利用近红外光谱分拣聚烯烃:确定最佳数据分析管道和机器学习分类器†‡

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY Digital discovery Pub Date : 2024-10-15 DOI:10.1039/D4DD00235K
Bradley P. Sutliff, Peter A. Beaucage, Debra J. Audus, Sara V. Orski and Tyler B. Martin
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引用次数: 0

摘要

聚烯烃 (PO) 是全球生产的最大一类聚合物。尽管这一类聚合物在化学性质上具有内在的相似性,但它们在物理上往往是不相容的。这种组合给高通量回收系统带来了巨大的障碍,因为该系统需要对各种类型的塑料进行分类。已有一些研究表明,近红外光谱(NIR)可以将 PO 从其他塑料中分拣出来,但它们通常无法将 PO 从彼此中分拣出来。在这项工作中,我们通过筛选超过 12000 个机器学习管道,增强了基于近红外光谱的分拣能力,从而实现了超出现有近红外数据库所能实现的 PO 种类分拣。这些管道包括一系列散射校正、过滤和区分、数据缩放、降维以及机器学习分类器。常见的散射校正和预处理步骤包括散射校正、线性去趋势和萨维茨基-戈莱滤波。此外,还研究了主成分分析(PCA)、功能主成分分析(fPCA)和均匀流形逼近与投影(UMAP)等降维技术,以提高分类能力。通过对预处理步骤和分类算法组合的分析,确定了多个数据管道,能够成功地对 PO 材料进行分类,准确率超过 95%。通过严格的测试,本研究为在不使数据分析过于复杂的情况下持续应用预处理和分类技术提供了建议。这项工作还提供了一套预处理步骤、一个选定的分类器和经过调整的超参数,这些可能对新模型和数据集的基准测试有用。最后,材料分类设备的开发人员可以随时应用本文概述的方法,从而提高回收塑料废物流的价值和纯度。
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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Sorting polyolefins with near-infrared spectroscopy: identification of optimal data analysis pipelines and machine learning classifiers†‡

Polyolefins (POs) are the largest class of polymers produced worldwide. Despite the intrinsic chemical similarities within this class of polymers, they are often physically incompatible. This combination presents a significant hurdle for high-throughput recycling systems that strive to sort various types of plastics from one another. Some research has been done to show that near-infrared spectroscopy (NIR) can sort POs from other plastics, but they generally fall short of sorting POs from one another. In this work, we enhance NIR spectroscopy-based sortation by screening over 12 000 machine-learning pipelines to enable sorting of PO species beyond what is possible using current NIR databases. These pipelines include a series of scattering corrections, filtering and differentiation, data scaling, dimensionality reduction, and machine learning classifiers. Common scattering corrections and preprocessing steps include scatter correction, linear detrending, and Savitzky–Golay filtering. Dimensionality reduction techniques such as principal component analysis (PCA), functional principal component analysis (fPCA) and uniform manifold approximation and projection (UMAP) were also investigated for classification enhancements. This analysis of preprocessing steps and classification algorithm combinations identified multiple data pipelines capable of successfully sorting PO materials with over 95% accuracy. Through rigorous testing, this study provides recommendations for consistently applying preprocessing and classification techniques without over-complicating the data analysis. This work also provides a set of preprocessing steps, a chosen classifier, and tuned hyperparameters that may be useful for benchmarking new models and data sets. Finally, the approach outlined here is ready to be applied by the developers of materials sortation equipment so that we can improve the value and purity of recycled plastic waste streams.

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Back cover Sorting polyolefins with near-infrared spectroscopy: identification of optimal data analysis pipelines and machine learning classifiers†‡ High accuracy uncertainty-aware interatomic force modeling with equivariant Bayesian neural networks† Correction: A smile is all you need: predicting limiting activity coefficients from SMILES with natural language processing Artificial intelligence-enabled optimization of battery-grade lithium carbonate production†
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