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

Water ◽  
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.

scholarly journals Soft Sensor Model Based on Improved Elman Neural Network with Variable Data Preprocessing and Its Application

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Hai-bo Zhu ◽  
Yong Zhang
Keyword(s):  
Neural Network ◽  
Data Preprocessing ◽  
Soft Sensor ◽  
Delay Systems ◽  
Mutual Distance ◽  
Elman Neural Network ◽  
Model Based ◽  
Sensor Model ◽  
Preprocessing Method ◽  
Variable Data

In order to solve the problems of strong coupling, nonlinearity, and complex mechanism in real-world engineering process, building soft sensor with excellent performance and robustness has become the core issue in industrial processes. In this paper, we propose a new soft sensor model based on improved Elman neural network (Elman NN) and introduce variable data preprocessing method to the soft sensor model. The improved Elman NN employs local feedback and feedforward network mechanism through context layer to accurately reflect the dynamic characteristics of the soft sensor model, which has the superiority to approximate delay systems and adaption of time-varying characteristics. The proposed variable data preprocessing method adopts combining Isometric Mapping (ISOMAP) with local linear embedding (LLE), which effectively maintains the neighborhood structure and the global mutual distance of dataset to remove the noises and data redundancy. The soft sensor model based on improved Elman NN with variable data preprocessing method by combining ISOMAP and LLE is applied in practical sintering and pelletizing to estimate the temperature in the rotary kiln calcining process. Comparing several conventional soft sensor model methods, the results indicate that the proposed method has more excellent generalization performance and robustness. Its model prediction accuracy and anti-interference ability have been improved, which provide an effective and promising method for the industrial process application.

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

2021 ◽  
Vol 25 (6) ◽  
pp. 963-973
Author(s):  
Yujie Li ◽  
Ming Zhang ◽  
Yu Zhu ◽  
Xin Li ◽  
Leijie Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Relative Error ◽  
Least Squares Method ◽  
Performance Measure ◽  
Soft Sensor ◽  
Estimation Accuracy ◽  
Dynamic Neural Network ◽  
Sensor Model ◽  
Flexible Deformation ◽  
Displacement Based

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.

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

2020 ◽  
Vol 203 ◽  
pp. 104050 ◽  
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

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

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
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.

An Integrated Evaluative Function and Water Bloom Predicting and Prewarning System

2012 ◽  
Vol 476-478 ◽  
pp. 2427-2434
Author(s):  
Zai Wen Liu ◽  
Xiao Yi Wang ◽  
Qiao Mei Wu
Keyword(s):  
Neural Network ◽  
Rough Set Theory ◽  
Influence Factor ◽  
Water Bloom ◽  
Elman Network ◽  
Short Term ◽  
Elman Neural Network ◽  
High Prediction ◽  
Basic Features ◽  
Long Term Prediction

An integrated evaluative function and prediction model and prewarning system for water bloom in lakes based on Elman neural network is proposed in this paper, in which main influence factor of outbreak of water bloom is analyzed by rough set theory. The study of the function involves some aspects: algal average activation energy of photosynthesis, integrated nutritional status index, and transparency, which are considered from the microcosmic level, the macroscopic level and the intuitionistic level respectively. The values of the function are classified properly. Combined with the basic features of outbreak of water-bloom, Elman network is studied from the angles of theory and experiment and a water-bloom prewarning system in short term based on Elman network is established. The results of simulation and application show that: Elman neural network improves the algorithm of BP neural network, it has long-term prediction period, strong generalization ability， high prediction accuracy; and needs a small amount of sample and this model provides an efficient new way for short-term water bloom prediction, And approaching ability of Elman network is more superficial than common static networks and its velocity of convergence is faster.

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

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
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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