Adaptive neuro-fuzzy inference system model with application in wastewater systems

Activated Sludge Process (ASP) is a highly complex and non-linear biological system; therefore, traditional mathematical modelling of this treatment process has remained a challenge. To improve treatment efficiency, quality of the effluent released into the receiving water body, find simple and easy...

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Main Author:	Saleh, Amjed Hassoon
Format:	Thesis
Language:	English
Published:	2014
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/50765/25/AmjedHassoonSalehMFKE2014.pdf
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id	my-utm-ep.50765
record_format	uketd_dc
spelling	my-utm-ep.507652020-07-12T03:06:45Z Adaptive neuro-fuzzy inference system model with application in wastewater systems 2014-06 Saleh, Amjed Hassoon TK Electrical engineering. Electronics Nuclear engineering Activated Sludge Process (ASP) is a highly complex and non-linear biological system; therefore, traditional mathematical modelling of this treatment process has remained a challenge. To improve treatment efficiency, quality of the effluent released into the receiving water body, find simple and easy model that can help the operator to predict the performance of the plant. Moreover, to take cost-effective and timely remedial actions that would ensure consistent treatment efficiency that meeting discharges consents. Therefore, this work highlights one of the techniques that have proved a great success in many scientific problems and applications. Adaptive Neuro-Fuzzy Inference System (ANFIS) has proven to be efficient, reliable and flexible. This work focus on the general concept and characteristics of Adaptive Neuro Fuzzy Inference System and its application in nonlinear and dynamic systems. Moreover, this work present MANFIS which is the extended of Adaptive Neuro Fuzzy Inference System (ANFIS). the proposed MANFIS is used in this project to make identification of four nonlinear outputs in the asp: biomass, recycled biomass, dissolved oxygen and substrate. the last part of this work focuses on controller design – ANFIS inverse controller and model predictive control (MPC) that are used to compare the ability of these two techniques in dealing with nonlinear and complex systems and to predict and control the concentration of the two outputs, substrate and dissolved oxygen. 2014-06 Thesis http://eprints.utm.my/id/eprint/50765/ http://eprints.utm.my/id/eprint/50765/25/AmjedHassoonSalehMFKE2014.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:86636 masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Saleh, Amjed Hassoon Adaptive neuro-fuzzy inference system model with application in wastewater systems
description	Activated Sludge Process (ASP) is a highly complex and non-linear biological system; therefore, traditional mathematical modelling of this treatment process has remained a challenge. To improve treatment efficiency, quality of the effluent released into the receiving water body, find simple and easy model that can help the operator to predict the performance of the plant. Moreover, to take cost-effective and timely remedial actions that would ensure consistent treatment efficiency that meeting discharges consents. Therefore, this work highlights one of the techniques that have proved a great success in many scientific problems and applications. Adaptive Neuro-Fuzzy Inference System (ANFIS) has proven to be efficient, reliable and flexible. This work focus on the general concept and characteristics of Adaptive Neuro Fuzzy Inference System and its application in nonlinear and dynamic systems. Moreover, this work present MANFIS which is the extended of Adaptive Neuro Fuzzy Inference System (ANFIS). the proposed MANFIS is used in this project to make identification of four nonlinear outputs in the asp: biomass, recycled biomass, dissolved oxygen and substrate. the last part of this work focuses on controller design – ANFIS inverse controller and model predictive control (MPC) that are used to compare the ability of these two techniques in dealing with nonlinear and complex systems and to predict and control the concentration of the two outputs, substrate and dissolved oxygen.
format	Thesis
qualification_level	Master's degree
author	Saleh, Amjed Hassoon
author_facet	Saleh, Amjed Hassoon
author_sort	Saleh, Amjed Hassoon
title	Adaptive neuro-fuzzy inference system model with application in wastewater systems
title_short	Adaptive neuro-fuzzy inference system model with application in wastewater systems
title_full	Adaptive neuro-fuzzy inference system model with application in wastewater systems
title_fullStr	Adaptive neuro-fuzzy inference system model with application in wastewater systems
title_full_unstemmed	Adaptive neuro-fuzzy inference system model with application in wastewater systems
title_sort	adaptive neuro-fuzzy inference system model with application in wastewater systems
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2014
url	http://eprints.utm.my/id/eprint/50765/25/AmjedHassoonSalehMFKE2014.pdf
_version_	1747817529298911232

Adaptive neuro-fuzzy inference system model with application in wastewater systems

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