Maximizing Green Hydrogen Production from Water Electrocatalysis: Modeling and Optimization
Abstract
:1. Introduction
- A reliable ANFIS model to replicate the electrolysis of water was developed.
- For the first time, an innovative application of the pelican optimization algorithm was developed to determine the optimum values for the amount of catalyst, electrolysis time, and electric voltage.
- Demonstrating the superiority and robustness of the proposed methodology.
- Boosting the production of hydrogen from the water electrolysis process.
2. Experimental Methods
3. Methodology
3.1. ANFIS Modeling
3.2. Pelican Optimization Algorithm (POA)
- Phase 1: Moving toward Prey (Exploration Phase)
- Phase 2: Winging on the Water Surface (Exploitation Phase)
4. Results and Discussion
4.1. Modeling Phase
4.2. Optimization Results
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
ANFIS Rules |
‘1. If (in1 is in1cluster1) and (in2 is in2cluster1) and (in3 is in3cluster1) then (Output is out1cluster1) (1)’ |
‘2. If (in1 is in1cluster2) and (in2 is in2cluster2) and (in3 is in3cluster2) then (Output is out1cluster2) (1)’ |
‘3. If (in1 is in1cluster3) and (in2 is in2cluster3) and (in3 is in3cluster3) then (Output is out1cluster3) (1)’ |
‘4. If (in1 is in1cluster4) and (in2 is in2cluster4) and (in3 is in3cluster4) then (Output is out1cluster4) (1)’ |
‘5. If (in1 is in1cluster5) and (in2 is in2cluster5) and (in3 is in3cluster5) then (Output is out1cluster5) (1)’ |
‘6. If (in1 is in1cluster6) and (in2 is in2cluster6) and (in3 is in3cluster6) then (Output is out1cluster6) (1)’ |
‘7. If (in1 is in1cluster7) and (in2 is in2cluster7) and (in3 is in3cluster7) then (Output is out1cluster7) (1)’ |
‘8. If (in1 is in1cluster8) and (in2 is in2cluster8) and (in3 is in3cluster8) then (Output is out1cluster8) (1)’ |
‘9. If (in1 is in1cluster9) and (in2 is in2cluster9) and (in3 is in3cluster9) then (Output is out1cluster9) (1)’ |
‘10. If (in1 is in1cluster10) and (in2 is in2cluster10) and (in3 is in3cluster10) then (Output is out1cluster10) (1)’ |
References
- Olabi, A.G.; Wilberforce, T.; Elsaid, K.; Sayed, E.T.; Maghrabie, H.M.; Abdelkareem, M.A. Large Scale Application of Carbon Capture to Process Industries—A Review. J. Clean. Prod. 2022, 362, 132300. [Google Scholar] [CrossRef]
- Olabi, A.-G. 100% Sustainable Energy. Energy 2014, 77, 1–5. [Google Scholar] [CrossRef]
- Efimov, I.; Povarov, V.G.; Rudko, V.A. Comparison of UNIFAC and LSER Models for Calculating Partition Coefficients in the Hexane–Acetonitrile System Using Middle Distillate Petroleum Products as an Example. Ind. Eng. Chem. Res. 2022, 61, 9575–9585. [Google Scholar] [CrossRef]
- Abdelkareem, M.A.; Lootah, M.A.; Sayed, E.T.; Wilberforce, T.; Alawadhi, H.; Yousef, B.A.A.; Olabi, A.G. Fuel Cells for Carbon Capture Applications. Sci. Total Environ. 2021, 769, 144243. [Google Scholar] [CrossRef]
- Osman, A.I.; Hefny, M.; Abdel Maksoud, M.I.A.; Elgarahy, A.M.; Rooney, D.W. Recent Advances in Carbon Capture Storage and Utilisation Technologies: A Review. Environ. Chem. Lett. 2021, 19, 797–849. [Google Scholar] [CrossRef]
- Nassef, A.M.; Rezk, H.; Alahmer, A.; Abdelkareem, M.A. Maximization of CO2 Capture Capacity Using Recent RUNge Kutta Optimizer and Fuzzy Model. Atmosphere 2023, 14, 295. [Google Scholar] [CrossRef]
