Design of advanced control strategies for a multivariable oil heating furnace
Keywords:
multivariable system; heating furnaces; Smith predictors; temperature control; energy efficiencyAbstract
Introduction: Crude oil refining is an energy-intensive process where heating furnaces are the main fuel consumers, as is the case with furnace F-101 at the Hermanos. Díaz Refinery in Santiago de Cuba. Precise temperature control and operation within optimal ranges improve its dynamics and energy efficiency.
Objective: To design, tune, and rigorously evaluate the performance of different advanced management strategies for the temperature control system in furnace F-101 as a multivariable system, to improve its dynamic behavior and the energy efficiency of the refining process.
Methods: To compare advanced control strategies — proportional integral (absolute error integral method and proportional integral derivative Tuner) and Smith predictors (classic, filtered and modified)— designed through rigorous simulation for temperature control in furnace F-101, modeled as a multivariable 2×2 system based on an experimentally identified model.
Results: Based on the previously identified experimental dynamic model of the crude oil heating furnace, characterized by dominant delays and interactions between variables, five control strategies were evaluated: conventional proportional integral tuned using the absolute error integral method, proportional integral with the proportional integral derivative Tuner tool, classic Smith predictor, filtered Smith predictor (Normey-Rico), and modified Smith predictor (Rivas-Pérez). The latter exhibited the best performance, with a 30% reduction in maximum overshoot, a 42.3% improvement in settling time, and a 45.2% decrease in temperature variability.
Conclusions: This study provides a technical basis for implementing advanced control in this type of furnace, offering the greatest potential for improving energy efficiency and reducing pollutant emissions. This research constitutes the first systematic comparative analysis of control strategies based on Smith predictors for the 2×2 multivariable model experimentally identified for furnace F-101, opening perspectives for the implementation of model predictive control and control strategies with artificial intelligence.
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