Forecasting CO2 Emissions from Cement Manufacturing with
iHOW-Tuned Machine Learning Models
Omnia M. Osama 1, El-Sayed M. El-Rabaie2, Marwa M. Eid3,4
1Department of Communications & Electronics,Delta Higher Institute of Engineering & Technology ,
Mansoura, Egypt
2Faculty of Electronic Engineering , Menoufia University , Department of Electronics and Communications ,
Menouf 32952, Egypt
3Faculty of Artificial Intelligence , Delta University for Science and Technology , Mansoura 35111, Egypt
4Jadara Research Center , Jadara University , Irbid 21110, Jordan
Emails: Omnya.osama@dhiet.edu.eg; srabie1@yahoo.com; mmm@ieee.org
Abstract
Cement production is a major contributor to global CO2 emissions, posing a challenge for climate
mitigation efforts. Accurate forecasting of these emissions is vital for guiding policy and industrial
decarbonization. This study addresses the need for improved predictive frameworks by developing
an optimized ensemble-based machine learning model for CO2 emissions forecasting. The model is
trained on a corrected global cement emissions dataset and enhanced through hyperparameter tuning
using ten metaheuristic algorithms. Among them, the Improved Henry’s Optimization Algorithm
(iHOW) achieved superior performance. The iHOW-optimized model attained an MSE of 1.21×10−6
and R2 of 0.9657, improving over the best baseline model (Gradient Boosting: MSE = 0.0164, R2 =
0.8621) by more than 99%. These results confirm the effectiveness of iHOW in producing accurate
and reliable forecasts. The proposed framework offers strong potential for integration into carbon
tracking systems and policy support tools.
Received: January 01, 2025 Revised: February 05, 2025 Accepted: March 03, 2025
Keywords: CO2 emissions; Cement industry; Ensemble models; iHOW algorithm; Metaheuristic
optimization