Projeto de estrutura de controle aplicado à sistema de tratamento de efluentes.

Abstract

The objective of this work was to formalize and implement a systematic and yet simple procedure for the selection of control structures in wastewater treatment plants (WWTPs) and to show that the application of the proposed methodology agrees with the "empirical"' findings regarding the operation of this process. The focus is to search for a control structure that leads to optimal economic operation, while promptly rejecting disturbances at lower layers in the control hierarchy avoiding thus violation of the more important regulation constraints on effluent discharge. We start by optimizing a steady-state nonlinear model of the process. The resulting active constraints must be chosen as economic controlled variables.In chapter 3, the results confirmed that it is economically optimal to control the oxygen concentration in the aerobic basins and the nitrate in the second anoxic tank at their respective lower bounds, whereas the effluent ammonia from the bioreactors should be controlled at its upper limit. In addition, because it is good practice to operate with minimal manipulation, the wastage flow rate should be fixed at its nominal optimal set point. The proposed decentralized control configuration, consisting of simple PI controllers, is capable of maintaining the process well within the regulatory limits at a small cost when dynamic disturbances represented by three weather files affect the process, therefore suggesting that, according to the applied systematic methodology, more complex (multivariable) regulators are not necessary for the ASM1 process. In chaper 4, a new steady-state secondary settler mathematical model is developed based on the theory of partial differential equations applied to the conservation law with discontinuous fluxes.The effluent ammonia from the bioreaction section and the final effluent total suspended solids at their respective upper limits, in addition to the internal recycle flow rate at its lower bound must be chosen as economic controlled variables. The remaining degrees of freedom need to be fulfilled, and we use several local (linear) sensitivity methods to find a set of unconstrained controlled variables that minimizes the loss between actual and optimal operation; particularly we choose to control linear combinations of readily available measurements so to minimize the effect of disturbances and implementation errors on the optimal static performance of the plant.