Maximum acceptable concentrations of metals in drinking water according to the US EPA [6].
\r\n\tIn the book the theory and practice of microwave heating are discussed. The intended scope covers the results of recent research related to the generation, transmission and reception of microwave energy, its application in the field of organic and inorganic chemistry, physics of plasma processes, industrial microwave drying and sintering, as well as in medicine for therapeutic effects on internal organs and tissues of the human body and microbiology. Both theoretical and experimental studies are anticipated.
\r\n\r\n\tThe book aims to be of interest not only for specialists in the field of theory and practice of microwave heating but also for readers of non-specialists in the field of microwave technology and those who want to study in general terms the problem of interaction of the electromagnetic field with objects of living and nonliving nature.
",isbn:"978-1-83968-227-8",printIsbn:"978-1-83968-226-1",pdfIsbn:"978-1-83968-228-5",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"8f6a41e4f5ce0e9c48628516d7c92050",bookSignature:"Prof. Gennadiy Churyumov",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10089.jpg",keywords:"Electromagnetic Wave, Microwave Energy Application, Electromagnetic Energy Generation, Intelligent Microwave Heating, Microwave Organic Chemistry, Microwave Reactor, Microwave Discharge, Microwave Plasma, Microwave Drying System, Tissue Microwave Heating, Measurement Automation, Industrial Microwave Process",numberOfDownloads:224,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"July 3rd 2020",dateEndSecondStepPublish:"July 24th 2020",dateEndThirdStepPublish:"September 22nd 2020",dateEndFourthStepPublish:"December 11th 2020",dateEndFifthStepPublish:"February 9th 2021",remainingDaysToSecondStep:"7 months",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:"Prof. Gennadiy I. Churyumov is a professor at two universities: Kharkiv National University of Radio Electronics, and Harbin Institute of Technology and a senior IEEE member.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"216155",title:"Prof.",name:"Gennadiy",middleName:null,surname:"Churyumov",slug:"gennadiy-churyumov",fullName:"Gennadiy Churyumov",profilePictureURL:"https://mts.intechopen.com/storage/users/216155/images/system/216155.jfif",biography:"Gennadiy I. Churyumov (M’96–SM’00) received the Dipl.-Ing. degree in Electronics Engineering and his Ph.D. degree from the Kharkiv Institute of Radio Electronics, Kharkiv, Ukraine, in 1974 and 1981, respectively, as well as the D.Sc. degree from the Institute of Radio Physics and Electronics, National Academy of Sciences of Ukraine, Kharkiv, Ukraine, in 1997. \n\nHe is a professor at two universities: Kharkiv National University of Radio Electronics, and Harbin Institute of Technology. \n\nHe is currently the Head of a Microwave & Optoelectronics Lab at the Department of Electronics Engineering at the Kharkiv National University of Radio Electronics. \n\nHis general research interests lie in the area of 2-D and 3-D computer modeling of electron-wave processes in vacuum tubes (magnetrons and TWTs), simulation techniques of electromagnetic problems and nonlinear phenomena, as well as high-power microwaves, including electromagnetic compatibility and survivability. \n\nHis current activity concentrates on the practical aspects of the application of microwave technologies.",institutionString:"Kharkiv National University of Radio Electronics (NURE)",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:null}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"24",title:"Technology",slug:"technology"}],chapters:[{id:"74623",title:"Influence of the Microwaves on the Sol-Gel Syntheses and on the Properties of the Resulting Oxide Nanostructures",slug:"influence-of-the-microwaves-on-the-sol-gel-syntheses-and-on-the-properties-of-the-resulting-oxide-na",totalDownloads:94,totalCrossrefCites:0,authors:[null]},{id:"75284",title:"Microwave-Assisted Extraction of Bioactive Compounds (Review)",slug:"microwave-assisted-extraction-of-bioactive-compounds-review",totalDownloads:12,totalCrossrefCites:0,authors:[null]},{id:"75087",title:"Experimental Investigation on the Effect of Microwave Heating on Rock Cracking and Their Mechanical Properties",slug:"experimental-investigation-on-the-effect-of-microwave-heating-on-rock-cracking-and-their-mechanical-",totalDownloads:28,totalCrossrefCites:0,authors:[null]},{id:"74338",title:"Microwave Synthesized Functional Dyes",slug:"microwave-synthesized-functional-dyes",totalDownloads:21,totalCrossrefCites:0,authors:[null]},{id:"74744",title:"Doping of Semiconductors at Nanoscale with Microwave Heating (Overview)",slug:"doping-of-semiconductors-at-nanoscale-with-microwave-heating-overview",totalDownloads:45,totalCrossrefCites:0,authors:[null]},{id:"74664",title:"Microwave-Assisted Solid Extraction from Natural Matrices",slug:"microwave-assisted-solid-extraction-from-natural-matrices",totalDownloads:25,totalCrossrefCites:0,authors:[null]}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"252211",firstName:"Sara",lastName:"Debeuc",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/252211/images/7239_n.png",email:"sara.d@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"45214",title:"Bioremediation of Waters Contaminated with Heavy Metals Using Moringa oleifera Seeds as Biosorbent",doi:"10.5772/56157",slug:"bioremediation-of-waters-contaminated-with-heavy-metals-using-moringa-oleifera-seeds-as-biosorbent",body:'Water is not only a resource, it is a life source. It is well established that water is important for life. Water is useful for several purposes including agricultural, industrial, household, recreational and environmental activities. Despite its extensive use, in most parts of the world water is a scarce resource. Ninety percent of the water on earth is seawater in the oceans, only three percent is fresh water and just over two thirds of this is frozen in glaciers and polar ice caps. The remaining unfrozen freshwater is found mainly as groundwater, with only a small fraction present above ground or in the air. Thus, almost all of the fresh water that is available for human use is either contained in soils and rocks below the surface, called groundwater, or in rivers and lakes.
The contamination of soil and water resources with environmentally harmful chemicals represents a problem of great concern not only in relation to the biota in the receiving environment, but also to humans. The continuing growth in industrialization and urbanization has led to the natural environment being exposed to ever increasing levels of toxic elements, such as heavy metals. Approximately 10% of the wastes produced by developed countries contain heavy metals. Figure 1 gives some indication of the amounts of metal-containing waste produced in developed countries. Much of the discharge of metals to the environment comes from mining, followed by agriculture activities.
Waste containing heavy metals produced in developed countries [1].
Many different definitions have been proposed for heavy metals, some based on density, some on atomic number or atomic weight, and others on chemical properties or toxicity, which are not necessarily appropriate. For example, cobalt, iron, copper, manganese, molybdenium, vanadium, strontium and zinc are required to perform vital functions in the body and therefore cannot be considered as compounds with high toxicity or ecotoxic properties. Regarding the meaning of the term “heavy metal” it was found that there can be misinterpretation due to the contradictory definitions and lack of a coherent scientific basis [2].
In conventional usage “heavy” implies high density and “metal” refers to the pure element or an alloy of metallic elements. According to Duffus [2], a new classification should reflect our understanding of the chemical basis of toxicity and allow toxic effects to be predicted. Various publications have used the term “heavy metals” related to chemical hazards and this definition will also be used herein. Among the classes of contaminants, heavy metals deserve greater concern because of their high toxicity, accumulation and retention in the human body. Moreover, heavy metals do not degrade to harmless end products [3, 4]. It is well established that the presence of heavy metals in the environment, even in moderate concentrations, is responsible for producing a variety of illnesses of the central nervous system (manganese, mercury, lead, arsenic), the kidneys or liver (mercury, lead, cadmium, copper) and skin, bones, or teeth (nickel, cadmium, copper, chromium) [5].
Due to its properties, water is particularly vulnerable to contamination with heavy metals. Table 1 shows the maximum limits for some metals in drinking water, according to the US Environmental Protection Agency (US EPA) [6]. The US EPA requires that lead, cadmium and total chromium levels in drinking water do not to exceed 0.015, 0.005 and 0.1 mg L-1, respectively. Corresponding values for other metals are presented in Table 1.
\n\t\t\t\t\tElement\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tUS EPA Limit (mg L-1)\n\t\t\t\t | \n\t\t\t
Antimony | \n\t\t\t0.006 | \n\t\t
Arsenic | \n\t\t\t0.010 | \n\t\t
Beryllium | \n\t\t\t0.004 | \n\t\t
Chromium (total) | \n\t\t\t0.1 | \n\t\t
Cadmium | \n\t\t\t0.005 | \n\t\t
Cupper | \n\t\t\t1.3 | \n\t\t
Lead | \n\t\t\t0.015 | \n\t\t
Mercury | \n\t\t\t0.002 | \n\t\t
Selenium | \n\t\t\t0.05 | \n\t\t
Silver | \n\t\t\t0.1 | \n\t\t
Maximum acceptable concentrations of metals in drinking water according to the US EPA [6].
Within this context, and considering that heavy metals do not decay and are toxic even at low concentrations, it is necessary to remove them from various types of water samples. Of the conventional treatments used for the removal of metals from liquid waste, chemical precipitation and ion exchange are the predominant methods. However, they have some limitations since they are uneconomical and do not completely remove metal ions, and thus new removal processes are required [7-9]. Table 2 illustrates in more detail the advantages and limitations of the traditional methods applied to treat effluents.
\n\t\t\t\t\tProcess \n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tDisadvantages\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tAdvantages\n\t\t\t\t | \n\t\t\t
Precipitation and filtration | \n\t\t\tFor high concentrations Separation difficult Not very effective Produces sludge | \n\t\t\tSimple Low cost | \n\t\t
Biological oxidation and reduction | \n\t\t\tWhen biological systems are used the conversion rate is slow and susceptible to adverse weather conditions | \n\t\t\tLow cost | \n\t\t
Chemical oxidation and reduction | \n\t\t\tRequires chemicals Applied to high concentrations Expensive | \n\t\t\tMineralization Enables metal recovery | \n\t\t
Reverse osmosis | \n\t\t\tHigh pressures Expensive | \n\t\t\tPure effluent (for recycling) \n\t\t | \n\t
Ion exchange | \n\t\tResponsive to the presence of particles Resins of high cost | \n\t\tEffective Enables metal recovery | \n\t
Adsorption | \n\t\tNot effective for some metals | \n\t\tConventional sorbents (coal) | \n\t
Evaporation | \n\t\tRequires an energy source Expensive Produces sludge | \n\t\tPure effluent obtained | \n\t
Traditional process used in wastewater treatment: advantages and disadvantages [10].
For these reasons, alternative technologies that are practical, efficient and cost effective for low metal concentrations are being investigated. Biosorption in the removal of toxic heavy metals is especially suited as a \'nonpolluted \' wastewater treatment step because it can produce close to drinking water quality from initial metal concentrations of 1-100 mg L-1, providing final concentrations of < 0.01-0.1 mg L-1 [11]. Biosorption has been defined as the ability of certain biomolecules or types of biomass to bind and concentrate selected ions or other molecules from aqueous solutions. It should to be distinguished from bioaccumulation which is based on active metabolic transport; biosorption by dead biomass is a passive process based mainly on the affinity between the biosorbent and the sorbate [12]. The biosorption of heavy metals by non-living biomass of plant origin is an innovative and alternative technology for the removal of these pollutants from aqueous solution and offers several advantages such as low-cost biosorbents, high efficiency, minimization of chemical and/or biological sludge, and regeneration of the biosorbent [13].
Recently, natural adsorbents have been proposed for removing metal ions due to their good adsorption capacity. Technologies based on the use of such materials offer a good alternative to conventional technologies for metal recovery. In this context, Moringa oleifera represents an alternative material for this purpose [14-16].
Moringa oleifera is the best known species of the Moringaceae family. Moringaceae is a family of plants belonging to the order Brassicales. It is represented by fourteen species and a single genus (Moringa), being considered an angiosperm plant. It is a shrub or small tree which is fast growing, reaching 12 meters in height. It has an open crown and usually a single trunk (Figure 2). It grows mainly in the semi-arid tropics and subtropics. Since its preferred habitat is dry sandy soil, it tolerates poor soils, such as those in coastal areas [17].
Tree of Moringa oleifera species [18].
