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.
ASJC Scopus subject areas
- Chemical Engineering(all)
- Industrial and Manufacturing Engineering