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IntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\\n\\nIntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
\\n\\nLaunching 2021
\\n\\nArtificial Intelligence, ISSN 2633-1403
\\n\\nVeterinary Medicine and Science, ISSN 2632-0517
\\n\\nBiochemistry, ISSN 2632-0983
\\n\\nBiomedical Engineering, ISSN 2631-5343
\\n\\nInfectious Diseases, ISSN 2631-6188
\\n\\nPhysiology (Coming Soon)
\\n\\nDentistry (Coming Soon)
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\\n\\nNote: Edited in October 2021
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\n\nDesigned to cover fast-moving research fields in rapidly expanding areas, our Book Series feature a Topic structure allowing us to present the most relevant sub-disciplines. Book Series are headed by Series Editors, and a team of Topic Editors supported by international Editorial Board members. Topics are always open for submissions, with an Annual Volume published each calendar year.
\n\nAfter a robust peer-review process, accepted works are published quickly, thanks to Online First, ensuring research is made available to the scientific community without delay.
\n\nOur innovative Book Series format brings you:
\n\nIntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\n\nIntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
\n\nLaunching 2021
\n\nArtificial Intelligence, ISSN 2633-1403
\n\nVeterinary Medicine and Science, ISSN 2632-0517
\n\nBiochemistry, ISSN 2632-0983
\n\nBiomedical Engineering, ISSN 2631-5343
\n\nInfectious Diseases, ISSN 2631-6188
\n\nPhysiology (Coming Soon)
\n\nDentistry (Coming Soon)
\n\nWe invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
\n\nNote: Edited in October 2021
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This book is a part of a four volume collection (covering material aspects, physical effects, characterization and modeling, and applications) and focuses on ways to obtain high-quality materials exhibiting large ferroelectric activity. The book covers the aspect of material synthesis and growth, doping and composites, lead-free devices, and thin film synthesis. The aim of this book is to provide an up-to-date review of recent scientific findings and recent advances in the field of ferroelectric materials, allowing a deep understanding of the material aspects of ferroelectricity.",isbn:null,printIsbn:"978-953-307-332-3",pdfIsbn:"978-953-51-4451-9",doi:"10.5772/702",price:159,priceEur:175,priceUsd:205,slug:"ferroelectrics-material-aspects",numberOfPages:532,isOpenForSubmission:!1,isInWos:1,isInBkci:!0,hash:"4489eb7544dc5c1014f4e1280e677371",bookSignature:"Mickaël Lallart",publishedDate:"August 24th 2011",coverURL:"https://cdn.intechopen.com/books/images_new/174.jpg",numberOfDownloads:78165,numberOfWosCitations:140,numberOfCrossrefCitations:38,numberOfCrossrefCitationsByBook:8,numberOfDimensionsCitations:102,numberOfDimensionsCitationsByBook:9,hasAltmetrics:0,numberOfTotalCitations:280,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 11th 2010",dateEndSecondStepPublish:"November 8th 2010",dateEndThirdStepPublish:"March 15th 2011",dateEndFourthStepPublish:"April 14th 2011",dateEndFifthStepPublish:"June 13th 2011",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7,8",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"10041",title:"Dr.",name:"Mickaël",middleName:null,surname:"Lallart",slug:"mickael-lallart",fullName:"Mickaël Lallart",profilePictureURL:"https://mts.intechopen.com/storage/users/10041/images/1517_n.png",biography:"Mickaël Lallart graduated from Institut National des Sciences Appliquées de Lyon (INSA Lyon), Lyon, France, in electrical engineering in 2006, and received his Ph.D. in electronics, electrotechnics, and automatics from the same university in 2008, where he worked for the Laboratoire de Génie Electrique et Ferroélectricité (LGEF). After working as a post-doctoral fellow in the Center for Intelligent Material Systems and Structures (CIMSS) in Virginia Tech, Blacksburg, VA, USA in 2009, Dr. Lallart has been hired as an Associate Professor in the Laboratoire de Génie Electrique et Ferroélectricité. 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It is likely that this signals that the current level of dividends is unsustainable, hence, we use and return little of value to our natural world. In our book, we address the questions related to environmental health challenges that include contamination of air, water, and soil, and car transportation. In order to better understand natural, industrial, and social-environmental hazards, we have to think of them in a broader context (i.e., physical, chemical, biological, and cultural). 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Thus, petroleum processing actually commences shortly after the production of fluids from reservoir, where pretreatment operations are applied to the crude oil prior to transportation. Any crude oil to be shipped by pipeline, or by any other form of transportation, must meet strict regulations in regard to water and salt content.
Desalting is a water-washing operation performed initially at the production field and thereafter at the refinery site for additional crude oil cleanup, where the salt and water content specifications are even more rigid because of their negative effect in the downstream processes due to scale formation, corrosion, and catalyst deactivation. Desalting involves mixing heated crude oil with washing water, using a mixing valve or static mixers to ensure a proper contact between the crude oil and the water, and then passing it to a separating vessel, where a proper separation between the aqueous and organic phases is achieved.
Since emulsions can be formed in this process, there is a risk of water carryover in the organic phase. In order to overcome this problem chemical demulsifiers are added to promote the emulsion breaking. When this operation is performed at a refinery, an electric field across the settling vessel is applied to coalesce the polar salty water droplets, and, therefore, a decreasing in water and salt content is achieved.
The understanding of the different variables that affect the desalting process, especially the effect of the amount of chemical demulsifiers used, is imperative in order to optimize operating costs. According to data published by Vafajoo [1], the demulsifier concentration used for desalting could reach up to 100 ppm. Considering a demulsifier cost about 1.5 $/lb [2] for a medium capacity refinery (200 MBPD), the operating cost related to chemical demulsifiers would be in the order of 2 MM$/year. Thus, any reduction in demulsifier concentration in crude oil desalting would generate savings in the order of many thousand dollars. In the current work, a technical description of desalting crude oil and a wide description of stability emulsion phenomena are carried out. Finally, this work aims mainly at understanding the demulsifiers role from the viewpoint of physicochemical formulation.
Salts in crude oil are mainly in the form of magnesium, calcium, and sodium chlorides, sodium chloride being the most abundant. These salts can be found in two forms: dissolved in emulsified water droplets in the crude oil, as a water-in-oil emulsion, or crystallized and suspended solids.
The negative effect of these salts in downstream processes can be summarized as follows: salt deposit formation as scales where water-to-steam phase change takes place and corrosion by hydrochloric acid formation. Hydrochloric acid is formed by magnesium and calcium chlorides’ decomposition at high temperatures (about 350 °C) as follows [3]:
CaCl2 + 2H2O → Ca(OH)2 + 2HCl
MgCl2 + 2H2O → Mg(OH)2 + 2HCl
In addition, other metals in inorganic compounds present in reservoir dirt and sand produce catalyst poisoning in downstream processes such as hydrotreaters and cat crackers because of they are chemically adsorbed on the catalyst surface.
The objective of desalting process is to remove chloride salts and other minerals from the crude oil by water-washing. Depending on the desired salt content in the desalted crude oil, a one- or two-step process could be applied. For refining purposes, a salt concentration of maximum 1, 5 PTB (pound of salt measured as NaCl per thousand barrels) is desired. By desalting, a considerable percentage of suspended solids (sand, clay, or soil particles, or even particles product from corrosion of pipelines and other upstream equipments) are removed. Figure 1 shows a general process flow diagram for one- and two-step desalting process.
Simplified desalting process flow diagrams for different configurations: (a) one-step and (b) two-step.
The process starts by mixing the raw crude oil with an appropriate amount of washing water also known as dilution water. The washing water as a volume percent of the crude oil processed could oscillate between 3 and 10%, depending on the API gravity of the crude oil – the heavier the crude oil, the more the water required [2]. Demulsifiers are added to the crude oil in this process step.
In order to promote the effective mixing between the organic and aqueous phases, and ensure the proper dilution of the salts and minerals in the aqueous phase, a mixing valve is used. This is a common globe valvewhich causes a pressure drop and, as a result, a shear stress over the droplets that promotes an intimate water and oil mixture. The main aspect that needs to be considered is the pressure drop, whose value it is about 10-50 psi and varies according to the flux through the valve (automatic differential pressure controllers could be used). In addition to the mixing valve, upstream premixing device could be used, such as spray nozzles at the point of water injection or static mixers, between the water injection point and the mixing valve [4]. High delta pressure in the mixing valve promotes smaller droplets, which is positive because it improves the contact among the phases; however, very small droplets could yield a more stable emulsion, which could cause problems in the separating vessel. Therefore, it is very important to balance both effects in the selection of the operation pressure drop.
