Forecasting CO₂ emissions and identifying key CCUS factors for achieving net-zero targets in the North American cement, iron, and steel industries

Emissions of pollutants and their consequences on the atmosphere have received special attention from international organizations in recent years due to the climate crisis previewed in the next decades. In this regard, Carbon Dioxide (CO2) emissions produced in industrial sectors is one of the most concerning compounds because of its impact on global warming and climate change. International organizations have emphasized the necessity of creating a plan to gradually reduce the concentration of this pollutant in the atmosphere. This study employs multi-objective mathematical models combined with machine learning algorithms to predict CO₂ emission trends and evaluate mitigation techniques on Cement and Iron and Steel (I&S) industries. Furthermore, Carbon Capture, Utilization, and Storage (CCUS) technologies are assessed as a potential solution, utilizing Fuzzy and Rough DEMATEL analyses to identify and prioritize factors for successful CCUS deployment in industrial applications. The findings reveal a steady increase in CO₂ emissions in both cement and I&S industries, with an estimated annual growth of 0.58–0.7 MtCO₂ from 2020 to 2050. An improved accuracy in forecasting was obtained through the Generalized Reduced Gradient and Whale Optimization algorithms, achieving up to a 48.13% reduction in Mean Absolute Error. In the same way, findings reveal the prominence order and causal relationships among the main factors associated with each stage of CCUS, based on a comprehensive review of existing literature and validation from seasoned experts on the field. This study aims to provide stakeholders and decision-makers with valuable insights to facilitate the successful implementation of CCUS projects in manufacturing, thereby advancing progress toward the Net-Zero emissions targets set by environmental organizations. Keywords: Carbon Dioxide; Green house gas; Cement, Iron and Steel; Forecasting; North America; CCUS; Rough set theory; DEMATEL; Fuzzy set theory

Name thesis:degree_name

Master of Applied Science (MASc)

Level thesis:degree_level

Master's

Discipline thesis:degree_discipline

Engineering - Industrial Systems

Grantor dc:publisher

Faculty of Graduate Studies and Research, University of Regina

Year dc:date.issued

2024

Author dc:creator

• Galaviz Roman, Angel

Advisor dc:contributor.advisor

• Kabir, Golam

Committee members dc:contributor.committeemember

• Khan, Sharfuddin
• Khondoker, Mohammad

Language dc:language.iso

en

DOI

https://doi.org/10.82465/4517

OAI identifier oai:identifier

oai:uregina.scholaris.ca:10294/16799