Rojek Izabela, Macko Marek, Mikołajewski Dariusz
Faculty of Computer Science, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, Poland.
Faculty of Mechatronics, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, Poland.
Polymers (Basel). 2024 Jun 28;16(13):1852. doi: 10.3390/polym16131852.
Artificial intelligence methods and techniques creatively support the processes of developing and improving methods for selecting shredders for the processing of polymer materials. This allows to optimize the fulfillment of selection criteria, which may include not only indicators related to shredding efficiency and recyclate quality but also energy consumption. The aim of this paper is to select methods of analysis based on artificial intelligence (AI) with independent rule extraction, i.e., data-based methods (machine learning-ML). This study took into account real data sets (feature matrix 1982 rows × 40 columns) describing the shredding process, including energy consumption used to optimize the parameters for the energy efficiency of the shredder. Each of the 1982 records in a .csv file (feature vector) has 40 numbers divided by commas. The data were divided into a learning set (70% of the data), a testing set (20% of the data), and a validation set (10% of the data). Cross-validation showed that the best model was LbfgsLogisticRegressionOva (0.9333). This promotes the development of the basis for an intelligent shredding methodology with a high level of innovation in the processing and recycling of polymer materials within the Industry 4.0 paradigm.
人工智能方法和技术创造性地支持了开发和改进用于选择聚合物材料加工用切碎机的方法的过程。这使得能够优化选择标准的实现，这些标准可能不仅包括与切碎效率和回收物质量相关的指标，还包括能源消耗。本文的目的是选择基于人工智能（AI）且具有独立规则提取功能的分析方法，即基于数据的方法（机器学习 - ML）。本研究考虑了描述切碎过程的真实数据集（特征矩阵为1982行×40列），包括用于优化切碎机能源效率参数的能源消耗。.csv文件中的1982条记录（特征向量）中的每一条都有40个用逗号分隔的数字。数据被分为学习集（数据的70%）、测试集（数据的20%）和验证集（数据的10%）。交叉验证表明最佳模型是LbfgsLogisticRegressionOva（0.9333）。这推动了在工业4.0范式下聚合物材料加工和回收中具有高度创新性的智能切碎方法基础的发展。
