Nonlinear MPC tracking control and set point control for wastewater treatment processes
Date
2019-01-21
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
This thesis concerns the design of feedback controls of a biological wastewater treatment plant (BWT), specifically the benchmark simulation model number 1, and methods for determining optimal set points. In BWT, biological organisms remove unwanted substances including nitrogen, ammonia, and organic material. The feedback controls can manipulate aeration and flow rates in order to control the dissolved oxygen concentration and nitrite/nitrate concentrations. The most basic function of the feedback controls is to ensure that effluent quality meets a pre-determined environmental standard in an energy-efficient manner. Identifying optimal set points can be as important, or more important, in reducing the contaminants/cost as the feedback/feedforward strategy used to track the set point. Thus, choosing an appropriate nitrate/nitrite and oxygen set point, and then maintaining the set point, defines the important objectives of the current work. Several novel methods are developed and compared with a PI control. Initially, Lyapunov-based adaptive controllers with fuzzy set point regulators are designed for both loops. Compared with the existing methods, the proposed methods demonstrate great potential for improving system performance. Moreover, switching techniques on an external carbon source input are proposed to prevent the risk of too much or too little food and/or too little dissolved oxygen. Then, design of the dissolved oxygen (DO) variable set point is presented in parallel to the DO set point tracking control, based on Artificial Neural Network (ANN) models used for set point design and for prediction within the DO Neural Networks Model Predictive Control (NNMPC) algorithm. The solution of an offline multi-objective optimization problem during the first two days of dry weather conditions is used as the initial set point, and then changes in the moving direction provided by an ANN model. Compared with the existing methods, the proposed method shows ability of reducing the effluent quality and the operational cost simultaneously. Next, a single-optimization problem along with an ANN model designs the nitrate/nitrite set point in order to reduce violations in the ammonium and nitrogen limits. The results prove the near-complete removal of violations by using the proposed method. The method in the last chapter includes a way to adjust set point to respond to varying conditions and a model predictive control scheme, which utilizes a cerebellar model arithmetic computer (CMAC). This controller is an adaptive one, since our model used in the MPC updates on-line and in real-time and can thus change due to unknown and changing dynamics. This technique avoids the need for any a-priori estimation step. The CMAC controller learn the desired control signal in a Lyapunov-stable scheme, which provides a guarantee of uniformly ultimately bounded signals.
Description
Keywords
Model Predictive Control, Wastewater Treatment Process, Benchmark Simulation Model Number One Model, Nonlinear Optimization, Two-Level Hierarchical Control
Citation
Sadeghassadi, M. (2019). Nonlinear MPC tracking control and set point control for wastewater treatment processes (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.