Modeling and Control of Wastewater Treatment Process

Studies and research in modeling and control tools of the biological wastewater treatment process is very significant to improve the conventional operation control strategies and eventually giving big positive impact to our own environment. Hence, the objectives of this work are: i. To study wastewater treatment process ii. To model and simulate an activated sludge process iii. To apply control strategies on the model A wastewater treatment plant has been studied and activated sludge process has become the subject of concern as it is the most widely used technology for biological treatment in a wastewater treatment plant. The activated sludge plant has been modeled following the Benchmark Simulation Model No. 1 (BSM1) developed by COST 682 Working Group No. 2 to provide standard process assessment and besides, this model is accepted internationally. The model is implemented using MATLAB/Simulink Software to capture the mathematical model describing the process. The control of dissolved oxygen level in the reactors plays an important role in the operation, thus conventional PI control strategy had been applied to the model to control the dissolve oxygen (DO) level in the aerated reactor. To further improvement of the DO control in the system, a new approach of advance control strategy had been also applied to the system. Model Predictive Control (MPC) is introduced to the activated sludge plant. The control strategies had been evaluated and comparison had been made between the conventional PI controller and the MPC. The results showed that the system shows control improvement with the MPC implemented to the plant. Research study and literature review on the topic will be discussed in Chapter 2 of the report. All resources had been used wisely to obtain the most information regarding wastewater treatment process and its control strategies. Methodology of this project is represented in Chapter 3 of this report. Gantt chart is also included to present the activity conducted throughout the semester which in all, most of them have been executed successfully. The results of the project are shown in Chapter 4. Finally in Chapter 5, conclusion of the project is represented with recommendation for future work

Deep ocean disposal of sewage sludge off Orange County, California: a research plan

Even though the discharge of sludge into the ocean via an outfall is not now permitted, this research plan has been prepared to show what could be learned with a full scale experimental sludge discharge of 150 dry tons/day by the County Sanitation Districts of Orange County into deep water (over 1000 feet). To provide a wide range of inputs and evaluation, a broad-based Research Planning Committee was established to advise the Environmental Quality Laboratory on the overall content and details of the research plan. Two meetings were held at EQL on: March 4-5, 1982: The entire Committee July 19-20, 1982: A working subgroup of the Committee The entire Committee is listed in Appendix B, with footnotes to indicate meeting attendance. Those unable to come to a meeting were asked to comment on the drafts by mail or telephone. We gratefully acknowledge the members of the Research Planning Committee for their generous help in formulating the research tasks and reviewing report drafts

Wiener modelling and model predictive control for wastewater applications

The research presented in this paper aims to demonstrate the application of predictive control to an integrated wastewater system with the use of the wiener modeling approach. This allows the controlled process, dissolved oxygen, to be considered to be composed of two parts: the linear dynamics, and a static nonlinearity, thus allowing control other than common approaches such as gain-scheduling, or switching, for series of linear controllers. The paper discusses various approaches to the modelling required for control purposes, and the use of wiener modelling for the specific application of integrated waste water control. This paper demonstrates this application and compares with that of another nonlinear approach, fuzzy gain-scheduled control

Economic linear parameter varying model predictive control of the aeration system of a wastewater treatment plant

This work proposes an economic model predictive control (EMPC) strategy in the linear parameter varying (LPV) framework for the control of dissolved oxygen concentrations in the aerated reactors of a wastewater treatment plant (WWTP). A reduced model of the complex nonlinear plant is represented in a quasi-linear parameter varying (qLPV) form to reduce computational burden, enabling the real-time operation. To facilitate the formulation of the time-varying parameters which are functions of system states, as well as for feedback control purposes, a moving horizon estimator (MHE) that uses the qLPV WWTP model is proposed. The control strategy is investigated and evaluated based on the ASM1 simulation benchmark for performance assessment. The obtained results applying the EMPC strategy for the control of the aeration system in the WWTP of Girona (Spain) show its effectiveness.This work has been co-financed by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the project SaCoAV (ref. MINECO PID2020- 114244RB-I00), by the European Regional Development Fund of the European Union in the framework of the ERDF Operational Program of Catalonia 2014–2020 (ref. 001-P-001643 Looming Factory), and by the DGR of Generalitat de Catalunya (SAC group ref. 2017/SGR/482).Peer ReviewedPostprint (author's final draft

Control and soft sensing strategies for a wastewater treatment plant using a neuro-genetic approach.

During the last years, machine learning-based control and optimization systems are playing an important role in the operation of wastewater treatment plants in terms of reduced operational costs and improved effluent quality. In this paper, a machine learning-based control strategy is proposed for optimizing both the consumption and the number of regulation violations of a biological wastewater treatment plant. The methodology proposed in this study uses neural networks as a soft-sensor for on-line prediction of the effluent quality and as an identification model of the plant dynamics, all under a neuro-genetic optimum model-based control approach. The complete scheme was tested on a simulation model of the activated sludge process of a large-scale municipal wastewater treatment plant running under the GPS-X simulation frame and validated with operational gathered data, showing optimal control performance by minimizing operational costs while satisfying the effluent requirements, thus reducing the investment in expensive sensor devices

EPSAC for wastewater treatment process (BSM1)

Predictive control is one of the most spread advanced control algorithms in industrial application field. Extended Prediction Self-Adaptive Control (EPSAC) is a part of this family of algorithms and is suitable for wastewater treatment plants control. The main goal of those industrial processes is to fulfil effluent water quality legal provisions with minimal energy consumption. In order to achieve this goal EPSAC control methodology has been applied to the wastewater treatment process. Benchmark Simulation Model No. 1 (BSM1) has been used to simulate the process dynamics. Two types of control strategies were implemented and tested: predictive control without taking into account measured disturbances and predictive control with feedforward. Feedforward control with two measured disturbances (the influent flow rate and ammonium concentration) has been tested