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Bamidele V. Ayodele
Malaysia

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Editorial article
Published: 18 June 2021 in Frontiers in Energy Research
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Editorial on the Research Topic Technology Advances in the Utilization of Fossil Natural Gas as a Strategy in Transition to a Sustainable Energy System Natural gas as a fossil fuel has lower CO2 emissions from its combustion compared to other fossil fuels such as coal and oil. Hence, its demand has increased globally as a substitute for coal and oil-based fuel for power generation and transportation. The utilization of natural gas for transportation helps to mitigate the emission of other gaseous pollutants such as SOx and NOx as lesser amounts of these gases are emitted when combusted per kilometer. Besides being used as a low-carbon energy source, natural gas has been the main source of hydrogen production by catalytic steam reforming. The hydrogen produced can be utilized for fuel cell vehicles which has zero emissions and can significantly decarbonize the transportation sector. In addition to hydrogen, syngas, a mixture of hydrogen and carbon monoxide is an important chemical intermediate for producing methanol and Fischer-Tropsch liquids. This subject set was put together to pursue expert contributions on developments and advancements in the use of fossil natural gas for renewable energy processes due to the strategic position natural gas plays in decarbonizing the power and energy market as well as moving to a sustainable energy process. Sustainable hydrogen production by solar-assisted natural gas thermal dissociation as a potential pathway for energy decarbonization was reported by Rodat and Abanades. A computation fluid dynamic technique was employed to model a windowless scalable solar reactor that could enable volumetric gas-phase methane cracking with possible hybridization. The process is expected to overcome the challenges of carbon deposition, continuous round-the-clock operation of the solar reactor with an intermittent energy resource, and technology scale-up. The interest in the production of natural gas from “shale” formation is gaining wide acceptance. He et al. reported the loss of shale gas during the coring process in the Eastern Sichuan Basin in China. The error reduction rate was employed to measure the shale gas loss to verify the simulated experimental method. The results showed that the error reduction rate had an improved performance compared to the United States Bureau of Mines (USBM) methods. Experts voiced their opinions on the need to optimize energy for sustainable development by setting an achievable target for carbon neutrality (Idowu et al.). One major constraint highlighted in the utilization of fossil fuel for sustainable energy processes is the emission of CO2. This challenge was addressed by Zubir et al. who analyzed the strategy for CO2 capture from the coal-fired power plant for dry reforming of natural gas. A significant CO2 emissions reduction was obtained using CO2 capture through calcium carbonate looping. Adeneye et al. established the link between carbon emissions, energy consumption, urbanization and economic growth in Asia using common correlated effects mean group estimator. The results necessitated the need for lawmakers to gain input into green energy policies and urban planning. Natural gas’ importance as a primary alternative energy source in the move to a clean green energy system was also emphasized by Mohammad et al. The subject set had a cumulative view of 7,230 at the time of writing this Editorial, with 731 downloads of the various articles, demonstrating the interest in sustainable natural gas use. All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Keywords: natural gas, sustainable energy, CO2 emissions, low carbon economy, energy transition Citation: Ayodele BV, Sarkodie SA, Aneke M and Al-Amin AQ (2021) Editorial: Technology Advances in the Utilization of Fossil Natural Gas as a Strategy in Transition to a Sustainable Energy System. Front. Energy Res. 09:712739. doi: 10.3389/fenrg.2021.712739 Received: 21 May 2021; Accepted: 04 June 2021;Published: 18 June 2021. Edited and reviewed by: Copyright © 2021 Ayodele, Sarkodie, Aneke and Al-Amin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. *Correspondence: Bamidele Victor Ayodele, [email protected]

ACS Style

Bamidele Victor Ayodele; Samuel Asumadu Sarkodie; Mathew Aneke; Abul Quasem Al-Amin. Editorial: Technology Advances in the Utilization of Fossil Natural Gas as a Strategy in Transition to a Sustainable Energy System. Frontiers in Energy Research 2021, 9, 1 .

AMA Style

Bamidele Victor Ayodele, Samuel Asumadu Sarkodie, Mathew Aneke, Abul Quasem Al-Amin. Editorial: Technology Advances in the Utilization of Fossil Natural Gas as a Strategy in Transition to a Sustainable Energy System. Frontiers in Energy Research. 2021; 9 ():1.

Chicago/Turabian Style

Bamidele Victor Ayodele; Samuel Asumadu Sarkodie; Mathew Aneke; Abul Quasem Al-Amin. 2021. "Editorial: Technology Advances in the Utilization of Fossil Natural Gas as a Strategy in Transition to a Sustainable Energy System." Frontiers in Energy Research 9, no. : 1.

Journal article
Published: 31 May 2021 in Chemical Engineering and Processing - Process Intensification
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This study aims to model the effect of process parameters on the conversion of carbon dioxide (CO2) and methane (CH4) during reforming reaction over Nickel (Ni) catalysts. Various supervised machine learning algorithms were employed for the model development. To determine the best model, different configurations of the multilayer perceptron (MLP) and nonlinear auto-regressive exogenous (NARX) neural network models and their performances were evaluated. The performance of the various models was tested through their ability to predict the conversion of the CO2 and CH4. The best MLP network configurations of 5–15–2, 5–4–2, and 5–7–2 were obtained for the Levenberg-Marquardt-, the Bayesian Regularization-, and the Scaled conjugate gradient-trained MLP, respectively. While optimized NARX neural network configurations of 5–18–2, 5–13–2, and 5–8–2 were obtained for the Levenberg-Marquardt, Bayesian Regularization, and the Scaled conjugate gradient training algorithms, respectively. The Bayesian Regularization trained NARX with a coefficient of determination (R2) of 0.998 and MSE of 3.24×10–9 displayed the best performance with an accurate prediction of the thermo-catalytic conversion of CH4 and CO2. The sensitivity analysis revealed that the predicted CH4 and CO2 conversion were influenced in the order of reaction temperature > reduction temperature > calcination temperature > time on stream > Ni loading.

