International journal of advanced industrial engineering最新文献

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Photocatalytic degradation of methylene blue dye using synthesized CoxNi1-xFe2O4 nanoparticles under direct visible light 直接可见光下合成CoxNi1-xFe2O4纳米颗粒光催化降解亚甲基蓝染料

International journal of advanced industrial engineering Pub Date : 2023-09-21 DOI: 10.14741/ijaie/v.11.3.1

Seemab Hussnain, Syed Salman Ali, Muhammad Qadeer Alam, Muhammad Hamad Safder, Atia Roman, Tehreem Fatima, Muhammad Asif

{"title":"Photocatalytic degradation of methylene blue dye using synthesized CoxNi1-xFe2O4 nanoparticles under direct visible light","authors":"Seemab Hussnain, Syed Salman Ali, Muhammad Qadeer Alam, Muhammad Hamad Safder, Atia Roman, Tehreem Fatima, Muhammad Asif","doi":"10.14741/ijaie/v.11.3.1","DOIUrl":"https://doi.org/10.14741/ijaie/v.11.3.1","url":null,"abstract":"A cobalt doped nickel ferrites were prepared in this study. Photo catalysts made of cobalt-doped nickel ferromagnetic materials nanoparticles were utilized to degrade dyes. These nanoparticles were synthesized by the co-precipitation technique. A UV-Vis spectrophotometer was used to determine the band gap. These ferrites had band gap between 2.32eV to 2.20eV. The spinel structure of ferrites was characterized X-ray diffraction (XRD) technique. The shape and average size of synthesized nanoparticles was determined by scanning electron microscopy (SEM). Fourier transform infrared FTIR shows metal-oxygen bond in the range of 400-1000cm-1. The photocatalytic degradation of dye under UV radiation and direct sunlight was studied. Almost 72 to 92 percent methylene blue dye was degraded in first 100 mints owing to the active octahedral to tetrahedral lattice sites and prevent the recombination of electron/hole pair. The photo-degradation of methylene blue (MB) and a few textile dyes was practised to examine the photocatalytic performance of Co-NiFe2O4. It was observed as the most effective photocatalyst for treating sewage.","PeriodicalId":474390,"journal":{"name":"International journal of advanced industrial engineering","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136239273","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}

引用次数: 0

Construction site safety and quality analysis utilizing KHNN Model 基于KHNN模型的施工现场安全质量分析

International journal of advanced industrial engineering Pub Date : 2023-03-20 DOI: 10.14741/ijaie/v.11.1.2

Muhammad Imran Khan, Muhammad Qamer, Atta Mehroz

{"title":"Construction site safety and quality analysis utilizing KHNN Model","authors":"Muhammad Imran Khan, Muhammad Qamer, Atta Mehroz","doi":"10.14741/ijaie/v.11.1.2","DOIUrl":"https://doi.org/10.14741/ijaie/v.11.1.2","url":null,"abstract":"Improving construction site safety through effective hazard identification and mitigation is critical. This study aims to predict rework, defects, and associated costs using artificial neural networks and optimization algorithms. Traditional safety planning approaches lack pre-construction hazard analysis. To examine deficiencies, various metrics were analyzed, including rework costs per $1M scope and injury rates. Ineffective safety practices like inadequate training and protection have led to accidents. This work identifies approaches to enhance worker safety performance through hazard identification. Inputs to a neural network model predict rework workers, defects, and costs. Safety execution aims to systematically identify hazards before construction. Model performance using actual data was evaluated. Two soft computing methods - artificial neural network and optimization algorithms - were implemented in MATLAB. Krill herd and grey wolf optimization techniques optimized hidden neuron weights in the neural network structure. Predictions from these algorithms outperformed other existing methods like particle swarm and genetic algorithms. This study provides a framework to quantitatively forecast rework, defects, and associated costs through systematic pre-construction hazard analysis and modeling. The proposed optimizationenhanced neural network models can help construction managers implement targeted safety improvements.","PeriodicalId":474390,"journal":{"name":"International journal of advanced industrial engineering","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135135874","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}

引用次数: 0