Bio-Hybrid Films from Chirich Tuber Starch: A Sustainable Approach with Machine Learning-Driven Optimization

被引:0
|
作者
Karaogul, Eyyup [1 ]
Sariisik, Gencay [2 ]
Ogutlu, Ahmet Sabri [2 ]
机构
[1] Harran Univ, Fac Engn, Dept Food Engn, TR-63300 Sanliurfa, Turkiye
[2] Harran Univ, Fac Engn, Dept Ind Engn, TR-63300 Sanliurfa, Turkiye
关键词
sustainability; chirich tuber; bio-hybrid films; machine learning; starch-based bioplastics; circular economy; CROSS-LINKING; DOLOMITE;
D O I
10.3390/su17051935
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This study investigates the potential of Chirich (Asphodelus aestivus) tuber, one of Turkey's natural resources, for sustainable bio-hybrid film production. Bio-hybrid films developed from Chirich tuber starch in composite form with polyvinyl alcohol (PVOH) were thoroughly examined for their physical, mechanical, and barrier properties. During the production process, twin-screw extrusion and hydraulic hot pressing methods were employed; the films' optical, chemical, and barrier performances were analyzed through FT-IR spectroscopy, water vapor permeability, solubility, and mechanical tests. To evaluate the films' durability against environmental factors and model their properties, advanced computational model algorithms such as Gradient Boosting Regression (GBR), Random Forest Regression (RFR), and AdaBoost Regression (ABR) were utilized. The results showed that the GBR algorithm achieved the highest accuracy with 99.92% R2 and presented the most robust model in terms of sensitivity to environmental factors. The results indicate that Chirich tuber-based bio-hybrid films exhibit significantly enhanced mechanical strength and barrier performance compared to conventional corn starch-based biodegradable polymers. These superior properties make them particularly suitable for industrial applications such as food packaging and medical materials, where durability, moisture resistance, and gas barrier characteristics are critical. Moreover, their biodegradability and potential for integration into circular economy frameworks underscore their environmental sustainability, offering a viable alternative to petroleum-derived plastics. The incorporation of ML-driven optimization not only facilitates precise property prediction but also enhances the scalability of bio-hybrid film production. By introducing an innovative, data-driven approach to sustainable material design, this study contributes to the advancement of bio-based polymers in industrial applications, supporting global efforts to mitigate plastic waste and promote environmentally responsible manufacturing practices.
引用
收藏
页数:21
相关论文
共 8 条
  • [1] Machine learning-driven optimization for sustainable CO2-to-methanol conversion through catalytic hydrogenation
    Nia, Seyyed Alireza Ghafarian
    Shahbeik, Hossein
    Shafizadeh, Alireza
    Rafiee, Shahin
    Hosseinzadeh-Bandbafha, Homa
    Kiehbadroudinezhad, Mohammadali
    Tajuddin, Sheikh Ahmad Faiz Sheikh Ahmad
    Tabatabaei, Meisam
    Aghbashlo, Mortaza
    ENERGY CONVERSION AND MANAGEMENT, 2025, 325
  • [2] Machine learning-driven prediction and optimization of pyrolysis oil and limonene production from waste tires
    Qi, Jingwei
    Xu, Pengcheng
    Hu, Ming
    Huhe, Taoli
    Ling, Xiang
    Yuan, Haoran
    Wang, Yijie
    Chen, Yong
    JOURNAL OF ANALYTICAL AND APPLIED PYROLYSIS, 2024, 177
  • [3] Adaptive optimization of natural coagulants using hybrid machine learning approach for sustainable water treatment
    Pallavi Randive
    Madhuri S. Bhagat
    Mangesh P. Bhorkar
    Rajesh M. Bhagat
    Shilpa M. Vinchurkar
    Sagar Shelare
    Shubham Sharma
    N. Beemkumar
    S. Hemalatha
    Parveen Kumar
    Ankit Kedia
    Ehab El Sayed Massoud
    Deepak Gupta
    Jasmina Lozanovic
    Scientific Reports, 15 (1)
  • [4] Predictive Maintenance Optimization in Zigbee-Enabled Smart Home Networks: A Machine Learning-Driven Approach Utilizing Fault Prediction Models
    Alijoyo, Franciskus Antonius
    Pradhan, Rahul
    Nalini, N.
    Ahamad, Shaik Shakeel
    Rao, Vuda Sreenivasa
    Godla, Sanjiv Rao
    WIRELESS PERSONAL COMMUNICATIONS, 2024,
  • [5] Machine learning-driven prediction and optimization of monoaromatic oil production from catalytic co-pyrolysis of biomass and plastic wastes
    Xu, Dan
    Zhang, Zihang
    He, Zijian
    Wang, Shurong
    FUEL, 2023, 350
  • [6] Optimization-Driven Machine Learning Approach for the Prediction of Hydrochar Properties from Municipal Solid Waste
    Velusamy, Parthasarathy
    Srinivasan, Jagadeesan
    Subramanian, Nithyaselvakumari
    Mahendran, Rakesh Kumar
    Saleem, Muhammad Qaiser
    Ahmad, Maqbool
    Shafiq, Muhammad
    Choi, Jin-Ghoo
    SUSTAINABILITY, 2023, 15 (07)
  • [7] Equation chapter 0 section 1Macro-driven stock market volatility prediction: Insights from a new hybrid machine learning approach
    Zeng, Qing
    Lu, Xinjie
    Xu, Jin
    Lin, Yu
    INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS, 2024, 96
  • [8] A machine learning-genetic algorithm based models for optimization of intensification process through microwave reactor: A new approach for rapid and sustainable synthesis of biodiesel from novel Hiptage benghalensis seed oil
    Ahmad, Aqueel
    Yadav, Ashok Kumar
    Singh, Achhaibar
    Singh, Dinesh Kumar
    FUEL, 2024, 374