Pub Date : 2024-08-08DOI: 10.32628/ijsrset24114119
Dr. H. M. Naveen
The NEP 2020 recommends for vibrant University-Industry linkage with an emphasis on exposing students to real-life situations and making them globally competent. Keeping this in view, the UGC has approved the guidelines entitled “A Sustainable and Vibrant University-Industry Linkage System for Indian Universities”. These guidelines will promote research and development through collaborations between universities and industries. Establishing the linkages between university and industry will help to create training and apprenticeship opportunities in the industries, R&D labs and research organizations. The Higher Educational Institutions (HEIs) have been advised by the UGC to initiate necessary measures to research and development by creating R&D clusters at the State or Regional levels through University-Industry (UI) linkages. These guidelines shall promote research and development through collaborations between Universities and Industries. It will also help students to develop skill sets among learners and make them fit for industrial skills through internships, including fields/industry/ on the job skills/vocational training/ life skills to achieve the learning objectives and attain desired outcomes effectively. Establishing the linkages between the university and the academic world will help to create training and apprenticeship opportunities for students in the industries, R&D labs, as well as in research organizations. Keeping all these aspects in view, the UGC approved guidelines for University-Industry linkages enlightens the practicians with regard to Objectives of the scheme; Mechanisms to boost R&D through University-Industry Linkages; University-Industry (UI) linkages for enhancement of student internship and apprenticeship in academic and industrial systems; and sustainability of the proposed UI linkage system. The present article will enlighten all the academicians to establish sustainable and vibrant linkage between Indian universities and industries.
{"title":"UGC Guidelines on Sustainable and Vibrant University- Industry Linkage System for Indian Universities, 2024","authors":"Dr. H. M. Naveen","doi":"10.32628/ijsrset24114119","DOIUrl":"https://doi.org/10.32628/ijsrset24114119","url":null,"abstract":"The NEP 2020 recommends for vibrant University-Industry linkage with an emphasis on exposing students to real-life situations and making them globally competent. Keeping this in view, the UGC has approved the guidelines entitled “A Sustainable and Vibrant University-Industry Linkage System for Indian Universities”. These guidelines will promote research and development through collaborations between universities and industries. Establishing the linkages between university and industry will help to create training and apprenticeship opportunities in the industries, R&D labs and research organizations. The Higher Educational Institutions (HEIs) have been advised by the UGC to initiate necessary measures to research and development by creating R&D clusters at the State or Regional levels through University-Industry (UI) linkages. These guidelines shall promote research and development through collaborations between Universities and Industries. It will also help students to develop skill sets among learners and make them fit for industrial skills through internships, including fields/industry/ on the job skills/vocational training/ life skills to achieve the learning objectives and attain desired outcomes effectively. Establishing the linkages between the university and the academic world will help to create training and apprenticeship opportunities for students in the industries, R&D labs, as well as in research organizations. Keeping all these aspects in view, the UGC approved guidelines for University-Industry linkages enlightens the practicians with regard to Objectives of the scheme; Mechanisms to boost R&D through University-Industry Linkages; University-Industry (UI) linkages for enhancement of student internship and apprenticeship in academic and industrial systems; and sustainability of the proposed UI linkage system. The present article will enlighten all the academicians to establish sustainable and vibrant linkage between Indian universities and industries.","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":"4 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141927910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-25DOI: 10.32628/ijsrset24114107
Miss. Isha Anand Bhagat, Miss. Komal Gajanan Wankhede, Mr. Navoday Atul Kopawar, Prof. Dipali A. Sananse
Artificial Intelligence (AI) is revolutionizing healthcare by enhancing diagnostic accuracy, personalizing treatments and streamlining administrative tasks through advanced algorithms and machine learning. This review examines AI’s impact across various areas, including medical imaging, diagnostics, personalized medicine, drug discovery, patient monitoring, and surgical procedures. AI’s capacity to analyze complex medical data improves clinical decision-making, predicts patient outcomes, and optimizes hospital operations. AI offers significant benefits, including reduced diagnostic errors and lower healthcare costs. The future of AI in healthcare promises further innovations, such as robotic-assisted surgery, virtual patient care via remote consultations, and advanced health monitoring with wearable devices. Embracing AI not only enhances patient outcomes but also transforms medical research and administrative efficiency, paving the way for a more accessible and effective global healthcare system. Ongoing research and regulatory oversight are essential to fully harness AI’s potential while ensuring ethical standards and patient safety.