ACS Style

Bamidele Victor Ayodele; May Ali Alsaffar; Siti Indati Mustapa; Ramesh Kanthasamy; Suwimol Wongsakulphasatch; Chin Kui Cheng. Carbon dioxide reforming of methane over Ni-based catalysts: Modeling the effect of process parameters on greenhouse gasses conversion using supervised machine learning algorithms. Chemical Engineering and Processing - Process Intensification 2021, 166, 108484 .

AMA Style

Bamidele Victor Ayodele, May Ali Alsaffar, Siti Indati Mustapa, Ramesh Kanthasamy, Suwimol Wongsakulphasatch, Chin Kui Cheng. Carbon dioxide reforming of methane over Ni-based catalysts: Modeling the effect of process parameters on greenhouse gasses conversion using supervised machine learning algorithms. Chemical Engineering and Processing - Process Intensification. 2021; 166 ():108484.

Chicago/Turabian Style

Bamidele Victor Ayodele; May Ali Alsaffar; Siti Indati Mustapa; Ramesh Kanthasamy; Suwimol Wongsakulphasatch; Chin Kui Cheng. 2021. "Carbon dioxide reforming of methane over Ni-based catalysts: Modeling the effect of process parameters on greenhouse gasses conversion using supervised machine learning algorithms." Chemical Engineering and Processing - Process Intensification 166, no. : 108484.

Journal article
Published: 20 May 2021 in International Journal of Hydrogen Energy
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This study investigates the kinetic modeling and reaction pathway for the thermo-catalytic conversion of methane (CH4) and Carbon dioxide (CO2) over alpha-alumina supported cobalt catalyst. Rate data was obtained from the thermo-catalytic reaction at a temperature range of 923–1023 K and varying CH4 and CO2 partial pressure (5–50 kPa). The rate data was significantly influenced by the changes in the reaction temperature as well as the CH4 and CO2 partial pressure. To estimate the kinetic parameters, the rate data were fitted with five Langmuir-Hinshelwood kinetic models. The discrimination of the kinetic models using different parameters revealed that the Langmuir-Hinshelwood kinetic model with the assumption of CH4 being associatively adsorbed on a single and CO2 being dissociative adsorbed with bimolecular surface reaction best described the rate data. The analysis of the kinetic model using a non-linear regression solver results in activation energies of 15.88 kJ/mol, 36.78 kJ/mol, 65.51 kJ/mol, and 41.08 kJ/mol for CH4 consumption, CO2 consumption, H2 production, and CO production, respectively. The thermo-catalytic reaction was influenced by carbon as indicated by the rate of carbon deposition which was mainly caused by methane cracking. The reaction pathway for the thermo-catalytic conversion of the CH4 and CO2 over the alpha-alumina supported cobalt catalyst can best be described as by CH4 associative adsorption on the alpha-alumina supported cobalt catalyst single site and CO2 dissociative adsorption with bimolecular surface reaction.

ACS Style

May Ali Alsaffar; Bamidele Victor Ayodele; Jamal M. Ali; Mohamed A. Abdel Ghany; Siti Indati Mustapa; Chin Kui Cheng. Kinetic modeling and reaction pathways for thermo-catalytic conversion of carbon dioxide and methane to hydrogen-rich syngas over alpha-alumina supported cobalt catalyst. International Journal of Hydrogen Energy 2021, 46, 30871 -30881.

AMA Style

May Ali Alsaffar, Bamidele Victor Ayodele, Jamal M. Ali, Mohamed A. Abdel Ghany, Siti Indati Mustapa, Chin Kui Cheng. Kinetic modeling and reaction pathways for thermo-catalytic conversion of carbon dioxide and methane to hydrogen-rich syngas over alpha-alumina supported cobalt catalyst. International Journal of Hydrogen Energy. 2021; 46 (60):30871-30881.

Chicago/Turabian Style

May Ali Alsaffar; Bamidele Victor Ayodele; Jamal M. Ali; Mohamed A. Abdel Ghany; Siti Indati Mustapa; Chin Kui Cheng. 2021. "Kinetic modeling and reaction pathways for thermo-catalytic conversion of carbon dioxide and methane to hydrogen-rich syngas over alpha-alumina supported cobalt catalyst." International Journal of Hydrogen Energy 46, no. 60: 30871-30881.

Research article
Published: 02 February 2021 in International Journal of Energy Research
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This study aimed to investigate the application of radial basis function (RBF) and multilayer perceptron (MLP) artificial neural networks for modeling hydrogen production by co‐gasification of rubber and plastic wastes. Both the RBF and MLP neural networks were configured by determining the best‐hidden neurons that could offer optimized performance. Based on the best‐hidden neurons, a model architecture of 4‐16‐1, 4‐20‐1, 4‐17‐1, and 4‐3‐1 was obtained for RBF (with standard activation function), RBF (with ordinary activation function), one‐layer MLP, and two‐layer MLP, respectively, indicating the number of input nodes, the hidden neurons, and the output nodes. The predicted hydrogen production from the co‐gasification closely agrees with the observed values. The 1‐layer MLP with R2 of .990 displayed the best performance with all the input parameters having a significant influence on 9 the model output. The neural network algorithm obtained in this study could be implemented in the eventuality of making a vital decision in the process operation of the co‐gasification process for hydrogen production.

ACS Style

Bamidele Victor Ayodele; Siti Indati Mustapa; Ramesh Kanthasamy; Mohammed Zwawi; Chin Kui Cheng. Modeling the prediction of hydrogen production by co‐gasification of plastic and rubber wastes using machine learning algorithms. International Journal of Energy Research 2021, 45, 9580 -9594.

AMA Style

Bamidele Victor Ayodele, Siti Indati Mustapa, Ramesh Kanthasamy, Mohammed Zwawi, Chin Kui Cheng. Modeling the prediction of hydrogen production by co‐gasification of plastic and rubber wastes using machine learning algorithms. International Journal of Energy Research. 2021; 45 (6):9580-9594.

Chicago/Turabian Style

Bamidele Victor Ayodele; Siti Indati Mustapa; Ramesh Kanthasamy; Mohammed Zwawi; Chin Kui Cheng. 2021. "Modeling the prediction of hydrogen production by co‐gasification of plastic and rubber wastes using machine learning algorithms." International Journal of Energy Research 45, no. 6: 9580-9594.

