Materials in industry: Types, main properties and uses.
\r\n\tAn important part of the book will consider electrodes (materials, configurations, contacts with biological matter) as responsible tools for the acquisition of bioimpedance data correctly. Implementations in wearable and implantable health monitors are the proposed book topics. Detecting of different pathogens by the aid of lab-on-chip (LoC) devices for point-of-care (PoC) and need-of-care (NoC) diagnostics is expected. Also, express analysis of biological matter (blood and other body fluids) is included. Electronics connected to electrodes for receiving the bioimpedance signals for further processing belongs to sensing techniques and will be considered.
\r\n\tDevelopment and application of software tools for information extracting from the acquired bioimpedance data, automatic identification of bioparticles and the decision making for diagnosing and treatment are very welcome chapters in the present book.
The economic development of any region, state or country, depends not only on its natural resources and productive activities, but also on the infrastructure that account for the exploitation, processing and marketing of goods. Irrigation systems, roads, bridges, airports, maritime, land and air transport, school buildings, offices and housing, industrial installations are affected by corrosion and therefore susceptible to deterioration and degradation processes.
Corrosion is a worldwide crucial problem that strongly affects natural and industrial environments. Today, it is generally accepted that corrosion and pollution are interrelated harmful processes since many pollutants accelerate corrosion and corrosion products such as rust, also pollute water bodies. Both are pernicious processes that impair the quality of the environment, the efficiency of the industry and the durability of the infrastructure assets. Therefore, it is essential to develop and apply corrosion engineering control methods and techniques.
Other critical problems, that impact on infrastructure and industry are climate change, global warming and greenhouse emissions, all interrelated phenomena.
This chapter presents important aspects of corrosion in industrial infrastructure, its causes, impacts, control, protection and prevention methods.
Metallic materials play a key role in the development of a country and its sustained growth in the context of the global economy. Table 1 shows a classification and the properties of different types of materials used in the industry. During the course of the metal production it undergoes various types of processes: mining of minerals, manufacturing and application and generation of gases, liquids or solids that are released into the environment. In the industrial development, production and use of materials in general, economic cycles are due to take effect that influence the environment (Raichev et al., 2010). The selection of a predominant group of materials depends on the particular industries; they determine to a greater or lesser extent the pattern of consumption of a given product, inducing the market to adapt itself to this new reality. The materials industry follows two general strategies: research the materials and the available technology recommended for their. Recycled materials typically require less capital and energy consumption, but need more manpower, for primary processing. Also, the costs of pollution control are lower than those required for primary processing of minerals. Recycling becomes more intense, as economies tend to be more sophisticated, since viable quantities of recycled material must be available for reuse (Garcia, R., et al, 2012, Lopez, G. 2011, Schorr, M., 2010).
\n\t\t\t\tMaterial\n\t\t\t | \n\t\t\t\n\t\t\t\tMain properties\n\t\t\t | \n\t\t\t\n\t\t\t\tUses\n\t\t\t | \n\t\t
Metals and alloys (carbon and stainless steels, non ferrous alloys) | \n\t\t\tMechanical resistance hardness | \n\t\t\tCars, aircraft, tanks, infrastructure reinforcement. | \n\t\t
Plastics (Synthetic polymers, rubbers) | \n\t\t\tLow density and corrosion resistance | \n\t\t\tProcess components, tubes, vessels, coatings, paints. | \n\t\t
Ceramics (Metallic carbides, silica, glass, alumina) | \n\t\t\tHigh hardness, high temperature and corrosion resistance | \n\t\t\tCutting tools, motor components, refractory bricks, ovens, etc. | \n\t\t
Composites (glass and carbon fibers reinforced plastics, plastic matrixes reinforced with metallic particles) | \n\t\t\tLight weight, high strength and hardness. | \n\t\t\tCar bodies, aircraft components, vessels, construction. | \n\t\t
Materials in industry: Types, main properties and uses.
In the production of a material waste is generated: for example, parts of material that was left aside, through the production steps. There are called effluent, which consist of waste that comes from the processes linked to the technology involved in each step of production, although not necessarily with the main material. Industrial processes for the recovery of ore from the mine to produce a metal, are related to technological development and therefore varies from one country to another, including regulatory laws, financial aspects etc.. Therefore, the environmental impacts vary widely. A low grade or poor quality of the ore, with low metal content, increase the cost of recovery, requiring large amounts of mineral raw material and energy invested for the recovery of small amounts of metal. Also important is the feasibility of the mineral that can be worked out e.g., the cost of physical removal of rock, accessibility to the mines, thickness and regularity of the ore zone, and its hardness. Figure 1, shows the material cycle, which involves processes from raw material, extraction from natural sources, processing and conversion into industrial materials, their processing and application, the deterioration rate effects, its mechanical properties, environmental behavior, corrosion, disposal and possible recovery of some of these through the use of recycling methods.
There are many examples of recovery of metals, which could help to describe step by step the various interactions with the environment itself. A mineral submitted to a production process will impact the environment, during four steps: extraction, processing, fabrication and manufacturing, of goods as seen in the cycle of materials. (Figure 1)
Materials production and use cycle.
In the mineral extraction step, the effluents of N, C, S, NOx, SOx and COx, from machinery and equipment, operation process water, particulate matter and ground movement in landfills.
The processing stage, chemical operations or extractive metallurgy for converting the concentrate into metal apply selected technologies. The effluents are gases such as SO2, NO2 and CO2, water contaminated with heavy metals, and hazard sediments.
In the manufacturing step the material undergoes operations that transform it into rods, bars, sheets; losses are scrap metal, such as cuts, burrs, mill scale, which recycled with no net loss of metal. In the manufacturing stage the metal is formed by stamping, machining and forging.
Focus on good operations management involves control of air emissions, water management and treatment, solid waste disposal and good land use, will greatly help to maintain a good balance with the environment. It is also necessary to analyze the production area to identify what improvements or measures should be implemented. The role of hydrometalurgist is particularly important and so he is responsible for the design of environmentally friendly processes in each of his steps, to promote sustainable production.
In addition to the common processes of deterioration of materials by chemical reactions and mechanical fracture, there are others who are concerned with the participation of various types of microorganisms that adhere in colonies or develop on their surfaces.
Biocorrosion and biodeterioration of metallic materials and nonmetallic materials are two important processes that cause serious problems to the infrastructure of various industrial systems. Generally, microorganisms do not deteriorate or corrode metals directly, but modify the conditions of interface material / environment and surroundings, favoring the degradation of these materials in such a way that induce or influence the development process.
Biofouling is a common term that indicates the presence of microbiological growth on the surfaces of structures built of different materials favoring the formation of biofilms with the colonies of various types of microorganisms.
In the case of metal, biocorrosion occurs due to corrosion electrochemical processes and biological agents due to the action of microorganisms and / or bacteria present in the system. The knowledge of these biological processes and their effects is necessary in order to establish preventive measures and control measures in industrial systems.
An industrial plant containing several biocorrosion environments is a potential risk:
In a heat exchangers system, usually dust accumulates biological waste; biocorrosion could occur, leading to corrosion film formation on walls surface. Therefore, it will be energy loss by increasing the resistance to fluid flow and heat transfer. Loss by evaporation of water favors the increase of the concentration of nutrients, the residence time, the water temperature and the surface / volume ratio, which leads to higher rate of microbial growth (Stoytcheva et al., 2010, Carrillo M. et al., 2010).
Until the early 80\'s of the twentieth century, we used mixtures of anodic and cathodic inhibitors, such as chromium, zinc and phosphates, to lessen the effects of corrosion in water systems. In some cases we added a polymer, as is still done to date, to avoid or eliminate the problems of fouling on the metal walls. On the other hand, to prevent microbiological growth, we added biocides such as chlorine and quaternary ammonium compounds under acidic conditions.
In the early 90\'s, the strategies for industrial water treatment changed because of pressure from laws inforcing for the preservation of the environment. Chromates and acid pH values are replaced by the use of organic phosphonates as corrosion inhibitors, while for the control of fouling polycarboxylate type polymers are used. However, this change brought about an increase in the amount of suspended solids, a greater number and variety of microorganisms and therefore a greater amount of inorganic deposits on the heat exchangers walls.
The metal nature has an effect on the distribution and development of microbial films on its surface. These films influence on the wear and corrosion of the metal substrate. The lack of homogeneity in the biofilm is a precursor of differential aeration processes with formation of differential cell concentration, for example, stainless steels (SS) and nickel-copper (Ni-Cu) alloys in seawater. The oxides passive films or hydrated hydroxides (corrosion products) are a good place for the establishment and growth of bacteria, especially when these products are at a physiological pH values (pH ≈ 7.4)
Carbon Steel (CS)
CS are very active metals in aggressive media, such as seawater. In this case, the action of microorganisms involves the dissolution of films of corrosion products, by processes of oxidation and reduction. This creates new metal active areas, exposed to the aggressive medium and suffers corrosion processes. In the case of sulfate-reducing bacteria (SRB), the species generated by their metabolism (sulfides) are corrosive to the metal. Figure 2 shows the final state pitting outside a CS pipe, which was affected by microbial growth inside, prompting a process of microbial corrosion with not uniform localized attack.
Stainless steel
The presence of chromium and molybdenum as alloying elements, enable passive behavior of stainless steels in different environments. However, the passive surface of these SS provides an ideal location for microbial adhesion and therefore are susceptible to corrosion pitting, crevice corrosion under stress or in solutions containing chlorides, as sea water.
External pitting caused by biocorrosion on the internal surface of a carbon steel pipe in a fire extinguisher system.
In marine environments, the generation of peroxides during bacterial metabolism causes an ennoblement of the pitting potential of SS, thus promoting corrosion. Obviously, not all SS have the same behavior, but in general they tend to deteriorated in the presence of colonies of microorganisms.
Copper and nickel alloys
Alloys of Cu with Zn, Sn and Al, brasses, bronzes, aluminum bronzes; also the nickel alloys: Monel, Hastelloy, nickel superalloys: Ni-Mo, Ni-Cr-Mo, Ni-Cr-Fe- Mo; the traditional nickel alloys: Ni-Cr-Fe, Ni-Fe-Cr, Fe-Ni-Cr-Mo), and the Cu-ni alloys CuNi\\70/30, CuNi\\90/10, have shown great corrosion resistance in different environments, so they have found a wide use in different industries and environments. However, despite these skills, there are reports that these alloys are colonized by bacteria after several months of exposure in seawater (Acuña, N. et al., 2004).
Aluminum and its alloys
Al is an active metal which is passivated rapidly in some neutral and acid media, thus offering a good resistance to corrosion. Al alloys with copper, magnesium and zinc, are widely used in the aviation industry. However, there have been cases of biocorrosion on fuel tanks of jet aircraft made of Al alloys by microbial contaminants in turbo combustibles. The presence of water (moisture), even in minimal amounts, allows growth of microorganisms (typically fungi), when these are able to utilize hydrocarbons as a carbon source.
Titanium
Ti is considered as the most resistant metal to biocorrosion, according to the results of tests carried in different conditions, due to its passive behavior that is reinforced in the presence of oxidizing agents. This is the reason why Ti is the material of choice, for example, for the manufacture of tubes in cooling systems that use seawater.
Nonmetallic materials
Non-metallic materials such as fiberglass reinforced polyester (FGRP), concrete and wood, are also affected by biodeterioration processes in the presence of microorganisms
In the case of FGRP, bacteria and algae are able to use the polyester matrix as a carbon source, consuming and considerably reducing the mechanical strength of composite material, ultimately causing its failure. This is easily observable in screens of this material in cooling towers or tanks containing fresh water or salt water. Wood suffers biodeterioration by the presence of fungi in moist environments that promote the delignification of this material (Valdez B., et al., 1996, 1999, 2008).
The inevitable presence of microorganisms in the feed water causes a sequence of biofouling, biocorrosion and biodeterioration of the materials component of the structures. This sequence depends on the degree of microbial contamination and the system operating characteristics.
The most common methods of controlling these problems involve the application of continuous or metered biocides such as chlorine. Currently, we use substances more compatible with the environment, since the use of chlorine is limited to certain concentrations. Such is the case of ozone, which is also ascribed with passivating effects on certain metals and alloys commonly applied in industry, and also in antifouling action.
In order to tackle a biodeterioration problem it is required a prior analysis of the problem, to know when conditions are suitable for the development of this process. In industrial systems we need to know some parameters: temperature, pH, nutrients; carbon, phosphorus, nitrogen, sulfate ion levels and flow rates. The places where we find biodeterioration are: biofouling deposits, under any deposit, zones of localized metal corrosion. to check their presence it is necessary to utilize sampling techniques, isolation and identification of microorganisms. It is interesting to note that there are commercial devices for in situ measurements that are practical and useful for the plant engineer.
Corrosion of device components, manufactured by the electronics industry, is a problem that has occurred during a long time. Often, especially corrosion of one or more of the metallic elements of an electronic component is the primary cause of failure in various electronic equipments. The high density of components required to reduce the size of electronic equipment, also for a better signal processing, leads to the generation of enclosed corrosion between thin metal sections. Furthermore, when electronic devices are in more severe environments such as tropical, subtropical, contaminated deserts, etc., they have high failure rates. Problems, due to the aggressiveness of the medium in electronic equipment for military use, have also occurred in aircraft and submarine guidance systems. Another common problem is corrosion damage suffered by components music players, when exposed to humid environments contaminated with chlorides, for example, during transport by ship, from the manufacture location to the consumer place. Thin layers of corrosion products on the surface of the metal component change their electrical characteristics: resistance, capacity and lead to partial or total failure of the electronic system. There are reported cases where small amounts of moisture have caused corrosion in tablets with printed circuits, nichrome resistors, fittings, electrical connectors and a wide range of components, and micro-electronic components, which have been coated with metallic films (Valdez B. et al., 2006, G. Lopez et. al., 2007)
Corrosion of metal components in the electronics industry may occur at different stages: during manufacture, storage, shipping and service. The main factors in the onset of corrosion and subsequent development are moisture and corrosive pollutants, such as chlorides, fluorides, sulfides and nitrogen compounds, organic solvent vapors, emanating from the resins used as label, or coatings formed during the curing process and packaging of microcircuits.
