{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T05:13:33Z","timestamp":1792214013690,"version":"4.4.0"},"reference-count":54,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T00:00:00Z","timestamp":1740096000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science Research Project of Hebei Education Department","award":["QN2019026"],"award-info":[{"award-number":["QN2019026"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The most important control parameters in the methanol distillation process, which are directly related to product quality and yield, are the temperature, pressure and water content of the finished product at the top of the column. In order to adapt to the development trend of modern industrial technology to be more accurate, faster and more stable, the fusion of multi-sensor data puts forward higher requirements. Traditional control methods, such as PID control and fuzzy control, have the disadvantages of low heterogeneous data processing capability, poor response speed and low control accuracy when dealing with complex industrial process detection and control. For the control of tower top temperature and pressure in the methanol distillation industry, this study innovatively combines generative artificial intelligence and a type II fuzzy neural network, using a GAN for data preprocessing and a type II fuzzy neural network for steady-state inverse prediction to construct the GAN-T2FNN temperature and pressure control model for an atmospheric pressure tower. Comparison experiments with other neural network models and traditional PID control models show that the GAN-T2FNN model has a better performance in terms of prediction accuracy and fitting effect, with a minimum MAE value of 0.1828, which is more robust, and an R2 Score of 0.9854, which is closer to 1, for the best overall model performance. Finally, the SHAP model was used to analyze the influence mechanism of various parameters on the temperature and pressure at the top of the atmospheric column, which provides a more comprehensive reference and guidance for the precise control of the methanol distillation process.<\/jats:p>","DOI":"10.3390\/s25051308","type":"journal-article","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:47:46Z","timestamp":1740109666000},"page":"1308","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Research on Fuzzy Control of Methanol Distillation Based on SHAP (SHapley Additive exPlanations) Interpretability and Generative Artificial Intelligence"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-1345-4460","authenticated-orcid":false,"given":"Yuhan","family":"Gong","sequence":"first","affiliation":[{"name":"College of Electrical Engineering, North China University of Science and Technology, Tangshan 063210, China"},{"name":"School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal 14300, Penang, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Science, North China University of Science and Technology, Tangshan 063210, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxian","family":"Ren","sequence":"additional","affiliation":[{"name":"College of \u2018Yisheng\u2019, North China University of Science and Technology, Tangshan 063210, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhike","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering, North China University of Science and Technology, Tangshan 063210, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1308-1984","authenticated-orcid":false,"given":"Mohamad Tarmizi","family":"Abu Seman","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal 14300, Penang, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"113281","DOI":"10.1016\/j.rser.2023.113281","article-title":"Statistical and analytical investigation of methanol applications, production technologies, value-chain and economy with a special focus on renewable methanol","volume":"179","author":"Shayan","year":"2023","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"133441","DOI":"10.1016\/j.fuel.2024.133441","article-title":"Parametric investigation of diesel\u2013methanol dual fuel marine engines with port and direct injection","volume":"381","author":"Karvounis","year":"2025","journal-title":"Fuel"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"112674","DOI":"10.1016\/j.jece.2024.112674","article-title":"Alternative sustainable routes to methanol production: Techno-economic and environmental assessment","volume":"12","author":"Scomazzon","year":"2024","journal-title":"J. Environ. Chem. