Cement Clinker Quality Prediction Using Advanced Analytics: A study of Machine Learning and Deep Learning Methods : WestminsterResearch

Title	Cement Clinker Quality Prediction Using Advanced Analytics: A study of Machine Learning and Deep Learning Methods
Authors	Iqbal Qasim, Habeeb Balogun, Wusu Godoyon and Hafiz Alaka
Type	Conference paper
Abstract	Free lime content (f-CaO) in cement clinker is a crucial determinant of product quality in rotary kilns. Accurate, real-time estimation of this parameter can significantly improve product quality and energy efficiency during production. While machine learning (ML) has shown promise for such predictive tasks, previous studies have primarily focused on predicting free lime content as a percentage, often resulting in high mean squared errors (MSE) and low R² values, making these approaches impractical for cement plants. This study developed and evaluated eight regression models, including a deep neural network model. The highest R² value of 0.39 was achieved using Support Vector Regression (SVR), while the lowest MSE of 0.08 was obtained with the deep neural network. However, these regression models did not meet the required confidence levels, prompting a shift to classification models. Subsequently, ten classification-based ML models were developed, evaluated, and compared, with Support Vector Machine with Radial Kernel (SVM R) emerging as the best performer, achieving an accuracy of 0.85 and an AUC of 0.91. These findings suggest that a robust classification-based ML model can be developed for predicting cement clinker quality
Keywords	Free lime content, Clinker quality, Predictive machine learning, Energy optimisation, Classification-based machine learning
Year	2026
Conference	6th International Conference on Electrical, Computer and Energy Technologies
Publisher	IEEE
Accepted author manuscript	cement clinker.pdf License CC BY 4.0 File Access Level Open (open metadata and files)
File	File Access Level Open (open metadata and files)

Related outputs

A Critical Review of Environmental Data Governance for Sustainable Infrastructure: A case of Offsite Construction in the United Kingdom
Balogun, H. 2026. A Critical Review of Environmental Data Governance for Sustainable Infrastructure: A case of Offsite Construction in the United Kingdom. Net-Zero Future. Germany 30 Sep - 02 Oct 2026 Springer Nature.

An AI-Assisted Environmental Data Governance Architecture for MRV in Sustainable Infrastructure
Balogun, H. 2026. An AI-Assisted Environmental Data Governance Architecture for MRV in Sustainable Infrastructure. International Conference on Electrical, Computer and Energy Technologies (ICECET 2026) . Rome, Italy 06 - 09 Jul 2026 IEEE .

An AI-Assisted Environmental Data Governance Architecture for MRV in Sustainable Infrastructure
Wusu, G., Balogun, H., Alaka, H., Oseghale, R., Sunmola, F. and Oriowo, O. 2026. An AI-Assisted Environmental Data Governance Architecture for MRV in Sustainable Infrastructure. International Conference on Electrical, Computer and Energy Technologies (ICECET 2026) . Rome, Italy 06 - 09 Jul 2026 IEEE . https://doi.org/10.1109/ICECET65726.2026.11632705

Built Environment Predevelopment Audit Automation
Balogun, H. 2025. Built Environment Predevelopment Audit Automation.

Deconstructability prediction for building using machine learning and ensemble feature selection techniques
Balogun, H., Alaka, H. and Demir, E. 2025. Deconstructability prediction for building using machine learning and ensemble feature selection techniques. Nature Scientific Reports. 15 22152. https://doi.org/10.1038/s41598-025-00790-0

Ensemble of ensembles for fine particulate matter pollution prediction using big data analytics and IoT emission sensors
Christian Nnaemeka Egwim, Hafiz Alaka, Youlu Pan, Balogun, H., Saheed Ajayi, Abdul Hye and Oluwapelumi Oluwaseun Egunjobi 2025. Ensemble of ensembles for fine particulate matter pollution prediction using big data analytics and IoT emission sensors. Journal of Engineering, Design and Technology. 23 (2), pp. 640-665. https://doi.org/10.1108/JEDT-07-2022-0379