Native to northern India, it currently grows in many regions including Africa, Arabia, Southeast Asia, the Pacific and Caribbean Islands and South America [3, 16, 19]. It is cultivated for its food, medicinal and culinary value and its leaves, fruits and roots are the parts used. It is commonly known as the ‘horseradish’ tree arising from the taste of a condiment prepared from the roots or ‘drumstick’ tree due to the shape of the pods. Figures 3 and 4 show the pods and seeds of this tree. M. oleifera has a host of other country-specific vernacular names, an indication of the significance of the tree around the world [16, 20-23].
Pods of Moringa oleifera [18].
Seeds of Moringa oleifera [18].
Research has focused on the use of M. oleifera seeds and fruits in water purification and the treatment of turbid water is the best-known application. The seeds of various species contain cationic polyelectrolytes which have proved to be effective in the treatment of water, as a substitute for aluminum sulfate. Interest in the study of natural coagulants for water clarification is not new. The coagulant is obtained from a byproduct of oil extraction and the residue can be used as a fertilizer or processed for animal fodder. Compared to the commonly used coagulant chemicals, Moringa oleifera has a number of advantages including low cost, biodegradable sludge production and lower sludge volume, and also it does not affect the pH of the water. Apart from turbidity removal, M. oleifera seeds also possess antimicrobial properties [24, 25], although the mechanism by which seeds act upon microorganisms is not yet fully understood.
Tissues of M. oleifera from a wide variety of sources have been analyzed for glucosinolates and phenolics (flavonoids, anthocyanins, proanthocyanidins, and cinnamates). M. oleifera seeds reportedly contain 4-(α-L-rhamnopyranosyloxy)-benzylglucosinolate in high concentrations. Roots of M. oleifera have high concentrations of both 4-(α-L-rhamnopyranosyloxy)-benzylglucosinolate and benzyl glucosinolate. Leaves contain 4-(α-L-rhamnopyranosyloxy)-benzylglucosinolate and three monoacetyl isomers of this glucosinolate and only 4-(α-L-rhamnopyranosyloxy)-benzylglucosinolate has been detected in M. oleifera bark tissue [26]. Every glucosinolate contains a central carbon atom which is bonded to the thioglucose group (forming a sulfated ketoxime) via a sulfur atom and to a sulfate group via a nitrogen atom. These functional groups containing sulfur and nitrogen are good metal sequesters from aqueous solution. The leaves of M. oleifera reportedly contain quercetin-3-O-glucoside and quercetin-3-O-(6‘ ‘-malonyl-glucoside), and lower amounts of kaempferol-3-O-glucoside and kaempferol-3-O-(6‘ ‘-malonyl-glucoside), along with 3-caffeoylquinic acid and 5-caffeoylquinic acid. Neither proanthocyanidins nor anthocyanins have been detected in any of the tissues [26]. Although M. oleifera seeds have been most widely applied as a coagulant agent, many studies have been performed in order to explore other potential applications of this material, especially in the removal of metals from aqueous systems.
Since Moringa oleifera seeds have the ability to retain metals, it is necessary to define and to understand the functional groups responsible for the adsorption phenomenon. Biosorption by dead biomass or by some molecules and/or their active groups is a passive process based mainly on the affinity between the biosorbent and the sorbate. In this case, the metal is sequestered by chemical sites naturally present in the biomass. The diagram in Figure 5 illustrates the main steps in this process. In most cases, the biosorption process is rapid and takes place under normal temperature and pressure. After the process of phase separation a biomass “charged” with metal ions and an effluent free of contamination are obtained. Two paths can be followed to deal with the “contaminated” biomass, the one of greatest interest being biosorbent regeneration and metal recovery. This process is the most attractive because biomass can be used for the removal of other metal species from other contaminated effluents. The other option is the destruction of the biomass, which offers no possibility of reuse.
Main steps in biosorption process [27].
The mechanisms associated with heavy metal biosorption by biomass are still not clear; however, it is important to note that this process is not based on a single mechanism. Since metals may be present in the aquatic environment in dissolved or particulate forms, they can be dissolved as free hydrated ions or as complex ions chelated with inorganic ligands, such as hydroxide, chloride or carbonate, or they may be complexed with organic ligands such as amines, humic or fulvic acids and proteins. Metal sequestration occurs through complex mechanisms, including ion-exchange and complexation, and it is quite possible that at least some of these mechanisms act simultaneously to varying degrees depending on the biomass, the metal ion and the solution environment.
In reference [28] indicated that ion-exchange is an important concept in biosorption, because it explains many of the observations made during heavy metal uptake experiments. In this context, the term ion-exchange does not explicitly identify the mechanism of heavy metal binding to biomass, and electrostatic or London–van der Waals forces should be considered as the precise mechanism of chemical binding, i.e., ionic and covalent bonds. Figure 6 provides a schematic representation of an ion-exchange mechanism for a biosorbent material where “Me” represents a metal with valence +2.
Schematic diagram of an ion exchange mechanism [29].
The seeds of Moringa oleifera and its parts can be classified as lignocellulosic adsorbents, consisting mainly of cellulose, hemicellulose and lignin. These functional groups are comprised of macromolecules that have the ability to absorb metal ions through ion exchange or complexation [30] phenomena which occur on the surface of the material through the interaction of the metal with the functional groups present. In order to understand the adsorption process it is also important to characterize the biomass material. Several techniques can be used to define the functional groups responsible for the adsorption phenomenon.
Infrared spectroscopy is an important technique in the qualitative analysis of organic compounds, widely used in the areas of natural products, organic synthesis and transformations. It is applied as a tool to elucidate the functional groups which may be present in substances [31], particularly with respect to the availability of the main groups involved in adsorption phenomena.
Figure 7 shows FT-IR spectra for Moringa oleifera seeds which verify the presence of many functional groups, indicating the complex nature of this material. The bandwidth centered at 3420 cm-1 may be attributed to the stretching of OH bonds present in proteins, fatty acids, carbohydrates and lignin units [32]. Due to the high content of protein present in the seed there is also a contribution in this region from N-H stretching of the amide bond. The peaks present at 2923 cm-1 and 2852 cm-1, respectively, correspond to asymmetric and symmetric stretching of the C-H bond of the CH2 group. Due to the high intensity of these bands it is possible to assign them to the predominantly lipid component of the seed, which is present in a high proportion similar to that of protein [33]. In the region of 1800-1500 cm-1 a number of overlapping bands are observed and between 1750 and 1630 cm-1 this can be attributed to C=O stretching. Due to the heterogeneous nature of the seed, the carbonyl group may be bonded to different neighborhoods as part of the fatty acids of the lipid portion or amides of the protein portion. The carbonyl component that appears due to the presence of lipids can be seen at 1740 and 1715 cm-1, as can be observed in the infrared spectra as small peaks, and the shoulders forming part of the main band that appears at 1658 cm-1 are attributed to the carbonyl amides present in the protein portion. The peak observed at 1587 cm-1 may be attributed to stretching connecting CN and also the deformation of the N-H bond present in the proteins of seeds [34, 35].
FT-IR spectrum of Moringa oleifera seeds. The arrows indicate the maximum signal obtained [36].
Among the various techniques for material characterization, the X-ray diffraction (XRD) technique is recommended for the evaluation of the presence of crystalline phases present in natural materials. In general, we can classify materials as amorphous, semicrystalline or crystalline. Figure 8 shows the XRD patterns for M. oleifera seeds. The XRD pattern for crushed seeds, due to the high amount of oils and proteins present in the composition of the material which represent around 69% of the total mass [36], shows unresolved signals (predominantly amorphous). For this reason intact seeds are analyzed, constituting a complex matrix comprised of a wide variation of substances including proteins, lipid structures and, to a lesser extent, carbohydrates. It was possible to separate a broad peak at around 2θ equals 10º. The presence of this peak is probably associated with the diffraction of the protein constituent surrounded by other components which have a more amorphous pattern [37]. The amorphous nature of the biosorbent suggests that the metal ion could more easily penetrate the biosorbent surface.
Thermogravimetric (TG) analysis was used to characterize the decomposition stages and thermal stability determined through the mass loss of a substance subjected to a constant heating rate for a specified time. The mass loss curve for a sample of Moringa oleifera seeds can be observed in Figure 9, showing a typical profile that indicates several stages of the decomposition process. This thermogravimetric curve verifies the sample heterogeneity, since the intermediates formed are a mixture of several components. The mass loss curve can be divided into three stages: i) the first step occurs from 30°C to 128°C where a mass loss in the order of 8%, associated with water desorption, was observed. The amount of water loss from seeds determined by this technique is similar to the value of 8.9% found in [38]; ii) in the second step 32% of mass loss was observed in the temperature range of 128–268°C. This stage occurs due to the decomposition of organic matter, probably the protein component, present in seeds; and iii) the third step occurs from 268°C to 541°C with decomposition of the greater part of the seed components, which probably includes fatty acids, for example, oleic acid has a boiling point of 360°C. At 950°C a total residue of around 14.6% was observed, due to the ash content and probably inorganic oxides.
X–ray diffractogram for Moringa oleifera seeds [36].
Thermogravimetric curve for Moringa oleifera seeds [36].
The morphological characteristics of the crushed seeds obtained using a scanning electron microscopy (SEM) can be seen in Figure 10. The results reveal that the material exhibits a relatively porous matrix with heterogeneous pore distribution. This feature is attributed to the fact that the whole seed comprises a wide variety of biomass components. The presence of some deformations on the surface of the plant tissue can be observed, containing available sites, from which it is possible to infer that the adsorbent provides favorable conditions for the adsorption of metal species in the interstices [35].
Scanning electron micrographs of Moringa oleifera. In the order of (a) 10 µm and (b) 50 µm [36].
Many variables can influence metal biosorption and experimental parameters such as temperature, stirring time, pH, particle size of the biomass, ionic strength and competition between metal ions can have a significant effect on metal binding to biomass. The biomass mass also influences the adsorption process because as the adsorbent dose increases the number of adsorbent particles also increases and there is greater availability of sites for adsorption. Some of the most important factors affecting metal binding are discussed below. In general, adsorption experiments are carried out in batch mode.
The pH is one of the most important parameters affecting any adsorption process. This dependence is closely related to the acid-base properties of various functional groups on the adsorbent surfaces [39]. The literature shows that a heterogeneous aqueous mixture of M. oleifera seeds contains various functional groups, mainly amino and acids groups. These groups have the ability to interact with metal ions, depending on the pH. An increase in metal adsorption with increasing pH values can be explained on the basis of competition between the proton and metal ions for the same functional groups, and a decrease in the positive surface charge, which results in a higher electrostatic attraction between the biosorbent surface and the metal [40]. Low pH conditions allow hydrogen and hydronium ions to compete with metal binding sites on the biomass, leading to poor uptake. Biosorbent materials primarily contain weak acidic and basic functional groups. It follows from the theory of acid–base equilibrium that, in the pH range of 2.5–5, the binding of heavy metal cations is determined primarily by the dissociation state of the weak acidic groups. Carboxyl groups (–COOH) are important groups for metal uptake by biological materials. At higher solution pH, the solubility of a metal complex decreases sufficiently for its precipitation, leading to a reduced sorption capacity. Therefore, it is recommendable to study biosorption at pH values where precipitation does not occur. Biomasses are materials with an amphoteric character; thus, depending on the pH of the solution, their surfaces can be positively or negatively charged. At pH values greater than the point of zero discharge (pHpzc), the biomass surface becomes negatively charged, favoring the adsorption of cationic species. However, adsorption of anionic species will be favored at pH < pHpzc. The pHpzc of the M. oleifera seeds is between 6.0 and 7.0 [41], indicating that the surface of the biosorbent presents acid characteristics. Figure 11 illustrates the surface charge or the point of zero net proton charge of Moringa\n\t\t\t\toleifera seeds. The surface charge of the seeds is positive at pH < PZC, is neutral at pH = PZC and is negative at pH > PZC. The variation in pH caused by protonation and deprotonation of the adsorbent reflects the presence of functional groups. Table 3 shows the use of components of the M oleifera in the pH range of 2.5 to 8.0.
Point of zero net proton charge of Moringa oleifera seeds.
It has been noted that the temperature can influence the sorption process. Simple physical sorption processes are generally exothermic, i.e., the equilibrium constant decreases with increasing temperature. According to data reported in the literature (Table 3), the binding of the metal to different parts of the M. oleifera plant can be observed when the temperature is raised from 22 to 50 °C.
