Title :
Moving Horizon Estimation and Control for an Industrial Gas Phase Polymerization Reactor
Author :
Hedengren, John D. ; Allsford, Kenneth V. ; Ramlal, Jasmeer
Author_Institution :
PAS, Inc., Houston
Abstract :
Moving horizon estimation (MHE) has been applied to an industrial gas phase polymerization reactor to improve estimates of current states and parameters. MHE is compared to implicit dynamic feedback (IDFtrade). With MHE, there is improved estimation of unmodeled disturbances in the UNIPOLtrade polyethylene plant. The UNIPOLtrade technology is licensed by Univation, a joint venture between ExxonMobil and Dow. The polymerization reactor and plant model is a large-scale set of differential and algebraic equations (DAEs) posed in open equation form. The DAE model is converted to algebraic equations by orthogonal collocation and solved with the MHE objective function in a simultaneous optimization. NOVAtrade, an active-set sparse NLP solver, is used to converge the problem that has 46,870 variables, 18 complementarity conditions, and a Jacobian sparsity of 0.01%. This large, sparse optimization problem is initiated every 5 minutes to update the model as new plant measurements become available and prior to the control optimization. The same plant model is used for nonlinear model predictive control (MPC) with 10 manipulated variables (MVs) and 26 controlled variables (CVs). In this case, a significant advantage is that with MHE a simpler rigorous model suffices for the application of nonlinear MPC.
Keywords :
Jacobian matrices; chemical reactors; differential equations; estimation theory; feedback; industrial plants; nonlinear control systems; nonlinear programming; optimal control; polymers; predictive control; sparse matrices; Dow; ExxonMobil; Jacobian sparsity; NOVA; UNIPOL polyethylene plant; UNIPOL technology; Univation; active-set sparse NLP solver; algebraic equations; control optimization; differential equations; implicit dynamic feedback; industrial gas phase polymerization reactor; moving horizon estimation; nonlinear model predictive control; sparse optimization problem; Differential algebraic equations; Feedback; Gas industry; Inductors; Industrial control; Phase estimation; Plastics industry; Polymers; Predictive models; State estimation;
Conference_Titel :
American Control Conference, 2007. ACC '07
Conference_Location :
New York, NY
Print_ISBN :
1-4244-0988-8
Electronic_ISBN :
0743-1619
DOI :
10.1109/ACC.2007.4282820