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PLS-based multi-loop robust H2 control for improvement of operating efficiency of waste heat energy conversion systems with organic Rankine cycle

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  • Zhang, Jianhua
  • Lin, Mingming
  • Chen, Junghui
  • Xu, Jinliang
  • Li, Kang

Abstract

Organic Rankine cycle (ORC) is a system suitable for generating electric power by recovering energy from a low-temperature heat source. The system is multivariate, coupled and ill-conditioned. In this paper, a multi-loop robust H2 control strategy based on the partial least squares (PLS), called PLS-H2, is proposed to improve the operating efficiency of a 100 kW waste heat recovery process with ORC. Using the PLS framework, the coupled ORC system can be decomposed into multiple single-loop systems in the PLS subspace to facilitate the controller design. The PLS framework compresses the original input and output variables into latent subspaces of the lower dimension, in which the collinearity of the original variables and the noise is eliminated. Taking into account model mismatch and disturbances from the heat source, the robust H2 controller is designed in each PLS subspace independently. The design is based on minimizing the 2-norm of the transfer function matrix from the exogenous inputs to the controlled outputs. The simulation results demonstrate the effectiveness of the proposed PLS-H2 and its superiority over PLS-PID control. The proposed control method can achieve better performance than PLS-PID control as the latter has more input efforts by1,800 times.

Suggested Citation

  • Zhang, Jianhua & Lin, Mingming & Chen, Junghui & Xu, Jinliang & Li, Kang, 2017. "PLS-based multi-loop robust H2 control for improvement of operating efficiency of waste heat energy conversion systems with organic Rankine cycle," Energy, Elsevier, vol. 123(C), pages 460-472.
    • Handle: RePEc:eee:energy:v:123:y:2017:i:c:p:460-472
    DOI: 10.1016/j.energy.2017.01.131
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    References listed on IDEAS

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    2. Zhu, Li & Li, Zhe & Chen, Junghui, 2021. "Evaluating and predicting energy efficiency using slow feature partial least squares method for large-scale chemical plants," Energy, Elsevier, vol. 230(C).

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