Analysis of Production System Automation Effects on Energy Efficiency in Manufacturing Industry

Ahmad Fauzan Pratama; Gede Arya Rachman; Paul R. Wilson

doi:10.70716/reswara.v4i1.367

Authors

Ahmad Fauzan Pratama Ahmad Fauzan Pratama Department of Mechanical Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia Author
Gede Arya Rachman Department of Mechanical Engineering, Politeknik Negeri Bali, Badung, Indonesia Author
Paul R. Wilson Department of Mechanical and Industrial Engineering, University of Sheffield, Sheffield, United Kingdom Author

DOI:

https://doi.org/10.70716/reswara.v4i1.367

Keywords:

production automation, energy efficiency, manufacturing industry, Industry 4.0, sustainable production

Abstract

The Brantas River Basin plays a strategic role in supporting domestic, agricultural, industrial, and ecological functions in East Java, Indonesia. Rapid urbanization, land-use change, and increasing anthropogenic activities have exerted significant pressure on river water quality, necessitating comprehensive and reliable assessment methods. This study aims to evaluate the water quality status of the Brantas River using the Pollution Index (PI) approach, which has been widely adopted for river quality assessment in Indonesia and other countries. Secondary water quality data were analyzed, encompassing key physical and chemical parameters, including biochemical oxygen demand, chemical oxygen demand, dissolved oxygen, total suspended solids, nutrients, and selected heavy metals. The PI values were calculated based on Indonesian water quality standards and interpreted according to established classification criteria. The results indicate that most monitoring points along the Brantas River fall into the moderately to heavily polluted categories, reflecting cumulative pollution loads from domestic wastewater, industrial discharges, and agricultural runoff. Comparative analysis with previous studies confirms the consistency of PI-based assessments and highlights spatial variability influenced by land use and human activities. This study demonstrates that the Pollution Index method is an effective and practical tool for evaluating river water quality and provides a scientific basis for pollution control strategies and sustainable river basin management in the Brantas River Basin.

References

Andrea Matta. (2019). Automation technologies for sustainable production [TC spotlight]. IEEE Robotics & Automation Magazine, 26(1), 8–10. https://doi.org/10.1109/MRA.2019.2892313

Benedetti, M., Cesarotti, V., & Introna, V. (2015). Improving energy efficiency in manufacturing systems—Literature review and analysis of the impact on the energy network of consolidated practices and upcoming opportunities. In Energy efficiency improvements in smart manufacturing systems (pp. 1–22). IntechOpen. https://doi.org/10.5772/59820

Bratukhin, A., Treytl, A., & Sauter, T. (2013). Energy aware manufacturing environments. In Proceedings of the IEEE International Conference on Emerging Technologies and Factory Automation (pp. 1–8). IEEE. https://doi.org/10.1109/ETFA.2013.6647956

Chen, G., Zhang, L., Arinez, J., & Biller, S. (2013). Energy-efficient production systems through schedule-based operations. IEEE Transactions on Automation Science and Engineering, 10(1), 27–37. https://doi.org/10.1109/TASE.2012.2202226

Christiansen, L., Linnenberg, T., Fay, A., & others. (2013). Energieeffizienz in der Fertigung bewerten. atp edition, 55(7–8), 50–58. https://doi.org/10.17560/ATP.V55I07-08.262

Clark, S. M., Wilson, P. R., & Scott, C. P. (2024). Energy efficiency optimization in mechanical systems: Innovative approaches in industrial applications. International Journal of Mechanical, Electrical and Computer Engineering, 1(2), 45–55. https://doi.org/10.61132/ijmecie.v1i2.76

Deepika, C., Taj, K., & Bedar, P. (2024). Automation in production systems: Enhancing efficiency and reducing costs in mechanical engineering. Nanotechnology Perceptions. https://doi.org/10.62441/nano-ntp.vi.3895

Hakimzoda, M. M., Safarov, I. M., & Khaliyev, M. S.-U. (2024). Automation and sustainable development impact on resource and energy saving in industry. Èkonomika i upravlenie: problemy, rešeniâ, 11, 70–82. https://doi.org/10.36871/ek.up.p.r.2024.11.07.001

