Investigation and development of methods for optimal control of the activated sludge process
Thesis (MTech (Electrical Engineering))--Cape Peninsula University of Technology, 2009. === This project was started as a result of strict environmental and health regulations together with a demand for cost effective operation of wastewater treatment plants (WNTPs). The main aim of this project i...
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ndltd-netd.ac.za-oai-union.ndltd.org-cput-oai-localhost-20.500.11838-22052018-05-28T05:09:51Z Investigation and development of methods for optimal control of the activated sludge process Kujane, Koketso Portia Tzoneva, Raynitchka Cape Peninsula University of Technology. Faculty of Engineering. Dept. of Electrical, Electronic and Computer Engineering. Sewage -- Purification -- Activated sludge process -- Mathematical models Sewage disposal -- South Africa Adaptive control systems MATLAB Thesis (MTech (Electrical Engineering))--Cape Peninsula University of Technology, 2009. This project was started as a result of strict environmental and health regulations together with a demand for cost effective operation of wastewater treatment plants (WNTPs). The main aim of this project is how to keep effluent concentration below a prescribed limit at the lowest possible cost Due to large fluctuations in the quality and quantity of the influent concentrations, traditional control methods are not adequate to achieve this aim The major drawback with these methods is that the disturbances affect the process before the controller has time to correct the error (Olsson and Newell, 1999: 454). This problem IS addressed through the use of modern control systems Modern control systems are model based predictive algorithms arranged as feed-forward controllers (Olsson and Newell, 1999: 454) Normally a controller is equipped with a constant set point; the goal In this project IS to calculate an optimal DO trajectory that may be sampled to provide a varying optimal set-point for the Activated Sludge Process. In this project an optimal control problem is formulated usmq 00 concentration as a control variable This requires a model of the process to be controlled, a mathematical expressions of the limitations on the process input and output variables and finally the objective functional which consists of the objectives of the control. The structures of the Benchmark plant (developed within the COST 682 working group) and the Athlone WWTPs are used to implement this optimal control strategy in MATLAB, The plant's full models are developed based on the mass balance principle incorporating the activated sludge biological models. ASM1, ASM2 ASM2d and ASM3 (developed by the IWA. working groups) To be able to develop a method that may later on be used ~or online control. the full models are reduced based on the technique in Lukasse (1996) To ensure that the reduced models keep the same prediction capabilities as the full models paran-eters of the reduced models are calculated based on the Least Squares principle. The formulated optimal control problem IS solved based on the decomposition-coordination method that involves time decomposition 111 a two layer structure, MATLAB software is developed to solve the problems for parameter estimation full and reduced model simulation and optimal control calculation for the considered different cases of plant structures and biological models, The obtained optimal DO trajectories produced the effluent state trajectories within prescribed requirements. These DO trajectories may be implemented in different SCADA systems to be tracked as set points or desired trajectories by different types of controllers. 2016-08-11T13:20:22Z 2016-09-09T10:04:54Z 2016-08-11T13:20:22Z 2016-09-09T10:04:54Z 2009 Thesis http://hdl.handle.net/20.500.11838/2205 en_ZA http://creativecommons.org/licenses/by-nc-sa/3.0/za/ Cape Peninsula University of Technology |
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language |
en_ZA |
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Sewage -- Purification -- Activated sludge process -- Mathematical models Sewage disposal -- South Africa Adaptive control systems MATLAB |
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Sewage -- Purification -- Activated sludge process -- Mathematical models Sewage disposal -- South Africa Adaptive control systems MATLAB Kujane, Koketso Portia Investigation and development of methods for optimal control of the activated sludge process |
description |
Thesis (MTech (Electrical Engineering))--Cape Peninsula University of Technology, 2009. === This project was started as a result of strict environmental and health regulations together
with a demand for cost effective operation of wastewater treatment plants (WNTPs). The
main aim of this project is how to keep effluent concentration below a prescribed limit at the
lowest possible cost Due to large fluctuations in the quality and quantity of the influent
concentrations, traditional control methods are not adequate to achieve this aim The major
drawback with these methods is that the disturbances affect the process before the controller
has time to correct the error (Olsson and Newell, 1999: 454). This problem IS addressed
through the use of modern control systems
Modern control systems are model based predictive algorithms arranged as feed-forward
controllers (Olsson and Newell, 1999: 454) Normally a controller is equipped with a constant
set point; the goal In this project IS to calculate an optimal DO trajectory that may be sampled
to provide a varying optimal set-point for the Activated Sludge Process. In this project an
optimal control problem is formulated usmq 00 concentration as a control variable This
requires a model of the process to be controlled, a mathematical expressions of the
limitations on the process input and output variables and finally the objective functional which
consists of the objectives of the control. The structures of the Benchmark plant (developed within the COST 682 working group) and
the Athlone WWTPs are used to implement this optimal control strategy in MATLAB, The
plant's full models are developed based on the mass balance principle incorporating the
activated sludge biological models. ASM1, ASM2 ASM2d and ASM3 (developed by the IWA.
working groups) To be able to develop a method that may later on be used ~or online
control. the full models are reduced based on the technique in Lukasse (1996) To ensure
that the reduced models keep the same prediction capabilities as the full models paran-eters
of the reduced models are calculated based on the Least Squares principle. The formulated
optimal control problem IS solved based on the decomposition-coordination method that
involves time decomposition 111 a two layer structure,
MATLAB software is developed to solve the problems for parameter estimation full and
reduced model simulation and optimal control calculation for the considered different cases
of plant structures and biological models, The obtained optimal DO trajectories produced the
effluent state trajectories within prescribed requirements. These DO trajectories may be
implemented in different SCADA systems to be tracked as set points or desired trajectories
by different types of controllers. |
author2 |
Tzoneva, Raynitchka |
author_facet |
Tzoneva, Raynitchka Kujane, Koketso Portia |
author |
Kujane, Koketso Portia |
author_sort |
Kujane, Koketso Portia |
title |
Investigation and development of methods for optimal control of the activated sludge process |
title_short |
Investigation and development of methods for optimal control of the activated sludge process |
title_full |
Investigation and development of methods for optimal control of the activated sludge process |
title_fullStr |
Investigation and development of methods for optimal control of the activated sludge process |
title_full_unstemmed |
Investigation and development of methods for optimal control of the activated sludge process |
title_sort |
investigation and development of methods for optimal control of the activated sludge process |
publisher |
Cape Peninsula University of Technology |
publishDate |
2016 |
url |
http://hdl.handle.net/20.500.11838/2205 |
work_keys_str_mv |
AT kujanekoketsoportia investigationanddevelopmentofmethodsforoptimalcontroloftheactivatedsludgeprocess |
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1718681794950201344 |