- Maghrabie, H.M.; Elsaid, K.; Sayed, E.T.; Abdelkareem, M.A.; Wilberforce, T.; Olabi, A.G. Building-Integrated Photovoltaic/Thermal (BIPVT) Systems: Applications and Challenges. Sustain. Energy Technol. Assess. 2021, 45, 101151. [Google Scholar] [CrossRef]
- Ahmed, S.D.; Al-Ismail, F.S.M.; Shafiullah, M.; Al-Sulaiman, F.A.; El-Amin, I.M. Grid Integration Challenges of Wind Energy: A Review. IEEE Access 2020, 8, 10857–10878. [Google Scholar] [CrossRef]
- Alsaqoor, S.; Marashli, A.; At-Tawarah, R.; Borowski, G.; Alahmer, A.; Aljabarin, N.; Beithou, N. Evaluation of Wind Energy Potential in View of the Wind Speed Parameters–A Case Study for the Southern Jordan. Adv. Sci. Technol. Res. J. 2022, 16, 275–285. [Google Scholar] [CrossRef]
- Pareek, A.; Dom, R.; Gupta, J.; Chandran, J.; Adepu, V.; Borse, P.H. Insights into Renewable Hydrogen Energy: Recent Advances and Prospects. Mater. Sci. Energy Technol. 2020, 3, 319–327. [Google Scholar] [CrossRef]
- Rezk, H.; Nassef, A.M.; Inayat, A.; Sayed, E.T.; Shahbaz, M.; Olabi, A.G. Improving the Environmental Impact of Palm Kernel Shell through Maximizing Its Production of Hydrogen and Syngas Using Advanced Artificial Intelligence. Sci. Total Environ. 2019, 658, 1150–1160. [Google Scholar] [CrossRef]
- Burton, N.A.; Padilla, R.V.; Rose, A.; Habibullah, H. Increasing the Efficiency of Hydrogen Production from Solar Powered Water Electrolysis. Renew. Sustain. Energy Rev. 2021, 135, 110255. [Google Scholar] [CrossRef]
- Abdelkareem, M.A.; Soudan, B.; Mahmoud, M.S.; Sayed, E.T.; AlMallahi, M.N.; Inayat, A.; Al Radi, M.; Olabi, A.G. Progress of Artificial Neural Networks Applications in Hydrogen Production. Chem. Eng. Res. Des. 2022, 182, 66–86. [Google Scholar] [CrossRef]
- Wilberforce, T.; Olabi, A.G.; Imran, M.; Sayed, E.T.; Abdelkareem, M.A. System Modelling and Performance Assessment of Green Hydrogen Production by Integrating Proton Exchange Membrane Electrolyser with Wind Turbine. Int. J. Hydrogen Energy 2023. [Google Scholar] [CrossRef]
- Salameh, T.; Sayed, E.T.; Abdelkareem, M.A.; Olabi, A.G.; Rezk, H. Optimal Selection and Management of Hybrid Renewable Energy System: Neom City as a Case Study. Energy Convers. Manag. 2021, 244, 114434. [Google Scholar] [CrossRef]
- Sharma, S.; Agarwal, S.; Jain, A. Significance of Hydrogen as Economic and Environmentally Friendly Fuel. Energies 2021, 14, 7389. [Google Scholar] [CrossRef]
- Qureshi, F.; Yusuf, M.; Kamyab, H.; Vo, D.-V.N.; Chelliapan, S.; Joo, S.-W.; Vasseghian, Y. Latest Eco-Friendly Avenues on Hydrogen Production towards a Circular Bioeconomy: Currents Challenges, Innovative Insights, and Future Perspectives. Renew. Sustain. Energy Rev. 2022, 168, 112916. [Google Scholar] [CrossRef]
- Tarkowski, M. On the Emergence of Sociotechnical Regimes of Electric Urban Water Transit Systems. Energies 2021, 14, 6111. [Google Scholar] [CrossRef]
- Mersin, K.; Bayirhan, I.; Gazioglu, C. Review of CO2 Emission and Reducing Methods in Maritime Transportation. Therm. Sci. 2019, 23, 2073–2079. [Google Scholar] [CrossRef]
- IMO. Third IMO GHG Study 2014 Executive Summary and Final Report; IMO: London, UK, 2014. [Google Scholar]
