Models involving decision variables in both discrete and continuous domain spaces
are prevalent in process design. Generalized Disjunctive Programming (GDP) has emerged
as a modeling framework to explicitly represent the relationship between algebraic
descriptions and the logical structure of a design problem. However, fewer formulation
examples exist for GDP compared to the traditional Mixed-Integer Nonlinear Programming
(MINLP) modeling approach. In this paper, we propose the use of GDP as a modeling
tool to organize model variants that arise due to characterization of different sections
of an end-to-end process at different detail levels. We present an illustrative case
study to demonstrate GDP usage for the generation of model variants catered to process
synthesis integrated with purchasing and sales decisions in a techno-economic analysis.
We also show how this GDP model can be used as part of a hierarchical decomposition
scheme. These examples demonstrate how GDP can serve as a useful model abstraction
layer for simplifying model development and upkeep, in addition to its traditional
usage as a platform for advanced solution strategies.