{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T21:15:59Z","timestamp":1783458959454,"version":"3.55.0"},"reference-count":60,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100006319","name":"Centre National pour la Recherche Scientifique et Technique","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100006319","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers &amp; Chemical Engineering"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.compchemeng.2026.109745","type":"journal-article","created":{"date-parts":[[2026,6,7]],"date-time":"2026-06-07T05:46:19Z","timestamp":1780811179000},"page":"109745","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Data-driven model to control the alkaline electrolyzer efficiency, for hydrogen production"],"prefix":"10.1016","volume":"213","author":[{"given":"Bouchra","family":"Oussmou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0534-6222","authenticated-orcid":false,"given":"Afaf","family":"Saaidi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Souad","family":"Abderafi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"25","key":"10.1016\/j.compchemeng.2026.109745_bib0001","doi-asserted-by":"crossref","first-page":"13063","DOI":"10.1016\/j.ijhydene.2014.07.001","article-title":"Influence of operation parameters in the modeling of alkaline water electrolyzers for hydrogen production","volume":"39","author":"Amores","year":"2014","journal-title":"Int. J. Hydrog. Energy"},{"key":"10.1016\/j.compchemeng.2026.109745_bib0002","article-title":"How properly are we interpreting the Tafel lines in energy conversion electrocatalysis?","volume":"29","author":"Anantharaj","year":"2022","journal-title":"Mater. Today Energy"},{"key":"10.1016\/j.compchemeng.2026.109745_bib0003","article-title":"How properly are we interpreting the Tafel lines in energy conversion electrocatalysis?","volume":"29","author":"Anantharaj","year":"2022","journal-title":"Mater. Today Energy"},{"key":"10.1016\/j.compchemeng.2026.109745_bib0004","doi-asserted-by":"crossref","DOI":"10.1016\/j.biortech.2022.128062","article-title":"Interpretable machine learning to model biomass and waste gasification","volume":"364","author":"Ascher","year":"2022","journal-title":"Bioresour. Technol"},{"key":"10.1016\/j.compchemeng.2026.109745_bib0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.cej.2024.155486","article-title":"Statistical analysis and comprehensive optimisation of zero-gap electrolyser: transitioning catalysts from laboratory to industrial scale","volume":"498","author":"Attar","year":"2024","journal-title":"Chem. Eng. J."},{"issue":"14","key":"10.1016\/j.compchemeng.2026.109745_bib0006","doi-asserted-by":"crossref","first-page":"5365","DOI":"10.3390\/en16145365","article-title":"Dynamics of gas generation in porous electrode alkaline electrolysis cells: an investigation and optimization using machine learning","volume":"16","author":"Babay","year":"2023","journal-title":"Energies"},{"issue":"1","key":"10.1016\/j.compchemeng.2026.109745_bib0007","doi-asserted-by":"crossref","DOI":"10.1149\/1945-7111\/abda57","article-title":"Evaluation of diaphragms and membranes as separators for alkaline water electrolysis","volume":"168","author":"Brauns","year":"2021","journal-title":"J. Electrochem. Soc."},{"issue":"6","key":"10.1016\/j.compchemeng.2026.109745_bib0008","doi-asserted-by":"crossref","DOI":"10.1149\/1945-7111\/acd9f1","article-title":"Model-based analysis and optimization of pressurized alkaline water electrolysis powered by renewable energy","volume":"170","author":"Brauns","year":"2023","journal-title":"J. Electrochem. Soc."},{"issue":"1","key":"10.1016\/j.compchemeng.2026.109745_bib0009","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"issue":"16","key":"10.1016\/j.compchemeng.2026.109745_bib0010","first-page":"140","article-title":"Digital twin of PEM electrolyzers based on phenomenological models","volume":"59","author":"Camacho","year":"2025","journal-title":"IFAC-Pap."