Moving horizon estimation in microalgae-bacteria based wastewater treatment using online and analytical multi-rate measurements

Population growth and industrialization have resulted into a substantial increase in wastewater production, thereby establishing water purification as a primary concern on a global scale. In this context, microalgae-bacteria based wastewater treatment has emerged as a solution for wastewater treatment and nutrient recovery at a low-energy demand. Nevertheless, operation of this type of wastewater treatment plants is more complex and requires of advanced control systems, capable of maintaining its key variables within appropriate ranges in spite of the periodic variations in environmental variables and wastewater composition. Very often, the implementation of state feedback control laws and model-based control techniques in these processes necessitates full information of the states and other variables of the system in real-time. However, in practical scenarios, only a subset of the variables of microalgae-bacteria processes can be measured online due to the need for more reliable measuring devices or the high costs of online sensors. In addition, these biological processes are subjected to frequent variations, so that the parameters of the models representing them requires continuous adaptation. This paper presents the application of a moving horizon estimation technique to a wastewater treatment process with microalgae and bacteria. The objective of this study is to estimate those variables or parameters that cannot be measured reliably online. This process was nonlinear and subject to uncertainties in the states and parameters. The estimation was coded using MATLAB® software, and simulation results demonstrated the effectiveness of estimation in this biological process, characterized by the availability of multi-rate measurements.