The sources providing aggressive pollutants are diverse, from flux residues used for welding processes, waste and vapors from electrolytic baths, arising volatile organic adhesives, plastics and acidification of their environment. Assays in artificial atmosphere, which simulates an indoor environment of an electronic plant have shown that the surface of the silver undergoes browning or tarnishing and the formation of dendrite whiskers due to corrosion (Figure 3).
The elemental chemical analysis of the surface (EDX - Scattered Electron Spectroscopy and XRD - X-rays) shows that the corrosion product formed on the silver surface is silver sulfide (Ag2S), due to the action of pollutant gases such as SO2 and H2S present in a humid environment (Figure 4). Moreover, the micrograph of the silver surface (SEM) shows a dendritic growth of corrosion products, characteristic for silver components.
The design of electronics equipment requires a great variety of different metals, due to their different physical and electrical features. Metals and alloys used in the electronics industry are:
Gold (Au) coating and / or foil in electrical connectors, printed circuits, hybrid and miniature circuits.;
Silver (Ag) for protective coating in contact relays, cables, EMI gaskets, etc..;
Magnesium (Mg) alloys for radar antenna dishes and light structures, chassis brackets, etc..;
Iron (Fe), steel and ferroalloys for guide components, magnetic shielding, magnetic coatings memory disks, processors, certain structures, etc..;
Aluminum (Al) alloys for armor equipment, chassis, mounting frames, brackets, trusses, etc..;
Copper and its alloys for cables, tablets printed circuit terminals, nuts and bolts, RF packaging, etc..;
Cadmium (Cd) for sacrificial protective coating on iron and safe electrical connectors;
Nickel (Ni) coating for layers such as barrier between copper and gold electrical contacts, corrosion protection, electromagnetic interference applications and compatibility of dissimilar material joints;
Tin (Sn) coating for corrosion protection of welding; for compatibility between dissimilar metals, electrical connectors, RF shielding, filters, automatic switching mechanisms;
Welding and weld coatings for binding, weldability, and corrosion protection.
Silver sulfide whiskers corrosion products on silver exposed in an electronics plant atmosphere.
Many of these metals are in contact with each other, so that in the presence of moisture, galvanic corrosion / bimetallic corrosion occurs. When using similar metals, due to design the following requirements must be taken into account.
Designing the contact of different metals such that the area of the more noble cathodic metal should be appreciably smaller than the area of the more active anodic metal. The area of the cathode can be decreased by applying paint or coating.
Coating the contact area of a metal with a compatible metal.
Interpose between dissimilar metals in a metal compatible packaging.
Sealing interfaces to prevent ingress of moisture.
Set the electronic device in a hermetically sealed arrangement.
Other corrosion problems can occur due to the characteristics of electronic components such as electromagnetic interference, electromagnetic pulse, flux residues, finishes and materials component tips, organic products that are used for various purposes and emitting gases during curing, whiskers, embrittlement inter-metallic electrical contacts.
Metal components may corrode during manufacture and storage prior to assembly, needing protection against corrosion. In plants and warehouses, air conditioning systems must operate efficiently, removing moisture and suspended particulate matter. Filters and traps should be cleaned and replaced regularly. For closed containers, we recommend the installation of dryers with visual indicators, and the use of volatile vapor phase corrosion inhibitors. In the case of sealed black boxes, the temperature inside these drops should never be below the dew point (Veleva L. et al., 2008, Vargas L. et al., 2009, Lopez G. et al., 2010).
Scanning Electron Microscopy and EDS analysis of silver corrosion products at indoor conditions of an electronics plant contaminated with H2S.
Abundant water sources are essential to a country\'s industrial development. Large quantities of this precious liquid are required for cooling products, machinery and equipment, to feed boilers, meet health needs and provide drinking water to humans. Estimates of water consumption for each country are different and depend on the degree of industrial development thereof. In first world countries like the United States, these intakes are as high as several hundred billion liters per day. These countries have implemented water reuse systems with certain efficiency due to the application of appropriate treatment for purification. Water, a natural electrolyte is an aggressive environment for many metals / alloys, so that they may suffer from corrosion, whose nature is electrochemical.
As raw water or fresh water we mean natural water from direct sources such as rivers, lakes, wells or springs. Water has several unique properties and one of these is its ability to dissolve to some degree the substances found in the earth\'s crust and atmosphere allowing the water to contain a certain amount of impurities, which causes problems of scale deposition on the metal surface, e.g. in pipelines, boiler tubes and all kinds of surfaces that are in contact with water (Valdez, B. et al., 1999, 2010).
Oxygen is the main gas dissolved in water, it is also responsible for the costly replacement of piping and equipment due to its corrosive attack on metals in contact with dissolved oxygen (DO). The origin of all sources of water is the moisture that has evaporated from the land masses and oceans, then precipitated from the atmosphere. Depending on weather conditions, water may fall as rain, snow, dew, or hail. Falling water comes into contact with gases and particulate matter in the form of dust, smoke and industrial fumes and volcanic emissions present in the atmosphere.
The concentrations of several substances in water in dissolved, colloidal or suspended form are low but vary considerably. A water hardness value greater than 400 parts per million (ppm) of calcium carbonate, for example, is sometimes tolerated in the public supply, but 1 ppm of dissolved iron should be unacceptable. In treated water for high pressure boiler or where radiation effects are important, as in nuclear reactors, impurities are measured in very small amounts such as parts per billion (ppb).
In the case of drinking water the main concern are detailed physicochemical analysis, to find contamination, and biological assays to detect bacterial load. For industrial water supplies it is of interest the analysis of minerals in particular salts. The main constituents of water are classified as follows:
Dissolved gases: oxygen, nitrogen, carbon dioxide, ammonia and sulfide gases;
Minerals: calcium, sodium (chloride, sulfate, nitrate, bicarbonate, etc.), Salts of heavy metals and silica;
Organic matter: plant and animal matter, oil, agricultural waste, household and synthetic detergents;
Microbiological organisms: include various types of algae, slime forming bacteria and fungi.
The pH of natural waters typically lies within the range of 4.5 to 8.5; at higher pH values, there is the possibility that the corrosion of steel can be suppressed by the metal passivation. For example, Cu is greatly affected by the pH value in acidic water and undergoes a slight corrosion in water releasing small amounts of Cu in the form of ions, so that it’s corroded surface because green stained clothing and sanitary ware. Moreover, deposition of the Cu ions on surfaces of aluminum or galvanized zinc corrosion cells leads to new bimetallic contact, which cause severe corrosion in metals.
The mineral water saturation produces a greater possibility of fouling on the metal walls, due to the ease with which the insoluble salts (carbonates) can be precipitated. To control this effect it is necessary to know and use the Saturation Indices. Water saturation refers to the solubility product of a compound and is defined as the ratio of the ion activity and the solubility product. For example, water is saturated with calcium carbonate when it is no more possible to dissolve the salt in water and then it begins to precipitate as scale. In fact, it is called supersaturated when carbonate precipitation occurs on standing the solution. The most common parameters that must be known to characterize the water corrosivity, be it raw or treated, for operation in an industrial facility are shown in Table 2.
\n\t\t\t\tWater properties\n\t\t\t | \n\t\t\t\n\t\t\t\tCorrosivity\n\t\t\t | \n\t\t
Hardness | \n\t\t\tSource of scaling that promotes corrosion | \n\t\t
Alkalinity | \n\t\t\tProduces foam and motion of solids | \n\t\t
pH | \n\t\t\tCorrosion depends on its value | \n\t\t
Sulphates | \n\t\t\tProduces scaling | \n\t\t
Chloride | \n\t\t\tIncreases water corrosivity | \n\t\t
Silica | \n\t\t\tGenerates scaling in hot water. Condensers and steam turbines | \n\t\t
Total Dissolved Solids (TDS) | \n\t\t\tIncreases electrical conductivity and corrosivity | \n\t\t
Temperature | \n\t\t\tElevated temperatures increases corrosion rates | \n\t\t
Water properties and corrosivity.
There six formulas to calculate Saturation Indices and embedding: Langelier index (LSI), Ryznar stability index, Puckorious index of scaling, Larson-Shold index, index of Stiff- Davis and Oddo-Tomson index. There is some controversy and concern for the correlation of these indices with the corrosivity of the waters, particularly regarding the Langelier (LSI).
A LSI saturation index with value "0" indicates that the water is balanced and will not be fouling, while the positive value indicates that the water may be fouling (Table 3). The negative value of the LSI suggests that water is corrosive and can damage the metal installation, increasing the content of metallic ions in water. While some sectors of the water management industry uses the values of the indices as a measure of the corrosivity of the water. Corrosion specialists are alerted and are very wary of issuing an opinion, or extrapolate the use of indices to measure the corrosivity of the environment.
\n\t\t\t\tLangelier Index\n\t\t\t | \n\t\t\t\n\t\t\t\tWater corrosivity and scaling\n\t\t\t | \n\t\t
-5.0 | \n\t\t\tSevere corrosion | \n\t\t
-4.0 to -2.0 | \n\t\t\tModerate corrosion | \n\t\t
-1.0 to -0.5 | \n\t\t\tLight corrosion | \n\t\t
0.0 | \n\t\t\tNo corrosion / no scale (balance) | \n\t\t
0.5 to 2.0 | \n\t\t\tLight incrustation | \n\t\t
3.0 | \n\t\t\tModerate incrustation | \n\t\t
4.0 to 5.0 | \n\t\t\tSevere incrustation | \n\t\t
Langelier index for water corrosivity and scaling.
Sometimes the raw water is contaminated with chemicals such as fertilizers and other chemicals coming from agricultural areas (Figure 5).
In these cases, ionic agents such as nitrites, nitrates, etc., in water causes an accelerated process of localized corrosion to many metals and the consequent failure of equipment.
Corrosion on the gates dam on the Colorado River, Baja California, Mexico
Raw water contaminants can be quite varied, including both heavy metals and organic chemicals, referred to as toxic pollutants. Among the heavy metals may be mentioned arsenic (As), mercury (Hg), cadmium (Cd), lead (Pb), zinc (Zn) and cadmium (Cd), which are sometimes at trace levels, but they tend to accumulate over time, so that priority pollutants are to be treated.
Pesticides, insecticides and plaguicides comprise a long list of compounds, for which we should be concerned: DDT (insecticide), aldrin (an insecticide), chlordane (pesticide), endosulfan (insecticide), diazinon (insecticide), among others.
Contaminants, such as polycyclic aromatic organic compounds, include what is known as volatile organic compounds such as naphthalene, anthracene and benzopyrene. There are two main sources of these pollutants: petroleum and combustion products found in municipal effluents. On the other hand, there are polychlorinated biphenyls or PCBs, which are mainly used in transformers for the electrical industry, heavy machinery and hydraulic equipment. This class of chemicals is extremely persistent in the environment and affects human health.
From the viewpoint of corrosion, these contaminants which are present even at low concentrations or trace in the raw water, favor the corrosivity the metals which are in contact with. The combination of the corrosive effects of these contaminants together with the oxidation by oxygen, minerals and other impurities, leads to consider raw water as a natural means capable of generating corrosion of metals. It is recommended at least, to carry out a process of treating raw water, to reduce significantly the hardness and remove suspended solids, which will help greatly in preventing subsequent problems of corrosion and fouling on metal surfaces, curbing economic losses and maintaining the industrial process in good operating condition.
Corrosion is a complex phenomenon that arises as a result of the interaction between water and the surface of metallic pipes or the equipment of storage and handling. The process is invariably a combination of oxidation and reduction, as already described in previous chapters. In drinking water, it should be noted that the corrosion products which are partially soluble in water in ionic form are toxic at certain concentrations, e.g. copper and lead. The existence of high concentrations of lead in water carried by copper tubing, indicate that the source of lead may be tin-lead solder at the junctions of the copper pipes. The consumption of domestic water contaminated with toxic metal ions (Pb+2, Cu+2, Zn+2, Cr+3), gives rise to acute chronic health problems. The regulations have set the following limits allowable concentration in drinking water: Cr (0.05 ppm), Cu (0.01 ppm), Pb (0.05 ppm) and Zn (5 ppm). These regulations are made in order to protect the public user and consumer of drinking water and are continuously striving for a reduction in the maximum allowable limits. Some concentrations reach zero as is the case of Pb in the United States due to the concerns Pb about poisoning of children. Still, many sources such as wells and springs are outside the control of law and toxic substances, bacteria and pathogens. Damage caused by corrosion of household plumbing may be accompanied by unpleasant aesthetic problems such as soiled clothing, unpleasant taste, stains and deposits in the toilets, floors of bathrooms, tubs and showers. To prevent corrosion of pipes, we recommend the use of PVC pipes for drinking water, replacing the metal, as a preventive measure.
Corrosion can occur anywhere on the pipes that carry drinking water, mainly at sites of contact between two dissimilar metals, thus forming a corrosion cell. In general, the metals will corrode to a greater or lesser degree in water, depending on the nature of the metal, on the ionic composition of water and its pH. Waters high in dissolved salts (water hardness), favor the formation of scale, more or less adherent, in different parts of the equipment (Figure 6). These deposits may be hard or brittle, sometimes acting as cement, creating a physical barrier between the metal and water, thereby inhibiting corrosion. Calcium carbonate (CaCO3) is the most common scale; its origin is associated with the presence of carbon dioxide gas (CO2) in water. Sometimes these deposits are filled with pasty or gelatinous hydrated iron oxides or colonies of bacteria (Valdez, B. et al., 1999, 2010).
Corrosion in potable water pipes.
Usually, groundwater CaCO3 saturated (calcareous soils), due to the presence of dissolved CO2, whose content depends on its content in the air in contact with the water and on temperature. These waters are often much higher in CO2 content, so they may dissolve substantial amounts of calcium carbonate. These waters are at pressures lower than they had in the ground, so CO2 gas lost with consequent supersaturation of carbonates. If conditions are appropriate, the excess of CaCO3 can precipitate as small agglomerates deposited in muddy or hard layers on solid surfaces, forming deposits. An increase in temperature is an important factor and also leads to supersaturation of carbonates, with the consequent possibility of fouling. To a lesser extent fouling can precipitate more soluble Mg carbonates (MgCO3) and Mn (MnCO3), and also oxides / hydroxides, dark colored and gelatinous. Except in very exceptional cases in sulfated water, it is normal to find deposits of gypsum (CaSO4•½ H2O) because their solubility is high, but decreases with increasing temperature. Hard silica scale (SiO2) may appear with oversaturated waters or appear as different silicates (SiO44-) trapped in the carbonate deposits. Generally, the silica appears trapped in other types of scale and it is not chemical precipitation.
