Modelling, control and analysis of moderate electric field in food processing

OLUWOLE-OJO, Oluwaloba (2023). Modelling, control and analysis of moderate electric field in food processing. Doctoral, Sheffield Hallam University.

[img] PDF
Oluwole-Ojo_2023_PhD_ModellingControlAnalysis.pdf - Accepted Version
Restricted to Repository staff only until 10 October 2024.
Creative Commons Attribution Non-commercial No Derivatives.

Download (4MB)
Link to published version:: https://doi.org/10.7190/shu-thesis-00565

Abstract

Ohmic heating (OH) is a Moderate Electric Field (MEF) processing technique in which electric current is applied to food products. The food product acts as a resistor and heat is generated and dissipated within the food volumetrically. OH is a very energy-efficient form of heating compared to conventional methods such as conduction and convection. The advantages of OH include rapid heating, reduced food processing times, bacterial inactivation, electroporation, and elimination of unwanted temperature peaks. Compared to other conventional methods of food processing, ohmic heating is over 95% energy efficient and high in energy saving. This research is majorly focused on the modelling, control and industrial application, and analysis of MEF in food processing. Firstly, the development of the batch ohmic heater model presents three distinct modelling approaches which are the first principle partial differential equation (PDE), lumped ordinary differential equation (ODE) model and regression models using system identification technique. The validation of the developed batch ohmic heater model is done using a commercially available batch ohmic heater and experimental data. Following this, collaboration with industrial partners and technicians led to the design and construction of a continuous flow ohmic heater (CFOH) pilot plant. Consequently, a novel approach to model the CFOH using the state space approach was developed. This work presents the quantitative results which demonstrate significant improvements in modelling the OH process with regard to food of varying conductivities, flow rates and initial temperatures. The results from the model development led to the application of classical to advanced model-based process control which include Proportional, Integral and Derivative (PID) control, Model Predictive Control (MPC) and adaptive model predictive control on the continuous flow ohmic heater pilot plant. These controllers are implemented on the CFOH pilot plant from MATLAB using Open Platform Communications (OPC). With OPC, server and client protocol are used to exchange data in real-time between the Programmable Logic Controller (PLC) and a stand-alone lab-based computer so that simple and advanced model-based control (e.g., Model Predictive Control) designed in MATLAB/Simulink can be applied on the continuous flow Ohmic Heater. This research demonstrates the following: •model-based design and validation of OH in the food industry •advantages of OH compared to conventional methods •the benefits advanced process control compared to simple control in food engineering •the technique of implementing advanced process control on a PLC based hardware. Industrial heating trial of tomato basil sauce was carried out to present a case study for the pilot scale heating of tomato basil sauce with advanced process control with applications in the food industry. The performances and energy efficiencies of the different control techniques implemented are compared.

Item Type: Thesis (Doctoral)
Contributors:
Thesis advisor - Zhang, Hongwei [0000-0002-7718-021X] (Affiliation: Sheffield Hallam University)
Additional Information: Director of studies: Dr. Hongwei Zhang
Research Institute, Centre or Group - Does NOT include content added after October 2018: Sheffield Hallam Doctoral Theses
Identification Number: https://doi.org/10.7190/shu-thesis-00565
Depositing User: Colin Knott
Date Deposited: 24 Nov 2023 15:27
Last Modified: 25 Nov 2023 02:00
URI: https://shura.shu.ac.uk/id/eprint/32734

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics