Academic literature on the topic 'Predicting biogas yields from biomass'
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Journal articles on the topic "Predicting biogas yields from biomass"
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Idika, C., and Aimikhe, Victor Joseph. "Non-linear Regression Models for Predicting Biogas Yields from Selected Bio-wastes." Journal of Energy Research and Reviews 13, no. 2 (March 16, 2023): 42–55. http://dx.doi.org/10.9734/jenrr/2023/v13i2261.
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The benefits of biogas as alternative energy to other fossil fuel sources, due to its renewability, environmentally friendly nature, health benefits, etc., cannot be overemphasized. There are numerous models for predicting biogas production rate from bio-materials, including the modified Gompertz equation. These models are primarily dependent on specific biomass parameters. When any of these parameters, like the slurry volume, changes, another round of experiments must be conducted and curve fitted before biogas yield predictions can be made. This could be time-consuming and costly. Using experimentally published data, simple empirical models can be developed for predicting biogas yields over a range of input parameters. This will eliminate the need for always performing experiments before biogas yield predictions can be made. In light of this, scarce literature provides explicit models for predicting biogas yield over a range of parameters based on published data. This study developed non-linear regression models using published data on parameters that affect biogas yields, like the slurry volume, carbon-to-nitrogen ratio, temperature, total solids, volatile solids, hydraulic retention time, and pH. The data covered seven readily available bio-wastes, including cow dung, cow dung with plant waste, cow dung with poultry dung, poultry dung with grass, pig dung, and plant wastes. On validation of the models, the results showed that the models had a relatively low standard error of estimates, Akaike information criterion, Schwarz criterion, and Hannan-Quinn information criterion. Furthermore, the coefficients of determination, R2, were between 94.62 and 98.93%. The percentage average absolute deviation (% AAD) for each model was less than 7 %. The non-linear models were found to adequately predict the biogas yields within the limits of the available data set.
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Fajobi, Moses Oluwatobi, Olumuyiwa Ajani Lasode, Adekunle Akanni Adeleke, Peter Pelumi Ikubanni, Ayokunle Olubusayo Balogun, and Prabhu Paramasivam. "Prediction of Biogas Yield from Codigestion of Lignocellulosic Biomass Using Adaptive Neuro-Fuzzy Inference System (ANFIS) Model." Journal of Engineering 2023 (February 6, 2023): 1–16. http://dx.doi.org/10.1155/2023/9335814.
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One of the major challenges confronting researchers is how to predict biogas yield because it is a herculean task since research in the field of modeling and optimization of biogas yield is still limited, especially with the adaptive neuro-fuzzy inference system (ANFIS). This study used ANFIS to model and predict biogas yield from anaerobic codigestion of cow dung, mango pulp, and Chromolaena odorata. Asides from the controls, 13 experiments using various agglomerates of the selected substrates were carried out. Cumulatively (for 40 days), the agglomerate that comprised 50% cow dung, 25% mango pulp, and 25% Chromolaena odorata produced the highest volume of biogas, 4750 m3/kg, while the one with 50% cow dung, 12.5% mango pulp, and 37.5% Chromolaena odorata produced the lowest volume of biogas, 630 m3/kg. The data articulated for modeling were those of the optimum biogas yield. Data implemented for modeling comprised two inputs (temperature in Kelvin and pressure in kN/m2) and one output (biogas yield). The Gaussian membership function (Gauss-mf) was implemented for the fuzzification of input variables, while the hybrid algorithm was selected for the learning and mapping of the input-output dataset. The developed ANFIS architecture was simulated at varied membership functions, MFs, and epoch numbers to determine the minimum root mean square error, RMSE, and maximum R-squared R2 values. The one that fulfilled the conditions was considered to be the optimized model. The minimum RMSE and maximum R2 values recorded for the developed model are 14.37 and 0.99784, respectively. The implication is that the model was able to efficiently predict not less than 99.78% of the experimental data. These results prove that the ANFIS model is a reliable tool for modeling data and predicting biogas yield in the biomass anaerobic digestion process. Therefore, the use of the developed ANFIS model is recommended for biogas producers and other allies for predicting biogas yield adequately.
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Bray, Douglas G., Gaurav Nahar, Oliver Grasham, Vishwanath Dalvi, Shailendrasingh Rajput, Valerie Dupont, Miller Alonso Camargo-Valero, and Andrew B. Ross. "The Cultivation of Water Hyacinth in India as a Feedstock for Anaerobic Digestion: Development of a Predictive Model for Scaling Integrated Systems." Energies 15, no. 24 (December 17, 2022): 9599. http://dx.doi.org/10.3390/en15249599.
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A novel, integrated system is proposed for the cultivation and co-digestion of the invasive macrophyte water hyacinth (WH) with cow manure (CM) for the production of biogas for cooking in rural India. This study investigates the pre-treatment approaches and performs a techno-economic analysis of producing biogas in fixeddome digesters as a replacement for liquefied petroleum gas (LPG). Methodologies have been developed for the cultivation of WH collected from wild plants in the Indrayani River, Pune, India. Cultivation trials were performed in 350 litre tanks using water, which was nutrient fed with CM. Cultivation trials were performed over a 3 week period, and growth rates were determined by removing and weighing the biomass at regular time intervals. Cultivation results provided typical yields and growth rates of biomass, allowing predictions to be made for cultivation scaling. Samples of cultivated WH have been co-digested with CM at a 20:80 ratio in 200 L anaerobic digesters, allowing for the prediction of bio-methane yields from fixed-dome anaerobic digesters in real world conditions, which are commonly used in the rural locations of India. A calculator has been developed, allowing us to estimate the scaling requirements for the operation of an integrated biomass cultivation and anaerobic co-digestion unit to produce an equivalent amount of biogas to replace between one and three LPG cylinders per month. A techno-economic analysis of introducing WH into fixed-dome digesters in India demonstrated that the payback periods range from 9 years to under 1 year depending on the economic strategies. To replace between one and three LPG cylinders per month using the discussed feedstock ratio, the cultivation area of WH required to produce sufficient co-feedstock ranges within 10–55 m2.
