2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 17 , Issue 2 , PP: 191-213, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Omnia M. Osama 1 * , El-Sayed M. El-Rabaie 2 , Marwa M. Eid 3
Doi: https://doi.org/10.54216/JISIoT.170212
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.
CO2 emissions , Cement industry , Ensemble models , iHOW algorithm , Metaheuristic optimization
[1] S. Wu et al., “Global co2 uptake by cement materials accounts 1930–2023,” Scientific Data, vol. 11, 1 Dec. 2024, ISSN: 2052-4463. DOI: 10.1038/s41597- 024- 04234- 8. [Online]. Available: https://doi.org/10.1038/s41597-024-04234-8.
[2] S. Kumar, A. Gangotra, and M. Barnard, “Towards a net zero cement: Strategic policies and systems thinking for a low-carbon future,” Current Sustainable/Renewable Energy Reports, vol. 12, 1 Mar. 2025, ISSN: 2196-3010. DOI: 10.1007/s40518-025-00253-0. [Online]. Available: https://doi. org/10.1007/s40518-025-00253-0.
[3] S. Barbhuiya, B. B. Das, D. Adak, K. Kapoor, and M. Tabish, “Low carbon concrete: Advancements, challenges and future directions in sustainable construction,” vol. 1, 1 Mar. 2025. DOI: 10.1007/ s44416-025-00002-y. [Online]. Available: https://doi.org/10.1007/s44416-025- 00002-y.
[4] E. A. Khalil and M. N. Abou-Zeid, “Framework for cement plants assessment through cement production improvement measures for reduction of co2 emissions towards net zero emissions,” Construction Materials, vol. 5, pp. 20–20, 2 Apr. 2025, ISSN: 2673-7108. DOI: 10 . 3390 / constrmater5020020. [Online]. Available: https : / / doi . org / 10 . 3390 / constrmater5020020.
[5] Y. hong Miao et al., “The effect of industrial byproducts fly ash and quartz powder on cement properties and environmental benefits analysis,” Applied Sciences, vol. 15, pp. 5093–5093, 9 May 2025, ISSN: 2076-3417. DOI: 10 . 3390 / app15095093. [Online]. Available: https : / / doi . org / 10 . 3390/app15095093.
[6] W.-H. Tsai and Y. Wu, “Research on multi-objective programming model of profits and carbon emission reduction in manufacturing industry,” Energies, vol. 18, pp. 1411–1411, 6 Mar. 2025, ISSN: 1996-1073. DOI: 10.3390/en18061411. [Online]. Available: https://doi.org/10.3390/ en18061411.
[7] Q. He, Q. Yuan, X. Chen, P. Jiang, Y.Wang, and Y. Li, “Research on industry–economy–energy–carbon emission relationships based on panel vector autoregressive modeling,” Processes, vol. 13, pp. 1107–1107, 4 Apr. 2025, ISSN: 2227-9717. DOI: 10.3390/pr13041107. [Online]. Available: https://doi.org/10.3390/pr13041107.
[8] M. Alghieth, “Sustain ai: A multi-modal deep learning framework for carbon footprint reduction in industrial manufacturing,” Sustainability, vol. 17, pp. 4134–4134, 9 May 2025, ISSN: 2071-1050. DOI: 10.3390/su17094134. [Online]. Available: https://doi.org/10.3390/su17094134.
[9] D. Effrosynidis, E. Spiliotis, G. Sylaios, and A. Arampatzis, “Time series and regression methods for univariate environmental forecasting: An empirical evaluation,” The Science of The Total Environment, vol. 875, pp. 162 580–162 580, Mar. 2023, ISSN: 0048-9697, 1879-1026. DOI: 10 . 1016 / j . scitotenv . 2023 . 162580. [Online]. Available: https : / / doi . org / 10 . 1016 / j . scitotenv.2023.162580.
