Development of a Nonlinear Model Predictive Controller of Flexible Structure for Greenhouse Gas Emissions Minimisation in Pulp and Paper Wastewater Treatment Processes

Abstract

It is a well-established fact that activated sludge Wastewater Treatment Plants (WWTP) are placed among the most significant contributors of Greenhouse Gas (GHG) emissions. The task of intelligent emission control of such plants is a great challenge, mainly due to the highly nonlinear dynamics and associated uncertainty of its constituent bioprocesses. The need for more efficient and environmentally conscious control is ever increasing, due to the upsurge of industrial and urban water usage. In the current work, a nonlinear Model Predictive Control (MPC) scheme that considers an activated sludge model is developed for pulp and paper industry. The nonlinear MPC employs a flexible controller input structure through a process superstructure formulation that enables the adaptive utilisation of the most suitable set of manipulated variables based on the prevailing operating conditions. A generalised framework of this flexible controller type along with a nonlinear dynamic model for the plant accounting for the simultaneous satisfaction of stringent control objectives on the effluent quality and the GHG minimisation is developed. The proposed controller incorporates manipulated variables for all admissible aeration intensities, internal flow recirculation streams, external carbon sources, influent flow distribution in each bioprocessing unit, unit volume variations and recirculation flow percentage. Accurate simulations indicate the achievement of a superior emission reduction performance compared to state-of-the-art MPC implementations in WWTP. Every consideration of a manipulated variable set is associated with respect to its contribution to the total GHG reduction for each mode of operation.