用机器学习预测并降低生物炭厂的氮氧化物排放。
Predicting NOx emissions in Biochar Production Plants using Machine Learning
- 用随机森林模型模拟热解机理，替代复杂物理建模。
- 测试中实现减排目标同时保持最大产量，适用于两家厂商设备。
- 可部署在普通物联网设备上，适合工业界快速落地。
全球生物炭产业在2023年已达到年产350千吨的规模。为达成气候目标，生物炭碳移除（BCR）技术需在2030年前将工厂数量提升至每年超1000座。然而大规模扩张带来技术和监管挑战，需在保证高产的同时满足排放标准。本文提出一种基于机器学习的优化方法：利用标准随机森林回归器建模热解机运行状态，其物理过程极为复杂。该模型作为机器的代理，用于数值优化，从而在保持最大产量的前提下显著降低氮氧化物（NOx）排放。初步测试表明，该方法在两家不同制造商的设备上均有效，且可部署于标准物联网（IoT）设备，具备广泛适用性。
原文摘要 · Abstract (English)
The global Biochar Industry has witnessed a surge in biochar production, with a total of 350k mt/year production in 2023. With the pressing climate goals set and the potential of Biochar Carbon Removal (BCR) as a climate-relevant technology, scaling up the number of new plants to over 1000 facilities per year by 2030 becomes imperative. However, such a massive scale-up presents not only technical challenges but also control and regulation issues, ensuring maximal output of plants while conforming to regulatory requirements. In this paper, we present a novel method of optimizing the process of a biochar plant based on machine learning methods. We show how a standard Random Forest Regressor can be used to model the states of the pyrolysis machine, the physics of which remains highly complex. This model then serves as a surrogate of the machine -- reproducing several key outcomes of the machine -- in a numerical optimization. This, in turn, could enable us to reduce NOx emissions -- a key regulatory goal in that industry -- while achieving maximal output still. In a preliminary test our approach shows remarkable results, proves to be applicable on two different machines from different manufacturers, and can be implemented on standard Internet of Things (IoT) devices more generally.
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