ADAPTIVE DIGITAL TWIN SYSTEM FOR CEMENT PRODUCTION OPTIMIZATION USING MARKOV CHAIN-BASED REACTION RATE MODELLING
Markov-Based Digital Twin for Cement Production Optimization
DOI:
https://doi.org/10.55766/sujst8764Keywords:
Digital twin technology, Cement production optimization, Markov chain modeling, Process automation, Sustainable manufacturing, Industrial internet of thingAbstract
Cement manufacturing remains one of the most energy - intensive and carbon - intensive industrial activities, largely because legacy control systems cannot accommodate the highly dynamic, multi - stage nature of clinker formation. This study proposes an Adaptive Digital Twin (ADT) framework that fuses real - time plant data with a Markov Chain - based reaction‑rate model to predict, and proactively steer, chemical transformations inside the kiln. The twin ingests high - frequency sensor streams, updates a time - varying state - space representation of material flows, and couples it with stochastic state - transition matrices calibrated from historical process logs. A reinforcement - learning controller then adjusts fuel injection, air flow, and feed ratios on the fly. The architecture has been given as a prototype design for two plants (Holcim, Switzerland; Fujian Ansha Jianfu, China) and benchmarked against their incumbent PID - driven systems. Over a three - month evaluation horizon, the ADT cut specific thermal energy consumption by 12%, lifted overall throughput by 8%, suppressed quality variability by an order of magnitude, and trimmed unplanned downtime by 25%. Sensitivity analysis confirms that the Markov component - by capturing probabilistic shifts between pre - heating, calcination, clinkering, and cooling states - accounts for more than half of the observed efficiency gain. Beyond delivering immediate cost and emissions benefits, the work demonstrates a transferable blueprint for merging stochastic reaction modelling with cyber - physical twins in other process industries. This research contributes to the field by demonstrating how Markov Chain - Based modeling can enhance industrial process optimization through digital twin frameworks, providing a scalable solution for sustainable cement production.
References
Montague, J. (2021). Building information modeling, digital twins create green, smart cement factory. Control Global. https://www.controlglobal.com/control/ai-ml/article/11290532/building-information-modeling-digital-twins-create-green-smart-cement-factory
Holcim. (2024). First cement plant digital twin. https://www.holcim.com/media/company-news/first-cement-plant-digital-twin
Hu, S., Li, C., Li, B., Yang, M., Wang, X., Gao, T., Xu, W., Dambatta, Y. S., Zhou, Z., and Xu, P. (2024). Digital twins enabling intelligent manufacturing: From methodology to application. Intelligent and Sustainable Manufacturing, 1(1):10007. https://doi.org/10.35534/ism.2024.10007
Becker, F., Bibow, P., Dalibor, M., Gannouni, A., Hahn, V., Hopmann, C., Jarke, M., Koren, I., Kröger, M., Lipp, J., Maibaum, J., Michael, J., Rumpe, B., Sapel, P., Schäfer, N., Schmitz, G. J., Schuh, G., and Wortmann, A. (2021). A conceptual model for digital shadows in industry and its application. In Conceptual modeling (ER 2021) (pp. 271-281). Springer. https://doi.org/10.1007/978-3-030-89022-3_22
Wallace, E. (2024). Using digital twins to drive manufacturing efficiency. RTInsights. https://www.rtinsights.com/using-digital-twins-to-drive-manufacturing-efficiency/
Bolender, T., Bürvenich, G., Dalibor, M., Rumpe, B., and Wortmann, A. (2021). Self-adaptive manufacturing with digital twins. In Proceedings of the IEEE International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) (pp. 156–166). IEEE Computer Society. https://doi.org/10.1109/SEAMS51251.2021.00029
SRMPR Cements. (2024). How cement companies are using new technologies to improve production efficiency. https://srmprcements.com/how-cement-companies-are-using-new-technologies-to-improve-production-efficiency/
TRC Companies. (2024). The future of digital twins in infrastructure. TRC Companies. https://www.trccompanies.com/insights/the-future-of-digital-twins-in-infrastructure/
Cement Equipment. (2024). Artificial intelligence in the cement industry: Comprehensive guide and new trends. Cement Equipment. https://www.cementequipment.org/home/artificial-intelligence-in-the-cement-industry-comprehensive-guide-and-new-trends/
Faststream Technologies. (2024). Smart cement factory solution. Faststream Technologies. https://www.faststreamtech.com/solutions/iot-solutions/smart-factory-solutions/smart-cement-factory-solution/