- Stark, C.; Xu, Y.; Zhang, M.; Yuan, Z.; Tao, L.; Shi, W. Study on Applicability of Energy-Saving Devices to Hydrogen Fuel Cell-Powered Ships. J. Mar. Sci. Eng. 2022, 10, 388. [Google Scholar] [CrossRef]
- Ritari, A.; Spoof-Tuomi, K.; Huotari, J.; Niemi, S.; Tammi, K. Emission Abatement Technology Selection, Routing and Speed Optimization of Hybrid Ships. J. Mar. Sci. Eng. 2021, 9, 944. [Google Scholar] [CrossRef]
- Kim, K.; Park, K.; Roh, G.; Choung, C.; Kwag, K.; Kim, W. Proposal of Zero-Emission Tug in South Korea Using Fuel Cell/Energy Storage System: Economic and Environmental Long-Term Impacts. J. Mar. Sci. Eng. 2023, 11, 540. [Google Scholar] [CrossRef]
- Wang, X.; Zhu, J.; Han, M. Industrial Development Status and Prospects of the Marine Fuel Cell: A Review. J. Mar. Sci. Eng. 2023, 11, 238. [Google Scholar] [CrossRef]
- Chun, K.W.; Kim, M.; Hur, J.-J. Development of a Marine LPG-Fueled High-Speed Engine for Electric Propulsion Systems. J. Mar. Sci. Eng. 2022, 10, 1498. [Google Scholar] [CrossRef]
- Shim, H.; Kim, Y.H.; Hong, J.-P.; Hwang, D.; Kang, H.J. Marine Demonstration of Alternative Fuels on the Basis of Propulsion Load Sharing for Sustainable Ship Design. J. Mar. Sci. Eng. 2023, 11, 567. [Google Scholar] [CrossRef]
- Ljulj, A.; Slapničar, V.; Grubišić, I.; Mihanović, L. Concept Design of a Hybrid Offshore Patrol Vessel. J. Mar. Sci. Eng. 2023, 11, 12. [Google Scholar] [CrossRef]
- Smyshlyaeva, K.I.; Rudko, V.A.; Kuzmin, K.A.; Povarov, V.G. Asphaltene Genesis Influence on the Low-Sulfur Residual Marine Fuel Sedimentation Stability. Fuel 2022, 328, 125291. [Google Scholar] [CrossRef]
- Ershov, M.A.; Savelenko, V.D.; Makhmudova, A.E.; Rekhletskaya, E.S.; Makhova, U.A.; Kapustin, V.M.; Mukhina, D.Y.; Abdellatief, T.M.M. Technological Potential Analysis and Vacant Technology Forecasting in Properties and Composition of Low-Sulfur Marine Fuel Oil (VLSFO and ULSFO) Bunkered in Key World Ports. J. Mar. Sci. Eng. 2022, 10, 1828. [Google Scholar] [CrossRef]
- Bolobov, V.I.; Latipov, I.U.; Popov, G.G.; Buslaev, G.V.; Martynenko, Y. V Estimation of the Influence of Compressed Hydrogen on the Mechanical Properties of Pipeline Steels. Energies 2021, 14, 6085. [Google Scholar] [CrossRef]
- Psaraftis, H.N.; Kontovas, C.A. Decarbonization of Maritime Transport: Is There Light at the End of the Tunnel? Sustainability 2020, 13, 237. [Google Scholar] [CrossRef]
- Rezk, H.; Olabi, A.G.; Abdelkareem, M.A.; Alami, A.H.; Sayed, E.T. Optimal Parameter Determination of Membrane Bioreactor to Boost Biohydrogen Production-Based Integration of ANFIS Modeling and Honey Badger Algorithm. Sustainability 2023, 15, 1589. [Google Scholar] [CrossRef]
- Li, S.; Kang, Q.; Baeyens, J.; Zhang, H.L.; Deng, Y.M. Hydrogen Production: State of Technology. In Proceedings of the 10th International Conference on Environment Science and Engineering (ICESE 2020), Vienna, Austria, 18–21 May 2020; IOP Publishing: Bristol, UK, 2020; Volume 544, p. 12011. [Google Scholar]
- Amin, M.; Shah, H.H.; Fareed, A.G.; Khan, W.U.; Chung, E.; Zia, A.; Farooqi, Z.U.R.; Lee, C. Hydrogen Production through Renewable and Non-Renewable Energy Processes and Their Impact on Climate Change. Int. J. Hydrog. Energy 2022, 47, 33112–33134. [Google Scholar] [CrossRef]
- Demirbas, A. Future Hydrogen Economy and Policy. Energy Sources Part B Econ. Plan. Policy 2017, 12, 172–181. [Google Scholar] [CrossRef]
