Developing a decision support system for operation and control of the high-purity oxygen-activated sludge process

Operation and control of the high-purity oxygen activated sludge process (HPO-AS) are more complex than for conventional open-air activated sludge process. The objective of this research is to provide both quantitative and qualitative support to the operators in their decision-making process. To realize this goal, a decision support system is developed in this investigation. The system consists of five major components: operator's interface, process simulator, on-line state and parameter estimator, knowledge base and data managing utilities. This dissertation presents the framework of the system.;The decision support system developed in this study is superior to a conventional expert system since it can quantify the operation and control, besides performing process diagnosis. The operator can obtain more information and has more choices when he/she is making operational changes to the process, so that the correct decision is more likely to be made.;A process simulator was built into the system. It consists of a group of ordinary differential equations. The operator can simulate and test the operational strategies before they are applied to the process operation. In this way, the strategies can be evaluated and refined. The simulator is a valuable tool for training new operators.;An on-line estimator was constructed to estimate biomass and substrate concentrations, maximum and specific growth rates of biomass, and oxygen uptake rate, based upon measured dissolved oxygen concentration in each stage. These estimated states and bio-kinetic parameters are very valuable to the operator, and provide important information for advanced process controls. The estimator is assisted by fuzzy estimations of influent substrate and effluent total suspended solids concentrations. The convergence of the estimator is fast and stable. The estimated values reasonably well agree with both steady state plant data and hypothetical data with artificial noise.;Two kinds of knowledge were formulated in the knowledge base: fuzzy knowledge for gas phase control and conventional knowledge for process diagnosis. Four fuzzy control strategies were developed to perform gas phase control. The results show that all four strategies are superior to the conventional proportional integral derivative (PID) control system in terms of stable oxygen feed, reducing dissolved oxygen oscillation and resisting process disturbances. The fuzzy control system also has the ability to adapt to process upsets, such as storm and extremely dry weather conditions.;A conventional rule-based knowledge base was developed specifically for the HPO-AS process operation. It can be executed in either on- and off-line modes. The arrangement of the logic trees has the advantages of easy maintenance and extendibility. More than 200 rules were formulated in the knowledge base.