Multi-objective optimization of the design of two-stage flash evaporators: Part 2. Multi-objective optimization |
| |
| Authors: | P Sebastian T Quirante V Ho Kon Tiat Y Ledoux |
| |
| Affiliation: | 1. CTU in Prague, Faculty of Mechanical Engineering, Department of Mechanics, Biomechanics and Mechatronics, Technická 4, 166 29 Prague, Czech Republic;2. CTU in Prague, Faculty of Civil Engineering, Department of Concrete and Masonry Structures, Thákurova 7, 166 29 Prague, Czech Republic;1. Department of Energy Engineering, University of Baghdad, Baghdad, Iraq;2. Young Researchers and Elite Club, Tonekabon Branch, Islamic Azad University, Tonekabon, Iran;3. Department of Mechanical Engineering, Babol University of Technology, Babol, Iran;4. Department of Mechanical Engineering and Energy Processes, Southern Illinois University, Carbondale, IL, USA;1. Aalto University, School of Engineering, Department of Energy Technology, P.O. Box 14400, FI-00076 Aalto, Finland;2. Sauter Finland Oy, Insinöörinkatu 7 B/PL 124, 00811 Helsinki, Finland;3. Australian College of Kuwait, P.O. Box 1411, Safat 13015, Kuwait;4. Energianhallinta Tapio Mäkilä, Betaniankatu 4, 20810 Turku, Finland;1. Department of Mechanical and Process Engineering, ETH Zurich, 8092 Zurich, Switzerland;2. Airlight Energy Holding SA, 6710 Biasca, Switzerland;3. Solar Technology Laboratory, Paul Scherrer Institute, 5232 Villigen PSI, Switzerland |
| |
| Abstract: | Flash evaporation process is currently developing in the wine industry where it is used for flash-cooling or concentration. The design of flash evaporators is faced with specific constraints and must take into account multiple design objectives. In this paper, the development of a multi-objective optimization method is investigated for the joint optimization of design objectives such as process transportability, environmental efficiency, operative cost or cooling power. The optimization method is based on the aggregation of design objectives through desirability functions and indexes. Desirability functions are suitable for formulating design constraints more precisely than inequality relations and, moreover, the global design model results in an unconstrained optimization problem. However, aggregation methods do make it difficult to compute the global optimum of the design problem. This difficulty has been addressed by developing a distributed genetic algorithm which is not so sensitive to this type of numerical solving difficulty. Another difficulty arises from the weighting method for the aggregation of desirability functions since weight parameters have no physical meaning. This weighting problem is approached through a sensitivity analysis of the weight parameters and by observing their relative influence. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
