A comparison of the open raceway and closed bioreactor systems for algae cultivation.
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\\n\\nLaunching 2021
\\n\\nArtificial Intelligence, ISSN 2633-1403
\\n\\nVeterinary Medicine and Science, ISSN 2632-0517
\\n\\nBiochemistry, ISSN 2632-0983
\\n\\nBiomedical Engineering, ISSN 2631-5343
\\n\\nInfectious Diseases, ISSN 2631-6188
\\n\\nPhysiology (Coming Soon)
\\n\\nDentistry (Coming Soon)
\\n\\nWe invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
\\n\\nNote: Edited in October 2021
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\n\nDesigned to cover fast-moving research fields in rapidly expanding areas, our Book Series feature a Topic structure allowing us to present the most relevant sub-disciplines. Book Series are headed by Series Editors, and a team of Topic Editors supported by international Editorial Board members. Topics are always open for submissions, with an Annual Volume published each calendar year.
\n\nAfter a robust peer-review process, accepted works are published quickly, thanks to Online First, ensuring research is made available to the scientific community without delay.
\n\nOur innovative Book Series format brings you:
\n\nIntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\n\nIntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
\n\nLaunching 2021
\n\nArtificial Intelligence, ISSN 2633-1403
\n\nVeterinary Medicine and Science, ISSN 2632-0517
\n\nBiochemistry, ISSN 2632-0983
\n\nBiomedical Engineering, ISSN 2631-5343
\n\nInfectious Diseases, ISSN 2631-6188
\n\nPhysiology (Coming Soon)
\n\nDentistry (Coming Soon)
\n\nWe invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
\n\nNote: Edited in October 2021
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These important natural events affect all ecosystems, causing important changes on biodiversity. Systematic and phylogenetic studies, biogeographic distribution analysis and evaluations of diversity richness are focal topics of this book written by international experts, some even considering economical effects and future perspectives on the managing and conservation plans.",isbn:null,printIsbn:"978-953-307-796-3",pdfIsbn:"978-953-51-4406-9",doi:"10.5772/1835",price:139,priceEur:155,priceUsd:179,slug:"changing-diversity-in-changing-environment",numberOfPages:404,isOpenForSubmission:!1,isInWos:1,isInBkci:!0,hash:"a9f3c1cbb7119a88079a1c07838455f8",bookSignature:"Oscar Grillo and Gianfranco Venora",publishedDate:"November 14th 2011",coverURL:"https://cdn.intechopen.com/books/images_new/1369.jpg",numberOfDownloads:47335,numberOfWosCitations:132,numberOfCrossrefCitations:30,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:112,numberOfDimensionsCitationsByBook:1,hasAltmetrics:0,numberOfTotalCitations:274,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 23rd 2010",dateEndSecondStepPublish:"December 21st 2010",dateEndThirdStepPublish:"April 27th 2011",dateEndFourthStepPublish:"May 27th 2011",dateEndFifthStepPublish:"July 26th 2011",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,8,9",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"51992",title:"PhD.",name:"Oscar",middleName:null,surname:"Grillo",slug:"oscar-grillo",fullName:"Oscar Grillo",profilePictureURL:"https://mts.intechopen.com/storage/users/51992/images/1749_n.jpg",biography:"Dr. Oscar Grillo is a food technologist with an international PhD in applied and environmental botany. Since 2003 he has been working as a researcher at the Stazione Sperimentale di Granicoltura per la Sicilia, a governmental institute of agronomic research, mainly working with computer vision applied to food matrices and plant structures. Currently, he is collaborating with the Sardinian Germplasm Bank of the Biodiversity Conservation Centre of the University of Cagliari on projects devoted to wild plant seed characterization and identification by image analysis. He also works as a supervisor for many MSc and PhD students. He is the author of about 50 research works published in many peer-reviewed journals and about 70 international conference papers. Dr. Grillo is a referee for a few peer-reviewed journals, and many times was invited as a visiting professor by national and international universities and research centres. 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Utilization of biomass resources is one option to meet the energy requirements for rapid industrialization and population growth with potential environmental and economic benefits. Energy could be derived from a variety of terrestrial, renewable, bio-based feedstocks like sugar-based biomass (e.g. corn, sugarcane, sugarbeet) and lignocellulosic biomass (e.g. wheat straw, corn stover, sugarcane bagasse, forestry residues, switchgrass, energy cane, sorghum, short rotation woody crops). However, production and conversion of these feedstocks could entail risks associated with disruption of the food chain and biodiversity, depletion of freshwater resources and eutrophication.
\nAquatic biomass like microalgae is a promising feedstock with many advantages over terrestrial plants. Its use dates to 1940s [1, 2]. To meet an energy shortage during this period, microalgal biomass was proposed to be used as a source for lipids. Microalgae have higher yield from incident solar energy and higher areal productivity. The photosynthetic efficiency of microalgae (around 3–8%) is substantially higher than that of terrestrial plants (typically 0.5%) due to their simple structure and convenient access to nutrients [3, 4, 5, 108]. Therefore, less land area is required and non-arable, non-productive land could be used for their cultivation. Some species could be cultivated using low quality water such as seawater, brackish water, desalination reject water and wastewater. A microalgae production facility could be operated as a closed loop system by allowing for recycling of water, nutrients and energy from downstream production processes [6, 7, 144]. Microalgae are characterized by high lipid/starch/protein content with a lack of lignin, which makes them well-suited for different conversion technologies [8, 9, 10]. Besides, microalgae cultivation has less potential to interfere with food and feed production. With such versatility, microalgae appear to be a promising biorenewable resource that has the potential to completely replace fossil resources [11]. Research in microalgae biotechnology has increased dramatically since 2005 and has been a very active field in recent years, especially to produce biomass and biofuels [12, 110, 111, 117, 118, 136, 143].
\nThough microalgae may demonstrate benefits over terrestrial feedstocks, the major challenges for their production include significant utilization of nutrients, high energy input for harvesting and dewatering, and complex downstream conversion processes for usable fuels like ethanol and biodiesel [6, 8, 100, 109, 131]. An alternative which can potentially decrease the energy footprint could be biogas production through anaerobic digestion [122, 125, 127, 137]. Anaerobic digestion (AD) is a biochemical process that mineralizes organic compounds to biogas through the synergistic and concerted action of microorganisms under anaerobic (O2 free) conditions. Dry biogas is primarily a mixture of methane and carbon dioxide with traces of ammonia, volatile organic compounds and hydrogen sulfide. Methane content of dry biogas usually ranges between 50 and 70% (by volume). Methane has a higher heating value on a mass basis when compared to liquid fuels, such as biodiesel and bioethanol [13, 145]. AD has been recognized as a mature technology to treat organic waste streams and is widely practiced due to its high energy output to input ratio, environmental benefits, as well as for its process simplicity—compared to bioethanol/biodiesel processes [13, 14]. It is suitable for organic feedstock with high moisture content [15] and so can directly be applied to wet algae biomass feedstock with perhaps little dewatering. Besides, no harsh pretreatment is necessary for algal biomass due to the negligible lignin content [14]. The algal biorefinery could be engineered to be resource efficient by recycling phosphorus and nitrogen nutrients in the digestate effluent and carbon dioxide from biogas upgrading processes for microalgae cultivation [13, 14, 16, 17].
\nIn addition to the physical and chemical properties of the fuel as specified by technical standards, the characteristics desired by the stakeholders, distributors and, consumers could also include sustainability indices related to environmental, social and economic performance. Techno-economic analysis (TEA) establishes a capital and operating cost profile to determine the potential economic viability of the production process for realizing its commercial feasibility. It can be an integral tool to direct research during development of specific technology and assist with investment by averting unnecessary expenditures. A number of techno-economic assessments have been completed to evaluate the economic feasibility of biodiesel derived from microalgae [9, 22, 69, 140, 141]. However, there is a lack of techno-economic analysis on anaerobic digestion of microalgae for biogas production, especially full-scale production taking the characteristics of algae species into consideration. In this chapter, the entire production process from algae cultivation to biogas upgrading will be discussed emphasizing the key cost drivers. TEA literature is reviewed for methodology and state of art technologies. An example of TEA was conducted based on the biogas production process from a microalgae/cyanobacteria species
An anaerobic digestion (AD) process can biochemically convert the whole, wet biomass rather than specific components. The emissions and effluents from the process can be captured for reuse of components like carbon dioxide, ammonia, and phosphorus, and therefore has the potential for economic and environmental benefits. The general biochemical steps in the AD process include: (1) hydrolysis: the breakdown of macromolecules like proteins, lipids, polysaccharides into simpler compounds such as amino acids, sugars, fatty acids and glycerol; (2) acidogenesis and acetogenesis: the hydrolyzed molecules are converted to volatile fatty acids, primarily acetate, hydrogen, and carbon dioxide; (3) methanogenesis: methane production from acetate, hydrogen and carbon dioxide. The hydrolysis step plays a crucial role in determining the successful production of methane [37, 145]. The biochemical processes in AD also occur in nature. AD technology is well established and recognized as a robust technology to convert biomass to bioenergy [146].