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Tsapekos, Panagiotis, Panagiotis G. Kougias, and Irini Angelidaki. "Mechanical pretreatment for increased biogas production from lignocellulosic biomass; predicting the methane yield from structural plant components." Waste Management 78 (August 2018): 903–10. http://dx.doi.org/10.1016/j.wasman.2018.07.017.
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Peyrelasse, Christine, Abdellatif Barakat, Camille Lagnet, Prasad Kaparaju, and Florian Monlau. "Anaerobic Digestion of Wastewater Sludge and Alkaline-Pretreated Wheat Straw at Semi-Continuous Pilot Scale: Performances and Energy Assessment." Energies 14, no. 17 (August 30, 2021): 5391. http://dx.doi.org/10.3390/en14175391.
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During the last decade, the application of pretreatment has been investigated to enhance methane production from lignocellulosic biomass such as wheat straw (WS). Nonetheless, most of these studies were conducted in laboratory batch tests, potentially hiding instability problems or inhibition, which may fail in truly predicting full-scale reactor performance. For this purpose, the effect of an alkaline pretreatment on process performance and methane yields from WS (0.10 g NaOH g−1 WS at 90 °C for 1 h) co-digested with fresh wastewater sludge was evaluated in a pilot-scale reactor (20 L). Results showed that alkaline pretreatment resulted in better delignification (44%) and hemicellulose solubilization (62%) compared to untreated WS. Pilot-scale study showed that the alkaline pretreatment improved the methane production (261 ± 3 Nm3 CH4 t−1 VS) compared to untreated WS (201 ± 6 Nm3 CH4 t−1 VS). Stable process without any inhibition was observed and a high alkalinity was maintained in the reactor due to the NaOH used for pretreatment. The study thus confirms that alkaline pretreatment is a promising technology for full-scale application and could improve the overall economic benefits for biogas plant at 24 EUR t−1 VS treated, improve the energy recovery per unit organic matter, reduce the digestate volume and its disposal costs.
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Roland, Gerhards, Bezhin Kostyantyn, and Santel Hans-Joachim. "Sugar beet yield loss predicted by relative weed cover, weed biomass and weed density." Plant Protection Science 53, No. 2 (January 25, 2017): 118–25. http://dx.doi.org/10.17221/57/2016-pps.
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Sugar beet yield loss was predicted from early observations of weed density, relative weed cover, and weed biomass using non-linear regression models. Six field experiments were conducted in Germany and in the Russian Federation in 2012, 2013 and 2014. Average weed densities varied from 20 to 131 with typical weed species compositions for sugar beet fields at both locations. Sugar beet yielded higher in Germany and relative yield losses were lower than in Russia. Data of weed density, relative weed cover, weed biomass and relative yield loss fitted well to the non-linear regression models. Competitive weed species such as Chenopodium album and Amaranthus retroflexus caused more than 80% yield loss. Relative weed cover regression models provided more accurate predictions of sugar beet yield losses than weed biomass and weed density.
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Hadiyanto, H., Figa Muhammad Octafalahanda, Jihan Nabila, Andono Kusuma Jati, Marcelinus Christwardana, Kusmiyati Kusmiyati, and Adian Khoironi. "Preliminary Observation of Biogas Production from a Mixture of Cattle Manure and Bagasse Residue in Different Composition Variations." International Journal of Renewable Energy Development 12, no. 2 (February 9, 2023): 390–95. http://dx.doi.org/10.14710/ijred.2023.52446.
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The need of renewable energy is paramount important as it is expected to replace fossil energy. One of renewable energy commonly used for rural area is biomass-based energy. Biogas is a biomass-based energy where organic materials are converted to methane gas via anaerobic digestion process. The limitations of mono-feedstock biogas are instability digestion process, low yield biogas produced and require readjusting C/N ratio, therefore co-digestion process was proposed to overcome these problems. This study aims to investigate the feasibility of anaerobic co-digestion of a mixture of cattle manure and bagasse residue in different weight ratio combinations. Biogas was generated by anaerobic digestion using a mixed substrate composed of a combination of weight ratios of bagasse:cattle manure (1:5, 1:2, 1:1, and 3:1). The kinetic analysis was evaluated by fitting Gompertz and Logistic model to experimental data of cumulative biogas. The result showed that the combination of 1:5 ratio of bagasse waste to cattle manure obtained the best biogas yield with cumulative biogas at 31,000 mL. The kinetic model of Gompertz and Logistic were able to predict the maximum cumulative biogas at ratio of 1:5 (cattle: bagasse) at 31,157.66 mL and 30,112.12 mL, respectively. The other predictions of kinetic parameters were maximum biogas production rate (Rm)= 1,720.45 mL/day and 1,652.31 mL/day for Gompertz and Logistic model, respectively. Lag periods were obtained at 2.403 day and 2.612 day for Gompertz and Logistic model, respectively. The potential power generation of 338.71 Watt has been estimated from biogas. This research has proven a positive feasibility of co-digestion of two feed-stocks (cattle manure and bagasse) for biogas production.
