However, during operation, an improvement of the hydrocyclone performance prediction (grade and overall efficiencies) is possible by fitting the model parameters to process data.
The hierarchical optimal control methodology is extended to account for variations in force process model parameters and process parameters.
Both methods are used to solve the NDDR problem in an industrial bulk propylene polymerization process, using real data in real time for the simultaneous estimation of model parameters and process states.
We did not estimate separate model parameters for process variance or observation error because we represented uncertainty using binomial distributions.
RSM was used for the evaluation of different operation parameters, process modelling, optimization study, and model prediction analysis.
Sensitivity analysis is conducted to optimally select the number of ensembles to be used for the model parameter calibration process.
The experimental data can be well fitted by Thomas model, offering some model parameters for the process design.
As agent-based models of social phenomenon become more complex, with many model parameters and endogenous processes, exploring and analyzing the generated data gets even more difficult.
Two simple methods for determining second-order dead time model parameters from underdamped process transients are presented.
The additional benefits of these approaches in relation to classical control have been demonstrated, laying primarily on the possibility of correcting not only the manipulated variables, but also the underlying model parameters as new process data become available.
