Countries with top biodiesel production in 2016.
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Fornace Jr. and Kamal Datta",authors:[{id:"74075",title:"Dr.",name:"Kamal",middleName:null,surname:"Datta",fullName:"Kamal Datta",slug:"kamal-datta"},{id:"121217",title:"Dr.",name:"Shubhankar",middleName:null,surname:"Suman",fullName:"Shubhankar Suman",slug:"shubhankar-suman"},{id:"121218",title:"Dr.",name:"Albert",middleName:null,surname:"Fornace",fullName:"Albert Fornace",slug:"albert-fornace"}]},{id:"28753",title:"The Stem Cell Environment: Kinetics, Signaling and Markers",slug:"the-significance-of-stem-cell-for-rectal-cancer-treatment",signatures:"George D. Wilson and Bryan J. Thibodeau",authors:[{id:"72804",title:"Dr.",name:"George",middleName:null,surname:"Wilson",fullName:"George Wilson",slug:"george-wilson"}]},{id:"28754",title:"Endoscopic Diagnosis and Treatment for Colorectal Cancer",slug:"endoscopic-diagnosis-and-treatment-for-colorectal-cancer",signatures:"Hiroyuki Kato, Teruhiko Sakamoto, Hiroko Otsuka, Rieko Yamada and Kiyo Watanabe",authors:[{id:"70877",title:"Prof.",name:"Hiroyuki",middleName:null,surname:"Kato",fullName:"Hiroyuki Kato",slug:"hiroyuki-kato"}]},{id:"28755",title:"Peri-Operative Care in Colorectal Surgery in the Twenty-First Century",slug:"peri-operative-colorectal-surgical-care-in-the-twenty-first-century",signatures:"Ned Abraham",authors:[{id:"77014",title:"Prof.",name:"Ned",middleName:"S",surname:"Abraham",fullName:"Ned Abraham",slug:"ned-abraham"}]},{id:"28756",title:"Follow Up and Recurrence of Colorectal Cancer",slug:"follow-up-and-recurrence-of-colorectal-cancer-",signatures:"Miroslav Levy",authors:[{id:"77208",title:"Dr.",name:"Miroslav",middleName:null,surname:"Levy",fullName:"Miroslav Levy",slug:"miroslav-levy"}]},{id:"28757",title:"Panitumumab for the Treatment of Metastatic Colorectal Cancer",slug:"panitumumab-for-the-treatment-of-metastatic-colorectal-cancer",signatures:"Béla Pikó, Ali Bassam, Enikő Török, Henriette Ócsai and Farkas Sükösd",authors:[{id:"69962",title:"Dr.",name:"Béla",middleName:null,surname:"Pikó",fullName:"Béla Pikó",slug:"bela-piko"},{id:"80745",title:"Dr.",name:"Ali",middleName:null,surname:"Bassam",fullName:"Ali Bassam",slug:"ali-bassam"}]},{id:"28758",title:"Resection for Colorectal Liver Metastases",slug:"resection-of-colorectal-liver-metastases",signatures:"Daniel Kostov and Georgi Kobakov",authors:[{id:"74015",title:"Prof.",name:"Daniel",middleName:null,surname:"Kostov",fullName:"Daniel Kostov",slug:"daniel-kostov"},{id:"74029",title:"Dr.",name:"Georgi",middleName:null,surname:"Kobakov",fullName:"Georgi Kobakov",slug:"georgi-kobakov"}]},{id:"28759",title:"Experimental Colorectal Cancer Liver Metastasis",slug:"experimental-colorectal-cancer-liver-metastasis",signatures:"Rania B. Georges, Hassan Adwan and Martin R. Berger",authors:[{id:"72759",title:"Prof.",name:"Martin",middleName:null,surname:"Berger",fullName:"Martin Berger",slug:"martin-berger"},{id:"72948",title:"Dr.",name:"Rania",middleName:null,surname:"Georges",fullName:"Rania Georges",slug:"rania-georges"},{id:"72949",title:"Dr.",name:"Hassan",middleName:null,surname:"Adwan",fullName:"Hassan Adwan",slug:"hassan-adwan"}]},{id:"28760",title:"Risk Factors for Wound Infection After Surgery for Colorectal Cancer: A Matched Case – Control Study",slug:"risk-factors-for-wound-infection-after-surgery-for-colorectal-cancer-a-matched-case-control-study",signatures:"Takatoshi Nakamura and Masahiko Watanabe",authors:[{id:"69637",title:"Prof.",name:"Masahiko",middleName:null,surname:"Watanabe",fullName:"Masahiko Watanabe",slug:"masahiko-watanabe"}]},{id:"28761",title:"Modelling and Inference in Screening: Exemplification with the Faecal Occult Blood 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Matsuo",slug:"keitaro-matsuo"},{id:"80214",title:"Prof.",name:"Kazuo",middleName:null,surname:"Tajima",fullName:"Kazuo Tajima",slug:"kazuo-tajima"},{id:"80217",title:"MSc.",name:"WenChang",middleName:null,surname:"Wang",fullName:"WenChang Wang",slug:"wenchang-wang"},{id:"80222",title:"Dr.",name:"Huan",middleName:null,surname:"Yang",fullName:"Huan Yang",slug:"huan-yang"}]},{id:"28763",title:"The Prognostic Significance of Number of Lymph Node Metastasis in Colon Cancer – Based on Japanese Techniques of Resection and Handling of Resected Specimens",slug:"prognostic-relationship-between-colon-cancer-and-metastatic-number-of-lymph-nodes-the-examination-ba",signatures:"Yoshito Akagi, Romeo Kansakar and Kazuo Shirouz",authors:[{id:"73177",title:"Dr.",name:"Yoshito",middleName:null,surname:"Akagi",fullName:"Yoshito Akagi",slug:"yoshito-akagi"},{id:"81612",title:"Dr.",name:"Kazuo",middleName:null,surname:"Shirouzu",fullName:"Kazuo Shirouzu",slug:"kazuo-shirouzu"},{id:"136281",title:"Dr.",name:"Romeo",middleName:null,surname:"Kansakar",fullName:"Romeo Kansakar",slug:"romeo-kansakar"}]},{id:"28764",title:"Minimally Invasive Robot – Assisted Colorectal Resections",slug:"minimally-invasive-robot-assisted-colorectal-resections",signatures:"Annibale D’Annibale, Graziano Pernazza, Vito Pende and Igor Monsellato",authors:[{id:"69534",title:"Dr.",name:"Graziano",middleName:null,surname:"Pernazza",fullName:"Graziano Pernazza",slug:"graziano-pernazza"},{id:"80719",title:"Dr.",name:"Annibale",middleName:null,surname:"D'Annibale",fullName:"Annibale D'Annibale",slug:"annibale-d'annibale"},{id:"80720",title:"Ph.D.",name:"Igor",middleName:null,surname:"Monsellato",fullName:"Igor Monsellato",slug:"igor-monsellato"},{id:"128019",title:"Dr.",name:"Vito",middleName:null,surname:"Pende",fullName:"Vito Pende",slug:"vito-pende"}]}]}],publishedBooks:[{type:"book",id:"2031",title:"Colorectal Cancer",subtitle:"From Prevention to Patient Care",isOpenForSubmission:!1,hash:"1f6f38a6a84434c19e02351ebe27c1da",slug:"colorectal-cancer-from-prevention-to-patient-care",bookSignature:"Rajunor Ettarh",coverURL:"https://cdn.intechopen.com/books/images_new/2031.jpg",editedByType:"Edited by",editors:[{id:"78549",title:"Dr.",name:"Rajunor",surname:"Ettarh",slug:"rajunor-ettarh",fullName:"Rajunor Ettarh"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3316",title:"Colonoscopy and Colorectal Cancer Screening",subtitle:"Future Directions",isOpenForSubmission:!1,hash:"e651855fc73415aa7c62622ab3b73b89",slug:"colonoscopy-and-colorectal-cancer-screening-future-directions",bookSignature:"Marco Bustamante",coverURL:"https://cdn.intechopen.com/books/images_new/3316.jpg",editedByType:"Edited by",editors:[{id:"41712",title:"Dr.",name:"Marco",surname:"Bustamante",slug:"marco-bustamante",fullName:"Marco Bustamante"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3529",title:"Colorectal Cancer",subtitle:"Surgery, Diagnostics and Treatment",isOpenForSubmission:!1,hash:"25832741e70e2fca568d88c1647786e9",slug:"colorectal-cancer-surgery-diagnostics-and-treatment",bookSignature:"Jim S Khan",coverURL:"https://cdn.intechopen.com/books/images_new/3529.jpg",editedByType:"Edited by",editors:[{id:"155719",title:"Dr.",name:"Jim",surname:"Khan",slug:"jim-khan",fullName:"Jim Khan"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4723",title:"Screening for Colorectal Cancer with Colonoscopy",subtitle:null,isOpenForSubmission:!1,hash:"1d7cbe85af06291d1d823919d7006ba0",slug:"screening-for-colorectal-cancer-with-colonoscopy",bookSignature:"Rajunor Ettarh",coverURL:"https://cdn.intechopen.com/books/images_new/4723.jpg",editedByType:"Edited by",editors:[{id:"78549",title:"Dr.",name:"Rajunor",surname:"Ettarh",slug:"rajunor-ettarh",fullName:"Rajunor Ettarh"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5122",title:"Colorectal Cancer",subtitle:"From Pathogenesis to Treatment",isOpenForSubmission:!1,hash:"5ab8ff026cf9fbd8e3b0097d7f11fe2c",slug:"colorectal-cancer-from-pathogenesis-to-treatment",bookSignature:"Luis Rodrigo",coverURL:"https://cdn.intechopen.com/books/images_new/5122.jpg",editedByType:"Edited by",editors:[{id:"73208",title:"Prof.",name:"Luis",surname:"Rodrigo",slug:"luis-rodrigo",fullName:"Luis Rodrigo"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],publishedBooksByAuthor:[]},onlineFirst:{chapter:{type:"chapter",id:"72722",title:"Biodiesel Production as a Renewable Resource for the Potential Displacement of the Petroleum Diesel",doi:"10.5772/intechopen.93013",slug:"biodiesel-production-as-a-renewable-resource-for-the-potential-displacement-of-the-petroleum-diesel",body:'\nThe need to substitute the conventional petroleum diesel with a renewable alternative, one that is sustainable and environmentally friendly, has driven various investigators over a decade now to research on the potentials of biodiesel [1]. This has risen due to depletion of fossil fuels and emission of greenhouse gases such as CO2 and methane upon combustion, which causes climate change, the result of which is the rise in the global temperature above the nominal margin of 2°C with the potential to extinct over 1 million species [2, 3]. Other adverse effects of this global temperature rise also known as global warming include receding of glaciers, rise in sea level and loss of biodiversity [4]. However, biodiesel is a renewable fuel produced from the reaction between triacylglycerol or fatty acid with alcohol in the presence of a catalyst [5]. The fuel exists as liquid and consists of mono-alkyl esters of long-chain fatty acids with similar characteristics as the conventional petroleum diesel, making it a potential substitute [6]. Biodiesel is biodegradable, sustainable, and nontoxic, and has less impact on the environment. The shortcomings of biodiesel include low energy density, relatively high production cost and poor cold flow [7]. The global production capacity of biodiesel is envisaged to reach 12 billion gallons by 2020 with Brazil, United States of America, Malaysia, Argentina, Netherlands, Spain, Philippines, Belgium, Indonesia and Germany meeting more than 80% of the world demand [8, 9]. In 2016, the biodiesel produced globally were contributed mostly by USA and Brazil (see Table 1). Larger proportions of which are consumed by countries such as USA, Brazil, Germany, Indonesia and France [2]. Countries like US, China and India are currently experiencing a great growth in the biodiesel market with their respective governments planning to replace about 15% of the conventional diesel with biodiesel by 2020.