[10] S. Rajvanshi, G. Kaur, A. Dhatwalia, Arunima, A. Singla, and A. Bhasin, “Research on problems and solutions of overfitting in machine learning,” in Springer Science+Business Media, Jan. 2024, pp. 637–651. DOI: 10 . 1007 / 978 - 981 - 97 - 2508 - 3 \ _47. [Online]. Available: https : //doi.org/10.1007/978-981-97-2508-3\_47.
[11] M. Tiomoko et al., “Human in the loop adaptive optimization for improved time series forecasting,” Jan. 2025. DOI: 10.48550/ARXIV.2505.15354. [Online]. Available: https://arxiv.org/ abs/2505.15354.
[12] X. Li and X. Zhang, “A comparative study of statistical and machine learning models on carbon dioxide emissions prediction of china,” Environmental Science and Pollution Research, vol. 30, no. 55, pp. 117 485–117 502, 2023. DOI: 10.1007/s11356-023-30428-5.
[13] D. Srivastava and A. K. Arya, “A multi-objective model to improve the profitability and reduce carbon emissions in a natural gas pipeline network,” Indian Chemical Engineer, vol. 0, no. 0, pp. 1–35, 2025. DOI: 10.1080/00194506.2025.2499834.
[14] W. Zhong et al., “Accurate and efficient daily carbon emission forecasting based on improved arima,” Applied Energy, vol. 376, p. 124 232, 2024. DOI: 10.1016/j.apenergy.2024.124232.
[15] P. Vaziri and B. Sedaee, “An application of a genetic algorithm in co-optimization of geological co2 storage based on artificial neural networks,” Clean Energy, vol. 8, no. 1, pp. 111–125, 2024. DOI: 10. 1093/ce/zkad077.
[16] Z. Chen, H. Cheng, Y. Liu, and M. Aljuaid, “An improved artificial bee colony algorithm for the multi objective cooperative disassembly sequence optimization problem considering carbon emissions and profit,” Engineering Optimization, vol. 57, no. 3, pp. 649–670, 2025. DOI: 10.1080/0305215X. 2024.2329988.
[17] S. Aras and M. Hanifi Van, “An interpretable forecasting framework for energy consumption and co2 emissions,” Applied Energy, vol. 328, p. 120 163, 2022. DOI: 10.1016/j.apenergy.2022. 120163.
[18] Q. Su, R. Latypov, and S. Chen, “Ann modeling for predicting nox and particulate matter emissions from cement kilns,” Journal of Cleaner Production, vol. 512, p. 145 707, 2025. DOI: 10.1016/j. jclepro.2025.145707.
[19] A. M. Nassef, A. G. Olabi, H. Rezk, and M. A. Abdelkareem, “Application of artificial intelligence to predict co2 emissions: Critical step towards sustainable environment,” Sustainability, vol. 15, no. 9, p. 7648, 2023. DOI: 10.3390/su15097648.
[20] P. Jiang, D. Zhao, C. Jin, S. Ye, C. Luan, and R. F. Tufail, “Compressive strength prediction and low-carbon optimization of fly ash geopolymer concrete based on big data and ensemble learning,”
PLOS ONE, vol. 19, no. 9, e0310422, 2024. DOI: 10.1371/journal.pone.0310422. [21] Y. Liu, Q. Xu, Z. Wang,L. Qi, and J. Lu, “Estimation of carbon dioxide emissions from the cement industry in beijing-tianjin-hebei using neural networks,” PLOS Climate, vol. 4, no. 3, e0000544, 2025. DOI: 10.1371/journal.pclm.0000544.
[22] J. Friedman, “Greedy function approximation: A gradient boosting machine,” The Annals of Statistics, vol. 29, 2000. DOI: 10.1214/aos/1013203451. [Online]. Available: https://doi.org/10. 1214/aos/1013203451.
[23] P. Geurts, D. Ernst, and L. Wehenkel, “Extremely randomized trees,” Machine Learning, vol. 63, no. 1, pp. 3–42, 2006. DOI: 10.1007/s10994-006-6226-1. [Online]. Available: https://doi. org/10.1007/s10994-006-6226-1.
