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The need to replace nonrenewable sources is a global challenge, especially for emerging countries with financial constraints to invest in energy transition. In Brazil, electrical generation is predominantly renewable, since the hydraulic source is responsible for more than half of the domestic electricity supply. Other alternative sources, such as biomass, solar, and wind, complement the renewable portion of the Brazilian electrical matrix. However, periods of drought and climate disturbances threaten the stability of the electrical system and increase dependence on thermal generation. Based on the global movement to intensify the use of renewable sources, which are volatile and influenced by climate variations, the objective of this research is to predict electricity generation using the Heston model. This model, originally applied to predict option prices, assumes nonconstant volatility and has the ability to estimate the average value including the stochastic volatility process. The database is made up of monthly values of the volume of electrical energy generation from biomass, coal, fuel oil, hydraulic, industrial waste, natural gas, nuclear, solar, and wind sources. Compared to the Black‐Scholes model, the chosen method proved to be superior in predicting the generation from coal, hydropower, and solar energy, according to the MAE, MAPE, and RMSE statistics. We conclude that a financial model can be effectively applied to the energy sector and the Heston model can be a useful instrument to assist in the planning and management of the national electrical system. One suggestion for the Brazilian case is to encourage the use of alternative energies and low‐carbon sources, such as solar, wind, and natural gas, providing greater energy security and a more reliable electrical system. [ABSTRACT FROM AUTHOR]

The success of anaerobic digestion (AD) process for biogas production is contingent upon complex mix of operating factors, process conditions, and feedstock types, which could be affected by inadequate understanding of microbial, kinetic, and physicochemical processes. To address these limitations, efforts have been directed toward developing mathematical and intelligent models. Although mathematical models provide near-optimal solutions, they are time consuming, highly expensive, and demanding. Intelligent standalone models are also limited by their low predictive capability and inability to guarantee global optimal solution for the prediction of cumulative biogas yield for FFV waste. However, hyperparameter optimization of such models is essential to improve the prediction performance for cumulative biogas yield for FFV waste. Therefore, this study applies a genetic algorithm (GA) to optimize an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of cumulative biogas production. Seven (7) input variables, organic loading rate (OLR), volatile solids (VS), pH, hydraulic retention time (HRT), temperature, retention time, and reaction volume, were considered with cumulative biogas production as the output. The effect of varying clustering techniques was evaluated. The three (3) clustering techniques evaluated are fuzzy c-means and subtractive clustering and grid partitioning. The hybrid model was evaluated based on some verified statistical performance metrics. Optimal root mean squared error (RMSE), mean absolute deviation (MAD), mean absolute percentage error (MAPE), and standard deviation error (error STD) of 0.0529, 0.0326,7.6742, and 0.0474, respectively, were reported at the model testing phase for the subtractive clustering technique being the best-performing model. The results confirm the capacity of hybrid evolutionary (genetic) algorithm based on subtractive clustering technique to predict the biogas yield from FFV and serve as an effective tool for the upscaling of anaerobic digestion units as well as in techno-economic studies toward more efficient energy utilization. [ABSTRACT FROM AUTHOR]

This study investigates the influence of wavelet decomposition on standalone adaptive neurofuzzy inference system (ANFIS) and its hybrid with evolutionary algorithms [genetic algorithm (GA) and particle swarm optimization (PSO)] at a medium-term temporal scale. Time series data of global horizontal irradiance used as inputs was decomposed according to 5-level wavelet sub-signals and modeled independently. Statistical metrics were used to evaluate the models, and these results were compared for all models. From our findings, the wavelet-based models incurred more computational time and lesser accuracy compared to the ANFIS and its hybrid models. ANFIS-GA and its wavelet hybrid had a high computational time of 7 s and 15.2 s, respectively., Among the six models, the standalone ANFIS model outperformed its hybrids with a root mean square error (RMSE) of 13.07, normalized root mean square error (NRMSE) of 0.11%, mean absolute percentage error (MAPE) of 4.57%, variance accounted for (VAF) of 92.03%, relative coefficient of variation (RCoV) of 0.24, and computational time (CT) of 3 s. The integration of wavelet decomposition reduced the performance of the standalone ANFIS and its hybrids with wavelet-based PSO-ANFIS recording the least model accuracy (RMSE = 31.02, NRMSE = 0.19, MAPE = 15.31, VAF = 55.89, RCoV = 0.21, CT = 8.4 s). Overall, standalone machine learning models could be more efficient, computationally less intensive, and computationally less costly than their hybrids if their hyperparameters are appropriately tuned. [ABSTRACT FROM AUTHOR]

