Optimizing Energy and Air Consumption in Smart Manufacturing: An Industrial Internet of Things-Based Monitoring and Efficiency Enhancement Solution

The rising cost of energy and the urgent need for sustainability have driven industries to adopt smarter solutions for monitoring and optimizing resource consumption. In this study, we present an Industrial Internet of Things (IIoT)-based approach for real-time energy and air consumption monitoring in manufacturing, focusing on a legacy Turret Punch Press (TPP) at Mitsubishi Electric Air Conditioning Systems Europe Ltd. (M-ACE). Due to its age and lack of modern monitoring capabilities, the machine was suspected to be inefficient, requiring a retrofitting strategy for improved transparency and optimization. To address these challenges, a structured IIoT-enabled monitoring system was deployed, integrating KEYENCE MP-F series sensors, an energy monitoring module, and Ethernet communication via Modbus TCP/IP. A comprehensive dashboarding system was developed for real-time visualization and analysis of energy consumption trends, identifying inefficiencies and optimizing machine usage. The data-driven approach revealed significant energy savings of up to 56% and uncovered hidden inefficiencies, including a persistent air leak. By implementing a smart shut-off valve triggered by real-time power consumption data, unnecessary air leakage was eliminated, reducing compressed air waste and overall energy costs. The results demonstrate the effectiveness of IIoT-based retrofitting for industrial energy efficiency, showcasing a scalable framework that can be applied across various machines and production environments. This study highlights the importance of data-driven decision-making in smart manufacturing, contributing to both cost reduction and sustainability goals in industrial settings.

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Publication Details

Journal
Applied Sciences
Published
2025-03-15
DOI
https://doi.org/10.3390/app15063222
Citations
4
Primary Topic
Energy Efficiency and Management
Type
article
Field-Weighted Citation Impact
1.45

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article

Optimizing Energy and Air Consumption in Smart Manufacturing: An Industrial Internet of Things-Based Monitoring and Efficiency Enhancement Solution

Shahram Hanifi, Gordon Lindsay, Babakalli Alkali, Don McGlinchey
4 citations
Applied Sciences
Energy Efficiency and Management
1.45
article

Optimizing Energy and Air Consumption in Smart Manufacturing: An Industrial Internet of Things-Based Monitoring and Efficiency Enhancement Solution

Shahram Hanifi, Gordon Lindsay, Babakalli Alkali, Don McGlinchey
article en
4 citations

Abstract

The rising cost of energy and the urgent need for sustainability have driven industries to adopt smarter solutions for monitoring and optimizing resource consumption. In this study, we present an Industrial Internet of Things (IIoT)-based approach for real-time energy and air consumption monitoring in manufacturing, focusing on a legacy Turret Punch Press (TPP) at Mitsubishi Electric Air Conditioning Systems Europe Ltd. (M-ACE). Due to its age and lack of modern monitoring capabilities, the machine was suspected to be inefficient, requiring a retrofitting strategy for improved transparency and optimization. To address these challenges, a structured IIoT-enabled monitoring system was deployed, integrating KEYENCE MP-F series sensors, an energy monitoring module, and Ethernet communication via Modbus TCP/IP. A comprehensive dashboarding system was developed for real-time visualization and analysis of energy consumption trends, identifying inefficiencies and optimizing machine usage. The data-driven approach revealed significant energy savings of up to 56% and uncovered hidden inefficiencies, including a persistent air leak. By implementing a smart shut-off valve triggered by real-time power consumption data, unnecessary air leakage was eliminated, reducing compressed air waste and overall energy costs. The results demonstrate the effectiveness of IIoT-based retrofitting for industrial energy efficiency, showcasing a scalable framework that can be applied across various machines and production environments. This study highlights the importance of data-driven decision-making in smart manufacturing, contributing to both cost reduction and sustainability goals in industrial settings.

Applied SciencesVol. 15(6)
Glasgow Caledonian University (GB)
Innovate UK
Openalex Percentile: Top 20%
Energy Efficiency and Management
1.45
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