Biogas composition.
\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. 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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:"62959",title:"Biogas for Clean Energy",doi:"10.5772/intechopen.79534",slug:"biogas-for-clean-energy",body:'\nBiogas is a byproduct of biomass which contains methane (CH4) and carbon dioxide (CO2) as a main gas component in a 3:2 ratio and it is produced through micro bacterial digestion processes under anaerobic conditions from a variety of organic material from animal, agricultural, industrial and domestic wastes [1]. The biogas production level is depending on the ingredient level in the feedstock. For example; if the material consists of mainly carbohydrates, like glucose and other simple sugars and high-molecular polymers such as cellulose and hemicelluloses, the methane production is low. However, if the fat content is high, the methane production is likewise high (Table 1) [2].
\nBiogas composition.
Methane and other additional hydrogen compounds make up the combustible part of biogas. Methane is a colorless and odorless gas with a boiling point of −162°C and it burns with a blue flame. At normal temperature and pressure, methane has a density of approximately 0.75 kg/m3. Due to carbon dioxide being somewhat heavier, biogas has a slightly higher density of 1.15–1.25 kg/m3. Pure methane has an upper calorific value of 39.8 MJ/m3 (11.06 kWh/m3) (Table 2) [2].
\nSubstrate | \nHRT (days) | \nSolid concentration (%) | \nTemperature (°C) | \nBiogas yield (m3/kg VS) | \nMethane (%) | \n
---|---|---|---|---|---|
Sewage sludge | \n25 | \n6 | \n35 | \n0.52 | \n68 | \n
Domestic garbage | \n30 | \n5 | \n35 | \n0.47 | \n— | \n
Piggery waste | \n20 | \n6.5 | \n35 | \n0.43 | \n69 | \n
Poultry waste | \n15 | \n6 | \n35 | \n0.5 | \n69 | \n
Cattle waste | \n30 | \n10 | \n35 | \n0.3 | \n58 | \n
Canteen waste | \n20 | \n10 | \n30 | \n0.6 | \n50 | \n
Food-market waste | \n20 | \n4 | \n35 | \n0.75 | \n62 | \n
Mango processing waste | \n20 | \n10 | \n35 | \n0.45 | \n52 | \n
Tomato-processing waste | \n24 | \n4.5 | \n35 | \n0.63 | \n65 | \n
Lemon waste | \n30 | \n4 | \n37 | \n0.72 | \n53 | \n
Citrus waste | \n32 | \n4 | \n37 | \n0.63 | \n62 | \n
Banana peel | \n25 | \n10 | \n37 | \n0.60 | \n55 | \n
Pineapple waste | \n30 | \n4 | \n37 | \n0.37 | \n60 | \n
Mixed feed of fruit waste | \n20 | \n4 | \n37 | \n0.62 | \n50 | \n
Potential biogas production from various biomass feedstocks on VS based.
Anaerobic digestion (AD) is a biochemical process during which complex organic matter is decomposed in absence of oxygen, by various types of anaerobic microorganisms. The result of the AD process is the biogas and the digestate. Biogas is a combustible gas, consisting primarily of methane and carbon dioxide. Digestate is the decomposed substrate, resulted from the production of biogas. If the substrate for AD is a homogenous mixture of two or more feedstock types (e.g., animal slurries and organic wastes from food industries), the process is called “co-digestion” and is common to most biogas applications today.
\nThe process of biogas formation is a result of linked process steps, in which the initial material is continuously broken down into smaller units. Specific groups of micro-organisms are involved in each individual step. The simplified diagram of the AD process, shown in Figure 1, highlights the four main process steps: hydrolysis, acidogenesis, acetogenesis, and methanogenesis. The process steps quoted in Figure 1 run parallel in time and space, in the digester tank. During hydrolysis, relatively small amounts of biogas are produced. Biogas production reaches its peak during methanogenesis [3].
\nBiogas production process by anaerobic digestion.
Methanogenesis is a critical step in the entire anaerobic digestion process, as it is the slowest biochemical reaction of the process. Methanogenesis is severely influenced by operation conditions. Composition of feedstock, feeding rate, temperature, water content, NH3 concentration and pH are examples of factors influencing the methanogenesis process.
\nTemperature for fermentation will greatly affect biogas production. The AD process can take place at different temperatures, divided into three temperature ranges: psychrophilic (below 20°C), mesophilic (30–42°C), and thermophilic (43–55°C). There is a direct relation between the process temperature and the HRT. The biogas production rate increases with increase the process temperature (Table 3).
\nThermal stage | \nProcess Temperature | \nMinimum HRT | \n
---|---|---|
Psychrophilic | \n< 20° C | \n70–80 days | \n
Mesophilic | \n30–42° C | \n30–40 days | \n
Thermophilic | \n43–55° C | \n15–20 days | \n
Biogas production thermal stage and their corresponding retention time [4].
In practice most modern biogas plants operate at thermophilic process temperatures because this process provides many advantages, compared to mesophilic and psychrophilic processes:
Effective destruction of pathogens
Fast grow rate of methanogenic bacteria at higher temperature
Minimization of biogas production period, making the process faster and more efficient
Improve digestibility and availability of substrates
better decomposition and utilization of solid substrates
Increase the chance to separate liquid and solid fractions
The metabolic processes in the production of biogas from different biomass feedstocks are hydrolysis, acidogenesis, acetogenesis and methanogenesis and their byproducts in the process is represented in the figure below.
\nIn this study thermophilic biogas temperature process is chosen in order to get higher biogas output and to achieve this target flat plate collector can be used to maintain digester process temperature at 55oc.
\nA biogas plant is a complex installation, consisting of a variety of elements. The layout of such a plant depends to a large extent on the types and amounts of feedstock supplied. Now there are several main types of biogas plants all over the world. Each time it is necessary to find the most suitable type in different case. Public acceptance, cost and energy efficiency are the main criteria to install biogas plant and efficiently utilize the biogas production. In smaller areas with scarcity of biogas feedstock or slurry to use low cost clay, concert or stone masonry made biogas digester.
\nInstallation and operation of a biogas plant is a combination of environmental, safety, economic and technical considerations. Acquiring maximum methane output, by complete digestion of feedstock substrate, would require a long fermentation or digestion time of the material inside the biogas digester and a correspondingly large digester size. The ultimate goal of biogas production is getting the highest possible methane output and having justifiable plant economy. Biogas plants have the following main components and operate with four different process stages [3].
\nProcess stages of biogas production:
Transport, delivery, storage and pre-treatment of feedstocks
Biogas production
Storage of digestate, conditioning and utilization
Storage of biogas, conditioning and utilization.
Main components of biogas plant:
Feedstock pre-storage tank
Substrate mixing Tank
Biogas digester
Post storage tank
Gas holder tank and
CHP system
The amount and type of available feedstock can determine the size, type and design structure of the biogas plant. The amount of biogas feedstock could determine the dimensioning of the digester size, storage capacities and CHP unit (Figure 2).
\nMain components and general process flow of biogas production.
The CHP system utilizes the biogas either in heat or electrical energy. The properties of the combustible methane gas (like as shown in Table 4) will affect the operation of the CHP equipment. The combustion nature of the gas must be guaranteed, to prevent damage to the engines. Further treatment and enhancing chemical and physical properties of biogas even possible to use it for other utilizations like as vehicle fuel or in fuel cells application.
\nNo. | \nParameter | \nSymbol | \nValue | \n
---|---|---|---|
1. | \nLower heat value | \nLHV | \n≥4 kWh/m3 | \n
2. | \nSulfur content | \nS | \n≤2.2 g/m3 CH4 | \n
3. | \nHydrogen sulfide | \nH2S | \n≤0.15 Vol. % | \n
4. | \nChlorine content | \nCl | \n≤100 mg/m3 CH4 | \n
5. | \nFluoride content | \nF | \n≤50 mg/m3 CH4 | \n
6. | \nDust (3–10 μm) | \n— | \n≤10 mg/m3 CH4 | \n
7. | \nRelative humidity | \nϕ | \n<90% | \n
8. | \nFlow pressure | \nPgas | \n20–100 mbar | \n
9. | \nGas pressure fluctuation | \n— | \n<±10% of set value | \n
10. | \nGas temperature | \nT | \n10–50oc | \n
11. | \nHydro carbon | \nHC | \n<0.4 mg/m3 CH4 | \n
12. | \nSilicon | \nSi | \n<10 mg/CH4 | \n
Biogas minimum requirement used in an electric engine [3].
The design of the biogas plant includes the design of:
The digester
The gas Holder
Digester heat maintaining system
Siting of biogas plant
To calculate the scale of a biogas plant, certain characteristic parameters are used. These are:
Daily fermentation slurry feeding (Sd), which is an equal mixture of biogas feedstock (animal dung, human feces, poultry waste and jatropha byproduct) with water feed in to the biogas digester.
Retention time (RT), the time by which the fermentation slurry stays in the digester. It is about 2–5 weeks.
Digester loading (R). This parameter indicates the amount of biogas feedstock material per day is fed to the digester or to be digested. It can be measured in kg/m3/day.
Specific gas production per day (Gd), which depends on the retention time, the digestion temperature and the feed material.
The size of the digester—the digester volume (VD)—is determined by the length of the retention time (RT) and by the amount of fermentation slurry supplied daily (SD). The amount of fermentation slurry consists of the feed material considered in this study (e.g., cattle dung) and the mixing water.
\nDaily average collectable biogas feedstock potential from cow dung, oxen dung, donkey, mule, and horse waste, chicken waste, human feces and jatropha byproduct in this study in tons/day is 10.867 = 10,867 kg/day = 15.53 m3/day. Since the average density of animal slurry mix is 700 kg/m3.
\nAdditional 15.53 m3/day water is required for proper digestion of biogas feedstock material to enhance biogas production.
\nHRT = 20 day, under thermophilic digestion temperature (55°C) the hydraulic retention time of the digestion process becomes short.