},{"key":"10.1016\/j.compchemeng.2026.109745_bib0011","doi-asserted-by":"crossref","DOI":"10.1016\/j.rineng.2022.100794","article-title":"Prediction of concrete porosity using machine learning","volume":"17","author":"Cao","year":"2023","journal-title":"Results Eng."},{"key":"10.1016\/j.compchemeng.2026.109745_bib0012","doi-asserted-by":"crossref","first-page":"e623","DOI":"10.7717\/peerj-cs.623","article-title":"The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation","volume":"7","author":"Chicco","year":"2021","journal-title":"Peerj Comput. Sci."},{"key":"10.1016\/j.compchemeng.2026.109745_bib0013","series-title":"2013 IEEE International Conference on Acoustics, Speech and Signal Processing","first-page":"8609","article-title":"Improving deep neural networks for LVCSR using rectified linear units and dropout","author":"Dahl","year":"2013"},{"key":"10.1016\/j.compchemeng.2026.109745_bib0014","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1016\/j.ijhydene.2024.08.157","article-title":"Advances in green hydrogen production through alkaline water electrolysis: a comprehensive review","volume":"83","author":"Dash","year":"2024","journal-title":"Int. J. Hydrog. Energy"},{"issue":"82","key":"10.1016\/j.compchemeng.2026.109745_bib0015","doi-asserted-by":"crossref","first-page":"34773","DOI":"10.1016\/j.ijhydene.2022.08.075","article-title":"Optimal operating parameters for advanced alkaline water electrolysis","volume":"47","author":"De Groot","year":"2022","journal-title":"Int. J. Hydrog. Energy"},{"key":"10.1016\/j.compchemeng.2026.109745_bib0016","doi-asserted-by":"crossref","DOI":"10.1016\/j.chemosphere.2020.126254","article-title":"Modeling and optimization of imidacloprid degradation by catalytic percarbonate oxidation using artificial neural network and Box-Behnken experimental design","volume":"251","author":"de Luna","year":"2020","journal-title":"Chemosphere"},{"key":"10.1016\/j.compchemeng.2026.109745_bib0017","doi-asserted-by":"crossref","DOI":"10.1016\/j.jpowsour.2022.231532","article-title":"Analysis of the effect of characteristic parameters and operating conditions on exergy efficiency of alkaline water electrolyzer","volume":"537","author":"Ding","year":"2022","journal-title":"J. Power. Sources."},{"key":"10.1016\/j.compchemeng.2026.109745_bib0018","doi-asserted-by":"crossref","first-page":"1013","DOI":"10.1016\/j.renene.2021.01.060","article-title":"Determination of design parameters to minimize LCOE, for a 1 MWe CSP plant in different sites","volume":"169","author":"El Hamdani","year":"2021","journal-title":"Renew. Energy"},{"issue":"1","key":"10.1016\/j.compchemeng.2026.109745_bib0019","doi-asserted-by":"crossref","first-page":"1","DOI":"10.23919\/CHAIN.2025.000003","article-title":"Integrating digital twins and machine learning for advanced control in green hydrogen production","volume":"2","author":"Feng","year":"2025","journal-title":"Chain"},{"issue":"1","key":"10.1016\/j.compchemeng.2026.109745_bib0020","first-page":"1","article-title":"Multivariate adaptive regression splines","volume":"19","author":"Friedman","year":"1991","journal-title":"Ann. Stat."},{"key":"10.1016\/j.compchemeng.2026.109745_bib0021","first-page":"1189","article-title":"Greedy function approximation: a gradient boosting machine","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"issue":"4","key":"10.1016\/j.compchemeng.2026.109745_bib0022","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","article-title":"Stochastic gradient boosting","volume":"38","author":"Friedman","year":"2002","journal-title":"Comput. Stat. Data Anal."},{"issue":"9","key":"10.1016\/j.compchemeng.2026.109745_bib0023","doi-asserted-by":"crossref","first-page":"1365","DOI":"10.1002\/sim.1501","article-title":"Multiple additive regression trees with application in epidemiology","volume":"22","author":"Friedman","year":"2003","journal-title":"Stat. Med."