Fuzzy-Supported Estimator for HPO-AS Process

Abstract

Lack of real-time measurements is a major problem in the operation and control of the high-purity oxygen activated-sludge process. A real-time estimator using dissolved oxygen measurements in each stage and liquid flow rates was developed to estimate biomass and substrate concentrations, biomass growth, and decay rates. A fuzzy algorithm was used to estimate unmeasured variables, such as influent substrate and recycle biomass concentrations. The convergence of the algorithms used for the estimator is fast and stable, even with a large range of initial inputs and noisy dissolved oxygen measurements. The estimated results compare well with both plant data and synthetic results produced by a process model and corrupted with white noise. The difference in the predictions of single substrate and structure models is demonstrated. The estimator was tested successfully for certain types of process upsets, such as shock hydraulic loading and high diluted sludge volume index.