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Research and Development in Techniques of Dyeing Wastewater Treatment
JING Xiao-hui 1,YOUKe-fei 2,DING Xin-yu 2,CAI Zai-sheng 1(1.School of Chemistry and Chemical Engineering,Donghua University,Shanghai200051,China; 2.School of Chemistry and Chemical Engineering,Nantong University,Nantong226007,China)Reviewof the progress on treating methods of dyeing wastewater is presented,especially the advanced techniques are introduced,suchas membrane extraction,ultrasonic processes,high-energyphysical processes,advanced electrocatalytic oxidaˉtion processes and advanced photocatalytic oxidation processes.The treating trend for the dyeing wastewater is discussed.
Overview of bio-based hydrogel fibers
ZHANG Yumin;LIU Yuxi;WU Yuting;PAN Zhijuan;Hydrogel fibers, which combine the one-dimensional macroscopic structure of fibers with the three-dimensional microscopic structure of hydrogels, have become a prominent research focus in the field of new fiber materials both domestically and internationally. Bio-based hydrogel fibers, in particular, have garnered significant attention due to their excellent biocompatibility and natural degradability. To gain a comprehensive and systematic understanding of the research status and development trends of bio-based hydrogel fibers, this study reviews domestic and international research achievements on various types of bio-based hydrogel fibers, including those based on proteins, cellulose, and alginates. The preparation methods, key properties, and composite advantages of these fibers are detailed. Protein-based hydrogel fibers, represented by silk fibroin and collagen, exhibit remarkable plasticity. Cellulose-based fibers are often constructed through physical cross-linking and are commonly used as reinforcing materials. Composite alginatebased hydrogel fibers exhibit diverse preparation methods and wide applicability. Furthermore, the applications of biobased hydrogel fibers in biomedical fields, such as wound dressings, drug delivery systems, and tissue engineering, are discussed. Their uses in flexible sensing areas, including strain, pressure, temperature, and humidity sensing, are also covered. Finally, the opportunities and challenges in developing hydrogel fibers based on natural biomass materials are presented, providing a valuable reference for further advancements in creating high-performance, multifunctional, and environmentally friendly bio-based hydrogel fibers.
Preparation and application of lignin-modified phenolic resin adhesive
WANG Shixing;Lü Wanwan;ZHAO Runge;GUO Haotian;JIANG Wei;Phenolic resin is an excellent adhesive material, but it has drawbacks such as the emission of toxic substances and the use of non-renewable raw materials. To effectively address these issues, a method of modifying phenolic resin with lignin is proposed. Lignin is modified under alkaline conditions, and then lignin-based phenolic resin is synthesized by the ortho-para substitution reaction of the modified lignin with phenol. The characteristics of phenolic resins prepared with different lignin substitution rates are investigated using Fourier transform infrared spectroscopy(FTIR) and ultraviolet spectrophotometry(UV), and the solid content and bonding strength of the modified adhesives are systematically tested. Through single-factor experiments, the optimal experimental conditions are determined to be an aldehyde-phenol mass ratio of 1.5∶1, a lignin substitution rate of 30%, and an alkali dosage of 15%. Under these conditions, the solid content of the prepared adhesive is 52.33%, and the bonding strength reaches 2.21 MPa. Woodbased panels are bonded using kenaf lignin-modified phenolic resin, industrial lignin-modified phenolic resin, and unmodified phenolic resin adhesives, and the bonding strength of different panels is tested. The results show that the bonding strength of industrial lignin-modified phenolic resin is 56% of that of unmodified phenolic resin, while the bonding strength of kenaf lignin-modified phenolic resin is 79% of that of unmodified phenolic resin, indicating that the prepared kenaf lignin-based phenolic resin is superior to industrial lignin-based phenolic resin.
Design of Simulation System for Buck Converter Based on PID Control
SANG Hui-hui,YANG Yi,SHEN Cai-lin(School of Electrical Engineering,Nantong University,Nantong 226019,China)Mathematical models of Buck converter and PID control algorithm are established through theoretical analysis in this paper.Based on these models,a simulation system for Buck converter based on PID control is designed by using C sharp language.Compared with other simulation systems,this system is convenient for changing simulation parameters whenever necessary and the impact on the output voltage of Buck converter is visible when PID parameters are changed.By using this simulation system can actual debugging time be saved.
