Textile and apparel industrial processes generate a huge amount of greenhouse gas emissions, which is a severe environmental issue for China. Aiming at greenhouse reduction, a carbon footprint calculation method is presented. In carbon footprint calculations, allocation methodology is among the most significant and controversial issues; it can be a major reason for the LCA uncertainty and robustness caused. What is more, allocation methodology impacts directly on the preparation of data collection and system boundary. Different outcomes can be achieved even for apparently similar systems by using a different allocation approach. Textile production has a large range of production process. During textile production process, it may be a single product production with co-products. The current CF calculation only evaluates GHGs emissions at product or plant level, so the difference of the technology on different processes cannot be deduced. Hence, the choice of proper allocation methodology is a crucial issue to be considered in textile and apparel industry. In this paper, based on characteristics of textile and apparel industry, process-level allocation methodology in textile and apparel industry was put forward. The application of allocation methodology was investigated and analyzed with a case study on cotton T-shirts. Firstly, case study results show that greenhouse gases of the ironing and sewing process are the two largest emissions (ironing, 40.82%, and sewing, 34.85%, respectively). Energy-saving refrigeration equipment needs to be chosen to reduce the greenhouse gases significantly. Secondly, for most processes, CF of S2 (auxiliary CF) accounts for the highest proportion of total CF. Preferred to S1, more attention should be paid to reduce the S2 emissions. Thirdly, GHGs emissions of the polo shirt in the sewing process are significantly higher than that of the T-shirt in the sewing stage (T-shirt, 0.167 kg CO₂ eq/piece, and polo shirt, 0.371 kg CO₂ eq/piece, respectively). This is the consequence that polo shirt’s style and structure determine the complexity of its sewing process. Finally, based on the pearson correlation coefficient, T-shirt production (kg) has a significant negative linear correlation (correlation coefficient: −0.868) with the CF (kg CO₂ eq/kg T-shirts), the similar with that (correlation coefficient: −0.963) of all production. Improving the textile and garment production efficiency is significant to reduce the CF of products (per mass) by technological innovation and management optimization. In this study, we demonstrate that the process-level allocation is a feasible method, and can serve as the basis for a textile-specific allocation approach in LCA. Process-level allocation may help to address textile allocation problems and might lead to more detailed LCA results for products. We recommend broad applications and testing of this new allocation approach.

Easy care for clothing is increasingly important, but the major problem is still the severe wrinkles formed on garments after machine washing. In this research, orthogonal tests and the analysis of variance (ANOVA) at a 95% confidence level were used to determine the critical parameters that affect fabric smoothness. We developed a video capturing and processing system for dynamic measurements of fabric in a top-loading washer to find the relationship between fabric movement and wrinkling performance during laundering. The ANOVA results showed that the smoothness of woven cotton fabrics was mainly affected by the washing load and the drum rotating speed, with p values of 0.000 and 0.036, respectively. Additionally, a relation models for fabric movements suggested by a curvilinear regression analysis indicated that the main causes of fabric wrinkling during top-loading washing were liquor ratio and the centrifugal force imposed on the fabric. The smoothness grade increased with the decrease of washing load and drum rotating speed, since these settings led to lower liquor ratio with a constant water volume and less stress on the fabric. The results of this study provide valuable information for the optimization of washing programs for fabric smoothness.

A mathematical model was developed to describe the processes of coupled heat and mass and momentum transfer occurring in convective drying of 100% porous cotton fabric. In this model, the variation in both air and fabric properties were expressed as a function of temperature and moisture content. The resulting systems of transient non-linear partial differential equations in the space-time domain together with the set of initial values and boundary conditions were numerically solved by utilizing the finite difference method. A series of laboratory drying experiments were performed using a computer-controlled dehydration system, which was developed to obtain data of drying kinetics under varying operating conditions for the validation of the predicted results from the proposed model. The experimental results has shown a very good agreement with the predictions of the model, which indicates that the proposed numerical model can be used with confidence as a tool in optimizing the design and operation of the fabric drying system. A parametric study was performed using the modeling tool has demonstrated the impact of key operational parameters on the drying kinetics, which has significant meaning on improving fabric’s appearance smoothness and decreasing the damage. It is expected that the model can be applied for other fiber products and processes involving similar phenomena.

The domestic tumble dryers are becoming indispensable household appliances and responsible for up to 10% of the total residential energy use in developed countries. However, their energy efficiency is low. In this paper, the development of a multi-sensor computer-controlled prototype platform for fabric drying is described for improving the efficiency of dryers. The prototype platform enables the real-time control and recording of key drying parameters including heater power, air flow velocity, rotating speed of drying drum, and drying cycle time. These parameters are automatically adjusted according to the exhaust air humidity instead of the temperature which is used traditionally. Additionally, a new drying model of dividing the drying process into four stages based on the humidity of the exhaust air has been investigated to further increase the energy saving. The performance of this staged drying model is experimentally evaluated in respect to energy consumption, drying time, and the smoothness of fabric after drying. The results clearly indicate that the staged controlling of heating power input not only decreases energy consumption by 21.5%, but also improves the fabric smoothness by 0.9 grade compared to using a single heating power input for the whole drying process. The research outcome can enable the design and production of new dryers that are more energy efficient and lead to dried clothes that require less ironing, which in turn further reduces energy consumption.

The transverse motion of fabrics in domestic tumble dryers plays an important role in fabric heat and mass transfer using hot air. However, owing to a lack of effective and accurate experimental methods, little attention has been paid to this dynamic behavior. This paper describes how a high-speed detection system was utilized to track a tracer fabric dyed yellow as it was tumbled amongst other fabrics under different drying conditions, with the aim of identifying transverse fabric motion in a domestic tumble dryer. The experimental results demonstrate that fabric motion is complicated and can be affected by the size of fabrics as well as by drying load mass. Most fabrics inhabited two main regions, the bulk region and the disperse region. Increasing the size of the disperse region, where fabrics are projected into the air by the motion of the rotary drum, also expands the contact area between the fabric and the surrounding air, and augments the mixing motions of textiles. This contributes positively to the mass and heat transfer, and to the prevention of wrinkling. A higher drying efficiency together with a better smoothness appearance was observed with a fabric size of 80 cm $\times$ 80 cm and a mass of 3 kg. The high-speed detection system identified that specific ranges of fabric size and load mass led to increased fabric mixing and air-fabric contact.

In order to investigate the impact of the repeated drying in dryer on physical properties of cotton fabric, smoothness appearance (SA), dimensional shrinkage, bending stiffness, Scanning electron microscopy (SEM), thermo-gravimetric analyzer (TGA) and x-ray diffraction (XRD) were employed. Results showed that smoothness appearance grade of cotton fabrics significantly depended on drying cycles during first 15 drying cycles. The dimensional shrinkage of cotton fabric was slight and had no relationship with drying cycles, and the shrinkage in the warp direction (1.29-1.71%) is higher than that of weft direction (0.002-0.045%). The bending stiffness was enhanced by drum-drying based on the bending stiffness data. And found that drying had little effect on thermal stability and crystallinity of cotton fibers. This indicated that drying only affected the appearance physical properties of cotton fabric such as appearance smoothness, dimensional stability, did not change composition and molecular structure. And from multiple levels including fabric-level, yarn-level and fiber-level, explained reasons of fabric properties change (namely, damage behavior). Moreover, this finding is not only helpful to understand of damage mechanisms of cotton fabrics drying in dryer, but also provided reference for dryer manufacturers to accurately specify the types of clothing that can be dried by using a dryer.