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Lingner, Stefan, Eiko Thiessen, and Eberhard Hartung. "Aboveground biomass estimation in linear forest objects: 2D- vs. 3D-data." Journal of Forest Science 64, No. 12 (December 20, 2018): 523–32. http://dx.doi.org/10.17221/106/2018-jfs.
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Wood-chips of linear forest objects (hedge banks and roadside plantings) are used as sustainable energy supply in wood-chip heating systems. However, wood yield of linear forest objects is very heterogeneous and hard to estimate in advance. The aim of the present study was to compare the dry mass estimation potentials of two different non-destructive data: (i) Canopy area (derived from aerial images) and mean age at stump level (2D), (ii) volume of vegetation cover based on structure from motion (SfM) via unmanned aerial vehicle (3D). These two types of data were separately used to predict reference dry mass (ground truth) in eleven objects (5 hedge banks and 6 roadside plantings) in Schleswig-Holstein, Germany. The predicting potentials were compared afterwards. The reference dry mass was ascertained by weighing after harvesting and drying samples to constant weight. The model predicting reference dry mass using canopy area and mean age at stump level achieved a relative root mean square error (RMSE) of 52% (42% at larger combined plot sizes). The model predicting reference dry mass using SfM volume achieved a relative RMSE of 30% (16% at larger combined plot sizes). This result indicates that biomass is better described by volume of vegetation cover than by canopy area and age.
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Dahunsi, S. O. "Mechanical pretreatment of lignocelluloses for enhanced biogas production: Methane yield prediction from biomass structural components." Bioresource Technology 280 (May 2019): 18–26. http://dx.doi.org/10.1016/j.biortech.2019.02.006.
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Quezada-Morales, Diana Laura, Juan Campos-Guillén, Francisco Javier De Moure-Flores, Aldo Amaro-Reyes, Juan Humberto Martínez-Martínez, Ricardo Chaparro-Sánchez, Carlos Eduardo Zavala-Gómez, et al. "Effect of Pretreatments on the Production of Biogas from Castor Waste by Anaerobic Digestion." Fermentation 9, no. 4 (April 20, 2023): 399. http://dx.doi.org/10.3390/fermentation9040399.
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Lignocellulosic biomass is a source of carbohydrates that can be used in the production of biogas. The aim of this study was to obtain biogas from biomass waste (leaves, stems and seed bagasse) of Ricinus communis, applying pretreatments such as temperature and humidity. We examined the effect of these pretreatments on the biomass, two enzymatic pretreatments (cellulase and cellobiohydrolase), two chemicals (NaOH and HCl) and two controls (dried castor straw and seed bagasse) on the methane content. The experiment was performed in two anaerobic digestion (AD) assays at a controlled temperature (37 °C) and at room temperature, with a hydraulic retention time (HRT) of 55 days. The results showed that the residues of the seed bagasse produced the highest biogas yields both at room temperature and at the controlled temperature since this material at 37 °C produced 460.63 mL gVS−1 under cellulase pretreatment; at room temperature, the highest level of production was found for the control (263.41 mL gVS−1). The lowest yields at the controlled temperature and room temperature were obtained from residues of Ricinus communis treated with cellobiohydrolase and the seed bagasse treated with alkaline (15.15 mL gVS−1 and 78.51 mL gVS−1, respectively). Meanwhile, the greatest amount of methane was produced by seed bagasse treated with cellobiohydrolase at a controlled temperature (92.2% CH4) and the lowest content of CH4 (15.5%) was obtained at a controlled temperature from castor straw under the control treatment.
Book chapters on the topic "Predicting biogas yields from biomass"
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Shiralipour, Aziz, Paul H. Smith, and Kenneth M. Portier. "Prediction of Methane Yields from Biomass." In Biomass Energy Development, 439–46. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4757-0590-4_35.
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Habyarimana, Ephrem, and Nicole Bartelds. "Yield Prediction in Sorghum (Sorghum bicolor (L.) Moench) and Cultivated Potato (Solanum tuberosum L.)." In Big Data in Bioeconomy, 219–33. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_17.
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AbstractSorghum and potato pilots were conducted in this work to provide a solution to current limitations (dependability, cost) in crop monitoring in Europe. These limations include yield forecasting based mainly on field surveys, sampling, censuses, and the use of coarser spatial resolution satellites. We used the indexes decribing the fraction of absorbed photosynthetically active radiation as well as the leaf areas derived from Sentinel-2 satellites to predict yields and provide farmers with actionable advice in sorghum biomass and, in combination with WOFOST crop growth model, in cultivated potatoes. Overall, the Bayesian additive regression trees method modelled best sorghum biomass yields. The best explanatory variables were days 150 and 165 of the year. In potato, the use of earth observation information allowed to improve the growth model, resulting in better yield prediction with a limited number of field trials. The online platform provided the potato farmers more insight through benchmarking among themselves across cropping seasons, and observing in-field variability Site-specific management became easier based on the field production potential and its performance relative to surrounding fields. The extensive pilots run in this work showed that farming is a business with several variables which not all can be controlled by the farmer. The technologies developed herein are expected to inform about the farming operations, giving rise to well-informed farmers with the advantage to be able to adapt to the circumstances, mitigating production risks, and ultimately staying longer in the business.
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Habyarimana, Ephrem, and Sofia Michailidou. "Genomic Prediction and Selection in Support of Sorghum Value Chains." In Big Data in Bioeconomy, 207–18. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_16.
