Abstract
In the process industry, advanced controllers usually aim at an economic objective, which usually requires closed-loop stability and constraints satisfaction. In this paper, the application of a MPC in the optimization structure of an industrial Propylene/Propane (PP) splitter is tested with a controller based on a state space model, which is suitable for heavily disturbed environments. The simulation platform is based on the integration of the commercial dynamic simulator Dynsim® and the rigorous steady-state optimizer ROMeo® with the real-time facilities of Matlab. The predictive controller is the Infinite Horizon Model Predictive Control (IHMPC), based on a state-space model that that does not require the use of a state observer because the non-minimum state is built with the past inputs and outputs. The controller considers the existence of zone control of the outputs and optimizing targets for the inputs. We verify that the controller is efficient to control the propylene distillation system in a disturbed scenario when compared with a conventional controller based on a state observer. The simulation results show a good performance in terms of stability of the controller and rejection of large disturbances in the composition of the feed of the propylene distillation column.
Highlights
Since the early applications of Model Predictive Control (MPC) in industry, more than three decades ago, this control method has shown a continuous development
As is usual in the process industry, there is a hierarchical control structure (Engell, 2007) in which, based on a complex non-linear stationary model of the plant and on an economic criteria, a Real Time Optimization (RTO) layer computes optimizing targets, which are sent to a MPC layer
The main scope of this work is the implementation of an advanced control strategy, based on the Infinite Horizon Model Predictive Control (IHMPC) in the highly non-linear industrial Propylene/Propane (PP) splitter
Summary
Since the early applications of Model Predictive Control (MPC) in industry, more than three decades ago, this control method has shown a continuous development. As is usual in the process industry, there is a hierarchical control structure (Engell, 2007) in which, based on a complex non-linear stationary model of the plant and on an economic criteria, a Real Time Optimization (RTO) layer computes optimizing targets, which are sent to a MPC layer. The main scope of this work is the implementation of an advanced control strategy, based on the Infinite Horizon Model Predictive Control (IHMPC) in the highly non-linear industrial Propylene/Propane (PP) splitter. The main purpose of this study is to verify if a multivariable advanced controller based on a state-space representation that does not require a state observer/estimator can give a good performance in terms of producing an economic benefit while maintaining the product qualities. Consider the system with nu inputs and ny outputs, which can be represented by the following where, A
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