Waters often carry considerable amounts of iron (ferrous ion, Fe+2), which may be often precipitated by oxidation upon contact with air as hydrated iron oxide (ferric, Fe+3) but sometimes can be Fe+2 form black sludge, more or less pasty or gelatinous and sometimes very large. The voluminous precipitate occupies the pores, significantly reducing the permeability of the fouling. Sometimes the Fe ions can come from corrosion of the pipe giving rise to simultaneous corrosion and scaling (Figure 6). Common bacteria of the genera Gallionella, Leptothrix Cremothrix are known as Fe bacteria, can give reddish-yellow voluminous precipitate and sticky ferric compounds from ferrous ion, which drastically reduce the permeability of the deposit, in addition to trap other insoluble particles.
The cost for impairment of domestic water systems and the impact on health, involves several consequences: premature corrosion and failure of the pipes and fittings that carry water in a house or building, a low thermal efficiency (up to 70%) of water heaters (boilers), which can cause their premature failure. High levels of metals or oxides, which usually are not properly, treated in drinking water cause red or blue-green deposits and stains in the toilets sinks. In addition to concerns about the aesthetic appearance, a corrosion process can result in the presence of toxic metals in our drinking water. For evaluating water quality and their tendency corrosive and / or fouling, LSI can be used. This analysis must be accompanied by measurements of water pH and conductivity, and corrosion tests applying international standards.
Corrosion control is complex and requires a basic knowledge of corrosion of the system and water chemistry. Systems can be installed for water pretreatment, using non-conductive connections, reducing the temperature of hot Cu water pipes employed and copper installing PVC or other plastic materials. It is important to note that the corrosiveness of water can be increased by the use of water softeners, aeration mechanisms, increasing the temperature of hot water, water chlorination, and attachment of various metals in the water conduction system. A proper balance between the treatment systems and water quality, can be obtained with acceptable levels of corrosivity. Thus, the lifetime of the materials that make the water system in buildings, public networks, homes and other systems will be longer.
A large part of steel structures: aqueducts, pipelines, oil pipelines, communications wire ropes, fuel storage tanks, water pipes, containers of toxic waste, are buried, in aggressive soils. Large amounts of steel reinforced concrete structures are also buried in various soil types. In the presence of soil moisture it is possible to have humid layer on the metal surface, whose aggressiveness depends on soil type and degree of pollution (decaying organic matter, bacterial flora, etc.). Thus, the soil can form on the metal surface an electrolyte complex with varying degrees of aggressiveness, a necessary element for the development of an underground electrochemical corrosion. The corrosion process of buried structures is extremely variable and can occur in a very fast, but insignificant rate, so that pipes in the soil can have perforations, presenting localized corrosion attack or uniform.
Metal structures are buried depending on their functionality and security. Most often they traverse large tracts of land, being exposed to soils with different degrees of aggressiveness exposed to air under atmospheric conditions (Figure 7).
Valve system of a desert water aqueduct.
When pipes or tanks are damaged by corrosion, the formation of macro-and micro-cracks can lead to leaks of contained products or fluids transported, causing problems of environmental pollution, accidents and explosions, which can end in loss of life and property (Guadalajara, Jalisco, Mexico, 1992). In the case of pipes used to carry and distribute water, a leak may cause loss of this vital liquid, so necessary for the development of society in general and especially important in regions where water is scarce, so the leakage through aqueducts pipes should be avoided. An important tool needed to prevent the most serious events, is the knowledge of the specific soil and its influence on the corrosion of metal structures.
A natural soil contains various components, such as sand, clay, silt, peat and also organic matter and organisms, gas, mineral particles and moisture. The soils are usually named and classified according to the predominant size range of individual inorganic constituent particles. For example, sandy soil particles (0.02 - 2 mm) are classified as fine sand (0.02 - 0.2 mm) or thick (0.20 -2.00 mm). Silt particles (0.002 to 0.02 mm) and clay, which have an average diameter 0.002 mm, are classified as colloidal matter. A comparison of the sizes of these typical soils is done in Figure 8.
Currently exists in the U.S. and in over 50 countries worldwide, a detailed classification for soils, which includes nine classes with 47 subgroups.
The variation in the proportion of the groups of soil with different sizes, determines many of its properties. Fine-textured soils due to high clay content, have amassed particles, so they have less ability to store and transport gases such as oxygen, that any ground-open e.g. sandy soil. The mineralogy of both clay types and their properties, are closely related to the corrosivity of the soil. Silica (SiO2) is the main chemical constituent of soils type clay, loam and silt, also in the presence of Al2O3. Common species in moist soil are dissolved ions H+, Cl-, SO42-, HCO3-. The chemical composition and mineralogy of the soil determine its corrosive aggressiveness; poorly drained soils (clay, silt and loam) are the most corrosive, while soils with good drainage (gravel and sand type) are less aggressive to metals. Vertically homogeneous soils do not exist, so it is convenient to consider the non-uniformity of ground, formed of different earth layers. To understand the corrosion behavior of a buried metal is very important to have information about the soil profile (cross section of soil layers). The physicochemical and biological nature of soil, corrosive aggressiveness and dynamic interactions with the environment, distinguishes the ground like a very complex environment and different from many others. Climate changes of solar radiation, air temperature and relative humidity, amount of rainfall and soil moisture are important factors in corrosion. Wind, mechanical action of natural forces, chemical and biological factors, human manipulation can alter soil properties, which directly affects the rate of corrosion of metals buried in the ground. Conditions may vary from atmospheric corrosion, complete immersion of the metal, depending on the degree of compactness of the soil (existence of capillaries and pores) and moisture content. Thus the variation in soil composition and structure can create different corrosion environments, resulting in different behavior of the metal and oxygen concentrations at the metal / soil interface.
Size of soil particles.
Two conditions are necessary to initiate corrosion of metal in soil: water (moisture, ionic conductor) and oxygen content. After startup, a variety of variables can affect the corrosion process, mentioned above, and among them of importance are the relative acidity or alkalinity of the soil (pH), also the content and type of dissolved salts.
Mainly three types of water provide moisture to the soil: groundwater (from several meters to hundreds below the surface), gravitational (rain, snow, flood and irrigation) and capillary (detained in the pores and capillary spaces in the soil particles type clay and silt). The moisture content in soils can be determined according to the methodology of ASTM D 2216 ("Method for Laboratory Determination of Water (Moisture) Content of Soil and Rock by Mass"), while its permeability and moisture retention can be measured the methods described in ASTM D2434 and D2980. The presence of moisture in soils with a good conductivity (presence of dissolved salts), is an indication for high ion content and possible strong corrosive attack.
The main factors that determine the corrosive aggressiveness of the soil are moisture, relative acidity (pH), ionic composition, electrical resistance, microbiological activity.
Given the electrochemical nature of corrosion of buried metals and specific soils, this can be controlled through the application of electrochemical techniques of control, such as cathodic protection. This method has been universally adopted and is appropriate to protect buried metallic structures. For an effective system of protection and cheaper maintenance, pipelines must be pre-coated, using different types of coatings, such as coal tar, epoxies, etc. This helps reduce the area of bare metal in direct contact with the ground, lowering the demand for protection during the corrosion process. The purpose of indirect inspection is to identify the locations of faulty coatings, cathodic protection and electrical Insufficient shorts (close-interval, on/off Potential surveys, electromagnetic surveys of attenuation current, alternating current voltage gradient surveys, etc..), interference current, geological surveys, and other anomalies along the pipeline.
One of the most common corrosion problems in pipes, ducts, tanks, preheaters, boilers and other metal structures, insulated heat exchange systems, is the wear and corrosion occurring on metal (steel, galvanized steel, Al, SS, etc.), below a deposit or in its immediate neighborhood. This corrosion is known as corrosion under deposit. The deposit may be formed by metal corrosion products and / or different types of coating applied for protection. For example, in the case of a calcareous deposit, formed in the walls of galvanized steel pipes which carry water with a high degree of hardness (dissolved salts), it might develop corrosion under deposit. These shells may be porous, calcareous deposit and / or partially detached from the metal surface, so that direct contact between metal, water and oxygen (the oxidizing agent in the corrosion process) allows the development of metal corrosion. For this reason the pipes could be damaged severely in these locations up to perforation, while in parts of the installation corrosion might occur at a much lower level.
There is a considerable amount of factors in the design, construction and maintenance, which can be controlled to avoid the effects of deterioration of metal by corrosion under deposit. In general, under these conditions the metal is exposed to frequent cycles of moisture, corrosivity of the aqueous medium or failure in the protective coatings (paint, metal, cement, fiberglass, etc.). Figure 9 shows a conductor tube steam in a geothermal power plant, where CS corrosion happened beneath the insulation.
Corrosion of a carbon steel pipe under insulation.
Seven factors can be controlled on the ground, to prevent this type of corrosion: design of equipment, operating temperature, selection of the insulation, protective coatings and paints, physical barriers from the elements, climate and maintenance practices of the facility. Any change in any of these factors may provide the necessary conditions for the corrosion process to take place. The management knowledge of these factors help explain the causes of the onset conditions of corrosion under deposits, and it will guide a better inspection of existing equipment and the best design.
The design of pressure vessels, tanks and pipes, generally includes accessories for support, reinforcement and connection to other equipment. Details about the installation of accessories are the responsibility of the engineers or designers, using building codes to ensure reliability of both insulated and non insulated equipment. The protective barrier against the environment surrounding the metal structure in such designs often breaks donor due to an inappropriate insulation, loss of space for the specified thickness of insulation or simply by improper handling during installation of the equipment. The consequence of a rupture or insulation failure means greater flow water ingress to the space between metal and coating hot-cold cycle, generating over time a buildup of corrosive fluid, increasing the likelihood of corrosive damage. Moreover, wet insulation will be inefficient and also cause economic losses. The solution of this factor is to meet the thickness specifications and spacing, as indicated in the code or equipment-building specifications and characteristics of the coating used.
The operating temperature is important for two reasons: a high temperature favors the water is in contact with the metal for less time, however, also provides a more corrosive environment, causes fast failures of coatings. Usually a team operating in freezing temperatures is protected against corrosion for a considerable life time. However, some peripheral devices, which are coupled to these cold spots and operating at higher temperatures, are exposed to moist, air and steam, with cycles of condensation in localized areas, which make them more vulnerable to corrosion. For most operating equipment at freezing conditions, the corrosion occurs in areas outside and below the insulation. The temperature range where this type of corrosion occurs is 60 °C to 80 °C; however, there have been failures in zones at temperatures up to 370 °C. Also, in good water-proof insulation, corrosion is likely to occur at points where small cracks or flaws are present, so that water can reach the hot metal and evaporate quickly. On the other hand, in machines where the temperature reaches extreme values, as in the case of distillation towers, it is very likely to occur severe corrosion problems.
The characteristics of the insulation, which have a greater influence on the corrosion processes deposits, are the ability to absorb water and chemical contribution to the aqueous phase. The polyurethane foam insulation is one of the most widely used; however, in cold conditions they promote corrosion due to water absorption present. The coatings of glass fiber or asbestos can be used in these conditions, always when the capacity of absorbing water do not becomes too high. Corrosion is possible under all these types of coating, such insulation. The selection of insulation requires considering a large group of advantages and disadvantages regarding the installation, operation, cost, and corrosion protection, which is not an easy task. The outside of the insulation is the first protective barrier against the elements and this makes it a critical factor, plus it is the only part of the system that can be readily inspected and repaired by a relatively inexpensive process. The durability and appearance, melting point fire protection, flame resistance and installation costs are other important factors that must be taken into account together with the permeability of the insulation. Usually the maintenance program should include repairs to the range of 2 to 5 years. Obviously the weather is important and corrosion under thermal insulation will more easily in areas where humidity is high. Sometimes conditions of microclimate can be achieved through the use of a good design team.
One of the most important elements of our daily life, which has great impact on economic activity, is represented by automotive vehicles. These vehicles are used to transport people, animals, grains, food, machinery, medicines, supplies, materials, etc. They range from compact cars to light trucks, heavy duty, large capacity and size. All operate mostly through the operation of internal combustion engines, which exploit the heat energy generated by this process and convert it in a mechanical force and provide traction to these vehicles.
The amount and type of materials used in the construction of automotive vehicles are diverse, as the component parts. They are usually constructed of carbon steel, fiberglass, aluminum, magnesium, copper, cast iron, glass, various polymers and metal alloys. Also, for aesthetic and protection against corrosion due to environmental factors, most of the body is covered with paint systems, but different metal parts are protected with metallic or inorganic coatings.
Corrosion in a car is a phenomenon with which we are in some way familiar and is perhaps for this reason that we often take precautions to avoid this deterioration problem.
A small family car, with an average weight of 1000 kg, is constructed of about 360 kg of sheet steel, forged steel 250 kg, 140 kg cast iron mainly for the engine block (now many are made of aluminum), 15 kg of copper wires, 35 kg and of plastic 50 kg of glass that usually do not deteriorate, and 60 kg for rubber tires; which wear and tear. The remaining material is for carpets, water and oil. Obviously, that is an advanced technology in the car industry, with automobiles incorporating many non-metallic materials into their structure. However, the problem of corrosion occurs at parts where the operation of the vehicle is compromised. Corrosion happens in many parts of the car (mostly invisible) it is not only undesirable for the problems it causes, but also reduces the vehicle\'s resale value and decreases the strength of the structure. To keep the car in good condition and appearance, its high price, it is necessary to pay attention to the hidden parts of the vehicle.
The main cause of corrosion of the car body is the accumulation of dust in different closed parts, which stays for a long time by absorbing moisture, so that in these areas metal corrosion proceeds, while in the clean and dry external parts it does not occur (Figure 10).