The contact time (or stirring time) is another important parameter that influences the efficiency of the adsorption process. As can be seen in Table 3, a period of 5 min was chosen for the nickel sorption process and good results were obtained; however, longer times (240 min) are required when using activated carbon.
Moringa oleifera is capable of directly sorbing metal ionic species from aqueous solutions. An interesting characteristic assigned to these biosorbents is the high abrasive content and the relative chemical resistance, allowing them to be subjected to different chemical treatments to increase their affinity and/or specificity for metal ions. Results previously published show the potential use of untreated seeds, although biosorbent materials are generally derived from plant biomass through different kinds of simple procedures. They may be chemically pretreated for better performance and/or suitability for process applications. However, good results have been obtained when the seeds were treated with NaOH. This treatment can remove organic and inorganic matter from the sorbent surface. Chemical treatments are commonly performed employing alkaline solutions or with phosphoric and citric acids [42]. Recently, however, efforts have been made to remove and subsequently also recover metals. Metal-saturated biosorbent materials can be easily regenerated applying a simple (e.g. acidic) wash which then contains a very high concentration of released metals in a small volume, making the solution quite amenable to metal recovery.
\n\t\t\t\t\tMoringa Oleifera\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tModifying agent(s)\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tHeavy metal\n\t\t\t\t | \n\t\t\t\tTemperature (°C) | \n\t\t\t\t\n\t\t\t\t\tpH\n\t\t\t\t | \n\t\t\t\tContact time (min) | \n\t\t\t\t\n\t\t\t\t\tRef. \n\t\t\t\t | \n\t\t\t
Seeds | \n\t\t\tPetroleum ether | \n\t\t\tCd (II) Cu (II) Co (II) Ni (II) Pb (II) | \n\t\t\t22 | \n\t\t\t3.5 – 8.0 | \n\t\t\t60 | \n\t\t\t[4] | \n\t\t
Leaves | \n\t\t\tNaOH and Citric acid | \n\t\t\tCd (II) Cu(II) Ni(II) | \n\t\t\t40 | \n\t\t\t5.0 | \n\t\t\t50 | \n\t\t\t[32] | \n\t\t
Bark | \n\t\t\tOriginal state | \n\t\t\tNi(II) | \n\t\t\t50 | \n\t\t\t6.0 | \n\t\t\t60 | \n\t\t\t[35] | \n\t\t
Wood | \n\t\t\tActivated carbon | \n\t\t\tCu(II) Ni(II) Zn(II) | \n\t\t\t30 | \n\t\t\t6.0 | \n\t\t\t240 | \n\t\t\t[31] | \n\t\t
Leaves | \n\t\t\tNaOH and Citric acid | \n\t\t\tPb(II) | \n\t\t\t40 | \n\t\t\t5.0 | \n\t\t\t50 | \n\t\t\t[34] | \n\t\t
Bark | \n\t\t\tOriginal state | \n\t\t\tPb(II) | \n\t\t\t25 | \n\t\t\t5.0 | \n\t\t\t30 | \n\t\t\t[19] | \n\t\t
Pod | \n\t\t\tOriginal state NaOH, H2SO4\n\t\t\t CTAB HCl Ca(OH)2\n\t\t\t Triton X-100 H3PO4\n\t\t\t Al(OH)3\n\t\t\t SDS | \n\t\t\tZn(II) | \n\t\t\t30 | \n\t\t\t7.0 | \n\t\t\t50 | \n\t\t\t[16] | \n\t\t
Shelled seeds | \n\t\t\tOriginal state | \n\t\t\tCd(II) Cr(III) Ni(II) | \n\t\t\t- | \n\t\t\t6.5 6.5 7.5 | \n\t\t\t40 | \n\t\t\t[15] | \n\t\t
Shells | \n\t\t\tOriginal state | \n\t\t\tAs (III) As (V) | \n\t\t\t- | \n\t\t\t7.5 2.5 | \n\t\t\t60 | \n\t\t\t[43] | \n\t\t
Husk and pods | \n\t\t\tUnmodified CTAB H3PO4\n\t\t\t H2SO4\n\t\t\t HCl | \n\t\t\tPb(II) | \n\t\t\t30 | \n\t\t\t5.8 | \n\t\t\t120 | \n\t\t\t[3] | \n\t\t
Shelled seeds | \n\t\t\tOriginal state | \n\t\t\tCd(II) | \n\t\t\t- | \n\t\t\t6.5 | \n\t\t\t40 | \n\t\t\t[14] | \n\t\t
Seeds | \n\t\t\tOriginal state | \n\t\t\tAg(I) | \n\t\t\t25 | \n\t\t\t6.5 | \n\t\t\t20 | \n\t\t\t[36] | \n\t\t
Seeds | \n\t\t\tNaOH | \n\t\t\tNi(II) | \n\t\t\t25 | \n\t\t\t4.0-6.0 | \n\t\t\t5 | \n\t\t\t[44] | \n\t\t
Study parameters for the removal of metal ions using Moringa oleifera.
CTAB: Cetyl trimethylammonium bromide, SDS: Sodium dodecyl sulfate
An important physicochemical aspect in terms of the evaluation of sorption processes is the sorption equilibrium. Adsorption isotherms are a basic requirement in understanding how the adsorbate is distributed between the liquid and solid phases when the adsorption process reaches the equilibrium state [45, 46]. Over the years a wide variety of isotherm models have been introduced. The most commonly used isotherm models include Langmuir [47], Freundlich [48], Dubinin-Radushkevich [49] and Temkin [50].
It can be observed that in most of the cases the Langmuir adsorption model has been successfully used to predict metal adsorption processes. The Langmuir isotherm model assumes monolayer adsorption onto an adsorbent surface containing a finite number of identical sites and without interaction between adsorbed molecules. The Langmuir isotherm model assumes that: each site can accommodate only one molecule or atom; the surface is energetically homogenous; there is no interaction between neighboring adsorbed molecules or atoms; and there are no phase transitions [51]. The Langmuir equation is expressed as follows:
where qe is the amount of metal adsorbed at equilibrium (mg g-1), Ce is the concentration of metal in solution at equilibrium (mg L-1), and qm (mg g-1) and KL (L mg-1) are the Langmuir constants related to the adsorption capacity (amount of adsorbate needed to form a complete monolayer) and adsorption energy, respectively. The constants qm and KL can be calculated from the intercepts and the slopes of the linear plots of Ce/qe versus Ce.
The Freundlich model describes adsorption onto an energetically heterogeneous surface not limited by the monolayer capacity [48]. It can be presented in the following form:
where qe is the amount of metal adsorbed at equilibrium (mg g-1), Ce is the concentration of metal in solution at equilibrium (mg L-1), and Kf (mg g-1)(L mg)(1/n) and n (g L-1) are the Freundlich constants related to the multilayer adsorption capacity and adsorption intensity, respectively. According to the theory, n values between 1 and 10 represent favorable adsorption conditions [52]. Values of Kf and n can be calculated from the slope and intercept of the plot of Log qe versus Log Ce. Experimental adsorption results with high coefficient correlation (R2) values obtained for Freundlich isotherms have been reported as shown in Table 4.
The Dubinin-Radushkevich model has been used to distinguish between physical and chemical adsorption [53]. The Dubinin-Radushkevich is more general than the Langmuir model because it does not assume a homogenous surface or constant sorption. The Dubinin-Radushkevich equation is given by:
where, qm (mg g-1) is the theoretical sorption capacity (mol g-1), ε is the Polanyi potential which is related to the equilibrium concentration and the constant β gives the mean energy of sorption, E (KJ mol-1). The constants qm and β are obtained from the intercept and slope of ln qe versus ε2, respectively. If the magnitude of E is between 8 and 16 KJ mol-1 the adsorption process proceeds by ion-exchange or chemisorptions, while for values of E < 8 KJ mol-1 the adsorption process is of a physical nature [54]. In [31] reported that the sorption energy (E) values obtained with the Dubinin-Radushkevich model showed that the interaction between metal ions and the adsorbent proceeded principally by physical adsorption.
The Temkin isotherm model is based on the assumption that the heat of adsorption of all the molecules in the layer decreases linearly with coverage due to adsorbent-adsorbate interactions, and the adsorption is characterized by a uniform distribution of binding energies, up to a maximum binding energy [50]. The model is represented by the following equation:
where A (L g-1) and B (J mg-1) are Temkin isotherm constants relating to adsorption potential and heat of adsorption, respectively. A plot of qe versus ln Ce gives the values of Temkin constants A and B. In the adsorption of copper, nickel and zinc onto activated carbon produced from Moringa oleifera wood [31] the Temkin isotherm showed a higher correlation coefficient, which may be due to the linear dependence of the heat of adsorption on low or medium coverage. The repulsive force probably occurs between the different adsorbate species or for intrinsic surface heterogeneity may be associated with the linearity.
Table 4 details some of the results for the biosorption studies using Moringa oleifera which have been reported in the literature from 2006 onwards. From this table it is clear that Moringa oleifera shows versatility, removing a variety metals under favorable conditions and is among the most promising metal biosorbents.
Comparing the Langmuir and Freundlich models, M. oleifera seeds demonstrated a good removal capacity for Co(II), Cu(II), Pb(II), Cd(II) and Ag(I), as compared to reports related to other parts of the plant (Table 3). The variations in the removal percentage for metal ions can be explained by the different ionic radii of chemical species. In general, for the single metal solutions, ions with larger ionic radii are preferentially adsorbed. Among the metals tested, Pb(II) has the largest ionic radius and hence shows the highest adsorption percentage, whereas Co(II) presents the lowest level of adsorption [55].
Kinetics models are important in evaluating the basic qualities of an adsorbent as well as the time required for the removal of particular metals, the effectiveness of the adsorbent and the identification of the types of mechanisms involved in an adsorption system [56-58]. In order to investigate the mechanism of biosorption and its potential rate-controlling steps, which include the mass transfer and chemical reaction processes, kinetics models are exploited to test experimental data obtained in kinetics studies. These usually show an initial period of rapid metal adsorption with a subsequent decreased until reaching equilibrium of the system. This occurs due to the rapid adsorption of metallic ions by the surface of the adsorbent followed by a step of slow diffusion of ions from the surface film to the adsorption sites in the micropores which are less accessible [59].
In practice, the kinetic studies are carried out in batch experiments, typically varying the adsorbate concentration, the adsorbent mass, the agitation time and the temperature, as well as the type of adsorbent and adsorbate. Subsequently, the data are processed and used in the linear regression to determine the kinetics model which provides the best fit. However, for the validity of the order of the adsorption process two criteria should be evaluated, the first based on the regression coefficient (R2) and the second on the calculated qe values, which must approach the experimental qe [60]. The main models used to evaluate the kinetics model profile are pseudo-first-order and pseudo-second order. However, other models are also are applied, such as Bangham\'s model and the Weber and Morris sorption kinetic model.
