Multi-objective optimisation and fast decision-making method for working fluid selection in organic Rankine cycle with low-temperature waste heat source in industry

Xu Zhang, Hao Bai, Xiancong Zhao, Ali Diabat, Jian Zhang, Huanmei Yuan, Zefei Zhang

    Research output: Contribution to journalArticle

    Abstract

    In China, the utilisation of low-temperature waste heat (especially at temperatures lower than 100 °C) plays a significant role in increasing the energy-consumption efficiency in the industry. The organic Rankine cycle (ORC) is considered as a promising method to recover the aforementioned part of the waste heat. In the study, six potential candidates, namely R141b, R142b, R245ca, R245fa, R600a, and R601a were screened from 12 dry or adiabatic organic working fluids based on their thermodynamic performances in the ORC. A multi-objective optimisation (MOO) was performed for the thermodynamic performance (exergy efficiency, EXE) and economic performance (levelised energy cost, LEC) by using non-dominated sorting genetic algorithm-II (NSGA-II). The Pareto frontiers were obtained for the six candidates with the algorithm, and each optimal compromise solution was accurately obtained with the fuzzy set theory. Based on the EXE and LEC of the optimal compromise solution, the total cost and power generation efficiency for the six candidates were determined. This was used to obtain an explicit evaluation index in economic performance, namely static investment payback period (SIPP), to identify that the R245ca corresponded to the most cost-effective working fluid with the shortest SIPP. This suggests R245ca was the fastest to cover the investment and cost of the ORC system. Furthermore, a fast decision-making method was introduced to select the optimal working fluid based on the grey relational analysis (GRA) by considering key physical property parameters of the working fluids. The results suggest that any potential working fluid to recover low-temperature waste heat in the ORC can be evaluated by the simplified grey relational degree (SGRD) proposed in the study.

    Original languageEnglish (US)
    Pages (from-to)200-211
    Number of pages12
    JournalEnergy Conversion and Management
    Volume172
    DOIs
    StatePublished - Sep 15 2018

    Fingerprint

    Rankine cycle
    Waste heat
    Multiobjective optimization
    Decision making
    Fluids
    Exergy
    Costs
    Industry
    Temperature
    Thermodynamics
    Waste utilization
    Economics
    Fuzzy set theory
    Sorting
    Power generation
    Energy utilization
    Physical properties
    Genetic algorithms

    Keywords

    • Fast decision-making method
    • Fuzzy set theory
    • Low temperature waste heat recovery
    • Multi-objective optimisation
    • Organic Rankine cycle
    • Working fluid selection

    ASJC Scopus subject areas

    • Renewable Energy, Sustainability and the Environment
    • Nuclear Energy and Engineering
    • Fuel Technology
    • Energy Engineering and Power Technology

    Cite this

    Multi-objective optimisation and fast decision-making method for working fluid selection in organic Rankine cycle with low-temperature waste heat source in industry. / Zhang, Xu; Bai, Hao; Zhao, Xiancong; Diabat, Ali; Zhang, Jian; Yuan, Huanmei; Zhang, Zefei.

    In: Energy Conversion and Management, Vol. 172, 15.09.2018, p. 200-211.

    Research output: Contribution to journalArticle

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    AU - Diabat, Ali

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