The viability of thermal waste-to-energy (WTE) plants and its optimal performance have informed intelligent predictive modelling of its significant variables critical to optimal energy recovery and plant operational planning using machine learning approach. However, the optimality of hyper-parameters is significant to accurate modelling of combustion enthalpy of waste in neuro-fuzzy models. In this study, the significant effect of hyper-parameters tuning of different clustering techniques, vis-à-vis fuzzy c-means (FCM), subtractive clustering (SC) and grid partitioning (GP), on the performance of the ANFIS model in its standalone and hybridized form was investigated. The ANFIS model was optimized with two evolutionary algorithms, namely particle swarm optimization (PSO) and genetic algorithm (GA), for predicting the lower heating value (LHV) of waste using the city of Johannesburg as a case study. The optimal model for LHV prediction was selected based on minimum error criteria after testing the models' performance using relevant statistical metrics like root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute deviation (MAD), relative mean bias error (rMBE) and coefficient of variation (RCoV). The result revealed a better performance of the hybridized ANFIS model than the standalone ANFIS model. Also, a significant variation in all models' performance at different clustering technique was noted. However, all GP-clustered models gave the most accurate prediction than others. The most accurate model was obtained using a GP-clustered PSO-ANFIS model with triangular input membership function (tri-MF) giving RMSE, MAD, MAPE, rMBE and RCoV values of 0.139, 0.064, 2.536, 0.071 and 0.181, respectively. This study established the significance of municipality-based LHV prediction model to enhance the efficiency of thermal WTE plants and the robustness of evolutionary-based neuro-fuzzy model for heating value prediction. [ABSTRACT FROM AUTHOR]

Parameter optimization is significant to a successful laser metal deposition (LMD). While conventional optimization methods have been used, the prowess of soft computing techniques is still less explored in LMD towards ensuring reduced experimental costs and throughput. This study develops a process optimization and wear volume prediction model for Ti-6Al-4 V using soft computing techniques. The particle swarm optimization (PSO) model was used to optimize a single objective function to determine optimal process parameters. A supervised learning model using artificial neural network (ANN) was developed to predict the wear volume from known process parameters. The model hyperparameters were tuned by several trials until optimal parameters were obtained. The ANN model was trained and tested with 70% and 30% of the dataset, respectively. The ANN model was evaluated using known statistical performance metrics and user-friendly interfaces, where process optimization can be carried out within upper and lower design bounds, which were developed for the two intelligent models. From the model evaluation result, a root mean square error (RMSE) of 0.0052, mean absolute deviation (MAD) of 0.0031, coefficient of determination (R2) of 0.9733, and a mean absolute percentage error (MAPE) of 14.0152 was obtained from the model testing phase. Overall, soft computing techniques prove helpful in ensuring process integrity, efficient, and cost-effective LMD. [ABSTRACT FROM AUTHOR]

Due to the inherent multiple response characteristics in many biological and separation processes, parameter optimization and modelling is usually a daunting task. The integration of Deng's grey incidence model (GRA) and Taguchi optimization (TM) therefore helps in transforming multiple quality characteristics into a single response presented as the grey relational grade (GRG). This was applied to optimize the multiple quality response characteristics in the maceration‐assisted extraction of African cucumber leaves. Two responses and five design factors were selected with L16(25) layout using signal‐to‐noise ratio as a point prediction feature. Under the optimized conditions, the optimum total phenolic content and antioxidant capacity of 0.8569 mg/ml gallic acid equivalence and 0.9259 mg/ml were achieved, respectively. The mass ratio was the highest contributor (38.2%), whereas the maceration time presented the least contribution (9.8%) to the cumulative response grade (GRG). In the neural network analysis, three models were deployed: Levenberg Marquardt backpropagation neural network (LMNN), gradient descent with adaptive learning rate neural network (GDALRNN), and the resilient back‐propagation neural network (RPNN). A better prediction of hold‐out data was achieved with the GDALRNN model, generating lesser absolute deviation error (MADGDALRNN = 0.099), root mean square error (RMSEGDALRNN = 0.1033), relative mean bias error (rMBEGDALRNN = − 0.24), and highest computational time (CTGDALRNN = 8.8), which is expected of an effective model. Based on the GRG and the signal‐to‐noise ratio, the optimum conditions and the neural network model succinctly provided a benchmark for future assessment of complex relationship among extraction variables, which could form the basis for a potential future scale‐up applications. [ABSTRACT FROM AUTHOR]