\n\t\t\t\tHeavy metal\n\t\t\t | \n\t\t\t\n\t\t\t\tLangmuir model\n\t\t\t | \n\t\t\t\n\t\t\t\tFreundlich model\n\t\t\t | \n\t\t\t\n\t\t\t\tRef. \n\t\t\t | \n\t\t||||
\n\t\t\t\tqm (mg g-1) | \n\t\t\t\tKL (L mg -1) | \n\t\t\t\tR2\n\t\t\t | \n\t\t\tKf (mg g-1) (L mg)(1/n)\n\t\t | \n\t\tn (g L-1) | \n\t\tR2\n\t\t | \n\t\t||
Cd (II) Cu(II) Ni(II) | \n\t\t\t171.37 167.90 163.88 | \n\t\t\t0.037 0.029 0.023 | \n\t\t\t> 0.99 > 0.99 > 0.99 | \n\t\t\t- | \n\t\t\t- | \n\t\t\t- | \n\t\t\t[32] | \n\t\t
Ni(II) | \n\t\t\t30. 38 | \n\t\t\t0.31 | \n\t\t\t0.9994 | \n\t\t\t- | \n\t\t\t- | \n\t\t\t- | \n\t\t\t[35] | \n\t\t
Cu(II) Zn(II) Ni(II) | \n\t\t\t11.534 17.668 19.084 | \n\t\t\t0.2166 0.1430 0.6165 | \n\t\t\t0.9979 0.9528 0.9973 | \n\t\t\t3.8563 3.7708 - | \n\t\t\t2.9214 2.2528 - | \n\t\t\t0.9976 0.9996 - | \n\t\t\t[31] | \n\t\t
Pb(II) | \n\t\t\t209.54 | \n\t\t\t0.038 | \n\t\t\t> 0.99 | \n\t\t\t- | \n\t\t\t- | \n\t\t\t- | \n\t\t\t[34] | \n\t\t
Ni(II) | \n\t\t\t29.6 | \n\t\t\t- | \n\t\t\t0.9913 | \n\t\t\t- | \n\t\t\t- | \n\t\t\t- | \n\t\t\t[44] | \n\t\t
Ag(II) | \n\t\t\t23.13 | \n\t\t\t0.1586 | \n\t\t\t0.9935 | \n\t\t\t- | \n\t\t\t- | \n\t\t\t- | \n\t\t\t[36] | \n\t\t
Zn(II) | \n\t\t\t52.08 | \n\t\t\t0.150 | \n\t\t\t0.9994 | \n\t\t\t50.35 | \n\t\t\t- | \n\t\t\t0.9953 | \n\t\t\t[16] | \n\t\t
Cd(II) Cr(III) Ni(II) | \n\t\t\t1.06 1.01 0.94 | \n\t\t\t0.51 0.40 0.34 | \n\t\t\t0.94 0.96 0.96 | \n\t\t\t- | \n\t\t\t- | \n\t\t\t- | \n\t\t\t[15] | \n\t\t
As (III) As (V) | \n\t\t\t1.59 2.16 | \n\t\t\t0.04 0.09 | \n\t\t\t0.96 0.98 | \n\t\t\t- | \n\t\t\t- | \n\t\t\t- | \n\t\t\t[43] | \n\t\t
Pb(II) | \n\t\t\t- | \n\t\t\t- | \n\t\t\t0.9981 | \n\t\t\t- | \n\t\t\t- | \n\t\t\t- | \n\t\t\t[3] | \n\t\t
Pb (II) | \n\t\t\t- | \n\t\t\t- | \n\t\t\t- | \n\t\t\t8.6 | \n\t\t\t2.8 | \n\t\t\t0.9981 | \n\t\t\t[19] | \n\t\t
Cd(II) | \n\t\t\t- | \n\t\t\t- | \n\t\t\t- | \n\t\t\t3.04 | \n\t\t\t1.37 | \n\t\t\t- | \n\t\t\t[14] | \n\t\t
Langmuir and Freundlich isotherm parameters for Moringa oleifera.
The pseudo-first order equation, also known as the Lagergren equation, is expressed as follows [16, 61]:
where qt and qe (mg g−1) are the amount of metal ions adsorbed per unit weight of the adsorbent at time t and equilibrium, respectively; and k1 (min−1) is the pseudo-first order rate constant of the sorption process and t (min) is the mixing time [60]. Table 5 presents the data of calculated qe, pseudo-first order rate (k1) and correlation coefficient (R2). This kinetics model is based on the assumption that the adsorption rate is proportional to the number of free sites available, occurring exclusively onto one site per ion [34, 62].
In most studies discrepancies occurred between the value of qe calculated by the pseudo-first order model and the experimental qe, as shown in Table 5, highlighting the inability of this model to describe the kinetics of the metal ion adsorption processes. In general, calculated qe values are smaller than the experimental qe, which may occur because of a time lag, probably due to the presence of the boundary layer or external resistance at the beginning of the sorption process [63]. Considering the papers detailed in Table 5, only in [43] and [14] used only the pseudo-first order kinetics model to examine the data obtained, even though in the latter case the correlation values obtained were relatively low. In [43] noted no change in the adsorption rate constant when varying the concentrations of As(III) and As(V) and therefore this model could describe the adsorption process. In [14] used this model to compare the adsorption rate constants of ternary metal ions and single metal ions and noted that these constants were lower for ternary metal ions. Their explanation for this was that metal ions compete for vacant sites and uptake by binding sites within the shortest possible time.
The pseudo-second-order kinetics model is also based on the assumption that the sorption rate is controlled by a chemical sorption mechanism involving electron sharing or electron transfer between the adsorbent and adsorbate [64]. It can be expressed as:
where qt and qe (mg g−1) are the amount of metal ions adsorbed per unit weight of the adsorbent at time t and equilibrium, respectively; and k2 (g mg-1 min−1) is the pseudo-second order rate constant of the sorption process and t (min) is the mixing time.
Table 5 presents the data of calculated qe, pseudo-second order rate (k2) and correlation coefficient (R2). For most of the pseudo-second order kinetics models the calculated qe values approach the experimental qe values and the correlation coefficients are close to 1, indicating a good ability of this model to describe the kinetics of the metal ion adsorption process. This observation indicates that the rate-limiting steps in the biosorption of metallic ions are chemisorption involving valence forces through the sharing or exchange of electrons between the sorbent and the sorbate, complexation, coordination and/or chelation, in which mass transfer in the solution was not involved.
Considering that neither the pseudo-first-order nor the pseudo-second-order model can identify the diffusion mechanism, other kinetic models are needed to study this process, such as Bangham\'s model and the Weber and Morris sorption kinetics model [65]. The latter model is also known as the intra-particle diffusion model, this process in many cases being the rate-limiting step, which can be determined through the following equation:
where qt (mg g−1) is the amount of metal ions adsorbed per unit weight of the adsorbent at time t, cid (mg g−1) is a constant of Weber and Morris, and kid (mg g−1 min−1/2) is the intra-particle diffusion rate constant and t (min) is the mixing time [66]. The value of the intercept gives an idea of the thickness of the boundary layer, i.e., the larger the intercept the greater the boundary layer effect will be. When there is a complete fit of the model the value of cid should be zero, and the deviation of this constant is due to differences in the mass transfer rate during the initial and final stages of adsorption. This is indicative that there is some degree of boundary layer control and shows that the intra-particle diffusion is not the only rate-limiting step, and thus several processes operating simultaneously may control the adsorption [34].
According to this model, if the plot of qt versus t1/2 gives a straight line, then the sorption process is controlled by intra-particle diffusion, while if the data exhibit multi-linear plots then two or more steps influence the adsorption process [67]. In two studies performed in [32, 19], multi-linear plots were observed with three distinct steps involved in the biosorption, the initial region of the curve relative relating to the adsorption on the external surface. The second region corresponds to the gradual uptake, where the intra-particle diffusion is the rate-limiting step. The final plateau region indicates the equilibrium uptake.
In reference [3] compared different types of carbon through the kid values and observed that the effect of intra-particle diffusion may be significantly increased by chemically modifying the adsorbents. Although none of the data collected in the studies detailed in Table 5 were well-described by the kinetics model proposed by Weber and Moris, the intraparticle diffusion may not be the only rate-limiting step in these studies.
Bangham\'s model evaluates whether pore diffusion is the only rate-controlling step in the adsorption process [65] and can be represented by the following equation:
where qt (mg g−1) is the amount of metal ions adsorbed per unit weight of the adsorbent at time t; co (mg L-1) is the initial metal ion concentration in liquid phase; m (g L‒1) is adsorbent concentration at time t (min); V (L) solution volume, t (min) is the mixing time and ko (L g-1) and σ (σ < 1) are constants of Bangham\'s model. Of the studies published, only in [3] used Bangham’s kinetic model to compare the rate constants for the adsorption of Pb(II) onto different types of functionalized carbon prepared from the seed husks and pods of M. oleifera and thereby assess the efficiency of the functionalization of this material.
The temperature is reportedly an important parameter for the adsorption of metal ions. An increase or decrease in temperature can cause a change in the amount of metal removed or adsorbed by the adsorbent. A change in temperature causes a change in the thermodynamic parameters of free energy (∆G°), enthalpy (∆H°) and entropy (∆S°). These parameters are important to understand the adsorption mechanism [68]. For a given temperature, a phenomenon is considered to be spontaneous if the ∆G° has a negative value. Moreover, if ∆H° is positive the process is endothermic and if it is negative the process is exothermic [69]. Negative values of ∆S° show a decreased randomness or increased order at the metal-biomass interface. The positive value showed a change in the biomass structure during the sorption process, causing an increase in the disorder of the system [68]. The parameters ∆G° (kJ mol-1), ∆H° (kJ mol-1) and ∆S° (J mol-1 K-1) can be evaluated from the following equations [70].
where R(8.314J mol-1 K-1) is the gas constant, T(K) the absolute temperature and Kc(mL g-1) the standard thermodynamic equilibrium constant defined by qe/Ce. ∆H° and ∆S° can be determined from the slope and the intercept of the linear plot of Ln Kc versus 1/T.
The studies performed on Moringa oleifera using chemically-modified leaves for the adsorption of Pb(II) [34], bark for Ni(II) [35] and leaves for Cd(II), Cu(II) and Ni(II) [32] showed the endothermic nature and spontaneity of the adsorption process. The positive values of ∆S° suggest an increase in randomness at the solid/liquid interface with some structural changes in the sorbate.
Although the biosorption of heavy metals from aqueous solutions is a relatively new process that has proven very promising in the removal of contaminants from aqueous effluents, offering significant advantages like the low-cost, availability, profitability, easy of operation and efficiency. Other technologies have also been very attractive ensuring an appropriate process to treat industrial waste effluents [71-77]. However, biosorption is becoming a potential alternative to the existing technologies for the removal and/or recovery of toxic metals from wastewater. The major advantages of biosorption technology are its effectiveness in reducing the concentration of heavy metal ions to very low levels and the use of inexpensive biosorbent materials.
The studies described herein indicate that Moringa oleifera seeds are an alternative sorbent for metal ion removal from contaminated waters. This can be found in most papers which report 60 to 90% removal of metals (Cd(II), Cu(II), Ni(II), Pb(II), As(III), As(V), Cr(III) and Zn(II)). In these cases, not only the seeds were used, but also leaves, bark and pods showing the great versatility of this plant. The results show that even with the high heterogeneity of the matrix confirmed through characterization techniques there is a great potential for the application of these seeds in effluent treatment without component separation, which makes the process economically and technically attractive.