Ghosh, A.K., Ullah, A.S., and Kubo, A. (2019). Hidden Markov model-based digital twin construction for futuristic manufacturing systems. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 33(3):317–331. https://doi.org/10.1017/S089006041900012X
Bhat, N.N., Dutta, S., Pal, S.K. and Pal, S. (2016). Tool condition classification in turning process using hidden Markov model based on texture analysis of machined surface images. Measurement, 90:500–509. https://doi.org/10.1016/j.measurement.2016.05.022
Cai, Y., Shi, X., Shao, H., Wang, R., and Liao, S. (2018). Energy efficiency state identification in milling processes based on information reasoning and hidden Markov model. Journal of Cleaner Production, 193:397-413. https://doi.org/10.1016/j.jclepro.2018.04.265
Grieves, M., and Vickers, J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In F.-J. Kahlen, S. Flumerfelt, & A. Alves (Eds.), Transdisciplinary perspectives on complex systems (pp. 85-113). Springer. https://doi.org/10.1007/978-3-319-38756-7_4
Kritzinger, W., Karner, M., Traar, G., Henjes, J., and Sihn, W. (2018). Digital twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine, 51(11):1,016-1,022. https://doi.org/10.1016/j.ifacol.2018.08.474
Kunath, M. and Winkler, H. (2018). Integrating the digital twin of the manufacturing system into a decision support system for improving the order management process. Procedia CIRP, 72:225-231. https://doi.org/10.1016/j.procir.2018.03.192
Rosen, R., von Wichert, G., Lo, G., and Bettenhausen, K.D. (2015). About the importance of autonomy and digital twins for the future of manufacturing. IFAC-PapersOnLine, 48(3):567-572. https://doi.org/10.1016/j.ifacol.2015.06.141
Perrot, A., Pierre, A., Nerella, V.N., Wolfs, R.J. M., Keita, E., Nair, S.A. O., Neithalath, N., Roussel, N., and Mechtcherine, V. (2021). From analytical methods to numerical simulations: A process engineering toolbox for 3D concrete printing. Cement and Concrete Composites, 122:104164. https://doi.org/10.1016/j.cemconcomp.2021.104164
Magistri, M. (2024). The cement industry: The challenges of the future. Mapei. https://www.mapei.com/it/en/realta-mapei/detail/the-cement-industry-the-challenges-of-the-future
Cementir Holding. (2024). The cement sector and the challenges of sustainability. Cementir Holding. https://www.cementirholding.com/en/sustainability/sustainability/cement-sector-and-challenges-of-sustainability
Softtek. (2024). Key transformation challenges in the cement industry. Softtek. https://blog.softtek.com/en/key-transformation-challenges-in-the-cement-industry
Chatterjee, A.K. (2021). Digital twin and its variants for advancing digitalization in cement manufacturing. In A. K. Chatterjee (Ed.), Intelligent and sustainable cement production: Transforming to Industry 4.0 standards (1st ed., pp. 245–275). CRC Press.
Wang, Y., Aslani, F., Dyskin, A., and Pasternak, E. (2023). Digital twin applications in 3D concrete printing. Sustainability, 15(3):2,124. https://doi.org/10.3390/su15032124
Opoku, D.-G. J., Perera, S., Osei-Kyei, R., & Rashidi, M. (2021). Digital twin application in the construction industry: A literature review. Journal of Building Engineering, 40, 102726. https://doi.org/10.1016/j.jobe.2021.102726
Buswell, R. A., da Silva, W. R. L., Bos, F. P., Schipper, H. R., Lowke, D., Hack, N., Kloft, H., Mechtcherine, V., Wangler, T., & Roussel, N. (2020). A process classification framework for defining and describing digital fabrication with concrete. Cement and Concrete Research, 134, 106068. https://doi.org/10.1016/j.cemconres.2020.106068
Heidarnezhad, F., & Zhang, Q. (2022). Shotcrete-based 3D concrete printing: State of the art, challenges, and opportunities. Construction and Building Materials, 323, 126545. https://doi.org/10.1016/j.conbuildmat.2022.126545
Wang, L., Aslani, F., & Mukherjee, A. (2022). Development of 3D printable self-sensing cementitious composites. Construction and Building Materials, 337, 127601. https://doi.org/10.1016/j.conbuildmat.2022.127601
Khan, A., Dahl, M., Falkman, P., & Fabian, M. (2018). Digital twin for legacy systems: Simulation model testing and validation. In Proceedings of the IEEE International Conference on Automation Science and Engineering (CASE) (pp. 421–426). IEEE. https://doi.org/10.1109/COASE.2018.8560338
Control Global. (2024). Building information modeling, digital twins create green, smart cement factory. Control Global. https://www.controlglobal.com/control/ai-ml/article/11290532/building-information-modeling-digital-twins-create-green-smart-cement-factory
ZKG International. (2024). Digital transformation of the cement industry. ZKG International. https://www.zkg.de/en/artikel/digital-transformation-of-the-cement-industry-3968132.html
