Engineering of intelligent systems for sustainable cement manufacturing - Oguntola, Olurotimi, author; Simske, Steve, advisor; Shahroudi, Kamran Eftekhari, committee member; Gallegos, Erika, committee member; Ortega, Francisco, committee member (Colorado State University. Libraries, 2025)
Cement-based materials have been used for urban development from historic times, remain important till the present day, and will be required for construction in the foreseeable future. However, cement manufacturing by its nature is carbon-intensive and consumes a lot of energy. The cement industry faces significant challenges in implementing sustainable practices and reducing its environmental footprint. Carbon dioxide emissions from global cement production have increased at a higher rate than cement production rates. While traditional carbon reduction efforts have focused on thermal energy use in the calcination process of cement manufacturing, electrical energy consumption represents a substantial but often overlooked opportunity for sustainability improvements. This dissertation employs a systems engineering approach to address this gap by developing intelligent systems for sustainable cement manufacturing with a focus on decarbonization through electrical energy consumption optimization. Through systematic review of research published between 1993-2023, life cycle assessment, and techno-economic analysis, this study demonstrates that substantial environmental and economic benefits can be achieved through innovative approaches. Analysis of four scenarios from a combination of two cement types (ordinary Portland cement, Portland-limestone cement) and two energy sources for thermal heating (coal, dried biosolids) indicates that increased production and adoption of Portland-limestone cement with up to 15% limestone can reduce carbon footprints by 6.4%, while using dried biosolids as combustion fuel can yield a 7.9% emission reduction compared to baseline. More significantly, the application of a memory-efficient hybrid variant of Causal Bayesian Optimization (CBO) to raw meal grinding indicates potential specific electrical energy consumption reductions of 26.7%. The study also introduces an IoT-inspired deployment framework for continuously assessing environmental and economic impacts and proposes that with Industry 4.0 digitalization and advancements in data analytics, artificial intelligence can extract operational insights from plant sensors and meters. This presents a cost-effective, high-return, and low-risk opportunity to optimize electrical energy consumption in cement manufacturing. By understanding causal relationships between cement plant system components and implementing targeted interventions to optimize electrical energy consumption in the production process, cement manufacturers can significantly contribute to decarbonization efforts, improve sustainability and resource efficiency, and enhance profitability and public image.
Keywords: artificial intelligence; intelligent systems; systems engineering; cement manufacturing; analytics; sustainability