\n\t\t\t\t\tMetal\n\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\tco\n\t\t\t\t\n\t\t\t\t \n\t\t\t\t(mg L−1)\n\t\t\t | \n\t\t\t\n\t\t\t\tqe, exp\n\t\t\t\t \n\t\t\t\t(mg g−1)\n\t\t\t | \n\t\t\t\n\t\t\t\tPseudo-first-order\n\t\t\t | \n\t\t\t\n\t\t\t\tPseudo-second-order\n\t\t\t | \n\t\t\t\n\t\t\t\tRef.\n\t\t\t | \n\t\t|||||
\n\t\t\t\tqe\n\t\t\t\t \n\t\t\t\t(mg g-1)\n\t\t\t | \n\t\t\t\n\t\t\t\tk1 (min−1)\n\t\t\t | \n\t\t\t\n\t\t\t\tR2\n\t\t\t\n\t\t | \n\t\t\n\t\t\tqe (mg g-1)\n\t\t | \n\t\t\n\t\t\tk2 (g mg-1 min-1)\n\t\t | \n\t\t\n\t\t\th0 (mg g−1 min−1)\n\t\t | \n\t\t\n\t\t\tR2\n\t\t\n\t | \n\t\n\t | |||
Zn (II) | \n\t\t50a\n\t\t 50b\n\t\t 50c\n\t | \n\t45.00 45.76 42.80 | \n\t- - - | \n\t- - - | \n\t- - - | \n\t46.94 47.16 43.47 | \n\t4.51 10-4 2.85 10-4 2.04 10-5 | \n\t- - - | \n\t0.997 0.999 0.997 | \n\t[16] | \n\t
Pb(II) | \n\t\t30d\n\t\t 30e\n\t\t 30f\n\t\t 30g\n\t\t 30h\n\t | \n\t- - - - - | \n\t- - - - - | \n\t- - - - - | \n\t- - - - - | \n\t24.57 27.70 28.49 29.08 29.46 | \n\t0.0085 0.0052 0.0060 0.0062 0.0087 | \n\t5.131 3.989 4.868 5.243 7.550 | \n\t0.999 0.998 0.998 0.999 0.999 | \n\t[3] | \n\t
As (III) As (V) | \n\t\t25 50 25 50 | \n\t\t- - - - | \n\t\t- - - - | \n\t\t0.047 0.049 0.063 0.065 | \n\t\t- - - - | \n\t\t- - - - | \n\t\t- - - - | \n\t\t- - - - | \n\t\t- - - - | \n\t\t[43] | \n\t
Cd(II) Cr(III) Ni(II) | \n\t\t25 25 25 | \n\t\t1.06 1.01 0.94 | \n\t\t- - - | \n\t\t0.51 0.40 0.34 | \n\t\t- - - | \n\t\t- - - | \n\t\t- - - | \n\t\t- - - | \n\t\t- - - | \n\t\t[14] | \n\t
Pb(II) | \n\t\t10 25 40 | \n\t\t12.7343 19.8988 23.9233 | \n\t\t- - - | \n\t\t- - - | \n\t\t- - - | \n\t\t13.26 20.64 25.01 | \n\t\t15.35 10.08 9.3 | \n\t\t0.20 0.21 0.23 | \n\t\t0.9974 0.997 0.9995 | \n\t\t[34] | \n\t
Pb(II) | \n\t\t10.4 30.1 50.4 | \n\t\t8.7 10.2 12.5 | \n\t\t- - - | \n\t\t- - - | \n\t\t- - - | \n\t\t8.8 10.3 12.53 | \n\t\t27.8 18.2 12.6 | \n\t\t2.5 1.9 1.6 | \n\t\t0.9999 0.9999 0.9998 | \n\t\t[19] | \n\t
Ni(II) | \n\t\t10 25 50 | \n\t\t9.7 6.74 3.27 | \n\t\t- - - | \n\t\t- - - | \n\t\t- - - | \n\t\t10.29 7.14 3.43 | \n\t\t1.91 2.70 12.74 | \n\t\t2.03 1.38 1.05 | \n\t\t0.9971 0.9964 0.996 | \n\t\t[35] | \n\t
Cu(II) Zn(II) Ni(II) | \n\t\t30 30 30 | \n\t\t8.3406 13.2537 9.5847 | \n\t\t- - - | \n\t\t- - - | \n\t\t- - - | \n\t\t8.3264 13.2450 9.6154 | \n\t\t0.0848 0.2457 0.0957 | \n\t\t- - - | \n\t\t0.9998 1 0.9999 | \n\t\t[31] | \n\t
Cd (II) \n\t\t Cu(II) \n\t\t Ni(II) | \n\t\t10 25 40 10 25 40 10 25 40 | \n\t\t13.54 13.80 20.86 11.92 13.55 16.01 10.24 12.49 14.07 | \n\t\t- - - - - - - - - | \n\t\t- - - - - - - - - | \n\t\t- - - - - - - - - | \n\t\t10.99 10.50 15.24 11.03 12.45 12.86 10.24 12.49 14.07 | \n\t\t1.39 1.46 1.22 3.4 1.66 1.56 1.51 1.31 1.52 | \n\t\t2.73 3.09 5.61 5.58 3.37 4.31 1.70 2.29 3.27 | \n\t\t0.9951 0.9969 0.9981 0.9992 0.9958 0.9977 0.9951 0.9952 0.9967 | \n\t\t[32] | \n\t
Kinetics parameters for metal biosorption using Moringa oleifera.
Biosorption is the most economical and eco-friendly method for the removal of heavy metals from domestic as well as industrial wastewater and it is particularly important to promote the development of biosorption for industrial processes. Notable advantages are: (a) low cost of the biosorbent, (b) high efficiency for metal removal at low concentration, (c) potential for biosorbent regeneration and metal valorization, (d) high sorption and desorption rates, (e) limited generation of secondary residues, and (f) relatively environmentally-friendly life cycle of the material (easy to eliminate compared to conventional resins, for example).
However, after the metal removal from aqueous solutions by the biomass, the recovery of the metal is an important issue. This can be achieved through a metal desorption process, aimed at weakening the metal-biomass linkage. Thus, studies to evaluate the reversibility of the adsorption reactions involved in the biosorption of heavy metals are of great importance. The problems associated with the disposal of exhausted adsorbent can be solved either by its activation or incineration or its disposal after proper treatment. For biosorption and desorption processes, another important aspect is the biosorbent reuse in successive biosorption-desorption cycles, the viability of which is determined by the cost-benefit relationship between the loss in biosorption capacity during the desorption steps and the operational yield in the metal recovery. Thus, further studies need to focus on the development of new clean environmentally-acceptable technologies.
The authors are grateful for financial support from the government agencies Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG), Fundação de Amparo à Pesquisa do Estado de Goiás (FAPEG) and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).
\n\t\t\t\tA\n\t\t\t | \n\t\t\tTemkin isotherm constant relating to adsorption potential, (L g-1) | \n\t\t
\n\t\t\t\tB\n\t\t\t | \n\t\t\tTemkin isotherm constant relating to heat of adsorption, (J mg-1) | \n\t\t
β | \n\t\t\tmean energy of sorption, E (KJ mol-1) | \n\t\t
\n\t\t\t\tco\n\t\t\t\n\t\t | \n\t\tinitial metal ion concentration in liquid phase, (mg L-1) | \n\t
Ce\n\t | \n\tconcentration of metal in solution at equilibrium, (mg L-1) | \n\t
\n\t\t\tc\n\t\t\tid\n\t\t | \n\t\tconstant of the Weber and Morris model, (mg g−1) | \n\t
CAPES | \n\t\tCoordination of Improvement of Higher Education Personnel | \n\t
CNPq | \n\t\tNational Council for Scientific and Technological Development | \n\t
\n\t\t\tε\n\t\t | \n\t\tPolanyi potential which is related to the equilibrium concentration | \n\t
FAPEMIG | \n\t\tResearch Foundation of the State of Minas Gerais | \n\t
FAPEG | \n\t\tResearch Foundation of the State of Goiás | \n\t
FT-IR | \n\t\tFourier transform infrared | \n\t
\n\t\t\t∆G°\n\t\t | \n\t\tfree energy, (kJ mol-1) | \n\t
\n\t\t\t∆H°\n\t\t | \n\t\tenthalpy, (kJ mol-1) | \n\t
\n\t\t\tKc \n\t\t\n\t | \n\tstandard thermodynamic equilibrium constant defined by qe/Ce\n\t, (mL g-1) | \n\t
KL\n\t | \n\tLangmuir constant related to adsorption energy, (L mg-1) | \n\t
Kf\n\t | \n\tFreundlich constant related to the multilayer adsorption capacity | \n\t
\n\t\t\tk1\n\t\t\n\t | \n\tpseudo-first order rate constant of the sorption process, (min−1) | \n\t
\n\t\t\tk2\n\t\t\n\t | \n\tpseudo-second order rate constant of the sorption process, (g mg-1 min−1) | \n\t
\n\t\t\tkid\n\t\t\n\t | \n\tintra-particle diffusion rate constant, (mg g−1 min−1/2) | \n\t
\n\t\t\tko\n\t\t\n\t | \n\tconstant of the Bangham\'s model, (L g-1) | \n\t
\n\t\t\tσ\n\t\t | \n\t\tconstant of the Bangham\'s model | \n\t
\n\t\t\tm\n\t\t | \n\t\tadsorbent concentration at time t (min), (g L‒1) | \n\t
n | \n\t\tFreundlich constant related to the multilayer adsorption intensity | \n\t
qe\n\t | \n\tamount of metal adsorbed per unit weight of the adsorbent at equilibrium, (mg g-1) | \n\t
qm\n\t | \n\tLangmuir constant related to the adsorption capacity, (mg g-1) | \n\t
\n\t\t\tqt\n\t\t\n\t | \n\tamount of metal ions adsorbed per unit weight of the adsorbent at time t, (mg g−1) | \n\t
\n\t\t\tR\n\t\t | \n\t\tgas constant, (8.314J mol-1 K-1) | \n\t
R2\n\t | \n\tcoefficient correlation | \n\t
\n\t\t\t∆S°\n\t\t | \n\t\tentropy, (J mol-1 K-1) | \n\t
SEM | \n\t\tscanning electron microscopy | \n\t
\n\t\t\tt\n\t\t | \n\t\tmixing time, (min) | \n\t
\n\t\t\tT\n\t\t | \n\t\tabsolute temperature, (K) | \n\t
TG | \n\t\tthermogravimetric | \n\t
US EPA | \n\t\tUnited States Environmental Protection Agency | \n\t
\n\t\t\tV\n\t\t | \n\t\tsolution volume, (L) | \n\t
XRD | \n\t\tX-ray diffraction | \n\t
The world’s human population increases by approximately 240,000 people every day: it is expected to reach 8 billion by 2025 and approximately 9.6 billion by 2050. Cultivated land is at a near-maximum, yet estimates predict that food production must be increased by 70% for worldwide peace to persist circa 2050 [1]. Thus, producing sufficient food to meet the ever-growing demand for this rising population is an exceptional challenge to humanity. To succeed at this vital objective, we must build more efficient—yet sustainable—food production devices, farms, and infrastructures. To accomplish that objective, the precision farming concept—a set of methods and techniques to accurately manage variations in the field to increase crop productivity, business profitability, and ecosystem sustainability—has provided some remarkable solutions.
Figure 1 summarizes the cycle of precision agriculture and distinguishes the activities based on analysis and planning (right) from those that rely on providing motion (left). The solutions for activities illustrated in Figure 1 right are being based on information and communication technologies (ICT), whereas the activities on the left rely on tractors, essential devices in current agriculture, that are being automated and robotized and will be also critical in future agriculture (smart farms).
UGVs in the cycle of precision agriculture.
The activities indicated in Figure 1 left can be applied autonomously in an isolated manner, i.e., a fertilization-spreading task, can be performed autonomously if the appropriate implement tank has been filled with fertilizer and attached to a fueled autonomous tractor (UGV); the same concept is applicable to planting and spraying. In addition, harvesting systems must offload the yield every time their collectors are full. However, tasks such as refilling, refueling/recharging, implement attachment, and crop offloading are currently primarily performed manually. The question that arises is: would it be possible to automate all these activities? And if so, would it be possible to combine these activities with other already automated farm management activities to configure a fully automated system resembling the paradigm of the fully automated factory? Then, the combination becomes a fully automated farm in which humans are relegated to mere supervisors. Furthermore, exploiting this parallelism, can we push new developments for farms to mimic the smart factory model? This is the smart farm concept that represents a step forward from the automated farm into a fully connected and flexible system capable of (i) optimizing system performances across a wider network, (ii) learning from new conditions in real- or quasi-real time, (iii) adapting the system to new conditions, and (iv) executing complete production processes in an autonomous way [2]. A smart farm should rely on autonomous decision-making to (i) ensure asset efficiency, (ii) obtain better product quality, (iii) reduce costs, (iv) improve product safety and environmental sustainability, (v) reduce delivery time to consumers, and (vi) increase market share and profitability and stabilize the labor force.
Achieving the smart farm is a long-term mission that will demand design modifications and further improvements on systems and components of very dissimilar natures that are currently being used in agriculture. Some of these systems are outdoor autonomous vehicles or (more accurately) UGVs, which are essential in future agriculture for moving sensors and implementing to cover crop fields accurately and guarantee accurate perception and actuation (soil preparation, crop treatments, harvest, etc.). Thus, this chapter is devoted to bringing forward the features that UGVs should offer to achieve the smart farm concept. Solutions are focused on incorporating the new paradigms defined for smart factories while providing full mobility of the UGVs. These two activities will enable the definition of UGV requirements for smart farm applications.
To this end, the next section addresses the needs of UGVs in smart farms. Then, two main approaches to configure solutions for UGVs in agricultural tasks are described: the automation of conventional vehicles and specifically designed mobile platforms. Their advantages and shortcomings regarding their working features are highlighted. This material enables the definition of other operating characteristics of UGVs to meet the smart farm requirements. Finally, the last section presents some conclusions.