Original paper
Published: 02 January 2021 in Topics in Catalysis
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Thermo-catalytic methane decomposition is a prospective route for producing COx free hydrogen. In this study, Bayesian regularization and Levenberg-Marquardt trained multilayer perceptron neural networks were employed in predictive modeling of hydrogen production by thermo-catalytic methane decomposition. Based on the non-linear relationship between the reaction temperature, weight of the catalysts, time of stream, calcination temperature, calcination time, specific volume, and the hydrogen yield, the various topology was configured for the neural network and tested to determine the artificial neuron that would result in the best model performance. The Levenberg-Marquardt trained neural network displayed the best performance with the model topology of 7–16-1 compared with the Bayesian regularization trained network. The model topology of 7–16-1 represents the input units, hidden neuron, and the output unit. The predicted hydrogen yield by the 7–16-1 configured neural network was in strong agreement with the observed value, evidenced by the coefficient of determination (R2) of 0.953 and mean square error of 0.03. A predicted hydrogen yield of 86.56 vol.% was obtained at the reaction temperature of 700 °C, 0.5 g catalyst weight, calcination temperature of 600 °C, calcination time of 240 min, catalyst specific surface area of 24.1 m2/g, the pore volume of 0.03 cm3/g, and 160 min time on stream which is at proximity with the observed value of 84 vol.%. The sensitivity analysis revealed that all the input parameters have varying levels of importance on the model output. However, the intrinsic properties of the catalysts (specific surface area, and the pore volume) have the most significant influence on the predicted hydrogen yield.

ACS Style

May Ali Alsaffar; Mohamed Abdel Rahman Abdel Ghany; Jamal Manee Ali; Bamidele Victor Ayodele; Siti Indati Mustapa. Artificial Neural Network Modeling of Thermo-catalytic Methane Decomposition for Hydrogen Production. Topics in Catalysis 2021, 64, 456 -464.

AMA Style

May Ali Alsaffar, Mohamed Abdel Rahman Abdel Ghany, Jamal Manee Ali, Bamidele Victor Ayodele, Siti Indati Mustapa. Artificial Neural Network Modeling of Thermo-catalytic Methane Decomposition for Hydrogen Production. Topics in Catalysis. 2021; 64 (5-6):456-464.

Chicago/Turabian Style

May Ali Alsaffar; Mohamed Abdel Rahman Abdel Ghany; Jamal Manee Ali; Bamidele Victor Ayodele; Siti Indati Mustapa. 2021. "Artificial Neural Network Modeling of Thermo-catalytic Methane Decomposition for Hydrogen Production." Topics in Catalysis 64, no. 5-6: 456-464.

Conference paper
Published: 22 December 2020 in IOP Conference Series: Materials Science and Engineering
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This study focuses on the non-linear effect of gas hourly space velocity (GHSV), oxygen (O2) concentration in the feed, the reaction temperature, and the CH4/CO2 ratio on hydrogen production by catalytic methane dry reforming using artificial neural networks (ANN). Ten different ANN models were configured by varying the hidden neurons from 1 to 10. The various ANN model architecture was tested using 30 datasets. The ANN model with the topology of 4-9-2 resulted in the best performance with the sum of square error (SSE) of 0.076 and coefficient of determination (R2) greater than 0.9. The predicted hydrogen yield and the CH4 conversions by the optimized ANN model were in close agreement with the observed values obtained from the experimental runs. The level of importance analysis revealed that all the parameters significantly influenced the hydrogen yield and the CH4 conversion. However, the reaction temperature with the highest level of importance was adjudged the parameter with the highest level of influence on the methane dry reforming. The study demonstrated that ANN is a robust tool that can be employed to investigate predictive modeling and determine the level of importance of parameters on methane dry reforming.

ACS Style

M A Alsaffar; A K Mageed; M A R Abdel Ghany; B V Ayodele; S I Mustapa. Elucidating the non-linear effect of process parameters on hydrogen production by catalytic methane reforming: an artificial intelligence approach. IOP Conference Series: Materials Science and Engineering 2020, 991, 012078 .

AMA Style

M A Alsaffar, A K Mageed, M A R Abdel Ghany, B V Ayodele, S I Mustapa. Elucidating the non-linear effect of process parameters on hydrogen production by catalytic methane reforming: an artificial intelligence approach. IOP Conference Series: Materials Science and Engineering. 2020; 991 (1):012078.

Chicago/Turabian Style

M A Alsaffar; A K Mageed; M A R Abdel Ghany; B V Ayodele; S I Mustapa. 2020. "Elucidating the non-linear effect of process parameters on hydrogen production by catalytic methane reforming: an artificial intelligence approach." IOP Conference Series: Materials Science and Engineering 991, no. 1: 012078.

Review
Published: 04 December 2020 in Sustainability
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This study presents an overview of the economic analysis and environmental impact of natural gas conversion technologies. Published articles related to economic analysis and environmental impact of natural gas conversion technologies were reviewed and discussed. The economic analysis revealed that the capital and the operating expenditure of each of the conversion process is strongly dependent on the sophistication of the technical designs. The emerging technologies are yet to be economically viable compared to the well-established steam reforming process. However, appropriate design modifications could significantly reduce the operating expenditure and enhance the economic feasibility of the process. The environmental analysis revealed that emerging technologies such as carbon dioxide (CO2) reforming and the thermal decomposition of natural gas offer advantages of lower CO2 emissions and total environmental impact compared to the well-established steam reforming process. Appropriate design modifications such as steam reforming with carbon capture, storage and utilization, the use of an optimized catalyst in thermal decomposition, and the use of solar concentrators for heating instead of fossil fuel were found to significantly reduced the CO2 emissions of the processes. There was a dearth of literature on the economic analysis and environmental impact of photocatalytic and biochemical conversion processes, which calls for increased research attention that could facilitate a comparative analysis with the thermochemical processes.

ACS Style

Freida Ayodele; Siti Mustapa; Bamidele Ayodele; Norsyahida Mohammad. An Overview of Economic Analysis and Environmental Impacts of Natural Gas Conversion Technologies. Sustainability 2020, 12, 10148 .

