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soft sensing of Overflow Particle Size Distributions in Hydrocyclones Using a Combined Method

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Tsinghua Science and Technology 2008年第1期13卷 47-53页

作者：孙喆王焕钢张曾科 Department of Automation Tsinghua University

Precise, real-time measurements of overflow particle size distributions in hydrocyclones are necessary for accurate control of the comminution circuits. soft sensing measurements provide real-time, flexible, and low-cost measurements appropriate for the overflow particle size distributions in hydrocyclones. Three soft sensing methods were investigated for measuring the overflow particle size distributions in hydrocyclones. Simulations show that these methods have various advantages and disadvantages. Optimal Bayesian estimation fusion was then used to combine three methods with the fusion parameters determined according to the performance of each method with validation samples. The combined method compensates for the disadvantages of each method for more precise measurements. Simulations using real operating data show that the absolute root mean square measurement error of the combined method was always about 2% and the method provides the necessary accuracy for beneficiation plants.

关键词： soft sensing overflow particle size distribution machine learning identification

A subspace ensemble regression model based slow feature for soft sensing application

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Chinese Journal of Chemical Engineering 2020年第12期28卷 3061-3069页

作者： Qiong Jia Jun Cai Xinyi Jiang Shaojun Li Key Laboratory of Advanced Control and Optimization for Chemical Processes East China University of Science and TechnologyMinistry of EducationShanghai 200237China

A novel adaptive subspace ensemble slow feature regression model was developed for soft sensing *** to traditional single models and random subspace models,the proposed method is improved in three ***,sub-datasets are constructed through slow feature directions and variables in each subdatasets are selected according to the output related importance ***,an adaptive slow feature regression is presented for ***,a Bayesian inference strategy based on a slow feature analysis process that monitors statistics is developed for probabilistic *** industrial examples were used to evaluate the proposed method.

关键词： soft sensing Slow feature regression Subspace modeling Ensemble learning

Hybrid Modeling for soft sensing of Molten Steel Temperature in LF

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Journal of Iron and Steel Research International 2009年第4期16卷 1-6页

作者： TIAN Hui-xin MAO Zhi-zhong WANG An-na School of Electrical and Automation Engineering Tianjin Polytechnic University Tianjin 300160 China School of Information Science and Engineering Northeastern University Shenyang 110004 Liaoning China Key Laboratory of Integrated Automation of Process Industry of Ministry of Education NortheasternUniversity Shenyang 110004 Liaoning China

Aiming at the limitations of traditional thermal model and intelligent model, a new hybrid model is established for soft sensing of the molten steel temperature in LF. Firstly, a thermal model based on energy conservation is described; and then, an improved intelligent model based on process data is presented by ensemble ELM （extreme learning machine） for predicting the molten steel temperature in LF. Secondly, the self-adaptive data fusion is pro- posed as a hybrid modeling method to combine the thermal model with the intelligent model. The new hybrid model could complement mutual advantage of two models by combination. It can overcome the shortcoming of parameters obtained on-line hardly in a thermal model and the disadvantage of lacking the analysis of ladle furnace metallurgical process in an intelligent model. The new hybrid model is applied to a 300 t LF in Baoshan Iron and Steel Co Ltd for predicting the molten steel temperature. The experiments demonstrate that the hybrid model has good generalization performance and high accuracy.

关键词： ladle furnace hybrid modeling soft sensing thermal model data fusion

Application of Online SVR on the Dynamic Liquid Level soft sensing

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Application of Online SVR on the Dynamic Liquid Level Soft S...

第25届中国控制与决策会议

作者： Bai Shan Jiang Zijian Wang Tong Lai Haozhe School of Electrical Engineering Shenyang University of Technology

ISBN: (纸本)9781467355339

There exist different kinds of drawbacks and security risks in the measurement methods of dynamic liquid level in oilfield, consequently ,each of them can’t provide reliable protection to the oilfield for its high efficiency production operations and easily to cause significant economic losses .In order to solve the problems above , soft sensing technology is found as a new way to measure the dynamic liquid level of the oilfield instead of original hardware technology. ANN is a kind of soft sensing technology which is earlier to be used as the way to measure the dynamic liquid level ,but there are some problems restricted its use of areas, such as easy to fall into local minimization, slow convergence speed and poor generalization *** to the oilfield data’s characteristics ,Online SVR algorithm is proposed to be a solution to measure the dynamic liquid level in this paper. As a kind of soft sensing technology ,Online SVR not only has the same performance of ANN, but also can update the forcast model by online .In addition, it also solve the problems of ANN .Online SVR algorithm is verified by the experiment in this paper and the result of this experiment shows that Online SVR has higher precision than ANN .The experimental results also show the Online SVR can be used in measuring the dynamic liquid level in oilfield.

关键词： Dynamic Liquid Level soft sensing Online SVR Artificial Neural Networks

soft sensing Model of Underflow Concentration for Thickener Process based on Data Reconciliation

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Soft Sensing Model of Underflow Concentration for Thickener ...

第32届中国控制与决策会议

作者： Hualu Zhang Fuli Wang Yihua Zhang Dakuo He School of Information Science and Engineering Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University

Measured data in industrial production inevitably contain stochastic errors that are influenced by the environment and equipment,and the stochastic errors will reduce the accuracy of the measured *** are widely used in mineral processing and sewage treatment,but the accuracy of the variables of the thickener process cannot meet the requirements,caused by the stochastic errors,and the underflow concentration cannot be real-time ***,pressure sensors were installed in the thickener to establish the soft sensing model of the underflow concentration,but the model accuracy is low owing to the stochastic errors of pressure *** paper presents a soft sensing model of underflow concentration based on data *** data reconciliation method is used to improve the accuracy of variables in thickener process,and then the soft sensing model of underflow concentration is established using the reconciled *** the data of impact thickener process,the validity of the model is confirmed.

