Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10399993 | Control Engineering Practice | 2005 | 14 Pages |
Abstract
This paper addresses the development and application of a first-principles, steady-state modeling framework in multivariable control applications. A rigorous approach based on detailed nonlinear models calibrated with reconciled online measurements is presented. Sensitivity analysis of this model is then applied in order to generate steady-state gain (inferential) models used in a DMC-based control application of a refinery unit. The benefits of using open-equation based inferential models to account for online product quality control are demonstrated in the context of a real-time model predictive control system, applied to a refinery. Finally, the direct economic impact of this application is assessed in a detailed quantitative manner and offered along with the relevant business process changes and operational practice recommendations for sustaining the benefits achieved.
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Authors
Minh Tran, Dimitrios K. Varvarezos, Mohamad Nasir,