\nDespite the potential, questions related to the economic feasibility and the net energy output are the main hurdles hampering the development of biogas production from microalgae [14, 18, 19, 20]. For example, due to the specific structure and composition of the microalgae cell wall, the yield of biogas could be low. Pretreatment to disrupt the cell walls could require high energy inputs. The algae productivity could be low and cultivation cost could be high. Thus, the viability of microalgal biogas production may depend on improvements of efficiency and economic performance. Ongoing efforts include developing inexpensive biomass feedstock, maximizing energy return on investment, and minimizing environmental risks. As only a few studies are available in the literature on the economic feasibility of microalgal biogas exploitation [14], the evaluation and analysis of microalgal biogas production cost will be based on conversion efficiency, technological design aspects as well as available cost information.
\nThe production of biogas from microalgae feedstock entails a series of steps starting with algae cultivation. Implementation of each step involves capital and operational expenditures. The key drivers such as algal biomass productivities, harvesting and dewatering techniques, AD designs, biogas utilization options, integration of algal production, and AD with other bioprocesses were addressed. The production cost breakdown was illustrated in a harmonized framework and a dynamic connection between the technological and economic/environmental assessments was established.
\nA photobioreactor is the essential component of an algae cultivation facility. An open raceway pond (ORP) and a closed photobioreactor (PBR) are two major cultivation platforms. These two platforms for algae biomass production have been extensively studied [22, 23, 24, 25, 26, 27, 83, 84, 85, 101]. The main differences are highlighted in Table 1. In addition, the steps from inoculum preparation to obtaining the wet algal paste typically include systems for culture circulation, growth medium supply, air/flue gas supply, culture cooling, culture harvesting, and process monitoring. Heat exchangers, pumps, and a piping network are also required. The location and climate are important factors for algae cultivation.
\n\n | Open raceway | \nClosed bioreactor | \n
---|---|---|
Biomass productivities | \nLow | \nHigh | \n
Harvesting biomass concentration | \nLow | \nHigh | \n
Total capital cost (CAPEX) | \nRelatively low | \nHigh | \n
Total operational cost (OPEX) | \nRelatively low | \nHigh | \n
Reliability (low contamination risk, stable yield) | \nLow | \nHigh | \n
Net energy ratio (energy output/input) | \n>1 | \n>1 in some cases | \n
Area required | \nHigh | \nLow | \n
Process control | \nLow | \nHigh | \n
CO2 loss | \nHigh | \nLow | \n
Water evaporation | \nHigh | \nLow | \n
Photosynthesis efficiency | \nLow | \nHigh | \n
Scale-up | \nEasy | \nDifficult | \n
A comparison of the open raceway and closed bioreactor systems for algae cultivation.
Due to the high methodological variation of TEA in literature, drawing a generic conclusion over the economic feasibility of microalgal cultivation could be impossible. From the technological and economic perspective, the factors presented below are the ones most prominent in the existing literature and identified as important topics in the development of algae fuels.
Algae harvesting and dewatering methods include gravity settling, chemical coagulation, flocculation, filtration, centrifuge, and drying. The economic feasibility and energy consumption are two criteria for assessing the performance of unit operations for harvesting and dewatering methods. It was found that the cost of separation takes 20–30% of the biomass production costs [32, 33]. Gravity settling, chemical coagulation, and flocculation usually concentrate the microalgal slurries to 2–7% while filtration and centrifugation concentrate microalgal slurry to 15–25% of total suspended solids [32]. The suitability of microalgal dewatering methods has been investigated for scalability, species flexibility, and downstream processing efficacy [33, 34, 35, 36]. Dewatering methods reaching high biomass concentrations are usually associated with high energy input and cost. Thus, a combination of dewatering methods such as flocculation followed by filtration is generally considered to be economical due to the increased harvest efficiency. For downstream processing, methods such as flocculation using flocculants comprised of cationic and anionic poly-electrolytes, synthetic polyacrylamide polymers and starch-based polymers can be employed. However, the detrimental effect of these flocculants on the subsequent microbial processes need to be considered. For example, anaerobic digester stability and gas production could be affected by metal contamination. Future work should include replacing chemical coagulants with natural and low-cost organic ones for harvesting algal biomass.
\nThe efficiency of biogas production has been shown to be species-dependent [39]. One crucial factor is the differences in structure of microalgae cell walls. The role of the cell wall in the microbial degradability of algae biomass is highlighted in many investigations [6, 13, 37, 38, 40, 41, 42, 43]. Many microalgae species (e.g.
The capital cost of the anaerobic digester could be reduced by using reactors designed for high OLR and low HRT [37]. The OLRs are typically between 1 and 6 g VS/L/d while the HRT varies between 10 and 30 days [37, 38]. Although high OLR will increase the methane productivity, overloading will decrease the biogas production efficiency due to the accumulation of inhibitors such as ammonia and acids [6, 37, 38]. Also, prolonged HRT could lead to ammonia inhibition due to slow liquid removal rate [41], while a low HRT could cause the washout of the anaerobic bacteria community [6]. Thus, an optimized OLR and HRT should be applied to achieve the expected specific methane yield. Possible solutions could be improving anaerobic digester configurations such as using membrane reactors or upflow anaerobic sludge blanket reactors to decouple the OLR and HRT [37, 119] and on-line control of anaerobic digester operation [124]. These have not been applied for digesting algal biomass. Additional costs for land and infrastructure and energy expenditures for heating the digesters should be included in the economic analysis.
\nAD microorganisms can grow in three temperature regimes: (1) psychrophilic (5–20°C); (2) mesophilic (25–45°C); and (3) thermophilic (45–65°C). The temperature effect on AD has been discussed [13, 37, 41]. The beneficial temperature regime for AD operation is anaerobic digester is species-specific [44, 45]. The rate of methane generation can be enhanced under mesophilic and thermophilic conditions. The increased temperature could improve enzymatic activity for degrading microorganisms, and at the same time, the photosynthesis activity of viable microalgae within the digester could be reduced [13, 37]. However, an increase in temperature beyond the tolerable range of each temperature regime could cause inactivation of the microbes. Thermophilic temperature may cause increased hydrolysis of nitrogenous compounds which may increase ammonia levels and in turn can cause inhibition [6]. For large-scale biogas productions, the energy required for heating may be more than 1/3 of the total energy output in the form of biogas [46]. Thus, the net energy production from algae biogas may still be limited due to the high heat input associated with a low concentration of algae substrates.
\nThe pH needs to be maintained at an appropriate level for efficient conversion of biomass to biogas. The growth of microbes, enzyme activity, and the biogas compositions are influenced by the pH [47]. The optimum pH level depends on each step of AD [41]. Generally, the pH values are maintained between 7 and 8 for single stage anaerobic digesters [13, 41].
\nMicroalgae grown in a saline environment offer a sustainable alternative to other biomass by utilizing non-arable land and seawater. Marine microalgae can usually grow in a salinity range of 35–125 ppt [48]. However, when a highly saline culture is processed in an anaerobic digester, the high salinity could be inhibitory to the AD process. The effects of salinity and concentration of sodium are discussed in previous studies [6, 38]. Adaptation of anaerobic digester microbial consortiums under different saline conditions was investigated by Mottet et al. [121]. In a promising study, methane production was observed from anaerobically digesting
Sulfide is a required micronutrient for anaerobic microorganisms, but high concentrations of sulfide (200 mg/L) could be toxic [6]. For saline microalgal species, the sulfur inhibition may occur due to the presence of oxidized sulfur compounds in saline algae growth medium. Proper inoculum selection for anaerobic digesters could favor the growth of methanogenic bacteria and limit the growth sulfate-reducing bacteria [49].
\nLipids can also be inhibitory to the AD process [6, 18, 50] although lipids have a high theoretical methane potential. Generally, inhibition would occur when lipids concentrations are higher than 30%. In this case, the high-lipid microalgae are suitable for lipid extraction for production of liquid fuels.