[24] M.-C. Popescu, V. Balas, L. Perescu-Popescu, and N. Mastorakis, “Multilayer perceptron and neural networks,” WSEAS Transactions on Circuits and Systems, vol. 8, 2009.
[25] T. Cover and P. Hart, “Nearest neighbor pattern classification,” IEEE Transactions on Information Theory, vol. 13, no. 1, pp. 21–27, 1967. DOI: 10.1109/TIT.1967.1053964. [Online]. Available: https://doi.org/10.1109/TIT.1967.1053964.
[26] A. Cutler, D. R. Cutler, and J. R. Stevens, “Random forests,” in Machine Learning - ML, vol. 45, Springer, 2011, pp. 157–176. DOI: 10.1007/978- 1- 4419- 9326- 7_5. [Online]. Available: https://doi.org/10.1007/978-1-4419-9326-7_5.
[27] H. Drucker, C. J. Burges, L. Kaufman, A. Smola, and V. Vapnik, “Support vector regression machines,” in Advances in Neural Information Processing Systems, vol. 9, MIT Press, 1996. [Online]. Available: https : / / proceedings . neurips . cc / paper _ files / paper / 1996 / hash / d38901788c533e8286cb6400b40b386d-Abstract.html.
[28] E.-S. M. El-Kenawy et al., “Ihow optimization algorithm: A human-inspired metaheuristic approach for complex problem solving and feature selection,” Journal of Artificial Intelligence in Engineering Practice, vol. 1, no. 2, pp. 36–53, 2024. DOI: 10 . 21608 / jaiep . 2024 . 386694. [Online]. Available: https://doi.org/10.21608/jaiep.2024.386694.
[29] J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of ICNN’95 – International Conference on Neural Networks, vol. 4, 1995, pp. 1942–1948. DOI: 10 . 1109 / ICNN . 1995 . 488968.
[30] X.-S. Yang, “A new metaheuristic bat-inspired algorithm,” in Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), J. R. Gonzalez, D. A. Pelta, C. Cruz, G. Terrazas, and N. Krasnogor, Eds., Springer, 2010, pp. 65–74. DOI: 10.1007/978-3-642-12538-6_6.
[31] S. Mirjalili and A. Lewis, “The whale optimization algorithm,” Advances in Engineering Software, vol. 95, pp. 51–67, 2016. DOI: 10.1016/j.advengsoft.2016.01.008.
[32] D. Simon, “Biogeography-based optimization,” IEEE Transactions on Evolutionary Computation, vol. 12, no. 6, pp. 702–713, 2008. DOI: 10.1109/TEVC.2008.919004.
[33] S. Mirjalili, S. M. Mirjalili, and A. Hatamlou, “Multi-verse optimizer: A nature-inspired algorithm for global optimization,” Neural Computing and Applications, vol. 27, 2015. DOI: 10.1007/s00521- 015-1870-7.
[34] C. Reeves, “Genetic algorithms,” in Handbook of Metaheuristics, vol. 146, 2010, pp. 109–139. DOI: 10.1007/978-1-4419-1665-5_5.
[35] H. Salimi, “Stochastic fractal search: A powerful metaheuristic algorithm,” Knowledge-Based Systems, vol. 75, pp. 1–18, 2014. DOI: 10.1016/j.knosys.2014.07.025.
[36] R. Storn and K. Price, “Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces,” Journal of Global Optimization, vol. 11, pp. 341–359, 1997. DOI: 10.1023/ A:1008202821328.
[37] X. Wang, V. Snašel, S. Mirjalili, J.-S. Pan, L. Kong, and H. A. Shehadeh, “Artificial protozoa optimizer (apo): A novel bio-inspired metaheuristic algorithm for engineering optimization,” Knowledge-Based Systems, vol. 295, p. 111 737, 2024. DOI: 10.1016/j.knosys.2024.111737.
