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. However, it is important to note that the
main difficulty of the dissolved oxygen control is due to noise and
delay in the sensors and actuators. These are taken into account in
this article with the aim of dissolved oxygen control improvement
using the benchmark simulation model no. 1. The main purpose of this
work is to highlight the need to take them into account and conduct
a first step in analyzing how they affect the usually considered dissolved
oxygen control approaches. The work proposes an approach for dissolved
oxygen control improvement within non-ideal sensors and actuators
using the benchmark simulation model no. 1, where a precise catalog
and characterization of sensors and actuators are also provided (although
not used). Filters are used to reduce the noise of the sensors. Artificial
neural networks are designed to predict the value of dissolved oxygen,
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. The artificial neural networks take into account the microorganisms
present in the wastewater, as well as their food and energy source,
to predict the value of dissolved oxygen. The article shows different
options of artificial neural networks for dry weather, rain, storm,
and variable set-point. The results show meaningful integral of square
error improvements, around 80% in dry weather and greater than 50%
with rain and storm influents, as well as a significant reduction
of abrupt changes in the actuator.