Nonlinear model predictive control of a smart-scale emulsion polymerization process

In this article a nonlinear model predictive control (NMPC) scheme for a smart-scale emulsion polymerization process is presented. The control objective is to maintain the product quality specifications and safe operating conditions even when the process is subject to disturbances or the product quality setpoint changes. The total monomer conversion and the polymer composition are used as a measure of product quality and the reactor temperature is used as a measure of process safety. The model is a set of differential algebraic equations, which captures the polymerization kinetics, mass transfer and heat transfer in the reactor as well as the dynamics of the auxiliary equipment namely thermostats, pumps and mixing chambers. An extended Kalman filter is used to estimate unmeasured states, from measurements of temperature and mass of reactant fed to the reactor. The performance of the NMPC scheme is demonstrated in simulation case studies.

[1]  W. Pauer,et al.  Process Model for Styrene and n-Butyl Acrylate Emulsion Copolymerization in Smart-Scale Tubular Reactor , 2016 .

[2]  W. Pauer,et al.  Properties of Smart-Scaled PTFE-Tubular Reactors for Continuous Emulsion Polymerization Reactions , 2013 .

[3]  James B. Rawlings,et al.  Postface to “ Model Predictive Control : Theory and Design ” , 2012 .

[4]  Christos Georgakis,et al.  How To NOT Make the Extended Kalman Filter Fail , 2013 .

[5]  Fredrik Gjertsen.  A Modeling Framework for Control of Smart-Scale Tubular Polymerization Reactors - A Case Study on Nonlinear Model-based Predictive Control of an Emulsion Copolymerization Process , 2014 .

[6]  M. P. Vega,et al.  Modeling and control of tubular solution polymerization reactors , 1997 .

[7]  Dan Simon,et al.  Optimal State Estimation: Kalman, H∞, and Nonlinear Approaches , 2006 .

[8]  Jennifer Puschke,et al.  A software environment for economic NMPC and dynamic real-time optimization of chemical processes , 2014, Autom..

[9]  Siep Weiland,et al.  Model predictive control algorithm for nonlinear chemical processes , 2003, 2003 IEEE International Workshop on Workload Characterization (IEEE Cat. No.03EX775).

[10]  Martin Mönnigmann,et al.  Systematic identifiability testing for unambiguous mechanistic modeling – application to JAK-STAT, MAP kinase, and NF-κB signaling pathway models , 2009, BMC Systems Biology.

[11]  José M. Asua,et al.  Emulsion polymerization: From fundamental mechanisms to process developments , 2004 .

[12]  Stuart C. Thickett,et al.  Emulsion polymerization : State of the art in kinetics and mechanisms , 2007 .

[13]  W. Schiesser.  The Numerical Method of Lines: Integration of Partial Differential Equations , 1991 .

[14]  Victor M. Zavala,et al.  Optimization-based strategies for the operation of low-density polyethylene tubular reactors: nonlinear model predictive control , 2009, Comput. Chem. Eng..