- Mallouppas, G.; Yfantis, E.A. Decarbonization in Shipping Industry: A Review of Research, Technology Development, and Innovation Proposals. J. Mar. Sci. Eng. 2021, 9, 415. [Google Scholar] [CrossRef]
- Wang, W.; Liu, Y.; Zhen, L.; Wang, H. How to Deploy Electric Ships for Green Shipping. J. Mar. Sci. Eng. 2022, 10, 1611. [Google Scholar] [CrossRef]
- Kim, K.; Roh, G.; Kim, W.; Chun, K. A Preliminary Study on an Alternative Ship Propulsion System Fueled by Ammonia: Environmental and Economic Assessments. J. Mar. Sci. Eng. 2020, 8, 183. [Google Scholar] [CrossRef] [Green Version]
- Park, S.W.; Kim, J.H.; Seo, J.K. Explosion Characteristics of Hydrogen Gas in Varying Ship Ventilation Tunnel Geometries: An Experimental Study. J. Mar. Sci. Eng. 2022, 10, 532. [Google Scholar] [CrossRef]
- Lee, J.; Choi, Y.; Choi, J. Techno-Economic Analysis of NH3 Fuel Supply and Onboard Re-Liquefaction System for an NH3-Fueled Ocean-Going Large Container Ship. J. Mar. Sci. Eng. 2022, 10, 1500. [Google Scholar] [CrossRef]
- Bertagna, S.; Kouznetsov, I.; Braidotti, L.; Marinò, A.; Bucci, V. A Rational Approach to the Ecological Transition in the Cruise Market: Technologies and Design Compromises for the Fuel Switch. J. Mar. Sci. Eng. 2023, 11, 67. [Google Scholar] [CrossRef]
- Huang, J.; Fan, H.; Xu, X.; Liu, Z. Life Cycle Greenhouse Gas Emission Assessment for Using Alternative Marine Fuels: A Very Large Crude Carrier (VLCC) Case Study. J. Mar. Sci. Eng. 2022, 10, 1969. [Google Scholar] [CrossRef]
- Lee, G.N.; Kim, J.M.; Jung, K.H.; Park, H.; Jang, H.S.; Lee, C.S.; Lee, J.W. Environmental Life-Cycle Assessment of Eco-Friendly Alternative Ship Fuels (MGO, LNG, and Hydrogen) for 170 GT Nearshore Ferry. J. Mar. Sci. Eng. 2022, 10, 755. [Google Scholar] [CrossRef]
- Panić, I.; Cuculić, A.; Ćelić, J. Color-Coded Hydrogen: Production and Storage in Maritime Sector. J. Mar. Sci. Eng. 2022, 10, 1995. [Google Scholar] [CrossRef]
- Reusser, C.A.; Pérez Osses, J.R. Challenges for Zero-Emissions Ship. J. Mar. Sci. Eng. 2021, 9, 1042. [Google Scholar] [CrossRef]
- Litvinenko, V.S.; Tsvetkov, P.S.; Dvoynikov, M.V.; Buslaev, G.V. Barriers to Implementation of Hydrogen Initiatives in the Context of Global Energy Sustainable Development. J. Min. Inst. 2020, 244, 428–438. [Google Scholar]
- Dutta, S. Review on Solar Hydrogen: Its Prospects and Limitations. Energy Fuels 2021, 35, 11613–11639. [Google Scholar] [CrossRef]
- Badea, G.E.; Hora, C.; Maior, I.; Cojocaru, A.; Secui, C.; Filip, S.M.; Dan, F.C. Sustainable Hydrogen Production from Seawater Electrolysis: Through Fundamental Electrochemical Principles to the Most Recent Development. Energies 2022, 15, 8560. [Google Scholar] [CrossRef]
- Du, Z.; Chen, Q.; Guan, C.; Chen, H. Improvement and Optimization Configuration of Inland Ship Power and Propulsion System. J. Mar. Sci. Eng. 2023, 11, 135. [Google Scholar] [CrossRef]
- Widera, B. Renewable Hydrogen Implementations for Combined Energy Storage, Transportation and Stationary Applications. Therm. Sci. Eng. Prog. 2020, 16, 100460. [Google Scholar] [CrossRef]
- Xu, Y.; Wang, C.; Huang, Y.; Fu, J. Recent Advances in Electrocatalysts for Neutral and Large-Current-Density Water Electrolysis. Nano Energy 2021, 80, 105545. [Google Scholar] [CrossRef]
