Target-oriented robust optimization of emissions reduction measures with uncertain cost and performance
- 75 Downloads
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.
KeywordsTarget-oriented robust optimization Integer linear programming Emissions reduction Pollution control Pollution prevention
We wish to acknowledge the support of the Philippine Commission on Higher Education (CHED) via the Philippine Higher Education Research Network (PHERNet) Sustainability Studies Program.
- Bandyopadhyay S, Foo DCY, Tan RR (2016) Feeling the pinch? Chem Eng Prog 112:46–49Google Scholar
- DETR (1999) Reducing fluoride emissions in brick, tile and pipe manufacture, Environmental technology best practice programme GG166. Department of the Environment, Transport and the Regions, United KingdomGoogle Scholar
- Fordyce FM (2018) Fluoride: human health risks. In: Nriagu JO (ed) Encyclopedia of environmental health, 2nd edn. Elsevier, Amsterdam, pp 776–785Google Scholar
- IMO MEPC (2011) Marginal abatement costs and cost effectiveness of energy-efficiency measures. Marine Environmental Protection Committee, International Maritime Organization. 62 Inf. 7Google Scholar
- Matthews J, Fink K (2004) Numerical methods using Matlab. Prentice-Hall Inc, Upper Saddle RiverGoogle Scholar
- Mavrotas G, Pechak O (2013) Combining mathematical programming and Monte Carlo simulation to deal with uncertainty in energy project portfolio selection. In: Cavallero F (ed) Assessment and simulation tools for sustainable energy systems: theory and applications. Springer, London, pp 333–356CrossRefGoogle Scholar
- Pistikopoulos EN (1995) Uncertainty in process design and operations. Comput Chem Eng 19(553):563Google Scholar
- Sy CL, Aviso KB, Ubando AT, Tan RR (2017) Synthesis of cogeneration, trigeneration, and polygeneration systems using target-oriented robust optimization. In: De S, Bandyopadhyay S, Assadi M, Mukherjee DA (eds) Sustainable energy technology and policies: a transformational journey, vol 1. Springer, Singapore, pp 155–171Google Scholar
- Yue X, Pye S, DeCarolis J, Li FGN, Rogan F, Gallachoir BO (2018) A review of approaches to uncertainty assessment in energy system optimization models. Renew Sustain Energy Rev 21:204–217Google Scholar