Ground mobile robots, equipped with advanced technologies for positioning and orientation, navigation, planning, and sensing, have already demonstrated their advantages in outdoor applications in industries such as mining [3], farming, and forestry [4, 5]. The commercial availability of GNSS has provided easy ways to configure autonomous vehicles or navigation systems to assist drivers in outdoor environments, especially in agriculture, where many highly accurate vehicle steering systems have become available [6, 7]. These systems aid operators in the precise guidance of tractors using LIDAR (light/laser detection and ranging) or GNSS technology but do not endow a vehicle or tool with any level of autonomy. Nevertheless, other critical technologies must also be incorporated to configure UGVs, such as the safety systems responsible for detecting obstacles in the robots’ path and safeguarding humans and animals in the robots’ surroundings as well as preventing collisions with obstacles or other robots. Finally, robot communications with operators and external servers (cloud technologies) through wireless communications that include the use of cyber-physical systems (CPSs) [8] and Internet of things (IoT) [9] techniques will be essential to incorporate decision-making systems based on big data analysis. Such integration will enable the expansion of decision processes into fields such as machine learning and artificial intelligence. Smart factories are based on the strongly intertwined concepts of CPS, IoT, big data, and cloud computing, and UGVs for smart farms should be based on the same principles to minimize the traditional delays in applying the same technologies to industry and agriculture.
The technology required to deploy more robotic systems into agriculture is available today, as are the clear economic and environmental benefits of doing so. For example, the global market for mobile robots, in which agricultural robots are a part, is expected to increase at a compound annual growth rate of over 15% from 2017 to 2025, according to recent forecast reports [10]. Nevertheless, manufacturers of agricultural machinery seem to be reluctant to commercialize fully robotic systems, although they have not missed the marketing potential of showing concepts [11, 12]. In any event, according to the Standing Committee on Agricultural Research [13], further efforts should be made by both researchers and private companies to invent new solutions.
Most of the robotics and automation systems that will be used in precision agriculture—including systems for fertilizing, planting, spraying, scouting, and harvesting (Figure 1)—will require the coordination of detection devices, agricultural implements, farm managing systems, and UGVs. Thus, several research groups and companies have been working on such systems. Specifically, two trends can be identified in the development of UGVs: the automation of conventional agricultural vehicles (tractors) and the development of specifically designed mobile platforms. The following sections discuss these two types of vehicles.
The tractor has been the central vehicle for executing most of the work required in a crop field. Equipped with the proper accessories, this machine can till, plant, fertilize, spray, haul, mow, and even harvest. Their adaptability to dissimilar tasks makes tractors a prime target for automation, which would enable productivity increases, improve safety, and reduce operational costs. Figure 2 shows an example of the technologies and equipment for automating agricultural tractors.
An example of agricultural tractor automation‑distribution of sensorial and actuation systems for transforming an agricultural tractor into a UGV (Gonzalez-de-Santos et al., 2017).
Numerous worldwide approaches to automating diverse types of tractors have been researched and developed since 1995 when the first GNSS was made available to the international civilian community of users, which opened the door for GPS-guided agricultural vehicles (auto-steering) and controlled-traffic farming.
The first evaluations of GPS systems for vehicle guidance in agriculture were also published in 1995 [14] demonstrating its potential and encouraging many research groups around the world to automate diverse types of tractors. The earliest attempts were made at Stanford University in 1996, where an automatic control system for an agricultural tractor was developed and tested on a large farm [15]. The system used a location system with four GPS antennas. Around the same time, researchers at the University of Illinois, USA, developed a guidance system for an autonomous tractor based on sensor fusion that included machine vision, real-time kinematics GPS (RTK-GPS), and a geometric direction sensor (GDS). The fusion integration methodology was based on an extended Kalman filter (EKF) and a two-dimensional probability-density-function statistical method. This system achieved a lateral average error of approximately 0.084 m at approximately 2.3 m s−1 [16].
A few years later, researchers at Carnegie Mellon University, USA, developed some projects that made significant contributions. The Demeter project was conceived as a next-generation self-propelled hay harvester for agricultural operations, and it became the most representative example of such activity [17]. The positional data was fused from a differential GPS, a wheel encoder (dead reckoning), and gyroscopic system sensors. The project resulted in a system that allowed an expert harvesting operator to harvest a field once, thus programming the field. Subsequently, an operator with lesser skill could “playback” the programmed field at a later date. The semi-autonomous agricultural spraying project, developed by the same research group, was devoted to making pesticide spraying significantly cheaper, safer, and more environmentally friendly [18]. This system enabled a remote operator to oversee the nighttime operation of up to four spraying vehicles. Another example is research conducted at the University of Florida, USA, [19], in which two individual autonomous guidance systems for use in a citrus grove were developed and tested along curved paths at a speed of approximately 3.1 m s−1. One system, based on machine vision, achieved an average guidance error of approximately 0.028 m. The other system, based on LIDAR guidance, achieved an average error of approximately 0.025 m.
Similar activities started in Europe in the 2000s. One example is the work performed at LASMEA-CEMAGREF, France, in 2001, which evaluated the possibilities of achieving recording-path tracking using a carrier phase differential GPS (CP-DGPS), as the only sensor. The vehicle heading was derived according to a Kalman state reconstructor and a nonlinear velocity independent control law was designed that relied on chained systems properties [20].
A relevant example of integrating UGVs with automated tools is the work conducted at the University of Aarhus and the University of Copenhagen, Denmark [21]. The system comprised an autonomous ground vehicle and a side shifting arrangement affixed to a weeding implement. Both the vehicle and the implement were equipped with RTK-GPS; thus, the two subsystems provided their own positions, allowing the vehicle to follow predefined GPS paths and enabling the implement to act on each individual plant, whose positions were automatically obtained during seeding.
Lately, some similar automations of agricultural tractors have been conducted using more modern equipment [22, 23], and some tractor manufacturers have already presented noncommercial autonomous tractors [11, 12]. This tendency to automate existing tractors has been applied to other types of lightweight vehicles for specific tasks in orchards such as tree pruning and training, blossom and fruit thinning, fruit harvesting, mowing, spraying, and sensing [24]. Table 1 summarizes the UGVs based on commercial vehicles for agricultural tasks.
Institution | Year | Description |
---|---|---|
Stanford University (USA) [15] | 1996 | Automatic large-farm tractor using 4 GPS antennas |
University of Illinois (USA) [16] | 1998 | A guidance system using a sensor based on machine vision, an RTK-GPS, and a GDS |
Carnegie Mellon University (USA)—Demeter project [17] | 1999 | A self-propelled hay harvester for agricultural operations |
Carnegie Mellon University (USA)—Autonomous Agricultural Spraying project [18] | 2002 | A ground-based vehicles for pesticide spraying |
LASMEA-CEMAGREF (France) [20] | 2001 | This study investigated the possibility of achieving vehicle guiding using a CP-DGPS as the only sensor |
University of Florida (USA) [19] | 2006 | An autonomous guidance system for citrus groves based on machine vision and LADAR |
University of Aarhus and the University of Copenhagen (Denmark) [21] | 2008 | An automatic intra-row weed control system connected to an unmanned tractor |
RHEA consortium (EU) [22] | 2014 | A fleet (3 units) of tractors that cooperated and collaborated in physical/chemical weed control and pesticide applications for trees |
Carnegie Mellon University (USA) [24] | 2015 | Self-driving orchard vehicles for orchard tasks |
University of Leuven (Belgium) [23] | 2015 | Tractor guidance using model predictive control for yaw dynamics |
UGVs based on commercial vehicles.
Nevertheless, UGVs suitable for agriculture remain far from commercialization, although many intermediate results have been incorporated into agricultural equipment—from harvesting to precise herbicide application. Essentially, these systems are installed on tractors owned by farmers and generally consist of a computer (the controller), a device for steering control, a localization system (mostly based on RTK-GPS), and a safety system (mostly based on LIDAR). Many of these systems are compatible only with advanced tractors that feature ISOBUS control technology [25], through which controllers connected to the ISOBUS can access other subsystems of the tractor (throttle, brakes, auxiliary valves, power takeoff, linkage, lights, etc.). Examples of these commercial systems are AutoDrive [26] and X-PERT [27].
An important shortcoming of these solutions is their lack of intelligence in solving problems, especially when obstacles are detected because they are not equipped with technology suitable for characterizing and identifying the obstacle type. This information is essential when defining any behavior other than simply stopping and waiting for the situation to be resolved. Another limitation of this approach is that the conventional configuration of a standard tractor driven by an operator is designed to maximize the productivity per hour; thus, the general architecture of the system (tractor plus equipment) is only roughly optimized.
The second approach to the configuration of mobile robots for agriculture is the development of autonomous ground vehicles with specific morphologies, where researchers develop ground mobile platforms inspired more by robotic principles than by tractor technologies. These platforms can be classified based on their locomotion system. Ground robots can be based on wheels, tracks, or legs. Although legged robots have high ground adaptability (that enables the vehicles to work on irregular and sloped terrain) and intrinsic omnidirectionality (which minimizes the headlands and, thus, maximizes croplands) and offer soil protection (discrete points in contact with the ground that minimize ground damage and ground compaction, an important issue in agriculture), they are uncommon in agriculture; however, legged robots provide extraordinary features when combined with wheels that can configure a disruptive locomotion system for smart farms. Such a structure (which consists of legs with wheels as feet) is known as a wheel-legged robot. The following sections present the characteristics, advantages, and disadvantages of these specifically designed types of robots.
The structure of a wheeled mobile platform depends on the following features:
Number of wheels: Three nonaligned wheels are the minimum to ensure platform static stability. However, most field robots are based on four wheels, an approach that increases the static and dynamic stability margins [28].
Wheel orientation type: An ordinary wheel can be installed on a platform in different ways that strongly determine the platform characteristics. Several wheel types can be considered:
Fixed wheel: This wheel is connected to the platform in such a way that the plane of the wheel is perpendicular to the platform and its angle (orientation) cannot change.
Orienting wheel: The wheel plane can change its orientation angle using an orientation actuator.
Castor wheel: The wheel can rotate freely around an offset steering joint. Thus, its orientation can change freely.
Wheel power type: Depending on whether wheels are powered, they can also be classified as follows:
Passive wheel: The wheel rotates freely around its shaft and does not provide power.
Active wheel: An actuator rotates the wheel to provide power.
Wheel arrangement: Different combinations of wheel types produce mobile platforms with substantially different steering schemes and characteristics.
Coordinated steering scheme: Two fixed active wheels at the rear of the platform coupled with two passive orienting wheels at the front of the platform are the most common wheel arrangement for vehicles. To maintain all wheels in a pure rolling condition during a turn, the wheels need to follow curved paths with different radii originating from a common center [29]. A special steering mechanism, the Ackermann steering system, which consists of a 4-bar trapezoidal mechanism (Figure 3a), can mechanically manage the angles of the two steering wheels. This system is used in all the vehicles presented in Table 2. It features medium mechanical complexity and medium control complexity. One advantage of this system is that a single actuator can steer both wheels. However, independent steering requires at least three actuators for steering and power (Figure 3b).
Skid steering scheme: Perhaps the simplest structure for a mobile robot consists of four fixed, active wheels, one on each corner of the mobile platform. Skid steering is accomplished by producing a differential thrust between the left and right sides of the vehicle, causing a heading change (Figure 3c). The two wheels on one side can be powered independently or by a single actuator. Thus, the motion of the wheels in the same direction produces backward/forward platform motion; and the motion of the wheels on one side in the opposite direction to the motion of wheels on the other side produces platform rotation.
Independent steering scheme: An independent steering scheme controls each wheel, moving it to the desired orientation angle and rotation speed (Figure 3d). This steering scheme makes wheel coordination and wheel position accuracy more complex but provides some advantages in maneuverability. In addition, this scheme provides crab steering (sideways motion at any angle α; 0 ≤ α ≤ 2π) by aligning all wheels at an angle α with respect to the longitudinal axis of the mobile platform. Finally, the coordination of driving and steering results in more efficient maneuverability and reduces internal power losses caused by actuator fighting. The independent steering scheme requires eight actuators for a four-wheel vehicle.
Steering driving systems: (a) Ackermann steering system; (b) independent steering; (c) skid steering system and (d) independent steering and traction system.
