Pub Date : 2024-01-01DOI: 10.22581/muet1982.2401.2625
Abdullah Abdullah, Uzma Rashid, Islam Ud Din, Muhammad Tahseen Aslam, Farzana Nazir, Ammarah Kanwal, Razia Kulsoom, Fouzia Hussain, Muhammad Afzal, Syed Hussain Abidi, Niaz Memon
One of the main issues confronting humanity in the twenty-first century is the lack of potable water availability. Around half of the world’s consumers face drinking water scarcity. Industrially rich areas have a high population and high-water contamination risk factors. Modern technologies that are quite effective for water purification, present economical limitations that impede their usefulness in developing countries. Conventional methods involving low energy, low chemical demand, and prevention of water-borne disease are therefore significant for water purification in developing countries like Pakistan. These limitations have led to improvising the conventional method for facile water purification. Herein we report the water purification assembly based on allow sand filtration; involving the raw materials grass, clay, sand, silt, pebbles, gravel and coal/ fly ash carbon to obtain clean and quality-controlled water treatment. Ground water samples collected from various areas of Sargodha city were subjected to the developed design Hybrid Multi-Layer Slow Sand Filter (HMLSSF). Based on pre- and post-treatment water analysis, it was determined that the filtration assembly was quite effective at reducing pH, turbidity, dissolved and suspended solids, hardness, and heavy metals percent removal by 87%, 77.7%, 91.3%, 95.4%, 84.4%, and to promising levels, respectively. Moreover, 99 % biological contamination such as total coliform was also removed by this method.
{"title":"Physicochemical and pathological assessment of groundwater quality from Sargodha, Pakistan using hybrid multi-layer slow sand filter: pre and post treatment analysis","authors":"Abdullah Abdullah, Uzma Rashid, Islam Ud Din, Muhammad Tahseen Aslam, Farzana Nazir, Ammarah Kanwal, Razia Kulsoom, Fouzia Hussain, Muhammad Afzal, Syed Hussain Abidi, Niaz Memon","doi":"10.22581/muet1982.2401.2625","DOIUrl":"https://doi.org/10.22581/muet1982.2401.2625","url":null,"abstract":"One of the main issues confronting humanity in the twenty-first century is the lack of potable water availability. Around half of the world’s consumers face drinking water scarcity. Industrially rich areas have a high population and high-water contamination risk factors. Modern technologies that are quite effective for water purification, present economical limitations that impede their usefulness in developing countries. Conventional methods involving low energy, low chemical demand, and prevention of water-borne disease are therefore significant for water purification in developing countries like Pakistan. These limitations have led to improvising the conventional method for facile water purification. Herein we report the water purification assembly based on allow sand filtration; involving the raw materials grass, clay, sand, silt, pebbles, gravel and coal/ fly ash carbon to obtain clean and quality-controlled water treatment. Ground water samples collected from various areas of Sargodha city were subjected to the developed design Hybrid Multi-Layer Slow Sand Filter (HMLSSF). Based on pre- and post-treatment water analysis, it was determined that the filtration assembly was quite effective at reducing pH, turbidity, dissolved and suspended solids, hardness, and heavy metals percent removal by 87%, 77.7%, 91.3%, 95.4%, 84.4%, and to promising levels, respectively. Moreover, 99 % biological contamination such as total coliform was also removed by this method.","PeriodicalId":44836,"journal":{"name":"Mehran University Research Journal of Engineering and Technology","volume":"43 2","pages":""},"PeriodicalIF":0.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139128900","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 : 2024-01-01DOI: 10.22581/muet1982.2401.2587
M. Yousuf, Muhammad Umair, Muhammad Rehan, Z. Umrani
South-facing collectors are the optimum choice for solar applications in the northern hemisphere. However, obstacles may limit the feasibility of this orientation. Therefore, altering the orientation of the collector impacts solar insolation. In this study, the Perez model is utilized to evaluate incoming solar radiation on tilted surfaces for solar collectors in four climatic zones across Pakistan. The results are presented in contour plots to analyze the optimal tilt and orientation for solar applications. The findings of the study indicate substantial energy gains when collectors are placed at optimum angles. More specifically, Quetta leads with a 14.54% increase, followed by Karachi and Multan at 9.81% and 9.3%, respectively, compared to horizontally placed collectors. Analysis of vertical surfaces reveals a notable decrease in monthly solar radiation, especially in Peshawar (37.22%). Monthly adjustments in tilt angles outperform fixed positions, enhancing solar energy intensity. When comparing yearly adjustments with monthly adjustments, Quetta shows the maximum increase of 5.92%, followed by Karachi (4.86%), Multan (4.01%), and Peshawar (3.65%). It is also observed that ±15° azimuth angle change from the south ensures receiving up to 98% of insolation, regardless of the climatic region. Lastly, the validation against the NASA SSE database further highlights the reliability of our simulation model. Overall, the outcomes of the study will contribute to informed solar energy planning in the studied regions.
