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

Samaneh Khademikia,Ghodratollah Shams Khorramabadi,Hatam Godini,Ali Haghizadeh

doi:10.28991/cej-2016-00000058

Abstract

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.

Highlights

Municipal and Industrial wastewaters are accounting for several types of contaminators released into the aquatic environment
The developed Cuckoo Optimization Algorithm (COA)-Artificial Neural Network (ANN) model was used with the available operational input variables for controlling the effluent biochemical oxygen demand (BOD), chemical oxygen demand (COD) and TSS during 6 months operation
The COA-ANN model in this study is expected to have a great application for controlling the Wastewater Treatment Plant (WWTP)

Summary

Introduction

Municipal and Industrial wastewaters are accounting for several types of contaminators released into the aquatic environment. In the last two decades, there has been constantly increasing interest in Artificial Neural Networks (ANNs) as a reliable model for efficient monitoring, predicting performance and controlling the operation and variables of the process in the complicated nonlinear and multivariable processes such as chemical engineering process, bioprocess and wastewater treatment process [5,6,7,8]. For any WWTP, the reliable ANN technique is essential in order to avoid process failure [9] To this end, ANNs have been developed to predict WWTP performance with a higher degree of accuracy and solve complex engineering problems more rapidly [10]. This study is expected to obtain a control system as the prediction model and controlling system for a WWTP, to keep process stabile with high performance in wastewater treatment. The approach used in this study will make WWTP more reliable, usable and give quicker process response

Material and Methods

Data Analyze

Predictive modeling with ANN

Conclusion