Work

Energy Saving Potential and Machine Learning-Based Prediction of Compressed Air Leakages in Sustainable Manufacturing

Year: 2026

Type: article

Abstract: Compressed air systems are widely used in industry, and air leaks that occur over time lead to significant and unnecessary energy losses. This study aims to quantify the energy-saving potential of compressed air leaks in a manufacturing plant and to develop machine learning (ML) regression models for sustainable leak management. A total of 230 leak points were identified by measuring three periods using an ultrasonic device. Using the measured acoustic emission level (dB) and probe distance (x) as inputs, the leak flow rate, annual energy-saving potential, cost loss, and carbon footprint were calculated. As a result of the repairs, energy consumption improved by 8% compared to the initial state. Three regression models were compared to predict leak flow: Linear Regression, Bagging Regression Trees, and Multivariate Adaptive Regression Splines. Among the models evaluated, the Bagging Regression Trees model demonstrated the best prediction performance, achieving an R2 value of 0.846, a mean squared error (MSE) of 389.85 (L/min2), and a mean absolute error (MAE) of 12.13 L/min in the independent test set. Compared to previous regression-based approaches, the proposed ML method contributes to sustainable production strategies by linking leakage prediction to energy performance indicators.

Source: Sustainability

DOI: 10.3390/su18020904

Author: Sinan Kapan

Institution: Fırat University

Language: English

Topic: Energy Efficiency and Management

Subfield: Renewable Energy, Sustainability and the Environment

Field: Energy

Domain: Physical Sciences

SDGs: Responsible consumption and production, Industry, innovation and infrastructure, Climate action

Open Access status: gold

Estimated APC paid: $2,896