- Ustolin, F.; Campari, A.; Taccani, R. An Extensive Review of Liquid Hydrogen in Transportation with Focus on the Maritime Sector. J. Mar. Sci. Eng. 2022, 10, 1222. [Google Scholar] [CrossRef]
- Wang, Z.; Wang, D.; Zhao, F.; Han, F.; Ji, Y.; Cai, W. Hydrogen Refueling Stations and Carbon Emission Reduction of Coastal Expressways: A Deployment Model and Multi-Scenario Analysis. J. Mar. Sci. Eng. 2022, 10, 992. [Google Scholar] [CrossRef]
- Rashid, M.D.; Al Mesfer, M.K.; Naseem, H.; Danish, M. Hydrogen Production by Water Electrolysis: A Review of Alkaline Water Electrolysis, PEM Water Electrolysis and High Temperature Water Electrolysis. Int. J. Eng. Adv. Technol. 2015. [Google Scholar]
- Gebreslase, G.A.; Martínez-Huerta, M.V.; Sebastián, D.; Lázaro, M.J. NiCoP/CoP Sponge-like Structure Grown on Stainless Steel Mesh as a High-Performance Electrocatalyst for Hydrogen Evolution Reaction. Electrochim. Acta 2023, 438, 141538. [Google Scholar] [CrossRef]
- Theerthagiri, J.; Cardoso, E.S.F.; Fortunato, G.V.; Casagrande, G.A.; Senthilkumar, B.; Madhavan, J.; Maia, G. Highly Electroactive Ni Pyrophosphate/Pt Catalyst toward Hydrogen Evolution Reaction. ACS Appl. Mater. Interfaces 2019, 11, 4969–4982. [Google Scholar] [CrossRef]
- Hu, S.; Tan, Y.; Feng, C.; Wu, H.; Zhang, J.; Mei, H. Synthesis of N Doped NiZnCu-Layered Double Hydroxides with Reduced Graphene Oxide on Nickel Foam as Versatile Electrocatalysts for Hydrogen Production in Hybrid-Water Electrolysis. J. Power Sources 2020, 453, 227872. [Google Scholar] [CrossRef]
- Ogumerem, G.S.; Pistikopoulos, E.N. Parametric Optimization and Control for a Smart Proton Exchange Membrane Water Electrolysis (PEMWE) System. J. Process. Control. 2020, 91, 37–49. [Google Scholar] [CrossRef]
- Kumar, S.S.; Himabindu, V. Hydrogen Production by PEM Water Electrolysis–A Review. Mater. Sci. Energy Technol. 2019, 2, 442–454. [Google Scholar]
- Kang, Z.; Alia, S.M.; Young, J.L.; Bender, G. Effects of Various Parameters of Different Porous Transport Layers in Proton Exchange Membrane Water Electrolysis. Electrochim. Acta 2020, 354, 136641. [Google Scholar] [CrossRef]
- Grigoriev, S.A.; Fateev, V.N.; Bessarabov, D.G.; Millet, P. Current Status, Research Trends, and Challenges in Water Electrolysis Science and Technology. Int. J. Hydrogen Energy 2020, 45, 26036–26058. [Google Scholar] [CrossRef]
- Okolie, J.A.; Epelle, E.I.; Nanda, S.; Castello, D.; Dalai, A.K.; Kozinski, J.A. Modeling and Process Optimization of Hydrothermal Gasification for Hydrogen Production: A Comprehensive Review. J. Supercrit. Fluids 2021, 173, 105199. [Google Scholar] [CrossRef]
- Yin, L.; Ju, Y. Process Optimization and Analysis of a Novel Hydrogen Liquefaction Cycle. Int. J. Refrig. 2020, 110, 219–230. [Google Scholar] [CrossRef]
- Zhu, J.; Wang, G.; Li, Y.; Duo, Z.; Sun, C. Optimization of Hydrogen Liquefaction Process Based on Parallel Genetic Algorithm. Int. J. Hydrogen Energy 2022, 47, 27038–27048. [Google Scholar] [CrossRef]
- Li, X.; Zhao, L.; Liu, Z. Topological Optimization of Continuum Structure Based on ANSYS. MATEC Web Conf. 2017, 95, 7020. [Google Scholar] [CrossRef] [Green Version]
- Alahmer, A.; Ajib, S. Solar Cooling Technologies: State of Art and Perspectives. Energy Convers. Manag. 2020, 214, 112896. [Google Scholar] [CrossRef]