\nThe volume of digester should be, VD = HRT × SD.
\n= 20 day × (15.53 × 2 m3/day) = 621 m3.
\nTherefore the size of the digester for site A could be 621 m3.
\nWhere, VD = the size of the digester, HRT = hydraulic retention time, and SD is the amount of fermentation slurry (water + feedstock) feed in to the digester per day. Biogas yield in m3/kg of fresh biogas feedstock mix is 1736.4 m3/31850 kg = 0.054 m3/kg; the biogas production rate is 10,867 kg/day × 0.054 m3/kg = 588 m3/day. Therefore the size of gasholder should account this daily biogas production.
\nDaily average collectable biogas feedstock potential from cow dung, oxen dung, donkey, mule, and horse waste, chicken waste, human feces and jatropha byproduct of Site-B in tons/day is 9.253 = 9253 kg/day = 13.22 m3/day. Since the average density of animal slurry mix is 700 kg/m3.
\nAdditional 13.22 m3/day water is required for proper digestion process of biogas feedstock material to enhance biogas production.
\nHRT = 20 day, under thermophilic digestion temperature the hydraulic retention time of the digestion process becomes short.
\nThe volume of digester should be, VD = HRT × SD.
\n= 20 day × (13.22 × 2 m3/day) = 529 m3. Therefore the size of the digester for site-B is 529 m3. The biogas gas production rate is 9253 kg/day × 0.054 m3/kg = 501 m3/day. Therefore the size of gasholder should account this daily biogas production.
\nDaily average collectable biogas feedstock potential from cattle dung, donkey, mule, and horse waste, chicken waste, human feces and jatropha byproduct of site-C in tons/day is 8.82 = 8820 kg/day = 12.6 m3/day, Since the average density of animal slurry mix is 700 kg/m3.
\nAdditional 12.6 m3/day water is required for proper digestion of biogas feedstock material to enhance biogas production.
\nThe volume of digester should be, VD = HRT × SD, HRT = 20 day.
\n= 20 day × (12.6 × 2 m3/day) = 504 m3.
\nTherefore the size of the digester for site-C is 504 m3.
\nThe gas production rate is 8820 kg/day × 0.054 m3/kg = 477 m3/day. Therefore the size of gasholder should account this daily biogas production also.
\nDaily average collectable biogas feedstock potential of Site-D in tons/day is 3.091 = 3091 kg/day = 4.42 m3/day, since the average density of animal slurry mix is taken as 700 kg/m3. Additional 4.42 m3/day water is required.
\nThe volume of digester should be, VD = HRT × SD, HRT = 20 day.
\n= 20 day × (4.42 × 2 m3/day) = 179 m3.
\nTherefore the size of the digester for site-D is 179 m3.
\nThe gas production rate is 3091 kg/day × 0.054 m3/kg = 168 m3/day. Therefore the size of gasholder should account this daily biogas production.
\nThe next planning step in a biogas plant project idea is to find a suitable site for the establishment of the plant. The list below shows some important considerations to be made, before choosing the location of the plant: [3].
The site should be located at suitable distance from residential areas in order to avoid inconveniences, nuisance and thereby conflicts related to odors and increased traffic to and from the biogas plant.
The direction of the dominating winds must be considered in order to avoid wind born odors reaching residential areas.
The site should have easy access to infrastructure such as to the electricity grid, in order to facilitate the sale of electricity and to the transport roads in order to facilitate transport of feedstock and digestate.
The soil of the site should be investigated before starting the construction.
The chosen site should not be located in a potential flood affected area.
The size of the site must be suitable for the activities performed and for the amount of biomass supplied.
The site should be located relatively close (central) to the agricultural feedstock production (manure, slurry, energy crops) aiming to minimize distances, time and costs of feedstock transportation.
For cost efficiency reasons, the biogas plant should be located as close as possible to potential users of the produced heat and electricity.
The required site space for a biogas plant cannot be estimated in a simple way. Experience shows that for example a biogas plant of 500 kWel needs an area of approximate 8000 m2. This figure can be used as a guiding value only, as the actual area also depends on the chosen technology [3]. Based on the above criteria of site selection of biogas plant, the location of the biogas plant for each site of the study area is chosen and the detail of it is found in the economic analysis section of the biogas plant in this paper.
\nVarious literatures show that methane yield of jatropha fruit hull is 0.438 m3/kg VS, and the VS is 76% of the TS of the jatropha fruit hull. Methane is 50% of the total biogas yield (1.153 m3/kg). The biogas yield of Jatropha seed presscake is approximately 1 m3/kg of presscake. The biogas yield of jatropha fruit hull is better than the seedcake [5]. Based on the jatropha fact sheet given in Table 5, the biomass, biogas and methane yield potential of the jatropha byproduct is estimated in Tables 6, 7 and 8.
\nParameter | \nUnit | \nMinimum | \nAverage | \nMaximum | \nSource | \n
---|---|---|---|---|---|
Seed yield | \ndry ton/hectare/year | \n0.3 | \n3.15 | \n6 | \nPosition Paper on Jatropha Large Scale Project Development, FACT 2007 | \n
Fruit hull yield | \ndry ton/hectare/year | \n0.2 | \n2.1 | \n4 | \n|
Rainfall requirements for seed production | \nmm/year | \n600 | \n1000 | \n1500 | \nPosition Paper on Jatropha Large Scale Project Development, FACT 2007 | \n
Oil content of seeds | \n% of mass | \n_ | \n34% | \n40% | \nJatropha bio-diesel production and use, W. Achten et al., 2008 | \n
Oil yield after pressing | \n% of mass of seed input | \n20% | \n25% | \n30% | \nJatropha handbook, 2010 | \n
Presscake yield after pressing | \n% of mass of seed input | \n70 | \n75 | \n80 | \n|
Energy content of Seed | \nMJ/kg | \n— | \n37 | \n— | \n
Jatropha fact sheet.
Biogas feedstock | \nJatropha biomass, tons/year | \nAverage jatropha biomass, tons/year | \nBiogas yield, m3/kg | \nMethane yield, m3/kg | \nTotal biogas yield, m3 | \nAverage biogas yield, m3/year | \nAverage methane yield, m3/year | \n
---|---|---|---|---|---|---|---|
Presscake | \n4.2–96 | \n50.1 | \n1 | \n0.5–0.6 | \n4200–96,000 | \n50,100 | \n25,050-30,060 | \n
Fruit hull | \n4–80 | \n42 | \n1.153 | \n0.576–0.69 | \n4612–92,240 | \n48,426 | \n27,894–33,414 | \n
Total | \n8.2–176 | \n92.1 | \n1.07 | \n0.575–0.689 | \n8812–188,240 | \n98,526 | \n52,944–63,474 | \n
Jatropha byproduct biomass potential in the study area.
Jatropha biomass (from presscake) = seed yield (ton/hectare) × % of presscake yield during oil production * total land for Jatropha farming (hectare)
Jatropha biomass (from fruit hull) = hull yield (ton/hectare) × total land for Jatropha farming (hectare).
Profile | \nJatropha biomass, tons | \nBiogas yield, m3/kg | \nBiogas yield, m3 | \nMethane yield, m3/kg | \nMethane yield, m3 | \n
---|---|---|---|---|---|
Yearly average | \n92.1 | \n1.07 | \n98,526 | \n0.575–0.689 | \n52,944–63,474 | \n
Daily average | \n0.253 | \n1.07 | \n270 | \n0.575–0.689 | \n145–174 | \n
Jatropha biogas potential of the study area.
Jatropha product | \nJatropha oil (liter/year) | \nJatropha biogas (m3/year) | \nJatropha fertilizer (kg/year) | \nJatropha biomass (ton/year) | \n
---|---|---|---|---|
Product yield | \n16,090–18,774 | \n98,526 | \n18,420 | \n92.1 | \n
Summary of Jatropha potential of the study area.
A wide range of biomass types can be used as substrates (feedstock) for the production of biogas from AD. The most common biomass categories used in biogas production are listed in Table 9 for this thesis work. To produce biogas from animal manure first we have to check whether we have animal livestock potential sufficient for biogas feedstock production or not. The following Table demonstrates the animal livestock potential for each sites of the study area.
\nAnimal livestock | \nSite-A | \nSite-B | \nSite-C | \nSite-D | \nAve. no. of animal/HH | \nTotal livestock in the study area | \n
---|---|---|---|---|---|---|
Cows | \n666 | \n566 | \n535 | \n172 | \n1.7 | \n1935 | \n
Oxen | \n719 | \n612 | \n577 | \n184 | \n1.85 | \n2092 | \n
Goats | \n163 | \n139 | \n131 | \n43 | \n0.42 | \n476 | \n
Sheep | \n1841 | \n1567 | \n1477 | \n472 | \n4.72 | \n5350 | \n
Mule | \n12 | \n10 | \n9 | \n3 | \n0.03 | \n29 | \n
Chickens | \n2340 | \n1992 | \n1878 | \n600 | \n6 | \n6810 | \n
Pigs | \n0 | \n0 | \n0 | \n0 | \n0 | \n0 | \n
Horse | \n48 | \n40 | \n37 | \n12 | \n0.12 | \n133 | \n
Donkey | \n345 | \n295 | \n278 | \n89 | \n0.89 | \n1007 | \n
Jama Woreda, Kebele-8 districts animal livestock potential.
Source: Jama Woreda rural development and Kebele-8 administration office, Nov 2012.
The average fresh manure obtained from, cattle is 4.5 kg/day/head [1, 6, 7], donkey, horse and mule is 10 kg/day/head [6, 7], sheep and goat 1 kg/day/head [6, 7], and chicken is 0.08 kg/day/head [6, 7]. The average biogas yield of cattle, horse, mule, and donkey manure is 0.24 m3/kg DM [2, 3, 8] and pigs, sheep and goat is 0.37 m3/kg DM whereas chicken is 0.4 m3/kg of DM [2, 3, 8]. The dry matter content from the total mass of fresh animal manure and the proportion of methane from the total biogas production is summarized in Table 10 [2, 3, 9] (Table 11).
