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Detailed dynamic simulations of three industrial columns (a propylene/propane splitter, a xylene/toluene column, and a depropanizer) have been used to evaluate configuration selection for single-ended and dual-composition control as well as compare conventional and advanced control approaches. For each case considered, the controllers were tuned by using setpoint changes and tested using feed composition upsets. Proportional Integral (PI) control performance was used to evaluate the configuration selection problem. For single ended control, the energy balance configuration was found to yield the best performance. For dual composition control, nine configurations were considered. It was determined that in order to identify the optimum configuration, detailed testing using dynamic simulation is required. The optimum configurations were used to evaluate the control performance of conventional PI controllers, DMC (Dynamic Matrix Control), PMBC (Process Model Based Control), and ANN (Artificial Neural Networks) control. It was determined that DMC works best when one product is much more important than the other while PI was superior when both products were equally important. PMBC and ANN were not found to offer significant advantages over PI and DMC

Comparison of advanced distillation control methods. Fourth annual report