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Water quality soft-sensor prediction in anaerobic process using deep neural network optimized by Tree-structured Parzen Estimator

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Frontiers of Environmental Science & Engineering 2023年第6期17卷 23-35页

作者： Junlang Li Zhenguo Chen Xiaoyong Li Xiaohui Yi Yingzhong Zhao Xinzhong He Zehua Huang Mohamed A.Hassaan Ahmed El Nemr Mingzhi Huang SCNU Environmental Research Institute Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety&MOE Key Laboratory of Theoretical Chemistry of EnvironmentSchool of EnvironmentSouth China Normal UniversityGuangzhou 510006China Fujian Environmental Protection Design Institute Co.Ltd Fuzhou 350000China National Institute of Oceanography and Fisheries NIOFAlexandria 21556Egypt SCNU Qingyuan Institute of Science and Technology Innovation Co. Ltd.Qingyuan 511517China

Anaerobic process is regarded as a green and sustainable process due to low carbon emission and minimal energy consumption in wastewater treatment plants(WWTPs).However,some water quality metrics are not measurable in real time,thus influencing the judgment of the operators and may increase energy consumption and carbon *** of the solutions is using a soft-sensor prediction *** article introduces a water quality soft-sensor prediction method based on Bidirectional Gated Recurrent Unit(BiGRU)combined with Gaussian Progress Regression(GPR)optimized by Tree-structured Parzen Estimator(TPE).TPE automatically optimizes the hyperparameters of BiGRU,and BiGRU is trained to obtain the point prediction with GPR for the interval ***,a case study applying this prediction method for an actual anaerobic process(2500 m^(3)/d)is carried *** show that TPE effectively optimizes the hyperparameters of *** point prediction of CODeff and biogas yield,R^(2)values of BiGRU,which are 0.973 and 0.939,respectively,are increased by 1.03%–7.61%and 1.28%–10.33%,compared with those of other models,and the valid prediction interval can be ***,the proposed model is assessed as a reliable model for anaerobic process through the probability prediction and reliable *** is expected to provide high accuracy and reliable water quality prediction to offer basis for operators in WWTPs to control the reactor and minimize carbon emission and energy consumption.

关键词： Water quality prediction soft-sensor Anaerobic process Tree-structured Parzen Estimator

MIMO soft-sensor Model of Nutrient Content for Compound Fertil- izer Based on Hybrid Modeling Technique

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Chinese Journal of Chemical Engineering 2007年第4期15卷 554-559页

作者：傅永峰苏宏业褚健 National Laboratory of Industrial Control Technology Institute of Advanced Process Control Zhejiang UniversityHangzhou 310027 China

In compound fertilizer production, several quality variables need to be monitored and controlled simultaneously. It is very diifficult to measure these variables on-line by existing instruments and sensors. So, soft-sensor technique becomes an indispensable method to implement real-time quality control. In this article, a new model of multi-inputs multi-outputs （MIMO） soft-sensor, which is constructed based on hybrid modeling technique, is proposed for these interactional variables. Data-driven modeling method and simplified first principle modelingmethod are combined in this model. Data-driven modeling method based on limited memory partial least squares（LM-PLS） *** is used to build soft-senor models for some secondary ***, the simplified first principle model is used to compute three primary variables on line. The proposed model has been used in practicalprocess; the results indicate that the proposed model is precise and efficient, and it is possible to realize on line quality control for compound fertilizer process.

关键词： multi-inputs multi-outputs soft-sensor limited memory partial least squares simplified first principle model nutrient content of compound fertilizer

Component Content soft-sensor Based on Neural Networks in Rare-earth Countercurrent Extraction Process

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自动化学报 2006年第4期32卷 489-495页

作者： YANG Hui CHAI Tian-You School of Electronics and Electrical Engineering East China Jiaotong University Nanchang 330013 Research Center of Automation Northeastern University Shenyang 110004

Throught fusion of the mechanism modeling and the neural networks modeling,a compo- nent content soft-sensor,which is composed of the equilibrium calculation model for multi-component rare earth extraction and the error compensation model of fuzzy system,is proposed to solve the prob- lem that the component content in countercurrent rare-earth extraction process is hardly measured *** industry experiment in the extraction Y process by HAB using this hybrid soft-sensor proves its effectiveness.

