Real-time multi-physical system identification and virtual sensing for a lab-scale chemical stirred tank using parallel estimators

In the initial stages of developing a new chemical process, chemists usually use a lab -scale stirred tank to analyze the yield amount and study the mass balance. However, understanding the energy balance is challenging. Monitoring the energy balance requires separating device and process -related effects, which is time-consuming and requires insight into the dynamics of the device and process. As a result, most labscale studies often overlook it. A real-time multi -physical virtual sensing and system identification approach is proposed in this study to address this issue by estimating desired unknown electro-thermo-mechanical parameters, states, and disturbances. The estimated unknowns include heat transfer coefficients, motor temperature, stirring torque, and heat generation rate. For this aim, a multi -physical lumped model is developed, which covers the electro-thermal and electro-mechanical responses of the device, as well as the process -related thermo-mechanical effects. The model is decoupled, and three parallel extended Kalman filters are employed. The developed model, estimation procedure, and measurement system are implemented in two experimental examples, including a hydrogenation reaction and the reductive catalytic fractionation of biomass. Both cases use a low-cost data acquisition system designed and manufactured to perform the measurements in this research. Comparing the logged measurements with the estimated values indicates considerable agreement in both cases. Based on the results, decoupling the model and using parallel estimators make the tuning easier, improve observability, and reduce the estimation time.