Micro-combined heat and power using dual fuel engine and biogas from discontinuous anaerobic digestion

Akkouche, Naïm; Loubar, Khaled; Nepveu, François; Kadi, Mohamed El Amine; Tazerout, Mohand

doi:10.1016/j.enconman.2019.112407

Cited by 32 publications

(12 citation statements)

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“…Accordingly, the diesel engines should be equipped with diesel and biogas dual-fuel operating modes to promote the efficiency of CHP for heat and electricity production. 26 GHG Emission and Carbon Footprint. The release of GHG during wastewater treatment was monitored in each unit separately to ascertain the most contributing operational sectors.…”

Section: ■ Materials and Methodsmentioning

confidence: 48%

Highly Efficient Reclamation of Meat-Processing Wastewater by Aerobic Hybrid Membrane Bioreactor-Reverse Osmosis Simulated System: A Comprehensive Economic and Environmental Study

Abyar

Nowrouzi

2020

ACS Sustainable Chem. Eng.

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A highly efficient, economic, and environmentally friendly hybrid membrane bioreactor-reverse osmosis (MBR-RO) was simulated for the treatment of meat-processing wastewater. The effluent elucidated ≥92% reduction in the COD, TKN, NO 3 − − N, TN, TSS, and TP parameters. The aerobic MBR accounted for over 62% of the total operation cost that predominantly dealt with the electricity demand (94.19%), while climate change (95.13%) and photochemical oxidation formation (4.66%) categories imposed the highest environmental burdens. Fossil fuel utilization as the major energy supplier (97.26%) significantly enhanced the CO 2 emission that led to augmented environmental impacts. Most sensitivity to change of prime parameters was reflected by the agricultural land occupation (19.25%) and ozone layer depletion (18.97%) categories. Accordingly, the proposed hybrid MBR-RO can be nominated as a promising system to overcome problems emerged from the meat-processing wastewater.

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Section: ■ Materials and Methodsmentioning

confidence: 48%

Highly Efficient Reclamation of Meat-Processing Wastewater by Aerobic Hybrid Membrane Bioreactor-Reverse Osmosis Simulated System: A Comprehensive Economic and Environmental Study

Abyar

Nowrouzi

2020

ACS Sustainable Chem. Eng.

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“…The results are obtained by using the "Radial basis" and "Purlin" hidden/output neuron activation functions ( 1 () and 2 (), respectively) and the 100-fold CV procedure. The "Radial basis" takes in input values between (-∞,∞) and produces in output values between (0,1) [44], [45], whereas "Purlin" transfers the inputs to the outputs without any change [44], [45].…”

Section: A Application Results Of the Proposed Local Training Stratementioning

confidence: 99%

A Local Training Strategy-Based Artificial Neural Network for Predicting the Power Production of Solar Photovoltaic Systems

2020

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Power production prediction from Renewable Energy (RE) sources has been widely studied in the last decade. This is extremely important for utilities to counterpart electricity supply with consumer demands across centralized grid networks. In this context, we propose a local training strategy-based Artificial Neural Network (ANN) for predicting the power productions of solar Photovoltaic (PV) systems. Specifically, the timestamp, weather variables, and corresponding power productions collected locally at each hour interval h, h=[1,24] (i.e., an interval of ∆ℎ=1 hour), are exploited to build, optimize, and evaluate H=24 different ANNs for the 24 hourly solar PV production predictions. The proposed local training strategy-based ANN is expected to provide more accurate predictions with short computational times than those obtained by a single (i.e., H=1) ANN model (hereafter called benchmark) built, optimized, and evaluated globally on the entire available dataset. The proposed strategy is applied to a case study regarding a 264kWp solar PV system located in Amman, Jordan, and its effectiveness compared to the benchmark is verified by resorting to different performance metrics from the literature. Further, its effectiveness is verified and compared when Extreme Learning Machines (ELMs) are adopted instead of the ANNs, and when the Persistence model is used. The prediction performance of the two training strategies-based ANN is also investigated and compared in terms of i) different weather conditions (i.e., seasons) experienced by the solar PV system under study and ii) different hour intervals (i.e., ∆ℎ=2, 3, and 4 hours) used for partitioning the overall dataset and, thus, establishing the different ANNs (i.e., H =12, 8, and 6 models, respectively).

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“…ANN modeling of micro combined heat unit operating in CNG diesel dual fuel mode was carried out by Akkouche et al [ 106 ]. Three different models developed to predict the airflow, pilot fuel flow, and exhaust temperature with 3-5-4-1, 3-3-5-1, and 3-5-3-1 network architecture, respectively, using biogas flow, methane contents, the power used as inputs.…”

Section: Modeling Of Internal Combustion Enginesmentioning

confidence: 99%

Application of Artificial Neural Network for Internal Combustion Engines: A State of the Art Review

Bhatt

Shrivastava

2021

Arch Computat Methods Eng

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The automotive industry is facing a crucial time. The transformation from internal combustion engines to new electrical technologies requires enormous investment, and hence the IC engines are likely to serve as a means of transportation for the coming decades. The search for sustainable green alternative fuel and operating parameter optimization is a current feasible solution and is a critical issue among the scientific community. Engine experiments are complicated, costly, and time-consuming, especially when the global economy is drastically down due to the COVID-19 pandemic and putting the limitation of social distancing. Industries are looking for proven computational solutions to address these issues. Recently, artificial neural network has been proven beneficial in several areas of engineering to reduce the time and experimentation cost. The IC engine is one of them. ANN has been used to predict and analyze different characteristics such as performance, combustion, and emissions of the IC engine to save time and energy. The complex nature of ANN may lead to computation time, energy, and space. Recent studies are centered on changing the network topology, deep learning, and design of ANN to get the highest performance. The present study summarizes the application of ANN to predict and optimize the complicated characteristics of various types of engines with different fuels. The study aims to investigate the network topologies adopted to design the model and thereafter statistical evaluation of the developed ANN models. A comparison of the ANN model with other prediction models is also presented.

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Micro-combined heat and power using dual fuel engine and biogas from discontinuous anaerobic digestion

Cited by 32 publications

References 50 publications

Highly Efficient Reclamation of Meat-Processing Wastewater by Aerobic Hybrid Membrane Bioreactor-Reverse Osmosis Simulated System: A Comprehensive Economic and Environmental Study

Highly Efficient Reclamation of Meat-Processing Wastewater by Aerobic Hybrid Membrane Bioreactor-Reverse Osmosis Simulated System: A Comprehensive Economic and Environmental Study

A Local Training Strategy-Based Artificial Neural Network for Predicting the Power Production of Solar Photovoltaic Systems

Application of Artificial Neural Network for Internal Combustion Engines: A State of the Art Review

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