Parameter Estimation For Multi-Stage Processes: A multiple shooting approach integrated with sensitivity analysis

Predictive capacity of models are becoming important due to increase in the development and deployment of digital twins in several areas of manufacturing. Therefore, it is important to keep the models up to date so they represent the process reliably. One way to keep these model calibrated is via parameter estimation. However, parameter estimation problems in nonlinearly-parametrized systems result in local optima and/or suffer from high computational costs. One way to address the aforementioned limitations is using multiple shooting parameter estimation, which provides a setting to deal with ill-defined regions of the search space. In this paper, we propose an improvement to the multiple shooting parameter estimation integrating sensitivity analysis. The approach divides the measured operating trajectory into different segments and performs sensitivity analysis to find the most contributing parameters in each of these segments. In this way, the proposed technique benefits from the selection of a subset of the most sensitive parameters per segment and the computational advantages of the multiple shooting method. The performance of the multiple shooting parameter estimation integrated with sensitivity analysis is tested on a case study, a batch reactive distillation column which is a multi-stage process.