AI-Driven Optimization of Biopolymer Composite Formulations Using IoT Data Streams

Year : 2025 | Volume : 13 | Issue : 05 | Page : 85 100
    By

    Praveena Murala,

  • Pedapati Veerababu,

  • Shailaja Mantha,

  • Subarno Bhattacharyya,

  • Reshma V. K,

  • C. M. Sheela Rani,

  1. Assistant Professor, Department of Computer Science and Engineering, QIS College of Engineering and Technology, Andhra Pradesh, India
  2. Assistant Professor, Department of CSE – Data Science, Vignan Institute of Technology and Science, Hyderabad, Telangana, India
  3. Associate Professor, Department of Electronics and Communication Engineering, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana, India
  4. Assistant Director, Office of Digital Learning and Online Education, O.P. Jindal Global University, Sonipat, Haryana, India
  5. Associate Professor, Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, India
  6. Professor, Department of Computer Science and Engineering, K L University, Vaddeswaram, Guntur District, Andhra Pradesh, India

Abstract

Biodegradable polymer composites have emerged as a sustainable alternative to petroleum-based materials in packaging, biomedical, and structural applications. However, traditional formulation techniques for reinforced polymer composites often lack precision and fail to adapt to real-time variations during processing, resulting in suboptimal material performance. This research proposes a real-time AI-IoT-enabled framework to optimize biopolymer composite formulations. The goal is to intelligently tune composite properties such as mechanical strength, moisture resistance, and biodegradation behavior by leveraging continuous sensor data and machine learning. A hybrid prediction model integrating Convolutional Neural Networks and Long Short-Term Memory (CNN-LSTM) with Extreme Gradient Boosting (XGBoost) was developed to predict composite behavior from IoT-based fabrication data. Key parameters polymer-filler ratio, temperature, humidity, and curing time were collected using an embedded sensor network during the composite processing stage. A Multi-Objective Genetic Algorithm (MOGA) was employed to optimize formulation targets across multiple property dimensions. Experimental validation was conducted using PLA-starch and PHA-lignin biopolymer composites. The proposed system achieved high predictive accuracy (R² > 0.90) across all polymer composite properties. Optimized reinforced formulations resulted in a 17.8% improvement in tensile strength and a 22.1% reduction in water absorption, while maintaining biodegradation above 90%. Experimental outcomes closely matched model predictions with less than 5% deviation. This study demonstrates a novel AI-driven optimization platform for biodegradable polymer composite formulations, offering a closed-loop, scalable solution for intelligent material design. The framework enables real-time formulation control for reinforced polymer systems, bridging performance, sustainability, and smart manufacturing.

Keywords: Biopolymer composites; polymer formulation; IoT data streams; AI optimization; hybrid CNN-LSTM model; genetic algorithm; sustainable materials; reinforced biodegradable polymer.

[This article belongs to Journal of Polymer and Composites ]

How to cite this article:
Praveena Murala, Pedapati Veerababu, Shailaja Mantha, Subarno Bhattacharyya, Reshma V. K, C. M. Sheela Rani. AI-Driven Optimization of Biopolymer Composite Formulations Using IoT Data Streams. Journal of Polymer and Composites. 2025; 13(05):85-100.
How to cite this URL:
Praveena Murala, Pedapati Veerababu, Shailaja Mantha, Subarno Bhattacharyya, Reshma V. K, C. M. Sheela Rani. AI-Driven Optimization of Biopolymer Composite Formulations Using IoT Data Streams. Journal of Polymer and Composites. 2025; 13(05):85-100. Available from: https://journals.stmjournals.com/jopc/article=2025/view=224080


