Chemical process
sustainability is mainly concerned with energy
and material efficiency, productivity and product quality, waste reduction,
process safety, heath impact, etc. Due to these multiple factors as
well as information and data uncertainty, development of optimal strategies
and effective action plans for sustainability enhancement becomes
a very challenging task. In this paper, we introduce a mathematical
framework for optimal process sustainability performance enhancement.
In this framework, we describe four types of optimization problems,
which are defined based on decision makers’ different objectives
for sustainability enhancement. In formulation, various economic,
environmental, and social concerns and technical feasibilities are
taken into account, where data uncertainty is dealt with by interval
parameters. Solution search is performed by a genetic algorithm method
and a Monte Carlo simulation technique. The methodological applicability
is illustrated by a comprehensive case study on biodiesel manufacturing.
The introduced methodology should be useful for decision makers to
compare optimization methods and select the most suitable for their
applications.