Dyeing and finishing wastewater treatment in China: State of the art and perspective

Hong, Chen; Yu, Xin; Wang, Xiaonuan; He, Yueling; Zhang, Chengji; Xue, Gang; Liu, Zhenhong; Lao, Hongbiao; Song, Hualong; Chen, Wei; Qian, Yanlong; Ai, Zhang; Li, Xiang

doi:10.1016/j.jclepro.2021.129353

Cited by 58 publications

(14 citation statements)

References 93 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Solar thermal storage insulation fibres are used to achieve thermal insulation. [8] According to the hypothesis, sunlight's visible and infrared rays are captured by the fibres, which subsequently radiate heat to the body, preserving body heat. Far infrared fibre performs better in terms of thermal insulation than sunlight thermal storage fibre.…”

Section: Temperature Control Fabricmentioning

confidence: 99%

SMART Wearable Fabric using Electronic Textiles – A Review

Hassabo

Gamal

Sediek

et al. 2022

J.Text.color. pol. sci.

View full text Add to dashboard Cite

LECTRONIC textiles, often known as SMART clothing, are fabrics that may be incorporated with digital components such as a battery, a light (including small processors), and electronics. SMART textiles are materials that have been created with innovative technology to offer value to the wearer. The growth of sensor technologies, nanotechnologies, embedded systems, wireless communication technologies, and downsizing enable the development of SMART systems to monitor human activities. Wearable computing envisions future electronic devices as an intrinsic part of our everyday attire Wearable systems will be distinguished by their capacity to automatically identify their own user's activity and behavioral status, as well as the circumstances surrounding her or him. and to utilize this information to alter the system's setup and operation. It is capable of detecting odd and unexpected events by monitoring bodily measurements as well as other signs. This study examines current breakthroughs in SMART textiles and their applicability in several industries.

show abstract

Section: Temperature Control Fabricmentioning

confidence: 99%

SMART Wearable Fabric using Electronic Textiles – A Review

Hassabo

Gamal

Sediek

et al. 2022

J.Text.color. pol. sci.

View full text Add to dashboard Cite

show abstract

“…The textile industry was one of the most water-consuming and key industrial branches, especially in developing countries. In China, the amount of textile wastewater, around 80% of which is from the printing and dyeing process [1], ranked third among all the 41 industries and accounted for 10.1% [2]. Generally, the discharged textile wastewater was treated either by on-site treatment plants in the factory or a combination of factories and urban wastewater treatment plants (WWTPs) to meet the wastewater discharge standard.…”

Section: Introductionmentioning

confidence: 99%

“…Reactive dye is the most important dyeing class for cellulosic fibers [4], up to 10-50% of which would flow into wastewater in the dyeing process [5]. ANL is an important intermediate in syntheses of benzidine azo dyes [2] as well as a product derived from the biotransformation of azo dyes [6]. ANL has been listed as a priority pollutant by the Environmental Protection Agency of the United States due to its carcinogenic and mutagenic effects [7].…”

Section: Introductionmentioning

confidence: 99%

“…During the Fe 2+ /PAA/H 2 O 2 process, PAA played a key role within the initial 5 s and H 2 O 2 became the dominant oxidant afterward due to the much higher reaction rate of PAA with Fe 2+ (>650 times) compared to that of H 2 O 2 [27]. Activation of PAA by Fe 2+ may primarily generate •OH, CH 3 C(O)O•, and Fe(IV) according to reactions (1)(2)(3)(4)(5) [27,28]. Moreover, •OH would also react with PAA and H 2 O 2 to generate secondary radicals, (e.g., CH 3 C(O)•, CH 3 C(O)OO•, and HO 2 •) via reactions (6)(7)(8)(9), and the reaction rate of •OH with PAA was much higher compared with H 2 O 2 [27,29].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of Fe2+/Peracetic Acid to Degrade Three Typical Refractory Pollutants of Textile Wastewater

et al. 2022

View full text Add to dashboard Cite

In this work, the degradation performance of Fe2+/PAA/H2O2 on three typical pollutants (reactive black 5, ANL, and PVA) in textile wastewater was investigated in comparison with Fe2+/H2O2. Therein, Fe2+/PAA/H2O2 had a high removal on RB5 (99%) mainly owing to the contribution of peroxyl radicals and/or Fe(IV). Fe2+/H2O2 showed a relatively high removal on PVA (28%) mainly resulting from ·OH. Fe2+/PAA/H2O2 and Fe2+/H2O2 showed comparative removals on ANL. Additionally, Fe2+/PAA/H2O2 was more sensitive to pH than Fe2+/H2O2. The coexisting anions (20–2000 mg/L) showed inhibition on their removals and followed an order of HCO3− > SO42− > Cl−. Humic acid (5 and 10 mg C/L) posed notable inhibition on their removals following an order of reactive black 5 (RB5) > ANL > PVA. In practical wastewater effluent, PVA removal was dramatically inhibited by 88%. Bioluminescent bacteria test results suggested that the toxicity of Fe2+/PAA/H2O2 treated systems was lower than that of Fe2+/H2O2. RB5 degradation had three possible pathways with the proposed mechanisms of hydroxylation, dehydrogenation, and demethylation. The results may favor the performance evaluation of Fe2+/PAA/H2O2 in the advanced treatment of textile wastewater.

show abstract

“…As the demand for functional clothing and accessories has been steadily growing, the dyeing and finishing quality is crucial for maintaining the competitiveness of the dyeing and finishing industry [1]. In the past, research conducted on dyeing and finishing primarily focused on poor product quality or environmental issues [2][3][4]. The abnormal operation of dyeing machines and the operating conditions have received less attention in the literature.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Based System for the Detection of Entanglement in Dyeing and Finishing Processes

Wang

2022

Sustainability

View full text Add to dashboard Cite

Many dyeing and finishing factories generally use old-fashioned dyeing machines. A key issue when using these machines is that the dyeing tank cannot detect entanglement problems, which may result in a lower dyeing quality. In this paper, imbalanced data with ensemble machine learning, such as Extreme Gradient Boosting (XGBoost) and random forest (RF), are integrated to predict the possible states of a dyeing machine, including normal operation, entanglement warning, and entanglement occurrence. To verify the results obtained using the proposed method, we worked with industry−academia collaborators. We collected 1,750,977 pieces of data from 1848 batches. The results obtained from the analysis show that after employing the Borderline synthetic minority oversampling technique and the Tomek link to deal with the data imbalance, combined with the model established by XGBoost, the prediction accuracy of the normal operation states, entanglement warning, and entanglement occurrence were 100%, 94%, and 96%, respectively. Finally, the proposed entanglement detection system was connected with the factory’s central control system using a web application programming interface and machine real-time operational parameter data. Thus, a real-time tangle anomaly warning and monitoring system was developed for the actual operating conditions.

show abstract

Dyeing and finishing wastewater treatment in China: State of the art and perspective

Cited by 58 publications

References 93 publications

SMART Wearable Fabric using Electronic Textiles – A Review

SMART Wearable Fabric using Electronic Textiles – A Review

Evaluation of Fe2+/Peracetic Acid to Degrade Three Typical Refractory Pollutants of Textile Wastewater

Machine-Learning-Based System for the Detection of Entanglement in Dyeing and Finishing Processes

Contact Info

Product

Resources

About