\nMicroalgae biomass generally has a higher composition of protein than terrestrial plants [6, 37]. The degradation of protein will cause ammonia accumulation and inhibit the methanogenesis process. The optimum C/N ratio for AD is between 15 and 30 while this C/N ratio for microalgal AD is generally below 10 [13, 38, 41]. Thus, increasing C/N ratio and reducing the ammonia toxicity are important to enhance the biogas yield and productivity from microalgae. Possible solutions to this issue could be; (1) using ammonia-tolerant inoculum generated either by bioaugmentation or by acclimation [37, 38]; (2) using microalgae biomass that was cultivated under nitrogen-limitation [41, 99, 102, 130]; (3) co-digestion with sludge, oil-greases, waste paper and food wastes [13, 41, 54]; and (4) using a two-stage AD for better control of the anaerobic microbial communities [6]. However, these solutions may add more complexity to the system, in which the economic and energetic performance is still clear. For example, the co-substrate needs to be secured for co-digestion; the digester volume and cost may increase due to the loading of the co-substrate; more environmental burdens may be associated with the shipping of biomass, and nitrogen-limitation cultivation may affect microalgae productivity.
\nMany other factors could affect the biogas yield and production of microalgal biomass. For example, the harvesting time influences the composition and biodegradability of algal biomass. Thus, it is essential to harvest algae in the appropriate stage of growth [13]. Storage conditions such as temperature also have an impact on biomass quality like macromolecular distribution and the content of organic compounds. Besides, inoculum to substrate ratio control is instrumental in avoiding inhibition problems such as drop in pH [51].
\nThe overall biogas yields depend on the chemical composition of the algae strains. The target strain should be highly digestible. The volatile solids/ash-free dry weight of microalgae plays a significant role in predicting theoretical biogas production potential, which is a critical factor in determining biogas productivities. Theoretically, the methane yield from different components of microalgae is as follows: lipids—1 L CH4/g VS, proteins—0.85 L CH4/g VS, carbohydrates—0.42 L CH4/g VS at standard conditions. Although the lipids have a high theoretical methane yield in AD, a high lipid content (more than 40%) will produce inhibitory substances such as long chain fatty acids [6]. Thus, for high-lipid content microalgae, lipid removal for biofuels production may be a better solution than biomass sent directly to AD.
\nThe impact of the algae cell wall is another critical factor affecting methane yield. Some species either lack cell wall or have cell walls rich in easily-biodegradable proteins as in
The microalgal strains which have been investigated extensively include
Components | \nSpecies | \n|||
---|---|---|---|---|
Protein % of DW | \n33 | \n65 | \n64 | \n19 | \n
Carbohydrates % of DW | \n35 | \n23 | \n18 | \n45 | \n
Lipids % of DW | \n22 | \n13 | \n10 | \n36 | \n
Approximate compositions of four microalgal species: Scenedesmus sp., Chlamydomonas reinhardtii, Chlorella vulgaris, and Nannochloropsis salina [41].
In published TEA works, the process complexity was often simplified in terms of limited pathways, few choices of economic drivers and implicit assumptions regarding the growth conditions, process modeling factors and financing of the production facility. Existing reviews in anaerobic digestion of microalgae biomass such as Ward et al. [6] focus on the integration of anaerobic digestion into biodiesel refineries. Considering that diesel or ethanol are more valuable products, anaerobic digestion was suggested to treat the residual biomass to improve the economic viability and sustainability of overall microalgae biodiesel/ethanol stages. Global research in various pathways is going on towards the sustainable development of algae biofuels. The following sections will review these works, highlight the variability of methods of estimating microalgal biogas production cost, find the key drivers of cost contributors, pointing out the convergence and difference in published results, and give a view of the whole value chain towards scaling-up and commercialization when performing a techno-economic analysis (TEA).
\nTo achieve an optimal facility design, it is necessary to evaluate the tradeoff resulting from the interactions between technical advances and financing parameters. The technical objectives include maximizing microalgal biomass productivity, maximizing biogas yield via AD of biomass, and process stabilization. The economic objectives are to minimize the production cost and maximize the economic benefits. Figure 1 shows the TEA framework for the sustainability analysis of biogas production from microalgal biomass through anaerobic digestion. The whole biomass processing value chain is determined by the technology framework and progress through experimentally validated process specifics. Economic analysis is based on the process design, which includes the cost assessments and investment analysis. A decision-making platform is built for raw material suppliers, producers and stake holders in an economic perspective. Correspondingly, the economic consequences will direct the research & development of new technologies, which could form a dynamic connection and optimization framework.
\nTEA framework for biogas production from algae biomass.
Environmental TEA (ETEA) extended the TEA framework with an environmental assessment based on a life cycle analysis [70]. The ETEA is based on the technology readiness level, which means the assessments are performed using the available data based on technology maturity. This would avoid a mismatch between the assessment methodology and the technology readiness level. For example, the whole biogas life cycle includes phases from the biomass cultivation to the final usage and end of life. Under current technology maturity, the whole data set is unavailable, which limits the assessments to certain life cycle phases.
\nBiorefinery optimization and full utilization of biomass addressing in the economic viability and environmental sustainability of the production of algae biofuels can be found in [39, 71, 72]. Dutta et al. [72] analyzed the sustainability of microalgae-derived biofuel production by performing a TEA and life-cycle assessment and found that coproducts valorization is more energy efficient than the processes focusing on specific components such as lipids. Biorefineries with coproducts and byproducts could have better utilization of the algal biomass and can increase the revenue, thus show greater possibility of achieving economic feasibility. In microalgae biodiesel and bioethanol productions, anaerobic digestion is usually integrated into the biorefinery to treat the residues for energy and nutrient recovery. Sialve et al. [18] compared the energy recovery ratio for two scenarios: direct AD of the whole algae biomass and AD of residue biomass after lipid extraction. Direct AD of the whole biomass was considered to have a higher energetic recovery when the cell lipid content does not exceed 40%. Also, increased lipids content in microalgae is not generally compensated with increased productivity due to nitrogen limitation. The potential of direct AD of microalgae biomass was addressed in their research, taking into account the energetic recovery and necessary nutrient recycle for large-scale productions. Chia et al. [73] discussed the economic potential of biohydrogen and biogas production in Germany and Spain. Two processes were compared: direct AD of microalgae biomass (DAD) and coupled hydrogen and biogas production (CHB). In the CHB process, hydrogen was first produced by dark fermentation then effluent from hydrogen fermentation was used for biogas production. The CHB was found to have a lower operating cost due to no additional water and nutrients requirements for the bioreactor feed while the DAD process requires algal biomass in combination with other feedstocks. Both cases have production costs 13–16 times higher than the market price for natural gas. A 1/3 higher biogas yield and a 1/2 lower labor cost did not change the economic status of both processes, due to the high cost of fertilizer and building photobioreactors for microalgae cultivation. Milledge and Heaven [74, 129] performed an energy balance of biogas production from microalgae. Their research emphasized a combination of dewatering methods, as well as the efficient exploitation of the heat generated by the combustion of biogas in combined heat and power (CHP) units to show the energetic viability of the whole process.
\nChew et al. [68] assessed the potential of microalgae biorefineries for producing high-value products such as pigments, proteins, lipids, carbohydrates, and vitamins. The high-value products were added to improve the biorefinery economics. Open pond cultivation and medium recycling were mentioned to have better economic performance than other biorefinery structures. Water, land usage and capital cost were challenging for the economic viability of algal biofuels. The high-value products also need to improve aspects such as separations method, energy consumption, and control of product loss. AD was emphasized to recycle a considerable amount of nutrient usage to make microalgal fuels head towards its large-scale production. Several authors [13, 17, 37, 38, 75, 133, 134] synthesized scientific literature on biogas production from algae and suggested integration of the technology with other technologies as well as co-digestion with other substrates for an optimized biorefinery that sustainably produces biogas. Singh and Gu [76] recommended integrated processes that combine algae cultivation and wastewater treatment for methane production, which could offset the higher cost in comparison to methane production from corn and woody biomass.
\nZamalloa et al. [8] evaluated the techno-economic potential of methane production from microalgae. The assessment was carried out using high rate anaerobic digesters (10–20 kg COD/m3/d) and preconcentrated algae biomass from a full-scale open pond. The energy production cost from microalgal biogas was estimated to be 0.087–0.17 euro/kWh with an algae biomass cost of 86–124 euro/tonne. The result was based on a feed-in tariff of 0.133 euro/kWh and a carbon credit of 30 euro/ton of carbon dioxide. This study is one of the limited works that has been done on a comprehensive technological and economic assessment of electrical and thermal energy produced by biogas through AD of microalgae.