关键词： Thickener soft sensing data reconciliation

Disturbance monitoring and compensation in soft sensing of mill load in thermal power plant

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Disturbance monitoring and compensation in soft sensing of m...

第40届中国控制会议

作者： Jianquan Shi Huihui Chen Zhang Guo School of Automation Nanjing Institute of Technology Jiangsu Province Robot and System Integration Military-civilian Integration Innovation Platform State Key Laboratory of Electrical Insulation and Power Equipment School of Electrical Engineering Xi'an Jiaotong University

In this paper, a monitoring method for dynamic disturbance in soft sensing of mill load in thermal power plant is proposed. Firstly, the proposed approach divides dynamic disturbance into detectable disturbance and undetectable disturbance according to whether the disturbance can be monitored or not. Then, the corresponding correction methods are proposed respectively. For detectable disturbance signals, the corresponding error correction model is established respectively, and the mill load of the soft sensing is compensated online when disturbance occurs. For undetectable disturbance, online monitoring and adaptive correction are realized based on the optimal order in the fractional order domain. Finally, for showing the efficacy and feasibility of our proposed algorithm, some comparing experiments are conducted, which are all favorable for our viewpoints. That is the method we proposed enables the monitoring of detectable disturbance and undetectable disturbances, and automatically compensates the value of soft sensors.

关键词： Disturbance monitoring soft sensing mill load fractional Fourier transform

Bioprocess soft sensing Based on Multiple Kernel Support Vector Machine

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Bioprocess Soft Sensing Based on Multiple Kernel Support Vec...

2011 Chinese Control and Decision Conference（CCDC）

作者： Cui Jinling,Wang Xianfang School of Computer and Information Technology,Henan Normal University,Xinxiang,P.R.China 453007

soft sensing technology is one of the topics of general interest in study on current process control,which has recently drawn considerable attention worldwide,and has stimulated researchers and engineers to make greater effort to reduce the cost/benefit-ratio for development and manufacture of bio-industrial processes both economically and *** paper introduced a kind of soft-sensor based on an improved support vector machine (SVM) for a polyacrylonitrile productive *** improved SVM called the multiple kernel support vector machine was presented,and the mathematical formulation of multiple kernel learning is *** the implementation for average molecular weight in polyacrylonitrile productive process,it demonstrates the good performance of the proposed method compared to single kernel.

关键词： Kernel function Support vector machine soft sensing Bioprocess

Application in soft sensing modeling of chemical process based on K-OPLS method

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Journal of Measurement Science and Instrumentation 2020年第1期11卷 17-27页

作者： LI Jun LI Kai School of Automation and Electrical Engineering Lanzhou Jiaotong UniversityLanzhou 730070China

Aiming at the problem of soft sensing modeling for chemical process with strong nonlinearity and complexity,a soft sensing modeling method based on kernel-based orthogonal projections to latent structures(K-OPLS)is *** projections to latent structures(O-PLS)is a general linear multi-variable data modeling *** can eliminate systematic variations from descriptive variables(input)that are orthogonal to response variables(output).In the framework of O-PLS model,K-OPLS method maps descriptive variables to high-dimensional feature space by using“kernel technique”to calculate predictive components and response-orthogonal components in the ***,the K-OPLS method gives the non-linear relationship between the descriptor and the response variables,which improves the performance of the model and enhances the interpretability of the model to a certain *** verify the validity of K-OPLS method,it was applied to soft sensing modeling of component content of debutane tower base butane(C4),the quality index of the key product output for industrial fluidized catalytic cracking unit(FCCU)and H 2S and SO 2 concentration in sulfur recovery unit(SRU).Compared with support vector machines(SVM),least-squares support-vector machine(LS-SVM),support vector machine with principal component analysis(PCA-SVM),extreme learning machine(ELM),kernel based extreme learning machine(KELM)and kernel based extreme learning machine with principal component analysis(PCA-KELM)methods under the same conditions,the experimental results show that the K-OPLS method has superior modeling accuracy and good model generalization ability.

关键词： kernel method orthogonal projection to latent structures(K-OPLS) soft sensing chemical process

A kind of soft sensing Method for Biomass Concentration of Phytoplankton in Seawater

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A kind of Soft Sensing Method for Biomass Concentration of P...

第26届中国控制与决策会议

作者： Zhang Ying Shi Jia College of Information Engineering Shanghai Maritime University

Effective monitoring the growth state of seawater phytoplankton plays an important role for the early warning of marine disasters, such as coastal red tides. Grey correlation analysis method was used to select the secondary variables of the soft sensing model. It can effectively reduce the dimension of the system. Extreme learning machine regression（ELMR） method was used to build the soft sensing model of biomass concentration of phytoplankton. Comparing with the generalized regression neural network, the testing result indicates that extreme learning machine regression has better accuracy, efficiency and generalization ability of measurement than the other methods. It adapts to be used for real time monitoring of biomass concentration of phytoplankton in seawater.

关键词： soft sensing Extreme learning machine regression Biomass concentration of phytoplankton Secondary variables Generalization ability