AMA Style

Freida Ayodele, Siti Mustapa, Bamidele Ayodele, Norsyahida Mohammad. An Overview of Economic Analysis and Environmental Impacts of Natural Gas Conversion Technologies. Sustainability. 2020; 12 (23):10148.

Chicago/Turabian Style

Freida Ayodele; Siti Mustapa; Bamidele Ayodele; Norsyahida Mohammad. 2020. "An Overview of Economic Analysis and Environmental Impacts of Natural Gas Conversion Technologies." Sustainability 12, no. 23: 10148.

Original paper
Published: 28 November 2020 in Topics in Catalysis
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Various anthropogenic activities often result in the emission of carbon dioxide (CO2), which is one of the principal components of greenhouse gases responsible for greenhouse effect. One vital strategy to mitigate the effect of the released CO2 on the environment is through sustainable utilization and conversion to value-added chemicals. This study employs the Radial Basis Function artificial neural network for modeling the prediction of thermo-catalytic CO2 oxidative coupling of methane to C2-hydrocarbons. The various architecture of the Radial Basis Function ANN was developed, trained, and tested using the non-linear relationship between the input parameters (reaction temperature, amount of CaO and MnO in the CaO-MnO/CeO2 catalysts and the CO2/CH4 ratio) and the output parameters (C2 hydrocarbon selectivity and yield). The Radial Basis Function ANN architecture with the topology of 4-20-2, representing the input layer, hidden neurons, and the output layer offers the best performance with a sum of square error (SSE) of 3.9 × 10−24 for training and 0.224 for testing. The R2 of 0.989 and 0.998 obtained for the prediction of the selectivity and the yield of the C2 hydrocarbon is an indication of the robustness of the Radial Basis Function ANN model. The sensitivity analysis revealed that the input parameters significantly influence the model output. However, the reaction temperature has the most significant influence on the model output based on the level of importance.

ACS Style

Bamidele Victor Ayodele; Siti Indati Mustapa; Thongthai Witoon; Ramesh Kanthasamy; Mohammed Zwawi; Chiedu N. Owabor. Radial Basis Function Neural Network Model Prediction of Thermo-catalytic Carbon Dioxide Oxidative Coupling of Methane to C2-hydrocarbon. Topics in Catalysis 2020, 64, 328 -337.

AMA Style

Bamidele Victor Ayodele, Siti Indati Mustapa, Thongthai Witoon, Ramesh Kanthasamy, Mohammed Zwawi, Chiedu N. Owabor. Radial Basis Function Neural Network Model Prediction of Thermo-catalytic Carbon Dioxide Oxidative Coupling of Methane to C2-hydrocarbon. Topics in Catalysis. 2020; 64 (5-6):328-337.

Chicago/Turabian Style

Bamidele Victor Ayodele; Siti Indati Mustapa; Thongthai Witoon; Ramesh Kanthasamy; Mohammed Zwawi; Chiedu N. Owabor. 2020. "Radial Basis Function Neural Network Model Prediction of Thermo-catalytic Carbon Dioxide Oxidative Coupling of Methane to C2-hydrocarbon." Topics in Catalysis 64, no. 5-6: 328-337.

Journal article
Published: 25 November 2020 in Processes
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This study investigates the use of a non-linear autoregressive exogenous neural network (NARX) model to investigate the nexus between energy usability, economic indicators, and carbon dioxide (CO2) emissions in four Association of South East Asian Nations (ASEAN), namely Malaysia, Thailand, Indonesia, and the Philippines. Optimized NARX model architectures of 5-29-1, 5-19-1, 5-17-1, 5-13-1 representing the input nodes, hidden neurons and the output units were obtained from the series of models configured. Based on the relationship between the input variables, CO2 emissions were predicted with a high correlation coefficient (R) > 0.9. and low mean square errors (MSE) of 3.92 × 10−21, 4.15 × 10−23, 2.02 × 10−19, 1.32 × 10−20 for Malaysia, Thailand, Indonesia, and the Philippines, respectively. Coal consumption has the highest level of influence on CO2 emissions in the four ASEAN countries based on the sensitivity analysis. These findings suggest that government policies in the four ASEAN countries should be more intensified on strategies to reduce CO2 emissions in relationship with the energy and economic indicators.

ACS Style

Siti Indati Mustapa; Freida Ozavize Ayodele; Bamidele Victor Ayodele; Norsyahida Mohammad. Nexus between Energy Usability, Economic Indicators and Environmental Sustainability in Four ASEAN Countries: A Non-Linear Autoregressive Exogenous Neural Network Modelling Approach. Processes 2020, 8, 1529 .

AMA Style

Siti Indati Mustapa, Freida Ozavize Ayodele, Bamidele Victor Ayodele, Norsyahida Mohammad. Nexus between Energy Usability, Economic Indicators and Environmental Sustainability in Four ASEAN Countries: A Non-Linear Autoregressive Exogenous Neural Network Modelling Approach. Processes. 2020; 8 (12):1529.

Chicago/Turabian Style

Siti Indati Mustapa; Freida Ozavize Ayodele; Bamidele Victor Ayodele; Norsyahida Mohammad. 2020. "Nexus between Energy Usability, Economic Indicators and Environmental Sustainability in Four ASEAN Countries: A Non-Linear Autoregressive Exogenous Neural Network Modelling Approach." Processes 8, no. 12: 1529.

Journal article
Published: 06 August 2020 in Process Safety and Environmental Protection
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The need for pollutant-free wastewater has necessitated a huge volume of research on the photocatalytic degradation of organic pollutants. The data obtained from various photocatalytic degradation experimental runs can be employed in data-driven machine learning modelling techniques such as artificial neural networks. In this study, the use of Levenberg-Marquardt-trained artificial neural network for modelling the photocatalytic degradation of chloramphenicol, phenol, azo dye, gaseous styrene, and methylene blue is presented. For each of the photocatalytic degradation processes, 20 neural network architectures were investigated by optimizing their hidden neurons. Optimized ANN configurations of 3-20-1, 3-5-1, 3-2-1, 4-17-1, 4-6-1, and 3-10-1 were obtained for modelling the photodegradation of chloramphenicol, phenol, phenol, azo dye, gaseous styrene, and methylene blue, respectively. The optimized ANN architectures were robust in predicting the degradation of the organic pollutants with R2 > 0.9 at a 95% confidence level with very low mean absolute errors. The sensitivity analysis using the modified Garson algorithm revealed that all the process parameters significantly influenced the photodegradation of the organic pollutants. The photocatalyst concentration, phenol concentration, pH of the solution, hydrothermal temperature, and methylene blue initial concentration were however found to have the most significant influence on the photodegradation processes. The ANN algorithm can be implemented in a photocatalytic degradation process for making vital decisions regarding the operation of the process.

