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Abstract：
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.
摘要：
人工智能方法和技术创造性地支持开发和改进选择用于聚合物材料加工的切碎机的方法的过程。这允许优化选择标准的实现，这可能不仅包括与切碎效率和回收质量相关的指标，还包括能源消耗。本文的目的是选择具有独立规则提取的基于人工智能（AI）的分析方法，即，基于数据的方法(机器学习-ML)。这项研究考虑了描述切碎过程的真实数据集（特征矩阵1982行×40列），包括用于优化碎纸机能效参数的能耗。1982年的每个记录在一个。csv文件（特征向量）有40个数字除以逗号。数据被分成一个学习集（70%的数据），测试集（20%的数据），和一个验证集（10%的数据）。交叉验证显示最佳模型为LbfgsLogisticRegressionOva(0.9333)。这促进了智能切碎方法的基础的发展，在工业4.0范式中对聚合物材料的加工和回收进行了高水平的创新。