The corrosion problem that occurs in the metal car body has been a serious problem that usually arises most often in coastal environments, contaminated with chlorides and rural areas with high humidity and specific contaminants. Many countries use salt (NaCl, CaCl2 or MgCl2) to keep the roads free of ice; under these conditions these salts, in combination with the dust blown by the car, provide conditions for accelerated corrosion. Therefore, it is recommended as a preventative measure, after a visit on the coast or being on dirty roads, to wash the car with water, and also the tires and the doors, especially their lower parts. In urban environments, the corrosion problem has been reduced due to the new design and application of protective coatings, introduced by major manufacturers in the early nineties of the twentieth century. The areas most affected are fenders, metal and chrome bumpers views which are used in some luxury vehicles as well as areas where water and mud are easily accumulated e.g. auctions of funds windshield and doors (Figure 11).
In regions with high incidence of solar radiation and the presence of abrasive dust, paint vehicles deteriorate rapidly. The hot, humid weather, combined with high levels of SO2 and NOx emissions that come from burning oil, chlorides salt. In the Gulf of Arabia, the blowing sand from the nearby desert, creates a very aggressive environment; statistics reveals that one in seven cars is damaged and due to corrosion the car life is estimated to an average of 8 months, also the car corrosion resistance decreases in the following order: manufactured in Europe, USA and Japan. White paints generally have shown a significantly better corrosion protection than other colors. Initially, corrosion defects appear as a kind of dots and spots of corrosion products formed under the paint and subsequently emerge from the steel sheet, leaving a free entry for moisture and air (oxygen), accelerating the corrosion process; in these cases reddish metal corrosion products.
Corrosion on a bodywork exposed to the Gulf of Mexico tropical coast.
Corrosion on a car door and bottom of the bodywork
The cooling system of a car combustion engine consists of several components, constructed of a variety of metals: radiators are made of copper or aluminum, bronze and solder couplings with tin water pumps; motors are made of steel, cast iron or aluminum. Most modern automobiles, with iron block engine and aluminum cylinder head, require inhibitor introduced into the cooling water to prevent corrosion in the cooling system. The inhibitor is not antifreeze, although there are in the market solutions which have the combination of inhibitor-antifreeze. The important thing is to use only the inhibitor recommended in the automobile manual and not a mixture of inhibitors, since these may act in different ways and mechanisms. The circulating water flow should work fine without loss outside the system. If the system is dirty, the water should be drain and filling the system with a cleaning solution. It is not recommended to fill the system with hard water, but with soft water, introducing again the inhibitor in the correct concentration. If there exhaust at the water cooling system, every time water is added the inhibitor concentration should be maintained to prevent.
In small cars, it is common for water pumps; constructed mainly of aluminum, to fail due to corrosion, cavitation, erosion and corrosion, making it necessary to replace the pump (Valdez, B. et al., 1995). Accelerated corrosion in these cases is often due to the use of a strong alkaline solution of antifreeze. On the other hand, in heavy duty diesel trucks, the cooling system is filled with tap water or use filters with rich conditioner chromates that can cause the pistons jackets to suffer localized corrosion. After 12 or 15 months, the steel jackets are perforated and the water passes into the cavity through which the piston runs, forcing to carry out repair operations (Figure 12).
Corrosion in a carbon steel jacket on the water face in a diesel combustion engine truck.
Corrosion causes great economic losses to the transport industry, since it must stop to repair the truck and abandon to provide the service with all the consequences that this entails. Furthermore, the use of chemical conditioning is now controlled by environmental regulations, so chromates and phosphates are restricted and novel mixtures of corrosion inhibitors have been produced to control the problem of corrosion in automobile cooling systems.
Exhaust pipes made of SS (0.6 - 0.8 mm thick) have a better resistance to chemical corrosion at high temperatures, which is why we are now using SS in many popular models. This SS resists corrosion much more than conventional CS and thus their long life covers the higher price. Another alternative is to use conventional CS tube, zinc coated or aluminum (Figure 13). These exhaust pipes are less expensive than stainless steel, but less resistant to corrosion.
Corrosion on carbon steel exhaust pipes coated with aluminum.
The acidic environment which is generated on the surface of accumulators supplying the energy necessary for starting the engine, favors conducting corrosion processes in the lead terminals, where the cables are connected by bronze or steel clamps. Thus, this environment and these contact zones predispose cells to a process galvanic corrosion, which gradually deteriorates the contact wires, generating bulky corrosion products. This phenomenon is called sulfation of the contacts due to the sulfuric acid containing the battery, thus forming white sulfates on the corroded metal surface. These products introduce high resistance to current flow and cause failure to the engine ignition system, and impede the battery charge process. This problem has been eliminated in batteries that have airtight seals, or are manufactured with new technologies as well as bases covered with organic coatings that prevent corrosion.
Some years ago it was common for starters to fail, because the moisture or water penetrated into the gear area preventing it sliding motion and causing burning of the electric motor. Currently, new designs avoid contact with moisture and other foreign agents, preventing the occurrence of corrosion problems in these devices. As a preventive measure is recommended to prevent spillage of battery acid, to periodically clean the battery terminals (with a brush of wire or a special instrument), also coat them with petroleum jelly to prevent corrosion in these contact areas. A fat based composition which contains several components: alkaline salts and oxides of lithium, sodium bicarbonate and magnesium oxide are applied to the terminals and the connector. In general, in wet weather, the contacts of the accumulators have a tendency to more accelerated corrosion, thus requiring greater care to disconnect the terminals when not being used.
To keep the vehicle for a longer time without the appearance of corrosion, it always requires washing with running water and, the use of very soft brush or cloth-like material, with a special detergent (not household detergents, which are very corrosive) and finally wash the vehicle with plenty of water. The floor carpet should be maintained clean and dry. A car should not be left wet in a hot garage, since under these conditions accelerated corrosion takes place since the water does not dry and can condense on the cold parts of the vehicle. In these cases, it is best not to close the garage door or use a roof space, to protect it from rain, and not allow moisture condensation. However, if the vehicle is left unused for a long time in a closed garage, it should be protected from dust, moisture and contaminants.
Electricity is a key element in ensuring economic growth and social development of a country. Many conventional power plants in recent years are being installed in combined cycle power plants, also called cogeneration. The latter, simultaneously generate electricity and / or mechanical power and useful heat, sometimes using thermal energy sources that are lost in conventional plants.
A power station is a thermoelectric energy conversion system, starting with the chemical energy of fuel that during combustion is converted into heat energy accumulated in the steam. This thermal energy generates mechanical energy from the hot steam, which expands in a turbine, turning on electricity in the generator. In this process of low energy thermal efficiency is lost in the hot gases that escape through the chimney and the cooling steam in the condenser.
Electricity generating plants burn fossil fuels such as coal, fuel oil and natural gas. These fuels containing as minor components sulfur compounds (S), nitrogen (N), vanadium (V) and chloride (Cl-). These are corrosive chemicals attacking the metal infrastructure; and polluting the environment by becoming acid gas emissions, also affecting the health of the population.
The three central equipment of a thermoelectric plant are the boiler, which converts the water into steam, the steam turbine to whom the pressure imparts a rotary motion and the condenser that condenses the vapor released by the turbine and the condensed water is returned to the boiler as feed water. The turbine itself transmits rotary motion to the generator of electricity, which will be distributed to industrial, commercial and homes in cities.
Corrosion in steam plant equipment occurs in two parts of the boiler: on the water side and the steam side, with the fire temperature up to 700 ° C, depending on the type, size and capacity of the boiler. The boiler feedwater must be treated to eliminate the corrosive components: salts such as chlorides and sulfates dissolved oxygen (DO); silicates and carbonates, producing calcareous scale on the boiler walls, regarded as precursors for the formation of corrosion under deposits. The water is softened by eliminating salts and treated to remove oxygen; the pH is controlled by addition of alkaline phosphate to reach a pH range of 10 to 11, and inhibitors are added to the feedwater to prevent corrosion.
The flue gases and ash solid particles reach temperatures up to 1000 to 1200 °C, impinging on the outer surface of the boiler water tubes and preheater, creating an atmosphere for aggressive chemical corrosion. The damaged tubes lose its thickness generating metal corrosion products; they often are fractured, suffering a stress corrosion due to the combined effects of mechanical stress and corrosion (Figure 14). Since the tubes lose steam and pressure, the operation of the plant is interrupted and the tubes or its sections should be changed incurring severe economic losses. For example, in the United States has been concluded that the costs of electricity are more affected by corrosion than any other factor, contributing 10% of the cost of energy produced.
Stress corrosion cracking, in a combustion gases pipe of a thermoelectric station.
A study reveals that in 1991 there were more than 1250 days lost in nuclear plants operating in the United States, due to failure by corrosion, which represented an economic loss of $ 250.000 per day. Such statistics indicate that the power generation industry needs to obtain a balance between cost and methods for controlling effectively corrosion in their plants. It is sometimes advisable to add additives to the fuel, for example, magnesium oxide which prevent the deposition of the molten salts on the boiler tubes. Corrosion occurs also in the combustion air preheater, by sulphurous gases which react with condense and form sulfuric acid. Metal components of the turbine rotor: disks and blades suffer from corrosion by salts, alkalis and solid particles entrained in the vapor. In these cases, it is common to observe the phenomena of erosion-corrosion, pitting and stress corrosion fracture; their damage can be ameliorated through a strict quality control of boiler water and steam.
Efficient maintenance and corrosion control in a power plant is based on the following:
Operation according to mechanical and thermal regime, indicated by the designer and builder of the plant;
Correct treatment of fuel, water and steam;
Chemical cleaning of the surfaces in contact with water and steam, using acidic solutions containing corrosion inhibitors, passivating ammoniacal solutions and solutions;
Mechanical cleaning of surfaces covered with deposits (deposits), using alkaline solutions and water under pressure;
Perform an optimum selection of the materials of construction for the components of the plant, including those suitable as protective coatings.
The installation of online monitoring of corrosion in critical plant areas will be one of the most effective actions to control corrosion. In addition, it is recommended same use and document to use corrosion expert system software and materials databases for the analysis of the materials corrosion behavior.
Corrosion in power plants can be controlled by applying the knowledge, methods, standards and materials, based on corrosion engineering and technology.
The development of alternative energy sources represents one of the most attractive challenges for engineering. There are several types of renewable energies already in operation, such as wind, solar and geothermal. Geothermal environments can lead to aggressive environments, e.g. the geothermal field of "Cerro Prieto", located in Baja California, Mexico.
The physical and chemical properties of the vapor at "Cerro Prieto" make it an aggressive environment for almost any type of material: metal, plastic, wood, fiberglass or concrete. The typical chemical composition of a geothermal brine, is shown in Table 4. Many engineering materials are present as components of the infrastructure and field equipment, required for the steam separation, purification and posterior operations for the generation of electricity. This entire infrastructure is a costly investment and therefore, failure or stoppage of one of them, means economic losses, regardless of how vital it is to maintain constant production of much-needed electricity.
Corrosion in concrete structures used to separate steam from water and to operate steam silencers.
In the process of the geothermal fluid exploitation, corrosion of metal structures occurs from the wells drilling operation, where the drilling mud used, causes corrosion of pumping and piping equipment. Subsequently, when the wells pipes are in contact with the steam, they can also suffer from corrosion-erosion problems, where the corrosive agent is hydrogen sulfide. Steam separators and the pipes are exposed to problems of fouling and localized corrosion due to the presence of aggressive components such as H2S and chloride ions (Cl-), present in the wells fluid. These agents lead to the deterioration of reinforced concrete foundations supporting steel pipes, or other concrete structures used to separate steam from water and to operate steam silencers. The reinforced concrete deterioration due to steel corrosion in this aggressive environment, and the steam pressure mechanical forces lead to concrete damage with formation of cracks and fractures.
\n\t\t\t\tComponent\n\t\t\t | \n\t\t\tNa+\n\t\t\t | \n\t\t\tK+\n\t\t\t | \n\t\t\tMg2+\n\t\t\t | \n\t\t\tCa2+\n\t\t\t | \n\t\t\tCl-\n\t\t\t | \n\t\t\tSO4\n\t\t\t\t2-\n\t\t\t | \n\t\t\tSiO2\n\t\t\t | \n\t\t\tHCO3-\n\t\t\t | \n\t\t
\n\t\t\t\t Ppm, mg/kg\n\t\t\t | \n\t\t\t6429 | \n\t\t\t1176 | \n\t\t\t18.6 | \n\t\t\t347 | \n\t\t\t11735 | \n\t\t\t15 | \n\t\t\t1133 | \n\t\t\t303 | \n\t\t
Typical chemical composition of typical “Cerro Prieto” geothermal brine
In the power plants, the observed corrosion affects components of the steam turbines, condensers and pipelines, and also the cooling towers and concrete structures inside and outside the building that houses the plant. In these cases, the effects of corrosive attack appears in the form of localized corrosion in metal walls and gas piping) or as corrosion fatigue or stress corrosion, caused by cyclic mechanical forces or residual stresses, in turbines and other metal equipment. Table 5 shows a list of equipment and materials used for construction, which are part of the infrastructure of a geothermal power (Valdez, B. et al., 1999, 2008)
The combination of an aerated moist environment with the presence of hydrogen sulfide gas (H2S) dissolved in water provides a very aggressive medium (Figure 16), which promotes the corrosion of metals and alloys, such as CS and SS. The presence of dust, from the geothermal field and condensation cycles favor the failure of protective coatings applied to steel, so that developed corrosion leads to constant repairs and maintenance of metal installations: pipes, machinery, cooling towers, vehicles, tools, fences, warehouses, etc.
\n\t\t\t\tEquipment\n\t\t\t | \n\t\t\t\n\t\t\t\tMaterials\n\t\t\t | \n\t\t
Pipelines | \n\t\t\tConcrete, steel | \n\t\t
Vertical and centrifugal pumps | \n\t\t\tSteel, copper alloys | \n\t\t
Valves | \n\t\t\tSteel | \n\t\t
Flanges and fits | \n\t\t\tSteel | \n\t\t
Silencers | \n\t\t\tConcrete, steel, FRGP | \n\t\t
Brine canals | \n\t\t\tReinforced concrete | \n\t\t
Evaporation ponds | \n\t\t\tPlastics | \n\t\t
Control and safety instruments | \n\t\t\tMetals and plastics | \n\t\t
Equipment and materials used to build infrastructure in a geothermal field
Cooling towers constructed of wood, steel and fiberglass in the presence of flowing and stagnant water and air currents (induced to complete cooling fans), suffer a serious deterioration of the steel by corrosion and biodeterioration, involving a variety of microorganisms. The timber is subjected to oxygen delignification under the effect of colonies of fungi and algae, as well as fiberglass reinforced polyester screens, which deteriorate due to colonies of aerobic and anaerobic bacteria e.g. sulfate reducers.
