Simulation–Optimization Framework for the Digital Design of Pharmaceutical Processes Using Pyomo and PharmaPy

The problem of performing model-based process design and optimization in the pharmaceutical industry is an important and challenging one both computationally and in the choice of solution implementation. In this work, a framework is presented to directly utilize a process simulator via callbacks during derivative-based optimization. The framework allows users with little experience in translating mechanistic ordinary differential equations and partial differential equations to robust and fully discretized algebraic formulations, required for executing simultaneous equation-oriented optimization and to obtain mathematically guaranteed optima at a competitive solution time when compared with the existing derivative-free and derivative-based frameworks. The effectiveness of the framework in the accuracy of the optimal solution as well as the computational efficiency is analyzed in two case studies: (i) an integrated 2-unit reaction synthesis train used for the synthesis of an anti-cancer active pharmaceutical ingredient and (ii) a more complex flowsheet representing a common synthesis-purification-isolation train of a pharmaceutical manufacturing process.