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

Zou, Guobin; Zhou, Junwu; Li, Kang; Zhao, Hongliang

doi:10.3390/ma15217586

Cited by 2 publications

(2 citation statements)

References 35 publications

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“…In the HGA optimization experiment, the modeling based on GA-LSTM is first carried out. The initial population size of the genetic algorithm [1] is set to 30, the crossover rate is 0.5, the mutation rate is 0.1, and the total number of iterations is set to 20. When the genetic algorithm is used to optimize the super parameters of the LSTM network, the optimal fitness function of the optimal chromosome in the population, namely the RMSE value of the LSTM network on the test set, gradually converges with the increase of iteration times.…”

Section: Input Data H1 H2 Hnmentioning

confidence: 99%

“…With the continuous depletion of resources and the increasing pressure of environmental protection and production costs, the production mode of mining enterprises is changing from an extensive production mode driven by human experience to an intelligent production mode driven by knowledge and data. The popularization of basic industrial process automation technology and the rapid development of big data and intelligent control technology also provide basic conditions and technical support for the intelligent operation of mineral processing [1,2]. More and more production problems that are difficult to solve by traditional methods need to be solved by artificial intelligence methods such as machine learning [3,4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hierarchical Intelligent Control Method for Mineral Particle Size Based on Machine Learning

Zou,

Zhou,

Song

et al. 2023

Minerals

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Mineral particle size is an important parameter in the mineral beneficiation process. In industrial processes, the grinding process produces pulp with qualified particle size for subsequent flotation processes. In this paper, a hierarchical intelligent control method for mineral particle size based on machine learning is proposed. In the machine learning layer, artificial intelligence technologies such as long and short memory neural networks (LSTM) and convolution neural networks (CNN) are used to solve the multi-source ore blending prediction and intelligent classification of dry and rainy season conditions, and then the ore-feeding intelligent expert control system and grinding process intelligent expert system are used to coordinate the production of semi-autogenous mill and Ball mill and Hydrocyclone (SAB) process and intelligently adjust the control parameters of DCS layer. This paper presents the practical application of the method in the SAB production process of an international mine to realize automation and intelligence. The process throughput is increased by 6.05%, the power consumption is reduced by 7.25%, and the annual economic benefit has been significantly improved.

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Section: Input Data H1 H2 Hnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hierarchical Intelligent Control Method for Mineral Particle Size Based on Machine Learning

Zou,

Zhou,

Song

et al. 2023

Minerals

Self Cite

View full text Add to dashboard Cite

show abstract

Research on the Critical Moisture Content of Frost Heaving in Gas-Containing Coal

Han,

Wang,

et al. 2024

ACS Omega

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As the depth of coal seam mining increases, coal and gas outburst prevention and control face unprecedented challenges. In recent years, freezing outburst prevention technology has received attention and been applied in industry; this method mainly improves the strength of the coal body while reducing its energy storage. Research has shown that due to the influence of moisture content, frost heaving occurs in coal bodies during the cooling process, weakening the coal bodies' strength. Therefore, it is necessary to explore the effect of the moisture content on the coal body frost heave. This paper uses a research method that combines laboratory experiments, theoretical analysis, and numerical simulation to study the critical moisture content of frost heaving in gas-containing coal. The results showed that the coal sample can be divided into three stages during the cooling process: the cold shrinkage stage, frost heave stage, and stabilization stage. When the moisture content is greater than 7%, as the moisture content increases, the amount of ice increases accordingly, which causes an increase in the frost heave deformation of the coal sample and produces an obvious frost heave effect. Utilizing COMSOL simulation software, the deformation of gas-containing coal samples at different moisture levels is simulated. By combining the results with laboratory measurements, the critical moisture content for the frost heave in gas-containing coal is determined to be 6.6%.

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

Cited by 2 publications

References 35 publications

Hierarchical Intelligent Control Method for Mineral Particle Size Based on Machine Learning

Hierarchical Intelligent Control Method for Mineral Particle Size Based on Machine Learning

Research on the Critical Moisture Content of Frost Heaving in Gas-Containing Coal

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