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Greenhouse Gas Emissions and Mitigation Strategies for Synthetic Textile Dyeing and Finishing Sector
GAN Jingjing, ZHANG Siyuan, XU Chenye, Lü Jiabin, GUO Ru, XU Jianlin, TIAN Qing, LI FangChina, as the world ’ s largest producer and consumer of synthetic textiles, faces ustainability challenges in the synthetic textile dyeing and finishing sector. The greenhouse gas (GHG) emission profiles and potential mitigation pathways for this sector require further classification. This study analyzed the GHG emissions from the synthetic textile dyeing and finishing process of eight representative life cycle assessment ( LCA ) cases. To explore the potential for emission mitigation, four mitigation strategies were developed, resulting in the formulation of 11 scenarios. The average GHG emissions per kilogram of synthetic textiles from the dyeing and finishing process were 3. 06 kg CO2 equivalent (eq) (ranging from 1. 83 to 5. 34 kg CO2 eq), primarily contributed by the dyeing unit and resulting from energy consumption (steam and electricity). The scenario analysis suggested that in the business-as-usual scenario, GHG emissions from the dyeing and finishing sector could reach 17. 79 Mt CO2 eq by the year of 2060. Emission mitigation potentials across scenarios ranged from 35. 72% to 71. 65%. In the most optimistic scenario, emissions could be reduced to as low as 5. 04 Mt CO2 eq by the year of 2060. These findings provide valuable insights to identify key mitigation pathways for the synthetic textile dyeing and finishing sector.
Synthesis of PEG-Modified PET Masterbatch: Preparation of Low-Temperature Easy-Dyeing, High-Fastness PET Fibers Based on Phase Domain Size Regulation
XIAO Yongbing , XU Chaochen , SHEN Wufeng , ZHANG Shengming , CHEN Xiangling , JI Peng, WANG Chaosheng , WANG HuapingAbstract: Polyethylene terephthalate (PET) fibers are the largest category of chemical fibers and are widely used. However, the dyeing of PET fibers requires high temperature and pressure (130 ℃ and 0. 2 MPa), and the dyeing process consumes huge amounts of energy. Existing studies have shown that the dyeing ability of PET is directly related to the size of the amorphous region, which determines the external conditions for dyeing. In this research, we synthesized a series of low-temperature easydyeing masterbatches, PET-co-polyethylene glycol (PETEG), using polyethylene glycol (PEG) with different number-average molecular masses Mn and additive amounts. The phase domain size of the amorphous region of PET fibers was regulated via the masterbatch method. The relationship between the phase domain size and dyeing performance was explored from three perspectives: theamount of masterbatch, type of masterbatch, and PEG relative molecular mass. The results indicate that the fiber sample with PEG (Mn = 2 000 g / mol) at a mass fraction of 20% modified masterbatch has a smaller crystalline lamellar thickness (5. 59 nm) and a larger interlamellar amorphous layer thickness (6. 43 nm). The increase in the long periodand lamellar inclination angle results in a looser structure, allowing small molecule dyes to diffuse into the fibers more easily. The dye-uptake increases from 63. 21% to 92. 66% at100 ℃ with the addition of the masterbatch. Additionally, the dye-uptake of the modified fibers increases with the relative molecular mass of PEG and the mass fraction of the masterbatch. All modified fibers achieve a staining color fastness of grade 4 or higher. This research demonstrates a simple masterbatch method that enables atmospheric pressure dyeing and provides a practical solution for efficient, low-temperature, and low-energy dyeing of PET fibers.
A Hybrid of RRT∗ and TD3 Deep Reinforcement Learning Algorithm for UAV Path Planning in 3D Partially Unknown Environments
HE Yanxi, QI Jie, WU NailongTo guide an unmanned aerial vehicle ( UAV) flying in complex three-dimensional ( 3D ) environments with unknown obstacles, a novel UAV path planning algorithm named IRRT∗-2TD3 is proposed. The algorithm combines the rapidly-exploring random tree star (RRT∗) algorithm with the twin delayed deep deterministic policy gradients (TD3) algorithm ( a deep reinforcement learning algorithm). By employing exploration strategies from reinforcement learning, IRRT∗-C2TD3 improves the RRT∗ algorithm. IRRT∗-C2TD3 is a two-stage path planning algorithm comprising pre-planning and real-time planning. It performs re-planning of paths by generating paths based on geometric connections toward the goal and smoothing them using cubic B-spline curves. By designing the network architecture and reward function of the TD3 algorithm, real-time planning in unknown environments is achieved based on the pre-planned path from the first stage. Simulation results show that IRRT∗-C2TD3 demonstrates better path planning performance in 3D partially unknown environments than RRT∗-C2TD3, M-C2TD3 and MODRRT∗ algorithms.
