Wastewater treatment is a complex process that removes and eliminates contaminants from wastewater and converts them into an effluent suitable for reintroduction into the water cycle. The subsequent discharge of this effluent can have a significant impact on the environment, and this process is typically carried out within Wastewater Treatment Plants (WWTPs). The waste residue generated during the treatment process is known as sludge and it is potentially hazardous. Hence, a primary objective of the WWTP is to process this sludge, ensuring it meets the necessary conditions for its intended use, i.e., a sewage plant. The goal of this project is the development and simulation of a WWTP by the implementation of a reduced order model. Furthermore, to be applied into a real-live application, which consisted of modelling the reduced model to be able to apply estimation methods to know the unknown components (or states) behavior in the plant. The inputs and measurements of those states were also unknown. Besides, the reduced model had to still represent the main processes and components from the original model without changing the behavior. To address the problem, extensive research on reduced-order models was undertaken to identify the most suitable model for practical application. The model used had to be implemented and verified to still maintain the same behavior. Later, applied the Extended Kalman Filter (EKF) algorithm to estimate those unknown states. The Unknown Input Observer (UIO) and augmentation plant algorithms were implemented into the filter to better estimate all the states. In the project, the reduced-order model will be explored in three versions: non-linear, linear, and state space models, and these will be compared with the behavior of the actual model to validate their correct behavior. Then, the EKF, EKF with the UIO, and the EKF with the augmented plant will be tested in different scenarios, alternating between the unknown measurements and inputs of the states to be estimated, to find the best solution to the problem. Moreover, several key performance indices (KPIs) will be computed to assess and compare the results, ultimately allowing for the selection of the most effective solution