\nBiomass source | \nAverage fresh manure, kg/day/head | \nm3 biogas/kg DM | \nDM % fresh manure | \nMethane % biogas | \n
---|---|---|---|---|
Cattle | \n4.5 | \n0.24 | \n16.7 | \n65 | \n
Pigs | \n2 | \n0.37 | \n4.4 | \n65 | \n
Sheep, goats | \n1 | \n0.37 | \n30.7 | \n65 | \n
Chickens | \n0.08 | \n0.40 | \n30.7 | \n65 | \n
Horse, mule | \n10 | \n0.24 | \n7 | \n65 | \n
Donkey | \n10 | \n0.24 | \n15 | \n65 | \n
Summary of fresh manure, biogas and methane yield of animal livestock.
Total fresh manure potential of the study area (tons/day) = Average fresh manure (kg/day/head) × Total no. of livestock in study area.
Total dry mater (DM) from fresh manure = DM % of fresh manure × Total fresh manure potential of the study area (tons/day).
Total biogas production, m3/day = Biogas m3/kg of DM × Total dry mater (DM) from fresh manure in kg/day.
Total electricity production in kWh/day = electricity production by biogas generator from 1 m3 biogas in kWh × total biogas production in m3/day.
By using biogas generator it is possible to generate 1kWh electricity from 0.7 m3 biogas [42].
Animal livestock | \nAve. fresh manure, kg/day/head | \nTotal no. of livestock in study area | \nTotal fresh manure (ton/day) | \nTotal DM (kg/day) | \nBiogas, m3/kg of DM | \nTotal biogas, m3/day | \nElectricity production, kWh/day | \n
---|---|---|---|---|---|---|---|
Cows | \n4.5 | \n1935 | \n8.708 | \n1455 | \n0.24 | \n350 | \n500 | \n
Oxen | \n4.5 | \n2092 | \n9.414 | \n1573 | \n0.24 | \n378 | \n540 | \n
Goats | \n1 | \n476 | \n0.476 | \n147 | \n0.37 | \n55 | \n79 | \n
Sheep | \n1 | \n5350 | \n5.350 | \n1643 | \n0.37 | \n608 | \n869 | \n
Mule | \n10 | \n29 | \n0.290 | \n24 | \n0.24 | \n6 | \n9 | \n
Chicken | \n0.08 | \n6810 | \n0.545 | \n168 | \n0.40 | \n68 | \n98 | \n
Pigs | \n2 | \n0 | \n0.000 | \n0.00 | \n0.37 | \n0.0 | \n0.0 | \n
Horse | \n10 | \n133 | \n1.330 | \n92 | \n0.24 | \n22 | \n32 | \n
Donkey | \n10 | \n1007 | \n10.070 | \n1511 | \n0.24 | \n363 | \n519 | \n
Total animal manure biomass | \n36.183 | \n6613 | \n0.28 | \n1850 | \n2646 | \n
Summary of expected animal manure potential of the study area.
For a given size of plant (rated gas production capacity per day) the amount of feedstock required can be estimated using the biogas yield data provided. The specific biogas consumption in biogas engines is 0.6–0.8 m3/kWh [1]. This specific fuel consumption value can be used to calculate the requirement for biogas for power generation purposes. The expected biomass potential from animal manure of the case study area is 36.2 tons/day and its biogas production capacity is 1850 m3/day. Various literatures show that the collection efficiency of animal manure varies from country to country and region to region.
\nMost significantly the collection efficiency varies from 50 to 100% [10]. Let as consider collection efficiency of 90% for cattle, donkey, mule, horse, pig and chicken manure, 50% for goat and sheep manure and 100% for human feces based on their difficulty of collecting it. Therefore the biomass potential available for biogas generation is estimated as follows.
\nThe total collectable fresh animal manure biomass potential of the study area is estimated to be 30.235 tons/day and its biogas production capacity is 1398.3 m3/day (Table 12).
\nAnimal livestock | \nAve. fresh manure, kg/day/head | \nTotal no. of livestock in study area | \nTotal collectable fresh manure, tons/day | \nTotal collectable DM, kg/day | \nBiogas, m3/kg of DM | \nTotal biogas, m3/day | \nElectricity production, kWh/day | \n
---|---|---|---|---|---|---|---|
Cows | \n4.5 | \n1935 | \n7.837 | \n1309.5 | \n0.24 | \n315 | \n450 | \n
Oxen | \n4.5 | \n2092 | \n8.473 | \n1415.7 | \n0.24 | \n340 | \n486 | \n
Goats | \n1 | \n476 | \n0.238 | \n73.5 | \n0.37 | \n27.3 | \n39 | \n
Sheep | \n1 | \n5350 | \n2.675 | \n821.5 | \n0.37 | \n304 | \n434.3 | \n
Mule | \n10 | \n29 | \n0.261 | \n21.6 | \n0.24 | \n5.2 | \n7.43 | \n
Chicken | \n0.08 | \n6810 | \n0.491 | \n151.2 | \n0.40 | \n60.5 | \n86.43 | \n
Pigs | \n2 | \n0 | \n0.000 | \n0.00 | \n0.37 | \n0.0 | \n0.0 | \n
Horse | \n10 | \n133 | \n1.197 | \n82.8 | \n0.24 | \n19.9 | \n28.43 | \n
Donkey | \n10 | \n1007 | \n9.063 | \n1360 | \n0.24 | \n326.4 | \n466.3 | \n
Total animal manure Biomass | \n30.235 | \n5235.8 | \n0.27 | \n1398.3 | \n1998 | \n
Summary of collectable animal manure potential of the study area.
Human feces are another feedstock for biogas production in the study area and the potential biogas production from human feces is discussed in this section. Feces are mostly made of water (about 75%). The rest is made of dead bacteria that helped us digest our food, living bacteria, protein, undigested food residue (known as fiber), waste material from food, cellular linings, fats, salts, and substances released from the intestines (such as mucus) and the liver (Table 13).
\nPopulation | \nSite-A | \nSite-B | \nSite-C | \nSite-D | \nTotal | \n
---|---|---|---|---|---|
Number of household | \n390 | \n332 | \n313 | \n100 | \n1135 | \n
Average Family per household | \n4.39 (5) | \n4.39 (5) | \n4.39 (5) | \n4.39 (5) | \n4.39 (5) | \n
Total population | \n1950 | \n1660 | \n1565 | \n500 | \n5675 | \n
Jama Woreda, Kebele-8 districts population data.
One person produces on average 100–140 g of feces per day, the dry matter content of which is about 25% and its biogas yield of about 0.2 m3/kg DM [11]. The total collectable fresh manure biomass potential of the case study area from humans is estimated to be 0.681 tons/day and its biogas production capacity is 34.05 m3/day. This figure accounts the collection efficiency of human excreta. Table 14 demonstrates the biogas potential of the study area from human feces.
\nLive stock | \nAve. fresh manure, kg/day/head | \nTotal no. of population | \nTotal fresh manure potential (ton/day) | \nTotal DM (kg/day) | \nBiogas, m3/kg DM | \nTotal biogas, m3/day | \nElectricity production, kWh/day | \n
---|---|---|---|---|---|---|---|
Human | \n0.12 | \n5675 | \n0.681 | \n170.25 | \n0.2 | \n34.05 | \n48.7 | \n
Biogas potential of study area from human feces.
The total biogas potential from Jatropha byproduct, Animal waste and human feces discussed above can be summarized in this section.
\nTaking the density of biogas 1.15 kg/m3 and calculating the gasification ratio (the mass of biogas produced per unit mass of feed stock consumed) of the biogas system. From Table 15 the mass of biogas feedstock consumed is 31,850 kg/day and the gas produced is 1736.4 m3/day. Therefore the gasification ratio of biogas feedstock mix is 1736.4 m3/31850 kg = 0.0545 m3/kg = 0.0626 kg/kg.
\nAs we have seen from Table 15, animal manure is the major biogas feedstock constitutes which accounts 97% from the total biogas feedstock potential whereas jatropha byproducts and human excreta constitute 1 and 2% of the total biogas feedstock potential of the study area respectively. However, the share of biogas production from, animal manure is 82%, and human excreta is 2% but biogas production from jatropha byproduct is increase to 16% regardless of its low contribution to the biomass potential since the biogas yield of jatropha byproduct is high as compared to both animal and human manure and this can be summarized in Figure 3 given below.
\nAnimal Livestock | \nAve. fresh manure, kg/day/head | \nTotal no. of live stock | \nTotal collectable fresh manure (ton/day) | \nTotal collectable DM (kg/day) | \nBiogas, m3/kg DM | \nTotal biogas production, m3/day | \nElectricity yield, kWh/day | \n
---|---|---|---|---|---|---|---|
Cows | \n4.5 | \n1935 | \n7.837 | \n1309.5 | \n0.24 | \n315 | \n450 | \n
Oxen | \n4.5 | \n2092 | \n8.473 | \n1415.7 | \n0.24 | \n340 | \n486 | \n
Goats | \n1 | \n476 | \n0.238 | \n73.5 | \n0.37 | \n27.3 | \n39 | \n
Sheep | \n1 | \n5350 | \n2.675 | \n821.5 | \n0.37 | \n304 | \n434.3 | \n
Mule | \n10 | \n29 | \n0.261 | \n21.6 | \n0.24 | \n5.2 | \n7.43 | \n
Chicken | \n0.08 | \n6810 | \n0.491 | \n151.2 | \n0.40 | \n60.5 | \n86.43 | \n
Pigs | \n2 | \n0 | \n0.000 | \n0.00 | \n0.37 | \n0.0 | \n0.0 | \n
Horse | \n10 | \n133 | \n1.197 | \n82.8 | \n0.24 | \n19.9 | \n28.43 | \n
Donkey | \n10 | \n1007 | \n9.063 | \n1360 | \n0.24 | \n326.4 | \n466.3 | \n
Human | \n0.12 | \n5675 | \n0.681 | \n170.25 | \n0.2 | \n34.05 | \n48.7 | \n
Jatropha byproduct biomass | \n0.253 | \n253 | \n1.07 | \n270 | \n386 | \n||
Total | \n31.85 | \n5829.3 | \n0.3 | \n1736.4 | \n2481.4 | \n
The total biogas and collectable feedstock potential of the study area.