Textile Manufacturing Carbon Emission Analysis Method Based on Holographic Process Model
GAO Jun, LI Jie, BAO Jinsong, ZHANG DanThe textile industry, while creating material wealth, also exerts a significant impact on the environment. Particularly in the textile manufacturing phase, which is the most energy-intensive phase throughout the product lifecycle, the problem of high energy usage is increasingly notable. Nevertheless, current analyses of carbon emissions in textile manufacturing emphasize the dynamic temporal characteristics while failing to adequately consider critical information such as material flows and energy consumption. A carbon emission analysis method based on a holographic process model (HPM) is proposed to address these issues. First, the system boundary in the textile manufacturing is defined, and the characteristics of carbon emissions are analyzed. Next, an HPM based on the object-centric Petri net (OCPN) is constructed, and simulation experiments are conducted on three different scenarios in the textile manufacturing. Subsequently, the constructed HPM is utilized to achieve a multi-perspective analysis of carbon emissions. Finally, the feasibility of the method is verified by using the production data of pure cotton products from a certain textile manufacturing enterprise. The results indicate that this method can analyze the impact of various factors on the carbon emissions of pure cotton product production, and by applying targeted optimization strategies, carbon emissions have been reduced by nearly 20%. This contributes to propelling the textile manufacturing industry towards sustainable development.
Gray Fabric Defect Detection Based on Statistical Template Matching
LI Saisai, YU Haiyan, WANG JunhuaTo address the high cost of online detection equipment and the low adaptability and accuracy of online detection models that are caused by uneven lighting, high noise, low contrast and so on, a block-based template matching method incorporating fabric texture characteristics is proposed. Firstly, the template image set is evenly divided into N groups of sub-templates at the same positions to mitigate the effects of image illumination, reduce the model computation, and enhance the detection speed, with all image blocks being preprocessed. Then, the feature value information is extracted from the processed set of subtemplates at the same position, extracting two gray-level cooccurrence matrix (GLCM) feature values for each image block. These two feature values are then fused to construct a matching template. The mean feature value of all image blocks at the same position is calculated and used as the threshold for template detection, enabling automatic selection of template thresholds for different positions. Finally, the feature values of the image blocks in the experimental set are traversed and matched with subtemplates at the same positions to obtain fabric defect detection results. The detection experiments are conducted on a platform that simulates a fabric weaving environment, using defective gray fabrics from a weaving factory as the detected objects. The outcomes demonstrate the efficacy of the proposed method in detecting defects in gray fabrics, the mitigation of the impact of uneven external lighting on detection outcomes, and the enhancement of detection accuracy and adaptability.
Effect of SiO2 Nanoparticles on Durability of Polyurethane-Coated Polyamide Tent Fabric
AFRIDA Noboni Rawnak, WANG Yiming, ZHOU Chuan, WEN Qingwen, LI Ni, SHAO XiaoqiangIn this paper, polyamide (PA) woven fabric was used as the base fabric, and polyurethane (PU) solution containing silica nanoparticles (PU@SiO2 NPs) was used as the coating solution to prepare composite tent fabric (PA /PU@SiO2 ). The morphology, structure, and durability of the tent fabric under ultraviolet ( UV) radiation, waterstained, or thermal conditions were investigated. The results show that compared with PA / PU fabric without SiO2 NPs, when the mass fraction of SiO2 NPs in PU coating is 5%, the air permeability of PA / PU@SiO2 fabric decreases from about 7. 5 to 6. 0 nm / s, while the reflectivity to UV-visible light is significantly improved. The surface wettability decreases, as indicated by the average water contact angle (WCA) on PA / PU@SiO2 remaining stable at 47° after 9 min. After thermal treatment, the PA / PU@SiO2 fabric shows superior mechanical stability. The degradation rate of the tensile strength is only 6. 3%, approximately half that of the PA / PU fabric. Meanwhile, the elongation at break increases to 98. 9%, compared to 61. 8% for the PA / PU fabric.
QSAR Model of Activated Carbon Adsorption Based on Langmuir Adsorption Isotherm
TAN Ting, WEI Qunshan, LIU Qiong, SHEN Zhemin, SONG Xinshan, WANG Yuhui, CHARLES Nzila, CHRISTOPHER W. K. ChowFrom a quantum chemistry standpoint, the impact of the structural properties of the compounds on activated carbon's adsorption ability was specifically investigated. The compounds whose adsorption behavior followed the Langmuir isotherm model were selected as the research objects. An optimal quantitative structure-activity relationship(QSAR) model was built by using the multiple linear regression(MLR) method, with the saturation adsorption capacity Qm from the Langmuir adsorption isotherm as the response variable and the structural parameters of 50 organic compounds as independent variables. The results show that the optimal model exhibits good stability, reliability and robustness, with a regression coefficient R2 of 0. 88, an adjusted regression coefficient Radj2 of 0. 87, an internal validation coefficient q2 of 0. 81, and an external validation coefficient Qext2 of 0. 68. The variables included in the optimal model indicate that the polarity of the molecule, the molecular potential energy, and the stability and bonding strength of the organic compound are the main factors affecting the adsorption on activated carbon. The results provide key information for predicting the adsorption capacity of organic compounds on activated carbon and offer a theoretical reference for adsorption treatment in water environments.