\nCollet et al. [77] performed a life-cycle assessment (LCA) of biogas production from the microalgae
The studies surveyed show considerable variability in the calculated fuel cost and identifying the significant cost contributors. The varied results come from different conversion pathways, technical assumptions (productivity, reactor design, process parameters, etc.) and economic factors (interest rate, raw material cost, etc.), diverse environmental and social conditions (consideration of season and location), and validation of sub-process models (lab/pilot plant/commercial scales). Nevertheless, the contributors to the production cost are mainly identified as microalgal strain selection, biomass cultivation and harvesting, AD operating conditions, biogas upgrading methods, waste management, and type of biorefinery. Thomassen et al. [78] evaluated the methodological reason for the wide variation in the results of multiple environmental and economic assessments. They proposed an environmental techno-economic assessment which can help to solve the challenges for a sustainability assessment: framework for methodology, harmonized assumptions, and integration of different dimensions (stages of technological maturity, technological process). This method is based on the dynamic technological process parameters and the same system boundaries for an integrated TEA and LCA.
\nGnansounou and Dauriat [79] investigated TEAs following different types of cost management systems in value engineering, target costing and a combination of value engineering and target costing. Value engineering includes process design via data collection and process flowsheeting. Process simulators such as Aspen Plus enables the evaluation of the whole process chain based on scale up of the pilot plant, state of art technologies and price quotes. For microalgae to biogas technologies, key issues along the process chain include the suitable choice and operation options of the microalgae species, harvesting/dewatering strategies, pretreatment methods, AD configurations, recycling the digestate, and energy integration. Not all the steps are necessary for technologies with simplified processes and high economic potential. Target costing is a market-oriented method, which means a target selling price was set for the cost evaluation based on market and societal values. Following the target price, the target cost of the final product and each step of the supply process will be estimated, which means the cost allowance will play a key role in the process design. Target costing could integrate with value engineering in the cost management activities, so the cost allowance and cost target could be reconciliated. In the case of biogas production, the target costing evaluation seems unfeasible for the whole process due to the weak financial position of the natural gas market [80].
\nReal options analysis framework was employed by Kern et al. [81] for TEA. The model was adapted to accept stochastic price data for energy and agricultural commodities as well as static operating parameters assumptions for the algal biofuel plants. The TEA work was combined with life cycle analysis in a dynamic system—the fluctuations in market prices for energy and agricultural commodities will influence the operation decisions of the biofuels plants and its associated environmental impacts. Areas such as carbon tax, resource shortage and market forces could be investigated for their impact on biofuel plant design and operations in a dynamic system in the future. This gives the stake holders and suppliers more flexibility in making decisions.
\nThe limitations of TEA include the potential competition for resources. For example, the microalgae biomass could have non-energy applications and has the potential for producing high value products besides biofuels. Then the biomass cost for the process will be influenced not only by the biomass production activities but also the market price which is determined by both the suppliers and purchase competitors.
\nThe sustainability of biogas production from microalgae will depend on not only the commercial viability but also environmental improvements such as greenhouse gas emission reduction, lack of direct and indirect impacts on land-use as well as biodiversity and eutrophication. The scope of TEA is limited for the environmental impact assessment, while these impact categories are appropriate for the goals of the overall sustainability analysis. Thus, an ETEA would allow assessing the sustainability of the entire value chain. Besides, TEA is not reflecting social impacts such as social awareness of algal biofuels’ non-food competitive characteristics, rural development, and public recognition.
\nThe microalgae used for this case study is a cyanobacterium,
Results from many experiments [149] conducted in the Bioprocess Engineering Laboratory, Department of Agricultural and Biological Engineering, University of Florida gave an average growth rate of 67.5 mg afdw/L/day (20.25 g afdw/m2/day) for BG0011 cell biomass and an EPS production rate of 52.5 mg afdw/L/day (15.75 g afdw/m2/day), resulting in cell density of 2.7 g/L and EPS concentration of 2.1 g/L. The areal rates were calculated by assuming that the depth of culture was 30 cm, which is typically the case for open ponds. In the laboratory, the cultures were cultivated under air sparging, a constant illumination of 1200 μmol photons m−2 s−1 light and 13 h to 11 h light-dark cycle. Open raceway ponds are generally used for large-scale commercial production of algal biomass [86]. Productivity in industrial-scale raceway ponds is generally lower than in small experimental reactors. In literature, algae biomass productivity performance claims range from 7 to 35 g afdw/m2/day [23, 87, 88, 89] with corresponding net photosynthetic efficiencies from under 1–4%. Among these, for studies involving techno-economic analyses, the baseline productivity assumed was 20 g/m2/day, with an optimistic value of 25–30 g afdw/m2/day, and a conservative value of 15 g afdw/m2/day. In this study, which assumed that BG0011 is cultivated in current large commercial open ponds, an average productivity of 12.4 g afdw/m2/day (corresponding to a net photosynthetic efficiency of under 1%) was used. Similar growth rates were obtained by [148] when the algae was cultivated by air sparging and exposed to a lower light intensity of 122 μmol photons m−2 s−1 light and 13 h to 11 h light-dark cycle. Here, laboratory-scale BG0011 cell biomass growth rate is comparable to algae cell growth rates reported from other studies, however, in the case of BG0011, it also produces EPS. The average mass ratio between EPS and cell biomass is 0.778: 1 and also EPS production is cell-growth associated, so for this study it is assumed that in the commercial system, in addition to BG0011 cells, EPS would be concomitantly produced at 0.778 × 12.4 g afdw/m2/d = 9.6 g afdw/m2/day. The total algae biomass productivity used was 22 g/m2/d. Henceforth, the term “algae biomass” will include both BG0011 cells and EPS.
\nThe scale of algae cultivation in literature for techno-economic analysis ranges from 200 to 700 ktonne afdw/year [22, 27, 72, 89]. In the present study, the scale of algae cultivation was determined based on a hypothetical 20 million gallons per year ethanol plant. The sugar required for such a plant would be 128 ktonnes afdw/year (assuming yield of around 0.42 g ethanol/g sugar, and 1.1 g sugar/g polysaccharide). Assuming this amount of sugar will be supplied in the form of EPS, the scale of the algae cultivation pond would be 293 ktonnes of algal biomass/year, which also includes BG0011 cell biomass. This scale falls into the range of values found in literature for TEA. To meet a production capacity of 293 ktonnes/year at algal biomass productivity of 22 g afdw/m2/day, land area required would be 3660 hectares (approximately 4 × 4 miles). For a sanity check, this cultivation area was compared to land area required to supplying corn grain for a 20 million gallon per year corn-ethanol plant. Based on annual corn grain yield of 7000 kg/ha with starch content of 72% [150], and assuming a conversion of 0.5 kg ethanol/kg of starch, land required would be 23,700 ha. In this case the total above ground biomass productivity of corn, including corn grain, stover and cobs, is 16,700 kg/ha/year [150] whereas for BG0011 it is anticipated to be 80,300 kg/ha/year.
\nThe BG0011 cultivation cost was estimated based on vendor quotes, literature, or engineering estimates. The installed pond capital cost includes civil work, liner, piping, electrical, other pond costs (such as paddlewheels). In addition, pumps for pumping water from ponds to refinery and for refilling the pond and required land also incur significant capital costs. Plastic lined earthen ponds were chosen for its lower cost compared to concrete ponds. Larger pond sizes would enable economically viable algal biomass production [23]. Here, the installed capital cost was estimated based on “dollars/hectare” of growth ponds for simplicity. The installed pond cost was set to be 80,000 $/ha. Literature value ranges from 46,000 $/ha to more than 150,000 $/ha (value adjusted for inflation) due to different liner scenarios (partial or full) and specific design (e.g. with or without equipment to minimize dead zones) [23, 86] which was not included here. A land cost of 3080 $/acre [90] was used for low-value land. The operation cost for algae cultivation such as utilities, chemicals, labor, overheads, maintenance, insurance tax, etc. were estimated using engineering estimates [91]. BG0011 was assumed to be cultivated in seawater or brackish water. The only fertilizer used for BG 0011 cultivation is phosphorus since it uses dinitrogen in air as a nitrogen source, and seawater would supply rest of micronutrients. From laboratory experiments it was determined that the phosphorous requirement of BG001 is 8.9 mg/L [149], so the annual requirement of phosphorous will be 1186.7 tonnes. Here, triple superphosphate (Ca(H2PO4)2 H2O) which contains 24.6% P is used as phosphorous source with a price of 270 $/tonne (Source: World Bank, 2017). The requirement of triple superphosphate is 4945 tonne/year.