Biogas feedstock contributions for biogas production in the study area.
The variation of jatropha byproduct feedstocks is assumed to be constant throughout the year and the potential biomass obtained from it was divided to each site regardless of the total house hold in each of the study area.
\nHowever, the biomass obtained from animal is highly depending on the availability and type of the animal feeding material. The animal feeding materials are varying in type and amount from month to month in the study area. In June and July there is enough root grass in addition to the usual animal food, let as consider this value as the annual average in ton/day (the data obtained by multiplying the biomass obtained per animal live stock in ton/day with the total number of animal live stock for each animal group in the district), as a reference frame. In January, February, and December there is excess dry agricultural farm grass for the animal food in the study area and assuming a 5% biomass resource increment is expected from the reference. March and April is a dry season and there is no enough food for the animal so considering a 5% biomass resource decrement from the reference. May, extremely drought month and August, animal grazing area are not permitted for animal food assuming a 10% animal based biomass resource drop is expected. From September to November there is excess animal food and a 10% biomass growth is assumed. Also assuming chicken manure and human feces are constant throughout the year. Taking in to account the assumption listed above the biogas feedstock potential month to month variation is presented in Tables 16–19.
\nMonth | \nBiomass, tons/day | \n||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Cow | \nOxen | \nMule | \nHorse | \nDonkey | \nSheep | \nGoats | \nChicken | \nJatroph | \nHuman | \nTotal | \n|
Jan | \n2.82 | \n3.069 | \n0.114 | \n0.4536 | \n3.28 | \n0.967 | \n0.086 | \n0.17 | \n0.0633 | \n0.234 | \n11.257 | \n
Feb | \n2.82 | \n3.069 | \n0.114 | \n0.4536 | \n3.28 | \n0.967 | \n0.086 | \n0.17 | \n0.0633 | \n0.234 | \n11.257 | \n
Mar | \n2.552 | \n2.775 | \n0.1031 | \n0.4104 | \n2.97 | \n0.875 | \n0.08 | \n0.17 | \n0.0633 | \n0.234 | \n10.233 | \n
Apr | \n2.552 | \n2.775 | \n0.1031 | \n0.4104 | \n2.97 | \n0.875 | \n0.08 | \n0.17 | \n0.0633 | \n0.234 | \n10.233 | \n
May | \n2.417 | \n2.63 | \n0.0972 | \n0.3654 | \n2.811 | \n0.83 | \n0.074 | \n0.17 | \n0.0633 | \n0.234 | \n9.693 | \n
Jun | \n2.686 | \n2.921 | \n0.108 | \n0.432 | \n3.123 | \n0.921 | \n0.082 | \n0.17 | \n0.0633 | \n0.234 | \n10.740 | \n
Jul | \n2.686 | \n2.921 | \n0.108 | \n0.432 | \n3.123 | \n0.921 | \n0.082 | \n0.17 | \n0.0633 | \n0.234 | \n10.740 | \n
Aug | \n2.417 | \n2.63 | \n0.0972 | \n0.3654 | \n2.811 | \n0.83 | \n0.074 | \n0.17 | \n0.0633 | \n0.234 | \n9.6912 | \n
Sep | \n2.954 | \n3.213 | \n0.119 | \n0.475 | \n3.4353 | \n1.013 | \n0.09 | \n0.17 | \n0.0633 | \n0.234 | \n11.767 | \n
Oct | \n2.954 | \n3.213 | \n0.119 | \n0.475 | \n3.4353 | \n1.013 | \n0.09 | \n0.17 | \n0.0633 | \n0.234 | \n11.767 | \n
Nov | \n2.954 | \n3.213 | \n0.119 | \n0.475 | \n3.4353 | \n1.013 | \n0.09 | \n0.17 | \n0.0633 | \n0.234 | \n11.767 | \n
Dec | \n2.82 | \n3.069 | \n0.114 | \n0.4536 | \n3.28 | \n0.967 | \n0.086 | \n0.17 | \n0.0633 | \n0.234 | \n11.257 | \n
Average | \n2.693 | \n2.958 | \n0.1096 | \n0.4335 | \n3.1628 | \n0.9327 | \n0.083 | \n0.17 | \n0.0633 | \n0.234 | \n10.867 | \n
Biomass resource of site-A—390 families.
Month | \nBiomass, tons/day | \n||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Cow | \nOxen | \nMule | \nHorse | \nDonkey | \nSheep | \nGoats | \nChicken | \nJatropha | \nHuman | \nTotal | \n|
Jan | \n2.40 | \n2.614 | \n0.095 | \n0.378 | \n2.788 | \n0.823 | \n0.073 | \n0.144 | \n0.0633 | \n0.183 | \n9.56 | \n
Feb | \n2.40 | \n2.614 | \n0.095 | \n0.378 | \n2.788 | \n0.823 | \n0.073 | \n0.144 | \n0.0633 | \n0.183 | \n9.56 | \n
Mar | \n2.17 | \n2.364 | \n0.086 | \n0.342 | \n2.523 | \n0.744 | \n0.066 | \n0.144 | \n0.0633 | \n0.183 | \n8.69 | \n
Apr | \n2.17 | \n2.364 | \n0.086 | \n0.342 | \n2.523 | \n0.744 | \n0.066 | \n0.144 | \n0.0633 | \n0.183 | \n8.69 | \n
May | \n2.056 | \n2.240 | \n0.081 | \n0.324 | \n2.390 | \n0.706 | \n0.062 | \n0.144 | \n0.0633 | \n0.183 | \n8.25 | \n
Jun | \n2.284 | \n2.489 | \n0.09 | \n0.36 | \n2.655 | \n0.784 | \n0.070 | \n0.144 | \n0.0633 | \n0.183 | \n9.12 | \n
Jul | \n2.284 | \n2.489 | \n0.09 | \n0.36 | \n2.655 | \n0.784 | \n0.070 | \n0.144 | \n0.0633 | \n0.183 | \n9.12 | \n
Aug | \n2.056 | \n2.240 | \n0.081 | \n0.324 | \n2.38 | \n0.706 | \n0.062 | \n0.144 | \n0.0633 | \n0.183 | \n8.24 | \n
Sep | \n2.513 | \n2.737 | \n0.099 | \n0.469 | \n2.921 | \n0.862 | \n0.077 | \n0.144 | \n0.0633 | \n0.183 | \n10.07 | \n
Oct | \n2.513 | \n2.737 | \n0.099 | \n0.469 | \n2.921 | \n0.862 | \n0.077 | \n0.144 | \n0.0633 | \n0.183 | \n10.07 | \n
Nov | \n2.513 | \n2.737 | \n0.099 | \n0.469 | \n2.921 | \n0.862 | \n0.077 | \n0.144 | \n0.0633 | \n0.183 | \n10.07 | \n
Dec | \n2.40 | \n2.614 | \n0.095 | \n0.378 | \n2.788 | \n0.823 | \n0.073 | \n0.144 | \n0.0633 | \n0.183 | \n9.56 | \n
Average | \n2.313 | \n2.52 | \n0.09 | \n0.383 | \n2.69 | \n0.794 | \n0.071 | \n0.144 | \n0.0633 | \n0.183 | \n9.25 | \n
Biomass resource of site-B—332 families.
Month | \nBiomass, tons/day | \n||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Cow | \nOxen | \nMule | \nHorse | \nDonkey | \nSheep | \nGoats | \nChicken | \nJatropha | \nHuman | \nTotal | \n|
Jan | \n2.263 | \n2.46 | \n0.085 | \n0.755 | \n2.637 | \n0.776 | \n0.069 | \n0.136 | \n0.0633 | \n0.188 | \n9.434 | \n
Feb | \n2.263 | \n2.46 | \n0.085 | \n0.755 | \n2.637 | \n0.776 | \n0.069 | \n0.136 | \n0.0633 | \n0.188 | \n9.431 | \n
Mar | \n2.048 | \n2.23 | \n0.077 | \n0.316 | \n2.385 | \n0.702 | \n0.062 | \n0.136 | \n0.0633 | \n0.188 | \n8.206 | \n
Apr | \n2.048 | \n2.23 | \n0.077 | \n0.316 | \n2.385 | \n0.702 | \n0.062 | \n0.136 | \n0.0633 | \n0.188 | \n8.206 | \n
May | \n1.94 | \n2.13 | \n0.073 | \n0.30 | \n2.26 | \n0.665 | \n0.059 | \n0.136 | \n0.0633 | \n0.188 | \n7.812 | \n
Jun | \n2.156 | \n2.35 | \n0.081 | \n0.333 | \n2.511 | \n0.739 | \n0.066 | \n0.136 | \n0.0633 | \n0.188 | \n8.618 | \n
Jul | \n2.156 | \n2.35 | \n0.081 | \n0.333 | \n2.511 | \n0.739 | \n0.066 | \n0.136 | \n0.0633 | \n0.188 | \n8.618 | \n
Aug | \n1.94 | \n2.123 | \n0.073 | \n0.30 | \n2.26 | \n0.665 | \n0.059 | \n0.136 | \n0.0633 | \n0.188 | \n7.812 | \n
Sep | \n2.37 | \n2.556 | \n0.089 | \n0.41 | \n2.76 | \n0.813 | \n0.072 | \n0.136 | \n0.0633 | \n0.188 | \n9.457 | \n
Oct | \n2.37 | \n2.556 | \n0.089 | \n0.41 | \n2.76 | \n0.813 | \n0.072 | \n0.136 | \n0.0633 | \n0.188 | \n9.457 | \n
Nov | \n2.37 | \n2.556 | \n0.089 | \n0.418 | \n2.76 | \n0.813 | \n0.072 | \n0.136 | \n0.0633 | \n0.188 | \n9.457 | \n
Dec | \n2.263 | \n2.463 | \n0.085 | \n0.755 | \n2.637 | \n0.669 | \n0.069 | \n0.136 | \n0.0633 | \n0.188 | \n9.328 | \n
Average | \n2.183 | \n2.372 | \n0.082 | \n0.449 | \n2.542 | \n0.739 | \n0.067 | \n0.136 | \n0.0633 | \n0.188 | \n8.820 | \n
Biomass resource of site-C—313 families.
