Event-based dynamic optimization for food thermal processing: High-quality food production under raw material variability

• Published in: Food and bioproducts processing Vol. 127; pp. 162 - 173
• Main Authors: Alonso, A.A., Pitarch, J.L., Antelo, L.T., Vilas, C.
• Format: Journal Article
• Language: English
• Published: Rugby Elsevier B.V 01.05.2021; Elsevier Science Ltd
• Subjects: biobased products; Canning industry; computer software; Convexity; Food; Food plants; Food processing; Food processing industry; Food production; Food quality; Food safety; foods; Mathematical models; Microbial lethality sentinel; Model-based optimization; Multi-objective optimization; Multiple objective analysis; Operators (mathematics); Optimization; Perturbation; Perturbations; Product variability; Raw materials; Real-time software sensor; Safety; Safety management; Sterilization; system optimization; Variability; Real-time software sensor; Multi-objective optimization; Microbial lethality sentinel; Product variability; Food quality; Model-based optimization
• ISSN: 0960-3085; 1744-3571; 1744-3571
• DOI: 10.1016/j.fbp.2021.02.013

•Average food quality, uniformity and duration of the thermal process are considered in a multi-objective optimization approach.•Properties of quality/safety indicators enable the efficient computation of optimal operation strategies in thermal processes.•Unexpected plant perturbations are compensated in real-time using available plant measurements. Industrial canneries are subject to perturbations that may compromise food safety requirements. In such cases, plant operators typically increase the processing time, leading to undesirable large processing cycles and excessive quality degradation. In addition, differences among the items in a batch lead to variability in terms of quality and safety which, if not explicitly considered in the processing strategy, forces the use of conservative operation policies. In this work, we present an event-based dynamic optimization approach that combines available plant measurements and mathematical model predictions to anticipate the effect of plant perturbations on food safety. A safety software sensor is build upon an on-line predictive simulation and a previous food-variability characterization such that, if any perturbation during the sterilization compromises food safety, a new processing strategy that optimizes a trade off among quality, uniformity and processing time is recomputed and implemented. Such multi-objective dynamic optimization problem under food product variability is efficiently addressed by taking advantage of the monotonicity and convexity properties of the food quality/safety dynamics.