Comparing the EKF with inverse black-box models in system identification of a chemical stirred tank

This paper compares the computational cost and accuracy of an extended Kalman filter with inverse black box models in multi-physical system identification of chemical stirred tanks. First, a physics-based coupled electro-thermo-mechanical lumped parameter model is presented with the heat transfer coefficient and stirring torque as unknowns. The developed model has two objectives: it is exploited by the extended Kalman filter to perform system identification and simulates the artificial data to train inverse black box models. The extended Kalman filter and the inverse black box models are compared in numerical and experimental cases in which the results show a good agreement between both approaches and reference values. The results show that introducing random variations to the heat transfer coefficient in simulating artificial data increases the accuracy of the inverse black box model.