Month | \nBiomass, tons/day | \n||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Cow | \nOxen | \nMule | \nHorse | \nDonkey | \nSheep | \nGoats | \nChicken | \nJatropha | \nHuman | \nTotal | \n|
Jan | \n0.723 | \n0.787 | \n0.029 | \n0.1134 | \n0.841 | \n0.496 | \n0.044 | \n0.0432 | \n0.0633 | \n0.06 | \n3.199 | \n
Feb | \n0.723 | \n0.787 | \n0.029 | \n0.1134 | \n0.841 | \n0.496 | \n0.044 | \n0.0432 | \n0.0633 | \n0.06 | \n3.199 | \n
Mar | \n0.654 | \n0.712 | \n0.026 | \n0.1026 | \n0.761 | \n0.448 | \n0.04 | \n0.0432 | \n0.0633 | \n0.06 | \n2.910 | \n
Apr | \n0.654 | \n0.712 | \n0.026 | \n0.1026 | \n0.761 | \n0.448 | \n0.04 | \n0.0432 | \n0.0633 | \n0.06 | \n2.910 | \n
May | \n0.620 | \n0.674 | \n0.024 | \n0.0972 | \n0.721 | \n0.425 | \n0.038 | \n0.0432 | \n0.0633 | \n0.06 | \n2.766 | \n
Jun | \n0.689 | \n0.750 | \n0.027 | \n0.108 | \n0.801 | \n0.472 | \n0.043 | \n0.0432 | \n0.0633 | \n0.06 | \n3.056 | \n
Jul | \n0.689 | \n0.750 | \n0.027 | \n0.108 | \n0.801 | \n0.472 | \n0.043 | \n0.0432 | \n0.0633 | \n0.06 | \n3.056 | \n
Aug | \n0.620 | \n0.675 | \n0.024 | \n0.0972 | \n0.721 | \n0.425 | \n0.038 | \n0.0432 | \n0.0633 | \n0.06 | \n2.766 | \n
Sep | \n0.757 | \n0.825 | \n0.03 | \n0.1188 | \n0.881 | \n0.519 | \n0.046 | \n0.0432 | \n0.0633 | \n0.06 | \n3.344 | \n
Oct | \n0.757 | \n0.825 | \n0.03 | \n0.1188 | \n0.881 | \n0.519 | \n0.046 | \n0.0432 | \n0.0633 | \n0.06 | \n3.344 | \n
Nov | \n0.757 | \n0.825 | \n0.03 | \n0.1188 | \n0.881 | \n0.519 | \n0.046 | \n0.0432 | \n0.0633 | \n0.06 | \n3.344 | \n
Dec | \n0.723 | \n0.787 | \n0.028 | \n0.1134 | \n0.841 | \n0.496 | \n0.044 | \n0.0432 | \n0.0633 | \n0.06 | \n3.199 | \n
Average | \n0.697 | \n0.759 | \n0.027 | \n0.1094 | \n0.811 | \n0.478 | \n0.043 | \n0.0432 | \n0.0633 | \n0.06 | \n3.091 | \n
Biomass resource of site-D—100 families.
The renewable energy potential of the site is estimated based on the primary data collected directly from the study area and secondary data obtained from various sources. The biogas feedstock mix potential of the study area is found to be 10.9 tons/day, 9.25 tons/day, 8.81 tons/day and 3.09 tons/day for Site-A, Site-B, Site-C and Site-D respectively with a gasification ratio of 0.0626 kg/kg. The study result shows that there is a sufficient biogas feedstock potential for all districts of the study area and the feasibility simulation result demonstrates there is an excess biogas after running a biogas generator in a hybrid system. The excess biogas left unused from a hybrid electric generating unit would go to biogas cooking application for the community cooking loads. Also, the biodiesel potential of the study area from Jatropha is estimated to be 18.5 m3/year.
\nVoltammetry is an electrochemical technique for current-voltage curves, from which electrode reactions at electrode-solution interfaces can be interpreted. Since current-voltage curves, called voltammograms, include sensitive properties of solution compositions and electrode materials, their analysis provides not only chemical structures and reaction mechanisms on a scientific basis but also electrochemical manufacture on an industrial basis. The voltammograms vary largely with measurement time except for steady-state measurements, and so it is important to pay attention to time variables. Voltage is a controlling variable in conventional voltammetry, and the current is a measured one detected as a function of applied voltage at a given time.
\nThe equipment for voltammetry is composed of electrodes, solution, and electric instruments for voltage control. Electrodes and electric instruments are keys of voltammetry. Three kinds of electrodes are desired to be prepared: a working electrode, a counter one, and a reference one. The three will be addressed below.
\nLet us consider a simple experiment in which two electrodes are inserted into a salt-included aqueous solution. When a constant current is applied to the two electrodes, reaction 2H+ + 2e− → H2 may occur at one electrode, and reaction 2OH− → H2O2 + 2e− occurs at the other. The current is the time variation of the electric charge, and hence it is a kind of reaction rate at the electrode. Since the applied current is a sum of the two reaction rates, one being in the positive direction and the other being in the negative, it cannot be attributed to either reaction rate. A technique of attributing the reactions is to use an electrode with such large area that an uninteresting reaction rate may not become a rate-determining step. This electrode is called a counter electrode. The current density at the counter electrode does not specifically represent any reaction rate. In contrast, the current density at the electrode with a small area stands for the interesting reaction rate. This electrode is called a working electrode. It is the potential difference, i.e., voltage, at the working electrode and in the solution that brings about the electrode reaction. However, the potential in the solution cannot be controlled with the working electrode or the counter one. The control can be made by mounting another electrode, called a reference electrode, which keeps the voltage between an electrode and a solution to be constant. However, the constant value cannot be measured because of the difference in phases. A conventionally employed reference electrode is silver-silver chloride (Ag-AgCl) in high concentrated KCl aqueous solution.
\nAn electric instrument of operating the three electrodes is a potentiostat. It has three electric terminals: one being a voltage follower for the reference electrode without current, the second being a current feeder at the counter electrode, and the third being at the working electrode through which the current is converted to a voltage for monitoring. A controlled voltage is applied between the working electrode and the reference one. These functionalities can readily be attained with combinations of operational amplifiers. A drawback of usage of operational amplifiers is a delay of responses, which restricts current responses to the order of milliseconds or 10 kHz frequency.
\nVoltammetry includes various types—linear sweep, cyclic, square wave, stripping, alternating current (AC), pulse, steady-state microelectrode, and hydrodynamic voltammetry—depending on a mode of the potential control. The most frequently used technique is cyclic voltammetry (CV) on a time scale of seconds. In contrast, currently used voltammetry at time as short as milliseconds is AC voltammetry. We describe here the theory and tips for practical use of mainly the two types of voltammetry.
\nThe theory of voltammetry is to obtain expressions for voltammograms on a given time scale or for those at a given voltage. First of all, it is necessary to specify rate-determining steps of voltammograms. There are three types of rate-determining steps under the conventional conditions: diffusion of redox species in solution near an electrode, adsorption on an electrode, and charging processes at the double layer (DL). Electric field-driven mass transport, called electric migration, belongs to rare experimental conditions, and hence it is excluded in this review. When a redox species in solution is consumed or generated at an electrode, it is supplied to or departed from the electrode by diffusion unless solution is stirred. When it is accumulated on the electrode, the change in the accumulated charge by the redox reaction provides the current. Whenever electrode voltage is varied with the time, the charging or discharging of the DL capacitor causes current. Therefore, the three steps are frequently involved in electrochemical measurements.
\nA mass transport problem on voltammetry is briefly described here. The redox species is assumed to be transported by one-directional (x) diffusion owing to heterogeneous electrode reactions. Then, the flux is given by f = −D(∂c/∂x), where c and D are the concentration and the diffusion coefficient of the redox species, respectively. Redox species in solution causes some kinds of chemical reaction through chemical reaction rates, h(c, t). Then the reaction rate is the sum of the diffusional flux and the chemical reaction rate, ∂c/∂t = −∂f/∂x − h(c, t). Here the equation for h = 0 is called an equation of continuum. Eliminating f with the above equation on the assumption of a constant value of D yields ∂c/∂t = D(∂2c/∂x2) − h(c, t). This is an equation for diffusion-chemical kinetics. The expression at h = 0 is the diffusion equation. A boundary condition with electrochemical significance is the control of c at the electrode surface with a given electrode potential. If the redox reaction occurs in equilibrium with the one-electron transfer at the electrode, the Nernst equation for the concentrations of the oxidized species, co, and the reduced one, cr, holds.
\nwhere Eo is the formal potential. If there is no adsorption, the zero-flux condition in the absence of accumulation is valid:
\nThe other conditions are concentrations in the bulk (x → ∝) and the initial conditions.
\nIf the mass transport is controlled only by x-directional diffusion, cr and co are given by the diffusion equations, ∂c/∂t = D(∂2c/∂t2) for c = cr or co. An electrochemically significant quantity is not concentration in any x and t, but a relation between the surface concentrations and the current (the flux at x = 0). On the assumption of Do = Dr = D, of the initial and boundary conditions, (cr)t = 0 = c*, (co)t = 0 = 0, and (cr)x = ∞ = c*, (co)x = ∞ = 0, a solution of the initial-boundary problem is given by [1].
