Predictive control of an activated sludge process for long term operation

Abstract

he application of a multivariable predictive controller to an activated sludge process is discussed in this work. Emphasis is given to the model identification and the long term assessment of the controller efficiency in terms of economical and environmental performances. A recurrent neural network model is developed for the identification problem and the dynamic matrix control is chosen as suitable predictive control algorithm for controlling the nitrogen compounds in the bioreactor. Using the Benchmark Simulation Model No. 1 as virtual platform, different predictive controller configurations are tested and further improvements are achieved by controlling the suspended solids at the end of the bioreactor. Based on the simulation results, this work shows the potentiality of the dynamic matrix control that together with a careful identification of the process, is able to decrease the energy consumption costs and, at the same time, reduce the ammonia peaks and nitrate concentration in the effluent

Multivariable Real-Time Control of Viscosity Curve for a Continuous Production Process of a Non-Newtonian Fluid

“…This task is addressed using the simulator to carry on step response tests from which the dynamic matrix F is obtained [5]. The simulator model is excited by varying the input corresponding to the manipulated variables, starting from the reference condition and considering step changes of different sizes [13,14]. It is worth noting that the simulator does not model the variations of ingredients quality (composition and rheological behavior).…”

Analytical Fuzzy Predictive Control Applied to Wastewater Treatment Biological Processes

“…Replacing (28) y m k + H by the second member of equality (35), which we have just obtained, we will finally have the expression that we were looking for u k , dependent on terms that can be determined using the plant model or the reference model or that can be measured at the kth instant. The final expression for u k is the following, where M a is given by (22) and P 10 by (27) and where the matrix coefficients that intervene must be updated in each iteration of the implementation of the control algorithm (due to their dependence on the antecedent vector and therefore on time)…”

“…The proposals that have been made to improve the control and operation of biological processes (generic or related to WWTPs) have been numerous and different, highlighting in our case those that include some variant of nonlinear 2 Complexity model-based predictive control (NLMPC) and, especially, the proposals framed in the field of the fuzzy model-based predictive control (FMBPC). In [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34], some of these contributions can be seen. Our work tries to be, precisely, a contribution more in this last area, proposing a strategy of predictive control based on a fuzzy model (obtained from numerical input-output data), but formalizing the law of control in an analytical and explicit way.…”

A recurrent neural network for wastewater treatment plant effuents' prediction

“…For instance, ANNs are adopted to forecast or predict some parameters to feed a control strategy. In that sense, Foscoliano et.al propose the adoption of RNN networks to predict the WWTP's nutrient concentrations and then, feed a Model Predictive Control (MPC) strategy to assure that concentrations are under the established limits [3]. Furthermore, Santín et.al adopt two Multilayer Perceptron (MLP) neural networks to predict the ammonia and total nitrogen concentrations in the effluent and determine whenever a violation of their limits is likely to occur [10].…”