Abstract
Optimal control policies are determined for the free radical polymerization of three different polymerization processes, in a non-isothermal batch reactor as follows: (1) bulk polymerization of n-butyl methacrylate; (2) solution polymerization of methyl methacrylate with monofunctional initiator; (3) solution polymerization of methyl methacrylate with bifunctional initiator. Four different optimal control objectives are realized for the above three processes. The objectives are: (i) maximization of monomer conversion in a specified operation time, (ii) minimization of operation time for a specified, final monomer conversation, (iii) maximization of monomer conversion for a specified, final number average polymer molecular weight, and (iv) maximization of monomer conversion for a specified, final weight average polymer molecular weight. The realization of these objectives is expected to be very useful for the batch production of polymers. To realize the above four different optimal control objectives, a genetic algorithms-based optimal control method is applied, and the temperature of heat exchange fluid inside reactor jacket is used as a control function. Necessary equations are provided in the above three processes to suitably transform the process model in the range of a specified variable other than time, and to evaluate the elements of Jacobian to help in the accurate solution of the process model. The results of this optimal control application reveal considerable improvements in the performance of the batch polymerization processes.
Highlights
Optimal control is an optimization of function (s) not variables
After an extensive literature survey on the free radical polymerization (FRP) of methacrylates, we found th a t no work has been done on the optimal control of n-Butyl M ethacrylate (BMA) polymerization
We found very few pub lication on the free radical polymerization of methyl m ethacrylate (MMA) with bifunctional initiator [Simionescu Gt al., 1990, 1988, Subramanian and Kapur, 1958], which convey the kinetic study of MMA polymerization with bifunctional initiator
Summary
Optimal control is an optimization of function (s) not variables. Ilie optimal con trol of process denotes ofi’-line determ ination of optimization function(s), the online api^lication of which would achieve a desired objective. The application of optimization function(s) in optimal control provides extreme flexibility and enhanced capabilities to realize process objectives with greater performance. To flnd the optimal policies for a process, techniques based on the calculus of variation, dynamic programming, and nonlinear programming are avaiiai)ie. Determin ing optimal control with nonlinear programming becomes very difficult, when the system becomes very complex to solve. The dimensionality becomes pro hibitively large even with modestly sized problem. Techniques based on Variational calculus are prone to be erroneous while solving the derivatives
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