(508a) Optimal Synthesis and Planning of SustainableChemical Process and Energy Systems

In this lecture we give an overview on the applications of recent models and algorithms for the discrete and continuous optimization of a variety of challenging applications in the optimal synthesis and planning of sustainable chemical processes and energy systems. We provide an overview of applications of deterministic models based on mixed-integer linear/nonlinear programming (MILP/MINLP), Generalized Disjunctive Programming (GDP) and global optimization techniques, many of which have been developed by chemical engineers in the area of process systems engineering. We first consider applications of MILP in energy systems that include optimization of hydrogen supply chains, and the long term expansion planning of reliable and resilient electric power systems with high penetration of renewables. We also briefly discuss the retrofit of crude oil refineries for processing biomass to produce sustainable aviation fuels. Next, we consider applications of MINLP and GDP that include synthesis of lignocellulosic ethanol, optimization of shale gas infrastructures, and synthesis of intensified distillation columns. We consider next recent algorithms for rigorous global optimization of nonconvex optimization models for which we consider applications in optimal process water networks that involve reuse and recycle, optimal maritime transportation planning under carbon intensity constraints, and optimal design of centralized and distributed manufacturing facilities for biomass production. Finally, we briefly discuss extensions of these models to handle uncertainties. The variety of applications that we cover in this lecture, illustrates the scope that optimization techniques have towards the optimal synthesis and planning of sustainable chemical processes and energy systems.