Unlocking advanced waste management models: Machine learning integration of emerging technologies into regional systems

IF 5.4 Q1 ENVIRONMENTAL SCIENCES
Nicolás Martínez-Ramón , Robert Istrate , Diego Iribarren , Javier Dufour
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Abstract

The waste management sector requires specialized systems analysis tools to facilitate decision-making and make waste management sustainable and efficient. While integrated systemic approaches exist for assessing conventional waste management systems, the integration of emerging technologies such as gasification, pyrolysis, and methane dry reforming remains largely overlooked. In this work, these three technologies have been integrated into a conventional regional waste management model by abstracting rigorous simulation models into machine-learning surrogate models. The resulting technology-rich waste management model incorporates material flow analysis and life-cycle assessment as tools for supporting policy and decision-making. The model was tested by assessing the environmental impacts and landfill rates for three technology implementation scenarios. Overall, the inclusion of these emerging technologies led to an environmental performance improvement compared to a reference system. For example, a 116.5 % reduction of the carbon footprint in the most optimistic scenario. Nevertheless, the mere addition of these technologies was not enough to achieve landfill rates below 10 %, reaching 37.6 % in the most optimistic scenario. Therefore, properly sizing capacity was found to be a key factor in minimizing both environmental impact and landfill rate.

Abstract Image

开启先进的废物管理模式:将新兴技术融入区域系统的机器学习
废物管理部门需要专门的系统分析工具来促进决策,并使废物管理具有可持续性和高效性。虽然已有评估传统废物管理系统的综合系统方法,但气化、热解和甲烷干重整等新兴技术的整合在很大程度上仍被忽视。在这项工作中,通过将严格的模拟模型抽象为机器学习代用模型,将这三种技术整合到传统的区域废物管理模型中。由此产生的技术丰富的废物管理模型将物料流分析和生命周期评估作为支持政策和决策的工具。通过评估三种技术实施方案的环境影响和垃圾填埋率,对模型进行了测试。总体而言,与参考系统相比,纳入这些新兴技术可改善环境绩效。例如,在最乐观的情况下,碳足迹减少了 116.5%。然而,仅仅增加这些技术还不足以使垃圾填埋率低于 10%,在最乐观的情况下,填埋率达到 37.6%。因此,适当确定处理能力是将环境影响和填埋率降到最低的关键因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Resources, conservation & recycling advances
Resources, conservation & recycling advances Environmental Science (General)
CiteScore
11.70
自引率
0.00%
发文量
0
审稿时长
76 days
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