References

  1. Rooney, K., Dong, Y., Basak, A. K., & Pramanik, A. (2024). Prediction of Mechanical Properties of 3D Printed Particle-Reinforced Resin Composites. Journal of Composites Science, 8(10), 416. https://doi.org/10.3390/jcs8100416
  2. Sharma, A., Mukhopadhyay, T., Rangappa, S. M., Siengchin, S., & Kushvaha, V. (2022). Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design. Archives of Computational Methods in Engineering, 29(5), 3341–3385. https://doi.org/10.1007/s11831-021-09700-9
  3. Champa-Bujaico, E., Garcia-Diaz, P., & Díez-Pascual, A. M. (2022). Machine Learning for Property Prediction and Optimization of Polymeric Nanocomposites: A State-of-the-Art. International Journal of Molecular Sciences, 23(18), 10712. https://doi.org/10.3390/ijms231810712
  4. Mamodiya and I. Kishor, “A Comparative Study on the Performance of DualAxis Solar Tracking Systems and Fixed Solar Arrays,” in Proc. 5th Int. Conf. on Information Management & Machine Intelligence (ICIMMI ’23), Jaipur, India, 2024, Art. no. 116, pp. 1–4, doi: 10.1145/3647444.3647943
  5. Nawafleh, N., & Al-Oqla, F. (2022). Artificial neural network for predicting the mechanical performance of additive manufacturing thermoset carbon fiber composite materials. Journal of the Mechanical Behavior of Materials, 31(1), 501–513. https://doi.org/10.1515/jmbm-2022-0054
  6. Pugar, J., Gang, C., Huang, C., Haider, K. W., & Washburn, N. R. (2022). Predicting Young’s Modulus of Linear Polyurethane and Polyurethane-Polyurea Elastomers: Bridging Length Scales with Physicochemical Modeling and Machine Learning. ACS Applied Materials & Interfaces, 14(14), 16568–16581. https://doi.org/10.1021/acsami.1c24715
  7. Arora, P. Agarwal, A. Mittal, U. Mamodiya and P. Juneja, “SA based Optimization of Controller Parameters for Crystallization Unit of Sugar Factory,” 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, 2022, pp. 341-346, doi: 10.1109/ICOEI53556.2022.9776904
  8. Moumen, A., Lakhdar, A., Laabid, Z., & Mansouri, K. (2022). Towards smart modeling of mechanical properties of a bio composite based on a machine learning. International Journal of Electrical and Computer Engineering, 12(3), 3138. https://doi.org/10.11591/ijece.v12i3.pp3138-3145
  9. Baturynska, I. (2019). Application of Machine Learning Techniques to Predict the Mechanical Properties of Polyamide 2200 (PA12) in Additive Manufacturing. Applied Sciences, 9(6), 1060. https://doi.org/10.3390/APP9061060
  10. Isabona, L. L. Ibitome, A. L. Imoize, U. Mamodiya, A. Kumar, M. M. Hassan, et al., “Statistical characterization and modeling of radio frequency signal propagation in mobile broadband cellular next generation wireless networks”, Computational Intelligence and Neuroscience, vol. 2023, no. 1, pp. 5236566, 2023.
  11. Kishor, A Prototype Framework for Customizing Virtual Reality Interactions for Paralyzed Patients Integrating Physical and Rehabilitation Modalities,” in Recent Advances in Sciences, Engineering, Information Technology & Management, 1st ed., London, U.K.: CRC Press, Taylor & Francis Group, 2025, pp. [insert actual page range if known]. doi: 10.1201/9781003598152-47.
  12. Baturynska, I. (2019). Application of Machine Learning Techniques to Predict the Mechanical Properties of Polyamide 2200 (PA12) in Additive Manufacturing. Applied Sciences, 9(6), 1060. https://doi.org/10.3390/APP9061060
  13. Isabona, L. L. Ibitome, A. L. Imoize, U. Mamodiya, A. Kumar, M. M. Hassan, et al., “Statistical characterization and modeling of radio frequency signal propagation in mobile broadband cellular next generation wireless networks”, Computational Intelligence and Neuroscience, vol. 2023, no. 1, pp. 5236566, 2023 S. K. Sunori,
  14. Cassola, S., Duhovic, M., Schmidt, T., & May, D. (2022). Machine learning for polymer composites process simulation– a review. 246, 110208. https://doi.org/10.1016/j.compositesb.2022.110208
  15. Mamodiya and N. Tiwari, “Design and implementation of an intelligent single axis automatic solar tracking system”, Mater. today Proc., vol. 81, pp. 1148-1151, 2023