},{"issue":"3","key":"10.1016\/j.compchemeng.2026.109745_bib0024","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/j.ijhydene.2006.10.062","article-title":"A review of specific conductivities of potassium hydroxide solutions for various concentrations and temperatures","volume":"32","author":"Gilliam","year":"2007","journal-title":"Int J Hydrog. Energy"},{"issue":"19","key":"10.1016\/j.compchemeng.2026.109745_bib0025","doi-asserted-by":"crossref","first-page":"13895","DOI":"10.1016\/j.ijhydene.2012.07.015","article-title":"New multi-physics approach for modelling and design of alkaline electrolyzers","volume":"37","author":"Hammoudi","year":"2012","journal-title":"Int J Hydrog. Energy"},{"key":"10.1016\/j.compchemeng.2026.109745_bib0026","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.jpowsour.2013.10.086","article-title":"Simulation tool based on a physics model and an electrical analogy for an alkaline electrolyser","volume":"250","author":"Henao","year":"2014","journal-title":"J. Power. Sources."},{"key":"10.1016\/j.compchemeng.2026.109745_bib0027","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2022.120099","article-title":"A comprehensive review of alkaline water electrolysis mathematical modeling","volume":"327","author":"Hu","year":"2022","journal-title":"Appl. Energy"},{"key":"10.1016\/j.compchemeng.2026.109745_bib0028","doi-asserted-by":"crossref","DOI":"10.1016\/j.molliq.2021.115509","article-title":"Rheological behavior of stabilized diamond-graphene nanoplatelets hybrid nanosuspensions in mineral oil","volume":"328","author":"Ilyas","year":"2021","journal-title":"J. Mol. Liq."},{"key":"10.1016\/j.compchemeng.2026.109745_bib0029","doi-asserted-by":"crossref","DOI":"10.1016\/j.jpowsour.2021.230106","article-title":"Numerical modeling and analysis of the temperature effect on the performance of an alkaline water electrolysis system","volume":"506","author":"Jang","year":"2021","journal-title":"J. Power. Sources."},{"key":"10.1016\/j.compchemeng.2026.109745_bib0030","doi-asserted-by":"crossref","first-page":"5625","DOI":"10.33263\/BRIAC124.56255637","article-title":"Liquid density prediction of ethanol\/water, using artificial neural network","volume":"12","author":"Jbari","year":"2021","journal-title":"Biointerface Recsearch Appl. Chem."},{"key":"10.1016\/j.compchemeng.2026.109745_bib0031","series-title":"Internal Combustion Engine Division Fall Technical Conference","article-title":"Application of a rectified linear unit (ReLU) based artificial neural network to cetane number predictions","volume":"58318","author":"Kessler","year":"2017"},{"key":"10.1016\/j.compchemeng.2026.109745_bib0032","doi-asserted-by":"crossref","first-page":"13793","DOI":"10.1016\/j.egyr.2022.10.127","article-title":"An overview of water electrolysis technologies for green hydrogen production","volume":"8","author":"Kumar","year":"2022","journal-title":"Energy Rep."},{"issue":"23","key":"10.1016\/j.compchemeng.2026.109745_bib0033","doi-asserted-by":"crossref","first-page":"8425","DOI":"10.3390\/app10238425","article-title":"Evolutionary design optimization of an alkaline water electrolysis cell for hydrogen production","volume":"10","author":"Le Bideau","year":"2020","journal-title":"Appl. Sci."},{"key":"10.1016\/j.compchemeng.2026.109745_bib0034","doi-asserted-by":"crossref","DOI":"10.1016\/j.cej.2021.131285","article-title":"Machine learning aided supercritical water gasification for H2-rich syngas production with process optimization and catalyst screening","volume":"426","author":"Li","year":"2021","journal-title":"Chem. Eng. J."},{"issue":"3","key":"10.1016\/j.compchemeng.2026.109745_bib0035","doi-asserted-by":"crossref","DOI":"10.1063\/5.0258326","article-title":"Digital twin model development and validation for megawatt-scale alkaline water electrolysis","volume":"17","author":"Liang","year":"2025","journal-title":"J. Renew. Sustain. Energy"},{"key":"10.1016\/j.compchemeng.2026.109745_bib0036","unstructured":"Liashchynskyi, P., & Liashchynskyi, P. (2019). Grid search, random search, genetic algorithm: a big comparison for NAS (arXiv:1912.06059). arXiv. https:\/\/doi.org\/10.48550\/arXiv.1912.06059."},{"key":"10.1016\/j.compchemeng.2026.109745_bib0037","first-page":"2616","article-title":"Mathematical modeling and experimental validation for a 50 kW alkaline water electrolyzer","volume":"12","author":"Liu","year":"2024","journal-title":"Process. 