Machine Learning Refinery Sensor Data to Predict Catalyst Saturation Levels.

In this research, we propose a novel data-centric way of optimizing a catalytic cracking unit. We first design a soft sensor to predict catalyst saturation levels within a Fluid Catalytic Cracking Unit (FCCU). To achieve this, we implement an established method and combine it with modern algorithms for accurate and robust results. The input for this model is data from a number of sensors throughout the refinery, combined with laboratory data. Catalyst saturation level is measured by way of manual refraction analysis and lookup tables. These manual measurements were combined with laboratory data to provide training input for our soft sensor models. Subsequently, we utilize this new soft sensor model in an input mix optimization in order to continuously optimize the use of the catalyst within the FCCU. This model leads to a higher product yield, less catalyst consumption, and a more efficient process. This proposed optimization pipeline can be introduced as smart process control tying into the development towards Industry 4.0.