Then this mixture goes to the desalter, a horizontal cylindrical tank that provides long enough residence time to separate the water and oil mixture in two phases. Some water droplets diameters are so small that they could not be separated by gravity; so, an electrostatic field between two electrodes installed into the desalter is used to promote coalescence. Due to the dipolar nature of the water molecule, this electric field promotes an attraction with the other water molecules in the neighbor droplets promoting coalescence. According to Gary et Al. [2], either AC or DC fields may be used and potentials from 12, 000 to 35, 000 volts. The attraction force (
where ε is voltage gradient,
Finally, after coalescence, water droplets settle according to the well-known Stock’s law given by:
where
In a one-step configuration, shown in Figure 1(a), 90% of salt removal can be achieved. For higher salt removal percentages, a two-step configuration, shown in Figure 1(b), should be required. As far as the last process configuration is concerned two demulsifier injection points are used, both before the mixing valve in the first and second step. In addition, freshwater is fed to the second stage, and effluent water from this stage is recycled to the first one. With this configuration, a 99% salt removal could be achieved.
Emulsions are present in the main operations of the petroleum industry. The more well-known emulsions in the petroleum industry are those when the crude oil is the external phase (W/O). Water-in-oil emulsions are robustly stabilized by natural surfactants from crude oil, where a “special film” takes place and it is the responsible for emulsion stability. Film at the oil–water interface is highly viscous and is formed by asphaltenes, resins, waxes, and small solid particles [5, 6].
A main feature for interfacial film is the irreversible adsorption of asphaltenes at the oil−water interface in combination with other natural surfactants [7-10].
In the study of water-in-oil emulsions, it is important to know the structure and properties of the crude oil components, their tendency to associate and accumulate at the interface, as well as solubility and sensitivity to pressure and temperature changes.
In secondary oil recovery processes, the oil is contacted with formation water or water injected to produce the oil drain water. Water is used as a piston to push the oil from the well during the production process and then remove to the refinery [11]. In the reservoir, the fluid velocity is very slow (1 ft/day) to produce the emulsion, and emulsion is not formed during the two phase flow in porous media but later in equipment where oil is processed. Hence, the shear is responsible of making emulsion by pumping through valves, pipes, elbows, and others [7].
Damage to environment may occurs when petroleum or its products are spilled into the sea or the river, due to which water–oil emulsions are also formed. These emulsions (called ‘‘chocolate mousse’’) has special properties and characteristics [12-13]. Changes in the emulsions properties, such as viscosity and stability, are notable due to internal phase increasing [13].
The formation of water/oil emulsions is generally caused by the presence of resins and asphaltenes present in the oil, which play the role of natural emulsifiers [9]. These emulsifiers have a mutual attraction resulting in the formation of an elastic membrane around the droplets, preventing water droplets from uniting and decanting by gravity. This membrane is thick and could be easily visible using an optical microscope [14].
Asphaltene is the petroleum most complex fraction which is insoluble in normal alkanes such as n-pentane, n-hexane, or n-heptane, but soluble in benzene or toluene. Asphaltenes are composed by high molecular weight molecules and the most polar components in crude oil [15].
Usually, asphaltene structure comprises fused polyaromatic rings substituted by short aliphatic chains (1-3 carbons atoms) and cycloalkanes. Aromatic rings containing some polar functional group than content heteroatom such as sulfur, nitrogen and oxygen and some metals like nickel, vanadium and iron, which confer polarity and amphiphilic characteristics to these macromolecules [16]. Asphaltenes can generate a wide distribution of molecular structures that can vary greatly from one crude to another [17].
Asphaltenes are composed of several polynuclear aromatic sheets surrounded by hydrocarbon tails, and form particles whose molar masses are between 500 and 20.000 g. They contain many functional groups, including some acids and bases [18].
The specific structure of asphaltenes is unknown; however, molecular weight averages about 750 and there is a planar aromatic structure surrounded by alkane groups, some with heteroatoms, sulfur (S), nitrogen (N), and oxygen (O) [19]. Deposition of asphaltenes often causes some changes in the flow behavior of the oil fields through processing facilities, and difficulties in oil recovery. The problems caused by asphaltenes are closely related to its stability. Due to pressure variations, oil composition, and temperature, asphaltenes tend to form aggregates [14].
Experimental evidence from a combination of spectroscopy techniques and elemental analysis suggest that resins contains several fused five-and-six carbons rings in addition to alkyl chains.
Crude oil can be defined as a dispersed system where asphaltenes are colloids. Asphaltene molecules are in the center of the structure of the liquid oil, the other components are organized around the layer of asphaltenes decreasing polarity (resins, saturated and aromatic hydrocarbons). This simple model allows reconciling the compatibility presence within the same fluid and asphaltenes.
Asphaltenes can be considered as a complex polydisperse system of molecules in which subcomponents with significantly different characteristics can be distinguished as the result of solubility differences [17, 20].
From the colloidal point of view, it has been stated that asphaltenes in general are a blend of two main fractions. Acevedo et al. [21] proposed a model for asphaltene colloids in toluene which consists of two subfractions with different solubilities, called A1 and A2. The subfraction A1 (90 mg/L in toluene), which is insoluble in aromatic solvents such as toluene, cumene, and others, is found in greater proportion than a second subfraction A2 (57 g/L in toluene), which is soluble in these solvents and acts as a promotor in the dispersion of A1. The solubility of the latter is similar to the total mixture. Asphaltene aggregation phenomena is well understanding under the vision of colloidal nature of both fractions. [22]. Furthermore, this colloidal model has been found very useful in accounting for other phenomena such as trapping of compounds, flocculation, aggregation promoted by A1, asphaltene aggregation at very low concentration, and metal distribution [17].
In the vapor pressure osmometry (VPO) technique for molecular weight measures, the polarity of solvent is a critical parameter. Using o-dichlorobenzene, a 1000 g mol-1 value was reported for A2 solutions at 120 °C. On the other side, A1 afforded higher values (around 2600 g mol-1), emphasizing the strong aggregation tendency of this fraction [23].
Asphaltenes can form different types of aggregates depending on the environment: oil, solvent, interface, and others. Mullins et al. [24] proposed a scheme to explain the aggregation of asphaltenes in toluene. At low concentration, asphaltene is a group of individual molecules that evolve into nanoclusters and are associated in particles until reaching a system susceptible to flocculate. Floccules are unstable aggregates that eventually precipitate. In certain cases, stable aggregates that give the crude viscoelastic properties are formed.
Due to the presence of polar groups, asphaltenes can behave as surfactants, exhibiting the phenomena of adsorption and aggregation. However, it is wrong to attribute a critical micelle concentration (CMC) for asphaltenes and other macromolecules from crude oil. CMC is a parameter exclusive for surfactants, such as sodium dodecyl sulfate (8.3 * 10-3 M in water). Parameters such as aggregation number, shape and size of the micelle, electrolyte effect on the micellization and other additives are well known for surfactants. For petroleum asphaltenes this knowledge does not exist, mainly because the structure of an asphaltenes molecule is unknown. Aggregates phenomena best describes asphaltenes behavior. Asphaltene in toluene begins to form aggregates at 50 ppm concentration [25].
In summary, the use of CMC term for asphaltenes and other natural surfactants contained in the crude oil should be avoided. The emulsions in crude oil are produced for a mechanism starting with asphaltenes migrate into the oil–water interface, a process which is regulated by the diffusion of the soluble asphaltenes [19]. Adsorption of asphaltenes at the interfaces is a very slow process. The initial diffusion step toward the interface is rapid and it is followed by a long reorganization and the progressive building of multilayers. Asphaltenes diffuse from the bulk of crude oil to oil–water interface and are then adsorbed, decreasing the interfacial tension [26]. Asphaltenes can also form multilayers on solid surfaces such as silica. It has been found that increasing the concentration of asphaltenes in solution tends to increase its state of aggregation, which decreases the rate of adsorption on the surface [22].
In terms of time, the stability of the emulsion has a range from few minutes to years and it varies by the characteristics of the crude oil and water [27]. The stability mechanism is, of course, related to the composition of crude oil, such as type and the concentration of natural surfactants, the physical properties of oil, and the interfacial rheology of the interface around water droplet, which provides information about elasticity and viscosity and, for instance, the presence of a film (called skin) for the specific case of petroleum crude oil at this interface [10, 28].
Gao et al. [29] quantitatively studied the formation of interfacial film by the asphaltenes on the oil–water interface. They propose to measure the appearance of crumpling in the interfacial film, using imaging drop following compression by extraction volume, by the following relationship:
where CR is crumpling ratio; A
Volume contraction scheme of a drop of oil in water system: (a) absence of crumpling phenomenon and (b) presence of crumpling phenomenon occurs.
As part of the stabilization mechanism, it is accepted that the water droplets are stabilized by the formation of a mechanically strong and viscoelastic film at the interface, composed of asphaltenes [30].
Subsequently, several workers reviewed emulsions and concluded that asphaltene content is the most important factor in the formation of emulsions [7-10]. Asphaltene fraction is mainly responsible for the high emulsion stability of water-in-crude-oil; thus, they were found to be capable of forming rigid, cross-linked, and elastic films [19].