\nCountry | \nBiodiesel production/billion liters | \n
---|---|
USA | \n5.5 | \n
Brazil | \n3.8 | \n
Germany | \n3 | \n
Indonesia | \n3 | \n
Argentina | \n3 | \n
France | \n1.5 | \n
Thailand | \n1.4 | \n
Spain | \n1.1 | \n
Belgium | \n0.5 | \n
Colombia | \n0.5 | \n
Canada | \n0.4 | \n
China | \n0.3 | \n
India | \n0 | \n
Singapore | \n0 | \n
Countries with top biodiesel production in 2016.
Biodiesel can be classified into three types based on the kind of feedstocks used in its production [10]. These are first-, second- and third-generation biodiesels.
\nThis type of biodiesel is produced using edible vegetable oils. These oils are discussed in the next section. Biodiesel produced from these oils usually has the following disadvantages [11, 12, 13, 14]:
Poor storage
Oxidation stability
High feedstock cost, up to 60−80% of biodiesel production cost
Low heating value
Higher NOx emission compared to the conventional diesel fuel
Loss of biodiversity
In order to minimize the over dependency on the edible vegetable oils feedstocks in biodiesel production, alternative sources from non-edible oils are explored. Biodiesel produced from this type of oils is known as second-generation biodiesel. The quality and yield obtained are similar to that from edible oils [15]. Lignocellulosic biomass (LCB) is also being considered as an alternative feedstock to edible oil in biodiesel production probably because it is suspected to promote faster production, less labour, more season and climate flexibility, easier scale-up, and potential economic advantage [16]. This biomass can be derived from food crops, non-food/energy crops, forest residue and industrial process residues (see Table 2). But, the most predominant is agricultural crop residues [17]. Although, some of the LCB resources might not be suitable for energy production, probably due to their wide dispersal or low bulk density, which makes energy recovery, transport and storage expensive [18]. Generally, the production of biodiesel from lignocellulosic biomass is hampered due to lack of economically feasible technologies [18].
\nFood crops | \nNon-food/energy crops | \nForest residue | \nIndustrial process residues | \n
---|---|---|---|
*Rice straw | \n*Cardoon ( | \n*Tree residue (twigs, leaves, bark, and roots) | \n*Rice husk | \n
*Wheat straw | \n*Giant reed ( | \n*Wood processing residues (sawmill off-cuts and sawdust) | \n*Rice bran | \n
*Sugarcane tops | \n*Salix | \n*Recycled wood (from demolition of buildings, pallets, and packing crates) | \n*Sugarcane bagasse | \n
*Maize stalks millet | \n*Jute stalks | \n\n | *Coconut husks | \n
*Groundnut stalks | \n*Willow | \n\n | *Maize husks | \n
*Corn straw | \n*Poplar | \n\n | *Groundnut husks | \n
*Soybean residue | \n*Eucalyptus | \n\n | \n |
*Residue from vegetables | \n*Miscanthus | \n\n | \n |
*Residence from pulses | \n*Reed canary grass | \n\n | \n |
\n | *Switch grass | \n\n | \n |
\n | *Hemp | \n\n | \n |
Sources of lignocellulosic biomass for biodiesel production [17].
This is produced from micro-and macro-species including algae [11]. Third-generation biodiesel is discussed further in Section 3.2.
\nThis can be produced from feedstocks that possess the capability of being genetically modified, accumulate large quantity of biomass, and can be utilized in photo-biological solar cells with the ability to convert solar energy directly to usable biodiesel. Example of such feedstocks is algal species. This concept focuses on producing biodiesel in addition to developing a means of trapping and storing CO2. The method of producing this energy is similar to that of the second-generation biofuels, except that CO2 is arrested at each stage of the production using techniques such as oxy-fuel combustion. The CO2 trapped is stored in saline aquifers, gas fields or old oils through the method known as geo-sequestration. The process has the capacity to trap carbon inclusively making it ‘carbon negative’ as opposed to ‘carbon neutral’ [2, 19].
\nThese include edible and non-edible oils, and are presented below.
\nAt the moment, over 95% of biodiesel globally is produced from edible vegetable oils. The commonly used of these oils are palm oil, soybean, coconut oil, rapeseed and sunflower due to their availability [12, 13, 14, 15, 19, 20]. Rapeseed oil, sunflower oil, palm oil and soybean oil are used in Europe, Malaysia, Indonesia, Philippians and US, respectively to produce biodiesel [21]. There is no doubt that the use of these feedstocks for biodiesel production competes with their need for human consumption and some other applications, the disadvantage of which is insecurity, high cost of production and potential depletion of ecological resources due to some agricultural practices. Biodiesel produced from these oils usually has the disadvantages highlighted in Section 2.1 [12, 13, 14, 21]. The sources of edible oil and the respective yield of oil are presented in Table 3.
\nS/N | \nSource | \nYield | \n
---|---|---|
1. | \nRapeseed (Brassica oilseed) | \n38–46 | \n
2. | \nCoconut | \n63–65 | \n
3. | \nSoybean | \n15–20 | \n
4. | \nPalm | \n30–60 | \n
5. | \nSunflower | \n25–35 | \n
Sources of edible oil used in biodiesel production [11].
Non-edible oils are cultivated on lands requiring minimum attention and as such are less expensive compared to edible oils [22]. These oils include jatropha, karanja, polanga, cotton seed
Sources of non-edible oils used in biodiesel production [
These include
\n\nSome of these non-edible oils are discussed in more detail below:
\nThis oil maybe yellow or brown grease obtained from palm, canola, corn, sunflower and other edible oils. Usually, it is ubiquitous and inexpensive, making it ideal for biodiesel production. In recent times, some researchers have demonstrated that biodiesel can be produced from WCO by pyrolysis and transesterification methods. The latter method is preferred due its low cost and simplicity [1]. The performance of the process is usually measured in terms of yield and it depends on factors such as catalyst, catalyst loading, temperature, time and methanol-to-oil molar ratio (see Table 4).
\nS/N | \nCatalyst | \nReaction condition | \nBiodiesel yield (%) | \nReferences | \n
---|---|---|---|---|
1. | \nCalcined chicken manure | \nCatalyst loading 7.5wt%, temperature 65°C and methanol-to-oil molar ratio 1:15 | \n90 | \n[36] | \n
2. | \nChicken manure biochar | \nTemperature 350°C | \n95 | \n[37] | \n
3. | \nCsPW-CB | \nCatalyst loading 2 wt%, methanol-to-oil molar ratio 11:1, temperature 70°C and time 2.5 h | \n95.1 | \n[38] | \n
4. | \nKOH | \nCatalyst loading 1 wt%, methanol-to-oil molar ratio 1:3, temperature 60°C and time 0.8 h | \n94 | \n[39, 40] | \n
5. | \nTitanium iso-propoxide (TiO2) + graphene oxide (GO) | \nCatalyst loading 1.5 wt%, methanol-to-oil molar ratio 1:12, temperature 65°C and time 3 h | \n98 | \n[41] | \n
6. | \nCalcium diglyceroxide | \nCatalyst loading 1.03 wt%, methanol-to-oil molar ratio 7.46:1, temperature 62°C and time 0.4 h | \n94.86 | \n[40] | \n
7. | \nKOH | \nCatalyst loading 1.5 wt%, methanol-to-oil molar ratio 7:1, temperature 60°C and time 1.5 h | \n92 | \n[42] | \n
8. | \nKOH | \nCatalyst loading 1.16 wt%, methanol-to-oil molar ratio 9.4:1, temperature 62.4°C and time 2 h | \n98.26 | \n[43] | \n
9. | \nCaO/MgO | \nCatalyst loading 6 wt%, methanol-to-oil molar ratio 1:15, temperature 90°C and time 2 h | \n96.47 | \n[44] | \n
10. | \nCaO | \nCatalyst loading 5 wt%, methanol-to-oil molar ratio 20:1, temperature 65°C and time 4 h | \n96.74 | \n[45] | \n
11. | \nBaSnO3\n | \nCatalyst loading 6 wt%, methanol-to-oil molar ratio 10:1, temperature 90°C and time 2 h | \n96 | \n[46] | \n
12. | \nSulphamic acid | \nCatalyst loading 1 wt%, methanol-to-oil molar ratio 10:1, temperature 110°C and time 2 h | \n95.6 | \n[47] | \n
13. | \nFusion waste chicken and fish bones | \nCatalyst loading 1.98 wt%, methanol-to-oil molar ratio 10:1, temperature 65°C and time 1.5 h | \n89.5 | \n[48] | \n
14. | \nBiomass fly ash | \nCatalyst loading 10 wt%, methanol-to-oil molar ratio 9:1, temperature 60°C and time 3 h | \n95 | \n[49] | \n
15. | \nKettle limescale | \nCatalyst loading 8.87 wt%, methanol-to-oil molar ratio 1.7:3, temperature 61.7°C and time 0.25 h | \n93.41 | \n[43] | \n
16. | \nCalcium oxide (CaO) nano-catalyst | \nCatalyst loading 1 wt%, methanol-to-oil molar ratio 8:1, temperature 50°C, time 1.5 h and particle size 29 nm | \n96 | \n[50] | \n
Dependence of the yield of biodiesel from WCO on reaction parameters.