Intelligent Manufacturing of High-End Equipment in the Era of "5G+Industrial Internet"
CHEN Xiaomin;ZHAO Taotao;YUAN Xueteng;LIU Xiaodong;In the high-end equipment intelligent manufacturing scenario, the communication networks should be reliable and fast, and can support the massive connection of sensors. With the knowledge of 5 G and its advantages,this paper summarizes the development and key technologies of intelligent manufacturing, and further analyzes the framework of "5 G+industrial Internet" and its potential applications. The findings prove that the advancement of 5G and industrial Internet can provide a good solution for intelligent manufacturing.
Review on Assessment of Flood and Waterlogging Disaster
GE Peng1,2,YUE Xian-ping1,2(1.Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology,Nanjing 210044,China; 2.School of Economics and Management,Nanjing University of Information Science and Technology, Nanjing 210044,China)Flood and waterlogging disaster is the most common one of all natural hazards in the world,occurring frequently,causing serious results,bringing about a lot of losses.It is the premise as well as the requirements of understanding and managing the disaster to perform an evaluation of flood and waterlogging disaster.There are three aspects of flood and waterlogging disaster assessment in the process of the disaster,including pre-disaster assessment,in-disaster assessment and post-disaster assessment.The paper focuses on pre-disaster assessment and post-disaster assessment,explains some concepts concerning flood and waterlogging disaster,summarizes the contents of its risk assessment from the perspective of the risk of hazard and disaster-generated environment and the vulnerability of disaster bearing body,introduces the main course of its loss assessment from the view of index determination,loss calculation and classification of the disaster.mainstream research methods are presentd.Finally,the tendency of flood disaster assessment research in the future is predicted.
Preparation of Low Molecular Weight Polyacrylic Acid
SUN Tong-ming TANG Yan-feng Zhu Jin-li School of Chemistry and Chemical Engineering,Nantong University,Nantong 226007,ChinaIn this paper,low molecular weight polyacrylic acid was prepared by solution polymerization in the presence of ammonium persulfate as initiator.The effects of polymerization conditions on the molecular weight of polyacrylic acid were investigated in detail.The results showed that the optimum conditions for proper molecular weight about 3 000 applicable to solution chain for hyperdispersant were as follows:the concentration of initiator was 4.5%,the monomer concentration was 25%,the polymerization temperature was 70℃,and the reaction time was 4h.
Application of Deep Learning in Image Recognition
LI Chaobo;LI Hongjun;XU Chen;School of Electronic and Information, Nantong University;Deep learning simulates the human brain by building deep neural networks to analyze, learn and interpret data. It is widely used in image recognition. Firstly, the research of deep learning is introduced in image recognition.Meanwhile, the typical network models of deep learning are discussed, such as convolutional neural networks, deep belief networks, recursive neural networks and generative adversarial nets. Then, some applications in image recognition are introduced, such as face recognition, human action recognition and fall detection. Finally, the difficulties and future works are pointed out. Deep learning can automatically extract similar features from different images which can be classified into several categories. It is excellent in recognition rate and robustness. Deep learning promotes the development of image recognition in artificial intelligence. Unsupervised learning and adversarial networks will be a hot topic in the future.
Application of ANSYS to Reinforced Concrete Beam
WANG Ya-ping 1,CHEN Jian-ping 2 ,CHEN Wu-zhou 3(1.Nantong Institute of Technology,Nantong226007,China;2.Nantong Architectural Design Institute of Industry,Nantong226001,China;3.Nantong Water Conservancy Construction Company,Nantong226005,China)In this article,taking features of reinforced concrete into account ,FEM software of ANSYS was used to calculate the beam' s deformation and the stress and strain of the normal section.At last,the answers to ANSYS(crack length,stress of reinforc-ing bar and concrete)and theoretical answers were compared in search of reasons and ways or measures that can amend it.
Deep Learning Air Quality Forecast with Divided Area Based on K-means
XU Ailan;ZHU Yanmin;SUN Qiang;YU Xiangxiang;PENG XiaoyanAiming at determining the monitoring stations with strong spatial correlation, a method based on the K-means clustering algorithm for dividing the air quality monitoring stations is proposed. With Nantong as an example,the historical pollutant data in the target area and the meteorological data were gathered. With them, the hybrid CNNLSTM model, which is composed of the convolutional neural network(CNN) and the long short-termmemory(LSTM)neural network, was used to predict the pollutants, and finally extracted the temporal and spatial evolution characteristics of the pollutant concentration to complete the high accuracy of air quality forecast. The experiments show that, with the historical pollutant concentration data of other stations in the area divided by K-means, the CNN-LSTM model can forecast PM2.5 concentrations more accurately.