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Show Multi-Objective Optimization - Waste Heat Recovery Systems in Food Processing Gabriel Legorburu Mechanical Engineering INTRODUCTION • A waste heat recovery system is designed for a Seasonal Cannery • System is optimized to minimize payback while minimizing equipment size • Similar systems have been optimized for payback but minimizing floor space is novel METHODS RESULTS/DISCUSSION • Process data collected from a Seasonal Cannery • A Multi-Objective Evolutionary Algorithm was implemented using Matlab • Pareto Front was analyzed using a K-means clustering algorithm Three Distinct Solution Sets: • Minimal Payback • 8.6 years - Quickest Payback • Selecting solution near the bottom of the Min. Payback cluster increases payback by 3.8% but reduces floor space by 30% • Balanced • Minimum Floor Space • As equipment gets smaller floor space is reduced but so is payback. Steps to Apply MOEA • Define load profile of the heat source (can pasteurizer) • Define load profile of the heat user (Process hot water) • Develop system of equations to represent heat recovery system • Pick decision variables: • Tank Volume • Heat Exchanger flow rate • Heat Exchanger Width • Heat Exchanger Height Key Decision Variables: • • • • Thermal Storage Tank Size Recirculation flow rate Heat Exchanger Height Heat Exchanger Width Optimization Overview • Identify constraints • Find Solution that operates within constraints Design functional System Environomic • Objectives are identified • Costs applied to each objective • Lowest Cost solution • Objectives are identified and left is native units • Solution set shows interrelation between objectives Multi-Objective Steps: 1.Random values are assigned to decision variables to generate a random solution space. 2.Solution set is sorted for solutions that are: 1.Better in at least one objective 2.Not worse in all objectives 3.These are called "Non-Dominated Solutions" 4.Non-Dominated solutions become the "parent set" 5.Parent set is a set of values that represent the decision variables 3.Create "offspring" of the parent set using crossover and mutation 1.Crossover combines (2) parent sets and creates an offspring that inherits the beneficial genes "values for the decision variables" 2.Mutation occurs at the gene level and random changes genes of the offspring by a small amount 4.Procedure iterates until the Pareto Front is formed 1.A Pareto front is the group of solutions that can't be improved with respect to one objective without negatively impacting another objective REFERENCES Pelster, D. Favrat, M. Von Spakovsky, The thermoeconomic and envi- ronomic modeling and optimization of the synthesis, design, and operation of combined cycles with advanced options, Journal of engineering for gas turbines and power 123 (4) (2001) 717-726. M. Burer, K. Tanaka, D. Favrat, K. Yamada, Multi-criteria optimization of a district cogeneration plant integrating a solid oxide fuel cell-gas turbine combined cycle, heat pumps and chillers, Energy 28 (6) (2003) 497-518. A. Molyneaux, G. Leyland, D. Favret, A new, clustering evolutionary mul- tiobjective optimization in, in: Proceedings of the Third International Sym- posium on Adaptive Systems-Evolutionary Computation and Probabilistic Graphical Models, pp. 41-47. A. Konak, D. W. Coit, A. E. Smith, Multi-objective optimization using genetic algorithms: A tutorial, Reliability Engineering & System Safety 91 (9) (2006) 992-1007. J. Wang, Z. Yan, M. Wang, M. Li, Y. Dai, Multi-objective optimization of an organic rankine cycle (orc) for low grade waste heat recovery using evolutionary algorithm, Energy Conversion and Management 71 (2013) 146-158. Z. Hajabdollahi, F. Hajabdollahi, M. Tehrani, H. Hajabdollahi, Thermo- economic environmental optimization of organic rankine cycle for diesel waste heat recovery, Energy 63 (2013) 142-151. M. Mokhtar, S. Burns, D. Ross, I. Hunt, Exploring multi-objective trade- offs in the design space of a waste heat recovery system, Applied Energy 195 (2017) 114-124. J. Miah, A. Griffiths, R. McNeill, I. Poonaji, R. Martin, A. Yang, S. Morse, Heat integration in processes with diverse production lines: a comprehensive framework and an application in food industry, Applied Energy 132 (2014) 452-464. ACKNOWLEDGEMENTS • Dr. Amanda Smith • Church of Latter-Day Saints • Dennis Group |