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AbstractGenomic prediction and selection models (GS) were deployed as part of DataBio project infrastructure and solutions. The work addressed end-user requirements, i.e., the need for cost-effectiveness of the implemented technologies, simplified breeding schemes, and shortening the time to cultivar development by selecting for genetic merit. Our solutions applied genomic modelling in order to sustainably improve productivity and profits. GS models were implemented in sorghum crop for several breeding scenarios. We fitted the best linear unbiased predictions data using Bayesian ridge regression, genomic best linear unbiased predictions, Bayesian least absolute shrinkage and selection operator, and BayesB algorithms. The performance of the models was evaluated using Monte Carlo cross-validation with 70% and 30%, respectively, as training and validation sets. Our results show that genomic models perform comparably with traditional methods under single environments. Under multiple environments, predicting non-field evaluated lines benefits from borrowing information from lines that were evaluated in other environments. Accounting for environmental noise and other factors, also this model gave comparable accuracy with traditional methods, but higher compared to the single environment model. The GS accuracy was comparable in genomic selection index, aboveground dry biomass yield and plant height, while it was lower for the dry mass fraction of the fresh weight. The genomic selection model performances obtained in our pilots are high enough to sustain sorghum breeding for several traits including antioxidants production and allow important genetic gains per unit of time and cost.
Conference papers on the topic "Predicting biogas yields from biomass"
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Wu, Zhiqiang, Shuzhong Wang, Jun Zhao, Lin Chen, and Haiyu Meng. "Investigation on Thermal and Kinetic Characteristics During Co-Pyrolysis of Coal and Lignocellulosic Agricultural Residue." In ASME 2014 Power Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/power2014-32162.
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Co-utilization of coal and lignocellulosic biomass has the potential to reduce greenhouse gases emission from energy production. As a fundamental step of typically thermochemical co-utilization (e.g., co-combustion, co-gasification), co-pyrolysis of coal and lignocellulosic biomass has remarkable effect on the conversation of the further step. Thermal behavior and kinetic analysis are prerequisite for predicting co-pyrolysis performance and modeling co-gasification and co-combustion processes. In this paper, co-pyrolysis behavior of a Chinese bituminous coal blended with lignocellulosic agricultural residue (wheat straw collected from north of China) and model compound (cellulose) were explored via thermogravimetric analyzer. Bituminous coal and lignocellulosic agricultural residue were heated from ambient temperature to 900 °C under different heating rates (10, 20, 40 °C·min−1) with various mass mixing ratios (coal/lignocellulosic agricultural residue ratios of 100, 75/25, 50/50, 25/75 and 0). Activation energy were calculate via iso-conversional method (eg. Kissinger-Akahira-Sunose, Flynn-Wall-Ozawa and Starink methods). The results indicated that pyrolysis rate of coal was accelerated by wheat straw under all mixing conditions. Cellulose promoted the pyrolysis rate of coal under equal or lesser than 50% mass ratio. Some signs about positive or passive synergistic effect were found in char yield. Char yields were lower than that calculated from individual samples for bituminous coal and wheat straw. With the increasing of cellulose mass ratio, the positive synergies on char yields were reduced, resulting in passive synergistic effect especially under higher coal/cellulose mass ratio (25/75). Nonlinearity performance was observed from the distribution of activation energy.
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Straume, Indulis, Imants Plume, Vilis Dubrovskis, Viktors Dreimanis, and Eriks Zukovskis. "Biogas potential from co-fermentation of food leftovers and lignocellulosic biomass at mesophilic temperatures." In 22nd International Scientific Conference Engineering for Rural Development. Latvia University of Life Sciences and Technologies, Faculty of Engineering, 2023. http://dx.doi.org/10.22616/erdev.2023.22.tf081.
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Every year, large amounts of food leftovers are thrown away in catering establishments and households. Industry and agriculture produce lignocellulosic residues, including paper dust and willow biomass, which cause environmental problems if not properly disposed of. The aim of this study is to investigate the biogas and biomethane yields of these biomasses during anaerobic co-fermentation under mesophilic conditions. Biogas yields were determined by co-fermentation of food (hospital canteen, cafeteria, and household) residues and lignocellulosic (paper dust and shredded willow) biomass in a number of 0.72 L bioreactors. All bioreactors were divided into groups having the same content in reactors within each group to ensure the reliability of the results. Groups of bioreactors used for anaerobic fermentation were inoculums (0.5 L) only, inoculums with individual biomass, and inoculums with mixture of two or more biomass. Bioreactors were placed in three different thermostats with 16 bioreactors in each thermostat. Single fill batch anaerobic fermentation (AF) process was provided at 28, 33 and 38 °C. Individual reactor groups were equipped with graphite electrodes connected to DC voltage of 0.7 V. The biogas released in the bioreactor was collected into a gas bag outside the reactor. AF was maintained until gas emission ceased. The highest biogas yield in the AF process was obtained from the bioreactors at a temperature of 38 °C and the lowest at a temperature of 28 °C. Co-fermentation of biomass increased biogas and methane yields compared to AF treatment of individual biomasses. Exposure to the electric field decreased the methane yield. The energy balance on the AF process with the application of the electric field should be calculated by considering also the energy of hydrogen released from substrates with electrodes installed.
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Lin, Leteng, Li Sun, Xiaodong Zhang, Xiaolu Yi, and Min Xu. "Simulation of Hydrogen Production From Biomass Pyrolysis Gas by Secondary Steam Reforming." In ASME Turbo Expo 2008: Power for Land, Sea, and Air. ASMEDC, 2008. http://dx.doi.org/10.1115/gt2008-51045.
