A new partition curve model of dense-medium cyclone based on process parameters

Dou, Dongyang; Zhou, Deyang; Yang, Jianguo

doi:10.1080/19392699.2018.1515075

Cited by 14 publications

(1 citation statement)

References 20 publications

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“…Besides, with only three input parameters, there is a possibility of a simple linear relationship between them and the quality index. So, the linear regression (LR) model (Dou et al, 2020a(Dou et al, , 2020b was chosen as the baseline for comparison with the SVR model.…”

Section: Model Optimization Processmentioning

confidence: 99%

Modeling and optimization of activated carbon carbonization process based on support vector machine

Liu¹,

Zhang²,

Dou³

et al. 2021

Physicochem. Probl. Miner. Process.

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Product prediction and process parameter optimization in the production process of activated carbon are very important for production. It can stabilize product quality and improve the economic efficiency of enterprises. In this paper, three process parameters of a carbonization furnace, namely feeding rate, rotation speed, and carbonization temperature, were adopted to build a quality optimization model for carbonized materials. First, an orthogonal test was designed to obtain the preliminary relationship between the process parameters and the quality indicators of a carbonized material and prepare data for modeling. Then, an improved SVR model was developed to establish the relationship between product quality indicators and process parameters. Finally, through the singlefactor experiments and the Monte Carlo method, the process parameters affecting the quality of a carbonized material were determined and optimized. This showed that a high-quality carbonized material could be obtained with a smaller feeding rate, larger rotation speed, and higher carbonization furnace temperature. The quality of activated carbon reached its maximum when the feeding rate was 1.0 t/h, the rotation speed was 90 r/h, and the temperature was 836°C. It can effectively improve the quality of carbonized materials.

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Section: Model Optimization Processmentioning

confidence: 99%