Asphaltenes stabilize water-in-oil emulsions if they are near or above the point of incipient flocculation, i.e., solid particles which can be. Other investigators have also suggested that asphaltene colloids are responsible for stable emulsions. Asphaltenes may be at the interface as fine solid particles or resin-asphaltene colloids. However, Yarranton et al. [31] reported that, at low asphaltene concentrations (<0.2 wt %), molecular state of asphaltenes predominant onto the colloidal form. It is also necessary to consider the effect of resins as they may influence asphaltene aggregation, adsorption on the interface, and emulsion stability [32].
One of the most used methods for studying emulsion stability is the bottle test. Figure 3 shows the bottle test sample for evaluating the stability of water-in-oil emulsion. It was observed that in the tubes on the left side there are stable emulsions, while in the tubes in the center and on the right side, there is a good separation of the two phases.
Stability bottle test of water-in-crude-oil emulsion (Silva, 2012).
Langmuir technique has been widely used for the characterization of the interfacial film formed by natural surfactants from crude oil [33-37]. Isotherms provide surface-pressure-area information on the compressibility of the film formed by the asphaltenes and other natural surfactants. The interfacial film can be collected with the Langmuir-Blodgett technique. Extensive morphological characterization of the interfacial film has been performed, including properties such as contact angle, thickness, and aggregate size, among other. Images obtained using Atomic Force Microscopy have contributed to the understanding of the mechanisms of interfacial adsorption, swelling, aggregation of colloidal interactions, and asphaltene [38, 39].
Aromaticity, length of the alkyl side chains, and branch reduction in the aliphatic chains of resins and asphaltenes are associated with the formation of stable emulsions. Another important parameter is the pH of the aqueous phase. Asphaltenes and resins are composed of polar functional groups; these groups can be ionized at alkaline pH. Surface charge density increases due to ionization of polar groups, generating internal repulsive forces destroying the mechanical properties preventing droplet coalescence [14].
When emulsion formation and stabilization take place, demulsifiers (generally a surfactant) must be used. A surfactant is a specie that, when present at low concentration in a medium, has the property of adsorbing onto the surfaces or interfaces of the system, altering the surface or interfacial free energies of those surfaces (or interfaces) [40].
Balsamo et al. [41] showed that effective demulsifiers formed complexes with natural anionic surfactants present in the oil phase, weakening the irreversible film; this is well correlated with low emulsion stability. When emulsion breaker was added to the system, a synergistic effect was observed between natural surfactant and demulsifier with further lowering of the interfacial tension (IFT).
It is known that an unstable emulsion occurs when amphiphilic formulation at the interface corresponds to an exact affinity balance of the surfactant mixture for the oil and water phase [7, 42-44]. From a physicochemical viewpoint, the optimum formulation is achieved when the standard potentials (µ*) of a surfactant (or mixture) in oil
A new methodology has been presented by several authors [7, 42-44]. It is essentially a formulation scan study carried out by adding a hydrophilic demulsifier at different concentrations, until a minimum emulsion stability is reached. At this optimum formulation point, the lipophilic natural surfactants (i.e., asphaltenes, resins, naphthenic acids, and other crude oil amphiphiles at the interface) referred to as asphaltenes (A) are balanced by the added hydrophilic demulsifiers (D). The hydrophilic−lipophilic contributions of both types in the interfacial mixture are dependent on their adsorbed concentration and on the strength of their effects. Consequently, the hydrophilic contribution of the demulsifier may be varied by changing its hydrophilicity or its concentration [44]. In general, it has been shown that the more hydrophilic the nature of the demulsifier, the lower is the concentration required to reach the minimum emulsion stability when they join asphaltenes [44], mixing rule is approximately linear, i.e., that the demulsifier is not too hydrophilic (because it would go essentially to water) or not too close to matching hydrophobicity (because it would not compensate the asphaltene hydrophobic effect). However, there are others factors to be considered such as the viscoelastic properties of the asphaltene interfacial layer than improve the understanding of demulsifier action.
Pensini et al. [45] measured the viscoelastic properties of the interface as a function of aging time as the asphaltenes diffused toward the interface and adsorbed on the water–toluene interface. With gradual accumulation of asphaltenes on the toluene–water interface, the interface changes from a viscous to an elastic-dominant microstructure. Focusing on the effect of demulsifier addition, at 6000 s of interfacial aging are not shown. Once the condition G’ (elastic component) = G” (viscous component) was reached, the demulsifier was added at 2.3 ppm (optimum dosage from the bottle tests). When the elastic contribution is predominant, asphaltenes are intimately linked, forming a cohesive interfacial network. To assess the demulsifier performance, in penetrating and breaking the cohesive network of asphaltenes, the shear viscoelastic moduli was measured. Upon addition of 2.3 ppm demulsifier in toluene, both viscous and elastic shear modulus of the interfacial film decreased.
The problem when handling formulation issues in a crude oil system is that only a small part of the asphaltenes is present at the interface. This implies that the total asphaltenes’ concentration in the crude oil does not participate with the demulsifier at the interfacial mixture. As a consequence, the optimum concentration of the demulsifier is not dependent on its mixture with all the asphaltenes in the system. Only a fraction of asphaltenes is adsorbed, and the rest forms aggregates, flocculates, or precipitates, an important amount of it being segregated close to the interface, but not at the interface (see Figure 4). This asphaltene excess is responsible for the characteristic skin at the water–crude oil interface [7, 9, 10].
Asphaltene excess than there are out of interface. This situation corresponds to Ca> 1, 000 ppm. Solvent can to penetrate in this layer asphaltene and interact with them for limit its aggregation due solvation effects (Pereira, 2009).
An experimental method has been recently proposed to estimate the asphaltene−demulsifier adsorbed mixture composition, so that interfacial formulation may be estimated [7]. It consists in diluting the crude oil so that the asphaltenes’ concentration in the oil phase is reduced. At some point, asphaltenes’ concentration is low enough so there is no excess of aggregates or flocculates in the oil phase. In this situation, asphaltenes and demulsifiers species may be considered as two types of surfactants adsorbed at the interface, according to the physicochemical conditions in the bulk phases at equilibrium. It has been found that below a concentration of asphaltenes 500−1000 ppm, the concentration of demulsifier required to attain optimum formulation varies proportionally to the concentration of asphaltenes (see Figure 5). In this so-called proportional regime [7], a quantitative formulation effect at the interface can be estimated without taking into account physical effects such as viscosity, convection, and diffusion through bulk oil (these are not formulation issues). This method allows evaluating the demulsification performance as a function of different formulation variables (demulsifier hydrophilicity, salinity, and crude oil characteristics) [7, 42-44], which represents a significant practical progress in designing commercial products.
Demulsifier concentration as a function of the asphaltene concentration to reach optimum formulation (Pereira, 2009).
HLB method (hydrophile–lipophile balance) is often used. In this method, a number (0–40), indicative of emulsification behavior and related to the balance between the hydrophilic and lipophilic (hydrophobic) portions of the molecule, has been assigned to many commercial surfactants [42].
Using HLB concept, the proportional regime can be estimated, as follows, [7, 42-44]:
where XA and XD are the fractions of A and D at the interface (XD = 1 − XA) and HLBOptMixt is a characteristic value (close to 10 correspond to the affinity) for the system. This can be calculated from the experimental correlation (bottle test) when the crude, brine, and temperature are fixed. The effect of any additive can be easily qualified as favorable or not when decreasing the fraction of demulsifier required (XD) to attain the optimum formulation for the lowest stability. Since the experiments are much easier and more accurate in the proportional regime, this is the zone where the tests of the additive effects are mostly carried out. Silva et al. [8] used this methodology to estimate, in a semiquantitative manner, the effects of organic acid and amine additives on the formulation of the components at the interface. The results indicate that water-soluble additives (acetic acid and ethylamine) have no significant effect on the required demulsifier concentration CD*. Even if they considerably alter the pH aqueous phase, additives do not reach a value to ionize asphaltenes species. The additives of lipophilic nature (hexanoic acid and hexylamine) both interact on the other side of the interface (i.e., asphaltenes in the oil phase). The hexanoic acid is found to marginally increase the emulsion stability, probably because it could lead to a slightly more hydrophobic co-surfactant effect. In contrast, the hexylamine, which is likely to interact with the asphaltenes through an acid−base mechanism, produces the delocalization of charges (ionization), resulting in an increase of hydrophilicity, and reducing the hydrophilic demulsifier requirement to attain optimum formulation.
Commercial demulsifiers are chemical cocktails or complex mixtures of surfactants (active substance), polymers, polar additives (alcohols, amines, ketones, and others), and solvents. Alcohols are useful substances to promote changes on the interfacial properties of surfactant–oil–water systems [46, 47]. Pei et al. [48] reported correlation between the interfacial tension and solubility in water of alcohols. Interfacial concentration of the surfactant is dependent on the less water-soluble alcohol additives will provide higher alcohol concentrations at the oil−water interface. Generally, the formulation of such products is protected by patents. The mechanisms and modes of action remain undisclosed in open literature. Therefore, the methodology explained constitutes a useful tool for the petroleum industry.