\nThe fixed capital investment was assumed to be borrowed at an interest rate of 10% for 20 years. The plant operates 24 h a day and 360 days annually. The prices were adjusted for Year 2017 using Chemical Engineering Plant Cost Index (CEPCI). These assumptions were also used for the analysis of subsequent biogas production, conversion and upgrading processes. The production cost was calculated as follows:
\nHere, the annual capital charges are calculated as follows:
\n* Total capital cost = Total fixed cost + Working capital.
\n* Working capital is 10% of fixed capital.
\nThe anaerobic digester was designed to treat the un-dewatered whole algae culture from the pond. The energy-intensive steps like algae harvesting and dewatering are avoided in this process which is different from most research [8, 22, 23]. The product biogas was analyzed for economic performance in two different applications: biogas purification or electricity production through combined heat and power.
\nThe first step in modeling mass flow rate of reactor outputs and determining energy requirements is to establish the stoichiometry of reactions. The stoichiometry of methane fermentation of algae biomass was developed based on the following assumptions: (1) microbial cells (cyanobacteria and bacteria) can be represented by the empirical formula CH1.8O0.5N0.2 [151]; (2) EPS is pure polysaccharide represented by the empirical formula C6H10O5; (3) algae biomass can be represented by an empirical formula containing the elements C, H, O and N in the mass ratios in which cells and EPS are produced that is 1:1.2; and (4) methane yield from laboratory assays corresponds to complete decomposition of substrate. The empirical formula for algae biomass was CH1.73O0.67N0.1. The stoichiometry for methane formation is written as follows:
\nMethane yield from algae biomass was measured in the laboratory to be 300 ml at STP (g afdw)−1. This corresponds to 0.35 moles of methane (mole algae biomass)−1, which is equal to value of ‘c’ in the above stoichiometry. The other stoichiometric coefficients can now be solved from elemental balances for C, H, O and N. The stoichiometry is
\nIn the anaerobic digester it was assumed that 98% of the algae biomass is converted. Different scenarios (three anaerobic digester types) were investigated to evaluate the economic and energetic performance. A schematic of biorefinery scenarios are shown in Figure 2.
\nSchematic diagram showing biorefinery scenarios.
Case 1. Above ground mesophilic anaerobic digester. In Aspen, the influent to the reactor was 15 ktonne/h. The temperature was maintained at 37°C. It was operated at an HRT of 25 days.
\nCase 2. Above ground low-temperature anaerobic digester. Anaerobic digestion at low temperatures (LTAD) was applied to improve the energy balance. In this scenario the digester is operated in the psychrophilic range (12–20°C) [92, 93, 94]. However, with the same flow rate, the digester volume is larger to achieve a higher HRT for LTAD than mesophilic and thermophilic anaerobic digestion. Here, the temperature of LTAD is set to 20°C with an HRT of 50 days.
\nCase 3. Covered anaerobic lagoon. Covered anaerobic lagoon (CAL) does not require additional energy for the biogas production because no heating or mixing processes are involved. Besides, it is economical to construct and operate. The CAL in this research was 6 meters deep and covers an area of 1.5 hectares based on literature data [95]. The HRT was set to 50 days. The cost includes anaerobic lagoon excavation, cut and fill, lagoon liner, inlet and out structures, lagoon cover, ancillaries, pipework & installation, contingencies, design, engineering, etc. Operating costs including utility usage are minimal.
\nIn all three cases above, the capital cost of anaerobic digester was estimated using vendor quotation or literature values. The operating cost was estimated by Aspen Process Economic Analyzer.
\nSeveral biogas upgrading or purification methods are available such as high-pressure water scrubbing, membrane, pressure swing, gas permeation and chemical scrubbing. High pressure water scrubbing and chemical scrubbing (using amine solutions—MEA) are two of the most commonly used processes.
\nThe MEA scrubbing method uses aqueous monoethanolamine (MEA) for acidic gas removal. The concentration of amine for acidic gas absorption is usually below 30% (by weight). The amine process has two main steps, absorption and stripping [96]. The detailed MEA scrubbing process is shown in Figure 3. Raw biogas goes through a scrubbing column in which MEA is flowing counter-current to biogas. The CO2-rich MEA is collected at the bottom of the scrubbing column and pumped into a stripping column to remove CO2 and regenerate MEA by heating. Similar to MEA scrubbing, high pressure water scrubbing was also employed for biogas upgrading: biogas is fed to the bottom of scrubber after compressing it to 10 bar. At the top of scrubber, pressurized water is fed. CO2-rich water is then transferred to a flash column with a lower pressure of 3 bar to release gases for feed recirculation and minimizing methane loss. Then the CO2-rich water goes through a CO2 desorption process from the water stream by air [97]. Both biogas purifying approaches were simulated in ASPEN Plus to determine the economics of each approach. The technical specification details are shown in Table 3. The table shows high pressure water scrubbing to be a more economical alternative and was chosen for the integrated process.
\nMEA scrubbing for biogas upgrading.
Specification | \nMEA | \nHigh pressure water scrubbing | \n
---|---|---|
Thermodynamic method | \nELECNRTL | \nPSRK | \n
Scrubbing column | \nRadFrac, 15 stages, pressure: 1.2 bar | \nRadFrac, 10 stages, pressure: 10 bar | \n
Stripping column | \nRadFrac, 15 stages, pressure: 8 bar | \nRadFrac, 10 stages, pressure: 1 bar | \n
Make up chemicals | \nWater: 150 kmol/h MAE: 750 kmol/h | \nWater: 11500 kmol/h | \n
Solvent recycle rate | \nMEA: 0.99 | \nWater: 0.95 | \n
Methane loss | \n1% | \n0.3% | \n
Product methane purity | \n95 wt% | \n99.2 wt% | \n
Capacity (raw biogas flow rate) | \n948.5 kmol/h | \n948.5 kmol/h | \n
Capital cost (million $) | \n8.2 | \n12 | \n
Operating cost (million $/year) | \n20 | \n4.6 | \n
Utility cost (million $/year) | \n17 | \n2 | \n
Purification cost ($/kg of methane) | \n0.3 | \n0.09 | \n
Technical and economic aspects of the biogas purifying systems in ASPEN V 8.8.
While the raw biogas can be purified to obtain biomethane, another option is to use the raw biogas to produce heat and power. Steam and electricity can be generated by burning the raw biogas through a combined heat and power (CHP) system. For reference, the CHP system uses General Electric Jenbacher JGS 420 system which is a 1425 kw generator. The total capital cost is $ 1,150,000 (including installation, tax, etc. 2007), which is 807 $/kw. The working capital is 10% of the total capital. The operating cost includes direct operating cost such as operating labor, supervised labor, maintenance and repairs, as well as indirect operating cost such as overhead, taxed, insurances. It is assumed that 40% biogas energy is for electricity, 50% for steam, 10% loss.
\nThe BG0011 cultivation economics analysis details are shown in Table 4. In the literature algae production costs range from 150 to 6000 $/tonne [19, 22, 27, 72, 89, 142], however, the studies vary from assumptions (production scale, chemical prices, plant life, etc.) to differences in technical specification (photobioreactor design, algal species, etc.). Some of the estimates also account costs for dewatering of algae [22, 27]. Thus, it is difficult to make a direct comparison between different studies. Besides, specific assumptions in each study could be based on different social-economic conditions, which makes comparisons more complicated [98].
\nParameters | \nValues | \n
---|---|
Production scale | \n\n |
BG0011 cells production (ktonne/year) | \n165 | \n
BG0011 EPS production (ktonne/year) | \n128 | \n
Capital cost (including fixed, installed and working capital) | \n\n |
Pond (million $) | \n308 | \n
Land (million $) | \n26.6 | \n
Pump (million $) | \n7.85 | \n
Total capital cost (million $) | \n342.45 | \n
Operating cost | \n\n |
Chemicals (P fertilizer: Ca (H2PO4)2 H2O) (million $/year) | \n1.3 | \n
Other operating cost (including utilities, maintenance and repairs, labor etc.) (million $/year) | \n3.26 | \n
BG0011 algae biomass production cost ($/tonne) | \n153 | \n
Algae cultivation economics.