ACS Style

Bamidele Victor Ayodele; May Ali Alsaffar; Siti Indati Mustapa; Chin Kui Cheng; Thongthai Witoon. Modeling the effect of process parameters on the photocatalytic degradation of organic pollutants using artificial neural networks. Process Safety and Environmental Protection 2020, 145, 120 -132.

AMA Style

Bamidele Victor Ayodele, May Ali Alsaffar, Siti Indati Mustapa, Chin Kui Cheng, Thongthai Witoon. Modeling the effect of process parameters on the photocatalytic degradation of organic pollutants using artificial neural networks. Process Safety and Environmental Protection. 2020; 145 ():120-132.

Chicago/Turabian Style

Bamidele Victor Ayodele; May Ali Alsaffar; Siti Indati Mustapa; Chin Kui Cheng; Thongthai Witoon. 2020. "Modeling the effect of process parameters on the photocatalytic degradation of organic pollutants using artificial neural networks." Process Safety and Environmental Protection 145, no. : 120-132.

Conference paper
Published: 18 July 2020 in IOP Conference Series: Materials Science and Engineering
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In this study, the modeling of photocatalytic degradation of 1,2 dihydroxybenzene using a multilayer perceptron neural network has been investigated. The multilayer perceptron neural network which consists of input layer, hidden layer with network configuration of 3, 17, 1 respectively were employed for predictive modeling using 20 datasets consisting the pH of the solution, the amount of the photocatalyst and the volume of the oxidant. The analysis of the network revealed that the volume of the oxidant was the most relevant factor that influences the degradation of the 1,2 dihydroxybenzene while the amount of photocatalyst has the least effect. The multilayer perceptron neural network model successfully predicts the photocatalytic degradation of the 1,2 dihydroxybenzene with coefficient of determination (R2) of 0.974. The predicted and the actual degradation of the 1,2 dihydroxybenzene was in close agreement with minimal error of prediction as indicated by the residual plot. This study has demonstrated the suitability of the multilayer perceptron neural network as a robust tool for modeling the prediction of 1,2 dihydroxybenzene degradation by photocatalytic process.

ACS Style

May Ali Alsaffar; Bamidele Ayodele; Mohamed A Abdel Ghany; Siti Indati Mustapa. Modeling the photocatalytic degradation of 1,2-Dihydroxybenzene using Multilayer Perceptron Neural Networks. IOP Conference Series: Materials Science and Engineering 2020, 870, 012057 .

AMA Style

May Ali Alsaffar, Bamidele Ayodele, Mohamed A Abdel Ghany, Siti Indati Mustapa. Modeling the photocatalytic degradation of 1,2-Dihydroxybenzene using Multilayer Perceptron Neural Networks. IOP Conference Series: Materials Science and Engineering. 2020; 870 (1):012057.

Chicago/Turabian Style

May Ali Alsaffar; Bamidele Ayodele; Mohamed A Abdel Ghany; Siti Indati Mustapa. 2020. "Modeling the photocatalytic degradation of 1,2-Dihydroxybenzene using Multilayer Perceptron Neural Networks." IOP Conference Series: Materials Science and Engineering 870, no. 1: 012057.

Journal article
Published: 09 July 2020 in Bulletin of Chemical Reaction Engineering & Catalysis
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In this study, the dehydrogenation of cyclohexanol to cyclohexanone over nitrogen-doped reduced graphene oxide (N-rGO) Cu catalyst has been reported. The N-rGO support was synthesized by chemical reduction of graphite oxide (GO). The synthesized N-rGO was used as a support to prepare the Cu/N-rGO catalyst via an incipient wet impregnation method. The as-prepared support and the Cu/N-rGO catalyst were characterized by FESEM, EDX, XRD, TEM, TGA, and Raman spectroscopy. The various characterization analysis revealed the suitability of the Cu/N-rGO as a heterogeneous catalyst that can be employed for the dehydrogenation of cyclohexanol to cyclohexanone. The catalytic activity of the Cu/N-rGO catalyst was tested in non-oxidative dehydrogenation of cyclohexanol to cyclohexanone using a stainless-steel fixed bed reactor. The effects of temperature, reactant flow rate, and time-on-stream on the activity of the Cu/N-rGO catalyst were examined. The Cu/N-rGO nanosheets show excellent catalytic activity and selectivity to cyclohexanone. The formation of stable Cu nanoparticles on N-rGO support interaction and segregation of Cu were crucial factors for the catalytic activity. The highest cyclohexanol conversion and selectivity of 93.3% and 82.7%, respectively, were obtained at a reaction temperature of 270 °C and cyclohexanol feed rate of 0.1 ml/min. Copyright © 2020 by Authors, Published by BCREC Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

ACS Style

Alyaa. K. Mageed; Dayang A. B. Radiah; A. Salmiaton; Shamsul Izhar; Musab Abdul Razak; Bamidele Victor Ayodele. Dehydrogenation of Cyclohexanol to Cyclohexanone Over Nitrogen-doped Graphene supported Cu catalyst. Bulletin of Chemical Reaction Engineering & Catalysis 2020, 15, 568 -578.

AMA Style

Alyaa. K. Mageed, Dayang A. B. Radiah, A. Salmiaton, Shamsul Izhar, Musab Abdul Razak, Bamidele Victor Ayodele. Dehydrogenation of Cyclohexanol to Cyclohexanone Over Nitrogen-doped Graphene supported Cu catalyst. Bulletin of Chemical Reaction Engineering & Catalysis. 2020; 15 (2):568-578.