{"title":"Effect of adjusting orientation for solar energy applications in multiple climatic zones","authors":"M. Yousuf, Muhammad Umair, Muhammad Rehan, Z. Umrani","doi":"10.22581/muet1982.2401.2587","DOIUrl":"https://doi.org/10.22581/muet1982.2401.2587","url":null,"abstract":"South-facing collectors are the optimum choice for solar applications in the northern hemisphere. However, obstacles may limit the feasibility of this orientation. Therefore, altering the orientation of the collector impacts solar insolation. In this study, the Perez model is utilized to evaluate incoming solar radiation on tilted surfaces for solar collectors in four climatic zones across Pakistan. The results are presented in contour plots to analyze the optimal tilt and orientation for solar applications. The findings of the study indicate substantial energy gains when collectors are placed at optimum angles. More specifically, Quetta leads with a 14.54% increase, followed by Karachi and Multan at 9.81% and 9.3%, respectively, compared to horizontally placed collectors. Analysis of vertical surfaces reveals a notable decrease in monthly solar radiation, especially in Peshawar (37.22%). Monthly adjustments in tilt angles outperform fixed positions, enhancing solar energy intensity. When comparing yearly adjustments with monthly adjustments, Quetta shows the maximum increase of 5.92%, followed by Karachi (4.86%), Multan (4.01%), and Peshawar (3.65%). It is also observed that ±15° azimuth angle change from the south ensures receiving up to 98% of insolation, regardless of the climatic region. Lastly, the validation against the NASA SSE database further highlights the reliability of our simulation model. Overall, the outcomes of the study will contribute to informed solar energy planning in the studied regions.","PeriodicalId":44836,"journal":{"name":"Mehran University Research Journal of Engineering and Technology","volume":"56 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139127459","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 : 2024-01-01DOI: 10.22581/muet1982.2401.2806
Mushtaque Ahmed Rahu, Muhammad Mujtaba Shaikh, Sarang Karim, A. Chandio, S. Dahri, Sarfraz Ahmed Soomro, Sayed Mazhar Ali
All living things, comprising animals, plants, and people require water to survive. The world is covered in water, just 1 percent of it is fresh and functional. The importance and value of freshwater have increased due to population growth and rising water demands. Approximately more than 70 percent of the world's freshwater is used for agriculture. Agricultural employees are the least productive, inefficient, and heavily subsidized water users in the world. They also utilize the most water overall. Irrigation consumes a considerable amount of water. The field's water supply needs to be safeguarded. A critical stage in estimating agricultural production is crop irrigation. The global shortage of fresh water is a serious issue, and it will only get worse in the years to come. Precision agriculture and intelligent irrigation are the only solutions that will solve the aforementioned issues. Smart irrigation systems and other modern technologies must be used to improve the quantity of high-quality water used for agricultural irrigation. Such a system has the potential to be quite accurate, but it requires data about the climate and water quality of the region where it will be used. This study examines the smart irrigation system using the Internet of Things (IoT) and cloud-based architecture. The water's temperature, pH, total dissolved solids (TDS), and turbidity are all measured by this device before the data is processed in a cloud using the range of machine learning (ML) approaches. Regarding water content limits, farmers are given accurate information. Farmers can increase production and water quality by using effective irrigation techniques. ML methods comprising support vector machines (SVM), random forests (RF), linear regression, Naive Bayes, and decision trees (DT) are used to categorize pre-processed data sets. Performance metrics like accuracy, precision, recall, and f1-score are used to calculate the performance of ML algorithms.