Solvents are used as carriers of the active molecules, and they play a critical role in the performance of a commercial demulsifier. Polar aromatic hydrocarbons are soluble in water, and are frequently used as dissolvent in the formulation of commercial products. Careful consideration is necessary of the solvent is surfactant dispersions then its addition in crude oil is most effective. An inadequate choice of solvent may result in a delay in the rate of breaking of emulsion and therefore other variables of the process physical o chemical are needed in order to improve crude oil desalter operations. Pereira shows that the solvent role can be understood using the criteria of proportional regime with solvents mixtures [7]. The position of optimum concentration of demulsifier (CD*) is influenced by the nature of the solvent.
Here it was explained the approach from the optimum formulation concept to assist emulsion breaking in desalting process. As part of the stabilization mechanism is accepted that the water droplets are stabilized by the formation of a mechanically strong and viscoelastic film at the interface composed of asphaltenes.
In the case of water-in-crude emulsions, a balanced optimum formulation is attained by adding demulsifiers which are hydrophilic to the lipophilic natural surfactants contained in the crude oil. This study aimed to determine an optimal and to relate the nature and concentration of demulsifier products added to the amphiphilic mixture at the interface. Proportional and saturation regimens can explaine the impact of formulation parameters, such as solvent, alcohols, kind and concentration of demulsifier, amoung others as useful tool by the formulating personal of petroleum industry.
Juan Pereira is grateful to the projects 2010-03 CDCH-UC and PEII2012-1184 PEI-FONACIT for funding.
Nowadays, bio-based molecules are more and more popular and used in everyday consumer products. Certain molecules such as phenolic compounds (PCs) are very appreciated for their biological activities which make it possible to fight against aging or to act as an antibacterial or anti-oxidant agent. Phenolic compounds are secondary metabolites of plants and are present in plant biomass as well as in agro-industrial by-products [1]. The latter are currently used in sectors with low added value such as methanization or animal feed. To provide additional value to its agro-industrial co-products, phenolic compounds could be extracted and concentrated [2]. For this, separation processes will have to be implemented and optimized. Thus, maximizing the extraction of phenolic compounds has become a topic of interest which would improve the profitability of crops and by-products resulting from their industrial transformation [3].
Many studies focus on maximizing extraction efficiency by optimizing using OFAT. This method, which seems simpler, is often either time consuming or leads to partial conclusions (e.g. no interpretation of the interactions between variables). Thus, to achieve such an optimization, it is recommended, if conditions allow it, to use the response surface methodology. RSM is a mathematical and statistical tool for exploring the relationships between several explanatory variables - called factors - and one or more variables to be optimized, called response(s). RSM is particularly relevant when the response is suspected to evolve in a curved way.
In this chapter, we will focus on the application of RSM for optimizing the extraction of phenolic compounds from
The studies reported in Table 1 deal with the optimization of the extraction process of phenolic compounds from Brassica. These all relate to the use of a design of experiments (DoE). OFAT optimization has been excluded.
Biomass | Extraction process | DOE | Factors | Response | Model | Ref |
---|---|---|---|---|---|---|
Mustard bran ( | CSE | CCF |
| Sinapine yield, sinapine purity | Second-order polynomial | [4] |
Rapeseed cake ( | CSE | 23 full factorial |
| TPC | Second-order polynomial | [10] |
Mustard seeds ( | CSE | Simplex centroid mixture | Mixture of solvents:
| TPC, AA | Quadratic or cubic regression | [11] |
Mustard bran ( | CSE | D-optimal |
| TPC | Second-order polynomial | [12] |
Canola meal ( | Accelerated Solvent Extraction | Factorial |
| TPC, TFC | First-order polynomial | [13] |
CSE | CC |
| TPA, TAA, AA | Second-order polynomial | [14] | |
CSE | CC |
|
| Second order polynomial | [15] | |
Red Cabbage ( | UAE | BB |
| TPC | Second order polynomial | [16] |
Broccoli leaves | CSE | CC |
| AA | Second order polynomial | [17] |
Rapeseed stems ( | UAE | BB |
| TPC | Second order polynomial | [18] |
White cabbage | UAE | CC rotational |
| Phenolic compound content (HPLC) | - | [19] |
Mustard seed ( | UAE | CC |
| TPC, AA | Second order polynomial | [20] |
Canola seed cake ( | MAE | BB |
| TPC, TFC, AA | Second order polynomial | [21] |
UAE enzymatic extraction |
| |||||
Broccoli ( | Alkaline Hydrolysis and UAE | BB |
| - | Second order polynomial | [22] |
Cauliflower Waste ( | UAE | 33 factorial |
| TPC | First-order polynomial | [23] |
Canola seed cake ( | CSE | Orthogonal experiment |
| AA | First order polynomial | [24] |
Maca flour ( | CSE | BB |
| TPC, AA | Second order polynomial | [25] |
Broccoli | MAE | CC |
| - | - | [26] |
Purple Cabbage | MAE | CC |
| Yield of proanthocyanidins | Second order polynomial | [27] |
Fresh cabbage | CSE | CC |
| TPC, AA | Second order polynomial | [28] |
Studies dealing with the extraction of PCs from
Twenty papers have been identified on various raw materials belonging to
The operating conditions the most often optimized are the extraction temperature, the solvent concentration in water, the solid-to-matter ratio, and the extraction time. Some specific conditions can also be investigated such as ultrasonic or microwave power when UAE and MAE are carried out.
The predicted responses are diverse whether they are measurement of individual phenolic content obtained by HPLC, total phenolic compounds (TPC), or content of total flavonoid (TFC), or antioxidant activity (AA) which can be measured by different methods (Table 2).
Assay | X1 | X2 | X3 | T(°C) | E (%v/v) | S/M (mL/gBDM) |
---|---|---|---|---|---|---|
1 | −1 | −1 | −1 | 45 | 45 | 10 |
2 | +1 | −1 | −1 | 75 | 45 | 10 |
3 | −1 | +1 | −1 | 45 | 95 | 10 |
4 | +1 | +1 | −1 | 75 | 95 | 10 |
5 | −1 | −1 | +1 | 45 | 45 | 30 |
6 | +1 | −1 | +1 | 75 | 45 | 30 |
7 | −1 | +1 | +1 | 45 | 95 | 30 |
8 | +1 | +1 | +1 | 75 | 95 | 30 |
9 | −1 | 0 | 0 | 45 | 70 | 20 |
10 | +1 | 0 | 0 | 75 | 70 | 20 |
11 | 0 | −1 | 0 | 60 | 45 | 20 |
12 | 0 | +1 | 0 | 60 | 95 | 20 |
13 | 0 | 0 | −1 | 60 | 70 | 10 |
14 | 0 | 0 | +1 | 60 | 70 | 30 |
15 | 0 | 0 | 0 | 60 | 70 | 20 |
16 | 0 | 0 | 0 | 60 | 70 | 20 |
17 | 0 | 0 | 0 | 60 | 70 | 20 |
CCF experimental design.
Most studies have used RSM to model and/or predict responses. A mixture design was also used to determine the composition of an extraction solvent from three pure solvents; a simplex centroid mixture was carried out [3]. Some studies model responses using first order polynomial equations. These models are obtained from factorial design of experiments [5, 15, 16]. Concerning the implementation of the RSM, the experimental design carried out are mainly Box–Behnken (BB) [8, 10, 13, 14, 17] and Central Composite (CC) [1, 6, 7, 9, 18, 19, 20]. We also found a D-optimal [4] and a full factorial [2]. However, these DoEs are rarely associated with RSM. The predictions made by RSM are associated with second order polynomial models.
Compared to all the studies that exist in the literature on the extraction of phenolic compounds from
Mustard bran is one of the main by-products of the mustard seed industry whose production peaked at 710 thousand tonnes in 2018 [29]. By-products from their processing represent up to 60%w of seeds [30]. Mustard bran is rich in water with a content between 53 ± 1%. The dry matter is mainly composed of proteins (27 ± 7%), lipids (18 ± 1%), carbohydrates (34 ± 5%) and ash (12 ± 5%) [30, 31, 32].
Phenolic compounds represent between 1 and 4% of the wet matter of defatted mustard seeds [33]. They are mainly derivatives of sinapic acid, present at 90% as sinapine with relatively small amounts of sinapic acid. Sinapine can be used directly due to its many bioactivities [5, 34] or be hydrolyzed to sinapic acid by chemical or enzymatic means [35]. Thus, our work will focus on the extraction of sinapine from mustard bran. Moreover, bio-based sinapic acid is highly sought after thanks to its many applications, whether in cosmetics (anti-aging, anti-UV) or in the field of polymers [6, 8].
Thus, the implementation of a green extraction process to recover sinapine seems particularly relevant. The most widely used process in the various studies found in the literature is conventional solvent extraction (CSE). This is a solid/liquid extraction, the liquid being a solvent whose properties will define the sustainability of the process. Solvents such as acetone, methanol, ethanol or water, as well as a mixture, have been used [36]. To follow the principles of green extraction [37], the extraction process developed will use aqueous ethanol as solvent, the percentage of which will be determined during the optimization of the process.