- Kargbo, H.O.; Zhang, J.; Phan, A.N. Optimisation of Two-Stage Biomass Gasification for Hydrogen Production via Artificial Neural Network. Appl. Energy 2021, 302, 117567. [Google Scholar] [CrossRef]
- Alahmer, A.; Alsaqoor, S.; Borowski, G. Effect of Parameters on Moisture Removal Capacity in the Desiccant Cooling Systems. Case Stud. Therm. Eng. 2019, 13, 100364. [Google Scholar] [CrossRef]
- Wilberforce, T.; Olabi, A.G.; Monopoli, D.; Dassisti, M.; Sayed, E.T.; Abdelkareem, M.A. Design Optimization of Proton Exchange Membrane Fuel Cell Bipolar Plate. Energy Convers. Manag. 2023, 277, 116586. [Google Scholar] [CrossRef]
- Mohammed, R.H.; Radwan, A.; Rezk, A.; Olabi, A.G.; Sharma, V.; Hossain, A.K.; Alaswad, A.; Abdelkareem, M.A. Energy and Exergy Study of the Integrated Adsorption-Absorption System Driven by Transient Heat Sources for Cooling and Desalination. Energy Convers. Manag. 2023, 277, 116614. [Google Scholar] [CrossRef]
- Alahmer, A.; Alsaqoor, S. Simulation and Optimization of Multi-Split Variable Refrigerant Flow Systems. Ain Shams Eng. J. 2018, 9, 1705–1715. [Google Scholar] [CrossRef]
- Nassef, A.M.; Fathy, A.; Sayed, E.T.; Abdelkareem, M.A.; Rezk, H.; Tanveer, W.H.; Olabi, A.G. Maximizing SOFC Performance through Optimal Parameters Identification by Modern Optimization Algorithms. Renew. Energy 2019, 138, 458–464. [Google Scholar] [CrossRef]
- Alahmer, H.; Alahmer, A.; Alkhazaleh, R.; Alrbai, M.; Alamayreh, M.I. Applied Intelligent Grey Wolf Optimizer (IGWO) to Improve the Performance of CI Engine Running on Emulsion Diesel Fuel Blends. Fuels 2023, 4, 35–57. [Google Scholar] [CrossRef]
- Olabi, A.G.; Abdelkareem, M.A.; Semeraro, C.; Al Radi, M.; Rezk, H.; Muhaisen, O.; Al-Isawi, O.A.; Sayed, E.T. Artificial Neural Networks Applications in Partially Shaded PV Systems. Therm. Sci. Eng. Prog. 2022, 37, 101612. [Google Scholar] [CrossRef]
- Alahmer, H.; Alahmer, A.; Alamayreh, M.I.; Alrbai, M.; Al-Rbaihat, R.; Al-Manea, A.; Alkhazaleh, R. Optimal Water Addition in Emulsion Diesel Fuel Using Machine Learning and Sea-Horse Optimizer to Minimize Exhaust Pollutants from Diesel Engine. Atmosphere 2023, 14, 449. [Google Scholar] [CrossRef]
- Sayed, E.T.; Rezk, H.; Abdelkareem, M.A.; Olabi, A.G. Artificial Neural Network Based Modelling and Optimization of Microalgae Microbial Fuel Cell. Int. J. Hydrogen Energy 2023. [Google Scholar] [CrossRef]
- Wilberforce, T.; Olabi, A.G.; Rezk, H.; Abdelaziz, A.Y.; Abdelkareem, M.A.; Sayed, E.T. Boosting the Output Power of PEM Fuel Cells by Identifying Best-Operating Conditions. Energy Convers. Manag. 2022, 270, 116205. [Google Scholar] [CrossRef]
- Nassef, A.M.; Sayed, E.T.; Rezk, H.; Abdelkareem, M.A.; Rodriguez, C.; Olabi, A.G. Fuzzy-Modeling with Particle Swarm Optimization for Enhancing the Production of Biodiesel from Microalga. Energy Sources Part A Recover. Util. Environ. Eff. 2019, 41, 2094–2103. [Google Scholar] [CrossRef]
- Salameh, T.; Sayed, E.T.; Olabi, A.G.; Hdaib, I.I.; Allan, Y.; Alkasrawi, M.; Abdelkareem, M.A. Adaptive Network Fuzzy Inference System and Particle Swarm Optimization of Biohydrogen Production Process. Fermentation 2022, 8, 483. [Google Scholar] [CrossRef]