所有生物，包括动物、植物和人类，都需要水才能生存。全世界都被水覆盖着，但只有 1% 的水是淡水和功能性水。由于人口增长和对水的需求不断增加，淡水的重要性和价值也随之增加。全世界约有 70% 以上的淡水用于农业。农业雇员是世界上生产率最低、效率最低、补贴最高的用水户。总体而言，农业用水量也最大。灌溉耗水量相当大。田间供水需要得到保障。估算农业产量的一个关键阶段是作物灌溉。全球淡水短缺是一个严重问题，而且在未来几年只会越来越严重。精准农业和智能灌溉是解决上述问题的唯一办法。必须利用智能灌溉系统和其他现代技术来提高农业灌溉的优质水量。这种系统有可能相当精确，但需要使用地区的气候和水质数据。本研究利用物联网（IoT）和云架构对智能灌溉系统进行了研究。水的温度、pH 值、总溶解固体 (TDS) 和浊度均由该设备测量，然后通过一系列机器学习 (ML) 方法在云端处理数据。在含水量限制方面，农民可以获得准确的信息。农民可以通过使用有效的灌溉技术提高产量和水质。支持向量机 (SVM)、随机森林 (RF)、线性回归、Naive Bayes 和决策树 (DT) 等 ML 方法用于对预处理数据集进行分类。准确率、精确度、召回率和 f1 分数等性能指标用于计算 ML 算法的性能。
{"title":"An IoT and machine learning solutions for monitoring agricultural water quality: a robust framework","authors":"Mushtaque Ahmed Rahu, Muhammad Mujtaba Shaikh, Sarang Karim, A. Chandio, S. Dahri, Sarfraz Ahmed Soomro, Sayed Mazhar Ali","doi":"10.22581/muet1982.2401.2806","DOIUrl":"https://doi.org/10.22581/muet1982.2401.2806","url":null,"abstract":"All living things, comprising animals, plants, and people require water to survive. The world is covered in water, just 1 percent of it is fresh and functional. The importance and value of freshwater have increased due to population growth and rising water demands. Approximately more than 70 percent of the world's freshwater is used for agriculture. Agricultural employees are the least productive, inefficient, and heavily subsidized water users in the world. They also utilize the most water overall. Irrigation consumes a considerable amount of water. The field's water supply needs to be safeguarded. A critical stage in estimating agricultural production is crop irrigation. The global shortage of fresh water is a serious issue, and it will only get worse in the years to come. Precision agriculture and intelligent irrigation are the only solutions that will solve the aforementioned issues. Smart irrigation systems and other modern technologies must be used to improve the quantity of high-quality water used for agricultural irrigation. Such a system has the potential to be quite accurate, but it requires data about the climate and water quality of the region where it will be used. This study examines the smart irrigation system using the Internet of Things (IoT) and cloud-based architecture. The water's temperature, pH, total dissolved solids (TDS), and turbidity are all measured by this device before the data is processed in a cloud using the range of machine learning (ML) approaches. Regarding water content limits, farmers are given accurate information. Farmers can increase production and water quality by using effective irrigation techniques. ML methods comprising support vector machines (SVM), random forests (RF), linear regression, Naive Bayes, and decision trees (DT) are used to categorize pre-processed data sets. Performance metrics like accuracy, precision, recall, and f1-score are used to calculate the performance of ML algorithms.","PeriodicalId":44836,"journal":{"name":"Mehran University Research Journal of Engineering and Technology","volume":"108 8","pages":""},"PeriodicalIF":0.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139128662","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 : 2024-01-01DOI: 10.22581/muet1982.2401.3006
S. Jiskani, Tanweer Hussain, A. A. Sahito, Faheemullah Shaikh, Ali Akbar Shah
Flashed over insulator faults are the most significant faults in high voltage line insulators. They are complicated to identify using traditional methods due to their labor-intensive nature. This study proposes a deep learning-based algorithm for detecting flashed over insulator faults in the real time. The algorithm is based on the Resnet 50 architecture, which has been shown to be effective for image classification tasks in the previous studies regarding image analysis. The algorithm is fast, robust and efficient, making it suitable for real-time applications. The algorithm is trained on a dataset of images of flashed over and non-flashed over insulators. This dataset was collected from various transmission lines and National Center of Robotics and Automation, which are located in Pakistan. For validating the effectiveness of the Resnet 50 algorithm, it was compared with the results obtained from the two other widely popular deep learning algorithms, Densenet 121 and VGG 16 (trained and validated on the same dataset). The results showed that the Resnet 50 was able to detect flashed over insulator faults with an accuracy of over 99%. Whereas the Densenet 121 and VGG 16 have achieved an accuracy of less than 51%.