Details of the production cost of renewable natural gas for the three anaerobic digestion scenarios are shown in Table 5. Case 2 contains two scenarios: The size of anaerobic digester in Case 2(a) is two times of that in Case 1. This is because the hydraulic retention time is longer under lower temperature, the volume of digester needs to be larger to keep the same production scale (the inflow rate). The size of anaerobic digester in Case 2(b) is the same as Case 1. Keeping the digester volume same as Case 1, because the temperature is lower, the productivity will be lower as well. Thus Case 2(b) has a lower production scale compared to other cases. The effect of temperature was incorporated by using the empirical relationship that for every 10°C rise in temperature the degradation rate is doubled. As the difference between the temperature for Case 1 and Case 2 is 17°C, it is expected that in Case 1, the digester has a processing capacity twice as much as that of the digester in Case 2b. The main contributor to the production cost of biogas is the biomass cost. Considering a carbon credit of 10 $/tonne of CO2, the production cost of biogas only drops 0.5 $/MMBtu. The results are comparable to Zamalloa et al.’s [8] research (the only paper focusing on the economics of renewable energy through AD, to our best knowledge): 32.2–61.5 $/MMBtu with the algae biomass cost of 115.4–166.4 $/tonne (0.087–0.17 euro/kwh with an algae biomass cost of 86–124 euro/tonne, 2011). The methane yield is 0.012 MMBtu/kg of VS biomass, which is in close agreement to our experimental result 0.0124 MMBtu/kg of VS biomass.
\nItem | \nCase 1 (mesophilic anaerobic digester) | \nCase 2(a) (low-temperature anaerobic digester) | \nCase 2(b) (low-temperature anaerobic digester | \nCase 3 (covered anaerobic lagoon) | \n
---|---|---|---|---|
Biogas production scale (106 MMBtu/year) | \n3.7 | \n3.7 | \n1.85 | \n3.7 | \n
Fixed capital cost of anaerobic digester (million $) | \n67.12 | \n102 | \n67.12 | \n7.5 | \n
Capital cost except anaerobic digester (million $) | \n16.3 | \n16.3 | \n12.3 | \n16.4 (including land: $11400) | \n
Annual capital charges (million $/year) | \n9.8 | \n13.9 | \n9.3 | \n2.8 | \n
Total raw materials (algae biomass) cost (million $/year) | \n44.8 | \n44.8 | \n44.8 | \n44.8 | \n
Other operating (labor, utility, indirect, etc.) cost (million $/year) | \n25.8 | \n7.1 | \n4.4 | \n7.1 | \n
Utility cost (million $/year) | \n21 | \n2.3 | \n1.4 | \n2.3 | \n
Renewable natural gas production cost ($/MMBtu) | \n21.7 | \n17.8 | \n31.6 | \n14.8 | \n
Process and economic assessment for purified biogas production through anaerobic digestion of Cyanothece BG0011 biomass.
On an energy potential basis, 40% of total methane produced per year could support a 50 MW power plant. Current residential electricity price is around 12 cents/kwh, while industrial price is around 7 cents/kwh. As shown in Table 6, the electricity production cost from biogas is 13 cents/kwh. Renewable energy technologies are usually more expensive than fossil fuel technologies. The reasons could be environmental costs associated with fossil fuels that are not paid by the rate payers, mechanical difficulty in bioenergy production, start-up issues and so on. European countries such as Germany and UK governments subsidize the production of renewable energy by introducing feed-in tariffs. These tariffs may be important to make bioenergy industry profitable.
\nItem | \nValue | \n
---|---|
Electricity capacity (million kwh/year) | \n435 | \n
Total capital cost of the CHP system (million $) (including fix capital cost and 10% working capital) | \n52.4 | \n
Capital charges (million $/year) | \n6.2 | \n
Steam credits (million $/year) | \n3.7 | \n
Raw biogas cost (million $/year) | \n47.7 | \n
Other operating cost (million $/year) | \n9.5 | \n
Electricity production cost ($/kwh) | \n0.13 | \n
The economics of biogas—electricity and steam system.
Nutrient (mostly nitrogen and phosphorous) recycling such as utilizing the digestate or wastewater for microalgae cultivation was highlighted in various studies [59, 60, 61, 62, 63, 104, 105, 106, 107, 126, 128, 138, 139]. Recycling the effluent from the anaerobic digester for algae cultivation could mitigate the costs associated with supplying nutrient for algal biomass growth and effluent treatment. Erkelens et al. [59] validated that microalgae
One option to increase algae biomass productivity and its concentration in the culture is to enrich the air with CO2. It has been shown that enriching the air with 1% CO2 increases cell concentration to 3.46 g afdw/L and EPS concentration to 2.91 g afdw/L, giving an algae biomass concentration of at least 6.37 g afdw/L [149], which is 1.33 times more than that used in the case study above. The increased productivity of algae biomass will reduce further the cost for biomass production. The CO2 released from the biogas upgrading process or waste gases from biogas combustion containing CO2 could be recycled to the algae growth ponds for enriching the air. The economic analysis for this scenario was also performed assuming algae biomass concentration is 1.33 times the previous value of 293 ktonne/year. The estimated production cost for
Upgrading biogas by fixation of the CO2 in biogas via photosynthesis by microalgae has been investigated with respect to CO2 removal capability, biomass productivity and O2 desorption minimization [16, 63, 64, 65, 66, 67]. Toledo-Cervantes et al. [16] optimized the biogas upgrading process by studying the influence of the recycling liquid to biogas ratio. The biomethane produced met specification for injection into natural gas grids. However, this technique requires closed photobioreactors. Hydrogen sulfide (H2S) is another contaminant to be removed from the biogas. Hydrogen sulfide removal was realized by the oxidation of H2S to sulfate by sulfur oxidizing bacteria that used the oxygen produced photosynthetically in situ. In this case, the algae-bacteria symbiosis was employed in the photobioreactors [67]. Nutrient recycling and biogas upgrading provides not only the opportunity for AD of microalgal biomass to be cost-effective, but also the potential to reduce the environmental impacts.
\nTo move industrial application of biogas production from microalgal biomass towards commercialization, additional assessment is required regarding large scale operations. These include (1) strain robustness, outdoor productivity, location and seasonal effects, yield from real production systems, and harvesting strategy for algae cultivation (2) for biomass to biogas conversion processes, the conceptual process design needs to take the following factors into consideration: costs associated with digester heating, land, and infrastructure as well as operational parameters such as maintaining pH, temperature, mixing, power consumption, and production of coproducts like fertilizer.
\nThe uncertainty of large-scale algae cultivation is still a challenge which prevents commercialization; process modeling could provide useful information about the performance of microalgae cultivation systems by estimation and optimization of microalgae productivity under different conditions [103]. A growth kinetic model is critical in a process model simulating microalgae cultivation which has a direct impact on downstream conversion processing systems [135] Lee et al. [31] classified the existing kinetic models into three groups: a single limiting substrate (phosphorus, or dissolved CO2 concentration), a physical limiting factor (light intensity or temperature), and multiple factors (e.g. both substrate and light). Based on their study, there was a tradeoff between the accuracy of the model representation and real-world usability. A future modeling framework should consider along with limiting nutrients, integration of light and temperature, and incorporation of species diversity.
\nAD can be integrated to biorefineries which produce high value products from algae such as chemicals for cosmetics, nutraceuticals and pharmaceuticals. This requires diversified business strategies which benchmark the market potential for the total raw materials and alternative products. In the economic perspective, three approaches could be possible for the development of microalgae AD: (1) implementing AD for biogasification of cell debris or waste streams in microalgal based processes such as biodiesel/bioethanol/high-value bioproducts (e.g. PHA)/fuel cell/hydrothermal liquefaction/hydrogen production [68, 120]; (2) investigation of high-value products from intermediate metabolites produced during AD such as carboxylic acids [37]; (3) electricity production from microalgae derived biogas. In previous sections, the cost of electricity from microalgae derived biogas is comparable with market value while cost of the renewable natural gas from microalgae is much higher than the current market value of natural gas.
\nThis chapter reviewed the literature on TEA of biogas production from algae. The key drivers to the overall production cost were identified and possible process improvements to reduce cost were discussed. The need for harmonization of resource, life cycle and techno-economic assessments in the methodology of TEA was highlighted. Modeling efforts, based on well-informed, rigorous engineering-based process models, should be integrated on a baseline framework such that different process technologies, subprocesses and alternative pathways can be directly compared at a system level. TEA model improvements include strategic planning and using reliable input data from simple mass balance calculations to geographically and seasonally specific assessments, as well as risk analysis for large-scale productivity. Nutrient recycling process has the potential to reduce both cost and environmental burdens.
\nThe cultivation of microalgae BG0011 and its economic feasibility as an energy source through anaerobic digestion was evaluated through a techno-economic analysis. The main contribution to the biogas cost is the biomass production cost. The best-case estimate was a biomethane production cost of 14.8 $/MMBtu using covered anaerobic lagoon and high-pressure water scrubbing purification. The cost of electricity production from biogas was estimated to be 13 cents/kwh. Even though these costs are higher than commercial prices in the United States, these are much lower than those costs with production of liquid fuels like ethanol or biodiesel from algae.
