Smart energy sustainability hub in light of Industry 4.0

Kluczek, Aldona; Buczacki, Aleksander

doi:10.1016/j.egyr.2023.10.048

Cited by 3 publications

(1 citation statement)

References 67 publications

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“…The Bulgarian industry and textile enterprises, in particular, are located at crossroads caused by the great dynamics of technological innovations continuously entering the industry (Oliveira Neto et al 2023; Kluczek and Buczacki 2023;Garcia et al 2024). The efficient conversion and use of energy through cogeneration are well known to provide significant primary energy savings in terms of separate production of the same amount of heat and electricity compared to conventional sources (Saini and Jakhar 2024;Zaporowski 2019;Stós et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Energy balance and analysis of the possibilities of implementing cogeneration in a textile plant

Kostov

2024

Polityka Energetyczna – Energy Policy Journal

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Section: Introductionmentioning

confidence: 99%

Energy balance and analysis of the possibilities of implementing cogeneration in a textile plant

Kostov

2024

Polityka Energetyczna – Energy Policy Journal

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Cooperative stochastic energy management of networked energy hubs considering environmental perspectives

Akbari,

Hashemi-Dezaki,

Martins

2024

Energy Reports

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Sustainable Textile Manufacturing with Revolutionizing Textile Dyeing: Deep Learning-Based, for Energy Efficiency and Environmental-Impact Reduction, Pioneering Green Practices for a Sustainable Future

Yılmaz,

Aksu,

Göçken

et al. 2024

Sustainability

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The textile industry, a substantial component of the global economy, holds significant importance due to its environmental impacts. Particularly, the use of water and chemicals during dyeing processes raises concerns in the context of climate change and environmental sustainability. Hence, it is crucial from both environmental and economic standpoints for textile factories to adopt green industry standards, particularly in their dyeing operations. Adapting to the green industry aims to reduce water and energy consumption in textile dyeing processes, minimize waste, and decrease the carbon footprint. This approach has become crucial in achieving sustainability in textiles following the signing of the Paris Climate Agreement. Important elements of this transformation include the reuse of washing waters used in the dyeing process, the recycling of wastewater, and the enhancement of energy efficiency through necessary methodological and equipment changes. This study analyzes the energy, labor, production, and consumption data since 2011 for a textile factories with four branches located in the Adana Organized Industrial Zone. Among these factories, the one designated as UT1, which has the highest average energy and water consumption compared to the other three branches, is selected. In recent years, the use of artificial intelligence and machine learning technologies in predicting industrial processes has been increasingly observed. The data are analyzed using LSTM (Long Short-Term Memory) and ANN (Artificial Neural Networks) forecasting methods. Particularly, the LSTM algorithms, which provided the most accurate results, have enabled advanced forecasting of electricity consumption in dyeing processes for future years. In 2020, electricity consumption was recorded as 3,717,224 kWh and this consumption was reflected in the total energy cost as TRY 1,916,032. Electricity consumption accounts for 22.34% of total energy consumption, while the share of this energy type in the cost is 43.25%. In the light of these data, the MAPE value for energy consumption forecasts using the LSTM model was 0.45%, which shows that the model is able to forecast with high accuracy. As a result, a solar power plant was installed to optimize energy consumption, and in 2023 60% energy savings were achieved in summer and 25% in winter. The electricity consumption forecasting results have been an essential guide in planning strategic initiatives to enhance factory efficiency. Following improvement efforts aimed at reducing energy consumption and lowering the carbon footprint, significant optimizations in processes and layouts have been made at specific bottleneck points within the facility. These improvements have led to savings in labor, time, and space, and have reduced unit production costs.

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Smart energy sustainability hub in light of Industry 4.0

Cited by 3 publications

References 67 publications

Energy balance and analysis of the possibilities of implementing cogeneration in a textile plant

Energy balance and analysis of the possibilities of implementing cogeneration in a textile plant

Cooperative stochastic energy management of networked energy hubs considering environmental perspectives

Sustainable Textile Manufacturing with Revolutionizing Textile Dyeing: Deep Learning-Based, for Energy Efficiency and Environmental-Impact Reduction, Pioneering Green Practices for a Sustainable Future

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