Optimizing pyrolysis and Co-Pyrolysis of plastic and biomass using Artificial Intelligence
Sharma Timilsina M, Chaudhary Y, Bhattarai P, Uprety B, Khatiwada D (2024)
Publication Type: Journal article
Publication year: 2024
Journal
Book Volume: 24
Article Number: 100783
DOI: 10.1016/j.ecmx.2024.100783
Abstract
The rapid increase in biomass and plastic waste poses significant environmental challenges. Co-pyrolysis of biomass with plastic wastes offers a promising avenue for sustainable waste management and renewable energy generation. This study covers several novel aspects: First, it investigates the impacts of feedstock composition and operating conditions in pyrolysis (individual feedstock) and co-pyrolysis (biomass and plastic wastes). The study reveals that synergistic effects, specifically improved yields and optimized temperature, exist in the co-pyrolysis of biomass and plastic wastes compared to individual feedstock. Secondly, a suitable blended machine learning predictive model (with Random Forest, Gradient Boosting Regressor, and XGBoost) and robust optimization framework are developed to address model accuracy, non-linear interactions, and uncertainties in pyrolysis such as temperature, heating rate, and biomass-to-plastic ratio. This study predicts the bio-oil yield quantitatively (amount) and qualitatively (composition) with high accuracy (R2 > 0.97). Thirdly, key factors contributing to yield include plastic content (18 %) and biomass type (13 %) have been identified through Gini feature importance and Shapley Additive Explanation (SHAP) analysis. Furthermore, multi-objective optimization techniques reveal the most optimal bio-oil yield under specific conditions, supported by uncertainty analysis, which confines bio-oil yield to a range of 30–50 %. Finally, it also demonstrates a case study to find the optimal bio-oil yield and quality conditions using co-pyrolysis of local resources, i.e., biomass (wood and bagasse) and plastic wastes. The case study suggests optimal conditions like > 50 °C heating rate, <50 min pyrolysis time, and > 60 % plastic content in a blend of wood and HDPE. This study assists industries and policymakers to assess and understand the viability of co-pyrolysis, optimal design parameters, and process impacts.
Additional Organisation(s)
Involved external institutions
Tribhuvan University / त्रिभुवन विश्वविद्यालय
Nepal (NP)
Kathmandu University (KU) / काठमाडौं विश्वविद्यालय
Nepal (NP)
Royal Institute of Technology / Kungliga Tekniska Högskolan (KTH)
Sweden (SE)
Kookmin University / 메인페이지
Korea, Republic of (KR)
Nepal (NP)
Kathmandu University (KU) / काठमाडौं विश्वविद्यालय
Nepal (NP)
Royal Institute of Technology / Kungliga Tekniska Högskolan (KTH)
Sweden (SE)
Kookmin University / 메인페이지
Korea, Republic of (KR)
How to cite
APA:
Sharma Timilsina, M., Chaudhary, Y., Bhattarai, P., Uprety, B., & Khatiwada, D. (2024). Optimizing pyrolysis and Co-Pyrolysis of plastic and biomass using Artificial Intelligence. Energy Conversion and Management: X, 24. https://doi.org/10.1016/j.ecmx.2024.100783
MLA:
Sharma Timilsina, Manish, et al. "Optimizing pyrolysis and Co-Pyrolysis of plastic and biomass using Artificial Intelligence." Energy Conversion and Management: X 24 (2024).
BibTeX: Download