A framework for multi-objective optimization of small-scale food processes
Résumé
Food process design is a challenging task, as several potentially conflicting objectives are targeted (product quality, cost reduction, environmental impact reduction...) under a number of constraints (e.g safety). Multi-objective optimization (MOO) is a powerful tool to solve this kind of problem but is still only partially used within design methods for food processes. By contrast, in other sectors such as mechanical engineering, Multi-criteria Decision Making (MCDM) methods are at the core of design optimization procedures.
To help design food processes in a more holistic way, we propose a framework to develop MOO methods, consisting of five “building blocks”:
i. a predictive process model;
ii. performance indicators;
iii. decision-maker and/or expert preferences;
iv. a selection method to select one or several “best” trade-offs;
v. an optimization algorithm to find the best design solutions among the feasible ones.
This framework was used to solve a number of design problems at the QualiSud Joint Research Unit, including a fish hot-smoking process, a cassava starch dryer, and a supply of bioenergy for a cereal based dryer. The authors used these examples to illustrate the design framework and highlight the interest and the potential of coupling design theory principles to MOO and MCDM methods.
The results first demonstrate the ability to find satisfying solutions despite multiple constraints and conflicting objectives. They also show the opportunity to provide guidelines for equipment design among numerous possible solutions. They finally show that decision-maker preferences and expert knowledge can be integrated in the design procedure thanks to MCDM methods.