This paper presents an analysis of the energy consumption of a continuous flow ohmic heater (CFOH) with advanced process controls for heating operations in the food and drinks industry. The study was carried out by using operational data collected from a CFOH pilot plant that was designed and constructed at the National Centre of Excellence for Food Engineering (NCEFE), Sheffield Hallam University. The CFOH is controlled by a PC and includes an onboard Programmable Logic Controller (PLC) and a Human Machine Interface (HMI) so that it can be operated as a stand-alone unit with basic on/off and power setting control but without any advanced control features. The technical solution presented in this paper for heating foods demonstrates significant energy saving compared with conventional heating methods. Using the CFOH, the electric current generated in the food products by the Joule effect produces a rapid temperature increase with very high energy efficiency. This technique eliminates the low efficiency of heat transfer from the surface of vessels typically used to heat and cook food products. The analysis presented in this paper describes the energy consumption of the CFOH and compares the efficiency of the CFOH when different advanced process control techniques are used. Experimental results and analysis have shown that the CFOH can achieve an energy efficiency conversion of at least 87.9%. It has also shown that the energy conversion percentage can be increased by applying advanced controllers such as model predictive control (MPC) or adaptive model predictive control (AMPC).

Ohmic heating is a highly efficient method for rapid fluid heating, with applications in fields such as food processing, pharmaceuticals, and chemical engineering. Prior to its industrial application, thorough analysis and modeling are crucial to ensure safe and efficient operations. Therefore, this research focuses on the development and validation of a transfer function-based model for a continuous flow ohmic heater (CFOH). Validation metrics include root mean square error (RMSE) and mean absolute percentage error (MAPE). The developed model achieves an RMSE of ±1.48 and a MAPE of ±2.58% compared to experimental results, demonstrating its accuracy. Furthermore, the research presents the implementation of robust real-time applications of advanced process controllers, including PID, MPC, and AMPC. These controllers were first simulated using the developed model and subsequently deployed in the pilot plant ohmic heater system to achieve precise temperature control and optimised input voltage. The reliability of this procedure was affirmed through a comparison between simulated results and empirical data obtained from the CFOH pilot plant. The study concludes by suggesting potential applications in fault diagnosis, educational training, system identification, and controller design.

Using moderate electric field (MEF) techniques, Ohmic heating (OH) provides the rapid and uniform heating of food products by applying electric fields to them. A range of theoretical Ohmic heating models have been studied by researchers, but model validation and comparisons using experimental data and model development using system identification techniques from experimental data have not been evaluated. In this work, numerical models, mathematical models, and system identification models for an MEF process were developed. The MEF models were developed and simulated using COMSOL and MATLAB/Simulink software. When simulated, the developed models showed a volumetric rise in the overall food temperature. It was found that upon the application of an electric field, the resultant temperature depends on the electrical conductivity, product temperature, and magnitude of the electric field. For this reason, a systematic approach was used to validate the developed models. Experimental data derived from a commercially available batch Ohmic heater from C-Tech Innovation were used to validate the simulated models. Validation, analysis, and model comparison were conducted to compare developed models with experimental data. The validated simulated model helped improve the understanding of the effect of different critical process parameters of foods with a range of initial conditions. The validated model could accurately predict the temperature of heating under varying electric fields and food products with different thermo–physical properties.

Improving the efficiency and performance of control systems in food processing remains a significant challenge for engineers and researchers. In this paper, Proportional, Integral, and Derivative (PID) control; Model Predictive Control (MPC); and Adaptive Model Predictive Control (AMPC) were implemented on a Continuous Flow Ohmic Heater (CFOH) pilot plant to process tomato basil sauce. The sauce, composed of tomato puree, basil, spices, and other ingredients, was used to assess the effectiveness of these advanced control strategies. This research presents a case study on the pilot-scale heating of tomato basil sauce, with applications in the broader food industry. The performances and energy efficiencies of the different control techniques were compared, demonstrating significant improvements in controlling the CFOH process. The results highlight the industrial practicality of using CFOH technology with advanced process controls for food processing.