Mustard bran, was supplied by Charbonneaux-Brabant (Reims, France). Mustard (
A CSE using an ethanol/water mixture was implemented to remove sinapine from mustard bran. A fixed volume of 100 mL of solvent was used for each experiment. The extraction temperature was regulated with a digital thermometer in contact with the solvent and connected to the heating plate (IKA-RCT). Magnetic stirring was ensured throughout the duration of the extraction (2 h). Centrifugation was used (4713 g, 10 min) to separate the liquid extract from the solid residue. The sinapine content was measured by HPLC. More details on the materials and the methods can be found in Reugoat
RSM is the recommended approach to optimize process operating conditions, for example to maximize extraction yield or minimize impurity content. Indeed, the implementation of the RSM, and therefore of a design of experiments, makes it possible to minimize the number of experiments, to determine the quadratic effect of a factor or the interaction between several factors and to obtain a high precision on the prediction of an optimal value.
The implementation of RSM requires the identification of the factors that will be involved in the model. Thus, RSM is often used after a screening plan which allows the discrimination of the operating conditions leading to a significant variation in the response. Sometimes, prior knowledge of the process is sufficient to avoid the screening step and RSM can be applied after arbitrary choice of factors by the experimenter. RSM is a relevant approach if the response surface is suspected to be curved. Indeed, the equation of the model used includes quadratic terms which make it possible to translate the curvature of the response.
In order to apply RSM, it is necessary to follow a rigorous approach so as not to end up with wrong conclusions or an unusable data set for the prediction of an optimum. This approach is illustrated in Figure 1.
Methodology for the implementation of RSM.
For each step, the reasoning adopted for our case study will be detailed, the choices will be explained so that the methodology can be easily implemented on other cases.
The objective of the optimization study must be defined according to the overall objective of the application. In our case, the operating conditions of the extraction process leading to a maximum yield of sinapine are sought. However, the global objective of the application is to produce sinapine, that is, to obtain a high purity sinapine extract. Thus, a second variable to be optimized emerges in addition to the yield of sinapine: the purity of the sinapine extract. Under such considerations, the optimum operating conditions sought will be a compromise between those allowing to maximize the yield of sinapine and the ones that maximize the purity of sinapine. Failure to correctly define the objective may lead to an incorrect definition of the responses, factors and their levels and thus induce a partial conclusion at the end of the study.
A response is defined as a variable to be explained. For the choice of responses, it is necessary to ensure that the measurement tools are sufficiently repeatable. Indeed, in statistics, it is common to say that the more the value is dispersed the more it will be difficult to highlight significant differences and therefore to obtain a valid prediction model. This is why the presence of a triplicate in the DOE is essential to quantify the repeatability of the measurement. If it is too large, the DOE will not be able to generate a valid model.
In our case study, the two responses to be optimized are the yield of sinapine in % (Y1) and the purity of sinapine in % (Y2) defined by Eq. (1, 2)
with Csinapine the sinapine content measured by HPLC in mg/L, Vsolvent the volume of solvent added during the extraction and mBDM the mass of dry matter in mustard bran.
with msinapine (g) the mass of sinapine in the extract determined from
A factor is defined as a variable that provides information to explain a response. Two strategies can be used to define the factors: to apply a screening plan (factorial or Plackett-Burman) or to use expertise on the process. In our case, the factors were chosen based on prior knowledge about the extraction process [4]. Note that the factors must be independent for the implementation of the experimental design. This should be checked before establishing the matrix of experiments.
According to theory, the liquid/solid extraction processes are influenced by a set of parameters which can modify their efficiency. These relate to: (i) the equipment used (stirring power, the configuration of the reactor), (ii) the operating conditions (extraction time, extraction temperature and pressure), and (iii) the biomass and the solvent (solvent-to-matter ratio, state of the biomass, nature of the solvent).
Some of these parameters are often fixed in the design of the experiments. Indeed, for laboratory experiments, the extraction reactor is always the same as well as the stirring system (type and power). Conventionally, the extraction time corresponds to the time required to reach the equilibrium. In our case, the biomass is wet and in the form of bran, so it cannot be crushed or sieved. This parameter cannot be taken as a factor. In addition, two constraints were imposed: to conduct the experiments at atmospheric pressure and working with ethanol (pure or aqueous) to design a sustainable process. Thus, the parameters that could be included as factors in the design of experiments are the solvent-to-matter ratio, the extraction temperature and the ethanol concentration. These parameters being independent, three factors will be used in models developed using RSM. The last point to be defined is the variation range of each factor.
Technological limits exist for the choice of extraction temperatures. The experimental domain cannot be extended above 75°C to avoid evaporation phenomena due to the boiling temperature of ethanol. Thus, the extraction temperature will be able to vary between room temperature and 75°C. However, according to the literature, it does not seem interesting to carry out experiments at temperatures close to room temperature. Indeed, it is known that an increase of temperature allows to improve the extraction of phenolic compounds. A range of values too large can adversely affect the quality and accuracy of the prediction model. We have, therefore, chosen to limit our temperature range between 45°C and 75°C.
There is also a technological limit for this factor. Indeed, it is not possible to extract with less than 10 mL per gram of mustard bran. In addition, the objective being not to consume too much solvent, no more than 30 mL per gram of mustard bran will be used. Thus, the range of the S/M factor will be between 10 and 30 mL/g.
No technological limit was found for this factor. The use of extreme values (water or pure ethanol) is not interesting because the better extraction yields are obtained with aqueous ethanol. According to preliminary experiments, to maximize the yield of sinapine (Y1), the values to be studied should be between 40 and 80%. Considering the purity of sinapine (Y2), the values to be studied should be between 60 and 100% in order to limit the extraction of impurities such as sugars and proteins.
To define the range of variation of the ethanol concentration, we merged the two previous intervals by removing the extreme values so as not to widen the range of values to be studied too much. Thus, ethanol concentrations between 45 and 95% were studied in the design of experiments.
The two most used design of experiments for the implementation of RSM are the composite center (CC) and Box–Behnken (BB) designs.
For a same number of factors and levels, a BB design generates fewer experiments than a CC design. However, BB designs have a certain rigidity in their implementation since the number of levels per factor is fixed. In addition, the BB designs do not include in the experiments the extreme values of the variation ranges of the factors. This can sometimes constitute a problem, when a precise knowledge of the interval is available and/or when the extreme values want to be tested.
In addition, CC design is to be able to integrate preliminary experiments. Thus, the results of certain experiments present in the screening plan carried out upstream can be used as experiments in the CC design. Thus, the number of new assays to realize will decrease.
The CCF design belongs to the category of the CC design. The experiments defined are located in the center of each face of the experimental domain.
In our application, a CCF design was used to optimize the extraction process for the recovery of sinapine. A total of 17 experiments including a repetition at the central point constitutes the set of the experiments to implement RSM. The different assays are presented Table 2 in the form of coded and uncoded variables.
X1 (extraction temperature; 45, 60, 75°C), X2 (concentration of ethanol; 45–70-95%v/v) and X3 (solvent-to-matter ratio; 10, 20, 30 mL/gBDM) are the independent variables used to explain the responses Y1 (sinapine yield on the mustard bran dry matter in g/gBDM) and Y2 (sinapine purity on the extract dry matter in %EDM).
The experimental data were fitted using a second-order polynomial (Eq. (3)):
where
This step corresponds to data collection. Assays can be performed in random order. The material and methods of analysis were briefly introduced. More details can be found in Reungoat et al. (2020) [1].
Once the data has been collected, they are processed by a software to generate a model and indicators that allow its quality to be assessed (fit to the data, ability to predict). The software used to carry out our case study is the commercial software MODDE v.12.0 (Umetrics AB, Sweden).
First, it is necessary to determine whether the model should be reduced. Reducing a model means removing variables whose coefficients are not significant. Significance tests are carried out for this purpose. The p-values obtained indicate whether the value of the coefficient can be considered equal to 0. In this case, the factors are considered to have no effect on the response. Table 3 presents the scaled and centered coefficients of the model associated with each term as well as the results of the significance test for each coefficient.
The p-values in red in Table 3 indicate the significant coefficients and factors to keep in the model.
The significant coefficients are β0 (constant), β1 (T) and β22 (E*E). Since the quadratic term E*E is significant, the variable E cannot be removed from the model. Thus, the factors to keep are temperature and ethanol. The S/M ratio has no effect on the sinapine yield. The data must be reprocessed by the software keeping the variables T, E and E*E. New values are found for the coefficients of the reduced model. Sinapine yield can be predicted according to Eq. (4) with unscaled coefficients.
The significant coefficients are β0 (constant), β1 (T), β2 (E), β3 (S/M), β23 (E*S/M) and β22 (E*E). Thus, all the factors should be kept. The data must be processed by the software by keeping the variables T, E, S/M, E*S/M and E*E. New values are found for the coefficients of the reduced model. Sinapine purity can be predicted according to Eq. (5) with unscaled coefficients.
Secondly, the indicators calculated on each reduced model are interpreted to assess whether the correlation between the model and the experimental data is acceptable and whether these models can be considered as good prediction tools. These indicators are presented in Table 4.
