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机器学习在分析聚合物回收物的机械破碎过程中的应用

Izabela Rojek¹, Marek Macko², Dariusz Mikołajewski¹

¹Faculty of Computer Science, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, Poland.

|July 13, 2024

概括

人工智能 (AI) 和机器学习 (ML) 通过分析能源消耗和回收质量来优化聚合物破碎机的选择. 该LbfgsLogisticRegressionOva模型实现了0.9333的准确性,推进了智能回收方法.

科学领域:

材料科学材料科学材料科学
计算机科学计算机科学
工业工程工业工程工业工程

背景情况:

聚合物材料加工需要高效的破碎方法.
碎机的选择标准通常包括效率,回收质量和能源消耗.
优化碎片机选择对于可持续的聚合物回收利用至关重要.

研究的目的:

确定人工智能 (AI) 方法来选择聚合物破碎机.
使用机器学习 (ML) 与独立规则提取用于破碎机分析.
根据能源效率和回收材料质量优化碎机的选择.

主要方法:

对现实世界数据集 (1982行x40列) 的分析,描述了聚合物破碎过程.
机器学习 (ML) 模型的应用,包括LbfgsLogisticRegressionOva.
数据分为学习 (70%),测试 (20%) 和验证 (10%) 集,并进行交叉验证.

主要成果:

该LbfgsLogisticRegressionOva模型表现出卓越的性能,精度为0.9333.3.
由人工智能驱动的分析成功优化了碎片机选择标准,考虑到能源消耗.
该研究提供了一种数据驱动的方法,以提高碎机的效率和回收材料的质量.

结论:

人工智能和机器学习方法为聚合物加工中的智能碎片机选择提供了创新的解决方案.
这项研究有助于在工业4.0框架内开发先进的回收方法.
优化的碎片机选择提高了聚合物回收利用的可持续性和经济可行性.

关键词:

人工智能的人工智能是人工智能.计算模型是一种计算模型.机器学习是机器学习.聚合物是一种聚合物.切碎切碎切碎切碎

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