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Wen, M., Cui, Y., Ming, Z., Wang, T., Zhang, C., Ampah, J.D., Jin, C., Huang, H., and Liu, H. (2022). Effects of Methanol Application on Carbon Emissions and Pollutant Emissions Using a Passenger Vehicle. Processes, 10.","DOI":"10.3390\/pr10030525"},{"key":"ref_5","first-page":"26","article-title":"Methanol market to stay tight","volume":"184","author":"Thomas","year":"2022","journal-title":"Chem. Week"},{"key":"ref_6","unstructured":"(2017). Growth of Global Methanol Market Driven by Applications in Chemical and Energy Sector, M2 Press."},{"key":"ref_7","first-page":"16","article-title":"Methanol markets in recovery mode","volume":"298","author":"Chavda","year":"2020","journal-title":"ICIS Chem. Bus."},{"key":"ref_8","first-page":"129","article-title":"Chrysanthemum response to foliar methanol application and nitrogen source in nutrient solution","volume":"28","year":"2010","journal-title":"Terra Latinoam."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"8813","DOI":"10.1021\/acsami.3c17383","article-title":"Reversible and Irreversible Structural Changes in Cu\/ZnO\/ZrOsub2\/sub Catalysts during Methanol Synthesis","volume":"16","author":"Warmuth","year":"2024","journal-title":"ACS Appl. Mater. Interfaces"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e202300425","DOI":"10.1002\/cplu.202300425","article-title":"Effect of Conversion, Temperature and Feed Ratio on In2O3\/In(OH)3 Phase Transitions in Methanol Synthesis Catalysts: A Combined Experimental and Computational Study","volume":"88","author":"Kampe","year":"2023","journal-title":"ChemPlusChem"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.cherd.2017.08.005","article-title":"A rigorous mathematical model for online prediction of tube skin temperature in an industrial top-fired steam methane reformer","volume":"126","author":"Darvishi","year":"2017","journal-title":"Chem. Eng. Res. Des."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1002\/ceat.202300256","article-title":"Nonlinear Behavior of Methanol Synthesis Compared to CO2 Methanation","volume":"47","author":"Leipold","year":"2023","journal-title":"Chem. Eng. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/s11144-013-0587-9","article-title":"Methanol synthesis from CO2 hydrogenation over copper based catalysts","volume":"110","author":"Ahouari","year":"2013","journal-title":"React. Kinet. Mech. Catal."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"886","DOI":"10.1134\/S0023158420060087","article-title":"Formation of Effective Copper-Based Catalysts of Methanol Synthesis","volume":"61","author":"Minyukova","year":"2020","journal-title":"Kinet. Catal."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhao, L., Zhang, L., Wu, Z., Huang, C., Chen, K., Wang, H., and Yang, F. (2023). Size Effect of Cu Particles on Interface Formation in Cu\/ZnO Catalysts for Methanol Synthesis. Catalysts, 13.","DOI":"10.3390\/catal13081190"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"599","DOI":"10.18280\/mmep.110303","article-title":"Enhancing Artificial Ventilator Systems: A Comparative Analysis of Traditional and Nonlinear PID Controllers","volume":"11","author":"Sheltag","year":"2024","journal-title":"Math. Model. Eng. Probl."},{"key":"ref_17","first-page":"28","article-title":"Are you applying a PID control to non-linear processes correctly?","volume":"70","author":"Petras","year":"2023","journal-title":"Control Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1016\/j.isatra.2023.10.005","article-title":"Comment on Optimal tuning of sigmoid PID controller using nonlinear sine cosine algorithm for the automatic voltage regulator system","volume":"143","author":"Debdoot","year":"2023","journal-title":"ISA Trans."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"107232","DOI":"10.1016\/j.istruc.2024.107232","article-title":"Temperature field prediction for a PC beam bridge with corrugated steel webs using BP neural network and measured data","volume":"68","author":"Zhang","year":"2024","journal-title":"Structures"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"173","DOI":"10.4028\/www.scientific.net\/AMM.722.173","article-title":"The Application of Adaptive BP Neural Predictive Fuzzy Control in Cement Decomposing Furnace Temperature Control System","volume":"722","author":"Li","year":"2014","journal-title":"AMM"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"112789","DOI":"10.1016\/j.scienta.2023.112789","article-title":"Storage quality prediction of winter jujube based on particle swarm optimization-backpropagation-artificial neural network (PSO-BP-ANN)","volume":"331","author":"Zhang","year":"2024","journal-title":"Sci. Hortic."