Monitoring batch suspension polymerization process based on calorimetric-state estimation technique

Abstract

This work introduces an approach to enhance the real-time monitoring of batch suspension polymerization processes through the integration of real-time calorimetry and state estimation techniques. The challenges inherent in monitoring dynamic and complex processes, particularly in the absence of direct measurements, are effectively addressed through the proposed approach. A dynamic model equation specific to batch operation is employed to assess conversion throughout the process, with timely updates of dynamic parameters in the model equation. A nonlinear high gain cascaded observer with calorimetric measurements estimates the overall heat transfer coefficient and reaction heat. Variations in other parameters such as heat capacity and density are derived from estimated conversion data and updated in the model equation. The study also accounts for the heat loss to the surroundings during isoperibolic batch processes. Validation of the proposed soft sensor model is carried out by comparing the estimated and experimental conversion data by conducting MMA suspension polymerization in a reaction calorimeter. Results demonstrate the potential of calorimetric state estimation as an alternative to direct conversion measurements, offering comprehensive and enhanced monitoring of the batch suspension polymerization process.