COVID-19 health crisis emerges at a time when African continent is pushing to increase her renewable energy (RE) uptake. To varying degrees, RE is susceptible to the global disruption engendered by COVID-19 pandemic, given its considerable impact and associated uncertainties related to economy and disruption of manufacturing processes. Therefore, there arises a need to highlight the possible impact of COVID-19 on the RE sector in Africa. Based on extensive desktop analysis and semi-structured interviews, this study discusses the socio-economic and policy impact of COVID-19 global pandemic on RE exploration. Most respondents suggested that their income level was largely unaffected, though the activities of 16% respondents in RE exploration decreased by more than 25%. Our study indicated that supply chain disruption, health and safety challenge, and financial risk, among other risks, were posed by COVID-19 pandemic. Overall, the opinion of the respondents varied on the summative impact of COVID-19 on the RE exploration in Africa. 40% of the respondents expressed that the pandemic affected their organization negatively, while 32% were positive on how the pandemic affected their business activities and income. Finally, the study proposed key priorities and strategies, such as digital transformation, domestication, and indigenization of RE technologies that can be deployed to accelerate renewable energy growth in the post-COVID-19 global pandemic era. This study will provide appropriate guidance for governments and renewable energy industries toward an accelerated uptake of renewable energy in Africa. [ABSTRACT FROM AUTHOR]

Biomass represents vast under-explored feedstock for energy generation across the globe. Among other factors, the location from where the feedstock is harvested may affect the overall properties and the efficiency of bioreactors used in the conversion process. Herein is reported some physicochemical properties, the kinetic study and thermodynamic analysis of corn cob sourced from two major economies in sub-Sahara African region. Brunauer Emmett and Teller (BET) analysis was performed to investigate the surface characteristics of corn cob while Fourier transform infrared (FTIR) spectroscopy revealed the corresponding functional group present in the selected biomass residue. The proximate and CHNSO analyses were performed using the standard equipment and following the standard procedures, then the result is reported and compared based on the geographical locations under consideration. Also, the thermal decomposition study was carried out at different heating rates (10, 15, 30 °C min−1) in inert atmosphere while the kinetic parameters were evaluated based on Flynn–Wall–Ozawa (FWO) and Kissinger–Akahira–Sunose (KAS) methods. The analysis of variance (ANOVA) showed that there is a statistically significant difference between ultimate constituents, the fixed carbon, and volatile matter obtained from the two countries at 95% confidence level. FTIR spectroscopy showed different spectra peak in both samples which means there are varying quantity of structural elements in each feedstock. The pore surface area (1.375 m2/g) obtained for corn cob from South Africa (SC25) was greater than the value (1.074 m2/g) obtained for Nigeria (NC25). From the result, the highest value of activation energy (Ea = 190.1 kJ mol−1 and 189.9 kJ mol−1) was estimated for SC25 based on KAS and FWO methods, respectively. The result showed that the geographical location may somewhat affect some energetic properties of biomass and further provides useful information about thermodynamic and kinetic parameters which could be deployed in the simulation, optimization, and scale-up of the bioreactors for pyrolysis process. [ABSTRACT FROM AUTHOR]

Energy generation from wind resources is now a mature technology with the ability to compete with traditional energy sources at utility scales in many countries, through the identification of suitable sites. However, beyond site suitability, predicting the wind resource variability of the potentially viable site presents overarching benefits in strategic and operational planning prior to site development. This study, therefore, combines geographical information systems multicriteria decision-making (GIS-MCDM) and hybrid neurofuzzy modeling tools for site suitability and resource variability forecast, respectively, in the Eastern Cape Province of South Africa. The GIS model uses two factors (climatological and environmental), and analytical hierarchical process was used for evaluating criteria degree of influence. Wind resource variability using diurnal satellite-based data for the candidate site was used on the models. Adaptive neurofuzzy inference system models hybrid with genetic algorithm (GA-ANFIS) and particle swarm optimization (PSO-ANFIS) were compared with standalone ANFIS and Levenberg–Marquardt backpropagation neural network (LMBP-ANN) using six statistical measures of error, accuracy, and variability. The GA-ANFIS and PSO-ANFIS accurately model the resource with PSO-ANFIS having lesser computational time compared to GA-ANFIS. However, LMBP-ANN is most robust and resistant in modeling the resource variability among the four models. Hence, wind resource variability investigation on a potentially viable site obtained from the GIS-MCDM model can complement on-site investigations prior to site development. Also, tuning ANFIS with evolutionary algorithms offers improved accuracy over standalone ANFIS model for wind resource forecast and further its robustness in predicting variability of the resource. From our findings, cross-boundary wind resource exploration between South Africa and Lesotho could foster regional interconnectivity. [ABSTRACT FROM AUTHOR]

