Implementation of adaptive optimal operation for a semi-batch reaction system

Abstract

This paper presents the results of on-line optimization of the acetoacetylation of pyrrole with diketene in a laboratory-scale reactor. In addition to the desired reaction of pyrrole to 2-acetoacetyl pyrrole, there are several undesired side reactions. The selectivity can be controlled by adjusting the feed rate of diketene to a given solution of pyrrole. Variable amounts of impurities in the crude pyrrole imply different rate constants for each batch. Consequently, on-line estimation of some rate constants and subsequent adjustment of the feeding strategy through dynamic optimization are necessary to reach a desired objective. The nonlinear differential-algebraic optimization problems for both the estimation and the optimization are transformed into nonlinear algebraic optimization problems (AOP). Due to the different characteristics of the estimation and optimization problems, the resulting AOPs are solved using different techniques. Successive quadratic programming is applied to solve the AOP in the estimation, and successive linear programming is used for the optimization. Both are fast infeasible-path methods. The estimation and optimization problems are solved sequentially at given points in time. Results are presented for the minimization of batch time subject to endpoint constraints with respect to yield and two of the concentrations. Runs with and without on-line optimization are compared to demonstrate the effectiveness of the on-line strategy.