Steering scheme | Characteristics |
---|---|
Coordinated | Advantages:
|
Skid | Advantages:
|
Independent | Advantages:
|
Characteristics of wheeled structures.
Table 2 summarizes the advantages and drawbacks of these schemes. Note that the number of actuators increases the total mass of a robot as well as its mechanical and control complexity (more motors, more drivers, more elaborate coordinating algorithms, etc.).
Some examples of wheeled mobile platforms for agriculture are the conventional tractor using the Ackermann steering system (Figure 2) with two front passive and steerable wheels and two rear fixed and active wheels.
Skid steering platforms can be found in many versions. For example,
Four fixed wheels placed in pairs on both sides of the robot
Two fixed tracks, each one placed longitudinally at each side of the robot,
Two fixed wheels placed at the front of the robot and two castor wheels placed at the rear (Figure 4c), etc.
Pictures of several specifically-designed agricultural platforms. (a) Robot for weed detection, courtesy of T. Bak, Department of Agricultural Engineering, Danish Institute of Agricultural Sciences; (b) ladybird, courtesy of J. P. Underwood, Australian Centre for Field Robotics at the University of Sydney [34]; (c) AgBot II, courtesy of O. Bawden, strategic Investment in Farm Robotics, Queensland University of Technology [31].
Regarding the independent steering scheme, the robot developed by Bak and Jakobsen [30] is one of the first representative examples (Figure 4a). This platform was designed specifically for agricultural tasks in wide-row crops and featured good ground clearance (approximately 0.5 m) and 1-m wheel separation. The platform is based on four-identical wheel modules. Each one includes a brushless electric motor that provides direct-drive power, and steering is achieved by a separate motor.
An example of a mobile platform under development that focuses on performing precision agricultural tasks is AgBot II (Figure 4c). This is a platform that follows the skid steering scheme with two front fixed wheels (working in skid or differential mode) and two rear caster wheels. It is intended to work autonomously on both large-scale and horticultural crops, applying fertilizer, detecting and classifying weeds, and killing weeds either mechanically or chemically [31, 32]. Another robot is Robot for Intelligent Perception and Precision Application (RIPPA), which is a light, rugged, and easy-to-operate prototype for the vegetable growing industry. It is used for autonomous high-speed, spot spraying of weeds using a directed micro-dose of liquid when equipped with a variable injection intelligent precision applicator [33]. Another example is Ladybird (Figure 4b), an omnidirectional robot powered with batteries and solar panels that follows the independent steering scheme. The robot includes many sensors (i.e., hyperspectral cameras, thermal and infrared detecting systems, panoramic and stereovision cameras, LIDAR, and GPS) that enable assessing crop properties [34]. One more prototype, very close to commercialization, is Kongskilde Vibro Crop Robotti, which is a self-contained track-based platform that uses the skid steering scheme. It can be equipped with implements for precision seeding and mechanical row crop cleaning units. This robot can work for 2–4 hours at a 2–5 km h−1 rate and is supplied by captured electric energy [35].
These robots are targeted toward fertilizing, seeding, weed control, and gathering information, and they have similar characteristics in terms of weight, load capacity, operational speed, and morphology. Tools, instrumentation equipment, and agricultural implements are connected under the robot, and tasks are performed in the area just below the robot, which optimizes implement weight distribution. These robots have limitations for use on farmland with substantial (medium to high) slopes or gully erosion. Nevertheless, some mobile platforms are already commercially available. Two examples of these vehicles are the fruit robots Cäsar [36] and Greenbot [37].
Cäsar is a remote-controlled special-purpose vehicle that can perform temporarily autonomous operations in orchards and vineyards such as pest management, soil management, fertilization, harvesting, and transport. Similarly, Greenbot is a self-driving machine specially developed for professionals in the agricultural and horticultural sectors who perform regular, repetitious tasks. This vehicle can be used not only for fruit farming, horticulture, and arable farming but also in the urban sector and even at waterfronts or on roadsides.
Despite their current features, the existing robots lack flexibility and terrain adaptability to cope with diverse scenarios, and their safety features are limited. For example:
They focus only on orchard and vineyard activities.
They have ground clearance limitations.
They are unsuitable for rough terrain or slopes.
They must be manually guided to the working area rather than freely and autonomously moving to different working areas around the farm.
They possess no advanced detection systems for weed or soil identification, which limits their use to previously planned tasks related to selective treatment.
They lack dynamic safety systems capable of recognizing or interpreting safety issues; thus, they are incapable of rescheduling or solving problems by themselves.
In addition, existing UGVs for agriculture lack communication mechanisms for providing services through cloud technologies, CPS, and IoT techniques, crucial instruments to integrate decision-making systems based on big data analysis, as is being done in the smart factory concept.
Table 3 summarizes the diverse robotic platforms, and Figure 4 depicts some of these platforms.
Vehicle | Type* | Year | Description |
---|---|---|---|
AgBot II [32] | P | 2014 | A platform that follows the skid steering scheme with two front fixed wheels (working in skid or differential mode) and two rear caster wheels |
Ladybird [34] | P | 2015 | An omnidirectional robot powered with batteries and solar panels that uses the independent steering scheme |
Greenbot [37] | C | 2015 | A self-driving robot for tasks in agriculture and horticulture |
Cäsar [36] | P | 2016 | A remotely controlled platform for temporary, autonomous use in fruit plantations and vineyards |
RIPPA [33] | P | 2016 | A light, rugged, and easy-to-operate prototype for the vegetable growing industry |
Vibro Crop Robotti [35] | C | 2017 | A self-contained track-based platform that uses the skid steering scheme |
Robots designed specifically for agriculture.
P-prototype; C-commercial.
The structure of a wheel-legged mobile platform depends on (i) the number of legs, (ii) the leg type, and (iii) the leg arrangement. The feet consist of 2-DOF steerable powered wheels as illustrated in Figure 5.
Wheel-legged structures. (a) 4-DOF articulated leg; (b) 3-DOF SCARA leg; (c) 2-DOF SCARA leg; (d) 1-DOF leg.
Number of legs: The minimum number of legs required for statically stable walking is four-three legs providing support in the form of a stable tripod while the other leg performs the transference phase [38]. Combining sequences of leg transferences with stable tripods produce a walking motion. A wheel-legged robot requires only three legs for translational motion, which provides additional terrain adaptation.
Leg type: Legs are based on the typical configurations of manipulators; thus, articulated, cylindrical, Cartesian, and pantographic configurations are the types used most often.
Leg arrangement: The normal arrangement for a 2n-legged robot is to distribute n legs uniformly on the longitudinal sides. Four-legged structures present some advantages regarding terrain adaptability, ground clearance, and track width control (crop adaptability) but also have some drawbacks, such as additional mechanical complexity (complex joints designs, including actuators and brakes) and control of redundant actuated systems, which exhibit complex interactions with the environment and make motion control more difficult than that of conventional wheeled platforms. Table 4 illustrates different theoretical wheel-legged structures.
Structure | Characteristics |
---|---|
A 4-DOF articulated leg with a 2-DOF wheeled foot (Figure 5a) | Advantages:
|
A 3-DOF motion-decoupled leg* with a 2-DOF wheeled foot (Figure 5b) | Advantages:
|
A 2-DOF motion-decoupled leg* with a 2-DOF wheeled foot (Figure 5c) | Advantages:
|
A 1-DOF leg with a 2-DOF wheeled foot (Figure 5d) | Advantages:
|
Wheel-legged structures.
Cylindrical, Selective Compliant Articulated Robot Arm (SCARA) or Cartesian.
Figure 6a illustrates the structure scheme of a wheel-legged robot based on the 3-DOF SCARA leg (See Figure 5b) with full terrain adaptability, ground clearance control, crop adaptability, and capability of walking, and Figure 6b shows the structure of a wheel-legged robot exhibiting full terrain adaptability, ground clearance control, and crop adaptability; however, it cannot walk under static stability.
Model of wheel-legs: (a) full terrain-crop adaptability, (b) full terrain and partial crop adaptability.
Another interesting example is the structure of BoniRob [39], a real wheel-legged platform for multipurpose agriculture applications, which consists of four independently steerable powered wheeled legs with the structure illustrated in Figure 5d (1-DOF legs with a 2-DOF wheeled foot). This robot can adjust the distance between its wheel sets, making it adaptable to many agricultural scenarios. The platform can be equipped with common sensorial systems used in robotic agricultural applications, such as LIDAR, inertial sensors, wheel odometry, and GPS. Moreover, the robotic platform can be retrofitted and upgraded with swappable application modules or tools for crop and weed identification, plant breeding applications, and weed control. This robotic platform is completely powered by electricity, which is more environmentally friendly but reduces its operational working time compared to conventional combustion-engine systems. Nevertheless, this robot configuration requires custom-built implements, which prevent the reuse of existing implements and, thus, jeopardize the introduction of this robot to the agricultural market.
In addition to their needed characteristics for infield operations, the robots fulfilling the demands of a smart farm will require the operating requirements summarized in the following paragraphs and Table 5.
Characteristics | Value |
---|---|
Dimensions | Length: ~3.0 m; width: ~1.50 m; height: ~1.00 m |
Weight | 1200–1700 kg |
Payload | 500–1000 kg |
Comments: These characteristics are estimations based on the current medium-sized vehicles reported in this chapter that are capable of carrying agricultural implements. Robots for carrying sensing systems can be truly small (low payloads), but vehicles for treatments need to carry medium to heavy loads (pesticides, fertilizes, etc.). For example, existing sprayers [45] weigh approximately 600–700 kg including 200–300 L of active ingredient. | |
Speed | 3–25 km h−1 |
Comments: Treatment speed is limited by the treatment process that depends on physical laws. However, robots need to move among working fields minimizing moving time; therefore, they must feature a reasonably high top speed. | |
Position accuracy | ±0.02 m |
Comments: The current DGPS accuracy seems to be sufficient for real applications. However, specific real-time localization systems, RTLS, can be used in small areas where GNSS is unavailable (radio frequency identification tags (RFID), ultra-wide band tags (UWB), etc.). These technologies will be essential in smart farms to ensure positioning precision in GNSS occluded areas. | |
Clearance | 0.35–1 m |
Comments: Weed control is performed at an early crop-growth stage; therefore, the minimum ground clearance of the robot must be approximately 0.35 m. A ground clearance of approximately 1 m will facilitate application of treatments at later crop-growth stages. The ideal approach would be to control the ground clearance to optimize the working height of the implements based on the crop. Existing robots cannot control their ground clearance, but some wheel-legged configurations can meet this specification (Figure 5a,b, and c). | |
Track width | 1.50–2.25 m |
Comments: To preserve crops in narrow-row situations, a tramline control is required; however, in wide-row crops, the tramlines must be located in the inter-row spacing. Taking maize as an example, which is planted at an inter-row spacing of approximately 0.75 m in some areas in Europe, a robot track width of 1.50 to 2.25 m is required to enable 2 or 3 rows to pass under the robot’s body. Controlling robot track width is imperative in a smart farm world. This characteristic is exhibited by wheeled-legged robots, which makes them a good candidate for UGVs in smart farms. | |
Energetic autonomy | ~10 h |
Comments: Robots based on combustion engines (e.g., tractors) can operate autonomously for approximately 10 hours, at minimum. The duration of autonomous operation for electrically driven systems should be similar. Some existing prototypes already meet this expectation [31]. In any case, the increasing improvement in battery technology will enlarge the energetic autonomy of future vehicles and robots. |
Prospective characteristics for UGVs in smart farms.
Small size: The idea that using small robots provides many advantages over the use of conventional large vehicles has been widely discussed over the past decade [22, 40]. It is broadly accepted that although several small robots can cost the same as a large machine and accomplish the same amount of work, using small robots allows a multi-robot system to continue a task even if a number of robots fail (re-planning the task). Moreover, the reduced weight of the small robots reduces terrain compaction and allows farmers to acquire robots incrementally.
Flexibility: Agricultural robots must be capable of adapting to many different scenarios (e.g., crops, row types, etc.) and tasks (e.g., plow, sow, fumigate, etc.). Thus, the robots must also be able to accommodate different agricultural implements, which should attach to or connect to (respectively, detach or disconnect from) the robots automatically.
