In many polymer processes semi-batch reactors are used, with only a few examples of continuous reactors in the patent literature. As the product demand of a polymer increases, larger or more semi-batch reactors are built to meet the demand, and the capital cost per production capacity is predictably lowered. However, this capital cost is still too high for re-investment, and alternative lower capital cost solutions, such as switching the production from semi-batch to continuous processes, could help sustain growth. Hence, continuous processes can reduce the capital cost and the cost required for heat transfer equipment, since the heat load is more evenly distributed over time. In addition, continuous reactors are easier to optimize at a steady state to maximize the overall production rate. However, continuous reactors are not as flexible regarding multiple products, product transition can be difficult, and scale-up of the products requires extra investment. The motivation of this project is to switch the current production from the existing semi-batch reactors to continuous reactors, such as plug flow and continuous stir tank reactors, to lower the capital cost.
In this talk the optimal performances of continuous stirred tank reactors (CSTR), plug flow reactors (PFR) and CSTR/PFR combinations are studied for two different polymer products. The development of the optimal reactor network from these combinations is formulated as a set of nonlinear programs (NLPs), aided by large-scale optimization models and solution strategies. For the optimization models with PFRs and CSTRs, the simultaneous collocation method is used to convert differential-algebraic equation (DAE) optimization problems into NLPs, in order to guarantee optimal, stable solutions for the overall process. In addition, retrofitting with existing batch reactors will also be considered in the design, which leads to a mixed-integer dynamic optimization problem (MIDO), which will be addressed through a tailored optimization strategy.