Soft Sensor Model for Estimating the POI Displacement Based on a Dynamic Neural Network

To satisfy the increasingly demanding requirements in throughput and accuracy, more lightweight structures and a higher control bandwidth are highly desirable in next-generation motion stages. However, these requirements lead to a more flexible deformation, causing the estimation accuracy of the point of interest (POI) displacement to be guaranteed under the rigid-body assumption. In this study, a soft sensor model is constructed using a dynamic neural network (DNN) to estimate the POI displacement. This model can reflect the dynamic characteristics of the POI and realize accurate estimations. Moreover, a method combining stepwise and weight methods is proposed to analyze the influence of different DNNs, and a performance measure is presented to evaluate the soft sensor model. In the simulation, the DNN with the hidden feedbacks is proved to be the most suitable soft sensor model. The relative error and correlation coefficient obtained were less than 2% and 0.9998, respectively, during training and 5% and 0.9989, respectively, during testing. Compared with the data-driven model using the least-squares method, the proposed method exhibits a higher precision, and the relative error is within the setting range using the proposed performance measure.

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Process Monitoring of Quality-Related Variables in Wastewater Treatment Using Kalman-Elman Neural Network-Based Soft-Sensor Modeling

Water ◽

10.3390/w13243659 ◽

2021 ◽

Vol 13 (24) ◽

pp. 3659

Author(s):

Yiqi Liu ◽

Longhua Yuan ◽

Dong Li ◽

Yan Li ◽

Daoping Huang

Keyword(s):

Neural Network ◽

Process Monitoring ◽

Oxygen Demand ◽

Soft Sensor ◽

Elman Network ◽

Elman Neural Network ◽

Dynamic Neural Network ◽

Sensor Model ◽

Marquardt Algorithm ◽

Gradient Descent Algorithm

Proper monitoring of quality-related but hard-to-measure effluent variables in wastewater plants is imperative. Soft sensors, such as dynamic neural network, are widely used to predict and monitor these variables and then to optimize plant operations. However, the traditional training methods of dynamic neural network may lead to poor local optima and low learning rates, resulting in inaccurate estimations of parameters and deviation of predictions. This study introduces a general Kalman-Elman method to monitor the effluent qualities, such as biochemical oxygen demand (BOD), chemical oxygen demand (COD), and total nitrogen (TN). The method couples an Elman neural network with the square-root unscented Kalman filter (SR-UKF) to build a soft-sensor model. In the proposed methodology, adaptive noise estimation and weight constraining are introduced to estimate the unknown noise and constrain the parameter values. The main merits of the proposed approach include the following: First, improving the mapping accuracy of the model and overcoming the underprediction phenomena in data-driven process monitoring; second, implementing the parameter constraint and avoid large weight values; and finally, providing a new way to update the parameters online. The proposed method is verified from a dataset of the University of California database (UCI database). The obtained results show that the proposed soft-sensor model achieved better prediction performance with root mean square error (RMSE) being at least 50% better than the Elman network based on back propagation through the time algorithm (Elman-BPTT), Elman network based on momentum gradient descent algorithm (Elman-GDM), and Elman network based on Levenberg-Marquardt algorithm (Elman-LM). This method can give satisfying prediction of quality-related effluent variables with the largest correlation coefficient (R) for approximately 0.85 in output suspended solids (SS-S) and 0.95 in BOD and COD.

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Soft-Sensor Modeling of PVC Polymerizing Process Based on F-GMDH-Type Neural Network Algorithm

Journal of Sensors ◽

10.1155/2017/7872030 ◽

2017 ◽

Vol 2017 ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

Wei-zhen Sun ◽

Jie-sheng Wang ◽

Shu-zhi Gao

Keyword(s):

Neural Network ◽

Least Squares Method ◽

Internal Standard ◽

Soft Sensor ◽

Polymerization Process ◽

Group Method ◽

Real Time Control ◽

Time Control ◽

Sensor Model ◽

Collinearity Problem

For predicting the conversion velocity of the vinyl chloride monomer (VCM) in the polymerization process of polyvinylchloride (PVC), an improved Group Method of Data Handling- (GMDH-) type neural network soft-sensor model is proposed. After analyzing the technique of PVC manufacturing process, the auxiliary variables for setting up the soft-sensor model are selected and the experimental data are normalized. Because the internal standard of the original GMDH-type neural cannot solve the problem of multiple-collinearity problem and the useful variables tend to be prematurely eliminated in the modeling process, a hybrid method combining the regression analysis method and the least squares method is proposed to solve the multiple-collinearity problem. On the same time, by adopting some optimization experiences in genetic algorithm (GA), the generational crossover combination variables method is proposed to solve the shortcoming of useful variable being eliminated prematurely. The simulation results show that the proposed soft-sensor model can significantly improve the prediction accuracy of economic and technical indicators in the PVC polymerization process and can meet the real time control requirements of polymerization reactor production process.

