Performance analysis and control of wastewater treatment plant using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multi-Linear Regression (MLR) techniques
1 Department of Electrical and Electronic Engineering, University of Abuja, Nigeria.
2 Department of Civil Engineering Bayero University, Kano.
3 Department of Civil Engineering, Baze University, Abuja, Nigeria.
4 Department of Electrical and Electronic Engineering, Baze University, Abuja, Nigeria.
5 Department of Civil Engineering, Kano University of science and technology, KUST, Wudil, Nigeria
6 Department of Civil Engineering, Federal University, Dutsin-ma, Katsina, Nigeria.
7 Department of Civil Engineering, Institute for Infrastructure & Environment, Heriot-Watt University, Edinburgh, UK.
8 Department of Electrical Engineering, Federal University Gusau, Nigeria.
Research Article
GSC Advanced Engineering and Technology, 2022, 04(02), 001–016.
Article DOI: 10.30574/gscaet.2022.4.2.0033
Publication history:
Received on 13 April 2022; revised on 06 October 2022; accepted on 09 October 2022
Abstract:
In order to provide an effective tool for the simulation of wastewater treatment plant performance and control, a reliable model is essential. In the present study, two different artificial intelligence (AI) models; Adaptive Neuro-Fuzzy inference system (ANFIS), and a classical multi-linear regression analysis (MLR) were applied for predicting the performance of Abuja wastewater treatment plant (AWWTP), in terms of Conductivity, pH, Iron content, BOD, COD, TSS and TDS. The daily data were obtained from Abuja Wastewater treatment plant, for this purpose, single and ensemble models were employed to compare and improve the prediction performance of the plant. The obtained result of single models proved that, MLR model provides an effective analysis in comparison to the other single model. The result showed that, conductivity influences the performance and efficiency of the water treatment plant by an increased efficiency performance of AI modelling up to 99.6% testing phase and 6.8% Error value of same phase. This shows that MLR model was more robust and reliable method for predicting the Abuja WWTP performance.
Keywords:
Artificial Intelligence; Adaptive Neuro-Fuzzy Inference system; Multi-Linear Regression; Performance Indices; Training; Testing
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