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Hydrogen is currently being widely regarded as a futural energy carrier to reduce carbon emissions and other NOx and SOx pollutants. Many researchers have proved that hydrogen can be efficiently used in solid oxide fuel cells -gas turbine system (SOFC-GT) and molten carbonate fuel cells-gas turbine system (MCFC-GT). Hydrogen production from biomass resources offers the advantage of providing a renewable energy carrier for extensive reduction of the CO2 emission. A secondary steam reforming process which consists of steam reforming of methane and water gas shift was proposed to further convert CH4, CO and other hydrocarbons in biomass pyrolysis gas for promoting hydrogen yield. According to respective reaction mechanism, simulating calculations were carried out in two reforming processes separately. With the favor of PRO/II, the effects of reaction temperature and steam to carbon ratio on hydrogen yield were discussed in details in the steam reforming of methane. A reasonable calculation method was established for simulating the water gas shift process in which the effects of temperature and steam to CO ratio was investigated. The simulation made good results in optimizing reaction conditions for two reformers and predicting the volume rate of all gas components. It is proved by simulation that hydrogen-rich gas with >68 mol% H2 could be produced, and the hydrogen yield could reach 48.18 mol H2/(Kg Biomass) and 45.85 mol/(Kg Biomass) respectively when using corn straw and rice husk as feedstock. The experiment data from a related reference was adopted to prove the reasonability of the simulation results which could show the feasibility of secondary steam reforming process, as well as provide good references for practical process operation.
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Lei, Hanwu, Shoujie Ren, James Julson, Lu Wang, Quan Bu, and Roger Ruan. "Microwave Torrefaction of Corn Stover and Tech-Economic Analysis." In ASME 2011 International Manufacturing Science and Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/msec2011-50230.
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Microwave torrefaction of corn stover with particle size of 4 mm was investigated and the effects of reaction temperature and time on the yields of volatile, bio-oil and torrefied biomass were determined. The response surface analysis of the central composite design (CCD) showed that the yields of volatile, bio-oil and torrefied biomass were significantly affected by the reaction temperature and time. Three linear models were developed to predict the yields of conversion products as a function of temperature and time. A first order reaction kinetics was also developed to model the corn stover torrefaction. Ph values of torrefaction bio-oils ranged from 2.3 to 2.76 which were similar to those of bio-oils from biomass pyrolysis. GC/MS analysis for torrefaction bio-oils showed that the organic acid was about 2.16% to 12.00%. The torrefaction bio-oils also contain valuable chemical compounds such as phenols, furan derivatives and aliphatic hydrocarbons determined by a GC/MS. There are no aromatic compounds and polycyclic aromatic hydrocarbons (PAHs) detected in the torrefaction bio-oils. The torrefaction biogas was mainly consisted of ch4, c2h6, c3h8, which was about 56 wt% of the total bio-gas. The biogas can be used for chemical synthesis or electricity generation. The heating values of torrefied biomass were from 18.64–22.22 MJ/kg depending on the process conditions. The heating values of torrefied biomass were significantly greater than those of raw biomass and similar to those of coals. The energy yields of torrefied biomass from 87.03–97.87% implied that most energy was retained in the torrefied biomass. Economic analysis indicated that the biomass microwave torrefaction plant located in a farm is profitable.
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Kessler, Travis, Thomas Schwartz, Hsi-Wu Wong, and J. Hunter Mack. "Evaluating Diesel/Biofuel Blends Using Artificial Neural Networks and Linear/Nonlinear Equations." In ASME 2021 Internal Combustion Engine Division Fall Technical Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/icef2021-67785.
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Abstract The use of biomass-derived additives in diesel fuel mixtures has the potential to increase the fuel’s efficiency, decrease the formation of particulate matter during its combustion, and retain the fuel’s behavior in cold weather. To this end, identifying compounds that enable these behaviors is paramount. The present work utilizes a series of linear and non-linear equations in series with artificial neural networks to predict the cetane number, yield sooting index, kinematic viscosity, cloud point, and lower heating value of multi-component blends. Property values of pure components are predicted using artificial neural networks trained with existing experimental data, and these predictions and their expected errors are propagated through linear and non-linear equations to obtain property predictions for multi-component blends. Individual component property prediction errors, defined by blind prediction median absolute error, are 4.91 units, 7.84 units, 0.06 cSt, 4.00 °C, and 0.55 MJ/kg for cetane number, yield sooting index, kinematic viscosity, cloud point, and lower heating value respectively. On average, property predictions for blends are shown to be accurate to within 6% of the blends’ experimental values. Further, a multitude of compounds expected to be produced from catalytically upgrading products of fast pyrolysis are evaluated with respect to their behavior in diesel fuel blends.
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Mayor, J. Rhett, and Alexander Williams. "Investigation Into the Effects of Reaction Duration on the Isothermal Fast Pyrolysis of Biomass." In ASME 2009 3rd International Conference on Energy Sustainability collocated with the Heat Transfer and InterPACK09 Conferences. ASMEDC, 2009. http://dx.doi.org/10.1115/es2009-90405.
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Bio-oils were produced within a fast-pyrolysis micro-reactor at 400°C from Loblolly Pine (Pinus Taeda) with varying residence times. This preliminary study has considered two boundary values for the residence time, evaluating the products of the reaction at 20 seconds and 120 seconds. The collected bio-oils were analyzed for their calorific values (LHV) and biomass conversion efficiencies. Heating rates greater than 100°C/s were achieved for the biomass, allowing for isothermal conditions to exist throughout the majority of the reaction despite short residence times. This study shows the effect that reaction duration has on the mass of the bio-oil yield and energy content present for the isothermal fast pyrolysis of Loblolly Pine and evaluates the predictive capabilities of TGA derived Arrhenius coefficients.
