Operation of an activated sludge plant for fellmongery wastewater treatment : a thesis submitted in partial fulfilment of the requirements of the degree of Master of Technology in Environmental Engineering at Massey University, Palmerston North, New Zealand

Activated sludge is one of the most common wastewater-treatment processes used to reduce pollutant loads on the receiving environment. For efficient operation, there must be an effective process control and operation strategy in place to ensure that process problems are avoided. This research is a case study into the process control and operation of an activated sludge plant used for fellmongery wastewater treatment. Analysis of the pretreated fellmongery wastewater showed that it is characterised by high total and volatile suspended solids concentrations, and high organic nitrogen concentrations. The plant was experiencing frequent problems that were attributed to the high influent suspended solids load coupled with ineffective solids management. Operation of bench-scale simulations showed that solids retention time (SRT) control at 5 or 10 days will produce acceptable effluent suspended solids concentrations and soluble chemical oxygen demand (COD) removal. Soluble COD removal for both 5 and 10 days was high at 85 and 80 % respectively at a hydraulic retention time of 6.4 days. Effluent suspended solids concentrations were 100 and 157 g/m 3 respectively. A steady state control model was developed based on, mass balances of biochemical oxygen demand (BOD) and volatile suspended solids (VSS), process performance equations, and the solids retention time (SRT). The model used three control points, the clarifier underflow pump, the clarifier influent pump and the waste sludge pump. The model was incorporated into an off-line Activated Sludge Operation Program (ASOP) to provide a user-friendly interface between the plant and operator. The main output from ASOP includes values for the three control points and suggestions to help avoid problems. A process control and operation strategy was developed using ASOP, the knowledge gained in this research, and an operation manual developed from accepted operation practises

Wastewater treatment improvement through an intelligent integrated supervisory system

This paper shows the result of years of work by a cooperative research group including chemical engineers, environmental scientists and computer scientists. This research has been focused on the development and implementation of new techniques for the optimisation of complex process management, mainly related to wastewater treatment plants (WWTP). The experience obtained indicates that the best approach is a Supervisory System that combines and integrates classical control of WWTP (automatic controller for maintaining a fixed dissolved oxygen level in the aeration tank, use of mathematical models to describe the process...) with the application of knowledge-based systems (mainly expert systems and case-based systems). The first part is an introduction to wastewater treatment processes and an explanation of the complexity of the management and control of such complex processes. The next section illustrates the architecture of the supervisory system and the work carried out to develop and build the expert system, the casebased system and the simulation model for implementation in a real plant (the Granollers WWTP). Finally, some results of the field validation phase of the Supervisory System when dealing with real situations in the plant are described.Aquest article mostra el resultat de la col·laboració portada a terme durant els darrers anys entre grups d'enginyeria química, enginyeria ambiental i intel·ligència artificial. El treball se centra en el desenvolupament de tècniques per a la millora i supervisió de processos complexos, especialment del tractament biològic d'aigües residuals. L'experiència demostra que la millor opció requereix desenvolupar un sistema supervisor que combini i integri tècniques de control clàssic (controlador automàtic del nivell d'oxigen dissolt en el reactor biològic, ús de models descriptius del procés, etc.) amb sistemes basats en el coneixement (concretament sistemes experts i sistemes basats en casos). El present article descriu la complexitat de la gestió del procés de tractament de les aigües residuals, l'arquitectura integrada que es proposa i el desenvolupament i la construcció de cadascun dels mòduls d'aquesta proposta per a la implementació real a l'estació depuradora d'aigües residuals de Granollers. Finalment, es detallen alguns resultats del procés de validació del seu funcionament enfront de situacions quotidianes de la planta

Wastewater treatment improvement through an intelligent integrated supervisory system

