Optimizing pyrolysis and Co-Pyrolysis of plastic and biomass using Artificial Intelligence

IF 7.1 Q1 ENERGY & FUELS

Energy Conversion and Management-X Pub Date : 2024-10-01 DOI:10.1016/j.ecmx.2024.100783

Manish Sharma Timilsina , Yuvraj Chaudhary , Prikshya Bhattarai , Bibek Uprety , Dilip Khatiwada

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引用次数: 0

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.

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利用人工智能优化塑料和生物质的热解和共热解过程

生物质和塑料废弃物的快速增长给环境带来了巨大挑战。生物质与塑料废弃物的共热解为可持续废物管理和可再生能源发电提供了一条前景广阔的途径。本研究涉及几个新的方面：首先，研究了热解（单独原料）和共热解（生物质和塑料废料）过程中原料成分和操作条件的影响。研究发现，与单独原料相比，生物质和塑料废料共热解存在协同效应，特别是提高了产量和优化了温度。其次，还开发了一个合适的混合机器学习预测模型（采用随机森林、梯度提升调节器和 XGBoost）和稳健的优化框架，以解决模型的准确性、非线性相互作用以及热解过程中的不确定性问题，如温度、加热速率和生物质与塑料的比例。这项研究从定量（数量）和定性（成分）两个方面预测了生物油的产量，准确度很高（R2 > 0.97）。第三，通过 Gini 特征重要性和 Shapley Additive Explanation（SHAP）分析，确定了影响产量的关键因素，包括塑料含量（18%）和生物质类型（13%）。此外，多目标优化技术揭示了特定条件下的最佳生物油产量，并辅以不确定性分析，将生物油产量限制在 30-50% 的范围内。最后，它还展示了一个案例研究，利用当地资源，即生物质（木材和甘蔗渣）和塑料废料的共热解，找到最佳的生物油产量和质量条件。该案例研究提出了最佳条件，如 50 °C 的加热速率、50 分钟的热解时间以及木材和高密度聚乙烯混合物中 60% 的塑料含量。这项研究有助于行业和政策制定者评估和了解共热解的可行性、最佳设计参数和工艺影响。

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来源期刊

Energy Conversion and Management-X Multiple-

CiteScore

8.80

自引率

3.20%

发文量

180

审稿时长

58 days

期刊介绍： Energy Conversion and Management: X is the open access extension of the reputable journal Energy Conversion and Management, serving as a platform for interdisciplinary research on a wide array of critical energy subjects. The journal is dedicated to publishing original contributions and in-depth technical review articles that present groundbreaking research on topics spanning energy generation, utilization, conversion, storage, transmission, conservation, management, and sustainability. The scope of Energy Conversion and Management: X encompasses various forms of energy, including mechanical, thermal, nuclear, chemical, electromagnetic, magnetic, and electric energy. It addresses all known energy resources, highlighting both conventional sources like fossil fuels and nuclear power, as well as renewable resources such as solar, biomass, hydro, wind, geothermal, and ocean energy.