关键词： Rare-earth countercurrent extraction soft-sensor equilibrium calculation model neural networks

soft-sensor Modeling of Rectification of Vinyl Chloride Based on Improved PSO-RBF Neural Network

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Soft-sensor Modeling of Rectification of Vinyl Chloride Base...

Estimation of Loose Status of Jigging Bed Based on Adaptive Neuro-Fuzzy Inference System

第24届中国控制与决策会议

作者： GAO Shuzhi 1, 2 , SUN Jie 2 ,GAO Xianwen 1 1.Northeastern University, Shenyang 110004, China.2.Shenyang University of Chemical Technology, Shenyang 110142, China

For the purity of vinyl chloride distillation process difficultly on-line detective timely, a strategy of vinyl chloride purity soft measurement modeling based on particle swarm optimization Improved RBF neural network is ***, we combine the PSO algorithm with RBF neural network to optimize RBF structure parameter. Then, vinyl chloride purity soft measurement modeling and optimization is ***, conducted a simulation *** the end, simulation results show that the soft measurement model has a faster convergence speed, a higher approximation accuracy,and a stronger real-time prediction ability.

关键词： rectification of vinyl chloride soft-sensor RBF neural network PSO particle swarm

来源： cnki会议评论

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Journal of China University of Mining and Technology 2006年第3期16卷 270-273页

作者： CHENG Jian GUO Yi-nan QIAN Jian-sheng School of lnformalion and Electrical Engineering China University of Mining ＆ Technology Xuzhou Jiangsu 221008 China

In the separation process with a jig washer, an accurate on-line measurement of loose status of a jigging bed is essential for a successful control of coal quality and loose status is difficult to measure on-line directly in industrial process situations. So a soft-sensor technology is needed for this purpose. The soft-sensor model is developed in the experiment by an adaptive neuro-fuzzy inference system （ANFIS） which has a remarkable ability of learning and generalization. Based on the analysis of the technologic mechanism of jigging bed, the structure of the ANFIS is established to build the soft-sensor model of loose status estimation. The ANFIS is trained by a hybrid learning algorithm. Finally, the simulation results and comparison analysis are presented, which indicate that the ANFIS has better abilities of learning and generalization than the RBF and the BP networks. Thus, it is possible that the loose status of the jigging bed can be estimated on-line bv using ANFIS.

关键词： soft-sensor ANFIS loose status jigging bed

Situation and Developing Trend of Rare-Earth Countercurrent Extraction Processes Control

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Journal of Rare Earths 2004年第5期22卷 604-610页

作者：柴天佑杨辉 Research Center of Automation Northeastern University Shenyang 110004 China School of Electronics and Electrical Engineering East China Jiaotong University Nanchang 330013 China

On the basis of the description of the rare-earth countercurrent extraction process, the on-line detecting method and equipments of rare-earth elements and the application in the process of the rare-earth countercurrent extraction are summarized. The procedure simulation of the computer, the automation control method and its current application are also mentioned in the process of rare-earth countercurrent extraction. The method of soft sensor is proposed. Optimal control method based on object-oriented rare-earth countercurrent extraction process and integrated automation system composed of process management system and process control system are presented, which are the developing direction of the automation of rare-earth countercurrent extraction process.

关键词： countercurrent extraction on-line analysis soft-sensor process control integrated automation system rare earths

An Ammonia Nitrogen Concentration Online soft Measure Method based on the Neural Network 36

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An Ammonia Nitrogen Concentration Online Soft Measure Method...