Furthermore, carbon steels corrode in the form of delamination due to sulfate reduction processes which induce the oxidation of iron, while the SS nails and screws undergoes localized corrosion, forming pits (Figure 17)
A humid corrosive environment in a geothermal field caused by steam and gases emission.
Corrosion in a cooling tower of a geothermal power plant.
The deterioration by microorganisms capable of living in these conditions is one of the processes that have provided more information to the study of corrosion induced by microorganisms. In "Cerro Prieto", for example, have been isolated and studied various bacteria capable of growing even at temperatures of 70 ° C under conditions of low nutrient concentrations, while in the geothermal field of "Azufres" bacteria have been isolated to survive at temperatures of 105 °C and pressures of downhole (Figure 18).
Biodeterioration of polyester polymeric matrix in a fiberglass screen exposed at geothermal temperatures.
Corrosion of the infrastructure used in the pulping and paper industry, is another serious problem for corrosion specialists. The wide experience, gathered from cases of corrosion in the various infrastructure components of the paper industry, has provided an extensive literature on mechanisms, types and control of corrosion in this environment.
In the early 60\'s of last century, when the continuous digester process was adopted, the paper industry had limited knowledge about caustic embrittlement. Currently, it is known that the digesters are subjected to caustic levels and temperatures too close to the fracture caustic range where the total relieves of stresses in the material are essential. To elucidate the mechanism of this phenomenon, it was necessary to conduct serious investigations, which subsequently provide solutions to the problem of corrosion and caustic embrittlement. Technology in the paper industry has evolved over the last forty years and in parallel we can talk about the solution of corrosion problems in different parts of its infrastructure. Components with high failure rate due to corrosion are those built of bronze, SS, cast iron. Corrosion occurs in the papermaking machinery, where the white water equipment is subjected to an aggressive environment. The metal surfaces are exposed to immersion in this water; to steam that promotes the formation of cracks, which favor the deposit of pulp and other compounds. CS undergoes rapid uniform corrosion, while the copper alloys and SS (austenitic UNS S30400 L: 18% Cr8% Ni, UNS S31600 L: 16% Cr10% Ni 2% Mo) develop localized pitting corrosion. In the mill bleach plants the pulp equipment has traditionally been made of SS which has good general corrosion resistance and weldability. The use of chlorine gas (Cl2) and oxygen in the bleach plant and pulp bleaching, favors a very aggressive oxidant and SS, as type 317 L (18% Cr14% Ni3.5% Mo). However, in the last 25 years the environment in these plants has become much more corrosive due to the wash systems employed for the paper pulp, which increased the emission of oxidizing and corrosive gases; so type "317 L" SS is not resistant and has a shorter service life. Many mills in the paper industry have opted for the use of high-alloy SS, nickel (Ni) and titanium (Ti), for better corrosion resistance in these particular environments. In general, SS exposed to corrosive environment of bleach plants are benefited by the share of chromium, nickel and molybdenum as alloying elements, which increase their resistance to the initiation of pitting and crevice corrosion. The addition of nitrogen (N) increases its resistance to pitting corrosion, particularly when it contains molybdenum (Mo). Furthermore, to avoid waste of elements such as carbon (C), where a concentration greater than 0.03%, can cause sensitization at affected by heat areas in the solder, causing the SS to be less resistant to corrosion. Other waste elements, such as phosphorus (P) and sulfur (S) can cause fractures in the hot steel, formed in the metal welding area. The corrosive environment of bleach plants contain residual oxidants such as chlorine (Cl2) and chlorine dioxide (ClO2), these are added to resists the effects of temperature and acidity, maintaining a very aggressive environment.
Corrosion also occurs in the pulping liquor facilities by sulfites, chemical recovery boilers, suction rolls and Kraft pulping liquors. The Kraft process is the method of producing pulp or cellulose paste, to extract the wood fibers, necessary for the manufacture of paper.
The process involves the use of sodium hydroxide (NaOH) and sodium sulfite (Na2SO3) to extract the lignin from wood fibers, using large high pressure digesters. High strength is obtained in the fiber and methods for recovery of chemicals explain the popularity of the Kraft process. The black liquor separated, is concentrated by evaporation and burned in a recovery boiler to generate high pressure steam, which can be used for the plant steam requirements for the production of electricity. The inorganic portion of the liquor is used to regenerate sodium hydroxide and sodium sulfite, necessary for pulping. Corrosion of metals in the facilities used in this process may occur during the acid pickling operation for the removal of carbonate incrustations on the walls and black liquor pipe heaters. It has been found that SS 304 L presents fracture failure and stress corrosion. In the recovery processes of chemical reagents, known as stage re alkalinization, metals can fail due to caustic embrittlement or corrosion-erosion under conditions of turbulent flow. Corrosion also occurs in the equipment used for mechanical pulping, such as stress corrosion cracking, crevice corrosion, cavitation and corrosion-friction.
Crude petroleum is one of the fundamental sources of energy in the world and plays an important role in economic growth and development of many economies. Because of the need for this product, the oil market is subjected to the market forces of demand and supply, which do lead to the fluctuation in the pricing. Hamilton [1], Blanchard and Gali [2], viewed, changes in the price of oil as an imperative source of economic fluctuations, in which the resultant effect led to global shock, capable of affecting many economic activities instantaneously. This shock is perceived generally to have a similar impact due to events like fall in growth rate, high unemployment rate, and high inflation rate, while the magnitude and the causes of the effect of these shocks may differ. For import-based economy, hike in the oil price will lead to shock in the economy, vice versa for the export-based economy [1, 3].
There are many established empirical analyses on the macroeconomic consequence of oil price shocks to net exporting countries, this is based on the dependency between oil price and the business cycle which can be explained through the impact of the oil price shocks on aggregate demand. Practitioners opined that an increase in oil price reduces aggregate supply since high energy prices mean that firms will purchase less energy. As a consequence, the productivity of any given volume of capital and labor will decline and leads to potential output loss. This invariably will lead to a decline in factors of production and real wages ([4, 5], p. 23; [6, 7]).
To expatiate further the influence of the oil price shocks on aggregate demand, Riaz et al. [5] submitted that oil is one of the basic inputs in manufacturing industries, any positive oil price shock increases the cost of manufacturing. As the cost of manufacturing rises the profit margins on investments fall will influence investors to postpone their irrevocable investments. Reductions in investment causes cuts in production level, consequently exports of the country are negatively affected and economy has to face adverse balance of trade. So also the effect permeates into households, oil price fluctuation induces the consumers to reschedule their expenditures on durable goods. This suggested that oil price shocks have serious concerns for all types of economies as aggregate demand is reduced from both consumption and investment sides. Increase in both oil prices and uncertainty in oil prices is detrimental for the economy (p. 24).
The negative effects of oil price shocks are more on the net-exporters of oil of the developing economies, the effect could be attributed to over-dependence on oil revenue, importation of basic necessity and susceptibility of their tradable lagging sectors to Dutch disease syndrome, the consequences of externalities, and economic pass-through (inflation) [8, 9, 10, 11, 12].
In the submissions of Abeng [8], opined that theoretically, an increase in oil price should reflect more revenue dividend for oil-exporting countries as it is expected to enhance foreign exchange earnings and build reserve in the short-run. Conversely, for net-importers of refined petroleum products for instance Nigeria with domestic regulation of oil prices (subsidies), oil price increase may not transform to the anticipated economic advantage, due to fiscal difficulties, restraining government’s ability to finance import in addition to meeting other international obligations (p.3). Nigerian has a deficit of ₦7114.49 and ₦8324.76 billion Naira for 2017 and 2018 periods for importation of non-oil products and spent about ₦2618.97 and ₦3833.82 billion on importation of refined petroleum product for the period of 2017 and 2018 [13]. These figures stress the vulnerability of the economy to the impulses of international oil price. The consequences may be unfavorable to economic growth arising from increased domestic production cost and decline in aggregate demand (p. 23).
In Ibrahim [14] remarks in studying the responses of non-oil productive sectors that is agriculture, manufacturing and service to shocks in change in oil price in Nigeria. In his submissions, the results obtained reveal that oil price impacted positively on aggregate output but negatively on agricultural, manufacturing and service sector suggesting that at the aggregate level, oil price is incline to increase aggregate output whereas an increase in oil price impacted negatively on the outputs of productive sectors as oil serves as an input factor in the production process of these sectors. This specifies that fluctuation in oil price creates uncertainty in the production capacity of the productive sectors and it also destabilizes the effectiveness of the government fiscal management of crude oil revenue.
Also Ayadi [15] posited that the forecast errors in industrial production are credited to volatility in real exchange rates and that changes in oil prices are only slightly important in influencing industrial production in Nigeria. Moreover, oil price changes affect real exchange rates, which, in turn, affect industrial production. He remarked that it should be noted that the indirect effect of oil prices on industrial production is not statistically significant. Therefore, the implication of the results presented in his paper is that an increase in oil prices does not cause an increase in industrial production in Nigeria.
According to [16, 17], the economy of Nigeria was affected by the decline in the revenue due to a fall in the price of crude oil alongside production. They cited that in about twenty months, the oil price has nosedived rapidly from as high as about one hundred and thirty dollars per barrel to as low as twenty-eight dollars and quantity also dropped from 2.15 Mbpd to 1.81 Mbpd in the earlier months of 2016, this resulted to a recession.
The crude petroleum industry is among the largest contributors to the economic growth, before the recession experienced by the country, in 2016 the growth rate shrank by −13.65%, a more substantial decline than that in 2015 of −5.45%. This reduced the oil sectors share of real GDP to 8.42% in 2016, compared to 9.61 per cent in 2015, (NBS, Q4 [18]). Aside from the contribution to the growth rate, the industry affects monetary variable and high unemployment rate [2]. According to Nweze and Edeme [19], as quoted by Adedokun [16], CBN [20] opined that on average, 75% of government revenues and on average 93% of foreign earnings from trade in goods and services, in the last ten years come from oil export, which informs part of the major sources used in financing the country’s imports.
Fluctuate in the price of natural resources is a term more related to the oil shocks because the majority of the problems encountered concerning recession is aggravated by a change in oil price. Hamilton [1], in his abstract, he opined that historical oil price shocks were principally caused by physical disruptions of supply, the price hike of 2007–2008 was caused by supply not meeting the excessive world demand. The consequences of recession are very similar with significant effects on consumption. According to Hamilton (1983) as cited by Sabiu [21], opined that ten out of eleven economic recessions were preceded by a sharp increase in oil prices in the United States.
Although, In a more recent development in the investigation of the causes of oil price shocks, many practitioners do not see supply as the sole cause of oil price shocks. The neo-monetarist, the likes of Bernanke et al. [22] sees oil and energy costs as insignificant relative to total production costs to account for the entire decline in output that, at least some events, has followed increases in the price of oil, they foresee that the monetary policy taken during spikes in the price of oil as the major contributing factors to the economic shocks.
Kilian [23] opined that historically, the decompositions of fluctuations in the real price of oil shows that oil price shocks have been driven mainly by a combination of global aggregate demand shocks and precautionary demand shocks, rather than oil supply shocks.
In furtherance to clear the air on the causes of oil price fluctuations, which was generally believed to have outgrown the traditional demand and supply factors, Humbatova and Hajiyev [24] made references, to the Er-Riad summit of 2007 where conclusions where reached on the oil market trend that, it is not related to OPEC decisions. They concluded that the current trend is due to financialisation factors, lack of production capacities in oil production, reduction in the world oil reserves, natural disasters, political events and processes.
The financialisation of oil market made oil a speculative commodity in the financial market contrary to the real commodity. This has been one among the major sources of oil price volatility [25, 26].
The exposure of the oil market to commodity market brought about the issue of speculation, that is investors’ expectations about future oil supply and demand. This breeds in the issue of inventory, either below or above the ground since oil can be stored. Others factors are the price of dollars, for net oil importers appreciation of dollar mean lower consumption of oil whereas the net exporters mean more revenue from the sales of oil, the reverse is the case when dollar price depreciate [26, 27].
The most recent factor in the front burner affecting fluctuation of oil price is the improvement of shale-oil technology (the shale revolution in the United States). The technological innovations that decreased the liquid fuel consumption and influenced the global energy markets to the point that many countries that are solely dependent on the oil resource plunged into economic crisis in 2016 due to falling in oil demand [26, 28]. Davig et al. [29] added that the fall in demand led to shifts in precautionary demand in the mid-2014 to mid-2015, this played a fundamental role in driving oil prices lower due to market glut and exacerbate the oil crisis to net exporters in 2016.
Fluctuation in the price of oil as a result of the aforesaid causes create the effect of uncertainty in the outputs of industries, not only to the manufacturing sector but also to the energy management sectors in process industries, that is oil and gas industries. According to Elder and Serletis [30] they posited that the theories of investment under uncertainty and real options predict that uncertainty about oil prices will tend to depress current investment. This uncertainty can be due to rise or fall in the oil prices.
Higher oil prices do come with a glade tidings for some industries. Apparently, they benefit oil and gas industries, but have both positive and negative multiplier effects to other components of an economy [31]. According to Hayes upstream firms face more hitches when oil prices fall since market forces is the determining factor at which oil is sold, and their costs of production are largely fixed. The higher the cost of production the higher the losses incurred by the producer. Downstream companies suffer a lesser consequences since they profit by purchasing crude oil and selling the refined products at a premium. Their earnings and profit margins always remain fairly stable even with fluctuating in oil prices. The submissions of Hayes is line with the suggestions of Jobert et al. [32] they posited that rise in the prices of oil are much desirable to the oil industries because they will make higher turnover, simultaneously, the rise in the oil prices correlate with waning outcomes for large capital expenditure projects for oil recovery. Large and capital-intensive drilling operations are hit harder in contrast to the smaller rigs, which can decide to shut down pending on when prices rise again.
Energy and the development of the shale oil is among the current drivers of US economy, new jobs opportunities has sprang up due to economy of scale (internal and external) for the Americans. Persistence, fall in oil price, could lead to folding up of operations for many onshore fracking wells that lack the working capital to continue drilling. Although the hydraulic fracturing is more expensive than typical drilling, so shale gas companies will be among the first hit if the cost of production prevail over profits [33].