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Soft sensor model for dynamic processes based on multichannel convolutional neural network

Chemometrics and Intelligent Laboratory Systems ◽

10.1016/j.chemolab.2020.104050 ◽

2020 ◽

Vol 203 ◽

pp. 104050 ◽

Cited By ~ 3

Author(s):

Xiaofeng Yuan ◽

Shuaibin Qi ◽

Yuri A.W. Shardt ◽

Yalin Wang ◽

Chunhua Yang ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Soft Sensor ◽

Dynamic Processes ◽

Sensor Model

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Hybrid Multiple Soft-Sensor Models of Grinding Granularity Based on Cuckoo Searching Algorithm and Hysteresis Switching Strategy

Scientific Programming ◽

10.1155/2015/146410 ◽

2015 ◽

Vol 2015 ◽

pp. 1-11 ◽

Cited By ~ 4

Author(s):

Jie-Sheng Wang ◽

Na-Na Shen

Keyword(s):

Neural Network ◽

Soft Sensor ◽

Grinding Process ◽

Real Time Control ◽

Switching Strategy ◽

Time Control ◽

Sensor Model ◽

Optimum Model ◽

Set Up ◽

Sampling Instant

According to the characteristics of grinding process and accuracy requirements of technical indicators, a hybrid multiple soft-sensor modeling method of grinding granularity is proposed based on cuckoo searching (CS) algorithm and hysteresis switching (HS) strategy. Firstly, a mechanism soft-sensor model of grinding granularity is deduced based on the technique characteristics and a lot of experimental data of grinding process. Meanwhile, the BP neural network soft-sensor model and wavelet neural network (WNN) soft-sensor model are set up. Then, the hybrid multiple soft-sensor model based on the hysteresis switching strategy is realized. That is to say, the optimum model is selected as the current predictive model according to the switching performance index at each sampling instant. Finally the cuckoo searching algorithm is adopted to optimize the performance parameters of hysteresis switching strategy. Simulation results show that the proposed model has better generalization results and prediction precision, which can satisfy the real-time control requirements of grinding classification process.

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RBF Neural Network Soft-Sensor Model of Electroslag Remelting Process Optimized by Artificial Fish Swarm Optimization Algorithm

Advances in Mechanical Engineering ◽

10.1155/2014/318195 ◽

2014 ◽

Vol 6 ◽

pp. 318195 ◽

Cited By ~ 1

Author(s):

Jie-sheng Wang ◽

Shuang Han ◽

Yang Yang

Keyword(s):

Neural Network ◽

Optimization Algorithm ◽

Rbf Neural Network ◽

Soft Sensor ◽

Electroslag Remelting ◽

Swarm Optimization ◽

Sensor Model ◽

Artificial Fish Swarm

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Features Extraction of Flotation Froth Images and BP Neural Network Soft-Sensor Model of Concentrate Grade Optimized by Shuffled Cuckoo Searching Algorithm

The Scientific World JOURNAL ◽

10.1155/2014/208094 ◽

2014 ◽

Vol 2014 ◽

pp. 1-17 ◽

Cited By ~ 2

Author(s):

Jie-sheng Wang ◽

Shuang Han ◽

Na-na Shen ◽

Shu-xia Li

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

Search Algorithm ◽

Cuckoo Search ◽

Cuckoo Search Algorithm ◽

Soft Sensor ◽

Features Extraction ◽

Image Processing Technique ◽

Flotation Process ◽

Sensor Model

For meeting the forecasting target of key technology indicators in the flotation process, a BP neural network soft-sensor model based on features extraction of flotation froth images and optimized by shuffled cuckoo search algorithm is proposed. Based on the digital image processing technique, the color features in HSI color space, the visual features based on the gray level cooccurrence matrix, and the shape characteristics based on the geometric theory of flotation froth images are extracted, respectively, as the input variables of the proposed soft-sensor model. Then the isometric mapping method is used to reduce the input dimension, the network size, and learning time of BP neural network. Finally, a shuffled cuckoo search algorithm is adopted to optimize the BP neural network soft-sensor model. Simulation results show that the model has better generalization results and prediction accuracy.

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