Integrated process and plant design optimisation of industrial scale batch systems: Addressing the inherent dynamics through stochastic and hybrid approaches

Abstract

This work explores stochastic and hybrid solution approaches for dealing with the problem of integrated batch process development and plant design. The simultaneous optimization of batch process synthesis, task allocation and plant design has been formulated in the literature as a mixed-logic dynamic optimization (MLDO) problem, including dynamic control profiles, continuous variables, integers and Booleans as degrees of freedom. In industrial scale situations, this formulation leads to numerically intractable problems when mathematical programming solution strategies are used. So, this work presents a 2-step approach that combines a differential genetic algorithm (DGA) with a deterministic directsimultaneous solution that transforms the problem into a non-linear programming (NLP) problem. The core idea is to combine in the DGA chromosomes the multiple decisions that characterize the problem, and then to use the solution obtained for reducing the complexity of this highly non-linear problem, so it can be managed by standard deterministic solvers. A comparative study of the stochastic and hybrid strategies with the purely deterministic solution is made for the specific case of primary copolymerization for acrylic fibre production. The results show that local optimal solutions of the deterministic method can be beaten by the proposed optimization strategy, becoming a suitable option for solving cases of industrial size.