2024"},{"issue":"15 April","key":"10.1016\/j.compchemeng.2026.109745_bib0038","article-title":"Evaluation of feature selection methods based on artificial neural network weights","volume":"168","author":"Lu\u00edza da Costa","year":"2021","journal-title":"Expert. Syst. Appl."},{"issue":"5","key":"10.1016\/j.compchemeng.2026.109745_bib0039","doi-asserted-by":"crossref","first-page":"178","DOI":"10.3390\/fluids6050178","article-title":"Rheological characterization of a concentrated phosphate slurry","volume":"6","author":"Maazioui","year":"2021","journal-title":"Fluids"},{"issue":"16","key":"10.1016\/j.compchemeng.2026.109745_bib0040","doi-asserted-by":"crossref","first-page":"13478","DOI":"10.1021\/acs.chemrev.2c00061","article-title":"Machine learning for electrocatalyst and photocatalyst design and discovery","volume":"122","author":"Mai","year":"2022","journal-title":"Chem. Rev"},{"key":"10.1016\/j.compchemeng.2026.109745_bib0041","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2025.126186","article-title":"Effect of exchange current density and charge transfer coefficient on performance characteristics of voltage of alkaline electrolysis","volume":"394","author":"Musa","year":"2025","journal-title":"Appl. Energy"},{"issue":"1","key":"10.1016\/j.compchemeng.2026.109745_bib0042","doi-asserted-by":"crossref","DOI":"10.1088\/1757-899X\/1279\/1\/012005","article-title":"Parametric modelling and optimization of alkaline electrolyzer for the production of green hydrogen","volume":"1279","author":"Niroula","year":"2023","journal-title":"IOP Conf. Ser.: Mater. Sci. Eng."},{"key":"10.1016\/j.compchemeng.2026.109745_bib0043","doi-asserted-by":"crossref","DOI":"10.1016\/j.rineng.2022.100815","article-title":"Clean technology selection of hydrogen production on an industrial scale in Morocco","volume":"17","author":"Ourya","year":"2023","journal-title":"Results Eng."},{"issue":"96","key":"10.1016\/j.compchemeng.2026.109745_bib0044","doi-asserted-by":"crossref","first-page":"37428","DOI":"10.1016\/j.ijhydene.2022.12.362","article-title":"Assessment of green hydrogen production in Morocco, using hybrid renewable sources (PV and wind)","volume":"48","author":"Ourya","year":"2023","journal-title":"Int J Hydrog. Energy"},{"key":"10.1016\/j.compchemeng.2026.109745_bib0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.rser.2025.116205","article-title":"Review of green hydrogen production technologies, to choose the optimal process of electrolysis-renewable energy","volume":"225","author":"Oussmou","year":"2026","journal-title":"Renew. Sustain. Energy Rev."},{"issue":"1","key":"10.1016\/j.compchemeng.2026.109745_bib0046","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-25842-3","article-title":"An experimentally validated model of electrolyte effects in alkaline water electrolysis","volume":"15","author":"Pinto","year":"2025","journal-title":"Sci. Rep."},{"issue":"10","key":"10.1016\/j.compchemeng.2026.109745_bib0047","first-page":"129","article-title":"Simple and precise approach for determination of ohmic contribution of diaphragms in alkaline water electrolysis","volume":"9","author":"Rodr\u00edguez","year":"2019","journal-title":"Membr. (Basel)"},{"key":"10.1016\/j.compchemeng.2026.109745_bib0048","doi-asserted-by":"crossref","DOI":"10.1016\/j.rineng.2023.101024","article-title":"Efficient machine learning model to predict dynamic viscosity in phosphoric acid production","volume":"18","author":"Saaidi","year":"2023","journal-title":"Results Eng."},{"key":"10.1016\/j.compchemeng.2026.109745_bib0049","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijhydene.2025.152830","article-title":"Single-input machine learning for gas evolution in membraneless alkaline water electrolysis: a comparative study of five regressors","volume":"199","author":"Sevim","year":"2026","journal-title":"Int J Hydrog. Energy"},{"issue":"24","key":"10.1016\/j.compchemeng.2026.109745_bib0050","first-page":"171","article-title":"A survey of forecast error measures","volume":"24","author":"Shcherbakov","year":"2013","journal-title":"World Appl Sci J"},{"key":"10.1016\/j.compchemeng.2026.109745_bib0051","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2024.108954","article-title":"Machine