Advanced Control Strategies for Continuous Flow Ohmic Heating: A Comparative Analysis of Conventional and Neural Network-Based Approaches for Sustainable Food Processing

Advanced Control Strategies for Continuous Flow Ohmic Heating: A Comparative Analysis of Conventional and Neural Network-Based Approaches for Sustainable Food Processing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Advanced Control Strategies for Continuous Flow Ohmic Heating: A Comparative Analysis of Conventional and Neural Network-Based Approaches for Sustainable Food Processing Tasmiyah Javed, Leo Pappukutty Luke, Walid Issa, James Spendlove, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9252718/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Continuous flow ohmic heating (CFOH) is a novel, energy-efficient, and sustainable food processing technique that employs the electrical resistance of food for rapid and uniform volumetric heating. The enhanced thermal efficiency and the absence of conventional coal or steam-based heating offer CFOH a feasible option for reducing greenhouse gas (GHG) emissions and advancing net-zero carbon objectives in industrial food processing. Precise temperature control is crucial, as fluctuations can compromise food quality, sensory attributes, and overall operational efficiency. The nonlinear dynamics of CFOH systems often challenge conventional proportional–integral–derivative (PID) controllers, requiring frequent tuning to maintain optimal performance. To overcome these limitations, advanced neural network (NN)-based control methods, proficient in managing nonlinear system dynamics by learning complex input-output relationships, can be utilised. In this research, NN-based control approaches, including nonlinear autoregressive moving average level 2 (NARMA-L2) and model reference control (MRC), are examined. This study performs a thorough performance comparison between traditional PID control and sophisticated neural network-based control methods, assessing their responsiveness, control precision, energy efficiency, and indirect greenhouse gas emission utilising a real-time validated physical model of a pilot-scale CFOH created in MATLAB/Simulink. The findings indicate that the NARMA-L2 controller outperforms both MRC and PID control, attaining a more rapid dynamic response, greater stability, superior energy efficiency without overshoot, and fewer GHG emissions. The MRC demonstrates moderate and consistent performance, while the PID has a slower response, significant overshoot, and elevated energy usage. Overall, NN-based control improves temperature regulation, lowers energy consumption, and promotes sustainable, low-carbon food processing systems. Continuous flow ohmic heating Precise temperature control Nonlinear dynamics NN-based controllers Performance comparison Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 19 Apr, 2026 Reviews received at journal 16 Apr, 2026 Reviewers agreed at journal 16 Apr, 2026 Reviews received at journal 02 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers invited by journal 02 Apr, 2026 Editor assigned by journal 31 Mar, 2026 Submission checks completed at journal 30 Mar, 2026 First submitted to journal 28 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9252718","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":618521785,"identity":"9b8ae083-15e7-42cc-8769-85fe9cff9f13","order_by":0,"name":"Tasmiyah Javed","email":"","orcid":"","institution":"Sheffield Hallam University","correspondingAuthor":false,"prefix":"","firstName":"Tasmiyah","middleName":"","lastName":"Javed","suffix":""},{"id":618521786,"identity":"2c9d9998-b2c0-4c4e-ab46-ca8f3825705f","order_by":1,"name":"Leo Pappukutty Luke","email":"","orcid":"","institution":"Sheffield Hallam 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