An AI-Based Modelling of a Sorption Enhanced Chemical-Looping Methane Reforming Unit

Document Type : Research Article

Authors

1 Department of Civil, Chemical, Environmental and Materials Engineering, University of Bologna, Bologna, ITALY

2 Department of Biosystems Engineering, Isfahan University of Technology, Isfahan, I.R. IRAN

3 Energy Department, Politecnico di Milano, Milan, ITALY

4 Department of Chemical Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, I.R. IRAN

Abstract

Hydrogen as a green fuel has attracted enormous attention recently. Although hydrogen combustion produces no harmful by-products, hydrogen production can be almost disastrous. Hydrogen production mainly originates from fossil fuels, and more than 80% of hydrogen production is produced using fossil fuel reformation with CO2 formation as a by-product. Light hydrocarbon gases, predominantly methane, are extensively used for hydrogen production. While methane reforming is an economical and efficient process, decarburization of flue gas can be a challenge. Processes involving chemical looping can be used to mitigate these challenges, and they are favorable for simultaneous CO2 capture during hydrogen generation. Intelligent models can help have accurate monitoring of such plants. The aim of this paper is to provide an Artificial Intelligence (AI) based approach to model a Sorption-Enhanced Chemical-Looping Reforming (SECLR) unit. To this end first, a SECLR unit was simulated using ASPEN Plus version 11. Then the simulation results were validated by experimental data, and the SECLR unit went through 31000 different scenarios. The derived data from ASPEN Plus was modeled and simulated with machine learning methods to estimate the CH4 conversion, H2 Purity, and CO2 removal in the SECLR process. Artificial neural networks, ensemble learning, and support vector machine methods were developed to predict the CH4 conversion, H2 Purity, and CO2 removal in a SECLR unit. All three models could provide satisfactory results for predicting CH4 conversion, CO2 removal, and H2 Purity. According to statistical evaluations, Artificial Neural Network (ANN) outperformed Support Vector Machine (SVM) and ensemble learning in producing results with lower error values and higher accuracy with an average 5.23e-5 of error and R2 of 0.9864.