},{"key":"ref_22","first-page":"2516","article-title":"Establishment and Effective Evaluation of Haikou Ozone Concentration Statistical Prediction Model","volume":"45","author":"Fu","year":"2024","journal-title":"Huan Jing Ke Xue Huanjing Kexue"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1108\/AEAT-02-2023-0044","article-title":"Analysis of influencing factors of aircraft fuel tank flammability","volume":"96","author":"Leiming","year":"2024","journal-title":"Aircr. Eng. Aerosp. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"100322","DOI":"10.1016\/j.etran.2024.100322","article-title":"Multi- forword-step state of charge prediction for real-world electric vehicles battery systems using a novel LSTM-GRU hybrid neural network","volume":"20","author":"Hong","year":"2024","journal-title":"eTransportation"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"119627","DOI":"10.1016\/j.ins.2023.119627","article-title":"Temporal distribution-based prediction strategy for dynamic multi-objective optimization assisted by GRU neural network","volume":"649","author":"Hou","year":"2023","journal-title":"Inf. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"103813","DOI":"10.1016\/j.csite.2023.103813","article-title":"Prediction model of goaf coal temperature based on PSO-GRU deep neural network","volume":"53","author":"Guo","year":"2024","journal-title":"Case Stud. Therm. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"104662","DOI":"10.1016\/j.robot.2024.104662","article-title":"Design and stabilization of a Coand\u0103 effect-based UAV: Comparative study between fuzzy logic and PID control approaches","volume":"175","author":"Sanguino","year":"2024","journal-title":"Robot. Auton. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"035018","DOI":"10.1088\/1361-665X\/acb577","article-title":"Robustness analysis of magnetorheological elastomer-based vibration isolation system with optimal fuzzy controller","volume":"32","author":"Fu","year":"2023","journal-title":"Smart Mater. Struct."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Li, W., and Wang, Y. (2024). Intelligent Temperature Control Method of Instrument Based on Fuzzy PID Control Technology. IJACSA, 15.","DOI":"10.14569\/IJACSA.2024.0150193"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Maya-Rodriguez, M.C., Carvajal-Mariscal, I., L\u00f3pez-Mu\u00f1oz, R., Lopez-Pacheco, M.A., and Tolentino-Eslava, R. (2023). Temperature Control of a Chemical Reactor Based on Neuro-Fuzzy Tuned with a Metaheuristic Technique to Improve Biodiesel Production. Energies, 16.","DOI":"10.3390\/en16176187"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s005210070014","article-title":"An Adaptive Neuro-Fuzzy Approach to Control a Distillation Column","volume":"9","author":"Cordero","year":"2000","journal-title":"Neural Comput. Appl."},{"key":"ref_32","first-page":"0959","article-title":"A very simple structure for neural network control of distillation","volume":"5","author":"Soundar","year":"1995","journal-title":"J. Process Control."},{"key":"ref_33","first-page":"196","article-title":"Advanced temperature control in ethanol fermentation using a PSO-PID controller with split-range control strategy","volume":"11\u201313","author":"Yerolla","year":"2024","journal-title":"Prep. Biochem. Biotechnol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"123447","DOI":"10.1016\/j.apenergy.2024.123447","article-title":"Real building implementation of a deep reinforcement learning controller to enhance energy efficiency and indoor temperature control","volume":"368","author":"Silvestri","year":"2024","journal-title":"Appl. Energy"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Jabari, M., Ekinci, S., Izci, D., Bajaj, M., Blazek, V., and Prokop, L. (2025). Efficient pressure regulation in nonlinear shell-and-tube steam condensers via a Novel TDn(1 + PIDn) controller and DCSA algorithm. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-86107-7"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"143618","DOI":"10.1016\/j.jclepro.2024.143618","article-title":"Optimization and discrete fractional-order PID controller applied to a pressure swing adsorption plant for the production and recovery of bioethanol","volume":"476","author":"Cantero","year":"2024","journal-title":"J. Clean. Prod."