\nwhere j is the current density. The common value of the diffusion coefficients yields co + cr = c* for any x and t. Inserting this relation and Eq. (3) into the Nernst equation, (co)x = 0 = c*/[1 + exp[−F(E − Eo)/RT]], we obtain the integral equation for j as a function of t or E.
\nWhen the voltage is linearly swept with the time at a given voltage scan rate, v, from the initial potential Ein, Eq. (3) through the combination with the Nernst equation becomes
\nThe above Abel’s integral equation can be solved by Laplace transformation. When the time variation is altered to the voltage variation through E = Ein + vt, the current density is expressed as
\nwhere ζ = (E − Eo)F/RT and ζi = (Ein − Eo)F/RT. Evaluation of the integral has to resort to numerical computation. Current at any voltage should be proportional to v1/2, as can be seen in Eq. (5). The voltammogram for v > 0 rises up from Eo, takes a peak, and then deceases gradually with the voltage. The decrease in the current is obviously ascribed to relaxation by diffusion. The peak current density is expressed by
\nat Ep = Eo + 0.029 V at 25°C, where 0.446 comes from the numerical calculation of the integral of Eq. (5).
\nPractical voltage-scan voltammetry is not simply linear sweep but cyclic voltammetry (CV), at which applied voltage is reversed at a given voltage in the opposite direction. The theoretical evaluation of the voltammogram should be at first represented in the integral form with the time variation and then express the time as the voltage. One of the features of the diffusion-controlled cyclic voltammograms is the difference between the anodic peak potential and the cathodic one, ΔEp (in Figure 1), of which value is 59 mV at 25°C.
\nVoltammograms calculated from Eq. (5) for v = (a) 180, (b) 80 and (c) 20 mV s−1.
AC voltammetry can be performed when the time variation of voltage is given by E = Edc + V0eiωt, where ω is the frequency of applied AC voltage, i is the imaginary unit, V0 is its voltage amplitude, and Edc is the DC voltage. A conventional value of V0 is 10 mV. When this voltage form is inserted into Eq. (3) together with the Nernst equation, the AC component of the current density is represented by [2].
\nA voltammogram (j vs. Edc) at a given frequency takes a bell shape, which is expressed by sech2{(Edc − Eo)/RT}. The functional form of sech2 is shown in Figure 2. The peak current appears at Edc = Eo.
\nVoltammogram calculated from Eq. (10).
The AC-impedance technique often deals with the real impedance, Z1, = 1/2Y1 and the imaginary one, Z2 = −1/2Y1, where Y1 is the real admittance given by
\nHere Y2 is the imaginary admittance, equal to Y1. Since Z1 = −Z2, the Nyquist plot, i.e., −Z2 vs. Z1, is a line with the slope of unity. The term 1 + i in Eq. (7) has come from (Dω)1/2, originating from (Diω)1/2. Therefore, it can be attributed to diffusion. In other words, diffusion produces the capacitive component as a delay.
\nWhen the redox species with reaction R = O + e− is adsorbed on the electrode and has no influence from the redox species in the solution, the sum of the surface concentrations of R and O is a constant, Γ*. Then the surface concentration of the oxidized species, Γo, is given by the Nernst equation:
\nThe time derivative of the redox charge corresponds to the current density, j = d(FΓo)/dt. Application of the condition of voltage sweep, E = Ein + vt, to Eq. (9) yields.
\nThe voltammogram takes a bell shape (Figure 2), of which peak is at E = Eo, similar to the AC voltammogram. The current at any voltage is proportional to v. Since the negative-going scan of the voltage provides negative current values, the cyclic voltammogram should be symmetric with respect to the I = 0 axis. The peak current is expressed as jp = F2Γ*v/4RT. The width of the wave at jp/2 is 90 mV at 25°C.
\nSince a phase has its own free energy, contact of two phases provides a step-like gap of the free energy, of which gradient brings about infinite magnitude of force. In order to relax the infinity, local free energy varies from one phase to the other as smoothly as possible at the interface. The large variation of the energy is compensated with spontaneously generated space variations of voltage, i.e., the electric field, which works as an electric capacitor. The capacitance at solution-electrode interface causes orientation of dipoles and nonuniform distribution of ionic concentration, of which layer is called an electric double layer (DL).
\nWhen the time variation of the voltage is applied to the DL capacitance, Cd, the definitions of the capacitance (q = CdV) and the current lead
\nwhere Cd generally depends on the time. This dependence is significant for understanding experimentally observed capacitive currents.
\nThe DL capacitance has exhibited the frequency dispersion expressed by Cd = (Cd) 1Hz f −λ, called the constant phase element [3, 4, 5] or power law [6, 7], where λ is close to 0.1. Inserting this expression and V = V0eiωt into Eq. (11) yields
\nThis is a simple sum of the real part of the current and the imaginary one, indicating that the equivalent circuit should be a parallel combination of a capacitive component and a resistive one, both depending on frequency. Since the ratio, −Z2/Z1, for Eq. (12) is 1/λ, the Nyquist plots have slopes less than 10 rather than infinity.
\nIf the capacitive charge is independent of the time, the capacitive current should be I = d(CV)/dt = C(E − Eo)/v. Therefore, it takes a horizontal positive (v > 0) and a negative line (v < 0), as shown in Figure 3 (dashed lines). When the time dependence of C, i.e., Cd = (Cd)0t−λ, is applied to Eq. (11), for the forward and the backward scans, respectively, we have
\nCapacitive voltammograms by CV at v= 0.5 V s−1 for (dashed lines) the ideal capacitance and for Eq. (13) (solid curves) at λ = 0.2.
The variation of CV computed from Eq. (13) (Figure 3, solid curves) is similar to our conventionally observed capacitive waves.
\nVoltammograms can identify an objective species by comparing a peak potential with a table of redox potentials and furthermore determine its concentration from the peak current. Their results are, however, sometimes inconsistent with data by methods other than electrochemical techniques if one falls in some pitfalls of analytical methods of electrochemistry. For example, a peak potential is influenced by a reference electrode and solution resistance relevant to methods. Peak currents are varied complicatedly with mass transport modes as well as associated chemical reactions. Since the theory on voltammetry covers only some restricted experimental conditions, it can rarely interpret the experimental data successfully. This review is devoted to some voltammetric tips which can lead experimenters to reasonable interpretation.
\nIt is rare to observe a reversible voltammogram in which both oxidation and reduction waves appear in a symmetric form with respect to the potential axis at a similar peak potential, as in Figure 1. Frequently observed voltammograms are irreversible, i.e., either a cathodic or an anodic wave appears; a value of a cathodic peak current is quite different from the anodic one in magnitude; a cathodic peak potential is far from the anodic one. These complications are ascribed to chemical reactions and/or phase transformation after the charge-transfer reaction. A typical example is deposition of metal ions on an electrode. The complications can be interpreted by altering scan rates and reverse potentials.
\nA wave at a backward scan is mostly attributed to electrode reactions generated by experimenters rather than to species latently present in the solution. That is, it is artificial. It is caused either by the reaction of the wave at the forward scan or the reaction of the rising-up current just before the reverse potential. A source of the backward wave can be found by changing the reverse potentials.
\nSome voltammograms have more than two peaks at one-directional scan. The appearance of the two can be interpreted as a two-step sequential charge-transfer reaction. However, multiple waves appear also by combinations of chemical reactions and adsorption. The peak current and the charge for this case are quite different from the predicted ones, as will be described in Section 3.2. Change in scan rates may be helpful for interpreting the multiple waves.
\nIt is possible to predict theoretically a controlling step of voltammograms from their shape (a bell type corresponding to an adsorption wave or a draw-out type corresponding to a diffusion wave). However, the shape strongly depends on chemical complications, adsorption, and surface treatment of the electrodes. When redox species in solution is partially adsorbed on an electrode, the electrode process is far from a prediction because of very high concentration in the adsorbed state. A draw-out-shaped wave can be observed even for the adsorbed control. It is important to estimate which state the reacting species takes on the electrode. Potentials representing of voltammetric features do not express a controlling step in reality although the theory does. One should pay attention to the current. The peak current controlled by diffusion with one-electron transfer is given by Ip = 0.27 cAv1/2 μA (c, bulk concentration mM; A, electrode area mm2; v, potential sweep rate mV s−1). The microelectrode behavior sometimes comes in view at v < 10 mV s−1, A < 0.1 mm2, so the measured current is larger than the estimated value. On the other hand, the peak current controlled by adsorption is given by Ip = 1.6 Av nA when one redox molecule is adsorbed at 1 nm2 on the electrode. The voltammogram by adsorption often differs from the ideal bell shape due to adsorbed molecular interaction and DL capacity. Division of the area of the peak by the scan rate yields the amount of adsorbed electricity. Comparison of this with the anticipated amount of adsorption may be helpful for understanding the electrode process.
\nThe peak potential difference ΔEp between the oxidation wave and the reduction wave (Figure 1) has been used for a prediction of the reaction mechanism. For example, ΔEp = 60 mM suggests the diffusion-controlled current accompanied by one-electron exchange, whereas ΔEp = 30 mM infers a simultaneous reaction with two electrons. Then what would happen for 120 mV which is sometimes found? A half-electron reaction might not be accepted. Potential shift over 60 mV occurs by chemical complications. In contrast, the voltammogram by adsorbed species shows theoretically a bell shape with the width, E1/2 = 90 mV, at the half height of the peak (Figure 2). This value is based on the assumption of the absence of interaction among adsorbed species. However, adsorption necessarily yields such high concentrations as strong interaction.
