Back-propagation Neural Network Adaptive Control of a Continuous Wastewater Treatment Process

1998 ◽  
Vol 37 (9) ◽  
pp. 3625-3630 ◽  
Author(s):  
Mei-J. Syu ◽  
Bow-C. Chen
Keyword(s):  
Neural Network ◽  
Wastewater Treatment ◽  
Adaptive Control ◽  
Treatment Process ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Wastewater Treatment Process

scholarly journals A Novel Model with GA Evolving FWNN for Effluent Quality and Biogas Production Forecast in a Full-Scale Anaerobic Wastewater Treatment Process

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Zehua Huang ◽  
Renren Wu ◽  
XiaoHui Yi ◽  
Hongbin Liu ◽  
Jiannan Cai ◽  
...  
Keyword(s):  
Neural Network ◽  
Wastewater Treatment ◽  
Treatment Process ◽  
Biogas Production ◽  
Anaerobic Treatment ◽  
Production Rates ◽  
Wastewater Treatment Process ◽  
Effluent Quality ◽  
Anaerobic Wastewater Treatment ◽  
Novel Model

The anaerobic treatment process is a complicated multivariable system that is nonlinear and time varying. Moreover, biogas production rates are an important indicator for reflecting operational performance of the anaerobic treatment system. In this work, a novel model fuzzy wavelet neural network based on the genetic algorithm (GA-FWNN) that combines the advantages of the genetic algorithm, fuzzy logic, neural network, and wavelet transform was established for prediction of effluent quality and biogas production rates in a full-scale anaerobic wastewater treatment process. Moreover, the dataset was preprocessed via a self-adapted fuzzy c-means clustering before training the network and a hybrid algorithm for acquiring the optimal parameters of the multiscale GA-FWNN for improving the network precision. The analysis results indicate that the FWNN with the optimal algorithm had a high speed of convergence and good quality of prediction, and the FWNN model was more advantageous than the traditional intelligent coupling models (NN, WNN, and FNN) in prediction accuracy and robustness. The determination coefficients R2 of the FWNN models for predicting both the effluent quality and biogas production rates were over 0.95. The proposed model can be used for analyzing both biogas (methane) production rates and effluent quality over the operational time period, which plays an important role in saving energy and eliminating pollutant discharge in the wastewater treatment system.

scholarly journals Real-Time Stripe Width Computation Using Back Propagation Neural Network for Adaptive Control of Line Structured Light Sensors

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2618 ◽  
Author(s):  
Jingbo Zhou ◽  
Laisheng Pan ◽  
Yuehua Li ◽  
Peng Liu ◽  
Lijian Liu
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Real Time ◽  
Exposure Time ◽  
Control Method ◽  
Structured Light ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Stripe Width ◽  
Accuracy Result

A line structured light sensor (LSLS) is generally constituted of a laser line projector and a camera. With the advantages of simple construction, non-contact, and high measuring speed, it is of great perspective in 3D measurement. For traditional LSLSs, the camera exposure time is usually fixed while the surface properties can be varied for different measurement tasks. This would lead to under/over exposure of the stripe images or even failure of the measurement. To avoid these undesired situations, an adaptive control method was proposed to modulate the average stripe width (ASW) within a favorite range. The ASW is first computed based on the back propagation neural network (BPNN), which can reach a high accuracy result and reduce the runtime dramatically. Then, the approximate linear relationship between the ASW and the exposure time was demonstrated via a series of experiments. Thus, a linear iteration procedure was proposed to compute the optimal camera exposure time. When the optimized exposure time is real-time adjusted, stripe images with the favorite ASW can be obtained during the whole scanning process. The smoothness of the stripe center lines and the surface integrity can be improved. A small proportion of the invalid stripe images further proves the effectiveness of the control method.

Decoupling control for wastewater treatment process based on recurrent fuzzy neural network

2018 ◽  
Vol 21 (3) ◽  
pp. 1270-1280 ◽  
Author(s):  
Jun‐Fei Qiao ◽  
Gai‐Tang Han ◽  
Hong‐Gui Han ◽  
Cui‐Li Yang ◽  
Wei Li
Keyword(s):  
Neural Network ◽  
Wastewater Treatment ◽  
Fuzzy Neural Network ◽  
Treatment Process ◽  
Decoupling Control ◽  
Wastewater Treatment Process ◽  
Fuzzy Neural ◽  
Recurrent Fuzzy Neural Network

scholarly journals Research on a soft measurement model of sewage treatment based on a case-based reasoning approach

2017 ◽  
Vol 76 (12) ◽  
pp. 3181-3189 ◽  
Author(s):  
Jiayan Zhang ◽  
Cuicui Du ◽  
Xugang Feng
Keyword(s):  
Neural Network ◽  
Wastewater Treatment ◽  
Treatment Process ◽  
Sewage Treatment ◽  
Oxygen Demand ◽  
Prediction Method ◽  
Measurement Model ◽  
Case Based Reasoning ◽  
Wastewater Treatment Process ◽  
Case Based

Abstract In this paper, the measurement of biochemical oxygen demand (BOD) in a wastewater treatment process is analyzed and an intelligent integrated prediction method based on case-based reasoning (CBR) is proposed in order to overcome difficulties. Due to the fact that there are many factors that influence the accuracy of the prediction model, the radial basis function, which is a neural network with a 3 layer feedforward network, is employed to reduce the dimension of input values. Under these circumstances, a back propagation neural network combining with a nearest neighbor retrieval strategy is adopted to match case. Then, the measurement of BOD in wastewater treatment process is analyzed. Finally, the validity of the improved CBR in sewage treatment is demonstrated by using numerical results.

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