Abstract:

Objective Textile manufacturing is reported to be the most energy-intensive in the entire lifecycle, and is associated to high energy consumption. However, existing methods to carbon emissions analysis in textile manufacturing focus on temporal dynamics while not adequately addressing the importance of critical factors like material circulation and energy usage. Therefore, it is essential to fully utilize neglected information in order to conduct a multi-perspective carbon emissions analysis of textile manufacturing.

Method To enable accurate carbon emissions analysis, this paper proposed a carbon emissions analysis method based on a holographic process model (HPM). The system boundary in the textile manufacturing was defined as the first step, followed by an analysis of the characteristics related to carbon emissions. A HPM based on the objective/centric petri net (OCPN) was constructed to simulate three different in the textile manufacturing of polyester-cotton blended fabrics.The constructed HPM was utilized to achieve a multi-perspective analysis of carbon emissions, and the feasibility of the method was verified using the polyester/cotton fabric production data from a textile enterprise.

Results By extracting and processing textile manufacturing data, a thorough dataset for the HPM was compiled. Utilizing the OCPM mining tool, the HPM was constructed and used to simulate actual textile manufacturing. By using the established HPM, it was possible to carry out carbon emissions analysis from three perspectives, i.e., entire process, each procedure, and influencing factors. From the perspective of the entire process, product 2(a standard polyester/cotton blended fabric) showed the lowest average carbon footprint at 4.74 kg CO_2e/kg, while product 3(a dark-colored polyester/cotton blended fabric) demonstrated the highest at 8.48 kg CO_2e/kg, nearly double that of product 2. Product 1(a light-colored polyester/cotton blended fabric) showed an average carbon footprint of 7.58 kg CO_2e/kg. From the perspective of each procedure, dyeing process was found to be the stage with the largest percentage of carbon emissions in the production of various fabrics. Carbon emissions from dyeing accounted for 50%-55% of the total energy consumption, with differences varying by product. From the perspective of influencing factors, the carbon emissions generated by the electricity consumed in fabric production showed the highest proportion, around 50%. After conducting an accurate carbon emissions analysis, a series of targeted clean production strategies were developed. Both product 1 and product 3 were fabrics in dark color, and the carbon emissions from the dyeing process accounted for a high proportion, nearly 55%. By implementing a single-step dyeing process for these two products, reducing the number of dyeing cycles, the carbon emissions were reduced by 20% while ensuring product quality. Compared to the traditional production process, the carbon emissions of product 1 decreased by 77 537.15 kg CO_2e, and the carbon emissions of product 3 decreased by 62 724.42 kg CO_2e. For product 2, a light-colored fabric, carbon emissions were decreased by streamlining unnecessary processes and reducing equipment downtime, which improved energy efficiency. The carbon emissions of product 2 decreased by 17 278.61 kg CO_2e, representing a reduction rate of 10%.

Conclusion This paper proposed a carbon emission analysis method based on HPM. This method first defined the system boundary in the textile manufacturing and analyzed the characteristics of carbon emissions. Subsequently, HPM based on OCPN was constructed to simulate the textile manufacturing process. Furthermore, the constructed HPM has been utilized to achieve a multi-perspective analysis of carbon emissions. This method was applied to a textile enterprise, and the feasibility of the method was verified using production data of polyester/cotton fabrics from three different scenarios. The results show that HPM outperforms traditional process model based on petri net. And proposed method is conducive to an all-encompassing carbon emissions analysis in textile manufacturing, offering robust support for the eco-transition of textile enterprises. Future work will focus on expanding the application scope of the holographic process model to accommodate a wider variety of textile manufacturing processes.

Key words: textile manufacturing, carbon emission, holographic process model, sustainable development, green manufacturing, industrial intelligence