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Reducing Air Pollution through Machine Learning

2023-03-22 03:24:52
Dimitris Bertsimas, Leonard Boussioux, Cynthia Zeng

Abstract

This paper presents a data-driven approach to mitigate the effects of air pollution from industrial plants on nearby cities by linking operational decisions with weather conditions. Our method combines predictive and prescriptive machine learning models to forecast short-term wind speed and direction and recommend operational decisions to reduce or pause the industrial plant's production. We exhibit several trade-offs between reducing environmental impact and maintaining production activities. The predictive component of our framework employs various machine learning models, such as gradient-boosted tree-based models and ensemble methods, for time series forecasting. The prescriptive component utilizes interpretable optimal policy trees to propose multiple trade-offs, such as reducing dangerous emissions by 33-47% and unnecessary costs by 40-63%. Our deployed models significantly reduced forecasting errors, with a range of 38-52% for less than 12-hour lead time and 14-46% for 12 to 48-hour lead time compared to official weather forecasts. We have successfully implemented the predictive component at the OCP Safi site, which is Morocco's largest chemical industrial plant, and are currently in the process of deploying the prescriptive component. Our framework enables sustainable industrial development by eliminating the pollution-industrial activity trade-off through data-driven weather-based operational decisions, significantly enhancing factory optimization and sustainability. This modernizes factory planning and resource allocation while maintaining environmental compliance. The predictive component has boosted production efficiency, leading to cost savings and reduced environmental impact by minimizing air pollution.

Abstract (translated)

这篇文章提出了一种基于数据的处理方法,通过将 operational decisions 与 weather conditions 联系起来,以减少工业工厂对周边城市空气污染的影响。我们的方法和预测与指令机器学习模型相结合,以预测短期风速和方向,并建议 operational decisions 以减少或暂停工厂的生产。我们展示了减少环境负面影响与维持生产活动之间的多个权衡。我们的框架中的预测部分使用各种机器学习模型,如梯度增强的Tree模型和集成方法,进行时间序列预测。指令部分使用可解释的最佳政策树提出多个权衡,例如减少危险的排放可以减少33-47%,不必要的成本可以减少40-63%。我们部署的模型 significantly 减少了预测错误,在小于12小时预测 lead 时间的情况下,准确率为38-52%,在12-48小时预测 lead 时间的情况下,准确率为14-46%。我们在OCP Safi工厂成功实施了预测部分,这是摩洛哥最大的化学工业工厂,目前正在部署指令部分。我们的框架通过消除污染工业活动权衡,通过数据驱动的天气操作决策,实现了可持续工业发展,极大地增强了工厂优化和可持续性。这种方法现代化了工厂规划和资源分配,同时保持了环境合规。预测部分提高了生产效率,通过最小化空气污染实现了成本节省和减少环境负面影响。

URL

https://arxiv.org/abs/2303.12285

PDF

https://arxiv.org/pdf/2303.12285.pdf


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