\nImproved algal biomass productivities could be essential for lowering the cost of algae-derived biogas. This could be achieved by recycling the CO2 released during biogas upgrading or combustion for algae cultivation. Algal biogas economics could be further improved by marketing the digester sludge as a soil-amendment product, considering that nitrogen in the sludge was fixed from atmospheric dinitrogen.
\nThe authors gratefully acknowledge funding provided by Office of Energy, Florida Department of Agricultural and Consumer Services under contract number 92420 for this project.
\nThe authors declare that there are no potential financial or other interests that could be perceived to influence the outcomes of the research.
Legumes are third largest family of flowering plants which consist of over 20,000 species, under the family of Fabaceae, amoung them
It is an important crop in the semiarid regions across India, Africa and Russia, naturally it is the farmers friendly crop due to crop survival, yield, resistance towards pest, tolerance in sandy soil, low rainfall, whole plant is used as cattle feed and also well-suited to intercropping with other crops as the plant fixes nitrogen with
There are many species can exist but the common sub-species are includes as
The bean crop is cultivated for the purpose of cattle feed and its seeds have high nutrient values, in terms of rich protein, carbohydrates, vitamins and minerals. The cow pea plant green stems and leaves and seed coats are high nutrients, so consider as cattle feed which increase cattle milk production for many application for in the dairy industry. Many varieties cowpeas were traditionally cultivated in some states India, but in Africa consider as primary cultivar. The countries such as United States of America, United Kingdom, Europe and some parts of Asia.
The cowpea was emerging crop for food and Nigeria considered as largest production country approximately 3.67 million tons and the second largest cultivar Niger global production of cowpea are approximately 2.6 million tons and other countries cowpea production is approximately 2 million tons in 2020. The production of cowpea is marginally reduced in globally approximately 8.2 million tons in 2020. Cow pea seeds are usually cooked and made into stews and curries, or ground into flour or paste.
Most cowpeas are grown on the African continent, particularly in Nigeria and Niger and also in India which account for 66% of world production. Recently the estimate suggests that cowpeas are cultivated on 12.5 million hectares of land. The cowpea has been cultivated in tons of three million and two hundred million humans consume in everyday. The large number of production of cowpea in many places should drastically reduction by the involvement of many pests and insects, particularly causing over ninety percentage loss in crop yield.
Kingdom | Plantae |
Sub kingdom | Trcheobionta |
Super division | Spermatophyta |
Division | Magnoliophyta |
Class | Magnoliopsida |
Sub class | Rosidae |
Order | Rosales |
Family | |
Genus | |
Species | |
Sub species |
Source: Wolfram Alpha Knowledge Base, 2011.
The most common important cultivar of cowpea is
The Magnoliopsida class of this plant describes the ovule enclosed with ovary. The order of the plant os Rosales means the petals separated. This group crops family Fabaceae is particularly bean or pea plants. There are many sub species such as
Cowpea are annual, small, sub erect herbaceous plant, have tap root system with root nodules, Glabrous stems and leaves are alternate and compound in nature. The fruits have pods and have cross pollination and epigeal seed germination. Inflorecence is receme, flowers bisexual, have five fused sepals and five free petals. It also has polyandrocium and monocarpellary superior ovary (Figures 1 and 2).
Morphology of Cowpea.
The word ‘cowpea’ primarily mentioned in America on 1798. The bean plant fodder was eagerly eating the cow animals, so the name as cowpea. The common name
Variety of cowpea grown by the many countries of people.
The cow pea does efficiently fix nitrogen to enhance to fertility of the different kinds soils. This is improvement of biodiversity on the particular cow pea growing area. The Excellent Manures are prepared by the senescence parts of long bean crop. The grains and leaves from cow pea food for all animals including humans and cattle, the cattle milk have rich nutrients as well as improve income of people. Below image describe the various uses of cowpea in daily life (Figure 4).
Cow pea consider as source of income and multidimensional applications.
The seeds from cowpea seeds have high content of proteins, carbohydrates, low cholesterol, minerals and vitamins. The leaves of cowpea also have high proteins. The calorie values are higher in cowpea when compare to other crops. The nutrient value will be more and easily cultivable even in polluted or low mineral soils, so many countries consider it is important chief food source.
The homo-polysaccharide starch is present in all crops but in cowpea has to do delay in digestion than other crop starch, so it is good for all animals as well as people consumption. The cowpeas seed have high content of vitamin B9 and helps in reduce or remove inborn error metabolism by means of efficiently develop neural tube in babies like fetus.
The cowpea cultivated by very poor people so it is called poor man crop and easy to grow under high drought conditions. These beans have phyticacids and some metabolic inhibitors which decrease the nutritional value. Some physiological methods like soaking, autoclaving fermentation, seed germination and stage of debranning used for the anti-nutritional properties of the cowpea by to is increased the cowpea nutrient bioavailability in soil.
Although the researchers has been identified importance of nutrients in many crop varieties. The anti-nutritional factors are high in dried seeds in some corps including cowpea than young or old leaves and pods. The below the images (Figure 5) describes the nutrient values, (Figure 6) describes the vitamin constituents and (Figure 7) describes the mineral quantity present in gram/100 gram of dried seeds or leaves.
Nutrient content of cow pea seeds (g/100 g).
Vitamin constituent of cow pea seeds (g/100 g).
Mineral quantity of cow pea seeds (g/100 g).
Vegetable-based food systems are more sustainable than meat-based ones because it requires less energy, land, and water resources. The proteins from pulses is used for the balanced growth with have high nutrients to prevent human diseases and maintain good health [2].
The cowpea plants have highest medical constituents such as high fibers, important vitamins like vitamin B complex, folic acids and vitamin K for to promote good health. Cowpea has more potassium content, it lowers cholesterol, low fat, have high iron and antioxidants and do weight loss (Figure 8). The phenolics and carotenoids components and anthocyanins pigments are involved and maintain good anabolic and catabolic processes [3, 4].
The seed content of cowpea is used for decrease cholesterol formation so to decrease the body weight [5], cowpea seeds also increase blood cells in hematopoiesis to improve cardiac circulation and do efficient food digestion and also reduce digestive problems such as constipation, etc. [6]. The cow plants have lack of glycemic content of index is to delay the digestion of starch, so the cowpea seeds have high dietary fiber involved in insulin metabolism to decrease hunger for approximately in twelve hours [7].
Cowpea regular consumption to cause prevent or reduce many chronic communicable and non communicable diseases [8], the cow pea seeds have high nutrients to remove many gastric syndromes [6]. Cowpea beans also eradicate many heart diseases, decrease cholesterol diseases and improve weight loss [9]. Cowpea seeds or leaves have many medical properties, including reduce carcinoma, improve insulin, reduce lipid diseases, decrease pathogenic infections and remove certain allergies [10, 11].
The high content of carbohydrates, proteins as well as essential amino acids of cowpea seeds used for many health or therapeutic purposes. Cowpea proteins are highly valuable for both consumption and some culinary and some cowpea seed proteins also used for designing the texture of foods [12]. High regular intake of cowpea seeds may reduce blood plasma cholesterol like low density lipoprotein and also reduce non communicable diseases like many cancers and atherosclerosis or myogenic heart diseases [13].
Legume plants are known to form symbioses with extremely broad range of beneficial soil microorganisms (BSM), the examples of almost all plant-microbe mutuality systems present in soil. The different legume plants have interaction with many beneficial microorganisms to increase nutritious, minerals and also do the intake of water from soil via prominent root hairs. (Figure 9).
Cowpea (Legumes) involved in N2 fixation and Carbon fixation. Courtesy: Sanjay K. Jaiswal, Frontiers in Sustainable Food Systems.
Cowpea plants promote the other plant development and also give protection to many bacterial of fungal plant pathogens and insect-pests. In the field of environmental sciences and agricultural aspects, the cowpea consider do most important beneficial legume to do the symbioses, they are bacterial specifically root nodule (RN) symbiosis and the fungi particularly arbuscular mycorrhiza (AM).
These symbioses explains the metabolic and genetically relations with host plants and have that plant growth promoting rhizosphere bacteria which are beneficial endophytic bacteria interact with legumes such as cowpea. The RNS requires a set of genes belonging to common symbiosis signaling pathway (CSSP), most of which are present in all land plants including legumes that can form a second symbiosis with, the fungi arbuscular mycorrhiza (AM) [14, 15].
