Dynamic optimization of batch reactors using adaptive stochastic algorithms

The dynamic optimization (optimal control) of chemical batch reactors is considered. The solution of these types of problems is usually very difficult due to their highly nonlinear and multimodal nature. In fact, although several deterministic techniques have been proposed to solve these problems, convergence difficulties have been frequently found. Here, two algorithms based on stochastic optimization are proposed as reliable alternatives. These stochastic algorithms are used to succesfully solve four difficult case studies taken from the recent literature: the Denbigh's system of reactions, the oil shale pyrolysis problem, the optimal fed-batch control of induced foreign protein production by recombinant bacteria, and the optimal drug scheduling of cancer chemotherapy. The advantages of these alternative techniques, including ease of implementation, global convergence properties, and good computational efficiency, are discussed.