The use of algae in biofuel production is gaining traction globally, especially as it is considered to be safer, non-competitive and made up of microorganisms with precocious growth. These organisms are aquatic and may be unicellular or multicellular with over 300,000 species. The number is greater than plant species and the organisms exhibit varying compositions, but are faced with higher cost of production. Also, more complexity of processes and technology are required for cultivation compared to plants [51]. Algae grow naturally in open ponds and can be cultivated through tubular photobioreactors. The former is the oldest method involving a simple and inexpensive process, compared to the latter, which enjoys high productivity rate, less maturity time and the capacity to selectively produced high lipid content using desirable algae species. Algae contain lipids, carbohydrates and complex oil depending on their species [52, 53, 54, 55]. The lipid content ranges from 20 to 80% depending on the various species. Some species such as
Algae are not edible and using them as a feedstock for biodiesel production poses no threat to food production. They have the capability to convert carbon dioxide to biofuels and oleochemical products [51]. The remaining biomass can be converted into useful chemicals to generate more revenue to ameliorate the high economic cost of the process.
\nDue to high lipid content and availability, several investigators have explored the potential of algae as a feedstock for biofuel production. This usually begins by selecting algae species with high lipid yield and very good fatty acid composition as shown in Figure 2. The desirable algae species for the production of biodiesel is usually selected based on growth rate, degree of survival and physicochemical properties and fatty acid composition.
\nProcesses involved in applying algae as feedstock for the production of biodiesel at a small scale or experimental level [
The typical properties of biodiesel algae oil compared with standards and biodiesel from other sources are presented in Table 5. Applying such biodiesel in an internal combustion engine usually consumes more fuel and has less thermal efficiency than petroleum diesel. This may be due to its physicochemical properties such as higher density and viscosity, lower calorific value and cetane number. The effect of this problem can be minimized by blending it with petroleum diesel (up to 30%) [57, 58, 59, 60, 61, 62, 63, 64, 65]. The presence of excess oxygen molecule in the algae biofuel ensures that complete combustion is attained, thereby eliminating the emission of undesirable substances such as hydrocarbons and carbon monoxide. But, NOx emission like biodiesel from other sources is high and can be reduced by the addition of n-butanol to the blends [63].
\nS/N | \nProperties | \nASTM 6751-12 | \nEN 14,214 | \nDiesel | \nAlgae oil | \nPalm oil | \nJatropha | \nKaranja | \n
---|---|---|---|---|---|---|---|---|
1 | \nCalorific value (kJ/kg) | \n— | \n— | \n43,000 | \n40,072 | \n37,800 | \n39,000 | \n39,200 | \n
2 | \nDensity (kg/L) | \n086–0.90 | \n0.86–0.90 | \n0.84 | \n0.912 | \n0.850 | \n0.940 | \n0.874 | \n
3 | \nViscosity @ 40°C (mm2/s) | \n1.9–6.0 | \n3.5–5.0 | \n2.64 | \n5.06 | \n4.32 | \n4.8 | \n5.21 | \n
4 | \nCetane number | \n>47 | \n>51 | \n53.3 | \n46.5 | \n55 | \n50 | \n50 | \n
5 | \nFlash point (°C) | \n100–170 | \n>120 | \n71 | \n145 | \n167 | \n135 | \n100 | \n
6 | \nAcid value (mg KOH/g) | \n<0.5 | \n<0.5 | \n0.0 | \n0.14 | \n0.24 | \n0.4 | \n0.43 | \n
7 | \nOxidation stability @ 110°C | \n3.0 | \n>6.0 | \n— | \n6.76 | \n10.3 | \n3.2 | \n— | \n
8 | \nOil yield (L/ha) | \n— | \n— | \n— | \n58,000 | \n5950 | \n1892 | \n2590 | \n
Comparison between the fuel properties of algae oil and the petroleum diesel [51].
S/N is serial number.
This is one of the cheapest vegetable oils with an average price of US $514 per ton. It is composed of predominantly unsaturated fatty acids with lower pour point, making it suitable for biodiesel production [64]. The characteristics of biodiesel from tea seed oil share some resemblances with those from vegetable oil, but it has lower pour point of −5 °C and is less viscous than biodiesel from palm oil, cotton seed oil and peanut oil [1, 64, 66]. Like in algae biodiesel, the application of tea seed oil biodiesel in internal consumption engine requires more fuel consumption and causes high emissions of CO and CO2. To solve these problems, hydrogen is usually added to the petroleum diesel and biodiesel blends, thereby improving the performance characteristics of the engine. This gain is possible since there is absence of carbon atoms in the chemical structure. But, the disadvantage is increased NOx emission [65].
\nThis sludge is a residue from the secondary/biological section of wastewater treatment plant and composed predominantly of microorganisms [67]. It is being investigated as a feedstock for biodiesel production probably due to its availability, lipid content and possibility of obtaining it without any cost implication [5, 68, 69, 70, 71, 72]. The lipid/oil content is relatively low and various researchers have investigated the potential of increasing the yield using different methods to ensure its adaptability as a substrate for biodiesel production. Notable among them are Edeh et al., who using the combination of subcritical water technology and optimization increased the lipid yield from 7.4 (wt./wt.)% to 41.0 (wt./wt.)% [28]. The predominant fatty acid in activated sludge is palmitic acid [27]. Researchers have shown that activated sludge can be used as a feedstock for biodiesel production. But, due to low yield of 3–6 wt%, (dry cell weight), which is below the minimum of 10 wt.% (dry cell weight) required for biodiesel to have an economic advantage over the conventional petroleum diesel, this feedstock is still unattractive [60, 73, 74]. Another problem is variation in the composition of fatty acids, which depends on the source and composition of wastewater and season of collection, which affect the quality and yield of the biodiesel [5, 75].
\nOther non-edible oils used in the production of biodiesel are presented in Table 6.
\nS/N | \nSource | \nCharacteristics | \nYield (wt.%) | \nFatty acid composition | \nReferences | \n
---|---|---|---|---|---|
1. | \nKaranja | \nGrown in Southeast Asia, flowers 3–4 years after planting while matures 4–7 years later, a single tree yields 9–90 kg of seeds | \n25–40 | \nOleic (44.5–71.3%), linoleic (10.8–18.3%) and stearic acids (2.4–8.9%) | \n[76, 77, 78, 79] | \n
2. | \nMahua | \nGrown in Indian forest, produces 20–200 kg of seeds annually per tree depending on maturity, starts to bear seeds after 10 years of planting and continues up to 60 years | \n35–50 | \nOleic (41–51%), stearic (20.0–25.1%), palmitic acid (16.0–28.2%) and linoleic acids (8.9–18.3%) | \n[51, 80, 81, 82] | \n
3. | \nCotton | \nGrown for cotton fiber in China, United States and Europe, the seeds contain non-glycerides such as gossypol, phospholipids, sterols, resins, carbohydrates and related pigments | \n17–25 | \nLinoleic (55.2–55.5%), palmitic (11.67–20.1%) and oleic acids (19.2–23.26%) | \n[83, 84, 85] | \n
4. | \nNeem | \nCan grow in different kinds of soils such as saline, clay, dry, shallow, alkaline and stony in Asian countries including India, Malaysia and Indonesia. It matures after 15 years and has a life span of 150–200 years | \n20–30 | \nLinoleic (6–16%), oleic (25–54%) and stearic (9–24%) acids | \n[17, 51, 85, 86, 87] | \n
5. | \nTobacco | \nGrown in countries such as Turkey, Macedonia and North America for leaf collection | \n35–49 | \nLinoleic acid (69.49–75.58%) | \n[88, 89, 90, 91] | \n
6. | \nRubber | \nForest-based tree largely grown in Malaysia, India, Thailand and Indonesia | \n50–60 | \nLinoleic (39.6–40.5%), oleic (17–24.6%) and linolenic acid (16.3–26%) | \n[20, 92, 93, 94] | \n
7. | \nJatropha | \nGrown in arid, semi-arid and tropical regions, such as United States, Brazil, Bolivia and Mexico. Produces seeds after 12 months of planting, attain optimum productivity by 5 years and has a life span of up to 30 years | \n20–60 | \nLinoleic (31.4–43.2%), oleic acid (34.3–44.7%), stearic (7.1–7.4%) and palmitic (13.6–15.1%) acids | \n[95, 96, 97, 98] | \n
Non-edible oils from the seeds of their respective trees used for biodiesel production.
These properties depend on the fatty acid and chemical composition of the non-edible oils. The fuel properties of biodiesel can be measured by using different standards including ASTM D6751 and EN 14214. The most essential properties used in assessing the suitability of biodiesel as fuel include density, flash point, cloud point, pour point, calorific value and cetane point (see Table 7). The standards for measuring each property are presented in Table 8 [99].