Legumes are able to fix nitrogen from the air in symbiosis with Rhizobium bacteria [16]. The symbiotic relationship between plants with Rhizobium bacteria or any fungi to do fix the atmospheric nitrogen and compensate the ecological gas balance and also do the growth of crop plants to ultimately improve agriculture and maintain sustainable development of life.
The cowpea plants is eager to absorb high heavy metals such as copper from highly polluted soil or industrial effluents or etc., so it is otherwise called hyper-accumulator plants [17]. The cow pea consider as great pollution eradicator.
Cowpea have morphologically and taxonomically well adapted in tropical countries. The herbaceous nature of cowpea is easy for crop cultivation, crop rotation as well as intercrop. It have able to withstand drought or seasonal conditions. The nutrient aspects have more starchy content to proof that stable food for poor people. Cowpeas have other high protein less fat and high vitamins and minerals for maintain excellent metabolism for consuming organisms. Cowpea do fixing atmospheric nitrogen by to symbiotic relations with microorganisms with do recycling with biogeochemical cycles to enhance atmospheric gases.
The cowpea is one of the the largest cultivator crop among legume family and is commonly cultivated crop in most of the countries globally. The cow pea has highest nutrient values so used for maintain good nutrient calories required for our body. This crop plant is considered as good medical applications to cure or reduce the most non-communicable diseases in animals and or human systems. Cow also pea maintain ecological balances by fixing nitrogen, to chelate heavy metals for water and soil pollution removal and it used as bio-fuel production to improve our green environment and reduce fossil fuels to control other environmental pollutions.
The morphological aspects ultimately deals with, the cowpea is recognized as an annual crop, herbaceous woody and greenish in nature. The seedlings were early to germinate in less moisture conditions. Cowpea leaves are rich in main green pigments such as chlorophyll
The medical aspects enumerate the cowpea is no limit. The cowpea has high potassium to improve cell signaling in animals. The legume lowers the blood glucose level to reduce the diabetes among humans and it lowers blood cholesterol. The seeds have high in full of dietary fibers to regulate metabolism. The fiber content of cowpea is responsible to maintain very low fat in our body. Cowpeas have marvelous cell antioxidant mechanism to remove reactive oxygen species and reduce the effect certain toxic substances due to stress and radiation. The seeds rich in protein contents, the ingredients of cowpea show wonderful circulatory system and improve cardiac muscle function and lower the blood pressure. The black eyed seeds also induce gall bladder bile secretion for digestion purpose. Cowpea role in to decrease body weight, it means that improve weight loss.
The ecological review deals with by symbiotic relationship among various types of beneficial microorganisms. The root nodules of cowpea have to fix atmospheric nitrogen to ammonia with the help of
This is a brief overview of the main steps involved in publishing with IntechOpen Compacts, Monographs and Edited Books. Once you submit your proposal you will be appointed a Author Service Manager who will be your single point of contact and lead you through all the described steps below.
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These geological elements have been evaluated to understand possible cause(s) of rapid decline in pro¬duction. The N5.2 reservoir, located in shallow marine sandstones, offshore Niger Delta, has experienced decline in oil rate with a corresponding increase in water-cut within two years of beginning of production. The main objective of this study is the determination of reservoir architecture in order to individuate the possible cause(s) of rapid production decline. To this aim, several methods have been used, including the seismic attribute analysis, electrofacies analysis, well log and petrophysical correlations. The obtained results show that the N5.2 reservoir is a massive sandy unit, occurring within the paralic Agbada Formation of about 2133 m thick. A contour depth map of the reservoir shows the occurrence of a structural saddle associated with an elongated closure having two structural culminations. Further analysis using the root mean square (RMS) and anti-tracking seismic attributes has indicated a seismic facies parallel to the paleo-coastline direction and several faults and fractures. The high quality of the reservoir, fractures, poor management and water injection may have induced rapid fluid flow and consequently early watercut and decline in production.",book:{id:"7768",slug:"sedimentary-processes-examples-from-asia-turkey-and-nigeria",title:"Sedimentary Processes",fullTitle:"Sedimentary Processes - Examples from Asia, Turkey and Nigeria"},signatures:"Prince Suka Momta",authors:[{id:"228567",title:"Dr.",name:"Prince Suka",middleName:null,surname:"Momta",slug:"prince-suka-momta",fullName:"Prince Suka Momta"}]},{id:"54964",title:"Intermittent Formation, Sedimentation and Deformation History of Cenozoic Forearc Basins along the Northwestern Pacific Margins as an Indicator of Tectonic Scenarios",slug:"intermittent-formation-sedimentation-and-deformation-history-of-cenozoic-forearc-basins-along-the-no",totalDownloads:3244,totalCrossrefCites:4,totalDimensionsCites:7,abstract:"This chapter examines the basin-filling stratigraphy and major unconformity events of the Cenozoic forearc basins in the NE Japan, SW Japan, Ryukyu and Izu-Bonin forearc territories along the northwestern Pacific margins to obtain information on the background tectonic scenarios along the plate subduction zones. The forearc basin type and tectonic history are characteristic for each forearc territory, reflecting the differences in plate tectonic processes. Several major unconformity events seem to be synchronous for a forearc territory or whole forearc territories around Japan, suggesting that these events originated from more or less wider scale plate tectonic events. In the NE Japan forearc territory, the Oligocene unconformity can be the largest events, which transformed the forearc basin styles from the trench slope break-uplifted, fluvial system-dominated type to the tensional, deeper marine sloped type. In the SW Japan and Ryukyu forearc territories, the latest Oligocene to Middle Miocene gap was the transformation phase from the Palaeogene Shimanto-type forearc and accretionary complex, to the Neogene compressive, sloped to ridged forearc basins, developments of which have been interrupted by several unconformity events possibly related to changes in plate tectonic condition. 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In today's highly integrated world, AI promises to become a robust and powerful means for obtaining solutions to previously unsolvable problems. This Series is intended for researchers and students alike interested in this fascinating field and its many applications.",coverUrl:"https://cdn.intechopen.com/series/covers/14.jpg",latestPublicationDate:"July 5th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:9,editor:{id:"218714",title:"Prof.",name:"Andries",middleName:null,surname:"Engelbrecht",slug:"andries-engelbrecht",fullName:"Andries Engelbrecht",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRNR8QAO/Profile_Picture_1622640468300",biography:"Andries Engelbrecht received the Masters and PhD degrees in Computer Science from the University of Stellenbosch, South Africa, in 1994 and 1999 respectively. He is currently appointed as the Voigt Chair in Data Science in the Department of Industrial Engineering, with a joint appointment as Professor in the Computer Science Division, Stellenbosch University. Prior to his appointment at Stellenbosch University, he has been at the University of Pretoria, Department of Computer Science (1998-2018), where he was appointed as South Africa Research Chair in Artifical Intelligence (2007-2018), the head of the Department of Computer Science (2008-2017), and Director of the Institute for Big Data and Data Science (2017-2018). 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He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"23",title:"Computational Neuroscience",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",isOpenForSubmission:!0,editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",slug:"magnus-johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",biography:"Dr Magnus Johnsson is a cross-disciplinary scientist, lecturer, scientific editor and AI/machine learning consultant from Sweden. \n\nHe is currently at Malmö University in Sweden, but also held positions at Lund University in Sweden and at Moscow Engineering Physics Institute. \nHe holds editorial positions at several international scientific journals and has served as a scientific editor for books and special journal issues. \nHis research interests are wide and include, but are not limited to, autonomous systems, computer modeling, artificial neural networks, artificial intelligence, cognitive neuroscience, cognitive robotics, cognitive architectures, cognitive aids and the philosophy of mind. \n\nDr. Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance, and medicine. \n\nWeb page: www.magnusjohnsson.se",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null},{id:"24",title:"Computer Vision",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",isOpenForSubmission:!0,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. Papakostas has received a diploma in Electrical and Computer Engineering in 1999 and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering in 2002 and 2007, respectively, from the Democritus University of Thrace (DUTH), Greece. Dr. Papakostas serves as a Tenured Full Professor at the Department of Computer Science, International Hellenic University, Greece. Dr. Papakostas has 10 years of experience in large-scale systems design as a senior software engineer and technical manager, and 20 years of research experience in the field of Artificial Intelligence. Currently, he is the Head of the “Visual Computing” division of HUman-MAchines INteraction Laboratory (HUMAIN-Lab) and the Director of the MPhil program “Advanced Technologies in Informatics and Computers” hosted by the Department of Computer Science, International Hellenic University. He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,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. 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We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. 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