Dynamic optimization of a polymerization reactor (original) (raw)
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2010
This work deals with the problem of dynamic optimization of an emulsion polymerization reaction in batch reactor. This process can be described as a hybrid system, i.e. dynamic system with both continuous and discrete character. We obtain optimal trajectories of control variable using control vector parameterization (CVP) method. This method translates infinite dynamic optimization problem into finite nonlinear programming (NLP) problem. The NLP computation requires gradient information which we provide using method of adjoint variables.
Optimal nonlinear control of an industrial emulsion polymerization reactor
Chemical engineering research & design, 2016
In this paper, the modelling, dynamic optimization and nonlinear control of an industrial emulsion polymerization reactor producing poly-vinyl acetate (PVAc) are proposed. The reaction is modeled as a two-phase system composed of an aqueous phase and a particle phase according to the model described in our previous work (Gil et al., 2014). The case study corresponds to an industrial reactor operated at a chemical company in Bogotá (Colombia). An industrial scale reactor (11 m 3 of capacity) is simulated. Three different dynamic optimization problems are solved from the more simplistic (only one control variable: reactor temperature) to the more complex (three control variables: reactor temperature, initiator flow rate and monomer flow rate) in order to minimize the reaction time. The results show that it is possible to minimize the reaction time while some polymer desired qualities (conversion, molecular weight and solids content) satisfy defined constraints. The optimal temperature profile and optimal feed policies of the monomer and initiator, obtained in a dynamic optimization step, are used as optimal set points for reactor control. A nonlinear geometric controller based on input/output linearization is implemented for temperature control.
Optimization and control of a continuous polymerization reactor
Brazilian Journal of Chemical Engineering, 2012
This work studies the optimization and control of a styrene polymerization reactor. The proposed strategy deals with the case where, because of market conditions and equipment deterioration, the optimal operating point of the continuous reactor is modified significantly along the operation time and the control system has to search for this optimum point, besides keeping the reactor system stable at any possible point. The approach considered here consists of three layers: the Real Time Optimization (RTO), the Model Predictive Control (MPC) and a Target Calculation (TC) that coordinates the communication between the two other layers and guarantees the stability of the whole structure. The proposed algorithm is simulated with the phenomenological model of a styrene polymerization reactor, which has been widely used as a benchmark for process control. The complete optimization structure for the styrene process including disturbances rejection is developed. The simulation results show the robustness of the proposed strategy and the capability to deal with disturbances while the economic objective is optimized.
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Optimizing Control and State Estimation in a Tubular Polymerization Reactor
Proceedings of the 19th IFAC World Congress, 2014
In this contribution we study the application of non-linear model-based optimizing control to the continuous polymerization of acrylic acid in a tubular reactor. Multiple side injections of monomer along the reactor and the reactor temperature which is controlled via cooling/heating jackets provide the means to control the product quantity and the product quality. The homo-polymerization reaction investigated here, can be modeled by a system of eight pdes which are transformed to an ode system. For this purpose, the spatial domain of the pdes is discretized using the weighted essentially non-oscillatory scheme (WENO). This method avoids the need for a very fine discretization grid while reproducing steep fronts well. The controller employs this model and aims at maximizing the product throughput while satisfying the product quality constraints. Four temperature measurements along the reactor and a molecular weight measurement, derived from a viscosity measurement, at the outlet of the reactor are assumed to be available. A particle filter is implemented that provides the initial condition of the prediction model. Simulation results show that the controller is robust against process and measurement noise and can meet the product constraints and increases the product throughput considerably.
Realization of Online Optimizing Control in an Industrial Polymerization Reactor
8th IFAC International Symposium on Advanced Control of Chemical Processes (2012), 2012
In this work, the operation of an industrial semi-batch polymerization reactor is economically optimized using an NMPC scheme. The goal is the minimization of the batch duration without violating the tight constraints for the product specification. Important issues for the practical implementation such as the development and experimental validation of a suitable process model, the estimation of unmeasured states and the real time solution of the nonlinear optimization problem are discussed. The effectiveness of the control scheme is illustrated by results taken from the implementation at the real plant.