  16. Kumar B N, D., & Dutt K, M. (2023). A Study On Mechanical Properties Of 3d Printed Hybrid Polymer Composites. Journal of Namibian Studies : History Politics Culture. https://doi.org/10.59670/btwhrn64
  17. Kishor, A Prototype Framework for Customizing Virtual Reality Interactions for Paralyzed Patients Integrating Physical and Rehabilitation Modalities,” in Recent Advances in Sciences, Engineering, Information Technology & Management, 1st ed., London, U.K.: CRC Press, Taylor & Francis Group, 2025, pp. [insert actual page range if known]. doi: 10.1201/9781003598152-47.
  18. Greco, P. F., Pepi, C., & Gioffrè, M. (2024). A novel biocomposite material for sustainable constructions: Metakaolin lime mortar and Spanish broom fibers. Journal of Building Engineering. https://doi.org/10.1016/j.jobe.2023.108425
  19. Yun Debbie, S. X., Muiruri, J. K., Wu, W.-Y., Yeo, J. C. C., Wang, S., Tomczak, N., Thitsartarn, W., Tan, B. H., Wang, P., Wei, F., Suwardi, A., Xu, J., Loh, X. J., Yan, Q., & Zhu, Q. (2024). Bio-Polyethylene and Polyethylene Biocomposites: An Alternative Towards a Sustainable Future. Macromolecular Rapid Communications, e2400064. https://doi.org/10.1002/marc.202400064
  20. Zulfiqar, A., Shah, A. ur R., Khalil, M. S., Azad, M. M., Zulfiqar, Y., Naseem, M. S., & Song, J.-I. (2023). Enhancing properties of jute/starch bio-composite material through incorporation of magnesium carbonate hydroxide pentahydrate: A sustainable approach. Materials Chemistry and Physics. https://doi.org/10.1016/j.matchemphys.2023.128690
  21. Wang, S., Muiruri, J. K., Soo, X. Y. D., Liu, S., Thitsartarn, W., Tan, B. H., Suwardi, A., Li, Z., Zhu, Q., & Loh, X. J. (2022). Bio-Polypropylene and Polypropylene-based Biocomposites: Solutions for a Sustainable Future. Chemistry-an Asian Journal, 18(2), e202200972. https://doi.org/10.1002/asia.202200972
  22. Rehman, N. U., Ullah, K. S., Sajid, M., Ihsanullah, I., & Waheed, A. (2024). Preparation of Sustainable Composite Materials from Bio‐Based Domestic and Industrial Waste: Progress, Problems, and Prospects‐ A Review. Advanced Sustainable Systems. https://doi.org/10.1002/adsu.202300587
  23. McNeill, D., Pal, A., Mohanty, A. K., & Misra, M. (2023). High Biomass Filled Biodegradable Plastic in Engineering Sustainable Composites. Composites Part C: Open Access. https://doi.org/10.1016/j.jcomc.2023.100388
  24. Benchouia, H. E., Boussehel, H., Guerira, B., Sedira, L., Tedeschi, C., Becha, H. E., & Cucchi, M. (2024). An experimental evaluation of a hybrid bio-composite based on date palm petiole fibers, expanded polystyrene waste, and gypsum plaster as a sustainable insulating building material. Construction and Building Materials. https://doi.org/10.1016/j.conbuildmat.2024.135735
  25. Zhang, H., Liao, W., Chen, G., & Ma, H. Q. (2023). Development and Characterization of Coal-Based Thermoplastic Composite Material for Sustainable Construction. Sustainability. https://doi.org/10.3390/su151612446
  26. Ferdousi, S., Advincula, R. C., Sokolov, A. P., Choi, W., & Jiang, Y. (2023). Investigation of 3D printed lightweight hybrid composites via theoretical modeling and machine learning. https://doi.org/10.1016/j.compositesb.2023.110958
  27. Chew, A. K., Afzal, M. A. F., Chandrasekaran, A., Kamps, J. H., & Ramakrishnan, V. (2024). Designing the Next Generation of Polymers with Machine Learning and Physics-Based Models. Machine Learning: Science and Technology. https://doi.org/10.1088/2632-2153/ad88d7
  28. Rabby, M. M., Das, P. P., Rahman, M., Vadlamudi, V., & Raihan, R. (2023). Fast and accurate prediction of cure quality and mechanical performance in fiber‐reinforced polymer composite using dielectric variables and machine learning. Polymer Composites. https://doi.org/10.1002/pc.27891
Regular Issue Subscription Original Research
Volume 13
Issue 05
Received 03/06/2025
Accepted 30/06/2025
Published 22/07/2025
Publication Time 49 Days
269

Login


My IP

PlumX Metrics