learning in PEM water electrolysis: a study of hydrogen production and operating parameters","volume":"194","author":"Shomope","year":"2025","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2026.109745_bib0052","doi-asserted-by":"crossref","first-page":"1120","DOI":"10.1016\/j.ijhydene.2024.08.184","article-title":"Integrative CFD and AI\/ML-based modeling for enhanced alkaline water electrolysis cell performance for hydrogen production","volume":"83","author":"Sirat","year":"2024","journal-title":"Int J Hydrog. Energy"},{"key":"10.1016\/j.compchemeng.2026.109745_bib0053","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1016\/j.jmsy.2022.06.015","article-title":"Digital twin modeling","volume":"64","author":"Tao","year":"2022","journal-title":"J. Manuf. Syst."},{"issue":"1","key":"10.1016\/j.compchemeng.2026.109745_bib0054","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1177\/875647939000600106","article-title":"Interpretation of the correlation coefficient: a basic review","volume":"6","author":"Taylor","year":"1990","journal-title":"J. Diagn. Med. Sonogr."},{"key":"10.1016\/j.compchemeng.2026.109745_bib0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.rineng.2021.100316","article-title":"A review of physics-based machine learning in civil engineering","volume":"13","author":"Vadyala","year":"2022","journal-title":"Results Eng."},{"key":"10.1016\/j.compchemeng.2026.109745_bib0056","doi-asserted-by":"crossref","DOI":"10.1016\/j.fuel.2024.132624","article-title":"Direct operational data-driven workflow for dynamic voltage prediction of commercial alkaline water electrolyzers based on artificial neural network (ANN)","volume":"376","author":"Wang","year":"2024","journal-title":"Fuel"},{"issue":"18","key":"10.1016\/j.compchemeng.2026.109745_bib0057","doi-asserted-by":"crossref","first-page":"7216","DOI":"10.1039\/D3GC01865B","article-title":"Machine learning-aided catalyst screening and multi-objective optimization for the indirect CO 2 hydrogenation to methanol and ethylene glycol process","volume":"25","author":"Yang","year":"2023","journal-title":"Green Chem."},{"issue":"2","key":"10.1016\/j.compchemeng.2026.109745_bib0058","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.gce.2024.04.004","article-title":"Machine learning-assisted prediction and optimization of solid oxide electrolysis cell for green hydrogen production","volume":"6","author":"Yang","year":"2025","journal-title":"Green Chem. Eng."},{"issue":"3","key":"10.1016\/j.compchemeng.2026.109745_bib0059","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/j.pecs.2009.11.002","article-title":"Recent progress in alkaline water electrolysis for hydrogen production and applications","volume":"36","author":"Zeng","year":"2010","journal-title":"Prog. Energy Combust. Sci."},{"key":"10.1016\/j.compchemeng.2026.109745_bib0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.egyai.2025.100657","article-title":"Machine learning-guided optimization of high-performance porous composite membranes for alkaline water electrolysis","volume":"22","author":"Zhao","year":"2025","journal-title":"Energy AI"}],"container-title":["Computers &amp; Chemical Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0098135426001985?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0098135426001985?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T20:39:15Z","timestamp":1783456755000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0098135426001985"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":60,"alternative-id":["S0098135426001985"],"URL":"https:\/\/doi.org\/10.1016\/j.compchemeng.2026.109745","relation":{},"ISSN":["0098-1354"],"issn-type":[{"value":"0098-1354","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Data-driven model to control the alkaline electrolyzer efficiency, for hydrogen production","name":"articletitle","label":"Article Title"},{"value":"Computers & Chemical Engineering","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compchemeng.2026.109745","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"109745"}}