{"title":"Aerial identification of flashed over faulty insulator using binary image classification","authors":"S. Jiskani, Tanweer Hussain, A. A. Sahito, Faheemullah Shaikh, Ali Akbar Shah","doi":"10.22581/muet1982.2401.3006","DOIUrl":"https://doi.org/10.22581/muet1982.2401.3006","url":null,"abstract":"Flashed over insulator faults are the most significant faults in high voltage line insulators. They are complicated to identify using traditional methods due to their labor-intensive nature. This study proposes a deep learning-based algorithm for detecting flashed over insulator faults in the real time. The algorithm is based on the Resnet 50 architecture, which has been shown to be effective for image classification tasks in the previous studies regarding image analysis. The algorithm is fast, robust and efficient, making it suitable for real-time applications. The algorithm is trained on a dataset of images of flashed over and non-flashed over insulators. This dataset was collected from various transmission lines and National Center of Robotics and Automation, which are located in Pakistan. For validating the effectiveness of the Resnet 50 algorithm, it was compared with the results obtained from the two other widely popular deep learning algorithms, Densenet 121 and VGG 16 (trained and validated on the same dataset). The results showed that the Resnet 50 was able to detect flashed over insulator faults with an accuracy of over 99%. Whereas the Densenet 121 and VGG 16 have achieved an accuracy of less than 51%.","PeriodicalId":44836,"journal":{"name":"Mehran University Research Journal of Engineering and Technology","volume":"72 10","pages":""},"PeriodicalIF":0.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139127528","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 : 2024-01-01DOI: 10.22581/muet1982.2401.2873
Muzamil Hussain Memon, Maria Mustafa, Zeeshan Ali
Humans generate massive amounts of plastic and electronic waste, which pollute our environment, particularly our water supplies, and cause fatal difficulties. In addition, the increased use of fossil fuels is wreaking havoc on the ecosystem. In order to solve these issues, we describe a simple, low-cost, and environmentally-friendly triboelectric nanogenerator (TENG) made of electronic waste and recycled plastic, and we add nanomaterial to improve power generation using biomechanical energy. The present investigation involves synthesizing carbon dots (CDs) nano-material through a single-step hydrothermal technique and CDs nano-material characterized via UV.Vis Spectroscopy. The proposed carbon dot-graphite nano composite-based TENGs (CGC-TENGs) are created by reusing dry cells (electronic waste) to obtain graphite, plastic bottles to obtain plastic, and synthesized CDs. CGC-TENGs manufactures a simple, low-cost, and environmentally friendly In-house quick and bulk fabrication printed electro hydrodynamics (EHD) electrospray process that uses less solvent and does not require specialist equipment or knowledge. Comparing fabricate TENG device results, in which CDs used produced high voltage (127.31 V)/current (107.12 μA), while not using CDs produced low voltage (95.23 V)/current (104.12 μA) at similar fabrication parameters, the size of the devices are 4.5 cm × 7 cm, and 15 N force applied. The CGC-TENG (δ) has maximum output performance and is thoroughly investigated using an open-circuit voltage of 171.30 V, a short circuit current of 111.39 μA, and a maximum output power density of 53.08 μW/cm2. CGC-TENG (δ) was used to power an electronic glucose monitoring device, and twenty-three blue light-emitting diodes (LEDs) to demonstrate its practical applications. The approach we propose produces renewable energy sources by reutilizing plastic waste and technological waste, providing a practical and sustainable path toward our goal of creating a green planet.