Multivariable nonlinear control of a continuous polymerization reactor: An experimental study
AIChE Journal, 1993
This experimental work concerns the multivariable nonlinear control of a continuous stirred‐tank polymerization reactor. The globally linearizing control (GLC) method is implemented to control conversion and temperature in the reactor in which the solution polymerization of methyl methacrylate takes place. Control of conversion and temperature is achieved by manipulating the flow rate of an inlet initiator stream and two coordinated heat input variables. Conversion is inferred from on‐line measurements of density and temperature. A reduced‐order state observer is utilized to estimate the concentrations of monomer, initiator and solvent in the reactor. The concentration estimates are used in the control law. This study demonstrates the considerable computational efficiency of the nonlinear controller, which is implemented on a microcomputer. The experimental results show the excellent performance of the controller in the presence of active state and input constraints. A systematic ap...
IFAC-PapersOnLine, 2015
The goal of this contribution is to study numeric solution techniques for implementing optimizing control of polymerization processes in tubular reactors with multiple side injections of monomer along the reactor. The configuration of the reactor causes long delays between the inputs and the measurements at the reactor outlet and sharp moving fronts when the inflows are changed. Moreover, the polymerization kinetics are strongly nonlinear. The process is described by 1D partial differential equations along the reactor length. This makes the application of optimizing control based on a rigorous process model challenging. The so called weighted essentially non-oscillatory scheme (WENO) is used to discretize the spatial dimension of the plant model. This method avoids the need for a very large number of discretization points and still the model can be simulated sufficiently accurately. The resulting ode model contains 1600 states and comprises five manipulated variables. We implement the optimizing controller using two different approaches: At first single shooting with control vector parametrization is used which is simple to implement and has fewer decision variables. This is compared to full discretization scheme using orthogonal collocation on finite elements which results in a very large but very sparse and structured nonlinear programing problem. The simulation results show that both approaches have a similar performance and drive the system to a significantly more productive steady state.
Computer Aided Chemical Engineering; vol. 28, pp. 559-564, 2010
One of the main characteristics of producing synthetic polymers is that the same process is used for the production of different kind of products (various molecular weights, compositions, etc.). Since the producers are forced to satisfy various demands of various costumers, frequent grade transitions are needed. These grade transitions are expected to be short and effective to avoid the production of so-called off-specification products. It became very popular to apply model predictive controllers (MPCs) to reduce the quantity of off-specification products, however most of them use linear models for prediction. Since polymerization reactions are highly non-linear, using linear models may cause significant difference between the response of the model and of the real plant and this can cause problems e.g. in predictive control. The difference appears mainly during grade transitions, hence it is important to tune the appropriate parameters of the regulators to realize the grade transitions as soon as possible. In this article a novel method – in the field of predictive control -is introduced for parameter tuning, although this method is well known in the field of experiment design. The statistical tools like design of experiments (DoE) permit the investigation of the process via simultaneous changing of factors' levels using reduced number of experimental runs. Through a case study the applicability of full factorial design is going to be examined. It will be proven that full factorial design is appropriate for finding the right tuning parameters of MPC controlled polymerization reactor. The aim of the case study is the reduction the time consumption of grade transitions, so applying the tools of design of experiments as a quasi-APC.
Trajectory following for the optimization of a batch polymerization reactor
2004
This paper considers the minimization of batch time for an industrial inverseemulsion polymerization reactor in the presence of uncertainty. A first optimization study resulted in the current industrial practice of a semi-adiabatic profile (isothermal operation followed by an adiabatic one), where the switching time between the two modes of operation is updated to meet the terminal constraint on reactor temperature, as well as a constraint on residual raw material levels. However, such a procedure requires several batches for convergence and, in addition, cannot compensate the effect of the within-run disturbances. As an alternative, we propose following a temperature trajectory with respect to conversion, for which the conversion is estimated on-line based on temperature measurements. Simulation results show an improved performance compared to the run-to-run strategy, with the additional advantage that this reduction is obtained immediately, i.e. without having to wait for run-to-run convergence.