Application of Advanced Process Control to a Continuous Flow Ohmic Heater: A Case Study with Tomato Basil Sauce  
[PERSON]  
\({}^{1}\)National Centre of Excellence for Food Engineering, Sheffield Hallam University, Howard Street, Sheffield S1 1 WB, UK; [EMAIL_ADDRESS] (T.J.); [EMAIL_ADDRESS] (O.O.-o.) \({}^{*}\) Correspondence: [EMAIL_ADDRESS] (M.R. ); [EMAIL_ADDRESS] (H.Z.) [PERSON]  
\({}^{1}\)Department of Mechanical Engineering, University of California, Berkeley, CA 94720, USA \({}^{2}\)Department of Mechanical Engineering, University of California, Berkeley, CA 94720, USA \({}^{3}\)Department of Mechanical Engineering, University of California, Berkeley, CA 94720, USA \({}^{4}\)Department of Mechanical Engineering, University of California, Berkeley, CA  
products. This ensures consistent product quality and enhances energy efficiency, making CFOH systems an ideal solution for large-scale food processing applications [7]. The general design of the CFOH includes an infeed pump to transport the food and holding tubes to attain the desired lethality, which adds to the complexity of the system. Various configurations, like parallel plate, co-axial, multi-point, and perforated electrodes, have been deployed for batch and continuous flow OH systems. The electrode material is also carefully selected to be highly conductive and corrosion-resistant, as well as compatible with the food product, to prevent any chemical reactions during the heating process that could compromise food safety and quality [1; 2; 3; 4; 5; 8; 9]. Foods with a high content of water and ionic salts, whether in homogeneous or heterogeneous form, are ideal for OH applications [1]. OH is utilized in various industrial processes, such as preheating [10], cooking [11], extraction [5; 6; 12], sterilization [13; 14], and pasteurization [15; 16; 17], where precise temperature control is essential to achieve effective microbial lethality while maintaining food quality. Additionally, energy efficiency in these processes is a growing concern, prompting industries to evaluate and optimize their control systems. The Proportional-Integral-Derivative (PID) controller is the most widely used traditional control method in these systems. However, due to its single-input nature, PID control often struggles to meet the increasingly stringent requirements for modern heating processes. It tends to provide limited temperature control precision and lacks built-in energy-saving features, making it less suitable for advanced applications that demand both high accuracy and optimized energy consumption [18; 19]. This highlights the need for more intelligent control strategies in the food processing industry. In response to the limitations of traditional control methods, studies have introduced advanced algorithms, such as artificial neural networks, and neuro-fuzzy systems to improve the energy efficiency and thermal comfort of centralized heating systems [20; 21; 22]. These algorithms have yielded continuous advancements in system performance. However, there is an inherent challenge in balancing the conflicting objectives of maximizing thermal comfort and minimizing energy consumption and computational demands. A promising solution to this challenge is Model Predictive Control (MPC), which uses predictive models to optimize control over a set time horizon, thereby minimizing cost functions. Unlike traditional controllers that target a single objective, MPC allows for a more flexible control by addressing multiple control goals simultaneously within an optimization framework [23]. Additionally, MPC offers significant potential for reducing energy consumption and greenhouse gas emissions while enhancing thermal comfort [24]. However, a traditional MPC system is designed around a nominal operating point, where the control model is valid, often relying on a linearized version of a nonlinear system to approximate a linear time-invariant (LTI) model. In contrast, Adaptive Model Predictive Control (AMPC) continuously updates the operating points of the system based on real-time data.