人类产生了大量的塑料和电子垃圾，污染了我们的环境，尤其是水源，并造成了致命的困难。此外，化石燃料使用量的增加也对生态系统造成了严重破坏。为了解决这些问题，我们介绍了一种由电子垃圾和回收塑料制成的简单、低成本、环保的三电纳米发电机（TENG），并添加了纳米材料，以利用生物机械能提高发电量。本研究通过一步水热技术合成碳点（CD）纳米材料，并通过紫外可见光谱对碳点纳米材料进行表征。所提出的基于碳点-石墨纳米复合材料的 TENGs（CGC-TENGs）是通过重复利用干电池（电子废料）获得石墨、塑料瓶获得塑料以及合成的 CD 而制成的。CGC-TENGs 采用简单、低成本和环保的内部快速批量制造印刷电动流体力学（EHD）电喷雾工艺，该工艺使用的溶剂较少，不需要专业设备或知识。比较制造 TENG 器件的结果，在相似的制造参数下，使用 CD 产生高电压（127.31 V）/电流（107.12 μA），而不使用 CD 产生低电压（95.23 V）/电流（104.12 μA），器件的尺寸为 4.5 cm × 7 cm，施加的力为 15 N。在开路电压为 171.30 V、短路电流为 111.39 μA、最大输出功率密度为 53.08 μW/cm2 的条件下，对 CGC-TENG (δ) 的最大输出性能进行了深入研究。CGC-TENG (δ) 用于为电子葡萄糖监测设备和 23 个蓝色发光二极管（LED）供电，以展示其实际应用。我们提出的方法通过重新利用塑料废料和技术废料来生产可再生能源，为我们实现创建绿色地球的目标提供了一条切实可行的可持续发展之路。
{"title":"Fabrication of low-cost and environmental-friendly EHD printable thin film nanocomposite triboelectric nanogenerator using household recyclable materials","authors":"Muzamil Hussain Memon, Maria Mustafa, Zeeshan Ali","doi":"10.22581/muet1982.2401.2873","DOIUrl":"https://doi.org/10.22581/muet1982.2401.2873","url":null,"abstract":"Humans generate massive amounts of plastic and electronic waste, which pollute our environment, particularly our water supplies, and cause fatal difficulties. In addition, the increased use of fossil fuels is wreaking havoc on the ecosystem. In order to solve these issues, we describe a simple, low-cost, and environmentally-friendly triboelectric nanogenerator (TENG) made of electronic waste and recycled plastic, and we add nanomaterial to improve power generation using biomechanical energy. The present investigation involves synthesizing carbon dots (CDs) nano-material through a single-step hydrothermal technique and CDs nano-material characterized via UV.Vis Spectroscopy. The proposed carbon dot-graphite nano composite-based TENGs (CGC-TENGs) are created by reusing dry cells (electronic waste) to obtain graphite, plastic bottles to obtain plastic, and synthesized CDs. CGC-TENGs manufactures a simple, low-cost, and environmentally friendly In-house quick and bulk fabrication printed electro hydrodynamics (EHD) electrospray process that uses less solvent and does not require specialist equipment or knowledge. Comparing fabricate TENG device results, in which CDs used produced high voltage (127.31 V)/current (107.12 μA), while not using CDs produced low voltage (95.23 V)/current (104.12 μA) at similar fabrication parameters, the size of the devices are 4.5 cm × 7 cm, and 15 N force applied. The CGC-TENG (δ) has maximum output performance and is thoroughly investigated using an open-circuit voltage of 171.30 V, a short circuit current of 111.39 μA, and a maximum output power density of 53.08 μW/cm2. CGC-TENG (δ) was used to power an electronic glucose monitoring device, and twenty-three blue light-emitting diodes (LEDs) to demonstrate its practical applications. The approach we propose produces renewable energy sources by reutilizing plastic waste and technological waste, providing a practical and sustainable path toward our goal of creating a green planet.","PeriodicalId":44836,"journal":{"name":"Mehran University Research Journal of Engineering and Technology","volume":"27 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139125796","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 : 2024-01-01DOI: 10.22581/muet1982.2401.2712
