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

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.