Summary: Wind energy uptake in South Africa is significantly increasing both at the micro‐ and macro‐level and the possibility of embedded generation cannot be undermined considering the state of electricity supply in the country. This study identifies a wind hotspot site in the Eastern Cape province, performs an in silico deployment of three utility‐scale wind turbines of 60 m hub height each from different manufacturers, develops machine learning models to forecast very short‐term power production of the three wind turbine generators (WTG) and investigates the feasibility of embedded generation for a potential livestock industry in the area. Windographer software was used to characterize and simulate the net output power from these turbines using the wind speed of the potential site. Two hybrid models of adaptive neurofuzzy inference system (ANFIS) comprising genetic algorithm and particle swarm optimization (PSO) each for a turbine were developed to forecast very short‐term power output. The feasibility of embedded generation for typical medium‐scale agricultural industry was investigated using a weighted Weber facility location model. The analytical hierarchical process (AHP) was used for weight determination. From our findings, the WTG‐1 was selected based on its error performance metrics (root mean square error of 0.180, mean absolute SD of 0.091 and coefficient of determination of 0.914 and CT = 702.3 seconds) in the optimal model (PSO‐ANFIS). Criteria were ranked based on their order of significance to the agricultural industry as proximity to water supply, labour availability, power supply and road network. Also, as a proof of concept, the optimal location of the industrial facility relative to other criteria was X = 19.24 m, Y = 47.11 m. This study reveals the significance of resource forecasting and feasibility of embedded generation, thus improving the quality of preliminary resource assessment and facility location among site developers. [ABSTRACT FROM AUTHOR]

The drive towards low-carbon economy in South Africa has necessitated alternative energy sources for electricity generation. More alternative sources have evolved in recent times with a view to making energy available to all and sundry. However, asides proliferation of these sources and extensions in form of micro-grids, the questions of increased availability and sustainability has become a growing concern. This survey investigates the state of the renewable energy system in South Africa with focus on the elements, which enhance energy availability and sustainability in the emerging transition to a low-carbon economy. Case studies of other countries were reviewed and considered in the South African context. It was observed that energy availability on the journey to the low-carbon economy is influenced by physical, climatic, human, prosumer concept and political factors. In sustaining the transition and progressing to a green economy, intelligent use of data from power generation, transmission, and distribution sectors for intelligent data-driven decision-making processes was also found as essential. As part of the sustainability roadmap, efficiency at the end-user side of the value chain and a system thinking paradigm in the harvesting of renewable energy sources (RES) and formulation of supporting policies were also identified. In the overall, the study reveals that South Africa is replete with abundance of RES, however, their continuous availability and sustainability depends on joint interventions of both stakeholders and the government with viable environment for the growth of the sector. [ABSTRACT FROM AUTHOR]

The drive towards low-carbon economy in South Africa has necessitated alternative energy sources for electricity generation. More alternative sources have evolved in recent times with a view to making energy available to all and sundry. However, asides proliferation of these sources and extensions in form of micro-grids, the questions of increased availability and sustainability has become a growing concern. This survey investigates the state of the renewable energy system in South Africa with focus on the elements, which enhance energy availability and sustainability in the emerging transition to a lowcarbon economy. Case studies of other countries were reviewed and considered in the South African context. It was observed that energy availability on the journey to the low-carbon economy is influenced by physical, climatic, human, prosumer concept and political factors. In sustaining the transition and progressing to a green economy, intelligent use of data from power generation, transmission, and distribution sectors for intelligent data-driven decision-making processes was also found as essential. As part of the sustainability roadmap, efficiency at the end-user side of the value chain and a system thinking paradigm in the harvesting of renewable energy sources (RES) and formulation of supporting policies were also identified. In the overall, the study reveals that South Africa is replete with abundance of RES, however, their continuous availability and sustainability depends on joint interventions of both stakeholders and the government with viable environment for the growth of the sector. [ABSTRACT FROM AUTHOR]

