Machine Learning Classifier-Based Metrics Can Evaluate the Efficiency of Separation Systems

Abstract

This paper highlights that metrics from the machine learning field (e.g., entropy and information gain) used to qualify a classifier model can be used to evaluate the effectiveness of separation systems. To evaluate the efficiency of separation systems and their operation units, entropy- and information gain-based metrics were developed. The receiver operating characteristic (ROC) curve is used to determine the optimal cut point in a separation system. The proposed metrics are verified by simulation experiments conducted on the stochastic model of a waste-sorting system. Machine learning classifier-based metrics has promising potential to gain information about the performance of separation systems. Industrial separation systems can be considered to perform a classification task. Initialized by this analogy, existing metrics from the machine learning field (e.g., entropy and information gain) to qualify a classifier can be used to evaluate the effectiveness of these systems. Our research investigates this idea generally, and also introduces a case study of an industrial manual waste-sorting system. The contributions of the paper are the following: (1) Overview of the possible applications of classifier-based metrics for process development aims. (2) Entropy and information gain are shown to be applicable to evaluate the efficiency of separation systems and their operation units as well. (3) Monte Carlo simulation is involved to produce robust results in a separation system with stochastic phenomena. (4) The ROC curve is shown to be applicable to determining the optimal cut point in a separation system. The ideas above are verified by simulation experiments conducted on the stochastic model of a waste-sorting system.