The extractive distillation of ethanol using glycerol as entrainer is studied in order to find its optimal design and operating conditions. The optimization is formulated as a mixed integer nonlinear programming (MINLP) problem. The discrete variables determine the number of stages of the columns and their feed stage locations. The continuous variables include the variables of the equilibrium model and operating variables. The solution of the optimization problem is achieved through a two-level strategy that combines stochastic and deterministic algorithms. The result obtained establishes the process that maximizes an economic criterion for the industrial production of bioethanol satisfying the problem constraints.

This paper provides a historical perspective and an overview of the pioneering work that Manfred Morari developed in the area of resiliency for chemical processes. Motivated by unique counter-intuitive examples, we present a review of the early mathematical formulations and solution methods developed by Grossmann and co-workers for quantifying Static Resiliency (Flexibility). We also give a brief overview of some of the seminal ideas by Manfred Morari and co-workers in the area of Dynamic Resiliency. Finally, we provide a review of some of the recent developments that have taken place since that early work.1

Optimization under uncertainty has been an active area of research for many years. However, its application in Process Synthesis has faced a number of important barriers that have prevented its effective application. Barriers include availability of information on the uncertainty of the data (ad-hoc or historical), determination of the nature of the uncertainties (exogenous vs. endogenous), selection of an appropriate strategy for hedging against uncertainty (robust optimization vs. stochastic programming), large computational expense (often orders of magnitude larger than deterministic models), and difficulty in the interpretation of the results by non-expert users. In this paper, we describe recent advances that have addressed some of these barriers.

Optimization under uncertainty has been an active area of research for many years. However, its application in Process Systems Engineering has faced a number of important barriers that have prevented its effective application. Barriers include availability of information on the uncertainty of the data (ad-hoc or historical), determination of the nature of the uncertainties (exogenous vs. endogenous), selection of an appropriate strategy for hedging against uncertainty (robust/chance constrained optimization vs. stochastic programming), large computational expense (often orders of magnitude larger than deterministic models), and difficulty of interpretation of the results by non-expert users. In this paper, we describe recent advances that have addressed some of these barriers for mostly linear models.

The extractive distillation of ethanol using glycerol as the entrainer was studied to determine its optimal control profiles when the azeotropic feed was subjected to composition disturbances. The process was modeled by a differential-algebraic equation (DAE) system that represents the dynamics of the equilibrium stages in the extraction column. The model equations were solved by discretizing the time domain using orthogonal collocation on finite elements. Initially, the effects of feed disturbances on the product flow rate and quality were analyzed. Subsequently, a profit objective function was formulated, and the optimal profiles of the manipulated variables (reflux ratio and reboiler duty) were determined, subject to quality constraints. The solution was obtained by solving the nonlinear programming (NLP) problem that resulted from the discretization. The problem was solved in GAMS using IPOPT as the nonlinear solver, testing two different linear solvers, the Harwell subroutines MA57 and MA86. The optimal control strategy was compared to a simple PI control scheme.

The design of resilient supply chains under the risk of disruptions at candidate locations for distribution centers (DCs) is formulated as a two-stage stochastic program. The problem involves selecting DC locations, determining storage capacities for multiple commodities, and establishing the distribution strategy in scenarios that describe disruptions at potential DCs. The objective is to minimize the sum of investment cost and expected distribution cost during a finite time-horizon. The rapid growth in the number of scenarios requires the development of an effective method to solve large-scale problems. The method includes a strengthened multi-cut Benders decomposition algorithm and the derivation of deterministic bounds based on the optimal solution over reduced sets of scenarios. Resilient designs for a large-scale example and an industrial supply chain are found with the proposed method. The

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