Generally,
synthesis and design of an optimal process is a challenging
task. The procedure includes specifying and optimizing system configurations
in order to achieve a certain aspect, such as maximizing economic
performance, minimizing environmental impact, etc. However, uncertainties
of the design parameters (e.g., volatility of raw materials and products
price, variability of feedstock supply and product demand, etc.) may
undermine the effectiveness of such systematic design approaches.
In response to this issue, various optimization works have been presented
to address such problems. In this paper, a robust mixed integer linear
programming (MILP) with input–output model is presented to
aid decision-makers in addressing process synthesis problems due to
uncertainties that arise from variation in feedstock supply and product
demand. This work primarily encompasses multifunctional energy systems
that can be described by a system of linear equations, which entails
“black box” modeling. The robust model helps to determine
the design capacity of each process unit in a flexible network which
involves sizing of equipment. This network is assumed to be able to
operate in all uncertain scenarios considered, with a minimum cost
of the plant. In addition, the intended model also helps to determine
the requirement to operate additional equipment in a plant in the
presence of uncertainties. This is especially true with designs of
plants that are incapable of meeting any increase in demand. These
aspects of the work are novel and an important contribution as such
analysis is not available in the literature, to date. Three case studies
that include a polygeneration plant and palm oil based integrated
biorefinery are presented to demonstrate the proposed novel approach
in a more descriptive manner.
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