Variables | Coefficients scaled and centered | Y1 | Y2 | ||
---|---|---|---|---|---|
Value | p-value | Value | p-value | ||
constant | β0 | 8.398 | < 0.001 | 3.491 | < 0.001 |
T | β1 | 0.622 | <0.01 | 0.140 | 0.030 |
E | β2 | −0.038 | 0.755 | 0.353 | <0.01 |
S/M | β3 | −0.021 | 0.863 | −0.231 | <0.01 |
T*E | β12 | 0.098 | 0.382 | 0.010 | 0.817 |
T*S/M | β13 | 0.227 | 0.070 | −0.022 | 0.584 |
E*S/M | β23 | −0.037 | 0.745 | −0.417 | <0.001 |
T*T | β11 | −0.136 | 0.472 | −0.061 | 0.473 |
E*E | β22 | −0.745 | <0.01 | −0.388 | 0.007 |
S/M*S/M | β33 | −0.258 | 0.194 | −0.121 | 0.185 |
Values of the model coefficients and the p-values of their significance tests.
Indicators | Reduced model (Y1) | Reduced model (Y2) |
---|---|---|
R2 | 0.90 | 0.97 |
R2 adj | 0.86 | 0.94 |
Model regression (p value) | 0.00003 | 0.00002 |
Reproducibility | 0.98 | 0.99 |
Condition number | 2.88 | 4.67 |
Indicators to assess the fit and quality of reduced models.
The coefficients of determination being close to 1, the reduced models have a good accuracy in their prediction. The values of the adjusted coefficients of determination are high enough to suggest a satisfactory correlation between the values predicted by the model and the values observed by the experiments. The p-values obtained by the ANOVA on the model regression are less than 0.01% which validates the models obtained. The condition number determines the correct orthogonality of the two models because it does not exceed 10. Each model reproducibility is also excellent with a value close to 1. All these statistical parameters indicate that the relationships between the variables and the responses are well described by the models.
In order to determine the optimal operating conditions for each response, the 3D response surfaces will be plotted. In a second time, the software optimizer tool based on the Nelder–Mead simplex method was implemented to obtain the optimal operating conditions. Figure 2 presents the evolution of Y1 according to the extraction temperature, the ethanol concentration for a solid-to-matter ratio of 10 mL/g.
3D response surface for a solid-to-matter ratio of 10 mL/g. for Y1.
Variations of the sinapine yield from 5.3 to 8.9 mg/gBDM were found among the 17 experiments of the CCF design.
As can be seen on Figure 2, the sinapine yield evolves in a parabolic shape. This can be explained by a strong influence of the quadratic term of the ethanol concentration. The maximum sinapine yield is achieved in the range 65–80% ethanol. The extraction temperature has a positive effect on the sinapine yield as observed in Figure 2 with the inclination of the response surface towards the high temperature zone.
The optimal operating conditions determined for Y1 by the software MODDE are 70% ethanol, 75°C.
An experimental sinapine yield of 8.8 ± 0.1 mg/g was achieved under these conditions.
Figure 3 presents the evolution of Y2 according to the extraction temperature, the ethanol concentration for a solid-to-matter ratio of 10 mL/gBDM.
3D response surface for a solid-to-matter ratio of 10 mL/g
Variations of the sinapine purity from 1.4%EDM and 4.4%EDM were found among the 17 experiments of the CCF design.
For a ratio of 20, the response surface is flat. Quadratic terms have little influence. The extract, containing the most sinapine compared to other extracted solutes, is obtained for a maximum temperature and ethanol concentration. This may be due to low solubility of proteins, sugars and minerals in ethanol compared to sinapine. However, an increase of the solvent-to-matter ratio increases the solubility of those impurities and decreases the sinapine purity in the extract.
The optimal operating conditions determined for Y2 by the software MODDE are 100% ethanol, 75°C and, 10 mL/gBDM. An experimental sinapine purity of 4.4 ± 0.1% EDM was achieved under these conditions.
Since the two optima are not the same, it will be necessary to find the operating conditions allowing to maximize the two responses at the same time. Figure 4 presents the response surfaces for Y1 and Y2 on the same graph.
3D response surface for a solid-to-matter ratio of 10 mL/gBDM for Y1 and Y2.
The MODDE software has determined that the optimal operating conditions that will provide the highest yield of sinapine while maintaining high purity, are 83% ethanol, 75°C, and 10 mL/gBDM. An experimental sinapine yield of 8.0 ± 0.1 mg/g was obtained under these conditions with a purity of 4.2 ± 0.1% EDM.
The last step to be carried out is the validation of the models on new experiments. For this, experiments were realized in duplicate under optimal conditions corresponding to the maximization of Y1 and for the compromise between Y1 and Y2. Student’s tests were performed to determine if the predicted values given by the models can be considered equivalent to the observed values. The results are shown in Table 5.
S/M (10 mL/gBDM) | Predicted values | Observed values | p-value (Student-tests) |
---|---|---|---|
Sinapine yield (mg/gBDM) | |||
70% EtOH, 75°C | 8.7 ± 0.2 mg/g | 8.8 ± 0.1 mg/g | 0.26 |
83% EtOH, 75°C | 8.2 ± 0.2 mg/g | 8.0 ± 0.1 mg/g | 0.11 |
Sinapine purity (%EDM) | |||
70% EtOH, 75°C | 3.6 ± 0.7% | 3.5 ± 0.2% | 0.43 |
83% EtOH, 75°C | 4.0 ± 0.7% | 4.2 ± 0.1% | 0.30 |
Validation of the models by performing student tests between predicted and observed values.
Experimental values correspond to predicted values since p-value>0.05. Thus, models developed by RSM are validated and can be used as prediction tool.
Concerning the extraction of sinapine from mustard bran, a CCF design was used to optimize the extraction process. A total of 17 experiments including a repetition at the central point constituted the set of the experiments to implement the RSM.
Two prediction models have thus been developed. These models have been validated, making it possible to predict the yield and the purity of sinapine from the operating conditions of the extraction process (extraction temperature, ethanol concentration and solvent-to-matter ratio).
An optimal sinapine content of 8.8 ± 0.1 mg/g was obtained at 75°C, 70% ethanol and 10 mL/gBDM whereas an optimal purity of sinapine in the extract (4.2 ± 0.1% EDM) was achieved under different operating conditions (75°C,100% ethanol and 10 mL/gBDM).
Wishing to situate us as close as possible to the 2 optima, the MODDE software determined that the most appropriate operating conditions were 75°C, 83% ethanol and 10 mL/gBDM. The loss in yield and purity remains low since the sinapine yield of 8.0 ± 0.1 mg/g and a purity of 4.0 ± 0.1% EDM are obtained.
The use of rigorous mathematical tools for optimization in process engineering remains under-exploited as we have shown for the extraction of phenolic compounds from
The case study presented was supported by Extractis (Amiens, France). We would like to thank the Region Grand Est, the Conseil Départemental de la Marne and the Grand Reims for their financial support, as well as Charbonneaux-Brabant for providing the mustard bran.
There is no conflict of interest.
