Dissolved oxygen control in wastewater treatment plants considering sensor noise and actuator delays

Abstract

The improvement of the dissolved oxygen control is one of the main objectives in the research works on control of wastewater treatment plants. In the research literature, most of the works are based on benchmark simulation models, where ideal sensors and ideal actuators are commonly considered. Noise and delay in sensors and actuators are however the main difficulty for control performance improvement in this loop. Much of the works that can be found in the literature are based on the well known and widely accepted Benchmark Simulation Model no. 1. Even this scenario provides standardised descriptions for real sensors and actuators, it is common practice to use the ideal version. In this work we propose an approach for dissolved oxygen control improvement within non ideal sensors and actuators by using the Benchmark Simulation Model no. 1. Filters are used to reduce the noise of the sensors. Artificial Neural Networks are designed to predict the value of dissolved oxygen, in order to compensate the delay produced by filters and sensors, as well as to anticipate the time needed by the actuator to obtain the desired value.