\nNon-edible oil | \nDensity at 40°C (kg/m3) | \nViscosity at 40°C (mm2/s) | \nFlash point (°C) | \nCloud point (°C) | \nPour point (°C) | \nCetane number | \nCalorific value (MJ/kg) | \nReferences | \n
---|---|---|---|---|---|---|---|---|
Karanja ( | \n876–890 | \n4.37–9.60 | \n163–187 | \n13–15 | \n−3 to 5.1 | \n52–58 | \n36–38 | \n[15, 77, 99, 100, 101] | \n
Polanga ( | \n888.6–910 | \n4–5.34 | \n151–170 | \n13.2–14 | \n4.3 | \n57.3 | \n39.25–41.3 | \n[15, 102] | \n
Mahua ( | \n904–916 | \n3.98–5.8 | \n127–129 | \n3–5 | \n1–6 | \n51–52 | \n39.4–39.91 | \n[15, 103, 104, 105, 106] | \n
Rubber seed oil ( | \n860–881 | \n5.81–5.96 | \n130–140 | \n4–5 | \n–8 | \n37–49 | \n36.5–41.07 | \n[92, 107, 108, 109] | \n
Cotton seed | \n874–911 | \n4–4.9 | \n210–243 | \n1.7 | \n−10 to −15 | \n41.2–59.5 | \n39.5–40.1 | \n[110, 111, 112] | \n
Jojoba oil ( | \n863–866 | \n19.2–25.4 | \n61–75 | \n6–16 | \n−6 to 6 | \n63.5 | \n42.76–47.38 | \n[113, 114, 115, 116] | \n
Tobacco oil ( | \n860–888.5 | \n3.5–4.23 | \n152–165.4 | \n— | \n−12 | \n49–51.6 | \n38.43–39.81 | \n[89, 90, 117] | \n
Neem (Azadirachta) | \n912–965 | \n20.5–48.5 | \n34 | \n— | \n— | \n51 | \n33.7–39.5 | \n[87, 110, 118, 119] | \n
Linseed oil ( | \n865–950 | \n16.2–36.6 | \n108 | \n1.7 | \n−4 to −18 | \n28–35 | \n37.7–39.8 | \n[110, 120, 121] | \n
Jatropha ( | \n864–880 | \n3.7–5.8 | \n163–238 | \n— | \n5 | \n46–55 | \n38.5–42 | \n[122, 123] | \n
Diesel | \n816–840 | \n2.5–5.7 | \n50–98 | \n−10 to −5 | \n−20 to 5 | \n45–55 | \n42–45.9 | \n[124, 125, 126] | \n
Properties of diesel fuel and those of biodiesel produced from non-edible oils.
S/N | \nProperty | \nCharacteristics | \nStandard | \nReferences | \n
---|---|---|---|---|
1. | \nDensity | \nHigher than the diesel | \nASTM D1298 and EN ISO 3675 | \n[127] | \n
2. | \nKinematic viscosity | \nHigh viscosity causes poor fuel flow resulting in delayed combustion | \nASTM D445 and EN ISO 3104 | \n[128] | \n
3. | \nFlash point | \nMeasures the flammability hazard of a substance. At flash point, if the source of ignition is removed, vapor ceases to burn | \nASTM D93 and EN ISO 3697 | \n[127] | \n
4. | \nCetane number (CN) | \nMeasures the ignition quality of fuel in a power diesel engine. Higher CN causes shorter ignition delay. Biodiesel has higher CN due to its longer fatty acid carbon chains | \nASTM D613 and EN ISO 5165 | \n[127] | \n
5. | \nCloud point (CP) | \nHigher CP than diesel | \nASTM D2500 | \n[129, 130] | \n
6. | \nPour point (PP) | \nHigher PP than diesel | \nASTM D97 | \n[129, 130] | \n
7. | \nCalorific value (HHV)* | \nMeasures the heat content of a fuel. Biodiesel has lower calorific value than diesel due to its higher oxygen content | \nASTM D2015 | \n[107, 131, 132] | \n
Standards for measuring properties of biodiesel [98].
In most cases oils are extracted from the oil-bearing biomass, for example oil seeds prior to use in biodiesel production. The methods used in oil extraction include solvent extraction, critical fluid extraction, mechanical extraction, enzymatic oil extraction, microwave-assisted extraction (MAE) and ultrasound-assisted extraction (UAE). They are discussed below:
\nThis extraction method utilizes organic solvents extract lipid/oil from the oil-bearing biomass. The organic solvents used include: hexane, chloroform, ethyl ether, petroleum ether, toluene, methanol, ethanol and acetone [5]. The solvents can also be combined together depending on their polarity to achieve higher yield of oil, for instance, chloroform and methanol, hexane and ethanol, dichloromethane and methanol [23, 133]. The properties that influence the selection of a particular solvent for oil extraction are polarity, volatility, non-miscibility with water, safety, boiling point, environmental factors, absence of toxic or reactive impurities, ability to form two phases with for easy separation, capacity to extract a large range of lipid classes and cost of the solvent [134, 135].
\nThe solvent extraction methods used in the laboratory include Soxhlet, Folch, and Bligh and Dyer methods. Soxhlet method is preferred due to the following advantages: it is easy to use, does not require filtration and inexpensive, and it ensures higher oil extraction, supports simultaneous and parallel extraction. Despite these advantages, its demerits include requirement of high volume of solvents, health and environmental risks, long extraction time, potential to thermally degrade sample and difficulty to automate due to selectivity issues [136]. The Soxhlet extraction is influenced by the following factors: temperature, sample preparation, extraction time, high solvent-to-sample ratio, type and the volume of solvent [137].
\nSoxhlet extraction is carried out by heating the distillation/boiling flask containing the organic solvent to its boiling point (see Figure 3). The vapor produced passes through the tube to the condenser where it is condensed and the liquid formed trickles down to the thimble containing the sample. The soluble part of the sample is dissolved by this liquid and the process continues until the liquid marked is reached. The solubilized sample is aspirated to the distillation/boiling flask and the process continues until the predetermined number of cycle or extraction time is attained [138].
\nSoxhlet apparatus.
This method was developed by Folch et al. [139]. The method utilizes a combination of organic solvents: chloroform and methanol in a ratio of 2:1 (v/v) for lipid/oil extraction. It is usually used for extracting and quantifying total lipids [140].
\nThis method has some similarities with the Folch method in terms of the solvent system and function. The method uses combined chloroform and methanol in a ratio of 1:2 (v/v) in converse to the Folch method to extract lipid/oil from samples. With this ratio, the Bligh and Dyer method is more economical than the Folch method [140].
\nThis involves the use of supercritical or subcritical fluids in oil extraction. These are discussed below:
\nThese are fluids with critical temperature and pressure above their critical points. For example, above the critical point of CO2 (31.1°C and 7.38 MPa) and that of water (374°C and 22.1 MPa), supercritical fluids exist [141, 142]. Supercritical fluids usually have high density, which increases their solubilization, and low viscosity, which enhances their mass transfer rate [143]. SCFs have the advantages of low operating cost; high product quality; ability to combine some operation units into one and to selectively extract certain lipids at different operating conditions of temperature, pressure, and time. The advantage of this is a reduction in cost and extraction time [143, 144]. The disadvantage of SCFs is that they required the use of high-pressure vessels which are usually expensive. A brief discussion on supercritical CO2 and supercritical water is presented below.
\nThis lipid extraction method uses CO2 as the supercritical fluid probably because it is cheap, non-toxic, non-explosive and non-flammable and possesses high purity and low critical temperature [141, 143]. The low critical temperature makes it the most suitable method for the extraction of thermal labile substances such as lipid/oil as the original properties of the materials are protected [143]. Supercritical CO2 is usually used to extract non-polar lipids but due to the introduction of co-solvents such as methanol, ethanol and water, it could recover polar lipids [143]. For instance, Hanif et al. increased the yield of phospholipid fatty acids (PLFAs) from 0.5 to 7.28 nmol/mg using methanol (10%, v/v) as a co-solvent [145].
\nWater is used as a supercritical fluid here. Supercritical water possesses liquid and gaseous properties including diffusivity, density and heat transfer, which can be manipulated through temperature and pressure to achieve an efficient extraction. For instance, a low-density supercritical water can be used to extract non-polar substances, and due to low dissolution it will not be effective in extracting ionic substances. At high temperature, it can dissolve organic substances, gases and salts due to its decrease in dielectric constant [146]. Supercritical water extraction has been used by Gungoren et al. for oil recovery and products distribution from sewage sludge at temperatures between 350 and 450°C and pressures of between 21.5 and 30 MPa [147].
\nSubcritical water as shown in Figure 4 is water at temperatures between its boiling point (Tb), 100°C and its critical point of 374°C with pressure sufficient to maintain water in the liquid state. Within this temperature range, water behaves like organic solvents due to decrease in its dielectric constant. At low temperature, subcritical water can extract both polar and ionic substances while at temperatures close to the critical temperature, extraction of non-polar substances is possible by the interaction with these substances and reduction in the binding forces [148, 149, 150]. Subcritical water has been demonstrated to be useful in decontaminating soil, removing polyhydroxyalkanoates (PAH), hydrocarbons and metals and extracting variety of natural products [151]. It has also been used to increase the lipid yield of activated sludge [34, 35].
\nPhase diagram.
This method uses the right enzymes in extracting oil from the oil-bearing biomass and it is environmentally friendly as there is no emission of volatile organic matter [2]. The disadvantages include: relatively high cost of enzyme production, prolonged incubation periods and requirement of de-emulsification during the downstream processing (DSP) [151, 152]. Some of these problems such as high cost of enzyme production can be minimized using enzyme immobilization, which helps to reduce enzyme losses, although, could reduce reaction rate due to steric hindrance. While others like de-emulsification during DSP can be made easier through the use of affinity chromatography and perfusion chromatography [2].
\nOil is extracted using a manual ram press or an engine-driven screw press. With the manual ram press extracting up to 60–65% and engine-driven press recovering 68–80% of the oil content of the feedstocks, respectively. Usually, the oil extract undergoes filtration and degumming as a way of obtaining a more refined oil. Mechanical extraction is inefficient in extracting oil from seeds, which it was not designed for, although, this problem can be solved by using pretreatment methods such as cooking of the seeds and using at least double passes in the screw press. This could give rise to up to 91% yield of oil [2, 151].
\nThis extraction method uses microwave oven in the extraction process. It has been utilized in extracting values from plant materials [153]. The method requires transferring heat through direct contact to the polar solvent and/to the target substance. This can be controlled through ionic conduction and dipole rotation, which occurs simultaneously. Comparing MAE with the conventional extraction method, the latter requires longer time and resources while the former supports high yield of extraction with lesser volume of solvents and controllable heating process [154]. MAE also emits smaller amount of CO2 and consumes lesser quantity of energy compared to the conventional extraction methods. The disadvantages are that the process is accompanied with the presence of solid residue, which limits heat and mass transfer, and the extraction using non-polar solvents or extracting non-polar substances is greatly affected [2, 151].
\nThis involves submerging the feedstocks usually of plant origin in a polar solvent (e.g. water) or non-polar solvent (e.g. ethanol) and subjecting the resulting mixture to an ultrasonic vibration. The vibration is made up of sound waves at the range of 18 kHz–100 MHz. This sound wave in the solvent enhances the biomass (flowers, seeds, leaves, etc.) solubilization resulting in the release of values such as oils entrapped within them, thereby increasing yield of the valuable materials. UAE has a very fast extraction rate and high efficiency, but could denature the structure of the extracted substance, for example, oil due to prolonged exposure to ultrasound. Also, it requires the use of large volume of solvent and repetition of the process in order to achieve an efficient extraction. This thus impacts on the operating cost of the entire process [155, 156, 157].