\nIt is necessary to pay attention to the validity of analyzing ΔEp and E1/2. The peak potential is the first derivative of a voltammogram. Since ΔEp is a difference between the two peaks, it is actually the second-order derivative of the curves in the view of accuracy. In other words, the accuracy of ΔEp is lower than that of peak current. Furthermore, peak potentials as well as E1/2 readily vary with scan rates owing to chemical reactions and solution resistance. One should use the peak current for data analysis instead of the potentials.
\nVoltammograms of a number of redox species have been reported to be diffusion controlled from a relationship between Ip and v1/2. The redox species exhibiting diffusion-controlled current is, however, limited to ferrocenyl derivatives under conventional conditions. Voltammograms even for [Fe(CN)6]3−/4− and [Ru(NH3)6]3+ are deviated from the diffusion control for a long-time measurement. Why have many researchers assigned voltammograms to be the diffusion-controlled step? The proportionality of Ip to v1/2 in Eq. (6) has been confused with the linearity, Ip = av1/2 + b (b ≠ 0). The plot for the adsorption control (Ip = kv) also shows approximately a linear relation for Ip vs. v1/2 plot in a narrow domain of v, as shown in Figure 4B. The opposite is true (Figure 4A). Therefore, it is the intercept that determines a controlling step of either the diffusion or adsorption. Some may say that the intercept can be ascribed to a capacitive current. If so, the peak current should be represented by Ip = av1/2 + bv, which exhibits neither linear relation with v1/2 nor v.
\nPlots of Ip of (A) K3Fe(CN)6 and (B) polyaniline-coated electrode against v1/2 and v. Both plots show approximately linear relations.
There is a simple method of determining a controlling step either by diffusion or adsorption. Current responding to diffusion-controlled potential at a disk electrode in diameter less than 0.1 mm would become under the steady state after a few seconds [8]. Adsorption-limited current should become zero soon after the potential application. Many redox species, however, show gradual decrease in the current because reaction products generate an adsorbed layer which blocks further electrode reactions.
\nIt is well known that currents vary not only with applied voltage but also with the time. It is not popular, however, to discuss quantitatively time dependence of CV voltammograms. Enhancing v generally increases the current and causes the peak potential to shift in the direction of the scan. A reason for the former can be interpreted as generation of large current at a shorter time (see Eqs. (6) and (10)), whereas the latter is ascribed to a delay of reaction responses as well as a voltage loss of the reaction by solution resistance. Then the voltage effective to the reaction is lower than the intended voltage, and so the observed current may be smaller than the predicted one. Although Ip is related strongly with Ep, the relationship has rarely been examined quantitatively.
\nA technique of analyzing the potential shift is to plot Ip against Ep, [9] as shown in Figure 5. If the plots on the oxidation side (Ip > 0) and the reduction side (Ip < 0) fall each on a straight line, the slope may represent conductivity. If values of both slopes are equal, the slope possibly stands for the conductivity of the solution or membrane regardless of the electrode reaction. The potential extrapolated to the zero current on each straight line should be close to the formal potential. Since this plot is simple technically, the analytical result is more reliable than at least discussion of time dependence of Ep.
\nPlots of Ip vs. Ep by CV of the first (circles) and the second (triangles) peak of tetracyanoquinodimethane (TCNQ), and ferrocene (squares) in 0.2 M (CH3)4NPF6 included acetonitrile solution when scan rates were varied, where triangles were displayed by 0.4 V shift.
Most researchers have quoted the Randles-Sevcik equation, jp = 0.446 (nF)3/2c*(Dv/RT)1/2, for the diffusion-controlled peak current without hesitation, where n is the electron transfer number of the reaction. According to Faraday’s law, the electrolytic quantity is proportional to nc*. Why is the peak current proportional to n3/2 instead of n? Let us consider voltammetry of metal nanoparticles (about 25 nm in diameter) composed of 106 metal atoms dispersed in solution. Faraday’s law predicts that the current is 106 times as high as the current by the one metal atom. However, Randles-Sevcik equation predicts the current further (106)1/2 = 1000 times as large, just by the effect of the potential scan. The order 3/2 is specific to CV. The order of n for AC current and pulse voltammetry is 2 [10]. On the other hand, the diffusion-controlled steady-state currents at a microelectrode and a rotating disk electrode are proportional to n. Comparing the differences in the order by methods, we can predict that the time variation of the voltage increases the power of n.
\nLet a potential width from a current-rising potential to Ep be denoted by ΔE. When an n-electron transfer reaction occurs through the Nernst equation at which F in Eq. (1) is replaced by nF, the concentration-potential curve takes the slope n times larger than that at n = 1 (see co/cr ≅ nF(E − Eo)/RT near E = Eo in Eq. (1)). Then we have (ΔE)n = (ΔE)n = 1/n. The period of elapsing for (ΔE)n becomes shorter by 1/n, as if v might be larger by n times. Then v in Eq. (6) should be replaced by (nv)1/2. Combining this result with the flux j/nF, the current becomes n3/2 times larger than that at n = 1. Therefore, the factor n3/2 results from the Nernst equation. This can be understood quantitatively by replacing F in Eq. (3) by nF. There are quite a few reactions for n ≥ 2 both for Nernst equation and in the bulk as stable species. The term n3/2 is valid only for a concomitant charge-transfer reaction, i.e., simultaneous occurrence n-electron transfer rather than a step-by-step transfer. Apparent two-electron transfer reactions in the bulk, for example, Cu, Fe, Zn, and Pb, cause other reactions immediately after the one-electron transfer.
\nAn electrochemical response is observed as a sum of the half reactions at the two electrodes. In order to extract the reaction at the working electrode, a conventional technique is to increase the area of the counter electrode so that the reaction at the counter electrode can be ignored. If the counter electrode area is increased by 20 times the area of the working electrode, the observed current represents the reaction of the working electrode with an error of 5%. Let us consider the experiment in which nanoparticles of metal are coated on a working electrode for obtaining capacitive currents or catalyst currents. Then, the actual area of the working electrode can be regarded as the area of the metal particles measured by the molecular level. Then, the area will be several thousand times the geometric area so that the observed current may represent the reaction at the counter electrode. This kind of research has frequently been found in work on supercapacitors. On the other hand, if the electrode reaction is diffusion controlled, the current is determined by the projected area of the diffusion layer. Then the current is not affected by the huge surface area of nanoparticles.
\nIt is important to examine whether or not a reaction is controlled by at a counter electrode. A simple method is to coat nanoparticles also on the counter electrode. Then the current in the solution may become so high that the potential of the working electrode cannot be controlled. It is better to use a two-electrode system. Products at the counter electrode are possible sources of contaminants through redox cycling.
\nThe Ag-AgCl electrode is most frequently used as a reference electrode in aqueous solution because of the stable voltage at interfaces of Ag-AgCl and AgCl-KCl through fast charge-transfer steps, regardless of the magnitude of current density. The “fast step” means the absence of delay of the reaction or being in a quasi-equilibrium. The stability without delay is supported with high concentration of KCl.
\nWhen an Ag-AgCl electrode is inserted to a voltammetric solution, KCl necessarily diffuses into the solution, associated with oxygen from the reference electrode. Thus, the reference electrode is a source of contamination by salt, dichlorosilver and oxygen. It is interesting to examine how much amount a solution is contaminated by a reference electrode [9]. Time variation of ionic conductivity in the pure water was monitored immediately after a commercially available Ag-AgCl electrode was inserted into the solution. Figure 6 shows rapid increase in the conductivity as if a solid of KCl was added to the solution. Oxygen included in the concentrated KCl may contaminate a test solution. Even the Ag-AgxO electrode, which was formed by oxidizing silver wire, increased also the conductivity, probably because the surface is in the form of silver hydroxide. As a result, no reference electrode can be used for studying salt-free electrode reactions. If neutral redox species such as ferrocene is included in a solution, the potential reference can be taken from redox potential of ferrocene.
\nTime-variation of conductivity of water into which (circles) Ag|AgCl, (triangles) Ag|AgxO, and (squares) AgCl-coated Ag wire were inserted. Conductivity measurement was under N2 environment.
When a constant voltage is applied to the ideal capacitance C, the responding current decays in the form of exp(−t/RC), where R is a resistance in series connected with C. It has been believed that a double-layer capacitance in electrochemical system behaves as an ideal capacitor, where R is regarded as solution resistance. However, any exponential variation cannot reproduce transient currents obtained at the platinum wire electrode in KCl aqueous solution, as shown in Figure 7. The current decays more slowly than by exp(−t/RC), because it is approximately proportional to 1/t. The property of non-ideal capacitance is the result of the constant phase element of the DL capacitance, as described in Section 2.3. The dependence of 1/t can be obtained approximately by the time derivative of q = V0C0t−λ for the voltage step V0.
\nChronoamperometric curves when 0.2 V vs. Ag|AgCl was applied to a Pt wire in 0.5 M KCl aqueous solution. Solid curves are fitted ones by exp(-t/RC) for three values of RC.
The slow decay is related with a loss of the performance of pulse voltammetry, in which diffusion-controlled currents can readily be excluded from capacitive currents. The advantage of pulse voltammetry is based on the assumption of the exponential decay of the capacitive current. Since the diffusion current with 1/t1/2 dependence is close to the 1/t dependence, it cannot readily be separated from the capacitive current in reality. A key of using pulse voltammetry is to take a pulse time to be so long as a textbook recommends.
\nHigh-performance potentiostats are equipped with a circuit for compensation of resistance by a positive feedback. Unfortunately, the circuit is merely useful because voltammograms depend on intensity of compensation resistances of the DL capacitance. It should work well if the DL capacitance is ideal.
\nAC techniques have an advantage of examining time dependence at a given potential, whereas CV has a feature of finding current-voltage curves at a given time. The former shows the dynamic range from 1 Hz to 10 kHz, while the latter does conventionally from 0.01 to 1 Hz. This wide dynamic range of the AC technique is powerful for examining dynamics of electrode reactions. Analytical results by the former are often inconsistent with those by the latter, because of the difference in the time domain. The other scientific advantage of the AC technique is to get two types of independent data set, frequency variations of real components and imaginary ones by the use of a lock-in amplification. The independence allows us to operate mathematically the two data, leading to the data analysis at a level one step higher than CV. An industrial advantage is the rapid measurement, which can be applied to quality control for a number of samples. The analysis of AC impedance necessarily needs equivalent circuits of which components do not have any direction relation with electrochemical variables.