Chicago/Turabian Style

Alyaa. K. Mageed; Dayang A. B. Radiah; A. Salmiaton; Shamsul Izhar; Musab Abdul Razak; Bamidele Victor Ayodele. 2020. "Dehydrogenation of Cyclohexanol to Cyclohexanone Over Nitrogen-doped Graphene supported Cu catalyst." Bulletin of Chemical Reaction Engineering & Catalysis 15, no. 2: 568-578.

Journal article
Published: 30 June 2020 in Sustainability
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The need to mitigate CO2 emissions from the transportation sector has necessitated the adoption of electric vehicles (EVs) and other forms of alternative vehicles. Despite the global rise of EVs demand as a complementary means of green transportations, the level of adoption in Malaysia is still not encouraging. Therefore, this study aimed to investigate the cost competitiveness of EVs in comparison with Hybrid Electric Vehicles (HEVs) and an Internal Combustion Vehicle (ICV) based on Malaysia scenarios. Using the existing data in Malaysia, life cost analysis (LCC) of two EVs was computed and compared with HEVs and ICVs. The study shows that Nissan leaf and BMW i3s EVs with LCC of $1.75 and $2.5 per km are not cost-competitive based on prevalent data available in Malaysia compared to the HEVs and ICV. Based on the sensitivity analysis, changes in the components of the operating costs significantly influence the accumulated cost of ownership of the EVs whereas the cost of ownership of the HEVs and ICVs did not experience any significant influence. The findings from this study could serve as bases for policymakers to formulate appropriate policies and strategies to improve the competitiveness of EVs in Malaysia.

ACS Style

Siti Mustapa; Bamidele Ayodele; Waznatol Mohamad Ishak; Freida Ayodele. Evaluation of Cost Competitiveness of Electric Vehicles in Malaysia Using Life Cycle Cost Analysis Approach. Sustainability 2020, 12, 5303 .

AMA Style

Siti Mustapa, Bamidele Ayodele, Waznatol Mohamad Ishak, Freida Ayodele. Evaluation of Cost Competitiveness of Electric Vehicles in Malaysia Using Life Cycle Cost Analysis Approach. Sustainability. 2020; 12 (13):5303.

Chicago/Turabian Style

Siti Mustapa; Bamidele Ayodele; Waznatol Mohamad Ishak; Freida Ayodele. 2020. "Evaluation of Cost Competitiveness of Electric Vehicles in Malaysia Using Life Cycle Cost Analysis Approach." Sustainability 12, no. 13: 5303.

Journal article
Published: 23 May 2020 in International Journal of Hydrogen Energy
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Running dry reforming of methane (DRM) reaction at low-temperature is highly regarded to increase thermal efficiency. However, the process requires a robust catalyst that has a strong ability to activate both CH4 and CO2 as well as strong resistance against deactivation at the reaction conditions. Thus, this paper examines the prospect of DRM reaction at low temperature (400–600 °C) over CeO2–MgO supported Nickel (Ni/CeO2–MgO) catalysts. The catalysts were synthesized and characterized by XRD, N2 adsorption/desorption, FE-SEM, H2-TPR, and TPD-CO2 methods. The results revealed that Ni/CeO2–MgO catalysts possess suitable BET specific surface, pore volume, reducibility and basic sites, typical of heterogeneous catalysts required for DRM reaction. Remarkably, the activity of the catalysts at lower temperature reaction indicates the workability of the catalysts to activate both CH4 and CO2 at 400 °C. Increasing Ni loading and reaction temperature has gradually increased CH4 conversion. 20 wt% Ni/CeO2–MgO catalyst, CH4 conversion reached 17% at 400 °C while at 900 °C it was 97.6% with considerable stability during the time on stream. Whereas, CO2 conversions were 18.4% and 98.9% at 400 °C and 900 °C, respectively. Additionally, a higher CO2 conversion was obtained over the catalysts with 15 wt% Ni content when the temperature was higher than 600 °C. This is because of the balance between a high number of Ni active sites and high basicity. The characterization of the used catalyst by TGA, FE-SEM and Raman Spectroscopy confirmed the presence of amorphous carbon at lower temperature reaction and carbon nanotubes at higher temperature.

ACS Style

Basem M. Al–Swai; Noridah Binti Osman; Anita Ramli; Bawadi Abdullah; Ahmad Salam Farooqi; Bamidele Victor Ayodele; David Onoja Patrick. Low-temperature catalytic conversion of greenhouse gases (CO2 and CH4) to syngas over ceria-magnesia mixed oxide supported nickel catalysts. International Journal of Hydrogen Energy 2020, 46, 24768 -24780.

AMA Style

Basem M. Al–Swai, Noridah Binti Osman, Anita Ramli, Bawadi Abdullah, Ahmad Salam Farooqi, Bamidele Victor Ayodele, David Onoja Patrick. Low-temperature catalytic conversion of greenhouse gases (CO2 and CH4) to syngas over ceria-magnesia mixed oxide supported nickel catalysts. International Journal of Hydrogen Energy. 2020; 46 (48):24768-24780.

Chicago/Turabian Style

Basem M. Al–Swai; Noridah Binti Osman; Anita Ramli; Bawadi Abdullah; Ahmad Salam Farooqi; Bamidele Victor Ayodele; David Onoja Patrick. 2020. "Low-temperature catalytic conversion of greenhouse gases (CO2 and CH4) to syngas over ceria-magnesia mixed oxide supported nickel catalysts." International Journal of Hydrogen Energy 46, no. 48: 24768-24780.

Research article chemical engineering
Published: 09 May 2020 in Arabian Journal for Science and Engineering
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This study investigates the optimization of multilayer graphene (MLG) growth on Co–Ni/Al2O3 substrate. The MLG synthesized by chemical vapor deposition technique (CVD) was characterized using various instrument techniques. The surface area and pore volume of the MLG were estimated as ~ 642 m2/g and ~ 2.7 cm3/g, respectively. The Raman spectrometric analysis showed evidence of MLG. The effects of parameters such as temperature, Co–Ni composition and ethanol flow rate were investigated using response surface methodology (RSM) and central composite design. The maximum MLG yield of 77% was attained at optimum conditions of 800 °C, Co–Ni composition of 0.3/0.7 and ethanol flow rate of 11 ml/min. The analysis of variance (ANOVA) results showed that the RSM quadratic model is significant with a p value < 0.0001. The coefficient of determination (R2) values of 0.9694 revealed the reliability of the RSM model. The potential of CVD as a technique to synthesize MLG growth of a highly ordered crystallinity structure has been demonstrated in this study. The resulting MLG films are promising materials for the use in improving graphene-based electronics, sensing and energy devices.