Although conventional tractors are proven and highly reliable machines, they lack some adaptability features. Tractors have normally fixed distances between wheels, which makes them unsuitable for working on crops with different distances between rows. Using mobile platforms capable of controlling the distance between wheels could alleviate this problem, allowing the machines to adapt to different crops under different situations.
Maneuverability: Robots must be capable of performing small radius turns while adapting to different terrain. This last feature requires independent vertical control of wheels with respect to the robot’s body.
A steering system capable of zero-radius turns would be a proper solution, and this feature can be implemented by different structures as discussed in the previous section. Thus, minimization of headlands and wheel distance control can be achieved using either conventional or new articulated structures. Among the conventional structures, the skid steering scheme based on wheels or tracks is capable of zero-radius turns without additional steering mechanism, which helps in minimizing the headlands. However, separating and controlling the distance between contralateral wheels/tracks requires an active system (which already exists for some tracked vehicles used in the building industry).
Mobile platform structures based on coordinated or independent steering schemes can achieve zero-radius turns, but they still lack intrinsic track width control and require additional mechanisms. Another structure is the wheel-legged mechanism. Legged robots exhibit high terrain adaptability on irregular ground, but wheeled robots have speed advantages on smooth terrain; that is, they complement each other. Therefore, the most complete wheel-legged mechanism (Figure 6a) is a leg with three degrees of freedom [38] with an active wheel as a foot, where the wheel is steered and driven separately. This is a disruptive design not verified yet that will provide extraordinary characteristics to robots for smart farm applications. Thus, the wheels drive and steer, while the legs provide track-width control and terrain adaptation, i.e., they control the robot’s body leveling and ground clearance. This is the most capable system regarding ground clearance and body pose control, but it comes at the cost of higher mechanical complexity. Nevertheless, intermediate solutions can be developed to reduce the number of actuators while maintaining appropriate robot characteristics. Table 4 summarizes different wheel-legged theoretical solutions indicating advantages and shortcomings, and Figure 5 shows some sketches of practical solutions.
Resilience: Resilience is the ability to recover from malfunctions or errors. Initializing complex robots is a time-consuming procedure, especially when several robots are collaborating on the same task. Agricultural mobile robots must be resilient enough to ensure profitability. Thus, they must be easily shut down and started up (essential for error recovery); moreover, they must facilitate changing between manual operation mode and autonomous operation mode and vice versa.
Efficiency: UGV should be more efficient than conventional, manned solutions. This can be accomplished by systems that:
Minimize energy consumption by optimizing the robot trajectories during the mission
Drastically reduce the use of herbicides and fertilizers by using intelligent detection systems, tools, and decision-making algorithms
Eliminate the need for a driver and minimize operator risk
Minimize unnecessary crop damage and soil compaction
Friendly human-machine interfaces (HMI): A friendly interface is required to facilitate the introduction of robots into agriculture and to achieve profitability. Intuitive, reliable, comfortable, and safe HMIs are essential for farmers to accept robotic systems. The HMIs should be implementable on devices such as smartphones and tablets.
Communications: Communications in the smart farm must capitalize on CPS and IoT to collect sufficient data to take advantage of the big data techniques and enable communication with the cloud for use via different services (software as a service, platform as a service, and infrastructure as a service) offered by cloud providers [41].
Wireless communications with the operator and/or a central controller for control commands and data exchanges, including images and real-time video, will be required. Wireless communication among robots will also be required for coordination and collaboration.
Standardization of mechanical and electrical/electronic interfaces: Commercial equipment must comply with well-defined standards and homologous procedures before adoption by industry. Subsystems such as LIDAR units, computers, and wireless or Internet communication (4G/5G) devices and GNNS receivers and antennas are already off-the-shelf components, but mobile platforms must also cope with some standards related to agricultural machinery [25, 42].
Safety: Safety systems for agricultural robots must focus on three stages: (i) safety to humans, (ii) safety to crops, and (iii) safety to the robots themselves.
Safety for humans and robots can usually be accomplished through a combination of computer vision, LIDAR, and proximity sensors to infer dangerous situations and halt robot motion, whereas safety to crops is achieved through precise steering that guides the robot to follow the crop rows accurately using the crop position acquired at seeding time or real-time crop-detection systems. Following these three stages, a step forward in safety for agricultural robots would be the integration of a two-level safety system relying on the following:
A low-level safety system that detects short-range obstacles with the purpose of avoiding imminent collisions. This level should be implemented within the robot controller and based on commercial components.
A high-level safety system that detects and discriminates obstacles at an adequate distance to allow the robotic system to make decisions (i.e., re-planning a trajectory). This level should include vision, infrared, and hyperspectral cameras that provide information about the surroundings. Optical flow methods should be applied to detect obstacles in motion and compute their speed and direction to predict potential collisions [43]. Hence, optical sensors should track obstacles and their movements, dynamically compute safe zones, and adjust a robot’s speed and direction of movement according to the given situation.
Regardless of the exact approach, standards on safety machinery must be taken into consideration [42] to ensure that systems will meet regulations and will be able to achieve certification.
Environmentally friendly impact: Both intervention mechanisms (implements) and mobile robots must be environmentally friendly (e.g., use fewer chemicals and cause less soil compaction) while improving the efficiency of the agricultural processes (i.e., reduce chemical costs while equaling or improving production). In addition, current agricultural vehicles use fossil fuels that emit large amounts of pollutants into the air such as carbon dioxide (CO2), nitrogen oxide (NOX), carbon monoxide (CO), and hydrocarbon (HC) [44]. Furthermore, fuel can be spilled onto the ground, which is a long-term pollutant. These elements alter the environment and damage the ecosystem. One possible solution—envisaged as the likely future solution—is the use of electric vehicles.
Implements: The use of the conventional three-point hitch to attach implements to tractors should be changed as robots are introduced into agriculture. Instead, implements should be aligned with the robot’s center of gravity to optimize the payload distribution and minimize compaction. Mechanical attachment and electrical connection to the implement should be automated. The definition of these types of interfaces is a pending issue; nevertheless, an intermediate solution allowing the use of both new and conventional attachment devices (three-point hitch) will facilitate the gradual introduction of robotic systems into the agricultural sector. Obviously, developing new robots and adapting existing implements to a new attachment/connection system is the only way to introduce the robots to real applications.
HMI: An HMI for operators to communicate with robots should be implementable on portable equipment (smartphones, tablets, etc.). Operators will use such devices to send commands and receive responses and data. Moreover, an additional device—an emergency button that works using radio signals—must be provided to stop the robots from malfunctioning or unsafe situations. These interfaces must be true user-friendly devices to be operated by farmers rather than by engineers, which is a vital aspect for the introduction of robotics into agriculture, as it is for industry and services.
Autonomy: Two basic types of autonomies will be needed in smart farms: behavioral autonomy and operational autonomy. Behavioral autonomy is primarily associated with autonomous robots and relies on artificial intelligence techniques. It refers to the robot’s ability to deal with uncertainty in its environment to accomplish a mission. Operational autonomy is associated with the tasks the robot has to accomplish autonomously to become a UGV, i.e., the tasks required for the robot to work continuously without human intervention: refueling or recharging (energetic autonomy, see Table 5), herbicide/pesticide refilling, implement attaching, and crop offloading. These tasks, which can be solved using current automatic techniques, are currently being done with human intervention and should be fully automated in the smart farms.
Based on the existing agricultural vehicles and robot prototypes, robots to be deployed in smart farms should meet also the characteristics presented in Table 5.
The world population is increasing rapidly, causing a demand for more efficient production processes that must be both safe and respect the ecosystem. Industry has already planned to meet production challenges in the coming decades by defining the concept of the smart factory; the agriculture sector should follow a similar path to design the concept of the smart farm: a system capable of optimizing its performance across a wide network, learning from new conditions in real time and adapting the system to them and executing the complete production process in an autonomous manner. Smart factory and smart farm concepts have many commonalities and include some common solutions, but some specific aspects of smart farms should be studied separately. For example, the design of UGVs for outdoor tasks in agriculture (field robots) presents specific characteristics worthy of explicit efforts.
This chapter focused on reviewing the past and present developments of UGVs for agriculture and anticipated some characteristics that these robots should feature for fulfilling the requirements of smart farms. To this end, this chapter presented and criticized two trends in building UGVs for smart farms based on (i) commercial vehicles and (ii) mobile platforms designed on purpose. The former has been useful for evaluating the advantages of UGV in agriculture, but the latter offers additional benefits such as increased maneuverability, better adaptability to crops, and improved adaptability to the terrain. Clearly, independent-steering and skid-steering systems provide the best maneuverability, but depending on their complexity, wheel-legged structures can provide similar maneuverability and improved adaptability to crops and terrain as well as increased stability on sloped terrain. For example, the 4-DOF articulated wheeled leg (Figure 5a) and the 3-DOF SCARA leg (Figure 5b and 6a) exhibit the best features at the cost of being the most complex. Note that although both structures have the same maneuverability features and adaptability to crops and terrain (ground clearance, body leveling, etc.), the 3-DOF SCARA leg involves one fewer motor per leg, which decreases the price and weight and improves the reliability of the robot. However, the 2-DOF SCARA leg also exhibits useful features regarding maneuverability, adaptability to crops, and adaptability to terrain (ground clearance control and body leveling) while using fewer actuators (Figure 5c and 6b). For agricultural tasks carried out on flat terrain, the 1-DOF leg with a 2-DOF wheeled foot provides sufficient maneuverability and adaptability to crops with very few actuators (leg structure as in Figure 5d).
However, these robots also require some additional features to meet the needs of the smart farm concept, such as the following:
Flexibility to work on very dissimilar scenarios and tasks.
Maneuverability to perform zero-radius turns, crab motion, etc.
Resilience to recover itself from malfunctions.
Efficiency in the minimization of pesticide and energy usage.
Intuitive, reliable, comfortable, and safe HMIs attractive to nonrobotic experts to ease the introduction of robotic systems in agriculture.
Wireless communications to communicate commands and data among the robots, the operator, and external servers for enabling CPSs, IoT, and cloud computing techniques to support services through the Internet.
Safety systems to ensure safe operations to humans, crops, and robots.
Environmental impact by reducing chemicals in the ground and pollutants into the air.
Standards: operational robots have to meet the requirements and specifications of the standards in force for agricultural vehicles.
Implement usage: although specific onboard implements for UGV are appearing, the capability of also using conventional implements will help in the acceptation of new technologies by farmers and, hence, the introduction of new-generation robotic systems.
Autonomy: both behavioral autonomy and operation autonomy. Regarding power supplies, automobiles worldwide will likely be electric vehicles powered by batteries within the next few decades; thus, agricultural vehicles should embrace the same solution.
Regardless of these characteristics, UGVs for smart farms have to fulfill the requirements of multi-robot systems, which is a fast-growing trend [22, 40, 46]. Multi-robot systems based on small-/medium-sized robots can accomplish the same work as a large machine, but with better positioning accuracy, greater fault tolerance, and lighter weights, thus reducing soil compaction and improving safety. Moreover, they can support mission coordination and reconfiguration. These capabilities position small/medium multi-robot systems as prime future candidates for outdoor UGVs in agriculture. Additionally, UGVs for smart farms should exhibit some quantitative physical characteristics founded on past developments and current studies that are summarized in Table 5.
Finally, autonomous robots of any type, working in fleets or alone, are essential for the precision application of herbicides and fertilizers. These activities reduce the use of chemicals generating important benefits: (i) a decrease in the cost of chemical usage, which impacts in the system productivity; (ii) an improvement in safety for operators, who are moved far from the vehicles; (iii) better health for the people around the fields, who are not exposed to the effects of chemical; and (iii) improved quality of foods that will reduce the content of toxic products.
The research leading to these results has received funding from (i) RoboCity2030-DIH-CM Madrid Robotics Digital Innovation Hub (“Robótica aplicada a la mejora de la calidad de vida de los ciudadanos. fase IV”; S2018/NMT-4331), funded by “Programas de Actividades I+D en la Comunidad de Madrid” and cofunded by Structural Funds of the EU; (ii) the Agencia Estatal Consejo Superior de Investigaciones Científicas (CSIC) under the BMCrop project, Ref. 201750E089; and (iii) the Spanish Ministry of Economy, Industry and Competitiveness under Grant DPI2017-84253-C2-1-R.
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