According to Adesina [34], he made references to the local key oil and gas corporation having a rough time due to the fall in oil price in the recent time with prices lower than local production in Nigeria. The local oil firms are fighting hard to survive as Crude and remains at the $20, which means Nigeria’s crude is being sold at a loss, coupled with the fact that oil demand has plummeted to the lowest level in more than a generation.
While on the other side Deloitte [35] views was on the impact of the oil price collapse on company accounts, fall in oil price tends to increase risk of loss of assets. They opined that lower oil price forecasts mean lower future profits from an asset. These leads to reduction in the present value of the asset, and the asset values on balance sheets cannot be fully recovered, this results in write-off, and tendencies of knock-on effect connected to deferring taxes and holding company investment balances.
In Nigeria one of the major contributing factors for 2016 recession was fall in the price of oil coupled with decreased in quantity of production, the recession was accompanied by high inflation rate on basic commodities (cost-push) [16]. Monetary policy on inflation is always been informed by the general price level. Before the recession, the inflation rate was at a single digit of 8.0% and 9.55% per cent for 2014 and 2015 [36]. During the recession, the inflation rate was about 18.55% per cent that is in 2016 and as expected, the monetary authority introduced a tight monetary policy by raising the cost of borrowing, the interest rate was steady at 14% from July 2017 to the first quarter of 2018 against 2016 which was 200 points higher. This is against the backdrop of relative improvement in the global economy.
Saban et al. [37] Investigated the responses of monetary policy variables of select emerging markets to oil market shocks. Using conventional and Fourier Toda Yamamoto methods. In their findings, the oil prices are sensitive to structural shifts and, the causality approach with gradual/smooth shifts indicates oil price shocks influencing the currencies of Indonesia and South Africa, interest rates in Brazil and India, and inflation in South Africa and Turkey.
Also in the summaries of Santos and Chris [38], used Johansen (1992) co-integration approach and the Toda and Yamamoto [39] causality testing procedure. Applying Wald coefficient test, the nominal interest rates, and expected inflation co-move together, in the long run, there is a uni-directional causality from expected inflation to nominal interest rates as suggested by the Fisher hypothesis in the closed economy context. While in the open economy context, the result showed that the expected inflation and international variables do not contain information that predicts the nominal interest rate.
In the empirical findings of Mohammed and Jauhari [40], they employed asymmetric causality test based on Toda and Yamamoto [39] causality approach to further the causal relationship between exchange rate and inflation differentials in Brunei, Malaysia, and Singapore. The results show the existence of Granger causality running from positive cumulative exchange rate shocks to shocks in inflation differentials for Brunei and Malaysia. Also, the asymmetric causality for Singapore runs from both positive and negative cumulative domestic inflation shocks to positive and negative exchange rate shocks respectively.
Chibvalo et al. [41] in their submissions, they employed the Toda-Yamamoto approach to Granger causality to test for a causal relationship between inflation and trade openness in Zambia. They established a bi-directional causality between inflation and trade openness. Further, there exists a positive relationship between inflation and trade openness in Zambia.
This analysis aims at investigating the effect and the interrelations existing between the impact of oil price fluctuation on the monetary instrument (Exchange rate, Inflation, Interest rate). The data were sourced from the Central Bank of Nigeria (CBN), National Bureau of Statistics (NBS) and Nigeria National Petroleum Corporation (NNPC). The data cover a period of 1995–2018 and the data is monthly. All our variables are in local currency. Therefore we used oil price, the interbank exchange rate as a proxy for exchange rate data, while the prime lending rate was used as a proxy for data on the interest rate and we used consumer price index for all commodity as a proxy for inflation.
A Toda and Yamamoto model (1995) (TY-VAR) was adopted in estimating the Modified WALD Granger Non-causality test (MWALD), Forecast Error Variance Decomposition (FEVD) and Impulse Response Function (IRF).
According to Salisu [42], Sims [43] and Toda and Yamamoto (TY-VAR) [39], Vector auto-regressions (VARs) are one of the widely used classes of models in applied econometrics, used as tools both for prediction and for model building and evaluation. It success lied on its flexibility and ease of application when dealing with the analysis of multivariate time series.
Practitioners have recently shown that the conventional asymptotic theory does not apply to hypothesis testing in levels VAR’s if the variables are integrated or co-integrated [39, 43]. And one of the deficiencies of the VAR application is the inability to ascertain the a priori expectation of the variables whether the variables are integrated, co-integrated, or (trend) stationary. This necessitates pretesting(s) for a unit root(s) and co-integration in the economic time series, asarequisite for estimating the VAR model, and also when the intentions are prioritized towards the estimation of cointegration and vector error correction model [44].
Conversely, the powers of the unit and also simulation experiments of Johansen tests for co-integrating are very sensitive to the values of the nuisance parameters in finite samples and hence not very reliable for sample sizes that are typical for economic time series [39, 45, 46].
To alleviate these problems, Toda and Yamamoto [39] as quoted by Shakya [47], Giles [48] proposes the augmented VAR modeling, that is the modified Wald test statistic (MWALD), which is more superiority to the ordinary Granger - causality tests, the method is flexible and easy to apply, since one can test linear or nonlinear restrictions on the coefficients by estimating a levels VAR and applying the Wald criterion, paying little attention or circumventing the integration and cointegration properties of the time series data [42, 44]. However, the model is not a substitute for the conventional pre-testing in time series analysis, but as a complementary to the conventional VAR [49].
In estimating the MWALD test for Granger causality, it is prerequisite to determine the maximum possible order of the integration of the basic variables (dmax). Although, the variables could be a mixture of I (0), I (1), and I (2), in such condition, dmax = 2. The determination of the optimal lag length (k) is very important, to avoid overstating or understating the true value of lag, to evade biased estimates of accepting the null hypothesis when it should be rejected, vice versa. By identifying dmax and k, a level VAR model of order (k + dmax) is estimated and zero restrictions test is conducted on lagged coefficients of the regressors up to lag k. This process certifies that the Wald test statistics have an asymptotical chi-square (χ2) distribution whose critical values can be used to draw a valid inference and conclusion [39, 44].
The model used in this research work borrowed a leave from the Toda and Yamamoto model (1995) as iterated in the work of Saban et al. [37], their model was adopted in this paper, to finding the inter-relationship between oil price and monetary variables. While they consider Granger Non-causality and structural shift, in our model we considered Granger Non-causality test, and substitute structural shift with Impulse Response Function (IRFs) and Forecast Error Variance Decomposition (FEVD). The TY-VAR is given by:
Where
The analysis aims at establishing the interrelationship that exist among the variables; i.e. oil price (lnoilpr), and monetary policy variable i.e. exchange rate (lnexchr), interest rates (lnintr), and inflation (lncpi). The specification considers each variable expressed as independent in the model as a function of its lag and the lag of other variables in the model. Here the exogenous error terms
Where
Although, the Todo-Yamamoto model, the MWALD test was introduced for ease of estimation by circumventing the presence of unit roots pre-testing problem, nevertheless, there is the need to determine the maximum order of integration of the variables, which is necessary for estimation of The MWALD test for Granger causality by Toda and Yamamoto [39]. Therefore, we ran the test for the Augmented Dickey-Fuller (ADF) test, Phillips – Perron (PP) test and Kwiatkowski–Phillips–Schmidt–Shin (KPSS) unit root test, to ascertain the stationarity of the variables [45, 50, 51, 52, 53, 54].
From Tables 1 and 2, the unit-roots tests confirmed all our process to be considered integrated at the first difference and 1% level of significance using Augmented Dickey-Fuller (ADF) test and Phillips – Perron (PP).
Variable | ADF | |||||||
---|---|---|---|---|---|---|---|---|
Level | First Difference | |||||||
Constant | Prob. | Constant & Trend | Prob. | Constant | Prob. | Constant & Trend | Prob. | |
lnoilpr | −1.2206 | 0.6663 | −2.3779 | 0.3904 | −14.3220*** | 0.0000 | −14.3037*** | 0.0000 |
lnexchr | 0.3070 | 0.9784 | −1.5899 | 0.7949 | −11.6443*** | 0.0000 | −11.6786*** | 0.0000 |
lncpi | −1.4401 | 0.5626 | −5.3282*** | 0.0000 | −13.3181*** | 0.0000 | −13.3666*** | 0.0000 |
lnintr | −1.8216 | 0.3696 | −2.3214 | 0.4250 | −16.2688*** | 0.0000 | −16.2400*** | 0.0000 |
ADF stationarity tests.
Note: ***, ** and * denote significance at 1%, 5% and 10% respectively. ADF test the null hypothesis of ‘not stationary’ against the alternative of ‘stationary’. Source: E-views Version 9 software was used in the estimation.
Variable | PP | |||||||
---|---|---|---|---|---|---|---|---|
Level | First Difference | |||||||
Constant | Prob. | Constant & Trend | Prob. | Constant | Prob. | Constant & Trend | Prob. | |
lnoilpr | −1.2921 | 0.6340 | −2.3897 | 0.3841 | −14.3491*** | 0.0000 | −14.3312*** | 0.0000 |
lnexchr | 1.0660 | 0.9972 | −1.5040 | 0.8271 | −9.8974*** | 0.0000 | −9.8872*** | 0.0000 |
lncpi | −1.7664 | 0.3968 | −5.5627*** | 0.0000 | −13.2950*** | 0.0000 | −13.3455*** | 0.0000 |
lnintr | −1.9316 | 0.3175 | −2.4972 | 0.3294 | −16.2641*** | 0.0000 | −16.2351*** | 0.0000 |
PP stationarity tests.
Note: Just like the ADF, the PP unit root test has the null hypothesis of ‘not stationary’ against the alternative, which is ‘stationary’. *, ** and *** indicate the level of significance at 10%, 5% and 1% respectively. Source: E-views Version 9 software was used in the estimation.
While Kwiatkowski, Phillips, Schmidt, and Shin (KPSS) in Table 3 is in contrast to ADF and PP which indicated that the variables are at levels. This corroborates with the work of Yakubu and Abdul Jalil in their test of stationarity. A quick check on the line graphs in Figure 1 indicated that all the variables are at first difference I(1). Therefore, we stick to ADF and PP, and agree that dmax = 1.
Variable | KPSS | |||||||
---|---|---|---|---|---|---|---|---|
Level | First Difference | |||||||
Constant | Prob. | Constant & Trend | Prob. | Constant | Prob. | Constant & Trend | Prob. | |
lnoilpr | 1.8432*** | 0.2905*** | 0.0615 | 0.0359 | ||||
lnexchr | 1.7493*** | 0.2035** | 0.1959 | 0.0771 | ||||
lncpi | 0.2299*** | 0.1406* | 0.2440 | 0.1035 | ||||
Intr | 0.9826*** | 0.1353* | 0.0457 | 0.0454 |
KPSS stationarity tests.
Note: In contrast to ADF and PP, KPSS unit root test has the null hypothesis of ‘stationarity’ against the alternative, ‘not stationary’. ***, ** and * represent 1%, 5% and 10% level of significance respectively. Source: E-views Version 9 software was used in the estimation.
Graphical representation of original series at I(1) for oil price (doilpr), exchange rate (dexcri), CPI (dcpi) and interest rate (dintr).
The Modified Wald (MWALD) Test for Granger Causality requires the determination of optimal lag which is presented in Table 4. By default, we use LR: sequentially modified LR test statistic, FPE: Final prediction error, AIC; Akaike information criterion, SBC: Schwarz information criterion and Hannan-Quinn information criterion to determine the optimal lag for the estimation of VAR system. The SC and HQ minimize its value at lag 2 while LR and FPE minimizes at lag 3. According to Liew [55], Asghar and Abid [56] Estimating the lag length of the autoregressive process for a time series is imperative in econometrics. The selection is done to minimize the chance of underestimation while at the same time maximizing the chance of recovering the true lag length. Another important aspect of the lag selection criteria is to overcome the structural break. Though, studies indicated that HQC is found to surpass the rest by correctly identifying the true lag length. In contrast, AIC and FPE are better choices for a smaller sample. In Table 4 out of the two criteria, we propose three lags (lag 3) as the optimal lag.
Endogenous variables: LNOILPR LNEXCHR LNCPI LNINTR | ||||||
---|---|---|---|---|---|---|
Lag | LogL | LR | FPE | AIC | SC | HQ |
0 | 1024.270 | NA | 8.68e-09 | −7.210389 | −7.158863 | −7.189729 |
1 | 3293.435 | 4458.148 | 1.05e-15 | −23.13382 | −22.87619 | −23.03052 |
2 | 3342.568 | 95.13951 | 8.35e-16 | −23.36797 | −22.90424* | −23.18203* |
3 | 3364.257 | 41.38540* | 8.02e-16* | −23.40817* | −22.73834 | −23.13959 |
4 | 3375.620 | 21.36093 | 8.29e-16 | −23.37540 | −22.49947 | −23.02418 |
5 | 3381.763 | 11.37514 | 8.89e-16 | −23.30575 | −22.22371 | −22.87189 |
VAR lag order selection criteria.
indicates lag order selected by the criterion. LR: sequential modified LR test statistic (each test at 5% level), FPE: Final prediction error, AIC: Akaike information criterion, SC: Schwarz information criterion and HQ: Hannan-Quinn information criterion. Source: E-views Version 9 software was used in the estimation.
The orthogonal impulse response are based on recursive causal ordering, if the ordering is reversed different sets of structural shocks will be identified, and this gives a different impulse response function (IRF) and forecast error variance decomposition (FEVD), except if the error terms contemporaneous correlations are low [57]. According to Lutkepohl [58] given a sample size of T, the determinant of the reordering of the variables is given by
The ordering of variables suggested by Sims (1981, 1980) as iterated in the work of Yakubu and Abdul Jalil [44], Duasa [46], is to start with the most exogenous variables in the system and ended by the most endogenous variable. Table 5 shows the residual correlation matrix result, the result shows that there is no instantaneous correlation between the variables because the variables are not significantly different from zero (at a 5% level of significance) [59]. This is based on the sample size in this analysis, we need at least a correlation of 31% that is above 5% level of significance to satisfy the call for reordering of the variables. Since there is no strong correlation among the variable we assumed the arrangement of our variables are in order.