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Kessler, Travis, Thomas Schwartz, Hsi-Wu Wong, and J. Hunter Mack. "Predicting the Cetane Number, Yield Sooting Index, Kinematic Viscosity, and Cloud Point for Catalytically Upgraded Pyrolysis Oil Using Artificial Neural Networks." In ASME 2020 Internal Combustion Engine Division Fall Technical Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/icef2020-2978.
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Abstract The conversion of biomass using fast pyrolysis has the potential to be significantly less expensive at scale compared to alternative methods such as fermentation and gasification. Selective upgrading of the products of fast pyrolysis through chemical catalysis produces compounds with lower oxygen content and lower acidity; however, identifying the specific catalytic pathways for producing viable fuels and fuel additives often requires a trial-and-error approach. Specifically, key properties of the compounds must be experimentally tested to evaluate the viability of the resultant compounds. The present work proposes predictive models constructed with artificial neural networks (ANNs) for cetane number (CN), yield sooting index (YSI), kinematic viscosity (KV), and cloud point (CP), with blind test set median absolute errors of 5.14 cetane units, 3.36 yield sooting index units, 0.07 millimeters squared per second, and 4.89 degrees Celsius, respectively. Furthermore, the cetane number, yield sooting index, kinematic viscosity, and cloud point were predicted for over three hundred expected products from the catalytic upgrading of pyrolysis oil. It was discovered that 130 of these compounds have predicted cetane numbers greater than 40, with four of these compounds possessing predicted yield sooting index values significantly less than that of diesel fuel and predicted viscosities and cloud points comparable to that of diesel fuel.
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Green, Alex E. S., and Sean M. Bell. "Pyrolysis in Waste to Energy Conversion (WEC)." In 14th Annual North American Waste-to-Energy Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/nawtec14-3196.
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Solid waste (SW), mostly now wasted biomass, could fuel approximately ten times more of USA’s increasing energy needs than it currently does. At the same time it would create good non-exportable jobs, and local industries. Twenty four examples of wasted or under-utilized solids that contain appreciable organic matter are listed. Estimates of their sustainable tonnage lead to a total SW exceeding 2 billion dry tons. Now usually disposal problems, most of these SW’s, can be pyrolyzed into substitutes for or supplements to expensive natural gas. The large proportion of biomass (carbon dioxide neutral plant matter) in the list reduces Greenhouse problems. Pyrolysis converts such solid waste into a medium heating value gaseous fuel usually with a small energy expenditure. With advanced gas cleaning technologies the pyrogas can be used in high efficiency gas turbines or fuel cells systems. This approach has important environmental and efficiency advantages with respect to direct combustion in boilers and even air blown or oxygen blown partial combustion gasifiers. Since pyrolysis is still not a predictive science the CCTL has used an analytical semi-empirical model (ASEM) to organize experimental measurements of the yields of various product {CaHbOc} yields vs temperature (T) for r dry ash, nitrogen and sulfur free (DANSF) feedstock having various weight % of oxygen [O] and hydrogen [H]. With this ASEM each product is assigned 5 parameters (W, T0, D, p, q) in a robust analytical Y(T) expression to represent yields vs. temperature of any specific product from any specified feedstock. Patterns in the dependence of these parameters upon [O], [H], a, b, and c suggest that there is some order in pyrolysis yields that might be useful in optimize the throughput of particular pyrolysis systems used for waste to energy conversion (WEC). An analytical cost estimation (ACE) model is used to calculate the cost of electricity (COE) vs the cost of fuel (COF) for a SW pyrogas fired combined cycle (CC) system for comparison with the COE vs COF for a natural gas fired CC system. It shows that high natural gas prices solid waste can be changed from a disposal cost item to a valuable asset. Comparing COEs when using other SW capable technologies are also facilitated by the ACE method. Implications of this work for programs that combine conservation with waste to energy conversion in efforts to reach Zero Waste are discussed.
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Kessler, Travis, Thomas Schwartz, Hsi-Wu Wong, and J. Hunter Mack. "Screening Compounds for Fast Pyrolysis and Catalytic Biofuel Upgrading Using Artificial Neural Networks." In ASME 2019 Internal Combustion Engine Division Fall Technical Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/icef2019-7170.
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Abstract There is significant interest among researchers in finding economically sustainable alternatives to fossil-derived drop-in fuels and fuel additives. Fast pyrolysis, a method for converting biomass into liquid hydrocarbons with the potential for use as fuels or fuel additives, is a promising technology that can be two to three times less expensive at scale when compared to alternative approaches such as gasification and fermentation. However, many bio-oils directly derived from fast pyrolysis have a high oxygen content and high acidity, indicating poor performance in diesel engines when used as fuels or fuel additives. Thus, a combination of selective fast pyrolysis and chemical catalysis could produce tuned bioblendstocks that perform optimally in diesel engines. The variance in performance for derived compounds introduces a feedback loop in researching acceptable fuels and fuel additives, as various combustion properties for these compounds must be determined after pyrolysis and catalytic upgrading occurs. The present work aims to reduce this feedback loop by utilizing artificial neural networks trained with quantitative structure-property relationship values to preemptively screen pure component compounds that will be produced from fast pyrolysis and catalytic upgrading. The quantitative structure-property relationship values selected as inputs for models are discussed, the cetane number and sooting propensity of compounds derived from the catalytic upgrading of phenol are predicted, and the viability of these compounds as fuels and fuel additives is analyzed. The model constructed to predict cetane number has a test set prediction root-mean-squared error of 9.874 cetane units, and the model constructed to predict yield sooting index has a test set prediction root-mean-squared error of 13.478 yield sooting index units (on the unified scale).
