The Investigation of Multiple and Blended Fuel Options in Carbon-Constrained Industrial Clusters

Abstract

With the increasing climate change concerns, governments around the world are working on setting targets to mitigate its upsurge. Greenhouse gases (GHG), more specifically carbon dioxide (CO2), are the highest contributors to global warming. As a result, strict restrictions on GHG emissions are being set worldwide to a limit. Industrial sectors rely extensively on fossil fuels, the highest carbon emitters, and in turn face many challenges towards the compliance of the set emission standards. Therefore, finding solutions that would both meet the environmental requirements while satisfying the industries’ energy and fuel demands has been the focus of several studies over the years. The integration of biomass, or a renewable source of energy as an alternative to fossil fuels, has been previously investigated by many. Other works involved the development of carbon utilisation strategies to reduce carbon footprints. However, few studies targeted the incorporation of more than one type of fossil fuel with biomass and/or renewable energy in a carbon constrained industrial cluster. Therefore, this thesis proposes a carbon reduction optimisation-based technique that integrates more than one source of energy with carbon utilisation technologies and assesses both its economic and environmental feasibilities. A systematic optimization-based model is introduced to develop sustainable utilization strategies to meet a cluster’s product requirements, while meeting the set CO2 emission limits and maximizing associated financial returns. A Mixed Integer Nonlinear Program (MINLP) was developed to select the optimal allocation of the different types of fuels, the optimal source to sink mapping and carbon utilization configuration to meet the mentioned targets. Five case studies consisting of a natural gas cluster, a coal cluster, a biomass cluster, a blended fuel cluster, and a multiple fuel cluster, were conducted to illustrate the proposed model. The multiple fuel case study resulted in the lowest post-capture emissions with 9469 t CO2/d generating a high-end profit of $907.16 million/y. Two sensitivity analyses were conducted on the multiple fuel case study to assess the effect of varying the net carbon capture rate (NCCR) and the renewable percentage on the previously mentioned variables. It was further proven that at higher carbon capture rates, the reliance on fossil fuels decreased and the switch to renewable energy was favoured.