Forecasting alarms using machine learning : Predicting tall oil production at Södra Cell

Abstract: Background. Tall oil production at Södra Cell is an important byproduct produced at the facility in Mörrum. This process is monitored using a vast system of interconnected sensors that continuously monitor the system. At this time, these systems are operated under manual control without any guidance from data-driven analysis. Therefore, we propose an integrated alarm detection system based on the sensor data. Objectives. This study investigates the possibility of using a data-driven analysis system to detect decreases in the targeted variable. Three different approaches are investigated and evaluated on their performance to understand how these approaches can be used to improve the production process by predicting the changes of the target value.  Methods. Three quasi-experiments are conducted to understand how well different machine learning methods can predict and be used in the production process of tall oil. Each experiment is executed independently of each other with their own setup. Results. Out of three different machine learning methods that were tested, had neural network perform the best, while the two methods that observe the historical data trends seem to have problems with the specific data set. Conclusions. From this research, it can be stated that a neural network algorithm can accurately predict changes in the chemical production process. There are multiple machine learning algorithms that can further be used to improve production at Södra Cell.