ACS Style

May Ali Alsaffar; Suraya Abdul Rashid; Bamidele Victor Ayodele; Mohd Nizar Hamidon; Faizah Md Yasin; Ismayadi Ismail; Soraya Hosseini; Farahnaz Eghbali Babadi. Response Surface Optimization of Multilayer Graphene Growth on Alumina-Supported Bimetallic Cobalt–Nickel Substrate. Arabian Journal for Science and Engineering 2020, 45, 7455 -7465.

AMA Style

May Ali Alsaffar, Suraya Abdul Rashid, Bamidele Victor Ayodele, Mohd Nizar Hamidon, Faizah Md Yasin, Ismayadi Ismail, Soraya Hosseini, Farahnaz Eghbali Babadi. Response Surface Optimization of Multilayer Graphene Growth on Alumina-Supported Bimetallic Cobalt–Nickel Substrate. Arabian Journal for Science and Engineering. 2020; 45 (9):7455-7465.

Chicago/Turabian Style

May Ali Alsaffar; Suraya Abdul Rashid; Bamidele Victor Ayodele; Mohd Nizar Hamidon; Faizah Md Yasin; Ismayadi Ismail; Soraya Hosseini; Farahnaz Eghbali Babadi. 2020. "Response Surface Optimization of Multilayer Graphene Growth on Alumina-Supported Bimetallic Cobalt–Nickel Substrate." Arabian Journal for Science and Engineering 45, no. 9: 7455-7465.

Conference paper
Published: 01 May 2020 in Journal of Physics: Conference Series
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ACS Style

Omer Al Haiqi; Abdurahman Hamid Nour; Bamidele Ayodele; Rushdi Bargaa. Bayesian Regularization-Trained Multi-layer Perceptron Neural Network Predictive Modelling of Phenol Degradation using ZnO/Fe2O3 photocatalyst. Journal of Physics: Conference Series 2020, 1529, 1 .

AMA Style

Omer Al Haiqi, Abdurahman Hamid Nour, Bamidele Ayodele, Rushdi Bargaa. Bayesian Regularization-Trained Multi-layer Perceptron Neural Network Predictive Modelling of Phenol Degradation using ZnO/Fe2O3 photocatalyst. Journal of Physics: Conference Series. 2020; 1529 ():1.

Chicago/Turabian Style

Omer Al Haiqi; Abdurahman Hamid Nour; Bamidele Ayodele; Rushdi Bargaa. 2020. "Bayesian Regularization-Trained Multi-layer Perceptron Neural Network Predictive Modelling of Phenol Degradation using ZnO/Fe2O3 photocatalyst." Journal of Physics: Conference Series 1529, no. : 1.

Research article
Published: 03 April 2020 in International Journal of Energy Research
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In this study, the thermo‐catalytic conversion of two principal greenhouse gases (methane and carbon dioxide) to carbon monoxide (CO)‐rich hydrogen (H2) is investigated over cerium oxide (CeO2) promoted calcium ferrite supported nickel (Ni/CaFe2O4) catalyst. The CeO2 promoted Ni/CaFe2O4 catalyst was prepared using wet‐impregnation technique. To ascertain the physicochemical properties, the as‐prepared catalyst was characterized using various instrument techniques. The characterization of the catalysts reveals that CeO2‐Ni/CaFe2O4 possesses suitable physicochemical properties for the conversion of methane (CH4) and carbon dioxide (CO2) to CO‐rich H2. The thermo‐catalytic reaction revealed that the CeO2 promoted Ni/CaFe2O4 catalyst displayed a higher CH4 and CO2 conversions of 90.04% and 91.2%, respectively, at a temperature of 1073 K compared to the unpromoted catalyst. The highest H2 and CO yields of 78% and 76%, respectively, were obtained over the CeO2‐Ni/CaFe2O4 at 1073 K and CH4/CO2 ratio of 1. The CeO2 promoted Ni/CaFe2O4 catalyst remained stable throughout the 30 hours time on stream (TOS) while that of the unpromoted Ni/CaFe2O4 catalyst sharply decreased after 22 hours TOS. The characterization of the used catalysts confirms the evidence of carbon depositions on the unpromoted Ni/CaFe2O4 which is solely responsible for its deactivation. Whereas, there was a slightly gasifiable carbon deposited on the CeO2 promoted Ni/CaFe2O4 catalyst which could be ascribed to the interaction effect of the CeO2 promoter on the Ni/CaFe2O4 catalyst.

ACS Style

Mohammed Anwar Hossain; Bamidele V. Ayodele; Huei R. Ong; Siti I. Mustapa; Chin K. Cheng; Maksudur R. Khan. Thermo‐catalytic conversion of greenhouse gases (CO 2 and CH 4 ) to CO‐rich hydrogen by CeO 2 modified calcium iron oxide supported nickel catalyst. International Journal of Energy Research 2020, 44, 6325 -6337.

AMA Style

Mohammed Anwar Hossain, Bamidele V. Ayodele, Huei R. Ong, Siti I. Mustapa, Chin K. Cheng, Maksudur R. Khan. Thermo‐catalytic conversion of greenhouse gases (CO 2 and CH 4 ) to CO‐rich hydrogen by CeO 2 modified calcium iron oxide supported nickel catalyst. International Journal of Energy Research. 2020; 44 (8):6325-6337.

Chicago/Turabian Style

Mohammed Anwar Hossain; Bamidele V. Ayodele; Huei R. Ong; Siti I. Mustapa; Chin K. Cheng; Maksudur R. Khan. 2020. "Thermo‐catalytic conversion of greenhouse gases (CO 2 and CH 4 ) to CO‐rich hydrogen by CeO 2 modified calcium iron oxide supported nickel catalyst." International Journal of Energy Research 44, no. 8: 6325-6337.

