(21c) Robust Optimisation for Sustainable Process Synthesis with Uncertainties

Generally, in any chemical processes design synthesising an optimised flowsheet is of the most essential phase which involves a great deal of challenges. Optimisation of processes involves specifying the configuration of the entire system so as to maximise or minimise a certain objective function. Nevertheless, data uncertainties may compromise the long-term effectiveness of such a systematic approach. Uncertainties can manifest in the volatility of purchase price, in the supply of process inputs (e.g., raw materials), as fluctuations in demand for the product portfolio or in their selling price. Such uncertainties may also be static or varying over time. In line with this, research works have been done previously to address such synthesis problems through robust optimisation methodology. Grossmann and Santibanez (1980) have presented a mathematical programming as a suitable algorithm in their work to solve problems of this class. Although the application of this technique is not straightforward as chemical processes mainly involve nonlinear correlations, the possibility of handling them with sensible assumptions on linearity still exist. In this paper, a robust optimisation model is presented to aid decision makers in addressing process synthesis problems due to the various uncertainties. A case study on palm oil biorefinery is presented to demonstrate the proposed approach.