- Alahmer, H.; Alahmer, A.; Alkhazaleh, R.; Alrbai, M. Exhaust Emission Reduction of a SI Engine Using Acetone–Gasoline Fuel Blends: Modeling, Prediction, and Whale Optimization Algorithm. Energy Rep. 2023, 9, 77–86. [Google Scholar] [CrossRef]
- Alahmer, H.; Alahmer, A.; Alkhazaleh, R.; Al-Amayreh, M.I. Modeling, Polynomial Regression, and Artificial Bee Colony Optimization of SI Engine Performance Improvement Powered by Acetone–Gasoline Fuel Blends. Energy Rep. 2023, 9, 55–64. [Google Scholar] [CrossRef]
- Alahmer, A.; Khalid, M.B.; Beithou, N.; Borowski, G.; Alsaqoor, S.; Alhendi, H. An Experimental Investigation into Improving the Performance of Thermoelectric Generators. J. Ecol. Eng. 2022, 23, 100–108. [Google Scholar] [CrossRef]
- Alahmer, A.; Alahmer, H.; Handam, A.; Rezk, H. Environmental Assessment of a Diesel Engine Fueled with Various Biodiesel Blends: Polynomial Regression and Grey Wolf Optimization. Sustainability 2022, 14, 1367. [Google Scholar] [CrossRef]
- Alahmer, A.; Rezk, H.; Aladayleh, W.; Mostafa, A.O.; Abu-Zaid, M.; Alahmer, H.; Gomaa, M.R.; Alhussan, A.A.; Ghoniem, R.M. Modeling and Optimization of a Compression Ignition Engine Fueled with Biodiesel Blends for Performance Improvement. Mathematics 2022, 10, 420. [Google Scholar] [CrossRef]
- Senthilraja, S.; Gangadevi, R.; Köten, H.; Marimuthu, R. Performance Assessment of a Solar Powered Hydrogen Production System and Its ANFIS Model. Heliyon 2020, 6, e05271. [Google Scholar] [CrossRef]
- Özgür, C.; Mert, M.E. Prediction and Optimization of the Process of Generating Green Hydrogen by Electrocatalysis: A Study Using Response Surface Methodology. Fuel 2022, 330, 125610. [Google Scholar] [CrossRef]
- Olabi, A.G.; Rezk, H.; Sayed, E.T.; Ghoniem, R.M.; Abdelkareem, M.A. Boosting Carbon Dioxide Adsorption Capacity Applying Jellyfish Optimization and ANFIS-Based Modelling. Ain Shams Eng. J. 2022, 14, 101931. [Google Scholar] [CrossRef]
- Rezk, H.; Mohammed, R.H.; Rashad, E.; Nassef, A.M. ANFIS-Based Accurate Modeling of Silica Gel Adsorption Cooling Cycle. Sustain. Energy Technol. Assess. 2022, 50, 101793. [Google Scholar] [CrossRef]
- Trojovský, P.; Dehghani, M. Pelican Optimization Algorithm: A Novel Nature-Inspired Algorithm for Engineering Applications. Sensors 2022, 22, 855. [Google Scholar] [CrossRef]
RMSE | Coefficient of Determination (R2) | ||||
---|---|---|---|---|---|
Train | Test | All | Train | Test | All |
3.4 × 10−6 | 0.2308 | 0.1371 | 1.00 | 0.9999 | 1.00 |
Method | Electrolysis Time (min) | Electric Voltage (V) | Catalyst Amount (μg) | Hydrogen Production mL | Prediction Error % |
---|---|---|---|---|---|
Dataset [86] | 30 | 3 | 11.35 | 106.2 | 0.0 |
RSM [86] | 30 | 3 | 11.35 | 103.677 | 2.38 |
ANFIS model | 30 | 3 | 11.35 | 105.254 | 0.89 |
Run | PSO | DO | SCA | POA | GWO | Run | PSO | DO | SCA | POA | GWO |
---|---|---|---|---|---|---|---|---|---|---|---|
1 | 105.45 | 105.451 | 105.45 | 105.451 | 105.451 | 16 | 105.336 | 105.451 | 105.148 | 105.451 | 105.449 |
2 | 105.451 | 105.45 | 105.292 | 105.45 | 105.451 | 17 | 79.048 | 105.451 | 104.418 | 105.45 | 105.451 |
3 | 105.45 | 105.451 | 105.442 | 105.451 | 105.45 | 18 | 105.448 | 105.449 | 105.44 | 105.451 | 105.451 |
4 | 105.45 | 105.45 | 105.449 | 105.451 | 105.451 | 19 | 105.43 | 105.451 | 105.111 | 105.451 | 105.451 |