\nAccording to Rezania et al., there are four commonly used methods for biodiesel production [1]. These are explained below:
\nThis involves preheating of vegetable oil or animal fat at a temperature of 300–1300°C in the presence of catalyst and absence of oxygen [2]. This may result in product possessing desirable properties such as low viscosity, high cetane number, low amount of sulfur and water content, and standard corrosion values [158].
\nThese are clear, thermodynamically stable, isotropic liquid mixtures of oil, water, surfactant, mostly in combination of cosurfactant [159]. This method using ethanol has been used with soybean as feedstock to produce biodiesel with similar properties as No. 2 diesel. These properties include cetane number and viscosity [81, 151].
\nThis is also known as dilution and it is simplest and oldest method used in biodiesel production. It involves the blending of preheated vegetable oil or animal fats with the conventional petroleum diesel in a ratio of 10–40% (w/w) [160].
\nThis involves the reaction between triglyceride from vegetable oil or animal fat with alcohol usually methanol in the presence of catalyst such as acidic, basic or enzymatic catalyst [161]. When methanol is used, the reaction is called methanolysis while it is called ethanolysis if ethanol is used as the alcohol. The schematic diagram representing the processes involved in biodiesel production via transesterification is shown in Figure 5. Transesterification of triglyceride to biodiesel (alkyl ester) and glycerol as the by-product is illustrated in Figure 6. The reaction mechanism involves the conversion of triglyceride (TG) to diglyceride (DG) followed by monoglyceride (MG) and then to a free glyceride. Each step is catalyzed by alkoxide, for instance methoxide when methanol is used as the alcohol [163]. The reaction mechanism is presented in Figure 7.
\nFlowchart of biodiesel (FAME) production through transesterification [
Production of biodiesel through a transesterification of triglyceride [
Reaction mechanism (chain reaction) of the transesterification of triglyceride to biodiesel (fatty acid methyl acid-FAME) [
Similarly, esterification as a method of producing biodiesel involves a reaction between fatty acid and alcohol in the presence of catalyst (see Figure 8).
\nEsterification of free fatty acid to methyl ester and water [
Both transesterification and esterification can occur simultaneously in the same process. This is most suitable for feedstocks with high free fatty acid and water content. The feedstock is firstly esterified using the acidic catalyst before transesterification by the alkali catalyst [2]. The performance of these reactions is measured in terms of yield.
\nTransesterification is the most commonly used method in biodiesel production probably due to its simplicity and low cost [165]. It can be carried out in situ using the oil-bearing biomass or ex situ directly with the oil extracted from the biomass-bearing oil. Some researchers have demonstrated the application of in situ transesterification of oil-bearing biomass to biodiesel. For instance, Mondala et al. investigated the production of biodiesel from municipal primary and secondary sludge (activated sludge) through in situ transesterification reaction [154]. On the other hand, numerous works have been conducted using lipid extracted from oil-bearing biomass (ex situ) to produce biodiesel. For example, Siddiquee and Rohani worked on the production of biodiesel via the methanolysis of lipids extracted from the primary and secondary sludge [133].
\nThese include catalysts type, reactor type, temperature, agitation speed, solvent type, alcohol-to-oil ratio, residence time and nature of feedstock (water content, quantity of free fatty acid and esterifiable substances present in the feedstock) [133]. The catalyst type and nature of the feedstock are the most influential factors as they determine the cost of the production of biodiesel [11]. High free fatty acid and water content can cause low yield of biodiesel production due to soap formation via saponification reaction [166, 167].
\nResearchers have posited that biodiesel is currently not competitive with the conventional petroleum diesel due to higher production cost despite numerous advantages [168]. This can be influenced by the type of raw materials, selling price of the by-product, labour and operation cost, catalyst and the reaction type [1]. The average production cost for biodiesel and diesel fuel is $0.50 and $0.35 per liter, respectively [169]. The price for producing biodiesel can be estimated using Eq. (1)\n
\nThe cost of biodiesel production can be reduced by increasing yield using improved technologies, reducing capital investment cost and reducing the raw materials cost [168, 170, 171, 172, 173].
\nThis involves the use of cheaper feedstocks including wastes from oils, fats and non-edible crops in order to reduce the unit cost of producing biodiesel [28, 174]. The major drawbacks to using these feedstocks are high free fatty acid (FFA) and water content with the capacity to reduce the yield and quality of the biodiesel [9, 12, 22, 175]. The effect of this can be reduced by using multiple chemical processes with the tendency to increase the overall production cost [176]. For instance, using alkali to catalyze the transesterification reaction may require feedstock pretreatment, product separation and purification, thereby rendering the entire process uneconomical due to additional cost incurred [177]. However, acid catalysts are most suitable for the conversion of WCO with high FFA and water content to biodiesel. But, the disadvantages of this are that the reaction is very slow, requires more alcohol and large volume of reactor, and the acid used may corrode equipment, causing them to break down [178]. The use of acid catalyst may also increase the production cost. Some of these problems may be solved using supercritical fluid. The process does not need catalyst, it is faster and may require large volume of alcohol, high temperature and pressure giving rise to a considerable cost implication [179, 180].
\nThe use of cheap and low-cost feedstock may affect the quality of the biodiesel, although, this can be improved. For example, poor cold properties can be improved using additives, although not without some cost implications. Despite the potential of cheap and low-cost feedstock to reduce the production cost of biodiesel, due to high level of impurity, it may require pretreatment prior to use, product purification due to poor quality and, thus, have some cost implications.
\nTechnologies used in biodiesel production to a large extent impact the cost of production. Some of these technologies require more unit operations than the other, which influences energy utilization and number of equipment [181]. For instance, the use of catalytic distillation (CD) process is more economical than conventional reactor as capital and production costs are reduced. This is possible due to reduction in the number of equipment, for example plug flow reactor and flash separation units, which are essential when using the conventional reactors are not needed [182].
\nAlkali catalyst technologies are preferred for producing biodiesel, especially heterogeneous catalyst technology using neat vegetable oil. The reason being that it requires less unit operation and number of equipment. It is also faster and cheaper and can easily be recovered. An example of such catalysts is calcium oxide [181, 183, 184, 185]. For use with high free fatty acid and water content feedstocks, alkali catalyst will cause such problems such as soap formation, which reduces the yield of biodiesel (Figure 9). The soap can gel at room temperature causing the production of semisolid mass instead of biodiesel and can cause difficulty in purifying glycerol [186]. Thus, when considering waste oils such as waste cooking oil with high free fatty acid and water content, acid catalyst technologies are the best option with the aim of reducing the overall production cost. The cost can be reduced because acid catalysts are less corrosive, easy to separate, can be reused and do not require additional washing steps. This will help to produce high-quality products in terms of biodiesel and glycerols [187, 188].
\nSoap formation during the transesterification of triglyceride to produce biodiesel.
Alternatively, enzyme and supercritical technologies can be used to process feedstock with high free fatty acid and water content to biodiesel, although they are more expensive than acid-catalysed technologies [173, 189]. Enzyme-catalysed transesterification is a slow process and takes longer time, and the soluble enzymes are not reusable except if immobilized enzyme is used. These disadvantages impact negatively on the cost of production [190]. On the other hand, supercritical technologies do not require the use of catalyst and encourage the production of by-product glycerol with high purity [192].
\nGenerally, technologies like feedstock and catalyst influence the overall cost of biodiesel production (Table 9).
\nProduction technology type | \nCapacity | \nFeedstock | \nProduction cost $/ton | \nReferences | \n
---|---|---|---|---|
KOH-catalyzed transesterification with methanol | \n8000 ton/yr | \nWaste cooking oil | \n868,60 | \n[173] | \n
H2SO4 | \nWaste cooking oil | \n750,38 | \n||
Lipase (Novozym-435) | \nWaste cooking oil | \n1047,97 | \n||
Alkali catalyst process | \nBatch process with a production capacity of 1000 tons | \nPalm oil | \n1166,67 | \n[191] | \n
Soluble lipase catalyst process | \nPalm oil | \n7821,37 | \n||
Immobilzed lipase catalyst process | \nPalm oil | \n2414,63 | \n||
Homogeneous H2SO4 | \nContinuous reactor operating at 30°C | \nMicroalgae oil | \n620 | \n[182] | \n
Homogeneous H2SO4 | \nMicroalgae oil | \n580 | \n||
Homogeneous KOH catalyst and hot water purification process | \nBatch process with a production capacity of 1452 | \nWaste cooking oil | \n921 | \n[193] | \n
Homogeneous KOH catalyst and vacuum FAME distillation process | \nWaste cooking oil | \n984 | \n||
Heterogeneous CaO catalyst and hot water purification process | \n\n | Waste cooking oil | \n911 | \n|
Heterogeneous CaO catalyst and vacuum FAME distillation process | \n\n | Waste cooking oil | \n969 | \n|
Homogeneous KOH catalyst and hot water purification process | \nBatch mode with a production capacity of 7260 tons/year | \nWaste cooking oil | \n598 | \n[193] | \n
Homogeneous KOH catalyst and vacuum FAME distillation process | \n\n | Waste cooking oil | \n641 | \n|
Heterogeneous CaO catalyst and hot water purification process | \nWaste cooking oil | \n584 | \n||
Heterogeneous CaO catalyst and vacuum FAME distillation process | \nWaste cooking oil | \n622 | \n
Dependence of biodiesel production cost on technologies [168].
The effect of alternative catalysts in the production of biodiesel can been seen in the reduction of production cost as supported by some of their characteristics such as being inexpensive, reusability and high catalytic potential. Examples of such catalysts are obtained from shells from egg, coconut, mussel, scallop and crustacean [183, 190, 194, 195, 196].
\nGenerally, catalysts used in catalysing the transesterification reaction leading to the production of biodiesel may be either homogeneous or heterogeneous. The choice of which to use is dependent on the free fatty acid and water content composition of the feedstock. Usually, heterogeneous catalysts unlike homogeneous catalysts are used to catalyse reactions involving feedstock with high free fatty acid and water content as they can be reused, require less products separation and purification steps, and possess the capacity to enable the production of pure by-products such as glycerol. Although, these advantages have some cost implications, heterogenous catalysts remain the best choice for biodiesel production unit cost reduction [168, 187, 197].