{"title":"Artificial Intelligence in Healthcare : A Review","authors":"Miss. Isha Anand Bhagat, Miss. Komal Gajanan Wankhede, Mr. Navoday Atul Kopawar, Prof. Dipali A. Sananse","doi":"10.32628/ijsrset24114107","DOIUrl":"https://doi.org/10.32628/ijsrset24114107","url":null,"abstract":"Artificial Intelligence (AI) is revolutionizing healthcare by enhancing diagnostic accuracy, personalizing treatments and streamlining administrative tasks through advanced algorithms and machine learning. This review examines AI’s impact across various areas, including medical imaging, diagnostics, personalized medicine, drug discovery, patient monitoring, and surgical procedures. AI’s capacity to analyze complex medical data improves clinical decision-making, predicts patient outcomes, and optimizes hospital operations. AI offers significant benefits, including reduced diagnostic errors and lower healthcare costs. The future of AI in healthcare promises further innovations, such as robotic-assisted surgery, virtual patient care via remote consultations, and advanced health monitoring with wearable devices. Embracing AI not only enhances patient outcomes but also transforms medical research and administrative efficiency, paving the way for a more accessible and effective global healthcare system. Ongoing research and regulatory oversight are essential to fully harness AI’s potential while ensuring ethical standards and patient safety.","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":"48 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-25DOI: 10.32628/ijsrset24114108
Prof. Nagare Kanchan. S
No one is unaware of the massive volumes of waste produced on a daily basis by human society. All waste produced from household and industrial sources decompose in open spaces throughout the urban waste process. After that, leachate including high quantities of common cat ions, dioxins, heavy metals (such Pb, Ni, Cu, Hg, and organic compounds), Zn, S, NH3, and Cl, as well as common cat ions, is created as a result of the waste's breakdown. The concentration of heavy metals in the leachate is negatively impacting the environment, soil, and vegetation. The goal of this research is to offer an alternative to leachate for its effective usage as fertilizer. This occurs when the material is thrown untreated on soil or in any landfill. This will provide an active, practical solution to India's leachate problem, which at the moment poses a major environmental concern.
{"title":"Leachate as a Fertilizer","authors":"Prof. Nagare Kanchan. S","doi":"10.32628/ijsrset24114108","DOIUrl":"https://doi.org/10.32628/ijsrset24114108","url":null,"abstract":"No one is unaware of the massive volumes of waste produced on a daily basis by human society. All waste produced from household and industrial sources decompose in open spaces throughout the urban waste process. After that, leachate including high quantities of common cat ions, dioxins, heavy metals (such Pb, Ni, Cu, Hg, and organic compounds), Zn, S, NH3, and Cl, as well as common cat ions, is created as a result of the waste's breakdown. The concentration of heavy metals in the leachate is negatively impacting the environment, soil, and vegetation. The goal of this research is to offer an alternative to leachate for its effective usage as fertilizer. This occurs when the material is thrown untreated on soil or in any landfill. This will provide an active, practical solution to India's leachate problem, which at the moment poses a major environmental concern.","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":"19 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141803151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-22DOI: 10.32628/ijsrset24114104
Dileep Kumar Gupta, Prof. (Dr.) Devendra Agarwal, Dr. Yusuf Perwej, Opinder Vishwakarma, Priya Mishra, Nitya
Human emotion recognition using machine learning is a new field that has the potential to improve user experience, lower crime, and target advertising. The ability of today's emotion detection systems to identify human emotions is essential. Applications ranging from security cameras to emotion detection are readily accessible. Machine learning-based emotion detection recognises and deciphers human emotions from text and visual data. In this study, we use convolutional neural networks and natural language processing approaches to create and assess models for emotion detection. Instead of speaking clearly, these human face expressions visually communicate a lot of information. Recognising facial expressions is important for human-machine interaction. Applications for automatic facial expression recognition systems are numerous and include, but are not limited to, comprehending human conduct, identifying mental health issues, and creating artificial human emotions. It is still difficult for computers to recognise facial expressions with a high recognition rate. Geometry and appearance-based methods are two widely used approaches for automatic FER systems in the literature. Pre-processing, face detection, feature extraction, and expression classification are the four steps that typically make up facial expression recognition. The goal of this research is to recognise the seven main human emotions anger, disgust, fear, happiness, sadness, surprise, and neutrality using a variety of deep learning techniques (convolutional neural networks).