Aquest article mostra el resultat de la col·laboració portada a terme durant els darrers anys entre grups d'enginyeria química, enginyeria ambiental i intel·ligència artificial. El treball se centra en el desenvolupament de tècniques per a la millora i supervisió de processos complexos, especialment del tractament biològic d'aigües residuals. L'experiència demostra que la millor opció requereix desenvolupar un sistema supervisor que combini i integri tècniques de control clàssic (controlador automàtic del nivell d'oxigen dissolt en el reactor biològic, ús de models descriptius del procés, etc.) amb sistemes basats en el coneixement (concretament sistemes experts i sistemes basats en casos). El present article descriu la complexitat de la gestió del procés de tractament de les aigües residuals, l'arquitectura integrada que es proposa i el desenvolupament i la construcció de cadascun dels mòduls d'aquesta proposta per a la implementació real a l'estació depuradora d'aigües residuals de Granollers. Finalment, es detallen alguns resultats del procés de validació del seu funcionament enfront de situacions quotidianes de la planta.This paper shows the result of years of work by a cooperative research group including chemical engineers, environmental scientists and computer scientists. This research has been focused on the development and implementation of new techniques for the optimisation of complex process management, mainly related to wastewater treatment plants (WWTP). The experience obtained indicates that the best approach is a Supervisory System that combines and integrates classical control of WWTP (automatic controller for maintaining a fixed dissolved oxygen level in the aeration tank, use of mathematical models to describe the process...) with the application of knowledge-based systems (mainly expert systems and case-based systems). The first part is an introduction to wastewater treatment processes and an explanation of the complexity of the management and control of such complex processes. The next section illustrates the architecture of the supervisory system and the work carried out to develop and build the expert system, the casebased system and the simulation model for implementation in a real plant (the Granollers WWTP). Finally, some results of the field validation phase of the Supervisory System when dealing with real situations in the plant are described

Knowledge-based supervision and control of WWTP: a real-time implementation

Fundação Calouste Gulbenkian (FCG) - post-doctoral research grant.Generalitat de Catalunya. Consell Interdepartamental de Recerca i Innovació Tecnològica (CIRIT) - predoctoral fellowship

A knowledge-based distributed system for supervision and control of wastewater treatment processes

This paper presents the hardware architecture and the software development of a real-time knowledge-based distributed control system for the supervision of a wastewater treatment pilot plant with biological removal of organic matter and nitrogen. A continuous monitoring of plant and controls data is used by an expert system developed in G2, a development environment based on object-oriented paradigm. A set of rules and procedures to help fault detection, plant maintenance, and nitrification - denitrification cycle operation was implemented and validated at pilot scale. The hardware architecture includes different supervision levels, including two autonomous process computers (plant control and analysers control).Fundação Calouste Gulbenkian (FCG) - postdoctoral research grant..Generalitat de Catalunya. Consell Interdepartamental de Recerca i Innovació Tecnològica (CIRIT) - predoctoral fellowship

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

Demand response within the energy-for-water-nexus - A review. ESRI WP637, October 2019

A promising tool to achieve more flexibility within power systems is demand re-sponse (DR). End-users in many strands of industry have been subject to research up to now regarding the opportunities for implementing DR programmes. One sector that has received little attention from the literature so far, is wastewater treatment. However, case studies indicate that the potential for wastewater treatment plants to provide DR services might be significant. This review presents and categorises recent modelling approaches for industrial demand response as well as for the wastewater treatment plant operation. Furthermore, the main sources of flexibility from wastewater treatment plants are presented: a potential for variable electricity use in aeration, the time-shifting operation of pumps, the exploitation of built-in redundan-cy in the system and flexibility in the sludge processing. Although case studies con-note the potential for DR from individual WWTPs, no study acknowledges the en-dogeneity of energy prices which arises from a large-scale utilisation of DR. There-fore, an integrated energy systems approach is required to quantify system and market effects effectively

State estimation of a reducer order activated sludge model

Wastewater treatment is a complex process that removes and eliminates contaminants from wastewater and converts them into an effluent suitable for reintroduction into the water cycle. The subsequent discharge of this effluent can have a significant impact on the environment, and this process is typically carried out within Wastewater Treatment Plants (WWTPs). The waste residue generated during the treatment process is known as sludge and it is potentially hazardous. Hence, a primary objective of the WWTP is to process this sludge, ensuring it meets the necessary conditions for its intended use, i.e., a sewage plant. The goal of this project is the development and simulation of a WWTP by the implementation of a reduced order model. Furthermore, to be applied into a real-live application, which consisted of modelling the reduced model to be able to apply estimation methods to know the unknown components (or states) behavior in the plant. The inputs and measurements of those states were also unknown. Besides, the reduced model had to still represent the main processes and components from the original model without changing the behavior. To address the problem, extensive research on reduced-order models was undertaken to identify the most suitable model for practical application. The model used had to be implemented and verified to still maintain the same behavior. Later, applied the Extended Kalman Filter (EKF) algorithm to estimate those unknown states. The Unknown Input Observer (UIO) and augmentation plant algorithms were implemented into the filter to better estimate all the states. In the project, the reduced-order model will be explored in three versions: non-linear, linear, and state space models, and these will be compared with the behavior of the actual model to validate their correct behavior. Then, the EKF, EKF with the UIO, and the EKF with the augmented plant will be tested in different scenarios, alternating between the unknown measurements and inputs of the states to be estimated, to find the best solution to the problem. Moreover, several key performance indices (KPIs) will be computed to assess and compare the results, ultimately allowing for the selection of the most effective solutio