\nThis is a measure of the amount of profit that can be obtained from an investment in biodiesel production. The profit is usually calculated from the difference between the income obtained from the sales of the products and the expenses incurred. Profitability of biodiesel can be determined using such economic parameters as net present value, break-even price of biodiesel, after-tax internal rate of return, gross margin [168].
\nThese include income variables such as biodiesel and glycerol and outcome variables, which are feedstock, catalyst, alcohol and washing water. Studies have shown that the major market variable that influences the profitability of biodiesel production is the cost of feedstock due to large quantity required, and others are selling price of biodiesel and glycerol, while outcome variables such as catalyst and washing water have less effect because less quantities are required [162, 198, 199].
\nThis is another factor affecting the profitability of biodiesel production. The higher the production scale, the lower the unit production cost of biodiesel, see Figure 10. This can be seen from the work of Van Kasteren et al. who compared three biodiesel production processes via supercritical method [201]. The results obtained show increase in profitability of biodiesel at high production scale compared to low production scale. The result was corroborated by the study conducted by You et al. on the effect of production scales 8000, 30,000 and 100,000 annually on the feasibility of biodiesel production from food grade soybean oil using NaOH-catalyzed transesterification [202]. This conclusion was reached as production scale of 100,000 gave higher net annual profit after taxes (NNP) and after-tax of return (ARR), and lower biodiesel break-even price (BBP) compared to other production scales.
\nEffect of plant capacity/production scale on unit production cost [
Commercial quantity of biodiesel is currently being produced from edible vegetable oils with the global production capacity envisaged to reach 12 billion gallons by 2020 and countries such as Brazil, United States of America, Malaysia, Argentina, Netherlands, Spain, Philippines, Belgium, Indonesia and Germany meeting more than 80% of the world demand. The problem with this type of biodiesel includes poor storage, oxidation stability, high feedstock cost, low heating value and higher NOx emission. The implication of these is that biodiesel is not competitive with the conventional petroleum diesel.
\nResearchers have suggested the utilization of non-edible oils as a way of minimizing cost since feedstocks consume up to 80% in biodiesel production. But, the problem with this is the presence of high free fatty acid (FFA) and water content, which reduces the yield and quality of the biodiesel. This can be reduced through the use of multiple chemical processes, although there is a tendency to increase the overall production cost.
\nGenerally, the cost of biodiesel production is influenced by factors such as raw materials, technologies and catalyst. The raw material and catalyst cost can be reduced using alternatives to these factors while improved technologies could help to minimize the production cost.
\nThe profitability of biodiesel can be determined using economic parameters such as net present value, break-even price of biodiesel, after tax internal rate of returns, and gross margin. These parameters are influenced by market variables and production scale.
\nIntergovernmental Panel on Climate Change
\nAmerican Society of Testing Materials
\nwaste cooking oil
\ncetane number
\ncloud point
\npour point
\nhigh heating value
\nmicrowave-assisted extraction
\nultrasound-assisted extraction
\nsupercritical fluid
\nphospholipid fatty acids
\npolyhydroxyalkanoate
\nboiling point
\ncritical temperature
\ncritical pressure
\ndownstream processing
\nfatty acid methyl ester
\ntriglyceride
\ndiglyceride
\nmonoglyceride
\nfree fatty acid
\ncatalytic distillation
\nnet annual profit after taxes
\nafter-tax of return
\nbiodiesel break-even price
\nlignocellulosic biomass
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DC.",slug:"advance-in-phytoremediation-research",totalDownloads:7474,totalCrossrefCites:2,totalDimensionsCites:8,abstract:null,book:{id:"5331",slug:"advanced-knowledge-application-in-practice",title:"Advanced Knowledge Application in Practice",fullTitle:"Advanced Knowledge Application in Practice"},signatures:"Woranan Nakbanpote, Natthanwoot Paitlertumpai, Kannika Sukadeetad, Orapan Meesungeon and Wattchara Noisa-nguan",authors:null}],mostDownloadedChaptersLast30Days:[{id:"69447",title:"Importance of Social Networks for Knowledge Sharing and the Impact of Collaboration on Network Innovation in Online Communities",slug:"importance-of-social-networks-for-knowledge-sharing-and-the-impact-of-collaboration-on-network-innov",totalDownloads:811,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Innovation results from interactions between different sources of knowledge, where these sources aggregate into groups interacting within (intra) and between (inter) groups. Interaction among groups for innovation generation is defined as the process by which an innovation is communicated through certain channels over time among members of a social system. Apart from the discussion about knowledge management within organizations and the discussion about social network analysis of organizations on the topic of innovation and talks about various trade-offs between strength of ties and bridging ties between different organizational groups, within the topic of open source software (OSS) development researchers have used social network theories to investigate OSS phenomenon including communication among developers. It is already known that OSS groups are more networked than the most organizational communities; In OSS network, programmers can join, participate and leave a project at any time, and in fact developers can collaborate not only within the same project but also among different projects or teams. One distinguished feature of the open source software (OSS) development model is the cooperation and collaboration among the members, which will cause various social networks to emerge. In this chapter, the existing gap in the literature with regard to the analysis of cluster or group structure as an input and cluster or group innovation as an output will be addressed, where the focus is on “impact of network cluster structure on cluster innovation and growth” by Behfar et al., that is, how intra- and inter-cluster coupling, structural holes and tie strength impact cluster innovation and growth, and “knowledge management in OSS communities: relationship between dense and sparse network structures.” by Behfar et al., that is, knowledge transfer in dense network (inside groups) impacts on knowledge transfer in sparse network (between groups).",book:{id:"8850",slug:"harnessing-knowledge-innovation-and-competence-in-engineering-of-mission-critical-systems",title:"Harnessing Knowledge, Innovation and Competence in Engineering of Mission Critical Systems",fullTitle:"Harnessing Knowledge, Innovation and Competence in Engineering of Mission Critical Systems"},signatures:"Stefan K. Behfar",authors:[{id:"293475",title:"Dr.",name:"Stefan Kambiz",middleName:null,surname:"Behfar",slug:"stefan-kambiz-behfar",fullName:"Stefan Kambiz Behfar"}]},{id:"69883",title:"Evolution of Communication Skills in Virtual Product Development Process: Experience From EGPR",slug:"evolution-of-communication-skills-in-virtual-product-development-process-experience-from-egpr",totalDownloads:736,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"More than a decade of continuous international collaboration of several European universities in teaching new product development in virtual environment gives unique opportunity to investigate evolution and development of communication techniques for NPD collaboration in virtual environment. This chapter provides theoretical and practical view on different aspects: technical evolution of ICT tools, development and fostering of communication flow, personal aspects of IT communication, with important emphasis on building of trust within virtual teams. The reader can extract from this chapter guidelines for work in collaborative virtual environment, to run effectively either small projects, meetings and lectures or even more complex projects, distributed among several dislocated teams. The chronological overview of the continuous virtual communication in the last 15 years gives also fair suggestions about future evolution for the next decade.",book:{id:"8850",slug:"harnessing-knowledge-innovation-and-competence-in-engineering-of-mission-critical-systems",title:"Harnessing Knowledge, Innovation and Competence in Engineering of Mission Critical Systems",fullTitle:"Harnessing Knowledge, Innovation and Competence in Engineering of Mission Critical Systems"},signatures:"Nikola Vukašinović, Janez Benedičič and Roman Žavbi",authors:[{id:"294317",title:"Dr.",name:"Nikola",middleName:null,surname:"Vukašinović",slug:"nikola-vukasinovic",fullName:"Nikola Vukašinović"},{id:"294322",title:"Prof.",name:"Roman",middleName:null,surname:"Žavbi",slug:"roman-zavbi",fullName:"Roman Žavbi"},{id:"308791",title:"Dr.",name:"Janez",middleName:null,surname:"Benedičič",slug:"janez-benedicic",fullName:"Janez Benedičič"}]},{id:"70099",title:"Knowledge Redundancy Cycles in Complex Mission-Critical Systems",slug:"knowledge-redundancy-cycles-in-complex-mission-critical-systems",totalDownloads:692,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Based on a 20-year, 10-million case study programme of research, 98% of all innovation attempts end in failure. The main aim of the research has been to decode the underpinning, first-principle-driven ‘DNA’ of the 2% of successful attempts. Sitting right at the centre of this DNA is a triad of fundamentals: the need to embrace the dynamics of complex adaptive systems, the need to actively seek out and eliminate compromises and contradictions, and the need for industry domains to periodically unlearn knowledge that has become redundant. The chapter discusses all three of these pillars. Particular attention is paid to the knowledge redundancy topic, where the fact that the life-cycle of knowledge follows distinct, repeating patterns of evolution at meta, macro and micro- hierarchical levels is demonstrated. The research further demonstrates how organizations can use these patterns to objectively identify redundancy ‘pulse-rates’ and thus objectively manage both the acquisition of required new knowledge and the disposal of knowledge that is no longer fit for purpose. The research shows too that a key aspect of this ‘unlearning’ activity demands that organizational leaders acknowledge and accommodate the very human emotions that accompany change initiatives where the things that define a person’s competence become a hazard to the future success of the enterprise.",book:{id:"8850",slug:"harnessing-knowledge-innovation-and-competence-in-engineering-of-mission-critical-systems",title:"Harnessing Knowledge, Innovation and Competence in Engineering of Mission Critical Systems",fullTitle:"Harnessing Knowledge, Innovation and Competence in Engineering of Mission Critical Systems"},signatures:"Darrell Mann",authors:[{id:"297423",title:"Prof.",name:"Darrell",middleName:null,surname:"Mann",slug:"darrell-mann",fullName:"Darrell Mann"}]},{id:"69932",title:"Simplexity: A Hybrid Framework for Managing System Complexity",slug:"simplexity-a-hybrid-framework-for-managing-system-complexity",totalDownloads:716,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Knowledge management, management of mission critical systems, and complexity management rely on a triangular support connection. Knowledge management provides ways of creating, corroborating, collecting, combining, storing, transferring, and sharing the know-why and know-how for reactively and proactively handling the challenges of mission critical systems. Complexity management, operating on “complexity” as an umbrella term for