Multi-layer Perception Neural Network Soft-sensor Modeling of Grinding Process Based on Swarm Intelligent Optimization Algorithms
In modern industrial production processes, hard instruments are often used to measure quality variables in industrial processes, but they are often subject to various economic or technical limitations and cannot successfully complete real-time monitoring tasks. In order to alleviate these problems,...
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| Published in: | Engineering letters Vol. 32; no. 3; p. 463 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Hong Kong
International Association of Engineers
01.03.2024
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| ISSN: | 1816-093X, 1816-0948 |
| Online Access: | Get full text |
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| Abstract | In modern industrial production processes, hard instruments are often used to measure quality variables in industrial processes, but they are often subject to various economic or technical limitations and cannot successfully complete real-time monitoring tasks. In order to alleviate these problems, the soft-sensor technology is used to estimate the quality variables in real time, and a suitable prediction model is established. The process variables that can be directly measured are selected as input variables to estimate the target variables that are difficult to measure directly. Taking the iron content of ore, a key economic and technical index in the grinding process, as the prediction object, a MLP neural network soft-sensor model of grinding process based on swarm intelligent optimization algorithms is proposed. Based on the MLP neural network, the neural network is regarded as a nonlinear function. For the error function composed of nonlinear functions, an intelligent optimization algorithm is introduced to optimize it. After the optimized optimal model parameters, namely weights and thresholds, are re-assigned to the neural network, the neural network is used to predict the target variables. The simulation results indicate that the improved model exhibits enhanced generalization performance and predictive accuracy, making it capable of meeting the real-time control demands of the grinding and classification production process. |
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| AbstractList | In modern industrial production processes, hard instruments are often used to measure quality variables in industrial processes, but they are often subject to various economic or technical limitations and cannot successfully complete real-time monitoring tasks. In order to alleviate these problems, the soft-sensor technology is used to estimate the quality variables in real time, and a suitable prediction model is established. The process variables that can be directly measured are selected as input variables to estimate the target variables that are difficult to measure directly. Taking the iron content of ore, a key economic and technical index in the grinding process, as the prediction object, a MLP neural network soft-sensor model of grinding process based on swarm intelligent optimization algorithms is proposed. Based on the MLP neural network, the neural network is regarded as a nonlinear function. For the error function composed of nonlinear functions, an intelligent optimization algorithm is introduced to optimize it. After the optimized optimal model parameters, namely weights and thresholds, are re-assigned to the neural network, the neural network is used to predict the target variables. The simulation results indicate that the improved model exhibits enhanced generalization performance and predictive accuracy, making it capable of meeting the real-time control demands of the grinding and classification production process. |
| Author | Xing, Cheng Wang, Jie-Sheng Shang-Guan, Yi-Peng Sun, Yong-Cheng Yang, Qing-Da |
| Author_xml | – sequence: 1 givenname: Yi-Peng surname: Shang-Guan fullname: Shang-Guan, Yi-Peng – sequence: 2 givenname: Cheng surname: Xing fullname: Xing, Cheng – sequence: 3 givenname: Jie-Sheng surname: Wang fullname: Wang, Jie-Sheng – sequence: 4 givenname: Yong-Cheng surname: Sun fullname: Sun, Yong-Cheng – sequence: 5 givenname: Qing-Da surname: Yang fullname: Yang, Qing-Da |
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| SubjectTerms | Algorithms Error functions Grinding Multilayers Neural networks Optimization Optimization algorithms Performance prediction Prediction models Process variables Real time Real variables Sensors |
| Title | Multi-layer Perception Neural Network Soft-sensor Modeling of Grinding Process Based on Swarm Intelligent Optimization Algorithms |
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