Constructed wetlands: Prediction of performance with case-based reasoning (part B)

The aim of this research was to assess the treatment efficiencies for gully pot liquor of experimental vertical- flow constructed wetland filters containing Phragmites australis (Cav.) Trin. ex Steud. (common reed) and filter media of different adsorption capacities. Six out of 12 filters received inflow water spiked with metals. For 2 years, hydrated nickel and copper nitrate were added to sieved gully pot liquor to simulate contaminated primary treated storm runoff. The findings were analyzed and discussed in a previous paper (Part A). Case-based reasoning (CBR) methods were applied to predict 5 days at 20°C N-Allylthiourea biochemical oxygen demand (BOD) and suspended solids (SS), and to demonstrate an alternative method of analyzing water quality performance indicators. The CBR method was successful in predicting if outflow concentrations were either above or below the thresholds set for water-quality variables. Relatively small case bases of approximately 60 entries are sufficient to yield relatively high predictions of compliance of at least 90% for BOD. Biochemical oxygen demand and SS are expensive to estimate, and can be cost-effectively controlled by applying CBR with the input variables turbidity and conductivity

Development of an intelligent dynamic modelling system for the diagnosis of wastewater treatment processes

In the 21st Century, water is already a limited and valuable resource, in particular the limited availability of fresh water sources. The projected increase in global population from 6 billion people in 2010 to 9 billion in 2050 will only increase the need for additional water sources to be identified and used. This situation is common in many countries and is frequently exacerbated by drought conditions. Water management planning requires both the efficient use of water sources and, increasingly, the re-use of domestic and industrial wastewaters. A large body of published research spanning several decades is available, and this research study looks specifically at ways of improving the operation of wastewater treatment processes.Process fault diagnosis is a major challenge for the chemical and process industries, and is also important for wastewater treatment processes. Significant economic and environmental losses can be attributed to inappropriate Abnormal Event Management (AEM) in a chemical/processing operation, and this has been the focus of many researchers. Many researchers are now focusing on the application of several fault diagnosis techniques simultaneously in order to improve and overcome the limitations experienced by the individual techniques. This approach requires resolution of the conflicts ascribed to the individual methods, and incurs additional costs and resources when employing more than one technique. The research study presented in this thesis details a new method of using the available techniques. The proposal is to use different techniques in different roles within the diagnostic approach based upon their inherent individual strengths. The techniques that are excellent for the detection of a fault should be employed in the fault detection, and those best applied to diagnosis are used in the diagnosis section of a diagnostic system.Two different techniques are used here, namely a mathematical model and data mining are used for detection and diagnosis respectively. A mathematical model is used which is based upon the principal of analytical redundancy in order to establish the presence of a fault in a process (the fault detection), and data mining is used to produce production rules derived from the historical data for the diagnosis. A dataset from an industrial wastewater treatment facility is used in this study.A diagnostic algorithm has been developed that employs the techniques identified above. An application in Java was constructed which allows the algorithm to be applied, eventually producing an intelligent modelling agent. Thus the focus of this research work was to develop an intelligent dynamic modelling system (using components such as mathematical model, data mining, diagnostic algorithm, and the dataset) for simulation of, and diagnosis of faults in, a wastewater treatment process where different techniques will be assigned different roles in the diagnostic system.Results presented in Chapter 5 (section 5.5) show that the application of this combined technique yields better results for detection and diagnosis of faults in a process. Furthermore, the dynamic update of the set value for any process variable (presented in Chapter 5, section 5.2.1) makes possible the detection of any process disturbance for the algorithm, thereby mitigating the issue of false alarms. The successful embedding of both a detection and a diagnostic technique in a single algorithm is a key achievement of this work, thus reducing the time taken to detect and diagnose a fault. In addition, the implementation of the algorithm in the purposebuilt software platform proved its practical application and potential to be used in the chemical and processing industries