Antioxidant activity
Accelerated solvent extraction
Box–Behnken
Central Composite
Central Composite Face Centered
Conventional Solvent Extraction
Ethanol concentration
Microwave-accelerated extraction
Phenolic compounds
Pulsed electric field
Solvent to Matter ratio
Temperature
Total Antioxidant Activity
Total of Phenolic Acids
Total Phenol Content
Ultrasound-accelerated extraction
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This chapter describes a tiered approach to NFM, commencing with strategic modelling to identify a range of NFM opportunities (tree-planting, distributed runoff attenuation features, and soil structure improvements), and their potential benefits, before engagement with catchment partners, and prioritisation of areas for more detailed hydrological modelling and uncertainty analysis. NFM measures pose some fundamental challenges in modelling their contribution to flood risk management because they are often highly distributed, can influence multiple catchment processes, and evidence for their effectiveness at the large scale is uncertain. This demands we model the ‘upstream’ in more detail so that we can assess the effectiveness of many small-scale changes at the large-scale. We demonstrate an approach to address these challenges employing the fast, high resolution, fully-distributed inundation model JFLOW, and visualisation of potential benefits in map form. These are used to engage catchment managers who can prioritise areas for potential deployment of NFM measures, where more detailed modelling may be targeted. We then demonstrate a framework applying the semi-distributed Dynamic TOPMODEL in which uncertainty plays an integral role in the decision-making process.",book:{id:"6018",slug:"flood-risk-management",title:"Flood Risk Management",fullTitle:"Flood Risk Management"},signatures:"Barry Hankin, Peter Metcalfe, David Johnson, Nick A. Chappell,\nTrevor Page, Iain Craigen, Rob Lamb and Keith Beven",authors:[{id:"203276",title:"Dr.",name:"Barry",middleName:null,surname:"Hankin",slug:"barry-hankin",fullName:"Barry Hankin"}]},{id:"55369",doi:"10.5772/intechopen.68924",title:"One- and Two-Dimensional Hydrological Modelling and Their Uncertainties",slug:"one-and-two-dimensional-hydrological-modelling-and-their-uncertainties",totalDownloads:2718,totalCrossrefCites:3,totalDimensionsCites:12,abstract:"Earth processes, which occur in land, air and ocean in different environment and at different scales, are very complex. Flooding is also a part of the complex processes, which need to be assessed accurately to know the accurate spatial and temporal changes of flooding and their causes. Hydrological modelling has been used by several researchers in river and floodplain modelling for flood analysis. In this chapter, factors affecting flash flood, possible options of basic input parameters in one- and two-dimensional hydrological models in data sparse environment, some case studies and uncertainty in hydrological modelling were discussed. This discussion will help the readers to understand the flooding factors, selection of input parameters in data sparse environment, a brief insight of one- and two-dimensional hydrological models and uncertainties in their input and model parameters and model structures.",book:{id:"6018",slug:"flood-risk-management",title:"Flood Risk Management",fullTitle:"Flood Risk Management"},signatures:"Mohd Talha Anees, Khiruddin Abdullah, Mohd Nawawi Mohd\nNordin, Nik Norulaini Nik Ab Rahman, Muhammad Izzuddin Syakir\nand Mohd. Omar Abdul Kadir",authors:[{id:"11196",title:"Dr.",name:"Khiruddin",middleName:null,surname:"Abdullah",slug:"khiruddin-abdullah",fullName:"Khiruddin Abdullah"},{id:"151303",title:"Prof.",name:"Nik Norulaini",middleName:null,surname:"Ab Rahman",slug:"nik-norulaini-ab-rahman",fullName:"Nik Norulaini Ab Rahman"},{id:"151344",title:"Prof.",name:"Mohd Omar",middleName:null,surname:"Ab Kadir",slug:"mohd-omar-ab-kadir",fullName:"Mohd Omar Ab Kadir"},{id:"201647",title:"Mr.",name:"Mohd Talha",middleName:null,surname:"Anees",slug:"mohd-talha-anees",fullName:"Mohd Talha Anees"},{id:"203217",title:"Prof.",name:"Mohd Nawawi",middleName:null,surname:"Mohd Nordin",slug:"mohd-nawawi-mohd-nordin",fullName:"Mohd Nawawi Mohd Nordin"},{id:"203218",title:"Dr.",name:"Muhammad Izzuddin",middleName:null,surname:"Syakir Ishak",slug:"muhammad-izzuddin-syakir-ishak",fullName:"Muhammad Izzuddin Syakir Ishak"}]},{id:"55735",doi:"10.5772/intechopen.69139",title:"Understanding Flood Risk Management in Asia: Concepts and Challenges",slug:"understanding-flood-risk-management-in-asia-concepts-and-challenges",totalDownloads:1941,totalCrossrefCites:6,totalDimensionsCites:11,abstract:"In this chapter, an attempt is made to review the behavior of flood in Asian region and mechanism of flood risk management adopted among Asian nations. Flood is the most frequent natural disaster at present and vulnerability is widespread across the globe. Though, Asian region is on a knife-edge. Distribution of natural disasters in Asia followed by economic damage and human killing is illustrated in this chapter. In addition, discourse of China, Pakistan, India, Bangladesh, Indonesia, Nepal, Vietnam, and Sri Lanka on flood risk management is examined. Flood risk management policies framed by these nations over the period of time are synthesized. Research and investment on forecasting, planning, preparedness, assessment, evaluation, and mitigation of flood risk are explained. This synthesis can present a pathway for better response and flood management for debated Asian countries through filling the identified policy gaps. This chapter also urges a need of holistic and inter-countries research and cross country analysis followed by increased funding for sustainable management of risk.",book:{id:"6018",slug:"flood-risk-management",title:"Flood Risk Management",fullTitle:"Flood Risk Management"},signatures:"Saleem Ashraf, Muhammad Luqman, Muhammad Iftikhar, Ijaz\nAshraf and Zakaria Yousaf Hassan",authors:[{id:"202027",title:"Dr.",name:"Muhammad Saleem",middleName:null,surname:"Ashraf",slug:"muhammad-saleem-ashraf",fullName:"Muhammad Saleem Ashraf"}]},{id:"45003",doi:"10.5772/55472",title:"Impact of Hurricane Katrina on the Louisiana HIV/AIDS Epidemic: A Socio-Ecological Perspective",slug:"impact-of-hurricane-katrina-on-the-louisiana-hiv-aids-epidemic-a-socio-ecological-perspective",totalDownloads:1909,totalCrossrefCites:2,totalDimensionsCites:6,abstract:null,book:{id:"3507",slug:"natural-disasters-multifaceted-aspects-in-management-and-impact-assessment",title:"Natural Disasters",fullTitle:"Natural Disasters - Multifaceted Aspects in Management and Impact Assessment"},signatures:"William T. Robinson",authors:[{id:"161386",title:"Associate Prof.",name:"William",middleName:null,surname:"Robinson",slug:"william-robinson",fullName:"William Robinson"}]},{id:"55628",doi:"10.5772/intechopen.68912",title:"Flood Risk Mapping in the Amazon",slug:"flood-risk-mapping-in-the-amazon",totalDownloads:1639,totalCrossrefCites:3,totalDimensionsCites:6,abstract:"Floods are part of the natural and cultural life in the Amazon. However, the issues and management of fluvial-disaster risks are poorly studied. Among the reasons for the lack of studies, the Amazon region has several gaps in information ranging from inadequate regional maps to spatially unsystematic local data. Flood patterns differ in urban and rural areas. Severe large-scale flooding took place during the previous and the current decades, such as those that occurred in 2009 and 2012. Between 1991 and 2010, official recorded data indicate about 3,292,888 people were affected in 6 regional states of the Amazon (Acre, Amapá, Amazonas, Pará, Rondônia, and Roraima) considering 7 different hazards. Because of the extensive damages, the national government started a mapping program for cities in Brazil that have a history of facing significant flood risks. The aim of this chapter is to analyse the flood-risk mapping conditions in the Amazon.",book:{id:"6018",slug:"flood-risk-management",title:"Flood Risk Management",fullTitle:"Flood Risk Management"},signatures:"Milena Marília Nogueira de Andrade, Iris Celeste Nascimento\nBandeira, Dianne Danielle Farias Fonseca, Paulo Eduardo Silva\nBezerra, Ádanna de Souza Andrade and Rodrigo Silva de Oliveira",authors:[{id:"203296",title:"Dr.",name:"Milena",middleName:"Marília Nogueira De",surname:"Andrade",slug:"milena-andrade",fullName:"Milena Andrade"},{id:"203302",title:"MSc.",name:"Iris Celeste Nascimento",middleName:null,surname:"Bandeira",slug:"iris-celeste-nascimento-bandeira",fullName:"Iris Celeste Nascimento Bandeira"},{id:"203352",title:"Mr.",name:"Paulo Eduardo Silva",middleName:null,surname:"Bezerra",slug:"paulo-eduardo-silva-bezerra",fullName:"Paulo Eduardo Silva Bezerra"},{id:"203353",title:"Mrs.",name:"Ádanna",middleName:null,surname:"Andrade",slug:"adanna-andrade",fullName:"Ádanna Andrade"},{id:"203354",title:"Mr.",name:"Rodrigo",middleName:null,surname:"Oliveira",slug:"rodrigo-oliveira",fullName:"Rodrigo Oliveira"},{id:"203421",title:"Mrs.",name:"Dianne",middleName:null,surname:"Fonseca",slug:"dianne-fonseca",fullName:"Dianne Fonseca"}]}],mostDownloadedChaptersLast30Days:[{id:"56370",title:"Flood Risk Management in Mexico",slug:"flood-risk-management-in-mexico",totalDownloads:1575,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"Mexico receives an average annual rainfall of 740 mm, which are distributed in the hydrological cycle as follows: 72% evapotranspiration, 21% becomes runoff and 6% as aquifer recharge. Within the Mexican territory, exist a great diversity of climates and high spatial and temporal variability in water resources availability. In the period 2000–2015, damages from hydrometeorological phenomena in Mexico represented between 60 and 99% of total damages and losses at national level due to natural and socioorganizational events. Considering global climate change impact on the selection, design and implementation of flood control measures, represents a major challenge, since the level of certainty regarding its influence on the variables involved, remains insufficient. This chapter provides a description of the main elements directly linked to flooding in México, such as a high spatial and temporal variability in water resources availability and presence of tropical cyclones in both coasts and climate change. A brief summary of the main disasters caused by hydrometeorological phenomena, the annual cost of the damages, the main non‐structural measures for flood control and the intervention from the Mexican Institute of Water Technology in the use, development and spread of technology focused on flood risk management are also included.",book:{id:"6018",slug:"flood-risk-management",title:"Flood Risk Management",fullTitle:"Flood Risk Management"},signatures:"Felipe I. Arreguín-Cortés and Claudia Elizabeth Cervantes-Jaimes",authors:[{id:"203037",title:"Dr.",name:"Felipe I.",middleName:null,surname:"Arreguin-Cortés",slug:"felipe-i.