Keywords

Main Subjects

References

[1] Cerqueira P., Soria M., Madeira L.M., Hydrogen Production through Chemical Looping and Sorption-Enhanced Reforming of Olive Mill Wastewater, Thermodynamic and Energy Efficiency Analysis. Energy Conversion and Management, 238: 114146. (2021).
[2] Mutlu A.Y., Yucel O., An Artificial Intelligence Based Approach to Predicting Syngas Composition for Downdraft Biomass Gasification, Energy, 165: 895-901 (2018).
[3] Kalamaras C.M., Efstathiou A.M., "Hydrogen Production Technologies: Current State and Future Developments", In Conference Papers In Science., Hindawi (2013).
[4] Powell J., et al., Optimisation of a Sorption-Enhanced Chemical Looping Steam Methane Reforming Process, Chemical Engineering Research and Design,. 173:183-192 (2021).
[5] Norouzi N., Kalantari G., Talebi S., Combination of Renewable Energy in the Refinery, with Carbon Emissions Approach, Biointerface Res. Appl. Chem., 10(4): 5780-5786 (2020).
[6] Norouzi N., et al., Heavy Oil Thermal Conversion and Refinement to the Green Petroleum: a Petrochemical refinement plant using the Sustainable Formic Acid for the Process, Biointerface Res. Appl. Chem., 10(5): 6088-6100 (2020).
[7] Kartal F., Özveren U., A Deep Learning Approach for Prediction of Syngas Lower Heating Value from CFB Gasifier in Aspen Plus, Energy., 209: 118457 (2020).
[8] Zhao Y., et al., Thermodynamic Analysis of a New Chemical Looping Process for Syngas Production with Simultaneous CO2 Capture and Utilization, Energy Conversion and Management, 171: 1685-1696 (2018).
[9] Saithong N., et al., Thermodynamic Analysis of the Novel Chemical Looping Process for Two-Grade Hydrogen Production with CO2 Capture, Energy Conversion and Management, 180: 325-337 (2019).
[10] Mattisson T., Lyngfelt A., Applications of Chemical-Looping Combustion with Capture of CO2, Second Nordic Minisymposium on CO2 Capture and Storage, Göteborg, Sweden, (2001).
[11] Di Z., et al., Thermodynamic Analysis on the Parametric Optimization of a Novel Chemical Looping Methane Reforming in the Separated Productions of H2 and CO, Energy Conversion and Management, 192: 171-179 (2019).
[12] Zhang Q., et al., Multifunctional Ni-Based Oxygen Carrier for H2 Production by Sorption Enhanced Chemical Looping Reforming of Ethanol, Fuel Processing Technology, 221: 106953 (2021).
[13] Li F., et al., Modelling of a Post-Combustion CO2 Capture Process Using Neural Networks, Fuel, 151: 156-163 (2015).
[14] Khajehpour H., Norouzi N., Fani M., An Exergetic Model for the Ambient Air Temperature Impacts on the Combined Power Plants and its Management Using the Genetic Algorithm, International Journal of Air-Conditioning and Refrigeration, 29: (2021).
[15] Norouzi N., et al., A 4E Analysis of Renewable formic Acid Synthesis from the Electrochemical Reduction of Carbon Dioxide and Water: Studying Impacts of the Anolyte Material on the Performance of the Process, Journal of Cleaner Production, 293: 126149 (2021).
[16] Norouzi N., et al., Energy, Exergy, and Exergoeconomic (3E) Analysis of Gas Liquefaction and Gas Associated Liquids Recovery Co-Process Based on the Mixed Fluid Cascade Refrigeration Systems, Iran. J. Chem. Chem. Eng. (IJCCE), 41(4): 1391-1410 (2022).
[17] Norouzi N., Talebi S., Exergy, Economical and Environmental Analysis of a Natural Gas Direct Chemical Looping Carbon Capture and Formic Acid-Based Hydrogen Storage System, Iranian Journal of Chemistry and Chemical Engineering (IJCCE), 41(4): 1436-1457 (2022).
[18] Norouzi N., Hydrogen Production in the Light of Sustainability: A Comparative Study on the Hydrogen Production Technologies Using the Sustainability Index Assessment Method, Nuclear Engineering and Technology, 54(4): 1288-1294 (2022).
[19] Norouzi N., The Pahlev Reliability Index: A Measurement for the Resilience of Power Generation Technologies Versus Climate Change, Nuclear Engineering and Technology, 53(5): 1658-1663 (2021).
[20] Norouzi N., Fani M., Talebi S., Exergetic Design and Analysis of a Nuclear SMR Reactor Tetrageneration (Combined Water, Heat, Power, and Chemicals) with Designed PCM Energy Storage and a CO2 Gas Turbine Inner Cycle, Nuclear Engineering and Technology, 53(2): 677-687 (2021).
[21] Khajehpour H., et al., Exergy Analysis and Optimization of Natural Gas Liquids Recovery Unit, International Journal of Air-Conditioning and Refrigeration, 29(01): 2150005 (2021).
[22] Norouzi N., Shiva N., Khajehpour H., Optimization of Energy Consumption in the Process of Dehumidification of Natural Gas, Biointerface Research in Applied Chemistry, 11: 14634-14639 (2021).
[23] Norouzi N., et al., Exergy and Exergoeconomic Analysis of Hydrogen and Power Cogeneration Using an HTR Plant, Nuclear Engineering and Technology, 53(8): 2753-2760 (2021).
[24] Norouzi N., H Khajehpour, Simulation of Methane Gas Production Process from Animal Waste in a Discontinuous Bioreactor, Biointerface Research in Applied Chemistry, 11:13850-13859 (2021).
[25] Norouzi N., Talebi S., Najafi P., Thermal-Hydraulic Efficiency of a Modular Reactor Power Plant by Using the Second Law of Thermodynamic, Annals of Nuclear Energy, 151:107936 (2021).
[26] Khajehpour H., Norouzi N., Fani M., An Exergetic Model for the Ambient Air Temperature Impacts on the Combined Power Plants and its Management Using the Genetic Algorithm, International Journal of Air-Conditioning and Refrigeration, 29(01): 2150008 (2021).
[27] Norouzi N., 4E Analysis and Design of a Combined Cycle with a Geothermal Condensing System in Iranian Moghan Diesel Power Plant, International Journal of Air-Conditioning and Refrigeration, 28(03): 2050022 (2020).
[28] Shalaby A., et al., A Machine Learning Approach for Modeling and Optimization of a CO­ Post-Combustion Capture Unit, Energy, 215: 119113 (2021).
[29] Bai Z., et al., Modelling of a Post-Combustion CO2 Capture Process Using Bootstrap Aggregated Extreme Learning Machines, in "Computer Aided Chemical Engineering", Elsevier. 2007-2012 (2016).
[30] Valera V.Y., Codolo M.C., Martins T.D., Artificial Neural Network for Prediction of SO2 Removal and Volumetric Mass Transfer Coefficient in Spray Tower, Chemical Engineering Research and Design, 170:1-12 (2021).
[31] Rydén M., Ramos P., H2 Production with CO2 Capture by Sorption Enhanced Chemical-Looping Reforming Using NiO as Oxygen Carrier and CaO as CO2 Sorbent, Fuel Processing Technology,. 96: 27-36 (2012).
[32] Elmaz F., Yücel Ö., Mutlu A.Y., Predictive Modeling of Biomass Gasification with Machine Learning-Based Regression Methods, Energy, 191: 116541 (2020).
[33] Ahmad, I., et al., Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions, Energies, 14(16): 5072 (2021).
[34] Nkulikiyinka P., et al., Prediction of Sorption Enhanced Steam Methane Reforming Products from Machine Learning Based Soft-Sensor Models, Energy and AI, 2:100037 (2020). 

Files

Share

How to cite

Statistics