A Multi-Objective Optimization (MOO) framework is proposed in this study, for optimization of the overall performance of forced extraction systems used in the kitchen environment. The target of the proposed framework is to achieve a balance (trade-off) among three conflicting objectives: minimization of discomfort, minimization of energy consumed by the extraction system fan, and minimization of the gradient in temperature and CO2 profiles, within the kitchen environment, during extraction system operation. To this end, the objectives are formulated in terms of criterion (objective) functions, using certain variables including: target/reference signals (set-points) and ambient environmental conditions. The balance between the objectives is obtained by determining the optimal values of these reference signals. A well known meta-heuristic called Non-dominated Sorting Genetic Algorithm (NSGA)-II is used for this purpose. Performance of the proposed multi-objective framework is simulated by using the best optimal values of the reference signals (i.e., the knee-point solution), evaluated by the framework, in the Computational Fluid Dynamics (CFD) model of the kitchen environment, developed using FloVENT (TM), which is validated against the experimental data collected from a test room (kitchen). The results of the investigation show that the optimal fan operation, is able to maintain an overall balance between the comfort, fan energy consumption and the gradient (in temperature and CO2 concentration) within the kitchen.
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