LNOILPR | LNEXCHR | LNCPI | LNINTR | |
---|---|---|---|---|
LNOILPR | 1.000000 | |||
LNEXCHR | 0.156275 | 1.000000 | ||
LNCPI | 0.025236 | 0.038583 | 1.000000 | |
LNINTR | 0.052056 | 0.144681 | −0.057944 | 1.000000 |
Correlation matrix for TY-VAR.
Source: Estimation was compiled using E-views Version 9 software.
Before the estimation of the Causality Test, Forecast Error Variance Decomposition (FEVD) and Impulse Response Functions (IRFs). The VAR residual serial correlation test is needed to verify the adequacy of the lag selection criterion used in the estimation of a chosen multivariate model, it is applied to test a set of restrictions on a model that is unrestricted, and it is based on the restricted maximum likelihood test (ML) [42, 60, 61]. From the TY-VAR estimated output for the residual serial correlation test in Table 6, the null hypothesis for the test is that there is no serial correlation. The result submits that there is no evidence of serial correlation. Which indicate the acceptance of the null hypothesis that the restriction (lags) place on the model is adequate.
Lags | LM-Stat | Prob |
---|---|---|
1 | 13.85744 | 0.6093 |
2 | 8.875657 | 0.9184 |
3 | 15.67327 | 0.4760 |
4 | 12.71378 | 0.6936 |
TY-VAR residual serial correlation LM tests.
Probs from chi-square with 16 df.
Source: Estimation was compiled using E-views Version 9 software.
In the test for normality, to examine whether the residuals are normally distributed. We employed the null hypothesis H0: residuals are normally distributed. From Table 7 we rejected the null hypothesis of normality of residuals of each equation as well as all the equations combined at 5% level of significance since p-value of all the variables are zero. Hence, we concluded that residuals are not normally distributed [62].
Component | Jarque-Bera | df | Prob. |
---|---|---|---|
1 | 15.36714 | 2 | 0.0005 |
2 | 4572.449 | 2 | 0.0000 |
3 | 389.0131 | 2 | 0.0000 |
4 | 382.0722 | 2 | 0.0000 |
Joint | 5358.902 | 8 | 0.0000 |
Jarque-Bera normality test result.
df and Prob stands for the degree of freedom and probability. Source: Estimation was compiled using E-views Version 9 software.
Although, the credibility of Iarque-Bera test of normality with application to VAR has been questioned specifically for an I(1). Jarque-Bera normality of the series does not guarantee normality of distributions, it only signifies normality of the first four moments of a distributions [58]. According to Lutz and Ufuk [63] in their remarks, they posited that Jarque-Bera test based on asymptotic critical values can be very unreliable. In their submissions, they gave the asymptotic critical values of 1–100% in their Monte Carlo analysis of VAR. They presented that the size distortions of the asymptotic test persevere even for sample sizes as large as 5000 observations.
From Table 8 we have the lnoilpr as the dependent variable, at 5% level of significance, we accept the null hypothesis that there is no causality between, the lnexchr, lncpi and lnintr on the dependent variable. Also, the combination of all the independent variables do not granger caused changes in the dependent variable. This indicates the exogeneity of oil price which is been determined by many factors that are exogenous to both net importers and exporters of oil, Nigerian inclusive. According to Humbatova and Hajiyev [24] posited that the determinants of oil price range from financial factors, lack of production capacities in oil production, the decline in the world oil reserves, natural disasters, political events and processes, and no one country has the monopoly of determining oil price.
Excluded | Chi-sq | df | Prob. |
---|---|---|---|
LNEXCHR | 0.297326 | 3 | 0.9605 |
LNCPI | 2.517571 | 3 | 0.4721 |
LNINTR | 2.072927 | 3 | 0.5574 |
All | 5.503884 | 9 | 0.7884 |
Granger causality test WALD test for Eq. (2) for the dependent variable: LNOILPR.
Source: Estimation was compiled using E-views Version 9 software. Note: significance at 10% and 5% levels of significance respectively.
From Table 9 we have the lnexchr as the dependent variable, at 10% level of significance, we reject the null hypothesis that there is no causality between loilpr and lnexchr. The exchange rate plays a significant role in determining the oil price both to net exporters and net importers. Specifically, oil is priced in U.S. dollars. According to Farley [64] submissions, each decrease and increase in the dollar or the price of the commodity (oil) generates an instantaneous realignment between the US dollar and other currencies. These correlated is more significant in countries with significant oil reserves that depend largely on crude exports and they experience more economic damage than those with more diverse resources. In the presentations of Bützer [65], he established that oil Net exporters tend to respond against depreciation pressures by running down foreign exchange reserves, particularly after oil demand shocks, but also global demand shocks (which also decrease oil prices). This is sometimes supplemented by a nominal depreciation of exchange rates. These invariably indicate that oil demand shocks are a relevant factor for their exchange rates. While we accept the null hypothesis that there is no causality between, the lncpi and lnintr on the dependent variable. Also, the combination of all the independent variables do not Granger cause changes in the dependent variable.
Excluded | Chi-sq | df | Prob. |
---|---|---|---|
LNOILPR | 6.426225* | 3 | 0.0926 |
LNCPI | 2.889761 | 3 | 0.4089 |
LNINTR | 1.567570 | 3 | 0.6668 |
All | 11.29767 | 9 | 0.2559 |
Granger causality test WALD test for Eq. (3) for the dependent variable: LNEXCHR.
Source: Estimation was compiled using E-views Version 9 software. Note: significance at 10% and 5% levels of significance respectively.
Also from Table 10 we have the lncpi as the dependent variable, at 10% level of significance, we reject the null hypothesis and accept the alternative hypothesis that there is causality from lnexchr and linintr to lncpi. Exchange rate plays a vital role in determining prices in Nigeria, as an economy that has some element of a Dutch disease syndrome, and relied heavily on importation of basic necessity, when we factor out oil exportation from the total export, the non-oil balance of trade approximately stood at negative 7114 billion for 2017 as stated in our introduction. Therefore, appreciation in the exchange rate can cause inflation (lncpi) (Katz, 1973). The interest rate is one of the instruments used by the monetary authority to regulate the economy either during inflation or deflationary periods, the interest rate affects the demand and allocation of the available loanable funds the level, and pattern of consumption and investment ([66] p. 15). Before 2016 recession in Nigeria, the inflation rate was at a single digit of 9.55% in 2015, during the recession, the inflation rate was at double-digit 18.55% in 2016 and the central bank introduced a tight monetary policy, by raising the interest rate steady at 14 per cent from July 2017 to the first quarter of 2018 against 2016 which is 200 points higher [36].
Excluded | Chi-sq | df | Prob. |
---|---|---|---|
LNOILPR | 1.151935 | 3 | 0.7646 |
LNEXCR | 6.824049* | 3 | 0.0777 |
LNINTR | 7.771454* | 3 | 0.0510 |
All | 14.75625** | 9 | 0.0979 |
Granger causality test WALD test for Eq. (4) for the dependent variable: LNCPI.
Source: Estimation was compiled using E-views Version 9 software. Note: * and ** show significance at 10% and 5% levels of significance.
Also, the combination of all the independent variables (lnoilpr, lnexchr and lnintr) does Granger cause changes in the dependent variable lncpi at 5%, but lnexchr and lnintr are more pronounced in the causality. While we accept the null hypothesis that lnoilpr do not granger cause lncpi.
In Table 11 we have lnintr as the dependent variable, we reject the null hypothesis and accept the alternative hypothesis that at 5% levels of significance that there is a causality which is from lnoilpr and lnexchr to the endogenous variable lnintr, while there is no any causality with the log of lncpi on the dependent variable. Also, the combination of all the independent variables Granger cause changes in the dependent variable at a 5% level of significance. The relationship of lnoilpr and lnintr may not be exclusive but via the exchange rate, in the boom period the net exporter of oil has more dollars to expend, vice versa during deflationary periods, both periods has a direct link to economic growth. To avoid these inflationary or deflationary tendencies, the central bank may engage in the sterilization process through open market operation, by manipulating the short-term interest rate, that is by increasing interest rates to discourage borrowing during inflationary periods or decrease the interest rate to encourage borrowing during deflationary periods. The relation is said to be inverse and this shows how oil price and exchange rate influences the monetary policy of net oil exporters.
Excluded | Chi-sq | df | Prob. |
---|---|---|---|
LNOILPR | 14.66233** | 3 | 0.0021 |
LNEXCR | 10.44319** | 3 | 0.0152 |
LNCPI | 3.488718 | 3 | 0.3222 |
All | 31.49615** | 9 | 0.0002 |
Granger causality test WALD test for Eq. (5) for dependent variable: LNINTR.
Source: Estimation was compiled using E-views Version 9 software. * and ** show significance at 10%, 5% and 1% levels of significance.
From the estimated TY-VAR, we compute forecast error variance decompositions (FEVD and impulse response functions (IRF), which serve as means for evaluating the dynamics of the interrelationship, interactions, and strength of causal relations among the variables in the system. The impulse response functions trace the effects of a shock to one endogenous variable on to the other variables in the VAR, variance decomposition separates the variation in an endogenous variable into the component shocks to the VAR [10, 46].
In simulating FEVD and IFRs, the VAR innovations can be contemporaneously correlated. That is a shock in one variable can work through the contemporaneous correlation with innovations in other variables. The responses of a variable to innovations in another variable of interest cannot be adequately represented in isolation, due to the facts that shock to individual variables cannot be separately identified due to contemporaneous correlation [46].
In our analyses, we applied Cholesky approach which uses the inverse of the Cholesky factor of the residual covariance matrix to orthogonalise impulses (innovations) as recommended by Sims (1980) as quoted by Duasa [46] and (Breitung, Bruggemann, and [58]) to solve this identification problem. The strategy requires a pre-specified causal ordering of the variables, which we estimated in Table 5 for the correlation matrix. The results of FEVD are displayed in Tables 12–15, while the IRFs represented in Figures 2–17 in appendix 1, respectively.
Period | S.E. | LNOILPR | LNEXCHR | LNCPI | LNINTR |
---|---|---|---|---|---|
1 | 0.039283 | 100.0000 | 0.000000 | 0.000000 | 0.000000 |
2 | 0.059667 | 99.56602 | 0.007555 | 0.357862 | 0.068566 |
3 | 0.074387 | 99.31622 | 0.077847 | 0.518729 | 0.087200 |
4 | 0.087239 | 99.17720 | 0.135794 | 0.615055 | 0.071949 |
5 | 0.099720 | 99.16728 | 0.123200 | 0.650960 | 0.058563 |
6 | 0.112282 | 99.16645 | 0.102858 | 0.650544 | 0.080151 |
12 | 0.191020 | 98.36406 | 0.104402 | 0.630791 | 0.900743 |
18 | 0.276129 | 96.71609 | 0.060657 | 0.908562 | 2.314688 |
24 | 0.366613 | 94.33976 | 0.064427 | 1.477383 | 4.118426 |
30 | 0.457642 | 91.03687 | 0.223173 | 2.331971 | 6.407984 |
36 | 0.541764 | 86.40256 | 0.693518 | 3.520289 | 9.383636 |
42 | 0.611323 | 79.78047 | 1.802594 | 5.120937 | 13.29600 |
43 | 0.621214 | 78.43050 | 2.090483 | 5.429983 | 14.04904 |
44 | 0.630655 | 77.00398 | 2.418306 | 5.749531 | 14.82819 |
45 | 0.639696 | 75.50135 | 2.790808 | 6.077919 | 15.62992 |
46 | 0.648412 | 73.92544 | 3.212962 | 6.412711 | 16.44889 |
47 | 0.656906 | 72.28230 | 3.689787 | 6.750497 | 17.27741 |
48 | 0.665310 | 70.58226 | 4.226078 | 7.086683 | 18.10498 |
Variance decomposition of LNOILPR.
Note: SE refers to the total variance error in forecasting LNOILPR. Other columns represent the percentage of the variance attributable to shocks in the residual of the respective variables. Sources: Compiled using Eviews version 9.
Variance Decomposition of LNEXCHR: | |||||
---|---|---|---|---|---|
Period | S.E. | LNOILPR | LNEXCHR | LNCPI | LNINTR |
1 | 0.008667 | 2.442191 | 97.55781 | 0.000000 | 0.000000 |
2 | 0.016018 | 1.303029 | 98.47056 | 0.226099 | 0.000307 |
3 | 0.020768 | 0.793908 | 98.43015 | 0.646775 | 0.129165 |
4 | 0.024011 | 0.693289 | 97.87271 | 1.034284 | 0.399717 |
5 | 0.026961 | 0.553215 | 97.54321 | 1.309243 | 0.594331 |
6 | 0.030343 | 0.647208 | 97.17916 | 1.485892 | 0.687736 |
12 | 0.059365 | 4.366737 | 92.68622 | 2.025025 | 0.922015 |
18 | 0.109801 | 12.31598 | 84.79549 | 2.160683 | 0.727839 |
24 | 0.199812 | 21.01359 | 76.31208 | 2.242682 | 0.431654 |
30 | 0.358345 | 28.27847 | 69.15410 | 2.361025 | 0.206413 |
36 | 0.633138 | 33.57260 | 63.83346 | 2.514318 | 0.079625 |
42 | 1.103690 | 37.11351 | 60.17595 | 2.683471 | 0.027067 |
43 | 1.209424 | 37.56150 | 59.70328 | 2.711909 | 0.023305 |
44 | 1.324903 | 37.97407 | 59.26509 | 2.740223 | 0.020615 |
45 | 1.451006 | 38.35307 | 58.85970 | 2.768351 | 0.018878 |
46 | 1.588692 | 38.70034 | 58.48544 | 2.796232 | 0.017984 |
47 | 1.739007 | 39.01764 | 58.14072 | 2.823810 | 0.017829 |
48 | 1.903092 | 39.30669 | 57.82396 | 2.851035 | 0.018316 |
Variance decomposition of LNEXCHR.
Note: SE refers to the total variance error in forecasting LNEXCHR. Other columns represent the percentage of the variance attributable to shocks in the residual of the respective variables. Source: Estimation was compiled using E-views Version 9 software.
