Gray and Neural Network Prediction of Effluent from the Wastewater Treatment Plant of Industrial Park Using Influent Quality

2008 ◽  
Vol 25 (5) ◽  
pp. 757-766 ◽  
Author(s):  
Tzu-Yi Pai
Keyword(s):  
Neural Network ◽  
Wastewater Treatment ◽  
Wastewater Treatment Plant ◽  
Treatment Plant ◽  
Industrial Park ◽  
Neural Network Prediction ◽  
Network Prediction

Research on Variable Metric Chaos Optimization Modeling for the Prediction of Wastewater Treatment Plants Performance

2012 ◽  
Vol 428 ◽  
pp. 169-175
Author(s):  
Guo Kai Fu ◽  
Yi Yue Hu ◽  
Zhi Zhang
Keyword(s):  
Neural Network ◽  
Wastewater Treatment ◽  
Wastewater Treatment Plant ◽  
Wastewater Treatment Plants ◽  
Treatment Plant ◽  
Treatment System ◽  
Variable Metric ◽  
Chaos Optimization ◽  
Actual Operation ◽  
The Stability

A reliable model for any wastewater treatment plant is essential in order to provide a tool for predicting its performance and to form a basis for controlling the operation of the process. This would minimize the operation costs and assess the stability of environmental balance. For the multi-variable, uncertainty, non-linear characteristics of the wastewater treatment system, a variable metric chaos optimization neural network (VMCNW) prediction model is established standing on the actual operation data in the wasterwater treatment system. The model overcomes several disadvantages of the conventional BP neural network. Namely:slow convergence, low accuracy and difficulty in finding the global optimum.The results of model calculation show that the predicted value can better match measured value,played a effect of simulating and predicting and be able to optimize the operation status. The establishment of the predicting model provide a simple and practical way for the operation and management in wastewater treatment plant,and have good research and engineering practical value.

scholarly journals Artificial Neural Network-Cuckoo Optimization Algorithm (ANN-COA) for Optimal Control of Khorramabad Wastewater Treatment Plant, Iran

2016 ◽  
Vol 2 (11) ◽  
pp. 555-567 ◽  
Author(s):  
Samaneh Khademikia ◽  
Ali Haghizadeh ◽  
Hatam Godini ◽  
Ghodratollah Shams Khorramabadi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wastewater Treatment ◽  
Mean Square Error ◽  
Optimization Algorithm ◽  
Wastewater Treatment Plant ◽  
Treatment Plant ◽  
Mean Square ◽  
Cuckoo Optimization Algorithm ◽  
Artificial Neural

In this study a hybrid estimation model ANN-COA developed to provide an accurate prediction of a Wastewater Treatment Plant (WWTP). An effective strategy for detection of some output parameters tested on a hardware setup in WWTP. This model is designed utilizing Artificial Neural Network (ANN) and Cuckoo Optimization Algorithm (COA) to improve model performances; which is trained by a historical set of data collected during a 6 months operation. ANN-COA based on the difference between the measured and simulated values, allowed a quick revealing of the faults. The method could obtain the fault detection and used in solving continuous and discrete optimization problems, successfully. After constructing and modelling the method, selected performance indices including coefficient of Regression, Mean-Square Error, Root-Mean-Square Error and Aggregated Measure used to compare the obtained results. This analysis revealed that the hybrid ANN-COA model offers a higher degree of accuracy for predicting and control the WWTP.

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