Reports on the topic "Predicting biogas yields from biomass"
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Hamada, Yuki, Colleen Zumpf, and John Quinn. Predicting Switchgrass Biomass Yields Using a Spectral Vegetation Index Derived from Multispectral Satellite Imagery. Office of Scientific and Technical Information (OSTI), October 2022. http://dx.doi.org/10.2172/1992815.
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Anderson, Gerald L., and Kalman Peleg. Precision Cropping by Remotely Sensed Prorotype Plots and Calibration in the Complex Domain. United States Department of Agriculture, December 2002. http://dx.doi.org/10.32747/2002.7585193.bard.
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This research report describes a methodology whereby multi-spectral and hyperspectral imagery from remote sensing, is used for deriving predicted field maps of selected plant growth attributes which are required for precision cropping. A major task in precision cropping is to establish areas of the field that differ from the rest of the field and share a common characteristic. Yield distribution f maps can be prepared by yield monitors, which are available for some harvester types. Other field attributes of interest in precision cropping, e.g. soil properties, leaf Nitrate, biomass etc. are obtained by manual sampling of the filed in a grid pattern. Maps of various field attributes are then prepared from these samples by the "Inverse Distance" interpolation method or by Kriging. An improved interpolation method was developed which is based on minimizing the overall curvature of the resulting map. Such maps are the ground truth reference, used for training the algorithm that generates the predicted field maps from remote sensing imagery. Both the reference and the predicted maps are stratified into "Prototype Plots", e.g. 15xl5 blocks of 2m pixels whereby the block size is 30x30m. This averaging reduces the datasets to manageable size and significantly improves the typically poor repeatability of remote sensing imaging systems. In the first two years of the project we used the Normalized Difference Vegetation Index (NDVI), for generating predicted yield maps of sugar beets and com. The NDVI was computed from image cubes of three spectral bands, generated by an optically filtered three camera video imaging system. A two dimensional FFT based regression model Y=f(X), was used wherein Y was the reference map and X=NDVI was the predictor. The FFT regression method applies the "Wavelet Based", "Pixel Block" and "Image Rotation" transforms to the reference and remote images, prior to the Fast - Fourier Transform (FFT) Regression method with the "Phase Lock" option. A complex domain based map Yfft is derived by least squares minimization between the amplitude matrices of X and Y, via the 2D FFT. For one time predictions, the phase matrix of Y is combined with the amplitude matrix ofYfft, whereby an improved predicted map Yplock is formed. Usually, the residuals of Y plock versus Y are about half of the values of Yfft versus Y. For long term predictions, the phase matrix of a "field mask" is combined with the amplitude matrices of the reference image Y and the predicted image Yfft. The field mask is a binary image of a pre-selected region of interest in X and Y. The resultant maps Ypref and Ypred aremodified versions of Y and Yfft respectively. The residuals of Ypred versus Ypref are even lower than the residuals of Yplock versus Y. The maps, Ypref and Ypred represent a close consensus of two independent imaging methods which "view" the same target. In the last two years of the project our remote sensing capability was expanded by addition of a CASI II airborne hyperspectral imaging system and an ASD hyperspectral radiometer. Unfortunately, the cross-noice and poor repeatability problem we had in multi-spectral imaging was exasperated in hyperspectral imaging. We have been able to overcome this problem by over-flying each field twice in rapid succession and developing the Repeatability Index (RI). The RI quantifies the repeatability of each spectral band in the hyperspectral image cube. Thereby, it is possible to select the bands of higher repeatability for inclusion in the prediction model while bands of low repeatability are excluded. Further segregation of high and low repeatability bands takes place in the prediction model algorithm, which is based on a combination of a "Genetic Algorithm" and Partial Least Squares", (PLS-GA). In summary, modus operandi was developed, for deriving important plant growth attribute maps (yield, leaf nitrate, biomass and sugar percent in beets), from remote sensing imagery, with sufficient accuracy for precision cropping applications. This achievement is remarkable, given the inherently high cross-noice between the reference and remote imagery as well as the highly non-repeatable nature of remote sensing systems. The above methodologies may be readily adopted by commercial companies, which specialize in proving remotely sensed data to farmers.
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Asvapathanagul, Pitiporn, Leanne Deocampo, and Nicholas Banuelos. Biological Hydrogen Gas Production from Food Waste as a Sustainable Fuel for Future Transportation. Mineta Transportation Institute, July 2022. http://dx.doi.org/10.31979/mti.2021.2141.
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In the global search for the right alternative energy sources for a more sustainable future, hydrogen production has stood out as a strong contender. Hydrogen gas (H2) is well-known as one of the cleanest and most sustainable energy sources, one that mainly yields only water vapor as a byproduct. Additionally, H2 generates triple the amount of energy compared to hydrocarbon fuels. H2 can be synthesized from several technologies, but currently only 1% of H2 production is generated from biomass. Biological H2 production generated from anaerobic digestion is a fraction of the 1%. This study aims to enhance biological H2 production from anaerobic digesters by increasing H2 forming microbial abundance using batch experiments. Carbon substrate availability and conversion in the anaerobic processes were achieved by chemical oxygen demand and volatile fatty acids analysis. The capability of the matrix to neutralize acids in the reactors was assessed using alkalinity assay, and ammonium toxicity was monitored by ammonium measurements. H2 content was also investigated throughout the study. The study's results demonstrate two critical outcomes, (i) food waste as substrate yielded the highest H2 gas fraction in biogas compared to other substrates fed (primary sludge, waste activated sludge and mixed sludge with or without food waste), and (ii) under normal operating condition of anaerobic digesters, increasing hydrogen forming bacterial populations, including Clostridium spp., Lactococcus spp. and Lactobacillus spp. did not prolong biological H2 recovery due to H2 being taken up by other bacteria for methane (CH4) formation. Our experiment was operated under the most optimal condition for CH4 formation as suggested by wastewater operational manuals. Therefore, CH4-forming bacteria possessed more advantages than other microbial populations, including H2-forming groups, and rapidly utilized H2 prior to methane synthesis. This study demonstrates H2 energy renewed from food waste anaerobic digestion systems delivers opportunities to maximize California’s cap-and-trade program through zero carbon fuel production and utilization.