size, mass, diversity, ambiguity, fuzziness, randomness, risk, change, chaos, instability, and disruption, delivers support to both knowledge and systems management: on the one hand, support for dealing with the complexity of managing knowledge, i.e., furnishing criteria for a common and operationalized terminology, for dealing with mediating and moderating concepts, paradoxes, and controversial validity, and, on the other hand, support for systems managers coping with risks, lack of transparence, ambiguity, fuzziness, pooled and reciprocal interdependencies (e.g., for attaining interoperability), instability (e.g., downtime, oscillations, disruption), and even disasters and catastrophes. This support results from the evident intersection of complexity management and systems management, e.g., in the shape of complex adaptive systems, deploying slack, establishing security standards, and utilizing hybrid concepts (e.g., hybrid clouds, hybrid procedures for project management). The complexity-focused manager of mission critical systems should deploy an ambidextrous strategy of both reducing complexity, e.g., in terms of avoiding risks, and of establishing a potential to handle complexity, i.e., investing in high availability, business continuity, slack, optimal coupling, characteristics of high reliability organizations, and agile systems. This complexity-focused hybrid approach is labeled “simplexity.” It constitutes a blend of complexity reduction and complexity augmentation, relying on the generic logic of hybrids: the strengths of complexity reduction are capable of compensating the weaknesses of complexity augmentation and vice versa. The deficiencies of prevalent simplexity models signal that this blended approach requires a sophisticated architecture. In order to provide a sound base for coping with the meta-complexity of both complexity and its management, this architecture comprises interconnected components, domains, and dimensions as building blocks of simplexity as well as paradigms, patterns, and parameters for managing simplexity. The need for a balanced paradigm for complexity management, capable of overcoming not only the prevalent bias of complexity reduction but also weaknesses of prevalent concepts of simplexity, serves as the starting point of the argumentation in this chapter. To provide a practical guideline to meet this demand, an innovative model of simplexity is conceived. This model creates awareness for differentiating components, dimensions, and domains of complexity management as well as for various species of interconnectedness, such as the aligned upsizing and downsizing of capacities, the relevance of diversity management (e.g., in terms of deviations and errors), and the scope of risk management instruments. 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The preliminary objectives of the study are to understand and develop the evidence-based tools and interventions for the control and prevention of malaria in different sites of the INDIA. Alongside, with the help of next-generation genomics study, the team has studied the antimalarial drug resistance in India. Further, he has extended his research in the development of Humanized mice for the study of liver-stage malaria and identification of molecular marker(s) for the Artemisinin resistance. At present, his research focuses on understanding the role of B cells in the activation of CD8+ T cells in malaria. Received the CSIR-SRF (Senior Research Fellow) award-2018, FIMSA (Federation of Immunological Societies of Asia-Oceania) Travel Bursary award to attend the IUIS-IIS-FIMSA Immunology course-2019',institutionString:"Nirma University",institution:{name:"Nirma University",country:{name:"India"}}},{id:"334383",title:"Ph.D.",name:"Simone",middleName:"Ulrich",surname:"Ulrich Picoli",slug:"simone-ulrich-picoli",fullName:"Simone Ulrich Picoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334383/images/15919_n.jpg",biography:"Graduated in Pharmacy from Universidade Luterana do Brasil (1999), Master in Agricultural and Environmental Microbiology from Federal University of Rio Grande do Sul (2002), Specialization in Clinical Microbiology from Universidade de São Paulo, USP (2007) and PhD in Sciences in Gastroenterology and Hepatology (2012). She is currently an Adjunct Professor at Feevale University in Medicine and Biomedicine courses and a permanent professor of the Academic Master\\'s Degree in Virology. She has experience in the field of Microbiology, with an emphasis on Bacteriology, working mainly on the following topics: bacteriophages, bacterial resistance, clinical microbiology and food microbiology.",institutionString:null,institution:{name:"Universidade Feevale",country:{name:"Brazil"}}},{id:"229220",title:"Dr.",name:"Amjad",middleName:"Islam",surname:"Aqib",slug:"amjad-aqib",fullName:"Amjad Aqib",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229220/images/system/229220.png",biography:"Dr. Amjad Islam Aqib obtained a DVM and MSc (Hons) from University of Agriculture Faisalabad (UAF), Pakistan, and a PhD from the University of Veterinary and Animal Sciences Lahore, Pakistan. Dr. Aqib joined the Department of Clinical Medicine and Surgery at UAF for one year as an assistant professor where he developed a research laboratory designated for pathogenic bacteria. Since 2018, he has been Assistant Professor/Officer in-charge, Department of Medicine, Manager Research Operations and Development-ORIC, and President One Health Club at Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Pakistan. He has nearly 100 publications to his credit. His research interests include epidemiological patterns and molecular analysis of antimicrobial resistance and modulation and vaccine development against animal pathogens of public health concern.",institutionString:"Cholistan University of Veterinary and Animal Sciences",institution:null},{id:"62900",title:"Prof.",name:"Fethi",middleName:null,surname:"Derbel",slug:"fethi-derbel",fullName:"Fethi Derbel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62900/images/system/62900.jpeg",biography:"Professor Fethi Derbel was born in 1960 in Tunisia. He received his medical degree from the Sousse Faculty of Medicine at Sousse, University of Sousse, Tunisia. He completed his surgical residency in General Surgery at the University Hospital Farhat Hached of Sousse and was a member of the Unit of Liver Transplantation in the University of Rennes, France. He then worked in the Department of Surgery at the Sahloul University Hospital in Sousse. Professor Derbel is presently working at the Clinique les Oliviers, Sousse, Tunisia. His hospital activities are mostly concerned with laparoscopic, colorectal, pancreatic, hepatobiliary, and gastric surgery. He is also very interested in hernia surgery and performs ventral hernia repairs and inguinal hernia repairs. He has been a member of the GREPA and Tunisian Hernia Society (THS). During his residency, he managed patients suffering from diabetic foot, and he was very interested in this pathology. For this reason, he decided to coordinate a book project dealing with the diabetic foot. Professor Derbel has published many articles in journals and collaborates intensively with IntechOpen Access Publisher as an editor.",institutionString:"Clinique les Oliviers",institution:null},{id:"300144",title:"Dr.",name:"Meriem",middleName:null,surname:"Braiki",slug:"meriem-braiki",fullName:"Meriem Braiki",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/300144/images/system/300144.jpg",biography:"Dr. Meriem Braiki is a specialist in pediatric surgeon from Tunisia. She was born in 1985. She received her medical degree from the University of Medicine at Sousse, Tunisia. She achieved her surgical residency training periods in Pediatric Surgery departments at University Hospitals in Monastir, Tunis and France.\r\nShe is currently working at the Pediatric surgery department, Sidi Bouzid Hospital, Tunisia. Her hospital activities are mostly concerned with laparoscopic, parietal, urological and digestive surgery. She has published several articles in diffrent journals.",institutionString:"Sidi Bouzid Regional Hospital",institution:null},{id:"229481",title:"Dr.",name:"Erika M.",middleName:"Martins",surname:"de Carvalho",slug:"erika-m.-de-carvalho",fullName:"Erika M. de Carvalho",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229481/images/6397_n.jpg",biography:null,institutionString:null,institution:{name:"Oswaldo Cruz Foundation",country:{name:"Brazil"}}},{id:"186537",title:"Prof.",name:"Tonay",middleName:null,surname:"Inceboz",slug:"tonay-inceboz",fullName:"Tonay Inceboz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/186537/images/system/186537.jfif",biography:"I was graduated from Ege University of Medical Faculty (Turkey) in 1988 and completed his Med. PhD degree in Medical Parasitology at the same university. I became an Associate Professor in 2008 and Professor in 2014. I am currently working as a Professor at the Department of Medical Parasitology at Dokuz Eylul University, Izmir, Turkey.\n\nI have given many lectures, presentations in different academic meetings. I have more than 60 articles in peer-reviewed journals, 18 book chapters, 1 book editorship.\n\nMy research interests are Echinococcus granulosus, Echinococcus multilocularis (diagnosis, life cycle, in vitro and in vivo cultivation), and Trichomonas vaginalis (diagnosis, PCR, and in vitro cultivation).",institutionString:"Dokuz Eylül University",institution:{name:"Dokuz Eylül University",country:{name:"Turkey"}}},{id:"71812",title:"Prof.",name:"Hanem Fathy",middleName:"Fathy",surname:"Khater",slug:"hanem-fathy-khater",fullName:"Hanem Fathy Khater",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/71812/images/1167_n.jpg",biography:"Prof. Khater is a Professor of Parasitology at Benha University, Egypt. She studied for her doctoral degree, at the Department of Entomology, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, USA. She has completed her Ph.D. degrees in Parasitology in Egypt, from where she got the award for “the best scientific Ph.D. dissertation”. She worked at the School of Biological Sciences, Bristol, England, the UK in controlling insects of medical and veterinary importance as a grant from Newton Mosharafa, the British Council. Her research is focused on searching of pesticides against mosquitoes, house flies, lice, green bottle fly, camel nasal botfly, soft and hard ticks, mites, and the diamondback moth as well as control of several parasites using safe and natural materials to avoid drug resistances and environmental contamination.",institutionString:null,institution:{name:"Banha University",country:{name:"Egypt"}}},{id:"99780",title:"Prof.",name:"Omolade",middleName:"Olayinka",surname:"Okwa",slug:"omolade-okwa",fullName:"Omolade Okwa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/99780/images/system/99780.jpg",biography:"Omolade Olayinka Okwa is presently a Professor of Parasitology at Lagos State University, Nigeria. She has a PhD in Parasitology (1997), an MSc in Cellular Parasitology (1992), and a BSc (Hons) Zoology (1990) all from the University of Ibadan, Nigeria. She teaches parasitology at the undergraduate and postgraduate levels. She was a recipient of a Commonwealth fellowship supported by British Council tenable at the Centre for Entomology and Parasitology (CAEP), Keele University, United Kingdom between 2004 and 2005. She was awarded an Honorary Visiting Research Fellow at the same university from 2005 to 2007. \nShe has been an external examiner to the Department of Veterinary Microbiology and Parasitology, University of Ibadan, MSc programme between 2010 and 2012. She is a member of the Nigerian Society of Experimental Biology (NISEB), Parasitology and Public Health Society of Nigeria (PPSN), Science Association of Nigeria (SAN), Zoological Society of Nigeria (ZSN), and is Vice Chairperson of the Organisation of Women in Science (OWSG), LASU chapter. She served as Head of Department of Zoology and Environmental Biology, Lagos State University from 2007 to 2010 and 2014 to 2016. She is a reviewer