-arreguin-cortes",fullName:"Felipe I. Arreguin-Cortés"},{id:"204319",title:"M.Sc.",name:"Claudia Elizabeth",middleName:null,surname:"Cervantes-Jaimes",slug:"claudia-elizabeth-cervantes-jaimes",fullName:"Claudia Elizabeth Cervantes-Jaimes"}]},{id:"55369",title:"One- and Two-Dimensional Hydrological Modelling and Their Uncertainties",slug:"one-and-two-dimensional-hydrological-modelling-and-their-uncertainties",totalDownloads:2716,totalCrossrefCites:3,totalDimensionsCites:12,abstract:"Earth processes, which occur in land, air and ocean in different environment and at different scales, are very complex. Flooding is also a part of the complex processes, which need to be assessed accurately to know the accurate spatial and temporal changes of flooding and their causes. Hydrological modelling has been used by several researchers in river and floodplain modelling for flood analysis. In this chapter, factors affecting flash flood, possible options of basic input parameters in one- and two-dimensional hydrological models in data sparse environment, some case studies and uncertainty in hydrological modelling were discussed. This discussion will help the readers to understand the flooding factors, selection of input parameters in data sparse environment, a brief insight of one- and two-dimensional hydrological models and uncertainties in their input and model parameters and model structures.",book:{id:"6018",slug:"flood-risk-management",title:"Flood Risk Management",fullTitle:"Flood Risk Management"},signatures:"Mohd Talha Anees, Khiruddin Abdullah, Mohd Nawawi Mohd\nNordin, Nik Norulaini Nik Ab Rahman, Muhammad Izzuddin Syakir\nand Mohd. Omar Abdul Kadir",authors:[{id:"11196",title:"Dr.",name:"Khiruddin",middleName:null,surname:"Abdullah",slug:"khiruddin-abdullah",fullName:"Khiruddin Abdullah"},{id:"151303",title:"Prof.",name:"Nik Norulaini",middleName:null,surname:"Ab Rahman",slug:"nik-norulaini-ab-rahman",fullName:"Nik Norulaini Ab Rahman"},{id:"151344",title:"Prof.",name:"Mohd Omar",middleName:null,surname:"Ab Kadir",slug:"mohd-omar-ab-kadir",fullName:"Mohd Omar Ab Kadir"},{id:"201647",title:"Mr.",name:"Mohd Talha",middleName:null,surname:"Anees",slug:"mohd-talha-anees",fullName:"Mohd Talha Anees"},{id:"203217",title:"Prof.",name:"Mohd Nawawi",middleName:null,surname:"Mohd Nordin",slug:"mohd-nawawi-mohd-nordin",fullName:"Mohd Nawawi Mohd Nordin"},{id:"203218",title:"Dr.",name:"Muhammad Izzuddin",middleName:null,surname:"Syakir Ishak",slug:"muhammad-izzuddin-syakir-ishak",fullName:"Muhammad Izzuddin Syakir Ishak"}]},{id:"55139",title:"Estimating Flood Quantiles on the Basis of Multi-Event Rainfall Simulation",slug:"estimating-flood-quantiles-on-the-basis-of-multi-event-rainfall-simulation",totalDownloads:1391,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"This chapter provides an insight into a new approach to estimating the flood quantiles based on rainfall-runoff modelling using multiple rainfall events. The approach is based on the prior knowledge about the probability distribution of annual maximum daily totals of rainfall in catchments, random disaggregation of the totals into hourly values and rainfall-runoff modelling. The new presented method called MESEF (Multi-Event Simulation of Extreme Flood) combines design event method based on single-rainfall event modelling and continuous simulation method used for estimating the maximum discharges of a given exceedance probability using rainfall-runoff models. The MESEF method considers varied moisture conditions in model catchment before the occurrence of rainfalls. To verify the efficiency of the proposed method, a comparison was carried out between the values of flood quantiles estimated by the MESEF method and the flood quantiles estimated by direct method. The proposed approach was tested in two catchments in the Upper Vistula River basin. The results of the MESEF method in both catchments were satisfactory; however, in order to verify its effectiveness, more research is needed within catchments of diverse features and landscape. Special attention should be paid to the proportion of moisture conditions that is a crucial factor in future use of the MESEF method in uncontrolled catchments.",book:{id:"6018",slug:"flood-risk-management",title:"Flood Risk Management",fullTitle:"Flood Risk Management"},signatures:"Elżbieta Jarosińska and Katarzyna Pierzga",authors:[{id:"202772",title:"Ph.D.",name:"Elżbieta",middleName:null,surname:"Jarosińska",slug:"elzbieta-jarosinska",fullName:"Elżbieta Jarosińska"},{id:"202833",title:"MSc.",name:"Katarzyna",middleName:null,surname:"Pierzga",slug:"katarzyna-pierzga",fullName:"Katarzyna Pierzga"}]},{id:"55735",title:"Understanding Flood Risk Management in Asia: Concepts and Challenges",slug:"understanding-flood-risk-management-in-asia-concepts-and-challenges",totalDownloads:1936,totalCrossrefCites:6,totalDimensionsCites:10,abstract:"In this chapter, an attempt is made to review the behavior of flood in Asian region and mechanism of flood risk management adopted among Asian nations. Flood is the most frequent natural disaster at present and vulnerability is widespread across the globe. Though, Asian region is on a knife-edge. Distribution of natural disasters in Asia followed by economic damage and human killing is illustrated in this chapter. In addition, discourse of China, Pakistan, India, Bangladesh, Indonesia, Nepal, Vietnam, and Sri Lanka on flood risk management is examined. Flood risk management policies framed by these nations over the period of time are synthesized. Research and investment on forecasting, planning, preparedness, assessment, evaluation, and mitigation of flood risk are explained. This synthesis can present a pathway for better response and flood management for debated Asian countries through filling the identified policy gaps. This chapter also urges a need of holistic and inter-countries research and cross country analysis followed by increased funding for sustainable management of risk.",book:{id:"6018",slug:"flood-risk-management",title:"Flood Risk Management",fullTitle:"Flood Risk Management"},signatures:"Saleem Ashraf, Muhammad Luqman, Muhammad Iftikhar, Ijaz\nAshraf and Zakaria Yousaf Hassan",authors:[{id:"202027",title:"Dr.",name:"Muhammad Saleem",middleName:null,surname:"Ashraf",slug:"muhammad-saleem-ashraf",fullName:"Muhammad Saleem Ashraf"}]},{id:"71247",title:"Dealing with Local Tsunami on Pakistan Coast",slug:"dealing-with-local-tsunami-on-pakistan-coast",totalDownloads:592,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Tsunami originating from a local source can arrive at Pakistan coastline within minutes. In the absence of a comprehensive and well-coordinated management plan, the fast-approaching tsunami might wreak havoc on the coast. To combat such a threat, a wide range of short- and long-term mitigation measures are needed to be taken by several government and private sector organizations as well as security agencies. Around 1000-km coastline is divided administratively into two provinces of Baluchistan and Sindh and further into seven districts. Most of the coastal communities were severely affected by an earthquake of magnitude 8+ on 28 November 1945 followed by a devastating tsunami. In contrast to the level of posed hazard and multiple-fold increase in vulnerabilities since then, the risk mitigation efforts are trivial and least coordinated. It is important to provide stakeholders with a set of prerequisite information and guidelines on standardized format to develop their organizational strategies and course of action for earthquake and tsunami risk mitigation in a well-coordinated manner, from local to the national level.",book:{id:"8979",slug:"tsunami-damage-assessment-and-medical-triage",title:"Tsunami",fullTitle:"Tsunami - Damage Assessment and Medical Triage"},signatures:"Ghazala Naeem",authors:[{id:"193736",title:"Ms.",name:"Ghazala",middleName:null,surname:"Naeem",slug:"ghazala-naeem",fullName:"Ghazala Naeem"}]}],onlineFirstChaptersFilter:{topicId:"665",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},allSeries:{pteSeriesList:[],lsSeriesList:[],hsSeriesList:[],sshSeriesList:[],testimonialsList:[]},series:{item:{id:"22",title:"Business, Management and Economics",doi:"10.5772/intechopen.100359",issn:"2753-894X",scope:"\r\n\tThis series will provide a comprehensive overview of recent research trends in business and management, economics, and marketing. Topics will include asset liability management, financial consequences of the financial crisis and covid-19, financial accounting, mergers and acquisitions, management accounting, SMEs, financial markets, corporate finance and governance, managerial technology and innovation, resource management and sustainable development, social entrepreneurship, corporate responsibility, ethics and accountability, microeconomics, labour economics, macroeconomics, public economics, financial economics, econometrics, direct marketing, creative marketing, internet marketing, market planning and forecasting, brand management, market segmentation and targeting and other topics under business and management. This book series will focus on various aspects of business and management whose in-depth understanding is critical for business and company management to function effectively during this uncertain time of financial crisis, Covid-19 pandemic, and military activity in Europe.
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