Multilevel input signal for multivariable state-space identification of wastewater treatment plant

The purpose of this work is to identify a linear time-invariant dynamic model of wastewater treatment plants with multilevel pseudo random signals as an excitation input. The plants naturally aim to remove suspended substances, organic material and phosphate. An activated sludge process becomes the best technology available to control the discharge of pollutants. For this purpose, state-space models that emphasize on subspace-based method such as numerical subspace state-space system identification (N4SID) and ‘robust’ N4SID besides predictive estimation models are explored. The performance of identified models perturbed by multilevel input signal is validated by variance accounted for and compared to pseudo random binary input signal. It was proved that the estimated model with multilevel input offers good predicted behavior’s as compared to two-level input signal. Benchmark simulation model (BSM1) was applied as data generator for identification procedures.