The application of evolutionary algorithms could be a rapid and acceptable means of predicting the elemental composition of biomass applicable in power generation. This article introduces a hybrid model comprising of adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) called PSO-ANFIS model. The model is based on the proximate values of biomass materials to predict their corresponding elemental compositions. This intelligent hybrid model was trained with 581 biomass data and further tested with a new 249 biomass data. The carbon, hydrogen, and oxygen contents of solid biomass were predicted based on the ash content (Ash), fixed carbon (FC), and volatile matter (VM) as the inputs. The model was evaluated based on some known performance criteria. A root mean squared error (RMSE), mean absolute deviation (MAD), mean absolute percentage error (MAPE), log of accuracy ratio (LAR), coefficient of correlation (CC), mean absolute error (MAE) value of 3.5816, 2.3279, 4.8521, 0.0012, 0.9329, 0.0227 at computation time (CT) of 47.04 secs for Carbon (C); 0.6383, 0.4047, 10.2187, 0.002, 0.7986, 0.0261 at CT of 35.2 secs for Hydrogen (H); 4.2042, 2.7417, 10.7612,0.0016, 0.9137 at CT of 28.16 secs for Oxygen (O) respectively was obtained. A regression analysis was also carried out to determine the level of correlation between actual and predicted values. The reported indices show that PSO-ANFIS can be used as a novel computation approach for the prediction of elemental compositions of biomass, which is vital to the combustion, thermochemical, gasification, and pyrolysis process toward energy production. [ABSTRACT FROM AUTHOR]

Non-renewable energy sources have detrimental environmental effects, which directly and indirectly affect the biosphere as environmental deposits from their use for energy generation exceed a threshold. This study performs a streamlined life cycle assessment (LCA) of a coal-fired plant in South Africa. The cradle-to-grave LCA focuses on the coal cycle to determine hotspots with high environmental impacts in the process. Four impact categories were considered in this study: global warming potential, photochemical ozone creation potential, eutrophication potential, and acidification potential. Coal transportation, coal pulverization, water use, and ash management were identified as hotspots in the coal cycle. The coal process has 95% potential for global warming, 4% potential for eutrophication, 1% potential for acidification, and a negligible percentage for photochemical ozone creation. Susceptibility to climate change, eutrophication, acid rain, soil degradation, and water contamination among others are major concerns of the coal cycle. Outsourcing coal from nearby mines with train as medium of transportation reduces environmental impact. Similarly, the use of mitigation technologies like flue gas desulphurization, carbon capture storage, or selective catalytic reduction will reduce the concentration of the flue gas emitted. Ultimately, substituting the coal process with renewable energy sources will ensure environmental sustainability in South Africa. This study will serve as a good resource for further studies on LCA of coal power plants not only in other African countries but in other developing countries with similar situation. [ABSTRACT FROM AUTHOR]

The global challenges of climate change have been compounded by an unprecedented level of environmental pollution consequent upon the municipal solid waste, MSW generation. Recent advances by researchers and policymakers are focused on sustainable and renewable energy sources which are technologically feasible, environmentally friendly, and economically viable. Waste-to-fuel initiative is therefore highly beneficial to our environment while also improves the socio-economic well-being the nations. This current study introduces an adaptive neuro-fuzzy inference systems (ANFIS) model optimised with Particle Swarm Optimisation (PSO) algorithm aimed at predicting the enthalpy of combustion of MSW fuel based on the moisture content (H2O), Carbon, Hydrogen, Oxygen, Nitrogen, Sulphur, and Ash contents. This model was trained with 86 MSW biomass data and further tested with a new 37 data points. The developed model was observed to performed better in term of the accuracy when compared with other existing models in the literature. The model was evaluated based on some known error estimation. The values of Root Mean Squared Error (RMSE), Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE), Log Accuracy ratio (LAR), Coefficient of Correlation (CC) were 3.6277, 22.6202, 0.0337, 0.8673 respectively at computation time (CT) of 36.96 secs. Regression analysis was also carried out to determine the level of correlation between the experimental and predicted high heating values (HHV). [ABSTRACT FROM AUTHOR]

The global challenges of climate change have been compounded by an unprecedented level of environmental pollution consequent upon the municipal solid waste, MSW generation. Recent advances by researchers and policymakers are focused on sustainable and renewable energy sources which are technologically feasible, environmentally friendly, and economically viable. Waste-to-fuel initiative is therefore highly beneficial to our environment while also improves the socio-economic well-being the nations. This current study introduces an adaptive neuro-fuzzy inference systems (ANFIS) model optimised with Particle Swarm Optimisation (PSO) algorithm aimed at predicting the enthalpy of combustion of MSW fuel based on the moisture content (H2O), Carbon, Hydrogen, Oxygen, Nitrogen, Sulphur, and Ash contents. This model was trained with 86 MSW biomass data and further tested with a new 37 data points. The developed model was observed to performed better in term of the accuracy when compared with other existing models in the literature. The model was evaluated based on some known error estimation. The values of Root Mean Squared Error (RMSE), Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE), Log Accuracy ratio (LAR), Coefficient of Correlation (CC) were 3.6277, 22.6202, 0.0337, 0.8673 respectively at computation time (CT) of 36.96 secs. Regression analysis was also carried out to determine the level of correlation between the experimental and predicted high heating values (HHV). [ABSTRACT FROM AUTHOR]