Fuzzy Adaptive Admittance Control of Hexapod Wheeled-Legged Robot Based on Real-Time Estimation
CHEN Mengqi, LI Yan, XU YangA fuzzy adaptive admittance control method based on real-time estimation is proposed for the motion of the hexapod wheeled-legged robot in various environments. Firstly, the mechanical structure of the robot is designed, and a control system framework is proposed according to the different motion environments. To address the adaptability issue of the robot foot contact with the ground, a position-based admittance control method is proposed. Secondly, to improve the tracking performance of the robot foot contact force when the ground environment changes, a fuzzy adaptive admittance parameter adjustment method is proposed. Furthermore, to address the problem of sudden changes in the tracking difference of the foot contact force when the ground environment changes, a real-time estimation method is proposed to estimate the dynamic foot contact force. Finally, a simulation experiment is conducted in MATLAB and Simscape to verify the effectiveness of the robot motion control system, admittance control, fuzzy adaptive admittance parameters adjustment, and the realtime estimation method. Through multi-scenario experiments with the robot prototype, the control method demonstrates its effectiveness and adaptability in various environments.
Distributed Feedback Quadratic Filter for Estimating Moving Target in Time- Varying Non-Gaussian Systems with Limited Sensing Range
SUO Jinghui, ZHU XuefengThis research considers the tracking problem of a moving target in distributed sensor etworks with a limited sensing range (LSR) affected by non-Gaussian noise. In such sensor networks, observation loss due to LSR is a prevalent issue that has received insufficient attention. We introduce a time-varying random variable to describe whether the sensor observes a moving target at each moment. When a single sensor node is unable to receive information from other nodes, it cannot update its state estimation of the moving target once the target moves beyond this node ’ s observation range. We propose an information flow topology within distributed sensor networks to facilitate the reception of prior state estimation data transmitted by neighboring nodes. Based on this information, a quadratic distributed estimator is designed for each sensor, and an output injection term is introduced to handle unstable systems. Finally, a numerical example is provided to illustrate the effectiveness of the proposed control scheme.
Multi-Modal Multi-View 3D Hand Pose Estimation
WANG Hao, WANG Ping, YU Haoran, DING Dong, XIANG WeimingWith the rapid progress of the artificial intelligence (AI) technology and mobile internet, 3D hand pose estimation has become critical to various intelligent application areas, e. g., human-computer interaction. To avoid the low accuracy of single-modal estimation and the high complexity of traditional multi-modal 3D estimation, this paper proposes a novel multi-modal multi-view (MMV) 3D hand pose estimation system, which introduces a registration before translation ( RT )-translation before registration (TR) jointed conditional generative adversarial network ( cGAN ) to train a multi-modal registration network, and then employs the multi-modal feature fusion to achieve high-quality estimation, with low hardware and software costs both in data acquisition and processing. Experimental results demonstrate that the MMV system is effective and feasible in various scenarios. It is promising for the MMV system to be used in broad intelligent application areas.
MTI: a Motor Imagery Strategy Assisted by Tactile Imagery with Strong Correlation
CHEN Jiale, FEI ShengweiIn order to improve the performance of the braincomputer interface system of motor imagery ( MI ), the optimization of the MI strategy is an effective means. Therefore, this paper proposes a motor-tactile imagery (MTI) strategy to improve the classification accuracy of the MI-based brain-computer interface (BCI) system by adding a corresponding strong correlation of tactile imagery to each imaginary action of the MI. Electroencephalogram (EEG) signals generated by different strategies were collected, and the corresponding classification accuracy was obtained. The experimental results showed that the performance of the MTI-training group (84. 08% ± 7. 36%) was significantly better than that of the MI group and the MTI-withouttraining group. The MTI strategy proposed in this study can significantly improve the performance of BCI.
Features of Prime Attributes in a Relation Scheme
XU Yulu, LIU Guohua, YU Xiaoxue, LIU Changqi, ZHU Dongyan, ZHANG Limeng, HE SongdaNormal forms have a significant role in the theory of relational database normalization. The definitions of normal forms are established through the functional dependency (FD) relationship between a prime or nonprime attribute and a key. However, determining whether an attribute is a prime attribute is a nondeterministic polynomial-time complete (NP-complete) problem, making it intractable to determine if a relation scheme is in a specific normal form. While the prime attribute problem is generally NP-complete, there are cases where identifying prime attributes is not challenging. In a relation scheme R(U,F) , we partition U into four distinct subsets based on where attributes in U appear in F:U1 ( attributes only appearing on the left-hand side of FDs ), U2 (attributes only appearing on the right-hand side of FDs), U3 (attributes appearing on both sides of FDs ), and U4 (attributes not present in F ). Next, we demonstrate the necessary and sufficient conditions for a key to be the unique key of a relation scheme. Subsequently, we illustrate the features of prime attributes in U3 and generalize the features of common prime attributes. The findings lay the groundwork for distinguishing between complex and simple cases in prime attribute identification, thereby deepening the understanding of this problem.
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Journal of Donghua University (English Edition)
Started: 1984
Supervised by: Ministry of Education, China
Administered by : Donghua University
Editor-in-Chief: Zhu Meifang
CN: 31-1920/TS
ISSN: 1672-5220