(706b) Opportunity Cost-Based Systematic Process Intensification Under Multiple Objectives

Authors: 
Gandhi, A., Artie McFerrin Department of Chemical Engineering, Texas A&M University
Hasan, M. M. F., Artie McFerrin Department of Chemical Engineering, Texas A&M University
Demirel, S. E., Artie McFerrin Department of Chemical Engineering, Texas A&M University
Li, J., Artie McFerrin Department of Chemical Engineering, Texas A&M University
Due to stringent regulations and strong competition in industry, the focus of process design and optimization has shifted beyond solely the economic return to also include other objectives, e.g. environmental impacts, process safety, raw material requirement [1-3]. Considering multiple objectives within the design phase is also useful to reduce the costs incurred while retrofitting for the possible future regulations [4]. Multi-objective optimization (MOO) includes Pareto-generating techniques that can identify trade-offs between conflicting objectives and provide a map of multiple optimal solutions. To reduce multi-objective problems to single-objective, Cohon proposed two methods [5], i) weighting method, which assigns individual weights to objective functions and reformulates the main objective to a sum of these weighted objectives; and ii) constraint method, which assigns the other objective functions into constraints. However, these weights or constraints the decision maker choose may include bias and the resultant solutions may deviate from the truly optimal ones. Additionally, designs obtained by single objectives rely on static functions with respect to time and often neglect the uncertainty accompanying it for future changes in regulations, demand, and supply.

In this work, we propose an optimization formulation that assists in the selection of the best design among several Pareto-optimal solutions. We use an opportunity cost-based approach which considers the trade-offs associated with different objective functions in terms of economic gain. The parameters are market-driven in contrast to the user-defined weights. Multiple objectives such as capital and operating costs, energy consumption, environmental footprint, fresh material acquisition, waste minimization, and safety are accounted for. Furthermore, accounting for the anticipated changes during the design phase itself yields better economic results by reducing the costs associated with future retrofits [4, 6]. The optimization model is also extended to account, from the design phase, for future uncertainties in the regulations, demand shift, and supply constraints. As a result, we can come up with a more flexible and sustainable process configuration. The formulation described can be applied also to retrofit existing designs, but it is best utilized during conceptual design phase, when there is maximum flexibility. The model is incorporated in the building block-based process design and intensification framework which provides plethora of non-intuitive design solutions [7–9]. We demonstrate the capability of the proposed formulation through several case studies with special focus on intensification of energy-intensive separation processes.

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