The global challenges of climate change have been compounded by an unprecedented level of environmental pollution consequent upon the municipal solid waste, MSW generation. Recent advances by researchers and policymakers are focused on sustainable and renewable energy sources which are technologically feasible, environmentally friendly, and economically viable. Waste-to-fuel initiative is therefore highly beneficial to our environment while also improves the socio-economic well-being the nations. This current study introduces an adaptive neuro-fuzzy inference systems (ANFIS) model optimised with Particle Swarm Optimisation (PSO) algorithm aimed at predicting the enthalpy of combustion of MSW fuel based on the moisture content (H2O), Carbon, Hydrogen, Oxygen, Nitrogen, Sulphur, and Ash contents. This model was trained with 86 MSW biomass data and further tested with a new 37 data points. The developed model was observed to performed better in term of the accuracy when compared with other existing models in the literature. The model was evaluated based on some known error estimation. The values of Root Mean Squared Error (RMSE), Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE), Log Accuracy ratio (LAR), Coefficient of Correlation (CC) were 3.6277, 22.6202, 0.0337, 0.8673 respectively at computation time (CT) of 36.96 secs. Regression analysis was also carried out to determine the level of correlation between the experimental and predicted high heating values (HHV). [ABSTRACT FROM AUTHOR]

The global challenges of climate change have been compounded by an unprecedented level of environmental pollution consequent upon the municipal solid waste, MSW generation. Recent advances by researchers and policymakers are focused on sustainable and renewable energy sources which are technologically feasible, environmentally friendly, and economically viable. Waste-to-fuel initiative is therefore highly beneficial to our environment while also improves the socio-economic well-being the nations. This current study introduces an adaptive neuro-fuzzy inference systems (ANFIS) model optimised with Particle Swarm Optimisation (PSO) algorithm aimed at predicting the enthalpy of combustion of MSW fuel based on the moisture content (H2O), Carbon, Hydrogen, Oxygen, Nitrogen, Sulphur, and Ash contents. This model was trained with 86 MSW biomass data and further tested with a new 37 data points. The developed model was observed to performed better in term of the accuracy when compared with other existing models in the literature. The model was evaluated based on some known error estimation. The values of Root Mean Squared Error (RMSE), Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE), Log Accuracy ratio (LAR), Coefficient of Correlation (CC) were 3.6277, 22.6202, 0.0337, 0.8673 respectively at computation time (CT) of 36.96 secs. Regression analysis was also carried out to determine the level of correlation between the experimental and predicted high heating values (HHV). [ABSTRACT FROM AUTHOR]

The construction of biogas system involves anthropogenic activities, which can have negative effects on the ecosystem. Environmental impact assessment of the biogas system at subsystem level before manufacture fosters design optimization. This minimizes environmental pollutants associated with the system during operation. In this study, a gate‐to‐gate life cycle assessment (LCA) of a domestic biogas digester at the design stage was performed. The biogas digester, which is to operate at mesophilic conditions with a functional unit of 24,900.3 kWh/10 years was investigated, and its anticipated outputs during operation were presented. The environmental processes investigated include the transportation of components, the digester manufacturing, the digester operations, and the recyclability and disposal of the digester after its useful life. Results showed that equipment manufacturing and transportation of components are of high environmental influence on the particulate matter deposit measuring 0.00493 ppm and 0.00932 ppm, respectively. However, the manufacturing process produces higher toxic waste (8.6 m3) than the transportation process (1.15 m3). More harmful than carbon dioxide in climate change is methane gas during biogas plant operation. As part of the measures to reduce environmental impacts around the product system, optimization of transportation trips is recommended. The use of mechanical seal, as opposed to the conventional rubber seal, is preferred to prevent methane leakage during the system operation. Integrating LCA studies at the early design stage of equipment enables informed decisions prior to equipment fabrication. © 2019 American Institute of Chemical Engineers Environ Prog, 38: e13228, 2019 [ABSTRACT FROM AUTHOR]