5 | 79.172 | 105.45 | 105.408 | 105.451 | 105.448 | 20 | 105.45 | 105.451 | 104.978 | 105.451 | 105.43 |
6 | 104.888 | 105.451 | 105.155 | 105.449 | 105.446 | 21 | 105.451 | 105.449 | 104.836 | 105.449 | 105.45 |
7 | 105.335 | 105.451 | 102.785 | 105.451 | 105.443 | 22 | 105.382 | 105.272 | 105.328 | 105.451 | 105.449 |
8 | 104.454 | 105.445 | 103.963 | 105.451 | 105.449 | 23 | 105.444 | 105.451 | 104.523 | 105.451 | 105.437 |
9 | 105.45 | 105.451 | 105.446 | 105.451 | 105.396 | 24 | 105.447 | 102.993 | 105.055 | 105.451 | 105.45 |
10 | 105.45 | 105.451 | 105.438 | 105.451 | 105.418 | 25 | 105.225 | 101.712 | 100.278 | 105.449 | 104.674 |
11 | 105.448 | 102.875 | 105.349 | 105.451 | 105.45 | 26 | 105.45 | 99.94 | 105.448 | 105.448 | 105.427 |
12 | 105.306 | 105.451 | 105.423 | 105.45 | 105.45 | 27 | 105.45 | 101.317 | 105.09 | 105.451 | 105.45 |
13 | 105.45 | 105.451 | 104.842 | 105.45 | 105.44 | 28 | 105.397 | 101.042 | 105.232 | 105.451 | 105.448 |
14 | 105.45 | 105.451 | 105.303 | 105.45 | 105.444 | 29 | 104.98 | 105.451 | 105.088 | 105.451 | 105.446 |
15 | 105.441 | 105.449 | 105.45 | 105.446 | 105.424 | 30 | 79.255 | 105.45 | 104.682 | 105.451 | 105.448 |
PSO | DO | SCA | POA | GWO | |
---|---|---|---|---|---|
Best | 105.451 | 105.451 | 105.45 | 105.451 | 105.451 |
Worst | 79.048 | 99.94 | 100.278 | 105.446 | 104.674 |
Mean | 102.728 | 104.683 | 104.895 | 105.45 | 105.417 |
STD | 7.86 | 1.591 | 1.019 | 0.001 | 0.139 |
MEDIAN | 61.772 | 2.531 | 1.038 | 1.3 × 10−6 | 0.019 |
Variance | 105.446 | 105.45 | 105.193 | 105.451 | 105.448 |
Source | SS | df | MS | F | p-Value > F |
---|---|---|---|---|---|
Columns | 149.49 | 4 | 37.3737 | 2.76 | 0.0298 |
Error | 1960.79 | 145 | 13.5227 | ||
Total | 2110.29 | 194 |
Strategy | Electrolysis Time (min) | Electric Voltage (V) | Catalyst Amount (μg) | Hydrogen Production mL | Change % |
---|---|---|---|---|---|
Measured [86] | 30 | 3 | 10 | 104.1 | 0.0 |
RSM [86] | 30 | 3 | 11.35 | 103.677 | −0.4 |
ANFIS & POA | 30 | 3 | 11.9 | 105.45 | 1.3 |
Extending the constrains by 5% | |||||
ANFIS & POA | 31.5 | 3.15 | 13.3 | 111.58 | 7.19 |
Extending the constrains by 10% | |||||
ANFIS & POA | 33 | 3.3 | 14.24 | 117.23 | 12.6 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Rezk, H.; Olabi, A.G.; Abdelkareem, M.A.; Alahmer, A.; Sayed, E.T. Maximizing Green Hydrogen Production from Water Electrocatalysis: Modeling and Optimization. J. Mar. Sci. Eng. 2023, 11, 617. https://doi.org/10.3390/jmse11030617
Rezk H, Olabi AG, Abdelkareem MA, Alahmer A, Sayed ET. Maximizing Green Hydrogen Production from Water Electrocatalysis: Modeling and Optimization. Journal of Marine Science and Engineering. 2023; 11(3):617. https://doi.org/10.3390/jmse11030617
Chicago/Turabian StyleRezk, Hegazy, A. G. Olabi, Mohammad Ali Abdelkareem, Ali Alahmer, and Enas Taha Sayed. 2023. "Maximizing Green Hydrogen Production from Water Electrocatalysis: Modeling and Optimization" Journal of Marine Science and Engineering 11, no. 3: 617. https://doi.org/10.3390/jmse11030617
APA StyleRezk, H., Olabi, A. G., Abdelkareem, M. A., Alahmer, A., & Sayed, E. T. (2023). Maximizing Green Hydrogen Production from Water Electrocatalysis: Modeling and Optimization. Journal of Marine Science and Engineering, 11(3), 617. https://doi.org/10.3390/jmse11030617