R. Bhamidimarri

Analysis of a municipal wastewater treatment plant using a neural network-based pattern analysis

This paper addresses the problem of how to capture the complex relationships that exist between process variables and to diagnose the dynamic behaviour of a municipal wastewater treatment plant (WTP). Due to the complex biological reaction mechanisms, the highly time-varying, and multivariable aspects of the real WTP, the diagnosis of the WTP are still difficult in practice. The application of intelligent techniques, which can analyse the multi-dimensional process data using a sophisticated visualisation technique, can be useful for analysing and diagnosing the activated-sludge WTP. In this paper, the Kohonen Self-Organising Feature Maps (KSOFM) neural network is applied to analyse the multi-dimensional process data, and to diagnose the inter-relationship of the process variables in a real activated-sludge WTP. By using component planes, some detailed local relationships between the process variables, e.g., responses of the process variables under different operating conditions, as well as the global information is discovered. The operating condition and the inter-relationship among the process variables in the WTP have been diagnosed and extracted by the information obtained from the clustering analysis of the maps. It is concluded that the KSOFM technique provides an effective analysing and diagnosing tool to understand the system behaviour and to extract knowledge contained in multi-dimensional data of a large-scale WTP.

Use of fuzzy neural-net model for rule generation of activated sludge process

Abstract Activated sludge plant is usually difficult to operate and control because of its complex operational behaviour and usual significant process disturbances. To increase safety and improve operating performance of this biological wastewater treatment process, it is important to develop computer operational decision support systems. This intelligent computing system is able to assist ordinary operators to work at the level of a domain expert in daily operation. Neural network techniques and fuzzy logic methods have become important and effective tools to help build such intelligent system. The artificial neural network technique is powerful because it can learn to represent complicated data patterns or data relationships between input and output variables of the system being studied. Nevertheless, it has limitations in performing heuristic reasoning of the domain problem. On the other hand, expert systems are good at performing heuristic reasoning by making use of logic rules. It is, however, generally weak for knowledge acquisition. In this study, a fuzzy neural model is developed for addressing the operating problems of activated sludge processes, relating to prediction and heuristic understanding of the sludge age. Neural network techniques and fuzzy logic are used in model development. Simulation studies show that this fuzzy-neural network model obtained is able to extract fuzzy rules from a set of numerical data that can be used to carry out heuristic reasoning.

Dynamic behavior of the activated sludge process under shock loading: Application of the floc model

An integrated system model is developed for an activated sludge system which not only couples the floc behavior to changes in the bulk liquid characteristics but also takes into account the floc size distribution in terms of mass in the reacting system. Intensive simulation is then carried out based on the comprehensive model developed to assess the impact of shock loading on the plant dynamic behavior and to build an understanding of the importance of the floc effects. Simulation results show that more complex dynamic behavior due to the floc effects is likely to occur under severe shock loads which must be considered in the development of the control policy for a safer and better operating performance.

Neural network analysis of the diffusional limitations in activated sludge flocs

Abstract This work deals with the development of a comprehensive but simplified version of floc model for the activated sludge process. The rate expression of the type used by the IAWPRC Task Group was employed in this study to represent the reaction kinetics inside the floc matrix. The intraparticle mass transport was then incorporated to account for the effects of the diffusional limitations. Solution to the floc model was obtained by orthogonal collocation technique. Since the actual reactions are not only related to the sludge floc size and the bulk characteristics but are also associated with the floc size distribution, the concept of overall effectiveness factor was therefore used. Evaluation of overall effectiveness factors using neural network analysis was carried out and potential control strategies were discussed.

Developments in Enhanced Biological Phosphorus Removal (EBPR)

Phosphorus removal using Phosphorus Accumulating Organisms (PAOs) has attracted significant interest in recent years for controlling nutrient pollution in the aquatic environment. Enhanced Biological Phosphorus Removal (EBPR) has been known for several decades since the concept of “luxury uptake” was proposed in 1965 but the mechanisms have not been understood until recently. Biological phosphorus removal may occur through growth associated coupling or storage within the biomass as polyphosphate. The latter mechanism is exploited in the EBPR systems. Until recently, Acinetobacter sp alone was thought to be responsible for EBPR., but it is now known that this may not be the case, although the organism(s) responsible for polyphosphate accumulation are still unknown. There are two biochemical mechanisms proposed for PAOs under anaerobic and aerobic conditions based on the reducing power required for the synthesis of polyhyroxyalkanoate (PHA) . A range of reactor configurations has been developed to accomplish EBPR essentially exploiting the anaerobic and aerobic metabolism of PAOs. The initial EBPR systems relied upon modifications to continuous flow activated sludge reactors incorporating the anaerobic stage prior to aeration basin for phosphorus removal and anaerobic and anoxic stages for nitrogen and phosphorus removal. More recently, Sequencing Batch Reactor (SBR) has been shown to be an effective reactor system for either phosphorus removal alone or for the removal of both nitrogen and phosphorus. Much of EBPR work over the last four decades has been done on low phosphorus concentration (about 15mg/L) domestic effluents. More recently, industrial effluents containing high phosphorus concentration (about 90 mg/L) have also been successfully treated by EBPR. The understanding of EBPR both in terms of mechanisms and reactor systems reached a level ensuring that EBPR is now more attractive alternative to chemical treatment for phosphorus removal.