Deep learning based system identification of industrial integrated grinding circuits

Miriyala, Srinivas Soumitri and Mitra, Kishalay (2020) Deep learning based system identification of industrial integrated grinding circuits. Powder Technology, 360. pp. 1-16. ISSN 0032-5910

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Abstract

Energy efficiency and maximum productivity in ore beneficiation processes can be ensured when integrated grinding circuits function in an optimal fashion. The complexity of first principles based models prevents online implementation of control and optimization algorithms, thus, creating the need for the development of accurate data-based models. In this work, deep recurrent neural networks (DRNNs) are implemented for nonlinear system identification of 3 input 6 output integrated grinding circuit from an industrial lead-zinc ore beneficiation set-up. Optimal long short term memory networks (LSTMs) with maximum predictability are obtained by solving a novel multi-objective framework for DRNN architecture design. The optimal LSTMs are trained and validated on pseudo random binary sequence (PRBS) signal with an accuracy of 99%, and tested successfully on unseen random Gaussian sequence (RGS) signal. Comprehensive comparison with conventional tools for nonlinear system identification, such as wavelet networks, is performed to show the efficacy of proposed optimal LSTMs.

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IITH Creators:

IITH Creators	ORCiD
Mitra, Kishalay	http://orcid.org/0000-0001-5660-6878

Item Type:

Article

Additional Information:

This work was supported by the SPARC project, Ministry of Human Resources Development ( MHRD ), Government of India [grant number: SPARC/2018-2019/P1084/SL ]. Appendix A

Uncontrolled Keywords:

Deep learning; Grinding circuit; LSTM networks; Multi-objective evolutionary optimization; System identification; Wavelets

Subjects:

Chemical Engineering

Divisions:

Department of Chemical Engineering

Depositing User:

Team Library

Date Deposited:

11 Nov 2019 05:55

Last Modified:

28 Oct 2022 05:21