Target-oriented robust optimization of emissions reduction measures with uncertain cost and performance

Emissions can be reduced through the implementation of various combinations of control or prevention measures, or combinations thereof. However, the total cost and performance of such emissions reduction measures can often be difficult to predict precisely. Such uncertainties result in techno-economic risks that firms will have to deal with when implementing a project aimed at cutting emissions. In this work, an integer linear programming model is extended using the target-oriented robust optimization (TORO) framework for determining the best mix of emissions reduction measures. This framework allows optimization to be carried out with uncertain model parameters given in the form of intervals. A range of potential solutions can then be generated and subjected to Monte Carlo simulation to gauge their robustness. The decision maker can select a solution to implement based on the information regarding the expected performance, cost and robustness of the mix of emissions reduction measures. Case studies on reduction of hydrogen fluoride emissions from brick manufacturing and CO₂ emissions from maritime vessels are solved to illustrate the methodology. The examples demonstrate the capability of the TORO model to identify good solutions that are able to perform well despite variations in techno-economic conditions.