A knowledge based intelligent control method for dehydration and mixing process

Li, Kang; Wang, Fuli; He, Dakuo; Zhang, Shuning

doi:10.1109/ccdc.2017.7978141

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…In industrial applications, when the nature of the conveying feed slurry is stable, the pump runs stably, the power frequency of the pump motor is fixed, and the pipeline characteristics are stable, there is a corresponding relationship between the feed density and the current, frequency, and velocity of flow [ 35 ]. Thus, the three related indicators were selected as the auxiliary variable set and are listed in Table 1 .…”

Section: Resultsmentioning

confidence: 99%

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

Zou

Zhou

et al. 2022

Materials

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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.

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Section: Resultsmentioning

confidence: 99%