利用机器学习进行人类情感识别是一个新领域，有可能改善用户体验、降低犯罪率和广告针对性。当今情感检测系统识别人类情感的能力至关重要。从安防摄像头到情感检测，各种应用一应俱全。基于机器学习的情绪检测可从文本和视觉数据中识别和解读人类情绪。在这项研究中，我们使用卷积神经网络和自然语言处理方法来创建和评估情感检测模型。人类的面部表情并不是清晰地说话，而是通过视觉传达大量信息。识别面部表情对于人机交互非常重要。面部表情自动识别系统的应用非常广泛，包括但不限于理解人类行为、识别心理健康问题和创建人造人类情感。计算机要想识别出识别率较高的面部表情仍有一定难度。基于几何和外观的方法是文献中广泛用于自动 FER 系统的两种方法。预处理、人脸检测、特征提取和表情分类是通常构成面部表情识别的四个步骤。本研究的目标是利用各种深度学习技术（卷积神经网络）识别人类的七种主要情绪：愤怒、厌恶、恐惧、快乐、悲伤、惊讶和中立。
{"title":"Sensing Human Emotion using Emerging Machine Learning Techniques","authors":"Dileep Kumar Gupta, Prof. (Dr.) Devendra Agarwal, Dr. Yusuf Perwej, Opinder Vishwakarma, Priya Mishra, Nitya","doi":"10.32628/ijsrset24114104","DOIUrl":"https://doi.org/10.32628/ijsrset24114104","url":null,"abstract":"Human emotion recognition using machine learning is a new field that has the potential to improve user experience, lower crime, and target advertising. The ability of today's emotion detection systems to identify human emotions is essential. Applications ranging from security cameras to emotion detection are readily accessible. Machine learning-based emotion detection recognises and deciphers human emotions from text and visual data. In this study, we use convolutional neural networks and natural language processing approaches to create and assess models for emotion detection. Instead of speaking clearly, these human face expressions visually communicate a lot of information. Recognising facial expressions is important for human-machine interaction. Applications for automatic facial expression recognition systems are numerous and include, but are not limited to, comprehending human conduct, identifying mental health issues, and creating artificial human emotions. It is still difficult for computers to recognise facial expressions with a high recognition rate. Geometry and appearance-based methods are two widely used approaches for automatic FER systems in the literature. Pre-processing, face detection, feature extraction, and expression classification are the four steps that typically make up facial expression recognition. The goal of this research is to recognise the seven main human emotions anger, disgust, fear, happiness, sadness, surprise, and neutrality using a variety of deep learning techniques (convolutional neural networks).","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":"31 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141815545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-22DOI: 10.32628/ijsrset24114109
Idris Seidu, Benjamin Olowu, Samuel Olowu
The paper provides a comprehensive review of the advancements in quadcopters development made possible through additive manufacturing (AM). The review begins with an introduction to quadcopter technology and the basics of AM, followed by an exploration of the various AM technologies and materials used for creating quadcopter components. It highlights the innovative designs and complex geometries enabled by AM, as well as the improvements in customization and integration of multiple functions into single components. Practical case studies demonstrate the application of AM in producing high-performance quadcopters for various sectors, including military, commercial, research, and recreational use. The paper also addresses the technical challenges, economic considerations, and regulatory issues associated with AM in quadcopter development. Finally, it discusses future trends and research directions, emphasizing the potential of emerging materials and technologies to further enhance quadcopter performance. This review underscores the significant impact of AM on the evolution of quadcopters and the importance of ongoing research in this field.