Variance Decomposition of LNCPI: | |||||
---|---|---|---|---|---|
Period | S.E. | LNOILPR | LNEXCHR | LNCPI | LNINTR |
1 | 0.006843 | 0.063687 | 0.122994 | 99.81332 | 0.000000 |
2 | 0.010614 | 0.111687 | 1.169617 | 97.78015 | 0.938541 |
3 | 0.013902 | 0.104867 | 1.709240 | 96.72400 | 1.461890 |
4 | 0.016436 | 0.118843 | 2.348794 | 96.05369 | 1.478675 |
5 | 0.018494 | 0.094052 | 3.766938 | 94.89832 | 1.240691 |
6 | 0.020348 | 0.110542 | 6.716716 | 92.14406 | 1.028684 |
12 | 0.034150 | 0.390382 | 43.39555 | 54.73213 | 1.481945 |
18 | 0.058790 | 0.800621 | 71.63000 | 26.51978 | 1.049596 |
24 | 0.102887 | 2.477121 | 83.86813 | 13.27275 | 0.382003 |
30 | 0.182422 | 6.425699 | 86.02039 | 7.403793 | 0.150115 |
36 | 0.326127 | 12.33701 | 82.73589 | 4.811637 | 0.115460 |
42 | 0.583692 | 18.92647 | 77.30909 | 3.668364 | 0.096074 |
43 | 0.642926 | 19.99991 | 76.35470 | 3.554354 | 0.091028 |
44 | 0.708053 | 21.05415 | 75.40480 | 3.455500 | 0.085552 |
45 | 0.779631 | 22.08580 | 74.46447 | 3.369987 | 0.079739 |
46 | 0.858270 | 23.09198 | 73.53809 | 3.296234 | 0.073694 |
47 | 0.944635 | 24.07026 | 72.62936 | 3.232858 | 0.067521 |
48 | 1.039451 | 25.01867 | 71.74135 | 3.178651 | 0.061323 |
Variance decomposition of LNCPI.
Note: SE refers to the total variance error in forecasting LNCPI. Other columns represent the percentage of the variance attributable to shocks in the residual of the respective variables. Source: Estimation was compiled using E-views Version 9 software.
Variance Decomposition of LNINTR: | |||||
---|---|---|---|---|---|
Period | S.E. | LNOILPR | LNEXCHR | LNCPI | LNINTR |
1 | 0.011298 | 0.270981 | 1.911153 | 0.411721 | 97.40614 |
2 | 0.015682 | 1.162856 | 3.384292 | 0.236829 | 95.21602 |
3 | 0.019164 | 0.778732 | 7.551086 | 0.251113 | 91.41907 |
4 | 0.021868 | 1.545252 | 10.35243 | 0.690563 | 87.41175 |
5 | 0.024147 | 4.317860 | 10.80310 | 1.548061 | 83.33098 |
6 | 0.026278 | 8.517769 | 10.19189 | 2.639437 | 78.65090 |
12 | 0.042469 | 43.36535 | 7.083532 | 7.537991 | 42.01312 |
18 | 0.068739 | 68.22092 | 7.817425 | 6.432329 | 17.52932 |
24 | 0.105922 | 75.25005 | 13.09986 | 4.196117 | 7.453977 |
30 | 0.154692 | 69.22610 | 23.97069 | 2.633864 | 4.169344 |
36 | 0.219876 | 52.16768 | 42.34294 | 1.710601 | 3.778773 |
42 | 0.320347 | 28.71560 | 65.93762 | 1.220443 | 4.126342 |
43 | 0.343392 | 25.06914 | 69.62387 | 1.173901 | 4.133092 |
44 | 0.369026 | 21.71282 | 73.04019 | 1.136972 | 4.110024 |
45 | 0.397622 | 18.71892 | 76.11686 | 1.109481 | 4.054735 |
46 | 0.429590 | 16.14604 | 78.79614 | 1.091170 | 3.966652 |
47 | 0.465375 | 14.03524 | 81.03610 | 1.081687 | 3.846977 |
48 | 0.505456 | 12.40799 | 82.81296 | 1.080569 | 3.698485 |
Variance decomposition of LNINTR.
Cholesky Ordering: LNOILPR LNEXCHR LNCPI LNINTR. Note: SE refers to the total variance error in forecasting LNINTR. Other columns represent the percentage of the variance attributable to shocks in the residual of the respective variables. Source: Estimation was compiled using E-views Version 9 software.
Impulse response function of lnoilpr to lnoilpr.
Impulse response function of lnoilpr to lnexchr.
Impulse response function of lnoilpr to lncpi.
Impulse response function of lnoilpr to lnintr.
Impulse response function of lnexchr to lnoilpr.
Impulse response function of lnexchr to lnexchr.
Impulse response function of lnexchr to lncpi.
Impulse response function of lnexchr to lnintr.
Impulse response function of lncpi to lnoilpr.
Impulse response function of lncpi to lnexchr.
Impulse response function of lncpi to lncpi.
Impulse response function of lncpi to lnintr.
Impulse response function of lnintr to lnoilpr.
Impulse response function of lnintr to lnexchr.
Impulse response function of lnintr to lncpi.
Impulse response function of lnintr to lnintr.
We explored the Cholesky factorization in the E-Views software and forecast the interrelationship of the variables up 48 months equal to 4 years. Table 10 is the Table for FEVD for lnoilpr as a dependent variable for 48 periods (4 years) forecast. In forecasting a variable, shocks in the residual of the forecasted variable contribute more to its variance than the shocks in other variables in the first period. The shocks in oil price-output contributed more to its variance, from 100% in the first period down to 70.58% in the 48 period (4th year) of the forecast period. This is followed by lnintr that contributed 4.11% in the 24th period to about 18.11% in the 48 period (4th year). This followed by lncpi that contributed 1.48% at the 24th period to 7.09 at the 48 periods and last is the lnexchr contributions from 0.06% in the 24th period to 4.22% in the 48 periods. This shows monetary policy influences the fluctuation inherent with the oil price and in the future, it shows that lnintr will respond highly to oil price shocks. While the contemporaneous relationship between the oil prices as the endogenous variables (lncpi and lnexchr) in our model are very insignificant. This is an indication that it will take a longer time into the future, for variables other than lnintr to influence the impact of oil prices.
Table 13, is the Variance Decomposition for dependent variable lnexchr, the contributions to itself were 97.56% in the 1st period, to about 57.82% in the 48 period (4th year) into the future. This followed by the contributions of lnoilpr with 28.28% at the 24th period and 39.31% at the 48th period. While lncpi and lnintr contributed 2.58% and 0.02% all at the 48th period. The error variance in forecasting lnexchr from lnoilpr is high, which indicates that shocks in the residuals of lnoilpr will have much effect in determining the lnexchr in the future.
Table 14 is forecast error variance decomposition of LNCPI as the predictant, the predictant contributes 99.81%, 54.73%, 3.18% in the 1st, 12th and 48th periods to itself, which indicates that the contributions of lncpi to itself declined in 4 years. While lnexchr contributes more to the error variance in forecasting lncpi, contributing about 43.40% up to 82.74% for the periods 12th and 36th then declined to 71.74%in the 48th period (4th year). While lnoilpr contributions started from 24th period with 2.47% and keep increasing up to 25.02% in the 48th period. Whereas lnintr contributions are insignificant. This has brought a clearer picture that lnexchr and lnoilpr are the major determinant of inflation in the economy.
Table 15 illustrated the forecast error variance decomposition of lnintr, contributing to its future error variation of 97.41%, 42.01% and 54.34% for the 1st, 12th and declined to 3.70% at the 48th period (4th year), this is followed by lnexchr which contributes 1.91%, 10.19% for the 1st and 6th periods, it declined for some periods and pick up again and continue rising to 82.81% in the 48th period (4th year).
This is trailed behind by lnoilpr, contributing 4.32% and 43.37% in the 6th and 12th, 75.25% at 24th period and started declining up to 12.41% at the 48th period (4th year). This indicates also a strong relationship into the future. The forecast error variance decomposition of the variables estimates also coincides with the result we obtained in the estimates we derived in Table 11, which also indicates that our estimates are good to go with for future implementation of policies.
In Figure 2, from appendix 1, the Oil price (lnoilp) responded contemporaneously by the change in its own shocks, which is positive and not dissipating. The implication is that hick in the price of oil may mean high revenue, but the consequences is, as an import based economic of non-oil goods and refined petroleum product, with domestic regulation of prices (subsidies), the policy will confine government’s ability to finance the import bills as well as meet other international obligations [8]. While the response of oil price (lnoilpr) to change in Exchange rate (lnexchr) is insignificant in Figure 3. Inflation (lncpi), and Interest rate (lnintr) in Figures 4, and 5 showed some level of positive response.
In Figure 6, there is a slightly positive response of Exchange (lnexchr) to change Oil price (lnoilpr) in the sixth lag period. This show how influential oil is in determining exchange rate, since high price of oil means more revenue (foreign income), also Exchange (lnexchr) responded instantaneously, a positive response, to change in its self (Figure 7.). In Figure 8, there is slight positive response of lnexchr to change in lncpi and Figure 9 showed a small inverse response of lnexchr to change in lnintr.
In Figures 10 and 13, Inflation (lncpi) did not show a meaningful response to orthogonal change in the price of oil (lnoilpr) and Interest rate (lnintr). While Figure 11, showed a positive response in Inflation (lncpi) to change in the Exchange rate (lnexchr), that is from the second lag period up to the tenth lag period in increasing order, this indicate that inflation will continue since the response is not dissipating unless there is a policy to induce deflation. Whereas in Figure 12 there is an instantaneous response of Inflation (lncpi) to change in Inflation (lncpi) in a high positive level, with a slight drop towards the tenth period which indicates tendencies of achieving normality in the future.
Figure 14, showed that there is an inverse response of Interest rate (lnintr) to one standard deviation change in the price of oil (lnoilpr) from the second lag period in an increasing order up to the tenth period, this is expected because the assumption is that interest rate has an inverse relationship with the oil price. Also Figure 15 indicated an instantaneous positive response of interest rate (lnintr) to change in the Exchange rate (lnexchr), in the third and fourth period, before it dying off which indicates that there is propensities of achieving normality in the long run. In Figure 16 Interest rate (lnintr) responds contemporaneously to change in Inflation (lncpi), with a positive increase from the fourth period and finally, in Figure 17 Inflation (lncpi) responded significantly to change Inflation (lncpi). The impulse response functions further complement the Forecast Error Variance Decomposition by given a portrait of the direction of the inter-relationships of variables.
In this research work, we explored the Toda-Yamamoto Modified Wald Test (MWALD) to examine the impact of oil price fluctuation on the monetary instrument in Nigeria, by looking at their causal relationships. The study covered the period 1995 to 2018 and the data are monthly data, to establish the contemporaneous relationships between these macroeconomic indicators. Among other analyses are the Granger Causality, FEVD and IRFs.
The review showed the direction of causality and FEVD into the future for 48 months equivalent to four years (short-run), between oil price, Exchange rate, Inflation, and Interest rate.
From the analyses of Toda-Yamamoto Granger Causality WALD Test, the review presented that there is unidirectional causality from lnoilpr to lnexchr in Table 9. This is consistence with the result we obtained in the estimation forecast error variance decomposition of lnexchr (Table 13) as the predictant, where the predictant contributes 97.56% in the 1st period, to about 57.82% in the 48 period (4th year) into the future. This was followed by the contributions of lnoilpr with 28.28% at the 24th period and 39.31% at the 48th period. While lncpi and lnintr contributed 2.58% and 0.02% all at the 48th period. This was also complemented by for IRFs in Figure 7 in the appendix.
Also from granger causality of lncpi as a dependent variable in Table 10 there is unidirectional causality from lnexchr and lnintr to lncpi, also the combination of all the three independent variables (lnoilpr, lnexchr and lnintr) granger cause lncpi but lnexchr and lnintr have more contributions. This is also in tandem with the result of FEVD for dependent variable lncpi in Table 14 where the dependent variable contributions to itself were 99.81%, 54.73%, 3.18% in the 1st, 12th and 48th periods, which indicates that the contributions of lncpi to itself declined in 4 years. While lnexchr contributes more to the error variance in forecasting lncpi, contributing about 43.40% up to 82.74% for the periods 12th and 36th periods (3rd years) then declined to 71.74%in the 48th period (4th year). While lnoilpr contributions started from 24th period with 2.47% and keep increasing up to 25.02% in the 48th period (4th year). This is also affirmed in Figure 11 in the appendix.
Similarly in the estimation of Granger Causality WALD Test for lnintr, it responded positively to change in lnoilpr and lnexchr. This is also in agreement with the estimation of forecast error variance decomposition of lnintr as an endogenous variable, contributing to its future error variation of 97.41%, 42.01% and 54.34% for the 1st, 12th periods and declined to 3.70% at the 48th period (4th year), this is followed by lnexchr which contributes 1.91%, 10.19% for the 1st and 6th perods, it declined for some periods and pick up again and continue rising to 82.81% in the 48th period (4th year). This is trailed behind by lnoilpr, contributing 4.32% and 43.37% in the 6th and 12th, 75.25% at 24th period and started declining up to 12.41% at the 48th period (4th year). This indicated that the major determinant factors of interest rate policy in Nigeria are change in price of oil and exchange rate in the long run. This also conforms to the outcome of the IRF in Figure 14, which specified further that the relation between lnintr and lnoilpr is an inverse relationship, while lnexchr, lncpi and lnintr in Figures 15–17 are positive.
The object of this is work is to establish a direct link between oil price and some selected monetary instruments in Nigeria, and our a priori expectations were achieved, we were able to established that oil price has a direct influence on the exchange rate, interest rate and inflation rate. It is known facts that Nigeria is an oil-producing economy and at the same time also an import-based economy of non-oil products. The major sources of financing the import come from oil revenue. As an oil-producing economy, there are tendencies of having Dutch disease syndrome and economic pass-through [9]. Both in theory and empirical analyses one can conclude that oil price is a strong determining factor of the rate of exchange, it has a direct link to inflationary or deflationary tendencies and also influences the monetary policies in Nigeria in terms of cost of borrowing.
Therefore, in implementation of monetary policy by the policymakers, attention should be drawn to price level of import from the external market, that is by concurrently monitoring the domestic market and the economy of the country’s trading partners. On a general note, there should be diversification of the economy from oil to the non-oil economy to avoid the Dutch disease syndrome.
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