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"118070","DOI":"10.1016\/j.enconman.2024.118070","article-title":"From catalyst development to reactor Design: A comprehensive review of methanol synthesis techniques","volume":"302","author":"Sarvestani","year":"2024","journal-title":"Energy Convers. Manag."},{"key":"ref_38","first-page":"156","article-title":"Research on a New Multi-Effect Four-Tower Distillation Process for Methanol","volume":"50","author":"Cui","year":"2022","journal-title":"Guangzhou Chem. Ind."},{"key":"ref_39","first-page":"1","article-title":"Thermolysis and analysis of TDI char by supercritical methanol using TGA, DSC, IR and SEM","volume":"146","author":"Yu","year":"2020","journal-title":"J. Therm. Anal. Calorim."},{"key":"ref_40","first-page":"2333","article-title":"A new intelligent system for diagnosing tumors with MR images using type-2 fuzzy neural network (T2FNN)","volume":"81","author":"Vahid","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_41","first-page":"e12678","article-title":"Real-time adaptive speed control of vector-controlled induction motor drive based on online-trained Type-2 Fuzzy Neural Network Controller","volume":"30","author":"Hakan","year":"2020","journal-title":"Int. Trans. Electr. Energy Syst."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.1109\/TCSI.2006.873184","article-title":"Comments on \u201cDynamical Optimal Training for Interval Type-2 Fuzzy Neural Network (T2FNN)\u201d","volume":"36","author":"Hagras","year":"2006","journal-title":"IEEE Trans. Syst. Man Cybern. Part B"},{"key":"ref_43","first-page":"49","article-title":"Establishment of Balance between Pressurized Tower and Atmospheric Tower","volume":"6","author":"Han","year":"2008","journal-title":"China Nitrogen. Fertil."},{"key":"ref_44","first-page":"43","article-title":"Technical Transformation of Atmospheric Tower\u2014Heat Recovery from Top Circulation","volume":"14","author":"Yu","year":"1993","journal-title":"Chem. Equip. Technol."},{"key":"ref_45","first-page":"25","article-title":"Negative Pressure Occurrence and Treatment in Methanol Three-Tower Distillation Atmospheric Tower","volume":"3","author":"Zhao","year":"2002","journal-title":"China Nitrogen. Fertil."},{"key":"ref_46","first-page":"3","article-title":"Research on Methanol Distillation in Atmospheric Tower","volume":"3","author":"Tianfu","year":"2008","journal-title":"Luthianhua Sci. Technol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"e30821","DOI":"10.1016\/j.heliyon.2024.e30821","article-title":"Ultra-early Prediction of the Process Parameters of Coal Chemical Production","volume":"10","author":"Li","year":"2024","journal-title":"Heliyon"},{"key":"ref_48","first-page":"50","article-title":"Methanol Three-Tower Distillation Process","volume":"33","author":"Lizhi","year":"2010","journal-title":"Hebei Chem."},{"key":"ref_49","first-page":"5","article-title":"A New Process for Methanol Multi-Effect Distillation with Heat Integration and Its Simulation","volume":"39","author":"Zhang","year":"2011","journal-title":"Chem. Eng."},{"key":"ref_50","first-page":"5","article-title":"Soft Measurement of Product Quality from the Side Stream of Atmospheric Pressure Distillation Column Based on Multi-Neural Network Structure","volume":"3","author":"Zeng","year":"2002","journal-title":"Autom. Instrum."},{"key":"ref_51","first-page":"5","article-title":"Design Optimization and Modification of the Feed Pump for Pressurized Distillation Column","volume":"55","author":"Li","year":"2018","journal-title":"Chem. Equip. Pip."},{"key":"ref_52","unstructured":"Wankat, P.C. (2007). Separation Process Engineering, Pearson College Div. [2nd ed.]."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1002\/oca.2473","article-title":"Hierarchical optimal control of a binary distillation column","volume":"40","author":"Ramezani","year":"2019","journal-title":"Optim. Control Appl. Methods"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"126027","DOI":"10.1016\/j.seppur.2023.126027","article-title":"Water assisted formaldehyde-containing chemicals refining with easily separable methanol over Cu\/ZnO\/Al2O3 catalyst","volume":"334","author":"Cheng","year":"2024","journal-title":"Sep. Purif. Technol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/5\/1308\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:39:16Z","timestamp":1760027956000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/5\/1308"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,21]]},"references-count":54,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["s25051308"],"URL":"https:\/\/doi.org\/10.3390\/s25051308","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,21]]}}}