An HGA-LSTM-Based Intelligent Model for Ore Pulp Density in the Hydrometallurgical Process.

Guobin Zou, Junwu Zhou, Kang Li, Hongliang Zhao

Abstract read

In one paragraph

Article in Materials (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

4 authors.

Guobin ZouCollege of Information Science and Engineering, Northeastern University, Shenyang 110004, China.

Junwu ZhouCollege of Information Science and Engineering, Northeastern University, Shenyang 110004, China.

Kang LiBGRIMM Technology Group, Beijing 100160, China.

Hongliang ZhaoSchool of Metallurgical and Ecological Engineering, University of Science and Technology, Beijing 100083, China.ORCID 0000-0002-9789-6178

Funding

Automation in Mining and Metallurgy/Beijing Key Laboratory of Process Automation in Mining and Metallurgy Research BGRIMM-KZSKL-2020-01National Key R\&D Program of China 2019YFE0105000

6 · The paper itself

Abstract

This study focused on the intelligent model for ore pulp density in the hydrometallurgical process. However, owing to the limitations of existing instruments and devices, the feed ore pulp density of thickener, a key hydrometallurgical equipment, cannot be accurately measured online. Therefore, aiming at the problem of accurately measuring the feed ore pulp density, we proposed a new intelligent model based on the long short-term memory (LSTM) and hybrid genetic algorithm (HGA). Specifically, the HGA refers to a novel optimization search algorithm model that can optimize the hyperparameters and improve the modeling performance of the LSTM. Finally, the proposed intelligent model was successfully applied to an actual thickener case in China. The intelligent model prediction results demonstrated that the hybrid model outperformed other models and satisfied the measurement accuracy requirements in the factory well.

Indexed as

hybrid genetic algorithmintelligent modellong short-term memoryore pulp densitythickening process

Identifiers

PMID36363175

DOI10.3390/ma15217586

PMCPMC9655047

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.