Optimization of dewatering process of concentrate pressure filtering by support vector regression

Liu, Huizhong; You, Keshun

doi:10.1038/s41598-022-11259-9

Cited by 8 publications

(6 citation statements)

References 35 publications

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“…Modeling is an important and integral part of optimization [26] , the adaptive adjustment method for multi-operating parameters of the Tabling combines the results of the MV, ML, automatic control technologies and it is an optimization algorithm proposed through the exploration of big data modelling, and the deep learning image processing algorithm as a whole, which is an important foundation for the intelligent control of the Tabling beneficiation process, and an effective idea to realize the monitoring and intelligent process of the Tabling beneficiation. ML with optimization methods have been widely and maturely applied in mining equipment [27] . With the help of these mature research results and applications, this study also designs a more reasonable optimization method by the corresponding applications.…”

Section: Adaptive Adjustment Methods Of the Tablingmentioning

confidence: 99%

Intelligent Deployment Solution for Tabling Adapting Deep Learning

You

Liu

2023

IEEE Access

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The deep learning object detection model can well extract the coordinate information of the separating point of the concentrate ore belt boundary line. However, with the drawbacks of acquired zonal image features, it is difficult to obtain the perfect prediction of the concentrate grade and recovery rate. In this study, by adapting deep learning semantic segmentation technique with DeepLab V3+, which can effectively extract multi-dimensional image features. The mapping relationship between image features and attributes of the Tabling equipment is achieved by constructing a multi-output support vector regression model optimized of a sparrow search algorithm (SSA-MSVR). The beneficiation indicators and operating parameters of the Tabling can be continually detected and optimized by an intelligent system that mainly includes image recognition softwares, automatic control units, data processing and communication workstations, and matching intelligent equipment, which accomplished the intelligent deployment solution of the Tabling beneficiation process.

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Section: Adaptive Adjustment Methods Of the Tablingmentioning

confidence: 99%

Intelligent Deployment Solution for Tabling Adapting Deep Learning

You

Liu

2023

IEEE Access

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“…Therefore, when building related models, we did not consider particularly complex machine learning algorithms, but chose a more practical support vector regression (SVR) algorithm for data modeling. First of all, considering that support vector machines and support vector regression have been widely used in industrial practice, and the accuracy and stability of their algorithms have become increasingly prominent, they have been well used in the fields of mineral dehydration 16 , 17 .…”

Section: A Data-driven Model For Characterizing the Relationship Betw...mentioning

confidence: 99%

Research on optimization of control parameters of gravity shaking table

You

Liu

2023

Sci Rep

Self Cite

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When image processing and machine vision technology are used to extract features from the image of the ore belt of the shaking table, so as to realize the analysis of the processing indictors and mapping of control parameters. To realize the adaptive optimization of the multiple control parameters of the shaking table, it is necessary to have thorough access to the parameters of the internal and external properties of the gravity shaker, such as internal control parameters and external ore zone characteristics, as well as the processing indicators. In this study, information on the multi-scale characteristics of the zone is obtained through a visual experimental system, and the data-driven model of the separation process is constructed to characterize the relationship between the properties of the internal and external parameters of the shaking table, eventually, an adaptive optimization method of control parameters of the shaking table based on maximizing beneficiation efficiency is proposed. The research results show that the data from the geometric characteristics of the ore belts obtained from practical experiments all satisfy the statistical distribution requirements. In the three optimized support vector regression (SVR) models, the sparrow search algorithm optimized SVR (SSA-SVR) has the best comprehensive performance, which overcomes the limits of data samples under objective conditions and basically meets the existing industrial requirements. With these helps, the proposed optimization method has realized the continuous optimization of multiple control parameters of the shaking table, and the optimization results have a good guarantee.

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“…Furthermore, a differential evolution (DE) technique is developed for optimizing the learning rate, several hidden layer nodes, and batch size of the LSTM network. Liu et al [16] implement the industry's application of the ML method of support vector regression (SVR) and optimization techniques for control variables that can not only reduce energy use and costs. Also fundamentally improve the efficiency of filter press operation.…”

Section: Related Workmentioning

confidence: 99%

Wind Speed Prediction Using Chicken Swarm Optimization with Deep Learning Model

Surendran¹,

Alotaibi²,

Subahi³

2023

Computer Systems Science and Engineering

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High precision and reliable wind speed forecasting have become a challenge for meteorologists. Convective events, namely, strong winds, thunderstorms, and tornadoes, along with large hail, are natural calamities that disturb daily life. For accurate prediction of wind speed and overcoming its uncertainty of change, several prediction approaches have been presented over the last few decades. As wind speed series have higher volatility and nonlinearity, it is urgent to present cutting-edge artificial intelligence (AI) technology. In this aspect, this paper presents an intelligent wind speed prediction using chicken swarm optimization with the hybrid deep learning (IWSP-CSODL) method. The presented IWSP-CSODL model estimates the wind speed using a hybrid deep learning and hyperparameter optimizer. In the presented IWSP-CSODL model, the prediction process is performed via a convolutional neural network (CNN) based long short-term memory with autoencoder (CBLSTMAE) model. To optimally modify the hyperparameters related to the CBLSTMAE model, the chicken swarm optimization (CSO) algorithm is utilized and thereby reduces the mean square error (MSE). The experimental validation of the IWSP-CSODL model is tested using wind series data under three distinct scenarios. The comparative study pointed out the better outcomes of the IWSP-CSODL model over other recent wind speed prediction models.

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Optimization of dewatering process of concentrate pressure filtering by support vector regression

Cited by 8 publications

References 35 publications

Intelligent Deployment Solution for Tabling Adapting Deep Learning

Intelligent Deployment Solution for Tabling Adapting Deep Learning

Research on optimization of control parameters of gravity shaking table

Wind Speed Prediction Using Chicken Swarm Optimization with Deep Learning Model

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