(340q) Subspace-Based Model Identification for Wastewater Treatment
AIChE Annual Meeting
Friday, November 20, 2020 - 8:00am to 9:00am
This work leverages some of the recent results in the area of data-driven modeling for batch processes [1,2] to obtain a model which relates a set of typical measured outputs from a WWTP to a set of typical manipulated inputs, such that the model is useful for predicting the behaviour of the outputs. A data-driven model is trained and subsequently validated on simulated output data generated for a simplified WWTP layout in GPS-X, a wastewater treatment simulator developed by Hydromantis. Subspace identification algorithms are utilized to obtain a discrete LTI model. The model is then validated on a unique dataset different from that used during model identification to evaluate how accurately the estimated model is able to reproduce the dynamic system behaviour. Following an initial state estimation period employing a Luenberger observer, the subspace model is allowed to predict the behaviour of the outputs using future values of the inputs and the predictions are compared to the observed process outputs. The efficacy of subspace identification in obtaining an appropriate model that is able to accurately describes the dynamics of the simplified wastewater treatment process and is valuable for prediction purposes is demonstrated in this work.
 Data-driven modeling and quality control of variable duration batch processes with discrete inputs, B. Corbett, P. Mhaskar, Industrial & Engineering Chemistry Research 56 (24), 6962-6980, 12, 2017
 Subspace identification for dataâdriven modeling and quality control of batch processes, B. Corbett, P. Mhaskar, AIChE Journal 62 (5), 1581-1601, 58, 2016.