Modeling of the primary sludge thickening process at a wastewater treatment plant with the use of machine learning

Sammanfattning: This thesis focuses on modeling the primary sludge in the thickening process at Henrikdals wastewater treatment plant in Stockholm, Sweden. The thickening process is one of the core processes at the wastewater treatment plant, where the goal is to thicken a residual product called primary sludge. Two thickener belts are used to thicken the sludge gravimetrically. Polymer is also added to increase the dewaterability and to thicken the sludge. The thickness of the sludge is measured by the total solids content (TS) in the sludge and is measured with total solid measurement sensors. These sensors have, however, been shown to be inaccurate. A long short-term memory network (LSTM) and a feed-forward neural network were compared by using sensor and instrument data to predict the TS in the thickened primary sludge. To validate the performance of the models, manual laboratory testing samples were compared with the predictions of the models. Simulations in Simulink were also performed with the intent of simulating the thickening process. By using a machine learning model that could predict the TS, hypotheses regarding reductions in the polymer dosage were explored. A feed-forward and feedback control strategy in combination with the LSTM architecture were used and it was shown that the TS of the thickened sludge could be controlled by regulating the polymer dosage. Thus, using a feedback control strategy gives further opportunities for the wastewater treatment plant to choose whether a lower polymer consumption or a higher TS is preferred, as these two variables correlate with each other.