Applying machine learning techniques to implement the technical requirements of energy management systems in accordance with ISO 50001:2018, an industrial case study

Abstract

ABSTRACT This article developed an innovative data-driven framework utilizing machine learning models to implement effective energy management systems in the energy-intensive industries to comply with ISO 50001:2018. By implementing this three-level framework, the following standard’s requirements are fulfilled: (a) indicating the variables that significantly affect energy performance, (b) energy performance indicator and energy baseline development, (c) analysis of the current energy performance, (d) energy conservation opportunity quantification, (e) determining the energy target, and (f) defining the energy action plan. The proposed data-driven energy management approach was implemented in an industrial-scale ethane decarbonization unit in South Pars Gas Complex in Iran. The dataset consists of 52,560 records of unit states sampled throughout 2020 as a baseline period. A high-accuracy model with 99% R-squared is developed by applying the Gradient Boosting Machines algorithm to predict the unit’s energy consumption as an energy baseline. As a critical finding, the offline analysis reveals a great potential to save energy by 10.31% or 63,119 metric tons of yearly steam consumption decreasing and a reduction of 7,027 tons of carbon dioxide emissions per year. By adjusting the unit working modes to the best practices, as an action plan, the intended outcome would be achievable.