Hehenberger, P., Leherbauer, D., Penas, O., & others. (2023). Holistic system modelling and analysis for energy-aware production: An integrated framework. Systems, 11(2), 100. https://doi.org/10.3390/systems11020100

Ioshchikhes, B., Frank, M., & Weigold, M. (2024). A systematic review on expert systems for improving energy efficiency in the manufacturing industry. Energies, 17(19), 4780. https://doi.org/10.3390/en17194780

Ioshchikhes, B., Frank, M., & Weigold, M. (2024). A systematic review on expert systems for improving energy efficiency in the manufacturing industry. Preprints. https://doi.org/10.20944/preprints202408.1783.v1

Klačková, I. (2025). Energy efficiency of automated manufacturing systems: Innovations, challenges and the future of industrial energy. MM Science Journal, 2025(3), 2024109. https://doi.org/10.17973/mmsj.2025_03_2024109

Kreitlein, S., Rackow, T., & Franke, J. (2014). E|Benchmark—Approaches and methods for assessing the energy efficiency of the industrial automated product manufacturing. Applied Mechanics and Materials, 655, 15–22. https://doi.org/10.4028/www.scientific.net/AMM.655.15

Li, Y., Chang, Q., Ni, J., & Xiao, G. (2018). Event-based supervisory control for energy efficient manufacturing systems. IEEE Transactions on Automation Science and Engineering, 15(1), 92–103. https://doi.org/10.1109/TASE.2016.2585679

Lu, J., Qian, Y. J., Yang, Y., & others. (2022). Can artificial intelligence improve the energy efficiency of manufacturing companies? Evidence from China. International Journal of Environmental Research and Public Health, 19(4), 2091. https://doi.org/10.3390/ijerph19042091

Lu, R., Lu, Y., Chen, Y.-W., & others. (2019). Research on effect on energy efficiency of manufacturing technological progress based on the differential analysis of different kinds of energy-consuming industries. Journal of Industrial Engineering and Engineering Management, 33(3), 1–10. https://doi.org/10.13587/j.cnki.jieem.2019.03.002

Mazhar, S. S., Khan, F. S., Akhlaq, N., & others. (2025). Industry 4.0. In Advances in environmental engineering and green technologies (pp. 350–370). IGI Global. https://doi.org/10.4018/979-8-3693-9471-7.ch019

Mechs, S., Müller, J. P., Lamparter, S., & others. (2012). Networked priced timed automata for energy-efficient factory automation. In Proceedings of the American Control Conference (pp. 5515–5520). IEEE. https://doi.org/10.1109/ACC.2012.6315349

Mechs, S., Lamparter, S., Peschke, J., & others. (2013). Start-stopp-automatik für nicht-produktivphasen. atp edition, 55(6), 34–41. https://doi.org/10.17560/ATP.V55I06.255

Morales Rovalino, V. F. (2025). Análisis energético y exergético de procesos de manufactura automatizados en la industria 4.0: Un enfoque integral. Polo del Conocimiento, 10(6), 120–135. https://doi.org/10.23857/pc.v10i6.9840

Rachman, G. A., Saiful, R., Ariati, R., & others. (2025). Energy efficiency strategy in manufacturing industry through implementation of automation technology. Journal of Mechanical Engineering, Manufactures, Materials and Energy, 9(1), 1–12. https://doi.org/10.31289/jmemme.v9i1.13998

Wilson, P. R., Clark, S. M., & Scott, C. P. (2024). Energy efficiency optimization in mechanical systems: Innovative approaches in industrial applications. International Journal of Industrial Innovation in Mechanical Engineering, 1(2), 30–40. https://doi.org/10.61132/ijiime.v1i2.61

Xia, T., An, X., Yang, H., & others. (2023). Efficient energy use in manufacturing systems—Modeling, assessment, and management strategy. Energies, 16(3), 1095. https://doi.org/10.3390/en16031095

Zhu, M. (2024). Maximizing green economic efficiency: The role of robot adoption in shaping the future of manufacturing. Research Square Preprint. https://doi.org/10.21203/rs.3.rs-4005193/v1