Boruta-grid-search least square support vector machine for NO2 pollution prediction using big data analytics and IoT emission sensors
Balogun, H., Alaka, H. and Egwim, C.N. 2025. Boruta-grid-search least square support vector machine for NO2 pollution prediction using big data analytics and IoT emission sensors. Applied Computing and Informatics. 21 (1/2), pp. 101-113. https://doi.org/10.1108/aci-04-2021-0092

Artificial intelligence for deconstruction: Current state, challenges, and opportunities
Balogun, H., Alaka, H., Demir, E., Egwim, C.N, Olu-Ajayi, R., Sulaimon, I. and Oseghale, R. 2024. Artificial intelligence for deconstruction: Current state, challenges, and opportunities. Automation in Construction. 166 105641. https://doi.org/10.1016/j.autcon.2024.105641

Critical factors for assessing building deconstructability: Exploratory and confirmatory factor analysis
Habeeb Balogun, Hafiz Alaka, Saheed Ajayi and Christian Nnaemeka Egwim 2024. Critical factors for assessing building deconstructability: Exploratory and confirmatory factor analysis. Cleaner Engineering and Technology. 21 100790. https://doi.org/10.1016/j.clet.2024.100790

Artificial Intelligence in the Construction Industry: A Systematic Review of the Entire Construction Value Chain Lifecycle
Christian Nnaemeka Egwim,, Hafiz Alaka,, Eren Demir, Habbeb Balogun, Razak Olu-Ajayi, Ismail Sulaimon, Godoyon Wusu, Wasiu Yusuf and Adegoke A. Muideen 2024. Artificial Intelligence in the Construction Industry: A Systematic Review of the Entire Construction Value Chain Lifecycle. Energies. 17 (1) 182. https://doi.org/10.3390/en17010182

Exploratory Analysis of Machine Learning Methods for Total Organic Carbon Prediction Using Well-Log Data of Kolmani Field
Longman, Fodio S., Balogun, Habeeb, Ojulari, Rasheed O., Olatomiwa, Olaniyi J., Balarabe, Husaini J., Edeh, Ifeanyichukwu S. and Joshua, Olabisi O. 2024. Exploratory Analysis of Machine Learning Methods for Total Organic Carbon Prediction Using Well-Log Data of Kolmani Field. IEEE 14th International Conference on Pattern Recognition Systems (ICPRS). London, United Kingdom 15 - 18 Jul 2024 IEEE . https://doi.org/10.1109/icprs62101.2024.10677822

Extraction of underlying factors causing construction projects delay in Nigeria
Egwim, C.N., Alaka, H., Toriola-Coker, L.O., Balogun, H., Ajayi, S. and Oseghale, R. 2023. Extraction of underlying factors causing construction projects delay in Nigeria. Journal of Engineering, Design and Technology. 21 (5), pp. 1323-1342. https://doi.org/10.1108/jedt-04-2021-0211

Systematic review of drivers influencing building deconstructability: Towards a construct-based conceptual framework
Balogun, H., Alaka, H., Egwim, C.N. and Ajayi, S. 2023. Systematic review of drivers influencing building deconstructability: Towards a construct-based conceptual framework. Waste Management and Research. 41 (3), pp. 512-530. https://doi.org/10.1177/0734242x221124078

Building energy performance prediction: A reliability analysis and evaluation of feature selection methods
Olu-Ajayi, R., Alaka, H., Sulaimon, I., Balogun, H., Wusu, G., Yusuf, W. and Adegoke, M. 2023. Building energy performance prediction: A reliability analysis and evaluation of feature selection methods. Expert Systems with Applications. 225 120109. https://doi.org/10.1016/j.eswa.2023.120109

Applied artificial intelligence for predicting construction projects delay
Christian Nnaemeka Egwim, Hafiz Alaka, Luqman Olalekan Toriola-Coker, Habeeb Balogun and Funlade Sunmola 2021. Applied artificial intelligence for predicting construction projects delay. Machine Learning with Applications. 6 100166. https://doi.org/10.1016/j.mlwa.2021.100166

Permalink - https://westminsterresearch.westminster.ac.uk/item/x5zz9/cement-clinker-quality-prediction-using-advanced-analytics-a-study-of-machine-learning-and-deep-learning-methods

Cement Clinker Quality Prediction Using Advanced Analytics: A study of Machine Learning and Deep Learning Methods

Related outputs

Share this

Usage statistics

Export as