Exploring multi-objective trade-offs in the design space of a waste heat recovery system

A waste heat recovery system (WHRS) is used to capture waste heat released from an industrial process, and transform the heat into reusable energy. In practice, it can be difficult to identify the optimal form of a WHRS for a particular installation, since this can depend on various design objectives, which are often mutually exclusive. More so when the number of objectives is large. To address this problem, a multiobjective evolutionary algorithm (MOEA) was used to explore and characterise the trade-off surface within the design space of a particular WHRS. A combination of clustering algorithm and parallel coordinates plots was proposed for use in analysing the results. The trade-off surface is first segmented using a clustering algorithm and parallel coordinates plots are then used to both visualise and understand the resulting set of Pareto-optimal designs. As a case study, a simulation of a WHRS commonly found in the food and drinks process industries was developed, comprising of a desuperheater coupled to a hot water reservoir. The system was parameterised, considering typical objectives, and the MOEA used to build a library of alternative Pareto-optimal designs that can be used by installers. The resulting visualisation are used to better understand the sensitivity of the system’s parameters and their trade-offs, providing another source of information for prospective installations.

[1]  Soheil Porkhial,et al.  Multi-objective optimization of a combined steam-organic Rankine cycle based on exergy and exergo-economic analysis for waste heat recovery application , 2016 .

[2]  Adeniyi J. Isafiade,et al.  Interval based MINLP superstructure synthesis of heat exchanger networks for multi-period operations , 2010 .

[3]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[4]  Gary B. Lamont,et al.  Applications Of Multi-Objective Evolutionary Algorithms , 2004 .

[5]  Marina Khoroshiltseva,et al.  A Pareto-based multi-objective optimization algorithm to design energy-efficient shading devices , 2016 .

[6]  Armin Schnettler,et al.  Multi-objective optimization and simulation model for the design of distributed energy systems , 2016 .

[7]  François Maréchal,et al.  Multi-objective, multi-period optimization of district energy systems: IV – A case study , 2015 .

[8]  Alfred Inselberg,et al.  Multidimensional detective , 1997, Proceedings of VIZ '97: Visualization Conference, Information Visualization Symposium and Parallel Rendering Symposium.

[9]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[10]  Mahmoud M. El-Halwagi,et al.  An MINLP model for the simultaneous integration of energy, mass and properties in water networks , 2014, Comput. Chem. Eng..

[11]  Marco Maglionico,et al.  Heat recovery from urban wastewater: Analysis of the variability of flow rate and temperature , 2014 .

[12]  Maizura Mokhtar,et al.  Optimising a Waste Heat Recovery System using Multi-Objective Evolutionary Algorithm , 2016, GECCO.

[13]  Tamer Khatib,et al.  Sizing of a standalone photovoltaic water pumping system using a multi-objective evolutionary algorithm , 2016 .

[14]  Poul Alberg Østergaard,et al.  Combining multi-objective evolutionary algorithms and descriptive analytical modelling in energy scenario design , 2016 .

[15]  Yiping Dai,et al.  Multi-objective optimization of an organic Rankine cycle (ORC) for low grade waste heat recovery using evolutionary algorithm , 2013 .

[16]  Martin John Atkins,et al.  Integration of industrial solar and gaseous waste heat into heat recovery loops using constant and variable temperature storage , 2014 .

[17]  Carlos A. Coello Coello,et al.  Multi-objective evolutionary algorithms: a review of the state-of-the-art and some of their applications in chemical engineering , 2007 .

[18]  Alfred Inselberg,et al.  Parallel Coordinates: Visual Multidimensional Geometry and Its Applications , 2003, KDIR.

[19]  Iftekhar A. Karimi,et al.  Efficient algorithm for simultaneous synthesis of heat exchanger networks , 2014 .

[20]  Gonzalo Guillén-Gosálbez,et al.  Enhanced thermal energy supply via central solar heating plants with seasonal storage: A multi-objective optimization approach , 2016 .

[21]  Li Zhou,et al.  A novel methodology for the design of waste heat recovery network in eco-industrial park using techno-economic analysis and multi-objective optimization , 2016 .

[22]  Michael A. Lones,et al.  Metaheuristics in nature-inspired algorithms , 2014, GECCO.

[23]  Thokozani Majozi,et al.  Optimum heat storage design for heat integrated multipurpose batch plants , 2011 .

[24]  Carlos A. Coello Coello,et al.  Multiobjective Evolutionary Algorithms in Aeronautical and Aerospace Engineering , 2012, IEEE Transactions on Evolutionary Computation.

[25]  Jorge Otávio Trierweiler,et al.  Optimal heat exchanger network synthesis: A case study comparison , 2013 .

[26]  Hartmut Schmeck,et al.  Comparison of Multi-objective Evolutionary Optimization in Smart Building Scenarios , 2016, EvoApplications.

[27]  M. Fallah,et al.  ANN-based modeling and reducing dual-fuel engine’s challenging emissions by multi-objective evolutionary algorithm NSGA-II , 2016 .

[28]  Ralph Evins,et al.  A review of computational optimisation methods applied to sustainable building design , 2013 .

[29]  Graham Ault,et al.  Multi-objective planning of distributed energy resources: A review of the state-of-the-art , 2010 .

[30]  Cheng-Liang Chen,et al.  Design of Indirect Heat Recovery Systems with Variable-Temperature Storage for Batch Plants , 2009 .

[31]  Antonio J. Nebro,et al.  Structural design using multi-objective metaheuristics. Comparative study and application to a real-world problem , 2016 .