Artificial Neural Network-Cuckoo Optimization Algorithm (ANN-COA) for Optimal Control of Khorramabad Wastewater Treatment Plant, Iran
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.
Keywords: Wastewater Treatment
Plant; Artificial Neural Networks; Cuckoo Optimization Algorithm; Prediction
Analysis; Reliability
Author: Samaneh Khademikia,
Ali Haghizadeh, Hatam Godini, Ghodratollah Shams Khorramabadi
Journal Code: jptsipilgg160058