Review
Published: 19 March 2020 in Sustainability
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The transportation sector has been reported as a key contributor to the emissions of greenhouse gases responsible for global warming. Hence, the need for the introduction of electric vehicles (EVs) into the transportation sector. However, the competitiveness of the EVs with the conventional internal combustion engine vehicles has been a bone of contention. Life cycle cost analysis (LCCA) is an important tool that can be employed to determine the competitiveness of a product in its early stage of production. This review examines different published articles on LCCA of EVs using Scopus and Web of Science databases. The time trend of the published articles from 2001 to 2019 was examined. Moreover, the LCC obtained from the different models of EVs were compared. There was a growing interest in research on the LCC of EVs as indicated by the upward increase in the number of published articles. A variation in the LCC of the different EVs studied was observed to depend on several factors. Based on the LCC, EVs were found not yet competitive with conventional internal combustion engine cars due to the high cost of batteries. However, advancement in technologies with incentives could bring down the cost of EV batteries to make it competitive in the future.

ACS Style

Bamidele Victor Ayodele; Siti Indati Mustapa. Life Cycle Cost Assessment of Electric Vehicles: A Review and Bibliometric Analysis. Sustainability 2020, 12, 2387 .

AMA Style

Bamidele Victor Ayodele, Siti Indati Mustapa. Life Cycle Cost Assessment of Electric Vehicles: A Review and Bibliometric Analysis. Sustainability. 2020; 12 (6):2387.

Chicago/Turabian Style

Bamidele Victor Ayodele; Siti Indati Mustapa. 2020. "Life Cycle Cost Assessment of Electric Vehicles: A Review and Bibliometric Analysis." Sustainability 12, no. 6: 2387.

Research article
Published: 12 March 2020 in Journal of Chemical Technology & Biotechnology
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BACKGROUND The advanced oxidation process using photocatalysts has been proven to be an efficient technique used for the degradation of organic pollutants in wastewater. However, there exists a non‐linear relationship between the process parameters of the photodegradation reaction which needs to be well‐understood for the design of an efficient photoreactor. This study employed a backpropagation artificial neural network (BPANN) for the modeling of photocatalytic degradation of indole, anthraquinone dye and methyl blue using undoped and Ag+ doped TiO2 catalysts. RESULTS A Levenberg‐Marquardt algorithm was employed to train the backpropagation artificial neural network by varying the hidden neurons to obtained an optimized architecture. Optimized architectures with 3 14 1, 4 12 1 and 3 16 1 consists of the input layers, hidden layer, and the output layer were obtained using the datasets from photodegradation of indole, anthraquinone dye and methyl blue, respectively. The optimized BPANN accurately predicts the indole, anthraquinone dye and methyl blue degradation as a function of colour removal from the wastewater. High coefficient of determination (R2) of 0.999, 0.961, and 0.993 were obtained for the prediction of the photodegradation of indole, anthraquinone dye and methyl blue, respectively with over 95% confidence level. The study revealed that dye concentration, catalyst dosage and reaction time have the highest level of importance for the photodegradation of indole, anthraquinone dye and methyl blue, respectively. CONCLUSION This study has demonstrated the robustness of backpropagation artificial neural network for predictive modelling of photodegradation of organic pollutants such as indole, anthraquinone dye, and methyl blue. This article is protected by copyright. All rights reserved.

ACS Style

Bamidele Victor Ayodele; May Ali Alsaffar; Siti Indati Mustapa; Dai‐Viet N. Vo. Back‐propagation neural networks modeling of photocatalytic degradation of organic pollutants using TiO 2 ‐based photocatalysts. Journal of Chemical Technology & Biotechnology 2020, 1 .

AMA Style

Bamidele Victor Ayodele, May Ali Alsaffar, Siti Indati Mustapa, Dai‐Viet N. Vo. Back‐propagation neural networks modeling of photocatalytic degradation of organic pollutants using TiO 2 ‐based photocatalysts. Journal of Chemical Technology & Biotechnology. 2020; ():1.

Chicago/Turabian Style

Bamidele Victor Ayodele; May Ali Alsaffar; Siti Indati Mustapa; Dai‐Viet N. Vo. 2020. "Back‐propagation neural networks modeling of photocatalytic degradation of organic pollutants using TiO 2 ‐based photocatalysts." Journal of Chemical Technology & Biotechnology , no. : 1.

Journal article
Published: 06 March 2020 in IOP Conference Series: Materials Science and Engineering
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The energy demand in Iraq has increased in the past decade as a result of growth in population and industrialization. Although, most of the energy demand is being met using energy derived from fossil fuels, which are fast depleting and have the problem of emission of greenhouse gases when combusted. Recently, there have been an increasing awareness in the quest for alternative source of cleaner and sustainable energy production. One of such alternatives is hydrogen energy, which has been tagged as the energy of the future with zero-emission when combusted with oxygen. Moreover, hydrogen has wide applications in electrochemical cells for electricity production or as fuel in internal combustion engines for powering vehicles. The present study gives an overview of the prospect and challenges of renewable hydrogen production in Iraq. Moreover, the availability of the different feedstocks for the production of renewable hydrogen as well as the state-of-the-art in Iraqi context were examined. The prospect and challenges in the production of renewable hydrogen in Iraq were also presented.

ACS Style

May Ali Alsaffar; Bamidele Ayodele; Mohamed A. Abdel Ghany; Zainab Yousif Shnain; Siti Indati Mustapa. The prospect and challenges of renewable hydrogen production in Iraq. IOP Conference Series: Materials Science and Engineering 2020, 737, 1 .

AMA Style

May Ali Alsaffar, Bamidele Ayodele, Mohamed A. Abdel Ghany, Zainab Yousif Shnain, Siti Indati Mustapa. The prospect and challenges of renewable hydrogen production in Iraq. IOP Conference Series: Materials Science and Engineering. 2020; 737 ():1.

Chicago/Turabian Style

May Ali Alsaffar; Bamidele Ayodele; Mohamed A. Abdel Ghany; Zainab Yousif Shnain; Siti Indati Mustapa. 2020. "The prospect and challenges of renewable hydrogen production in Iraq." IOP Conference Series: Materials Science and Engineering 737, no. : 1.