Performance limits of the single screw expander in organic Rankine cycle with ensemble learning and hyperdimensional evolutionary many-objective optimization algorithm intervention

Xu Ping, Fubin Yang, Hongguang Zhang, Yan Wang, Biao Lei and Yuting Wu

Energy, 2022, vol. 245, issue C

Abstract: The operating performance of the expander, as the core component of the ORC system, directly determines the comprehensive performance of the system. Single screw expander (SSE) has received widespread attention due to its advantages such as large single-stage expansion pressure ratio, insensitivity to liquid, wide working range, and balanced forces. This study fully considers the strong coupling, high nonlinearity, large time variability and uncertainty between SSE performance evaluation indexes and operating parameters. On this basis, a scatter–bilinear interpolation hybrid feature selection approach is proposed, and the feature selection and analysis of the correlation degree among SSE performance evaluation indexes are conducted. In the high-dimensional space, the influence of operating parameters on different performance evaluation indexes is analyzed. Then, this study proposes the ensemble learning-driven non-dominated sorting genetic algorithm-III (NSGA-III) approach. An ensemble learning-driven NSGA-III model for SSE performance limit optimization is constructed on the basis of the scatter–bilinear interpolation hybrid feature selection method and the ensemble learning-driven NSGA-III approach. In the hyperdimensional space, the performance limits of SSE are obtained. When the comprehensive performance of SSE reaches its limit, the shaft efficiency, expansion pressure ratio, and power output are 53.71%, 7.76, and 10.83 kW, respectively. This research provides a reliable reference for analyzing, designing, and optimizing the SSE performance limits, and it also provides a direct guidance for obtaining the SSE performance limits in the ORC system.

Keywords: Organic rankine cycle; Single screw expander; Performance limits; Ensemble learning; Evolutionary many-objective optimization (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:245:y:2022:i:c:s0360544222001578

DOI: 10.1016/j.energy.2022.123254

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