Multi-Objective Optimization of Raw Mix Design and Alternative Fuel Blending for Sustainable Cement Production

Cement production is a carbon-intensive process that contributes significantly to global greenhouse gas emissions. Approximately 50–60% of these emissions result from limestone calcination, while 30–40% result from fossil fuel combustion in kilns. This study presents a multi-objective optimization (MOO) framework that integrates raw mix design and alternative fuel blending to simultaneously reduce production costs and carbon dioxide (CO2) emissions while maintaining clinker quality. A hybrid Genetic Algorithm–Linear Programming (GA-LP) model was developed to navigate the balance between economic and environmental objectives under stringent chemical and operational constraints. The approach models the impact of raw materials and fuel ash on critical clinker quality indices: the Lime Saturation Factor (LSF), Silica Modulus (SM), and Alumina Modulus (AM). It incorporates practical constraints such as maximum substitution rates and specific fuel compositions. A case study inspired by a medium-sized African cement plant demonstrates the utility of the model. The results reveal a Pareto front of optimal solutions, highlighting that a 20% reduction in CO2 emissions from 928 to 740 kg/ton clinker is achievable with only a 24% cost increase. Optimal strategies include 10% fly ash and 30–50% alternative fuels, such as biomass, tire-derived fuel (TDF), and dynamic raw mix adjustments based on fuel ash contributions. Sensitivity analysis further illustrates how biomass cost and LSF targets affect clinker performance, emissions, and fuel shares. The GA-LP hybrid model is validated through process simulation and benchmarked against African case studies. Overall, the findings provide cement producers and policymakers with a robust decision-support tool to evaluate and adopt sustainable production strategies aligned with net-zero targets and emerging carbon regulations.