Empirical Modelling of Activated Sludge Process via System Identification

Abstract

Activated sludge process is an important stage in Wastewater Treatment Plant (WWTP). In this study, model of the activated sludge process from Bunus Regional Sewage Treatment Plant Kuala Lumpur, Malaysia is developed. This paper focuses on modelling and model reduction of the WWTP system. The model with best fits of higher than 80% and the order of less than 10 is selected. For modelling purposes, data obtained is stimulated and modelled using System Identification technique which employ linear model ARX. For model reduction purposes, the high order model is reduced using model order reduction (MOR) of a combination of Singular Perturbation Approximation (SPA) and Frequency Domain Gramian based Model Reduction (FDIG) method. From the modelling results obtained, the ARX model with best fit of 85.11% is selected. Meanwhile, for the MOR FD-SPA technique, a 9th order model is selected with reduction error between frequencies 0.05 rad/s and 1.4 rad/s.

A Software Emulator for the Modelling and Control of an Activated Sludge Process in a Wastewater Treatment Plant

“…The flow of recycled sludge from the clarifier-settler is the major parameter that allows the adjustment of the activated sludge concentration. This succinct description is quite generic; in fact, the ASP is a complex, nonlinear, and uncertain process [20,23].…”

“…The control goal for the ASP within the WWTP is to obatin an imposed small level for pollution, with a proper aeration, in severe conditions owing to the unavailability and uncertainty of several key variables and parameters [19][20][21]. According to the particularities of the WWTP Făcăi, the controlled variables are C S and C DO , while the control inputs are the recycled sludge rate r and the aeration rate F O [25].…”

“…In most WWTPs, the activated sludge process is a key component of the biological treatment stage. Several works indicated that there are numerous hurdles in the ASP control design and implementation, such as kinetics nonlinearity and uncertainty, unavailability of online data, and noisy measurements [17][18][19][20]. To handle these problems, various control techniques were designed: adaptive and robust-adaptive approaches [19,21], sliding mode and neural networks-based control [22], event-based control [23], optimal and predictive control [24], and so on.…”