Modelling and Operational Control of the Activated Sludge Process in Wastewater Treatment

A report is presented on a collaborative study of dynamic modelling and control of the activated sludge process in wastewater treatment. The report divides into four major parts, the first of which presents and discusses the time-series of field data from the Norwich Sewage Works in England. The second part of the paper is concerned with the identification of a model for nitrification in the activated sludge process from the given field data; the technique used for this purpose is an extended Kalman filtering algorithm. A third section deals with the construction of a detailed simulation model which has been used for control system design and evaluation. The final major part of the report introduces some basic ideas of fuzzy control, suggests why conventional control schemes may be of limited value in wastewater treatment systems, and proceeds to define a fuzzy controller developed from the empirical experience of the Norwich Treatment Plant manager. The paper also offers some thoughts on future perspectives for the study and for the use of mathematical models as aids to the operational control of wastewater treatment

IMPLEMENTATION OF FUZZY NEURAL NETWORK IN ACTIVATED SLUDGE PROCESS OF THE WASTEWATER TREATMENT PLANT

Wastewater treatment plants play an important role in maintaining water quality and preserving the environment. The problem addressed in this study is the inefficiency of controller of the activate sludge process due to high energy consumption of the activated sludge process, lack of adaptability of the default controller, and strict effluent quality compliance set by local and national authorities. The objectives of this research are to develop an effective control strategy for the activated sludge process in tank 5 and to enhance the overall performance of the wastewater treatment plant. The proposed method of research utilizes a fuzzy neural network to model and optimize the control parameter of tank 5 which is the oxygen transfer coefficient. The proposed control strategy combines the benefits of fuzzy logic and neural network techniques to provide robust and adaptive control in complex and uncertain wastewater treatment systems. The modelling of the proposed controller is by employing the data of default controller. The outcomes of this study are expected to include improved process efficiency, enhanced treatment quality, reduced operational costs, and minimized environmental impact. The results will provide valuable insights for the wastewater treatment plant operators and contribute to the advancement of control strategies in wastewater treatment systems

Fuzzy Control of the Activated Sludge Wastewater Treatment Process

The activated sludge process is a commonly used method for treating sewage and waste waters. It is characterized by a lack of relevant instrumentation, control goals that are not always clearly stated, the use of qualitative information in decision making and poorly understood basic behavior mechanisms. In this brief paper we examine the behavior of an experimental fuzzy control algorithm constructed to reflect actual operational practice. We conclude that this algorithm does rather well and that a fuzzy controller would be a useful and practical way of regulating the activated sludge process

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

Neural Observer Based Hybrid Intelligent Scheme for Activated Sludge Wastewater Treatment

Activated sludge wastewater treatment plants have received considerable attention due to their efficiency to eliminate biodegradable pollution and their robustness to reject disturbances. Different control strategies have been proposed, but most of these techniques need sensors to measure process main variables. This paper presents a discrete-time recurrent high order neural observer (RHONO) to estimate substrate and biomass concentrations in an activated sludge wastewater treatment plant. The RHONO is trained on-line with an extended Kalman filter (EKF)-based algorithm. Then this observer is associated with a hybrid intelligent system based on fuzzy logic to control the substrate/biomass concentration ratio using the external recycle flow rate and the injected oxygen as control actions. The intelligent system and neural observer performance is illustrated via simulations

Modelling activated sludge wastewater treatment plants using artificial intelligence techniques (fuzzy logic and neural networks)

Activated sludge process (ASP) is the most commonly used biological wastewater treatment system. Mathematical modelling of this process is important for improving its treatment efficiency and thus the quality of the effluent released into the receiving water body. This is because the models can help the operator to predict the performance of the plant in order to take cost-effective and timely remedial actions that would ensure consistent treatment efficiency and meeting discharge consents. However, due to the highly complex and non-linear characteristics of this biological system, traditional mathematical modelling of this treatment process has remained a challenge. This thesis presents the applications of Artificial Intelligence (AI) techniques for modelling the ASP. These include the Kohonen Self Organising Map (KSOM), backpropagation artificial neural networks (BPANN), and adaptive network based fuzzy inference system (ANFIS). A comparison between these techniques has been made and the possibility of the hybrids between them was also investigated and tested. The study demonstrated that AI techniques offer viable, flexible and effective modelling methodology alternative for the activated sludge system. The KSOM was found to be an attractive tool for data preparation because it can easily accommodate missing data and outliers and because of its power in extracting salient features from raw data. As a consequence of the latter, the KSOM offers an excellent tool for the visualisation of high dimensional data. In addition, the KSOM was used to develop a software sensor to predict biological oxygen demand. This soft-sensor represents a significant advance in real-time BOD operational control by offering a very fast estimation of this important wastewater parameter when compared to the traditional 5-days bio-essay BOD test procedure. Furthermore, hybrids of KSOM-ANN and KSOM-ANFIS were shown to result much more improved model performance than using the respective modelling paradigms on their own.Damascus Universit

Knowledge-based fuzzy system for diagnosis and control of an integrated biological wastewater treatment process

A supervisory expert system based on fuzzy logic rules was developed for diagnosis and control of a lab- scale plant comprising anaerobic/anoxic/aerobic modules for combined high rate biological N and C removal. The design and implementation of a computational environment in LabVIEW for data acquisition, plant operation and distributed equipment control is described. A step increase in ammonia concentration from 20 to 60 mg N/L was applied during a trial period of 73 hours. Recycle flow rate from the aerobic to the anoxic module and by-pass flow rate from the influent directly to the anoxic reactor were the output of the fuzzy system that were automatically changed (from 34 to 111 L/day and from 8 to 13 L/day, respectively), when new plant conditions were recognized by the expert system. Denitrification efficiency higher than 85% was achieved 30 hours after the disturbance and 15 hours after the system response at an HRT as low as 1.5 hours. Nitrification efficiency gradually increased from 12 to 50% at an HRT of 3 hours. The system proved to properly react in order to set adequate operating conditions that timely led to recover efficient N and C removal rates.Fundação para a Ciência e a Tecnologia , Fundo Social Europeu - BD/1299/2000 , BD/13317/2003

Design of feedback control strategies in a plant-wide wastewater treatment plant for simultaneous evaluation of economics, energy usage, and removal of nutrients

Simultaneous removal of nitrogen and phosphorous is a recommended practice while treating wastewater. In the present study, control strategies based on proportional-integral (PI), model predictive control (MPC), and fuzzy logic are developed and implemented on a plant-wide wastewater treatment plant. Four combinations of control frameworks are developed in order to reduce the operational cost and improve the effluent quality. As a working platform, a Benchmark simulation model (BSM2-P) is used. A default control framework with PI controllers is used to control nitrate and dissolved oxygen (DO) by manipulating the internal recycle and oxygen mass trans-fer coefficient (KLa). Hierarchical control topology is proposed in which a lower-level control framework with PI controllers is implemented to DO in the sixth reactor by regulating the KLa of the fifth, sixth, and seventh reactors, and fuzzy and MPC are used at the supervisory level. This supervisory level considers the ammonia in the last aerobic reactor as a feedback signal to alter the DO set-points. PI-fuzzy showed improved effluent quality by 21.1%, total phosphorus removal rate by 33.3% with an increase of operational cost, and a slight increase in the production rates of greenhouse gases. In all the control design frameworks, a trade-off is observed between operational cost and effluent quality