Selection of Sustainable Process and Essential Indicators for Decision Making Using Machine Learning Algorithms

Abstract

Sustainability of processes or systems is generally evaluated with metrics or indicators. When a set of indicators is identified for a particular system of study, it is important to ensure that all the indicators are necessary and sufficient in defining system sustainability. The indicators should be able to distinguish sustainable process options from unsustainable ones. Generally, a large number of indicators are used to characterize a system. While some of the indicators are essential sustainable indicators, they may not be relevant in defining the particular system of interest. It is essential to select the most important indicators from a large set so that reduced set of indicators can enhance the generalized performance of sustainable process selection algorithm. In this paper, we propose the use of machine learning algorithms including k-means clustering for sustainable process selection and support vector machine recursive feature elimination algorithm (SVM-RFE) for relevant indicator identification that is important in sustainable process classification from a group of competing process options. The method is based on weight vector derivatives or generalized error bound sensitivity with respect to each indicator. The proposed method of relevant indicator selection is compared with previous work on important indicator identification using partial least square-variable importance in projection (PLS-VIP) method. We have used two case studies to demonstrate the methodologies. While the PLS-VIP method was successful in identifying indicators that are sufficient for finding relative importance of different process options, SVM-RFE method is useful for identifying indicators that are sufficient in sustainable process classification.