本文全面回顾了通过增材制造（AM）实现的四旋翼飞行器开发进展。综述首先介绍了四旋翼飞行器技术和 AM 基础知识，然后探讨了用于制造四旋翼飞行器部件的各种 AM 技术和材料。它强调了 AM 带来的创新设计和复杂几何形状，以及在定制和将多种功能集成到单个组件方面的改进。实际案例研究展示了应用 AM 技术生产高性能四旋翼飞行器的情况，适用于军事、商业、研究和娱乐等不同领域。本文还讨论了四旋翼飞行器开发中与 AM 相关的技术挑战、经济考虑因素和监管问题。最后，本文讨论了未来趋势和研究方向，强调了新兴材料和技术进一步提高四旋翼飞行器性能的潜力。这篇综述强调了 AM 对四旋翼飞行器发展的重大影响，以及该领域当前研究的重要性。
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This paper provides information on Human Computer Interaction, it actually means that, its present use in the world of technology and its importance in the tech- savvy world of today. The paper goes through the various types of interaction that can take place between a human and a computer, the technicalities of the same, and the future scope of each interface. We always want a path of communication which is fast, is efficient and is user friendly which gives us maximum output and good performance consistently. Vision based interface between human and computer is studied in detail. It can be stated as the most popular types of interaction between human beings and computers. Vision based interaction in HCI makes use of four main techniques; gesture recognition, eye movement recognition or tracking, head tracking and facial expressions recognition and judgement. Analysis for their usage in practical systems has been made. This is the most worked upon area is the vision based hand gesture recognition which has been discussed in this paper. The present studies and key findings in this area have been listed and its future scope and utility has also been discussed in this paper.
{"title":"Vision Based Interface in Human Computer Interaction","authors":"Aditya Verma, Ankita Verma","doi":"10.32628/ijsrset241147","DOIUrl":"https://doi.org/10.32628/ijsrset241147","url":null,"abstract":"This paper provides information on Human Computer Interaction, it actually means that, its present use in the world of technology and its importance in the tech- savvy world of today. The paper goes through the various types of interaction that can take place between a human and a computer, the technicalities of the same, and the future scope of each interface. We always want a path of communication which is fast, is efficient and is user friendly which gives us maximum output and good performance consistently. Vision based interface between human and computer is studied in detail. It can be stated as the most popular types of interaction between human beings and computers. Vision based interaction in HCI makes use of four main techniques; gesture recognition, eye movement recognition or tracking, head tracking and facial expressions recognition and judgement. Analysis for their usage in practical systems has been made. This is the most worked upon area is the vision based hand gesture recognition which has been discussed in this paper. The present studies and key findings in this area have been listed and its future scope and utility has also been discussed in this paper.","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":"20 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141819276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Irfan Ul Haq, Sandeep Kumar Tiwari, Pradeep Porwal, Naveed Ul Haq
In this research paper, we propose classical numerical technique for solving some simple harmonic problems arising in some applications of science. Adomian decomposition method (ADM) are used. Some numerical examples have been solved to illustrate the accuracy and efficiency of this numerical method.
{"title":"Solving Simple Hormonic Problems Using Adomian Decomposition Method","authors":"Irfan Ul Haq, Sandeep Kumar Tiwari, Pradeep Porwal, Naveed Ul Haq","doi":"10.32628/ijsrset241145","DOIUrl":"https://doi.org/10.32628/ijsrset241145","url":null,"abstract":"In this research paper, we propose classical numerical technique for solving some simple harmonic problems arising in some applications of science. Adomian decomposition method (ADM) are used. Some numerical examples have been solved to illustrate the accuracy and efficiency of this numerical method.","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":"115 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141821932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dr. Sonali Nemade, Dr. Sujata Patil, Mrs. Deepashree Mehendale, Mrs. Vidya Shinde, Mrs. Reshma Masurekar
The customer churn prediction (CCP) is one of the challenging problems in the E-Commerce industry. With the advancement in the field of machine learning and artificial intelligence, the possibilities to predict customer churn has increased significantly. Our proposed methodology, consists of six phases. In the first two phases, data pre-processing and feature analysis is performed. In the third phase, feature selection is taken into consideration. Next, the data has been split into two parts train and test set in the ratio of 80% and 20% respectively. In the prediction process, most popular predictive models have been applied, namely, logistic regression, random forest classifier etc. on train set are applied to see the effect on accuracy of models. In addition, K-fold cross validation has been used over train set for hyper parameter tuning and to prevent overfitting of models. Finally, the obtained results on test set have been evaluated using confusion matrix and AUC curve.
