Use Model-Predictive Control to Improve Distillation Operations

CEP January 2008 www.aiche.org/cep 35 I magine an equipment investment with a payback time of only 10 days. A major petroleum refiner experienced just that when it implemented linear multivariable model-predictive control (MPC) on its cat-cracker. Using a linear programming optimizer, the MPC determined the unit’s optimal constraints, and then moved the unit close to those limits over a few days. Although this is an extreme example, it is generally accepted that MPC can add significant value when coupled with an optimizer that drives the plant to maximum profitability, with typical paybacks of two years or less. Model-predictive control has been practiced commercially for well over 25 years, and many papers have been published on the theory and practice of MPC (e.g., 1, 2). This article summarizes what has been learned in the art of implementing MPC on large-scale fractionation plants, using first-principles steady-state and dynamic simulation methods to enhance the quality of the MPC models, and it recommends a procedure that avoids the need for step testing, which is often difficult and expensive. Traditionally, implementing model-predictive controllers in the process industries has required the creation of a fixed, linear, dynamic model that relates changes in each input to each output. The vast majority of projects described in the literature have been executed using extensive step tests to develop such linearized control models, using processmodel identification techniques (3). Because such deliberate step tests can be quite costly, disruptive, invasive and lengthy in duration (often lasting many weeks or months in a large unit), a significant incentive exists to minimize step tests, if not eliminate them entirely. However, the importance of a reliable and predictable base-level regulatory-control scheme cannot be overemphasized. In numerous instances, a poor regulatory control configuration in a distillation column has jeopardized the viability of any MPC control scheme.