\nData of the electrochemical AC impedance are represented by Nyquist (Cole-Cole) plots, that is, plots of the imaginary component (Z2) of the impedance against the real one (Z1), as shown in Figure 8. The simplest equivalent circuit for electrochemical systems is the DL capacitance Cd in series with the solution resistance RS. The Nyquist plot for this series circuit is theoretically parallel to the vertical axis (Figure 8A-a), but experiments show a slope of 5 or more (Figure 8A-b). This behavior, called constant phase element (CPE) and the power law, has been verified for combinations of various materials and solvents [6, 7, 11, 12]. The equivalent circuit for Eq. (12) is a parallel combination of capacitance and resistance (Figure 8B). Even without an electrode reaction, current always includes a real component.
\n(A) Nyquist plots for a RC-series circuit with ideal capacitor (a) and DL capacitor (b). (B) Equivalent circuit with the power-law of Cd. (C) Randles circuit.
The equivalent circuit with the Randles type is a parallel combination of the ideal DL capacitor Cd with the ideal resistance Rct representing the Butler-Volmer-type charge-transfer resistance. Practically, the Warburg impedance (the inverse of Eq. (8)) due to diffusion of redox species is incorporated in a series into Rct (Figure 8C). Rct cannot be separated from the DL resistance because of the frequency dispersion. Since even the existence of Rct is in question (Section 3.12), it is difficult to determine and interpret Rct. The usage of a software that can analyze any Nyquist plots will provide values of R and C. Even if analyzed values are in high accuracy, researches should give them electrochemical significance.
\nResidual current varies with treatments of electrodes such as polishing of electrode surfaces and voltage applications to an extremely high domain. It can often be suppressed to yield reproducible data when the electrode is replaced by simple platinum wire or carbon rod having the same geometric area. Simple wire electrodes are quite useful especially for measurements of DL capacitance and adsorption. One of the reasons for setting off large residual current is that the insulator of confining the active area is not in close contact with the electrode, so that the solution penetrated into the gap will give rise to capacitive current and floating electrode reactions. Since the coefficient of thermal expansion of the electrode is different from that of the insulator, the residual current tends to get large with the elapse from the fabrication of the electrode. This prediction is based on experience, and there are few quantitative studies on residual currents.
\nUnexpected gap has been a technical problem at dropping mercury electrodes. If solution penetrates the inner wall of the glass capillary containing mercury, observed currents become irreproducible. Water repellency of the capillary tip has been known to improve the irreproducibility in order to reduce the penetration. A similar technique has been used for voltammetry at oil-water interfaces and ionic liquid-water interfaces at present.
\nVoltammograms are said to vary with electrode reaction rates, and the rate constants have been determined from time dependence of voltammograms. The fast reaction of which rate is not rate determining has historically been called “reversible.” In contrast, such a slow reaction that a peak potential varies linearly with log v is called “irreversible.” A reaction between them is called “quasi-reversible.” The distinction among the three has been well known since the theoretical report on the quasi-reversible reaction by Matsuda [1]. This theory is devoted to solving the diffusion equations with boundary conditions of the Butler-Volmer (BV) equation under the potential sweep. As the standard rate constant ks in the BV equation becomes small, the peak shifts in the direction of the potential sweep from the diffusion-controlled peak. Steady-state current-potential curves in a microelectrode [13] and a rotating disk electrode also shift the potential in a similar way. According to the calculated CV voltammograms in Figure 9, we can present some characteristics: (i) if the oxidation wave shifts to the positive potential, the negative potential shift should also be found in the reduction wave. (ii) Both the amounts of the shift should have a linear relationship to log v. (iii) The shift should be found in iterative measurements. (iv) The peak current should be proportional to v1/2.
\nCV voltammograms (solid curves) at a normally sized electrode and steady-state voltammograms (dashed curves) at a microelectrodes in 12 μm in diameter, calculated theoretically for v = 0.5 V s−1, D = 0.73 × 10−5 cm2 s−1, ks = (a) 0.1, (b) 0.01, (c) 0.001, (d) 0.0001 cm s−1. The potential shift of CV is equivalent to the wave-shift at a microelectrode through the relation, v = 0.4RTD/αFa2 (a: radius).
The authors attempted to find a redox species with the above four behaviors. Some redox species can satisfy one of the four requirements, but do not meet the others. Most reaction rate constants have been determined from the potential shift in a narrow time domain. They are probably caused by follow-up chemical reactions, adsorption, or DL capacitance. For example, CV peak potentials of TCNQ and benzoquinone were shifted at high scan rates, whereas their steady-state voltammograms were independent of diameters of microdisk electrodes even on the nanometer scale [14]. The shift at high scan rates should be due to the frequency dispersion of the DL capacitance, especially the parallel resistance in the DL (Figure 8B). Values of the heterogeneous rate constants and transfer coefficients reported so far have depended not only on the electrochemical techniques but also research groups. Furthermore, they have not been applied or extended to next developing work. These facts inspire us to examine the assumptions and validity of the BV formula.
\nLet us revisit the assumptions of the BV equation when an overvoltage, i.e., the difference of the applied potential from the standard electrode potential, causes the electrode reaction. The rate of the oxidation in the BV equation is assumed to have the activation energy of α times the overvoltage, while that of the reduction does that of (1 − α) times. This assumption seems reasonable for the balance of both the oxidation and the reduction. However, the following two points should be considered. (i) Once a charge or an electron is transferred within the redox species, the molecular structure changes more slowly than the charge transfer itself occurs. The structure change causes solvation as well as motion of external ions to keep electric neutrality. These processes should be slower than the structure change. If the overvoltage can control the reaction rate, it should act on to the slowest step, which is not the genuine charge-transfer process. (ii) Since a reaction rate belongs to the probability theory, the reaction rate (dc/dt) at t is determined with the state at t rather than a state in the future. In other words, the rate of the reduction should have no relation with the oxidation state which belongs to the future state. The BV theory assumes that the α times activation energy for the oxidation is related closely with 1-α times one for the reduction. This assumption is equivalent to predicting a state at t + Δt from state at t + 2Δt, like riding on a time machine. This question should be solved from a viewpoint of statistical physics.
\nDevelopment of scanning microscopes such as STM and AFM has allowed us to obtain the molecularly and atomically regulated surface images, which have been used for interpreting electrochemical data. Then the electrochemical data are expected to be discussed on a molecular scale. However, there is an essential problem of applying photographs of regularly arranged atoms on an electrode to electrochemical data, because the former and the latter include, respectively, microscopically local information and macroscopically averaged one. A STM image showing molecular patterns is information of only a part of electrode, at next parts of which no atomic images are often observed but noisy images are found. Electrochemical data should be composed of information both at a part of the electrode showing the molecular patters and at other parts showing noisy, vague images. Noisy photographs are always discarded for interpreting electrochemical data although the surfaces with noisy images also contribute electrochemical data.
\nAn ideal experiment would be made by taking STM images over all the electrodes that provide electrochemical data and by obtaining an averaged image. However, it is not only impossible to take huge amounts of images, but the averaged image might be also noisy. It may be helpful to describe only a possibility of reflecting the STM-imaged atomic structure on the electrochemical data.
\nVoltammograms by adsorbed redox species, called surface waves, are frequently different from a bell shape (Figure 2). Really observed features are the following: (i) the voltammogram does not suddenly decay after the peak, exhibiting a tail-like diffusional wave; (ii) the peak current and the amount of the electricity are proportional to the power less than the unity of v; (iii) the oxidation peak potential is different from the reduction one; (iv) the background current cannot be determined unequivocally; and (v) voltammograms depend on the starting potential. Why are experimental surface waves different from a symmetric, bell shape in Figure 2?
\nA loss of the symmetry with respect to the vertical line passing through a peak can be ascribed to the difference in interactions at the oxidized potential domain and at the reduced one. Since redox species takes extremely high concentration in the adsorbed layer, interaction is highly influenced on voltammetric form. When the left-right asymmetry is ascribed to thermodynamic interaction, it has been interpreted not only with Frumkin’s interaction [15] but also Bragg-Williams-like model for the nearest neighboring interactive redox species [16]. On the other hand, most surface waves are asymmetric with respect to the voltage axis even at extremely slow scan rates. This asymmetry cannot be explained in terms of thermodynamics of intermolecular interaction, but should resort to kinetics or a delay of electrode reactions. There seems to be no delay in the electrode reaction of the monomolecular adsorption layer, different from diffusion species. The delay resembles the phenomenon of constant phase element (CPE) or frequency power law of DL capacitance, in that the redox interaction may occur two-dimensionally so that the most stable state can be attained. This behavior belongs to a cooperative phenomenon [17]. A technique of overcoming these complications is to discuss the amount of charge by evaluating the area of the voltammogram. It also includes ambiguity of eliminating background current and assuming the independence of the redox charge from the DL charge.
\nThe simplest theories for voltammetry are limited to the rate-determining steps of diffusion of redox species and reactions of adsorbed species without interaction. Variation of scan rates as well as a reverse potential is helpful for predicting redox species and reaction mechanisms. Furthermore, the following viewpoints are useful for interpreting mechanisms:
comparison of values of experimental peak currents with theoretical ones, instead of discussing ΔEp and E1/2;
examining the proportionality of Ip vs. v or vs. v1/2, i.e., zero or non-zero values of the intercept of the linearity;
a reference electrode and a counter electrode being a source of contamination in solution;
attention to very slow relaxation of DL capacitive currents;
inclusion of ambiguity in the equivalent circuit with the Randles type.
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