Estimation of reaction mass viscosity for suspension polymerization process using combined Kalman filter–fuzzy model

Abstract This study investigated the usefulness of measurements from an agitator torque sensor in monitoring the dynamics of suspension polymerization. The main focus was to estimate the viscosity of the reaction mass during polymerization using the agitator torque as a secondary variable. Viscosity is a crucial parameter that plays a vital role in determining the efficiency of the process and the quality of the final product. Accurate viscosity monitoring is essential as it provides valuable insights into the progression of the polymerization process and its dynamic behaviour. This study developed a combined Kalman filter (KF) and fuzzy logic (FL) model to estimate viscosity in real time, addressing the challenges of noise in torque measurements. Experimental validation showed that the KF‐fuzzy model improved the accuracy and stability of viscosity predictions, particularly during the critical stages of polymerization. This approach enables better monitoring of reaction dynamics, thereby supporting process optimization and control.