for several local and international journals such as Unilag Journal of Science, Libyan Journal of Medicine, Journal of Medicine and Medical Sciences, and Annual Research and Review in Science. \nShe has authored 45 scientific research publications in local and international journals, 8 scientific reviews, 4 books, and 3 book chapters, which includes the books “Malaria Parasites” and “Malaria” which are IntechOpen access publications.",institutionString:"Lagos State University",institution:{name:"Lagos State University",country:{name:"Nigeria"}}},{id:"273100",title:"Dr.",name:"Vijay",middleName:null,surname:"Gayam",slug:"vijay-gayam",fullName:"Vijay Gayam",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/273100/images/system/273100.jpeg",biography:"Dr. Vijay Bhaskar Reddy Gayam is currently practicing as an internist at Interfaith Medical Center in Brooklyn, New York, USA. He is also a Clinical Assistant Professor at the SUNY Downstate University Hospital and Adjunct Professor of Medicine at the American University of Antigua. He is a holder of an M.B.B.S. degree bestowed to him by Osmania Medical College and received his M.D. at Interfaith Medical Center. His career goals thus far have heavily focused on direct patient care, medical education, and clinical research. He currently serves in two leadership capacities; Assistant Program Director of Medicine at Interfaith Medical Center and as a Councilor for the American\r\nFederation for Medical Research. As a true academician and researcher, he has more than 50 papers indexed in international peer-reviewed journals. He has also presented numerous papers in multiple national and international scientific conferences. His areas of research interest include general internal medicine, gastroenterology and hepatology. He serves as an editor, editorial board member and reviewer for multiple international journals. His research on Hepatitis C has been very successful and has led to multiple research awards, including the 'Equity in Prevention and Treatment Award” from the New York Department of Health Viral Hepatitis Symposium (2018) and the 'Presidential Poster Award” awarded to him by the American College of Gastroenterology (2018). He was also awarded 'Outstanding Clinician in General Medicine” by Venus International Foundation for his extensive research expertise and services, perform over and above the standard expected in the advancement of healthcare, patient safety and quality of care.",institutionString:"Interfaith Medical Center",institution:{name:"Interfaith Medical Center",country:{name:"United States of America"}}},{id:"93517",title:"Dr.",name:"Clement",middleName:"Adebajo",surname:"Meseko",slug:"clement-meseko",fullName:"Clement Meseko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/93517/images/system/93517.jpg",biography:"Dr. Clement Meseko obtained DVM and PhD degree in Veterinary Medicine and Virology respectively. He has worked for over 20 years in both private and public sectors including the academia, contributing to knowledge and control of infectious disease. Through the application of epidemiological skill, classical and molecular virological skills, he investigates viruses of economic and public health importance for the mitigation of the negative impact on people, animal and the environment in the context of Onehealth. \r\nDr. Meseko’s field experience on animal and zoonotic diseases and pathogen dynamics at the human-animal interface over the years shaped his carrier in research and scientific inquiries. He has been part of the investigation of Highly Pathogenic Avian Influenza incursions in sub Saharan Africa and monitors swine Influenza (Pandemic influenza Virus) agro-ecology and potential for interspecies transmission. He has authored and reviewed a number of journal articles and book chapters.",institutionString:"National Veterinary Research Institute",institution:{name:"National Veterinary Research Institute",country:{name:"Nigeria"}}},{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. He is also an international opinion leader/expert in vaccination for Japanese encephalitis by IPIC (UK).",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",country:{name:"India"}}},{id:"94928",title:"Dr.",name:"Takuo",middleName:null,surname:"Mizukami",slug:"takuo-mizukami",fullName:"Takuo Mizukami",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94928/images/6402_n.jpg",biography:null,institutionString:null,institution:{name:"National Institute of Infectious Diseases",country:{name:"Japan"}}},{id:"233433",title:"Dr.",name:"Yulia",middleName:null,surname:"Desheva",slug:"yulia-desheva",fullName:"Yulia Desheva",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/233433/images/system/233433.png",biography:"Dr. Yulia Desheva is a leading researcher at the Institute of Experimental Medicine, St. Petersburg, Russia. She is a professor in the Stomatology Faculty, St. Petersburg State University. She has expertise in the development and evaluation of a wide range of live mucosal vaccines against influenza and bacterial complications. Her research interests include immunity against influenza and COVID-19 and the development of immunization schemes for high-risk individuals.",institutionString:'Federal State Budgetary Scientific Institution "Institute of Experimental Medicine"',institution:null},{id:"238958",title:"Mr.",name:"Atamjit",middleName:null,surname:"Singh",slug:"atamjit-singh",fullName:"Atamjit Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/238958/images/6575_n.jpg",biography:null,institutionString:null,institution:null},{id:"333753",title:"Dr.",name:"Rais",middleName:null,surname:"Ahmed",slug:"rais-ahmed",fullName:"Rais Ahmed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/333753/images/20168_n.jpg",biography:null,institutionString:null,institution:null},{id:"252058",title:"M.Sc.",name:"Juan",middleName:null,surname:"Sulca",slug:"juan-sulca",fullName:"Juan Sulca",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252058/images/12834_n.jpg",biography:null,institutionString:null,institution:null},{id:"191392",title:"Dr.",name:"Marimuthu",middleName:null,surname:"Govindarajan",slug:"marimuthu-govindarajan",fullName:"Marimuthu Govindarajan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/191392/images/5828_n.jpg",biography:"Dr. M. Govindarajan completed his BSc degree in Zoology at Government Arts College (Autonomous), Kumbakonam, and MSc, MPhil, and PhD degrees at Annamalai University, Annamalai Nagar, Tamil Nadu, India. He is serving as an assistant professor at the Department of Zoology, Annamalai University. His research interests include isolation, identification, and characterization of biologically active molecules from plants and microbes. He has identified more than 20 pure compounds with high mosquitocidal activity and also conducted high-quality research on photochemistry and nanosynthesis. He has published more than 150 studies in journals with impact factor and 2 books in Lambert Academic Publishing, Germany. He serves as an editorial board member in various national and international scientific journals.",institutionString:null,institution:null},{id:"274660",title:"Dr.",name:"Damodar",middleName:null,surname:"Paudel",slug:"damodar-paudel",fullName:"Damodar Paudel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/274660/images/8176_n.jpg",biography:"I am DrDamodar Paudel,currently working as consultant Physician in Nepal police Hospital.",institutionString:null,institution:null},{id:"241562",title:"Dr.",name:"Melvin",middleName:null,surname:"Sanicas",slug:"melvin-sanicas",fullName:"Melvin Sanicas",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241562/images/6699_n.jpg",biography:null,institutionString:null,institution:null},{id:"337446",title:"Dr.",name:"Maria",middleName:null,surname:"Zavala-Colon",slug:"maria-zavala-colon",fullName:"Maria Zavala-Colon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Puerto Rico, Medical Sciences Campus",country:{name:"United States of America"}}},{id:"338856",title:"Mrs.",name:"Nur Alvira",middleName:null,surname:"Pascawati",slug:"nur-alvira-pascawati",fullName:"Nur Alvira Pascawati",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Universitas Respati Yogyakarta",country:{name:"Indonesia"}}},{id:"441116",title:"Dr.",name:"Jovanka M.",middleName:null,surname:"Voyich",slug:"jovanka-m.-voyich",fullName:"Jovanka M. Voyich",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Montana State University",country:{name:"United States of America"}}},{id:"330412",title:"Dr.",name:"Muhammad",middleName:null,surname:"Farhab",slug:"muhammad-farhab",fullName:"Muhammad Farhab",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Agriculture Faisalabad",country:{name:"Pakistan"}}},{id:"349495",title:"Dr.",name:"Muhammad",middleName:null,surname:"Ijaz",slug:"muhammad-ijaz",fullName:"Muhammad Ijaz",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Veterinary and Animal Sciences",country:{name:"Pakistan"}}}]}},subseries:{item:{id:"25",type:"subseries",title:"Evolutionary Computation",keywords:"Genetic Algorithms, Genetic Programming, Evolutionary Programming, Evolution Strategies, Hybrid Algorithms, Bioinspired Metaheuristics, Ant Colony Optimization, Evolutionary Learning, Hyperparameter Optimization",scope:"Evolutionary computing is a paradigm that has grown dramatically in recent years. This group of bio-inspired metaheuristics solves multiple optimization problems by applying the metaphor of natural selection. It so far has solved problems such as resource allocation, routing, schedule planning, and engineering design. Moreover, in the field of machine learning, evolutionary computation has carved out a significant niche both in the generation of learning models and in the automatic design and optimization of hyperparameters in deep learning models. This collection aims to include quality volumes on various topics related to evolutionary algorithms and, alternatively, other metaheuristics of interest inspired by nature. For example, some of the issues of interest could be the following: Advances in evolutionary computation (Genetic algorithms, Genetic programming, Bio-inspired metaheuristics, Hybrid metaheuristics, Parallel ECs); Applications of evolutionary algorithms (Machine learning and Data Mining with EAs, Search-Based Software Engineering, Scheduling, and Planning Applications, Smart Transport Applications, Applications to Games, Image Analysis, Signal Processing and Pattern Recognition, Applications to Sustainability).",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",hasOnlineFirst:!1,hasPublishedBooks:!0,annualVolume:11421,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. Dr Ventura also holds the positions of Affiliated Professor at Virginia Commonwealth University (Richmond, USA) and Distinguished Adjunct Professor at King Abdulaziz University (Jeddah, Saudi Arabia). Additionally, he is deputy director of the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) and heads the Knowledge Discovery and Intelligent Systems Research Laboratory. He has published more than ten books and over 300 articles in journals and scientific conferences. Currently, his work has received over 18,000 citations according to Google Scholar, including more than 2200 citations in 2020. In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. He is a Senior Member of the IEEE Computer, the IEEE Computational Intelligence, and the IEEE Systems, Man, and Cybernetics Societies, and the Association of Computing Machinery (ACM). Finally, his main research interests include data science, computational intelligence, and their applications.",institutionString:null,institution:{name:"University of Córdoba",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,series:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403"},editorialBoard:[{id:"111683",title:"Prof.",name:"Elmer P.",middleName:"P.",surname:"Dadios",slug:"elmer-p.-dadios",fullName:"Elmer P. 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Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. 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