客户流失预测（CCP）是电子商务行业中极具挑战性的问题之一。随着机器学习和人工智能领域的进步，预测客户流失的可能性大大增加。我们提出的方法包括六个阶段。在前两个阶段，进行数据预处理和特征分析。第三阶段是特征选择。接下来，数据被分成训练集和测试集两部分，比例分别为 80% 和 20%。在预测过程中，在训练集上应用了最流行的预测模型，即逻辑回归、随机森林分类器等，以了解模型对准确率的影响。此外，还在训练集上使用了 K 折交叉验证来进行超参数调整，防止模型过度拟合。最后，使用混淆矩阵和 AUC 曲线对测试集上获得的结果进行评估。
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This paper presents a novel system for personalized drone interaction, integrating adaptive hand gesture control with facial authentication. Utilizing the DJI Tello drone equipped with a 5 MP camera, the system employs advanced computer vision and machine learning techniques to ensure secure and intuitive control. Facial recognition using the Histogram of Oriented Gradients (HOG) method and FaceNet model verifies user identity, while MediaPipe and a custom convolutional neural network (CNN) facilitate accurate hand gesture recognition. The system’s real-time processing capabilities ensure seamless and responsive user interaction. Experimental results demonstrate the system’s robustness and accuracy in various scenarios, highlighting its potential for diverse applications such as security, entertainment, and personal assistance.
{"title":"Personalized Drone Interaction : Adaptive Hand Gesture Control with Facial Authentication","authors":"Idris Seidu, Jafaar Olasunkanmi Lawal","doi":"10.32628/ijsrset241146","DOIUrl":"https://doi.org/10.32628/ijsrset241146","url":null,"abstract":"This paper presents a novel system for personalized drone interaction, integrating adaptive hand gesture control with facial authentication. Utilizing the DJI Tello drone equipped with a 5 MP camera, the system employs advanced computer vision and machine learning techniques to ensure secure and intuitive control. Facial recognition using the Histogram of Oriented Gradients (HOG) method and FaceNet model verifies user identity, while MediaPipe and a custom convolutional neural network (CNN) facilitate accurate hand gesture recognition. The system’s real-time processing capabilities ensure seamless and responsive user interaction. Experimental results demonstrate the system’s robustness and accuracy in various scenarios, highlighting its potential for diverse applications such as security, entertainment, and personal assistance.","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":" 47","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141826065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Herein, we discussed the pure water flux recovery of carbon based nanofillers embedded polyvinyl chloride nanocomposite membranes after physical cleaning through backwashing & flushing with flow reversal and after chemical cleaning using hydrochloric acid and caustic soda. The water flux recovery by backwashing is more than that by forward flushing for all the membrane but it is more effective in carbon black (CB) & graphitized carbon black (GCB) carbon-based membranes than relatively hydrophilic multiwalled carbon-nanotube (MWCNT) & carboxylated multiwalled carbon-nanotube (CMWCNT) based nanocomposite membranes. The highest water flux recovery was found ~96% for nanocomposite membranes by backwashing and caustic soda cleaning. The combination of backwashing and caustic soda cleaning could be the most effective method of cleaning of oil-water fouled membranes to restore the maximum water flux.
{"title":"Studies on Recovery of Performance of Fouled Nanocomposite Ultrafiltration Membranes on Cleaning after Treatment of Oil–Water Emulsions","authors":"A. K. Ghosh, V. S. Mamtani, A. K. Adak","doi":"10.32628/ijsrset241142","DOIUrl":"https://doi.org/10.32628/ijsrset241142","url":null,"abstract":"Herein, we discussed the pure water flux recovery of carbon based nanofillers embedded polyvinyl chloride nanocomposite membranes after physical cleaning through backwashing & flushing with flow reversal and after chemical cleaning using hydrochloric acid and caustic soda. The water flux recovery by backwashing is more than that by forward flushing for all the membrane but it is more effective in carbon black (CB) & graphitized carbon black (GCB) carbon-based membranes than relatively hydrophilic multiwalled carbon-nanotube (MWCNT) & carboxylated multiwalled carbon-nanotube (CMWCNT) based nanocomposite membranes. The highest water flux recovery was found ~96% for nanocomposite membranes by backwashing and caustic soda cleaning. The combination of backwashing and caustic soda cleaning could be the most effective method of cleaning of oil-water fouled membranes to restore the maximum water flux.","PeriodicalId":14228,"journal":{"name":"International Journal of Scientific Research in Science, Engineering and Technology","volume":"105 48","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141667037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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