Abstract:
Data Envelopment Analysis (DEA) is a method for measuring relative efficiencies of Decision Making Units (DMUs). DEA is used to rank the DMUs according to their relative efficiencies. For the last three decades, there have been incredible numbers of works on DEA and its applications. However, as of today, there is no application of DEA to Chemical Engineering. Hence, this thesis is focused on the applicability of the DEA to Chemical Engineering problems in order to judge its effectiveness, and possibly, to open up new research area particularly in chemical process systems engineering. In this thesis, two different problems of chemical engineering are solved via DEA by using Constant Returns-to-Scale (CRS) model. One of the problems is the ranking of the efficiencies of the alternative Heat Exchanger Network (HEN) structures, and the other is the ranking of the efficiencies of the alternative flowsheets of the Hydrodealkylation of Acetone (HDA) process. The DEA formulations are developed for both problems firstly by determining the DMUs, inputs, and outputs of the systems. Then, the DEA models are transformed into Linear Programming (LP) problems. The LPs are solved using the Excel Solver. The effects of the addition of value-judgement constraints in the DEA models are also considered. As a result of this thesis work, it is concluded that if a chemical engineer can clearly define the measure of efficiency and analyzes the relationships among the DMUs, inputs, and outputs, then the DEA is an easily applicable and trustable method to compute and rank the relative efficiencies of the alternative process flowsheets or designs. DEA is also applicable to very large-scale systems with many alternatives (DMUs), and many inputs and outputs since it requires the solution of LPs only.