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Asvapathanagul, Pitiporn, Leanne Deocampo, and Nicholas Banuelos. Biological Hydrogen Gas Production from Food Waste as a Sustainable Fuel for Future Transportation. Mineta Transportation Institute, July 2022. http://dx.doi.org/10.31979/mti.2022.2141.
Full textAbstract:
In the global search for the right alternative energy sources for a more sustainable future, hydrogen production has stood out as a strong contender. Hydrogen gas (H2) is well-known as one of the cleanest and most sustainable energy sources, one that mainly yields only water vapor as a byproduct. Additionally, H2 generates triple the amount of energy compared to hydrocarbon fuels. H2 can be synthesized from several technologies, but currently only 1% of H2 production is generated from biomass. Biological H2 production generated from anaerobic digestion is a fraction of the 1%. This study aims to enhance biological H2 production from anaerobic digesters by increasing H2 forming microbial abundance using batch experiments. Carbon substrate availability and conversion in the anaerobic processes were achieved by chemical oxygen demand and volatile fatty acids analysis. The capability of the matrix to neutralize acids in the reactors was assessed using alkalinity assay, and ammonium toxicity was monitored by ammonium measurements. H2 content was also investigated throughout the study. The study's results demonstrate two critical outcomes, (i) food waste as substrate yielded the highest H2 gas fraction in biogas compared to other substrates fed (primary sludge, waste activated sludge and mixed sludge with or without food waste), and (ii) under normal operating condition of anaerobic digesters, increasing hydrogen forming bacterial populations, including Clostridium spp., Lactococcus spp. and Lactobacillus spp. did not prolong biological H2 recovery due to H2 being taken up by other bacteria for methane (CH4) formation. Our experiment was operated under the most optimal condition for CH4 formation as suggested by wastewater operational manuals. Therefore, CH4-forming bacteria possessed more advantages than other microbial populations, including H2-forming groups, and rapidly utilized H2 prior to methane synthesis. This study demonstrates H2 energy renewed from food waste anaerobic digestion systems delivers opportunities to maximize California’s cap-and-trade program through zero carbon fuel production and utilization.
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Pullammanappallil, Pratap, Haim Kalman, and Jennifer Curtis. Investigation of particulate flow behavior in a continuous, high solids, leach-bed biogasification system. United States Department of Agriculture, January 2015. http://dx.doi.org/10.32747/2015.7600038.bard.
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Recent concerns regarding global warming and energy security have accelerated research and developmental efforts to produce biofuels from agricultural and forestry residues, and energy crops. Anaerobic digestion is a promising process for producing biogas-biofuel from biomass feedstocks. However, there is a need for new reactor designs and operating considerations to process fibrous biomass feedstocks. In this research project, the multiphase flow behavior of biomass particles was investigated. The objective was accomplished through both simulation and experimentation. The simulations included both particle-level and bulk flow simulations. Successful computational fluid dynamics (CFD) simulation of multiphase flow in the digester is dependent on the accuracy of constitutive models which describe (1) the particle phase stress due to particle interactions, (2) the particle phase dissipation due to inelastic interactions between particles and (3) the drag force between the fibres and the digester fluid. Discrete Element Method (DEM) simulations of Homogeneous Cooling Systems (HCS) were used to develop a particle phase dissipation rate model for non-spherical particle systems that was incorporated in a two-fluid CFDmultiphase flow model framework. Two types of frictionless, elongated particle models were compared in the HCS simulations: glued-sphere and true cylinder. A new model for drag for elongated fibres was developed which depends on Reynolds number, solids fraction, and fibre aspect ratio. Schulze shear test results could be used to calibrate particle-particle friction for DEM simulations. Several experimental measurements were taken for biomass particles like olive pulp, orange peels, wheat straw, semolina, and wheat grains. Using a compression tester, the breakage force, breakage energy, yield force, elastic stiffness and Young’s modulus were measured. Measurements were made in a shear tester to determine unconfined yield stress, major principal stress, effective angle of internal friction and internal friction angle. A liquid fludized bed system was used to determine critical velocity of fluidization for these materials. Transport measurements for pneumatic conveying were also assessed. Anaerobic digestion experiments were conducted using orange peel waste, olive pulp and wheat straw. Orange peel waste and olive pulp could be anaerobically digested to produce high methane yields. Wheat straw was not digestible. In a packed bed reactor, anaerobic digestion was not initiated above bulk densities of 100 kg/m³ for peel waste and 75 kg/m³ for olive pulp. Interestingly, after the digestion has been initiated and balanced methanogenesis established, the decomposing biomass could be packed to higher densities and successfully digested. These observations provided useful insights for high throughput reactor designs. Another outcome from this project was the development of low cost devices to measure methane content of biogas for off-line (US$37), field (US$50), and online (US$107) applications.