Iatizaz Hassan, N. A. Khan, N. H. Syed, Najma Memom, Muddasar Habib, Khalid Mehmood Barki
In Pakistan raw coal and a little quantity of waste plastics are burnt to sustain high temperature inside brick making kilns. The gaseous emissions of the kilns contain a considerable amount of darkish colored particulates. It is currently believed that the plastic burning produces these particulates. Advanced characterization instruments, such as a scanning electron microscope, energy dispersive spectroscopy, X-ray fluorescence, X-ray diffractometer, surface area analyzer using nitrogen gas adsorption isotherms, and thermogravimetric analyzer, were used to find out the chemistry and physics of the particulates. At a magnification of 30,000x, the SEM picture shows masses that are roughly roundish in shape and their size is in between 0.1 to 0.5 microns. The elements detected in these particles are carbon, oxygen, and sulfur (EDS analysis), or in other words, these elements are a typical composition of raw coal. This elemental analysis suggest that fine coal particles come out with usual combustion gases and these emitted particulates are not plastic combustion product. To strengthen this finding, the sample when calcined discarded a significant amount of sulphur oxides species, as determined in the XRF study by noticing a considerable decrease of sulphur content in the calcined particles, suggesting that the particles are actually a coal. The N2 isotherm graph reveals that the light weight flying coal particles has a very low surface area. Additionally, the XRD and TGA studies supports the conclusion that these dark colored particulate emissions are primarily fine coal particles (cenosphere).
{"title":"Compositional analysis of dark colored particulates homogeneously emitted with combustion gases (dark plumes) from brick making kilns situated in the area of Khyber Pakhtunkhwa, Pakistan","authors":"Iatizaz Hassan, N. A. Khan, N. H. Syed, Najma Memom, Muddasar Habib, Khalid Mehmood Barki","doi":"10.22581/muet1982.2401.2712","DOIUrl":"https://doi.org/10.22581/muet1982.2401.2712","url":null,"abstract":"In Pakistan raw coal and a little quantity of waste plastics are burnt to sustain high temperature inside brick making kilns. The gaseous emissions of the kilns contain a considerable amount of darkish colored particulates. It is currently believed that the plastic burning produces these particulates. Advanced characterization instruments, such as a scanning electron microscope, energy dispersive spectroscopy, X-ray fluorescence, X-ray diffractometer, surface area analyzer using nitrogen gas adsorption isotherms, and thermogravimetric analyzer, were used to find out the chemistry and physics of the particulates. At a magnification of 30,000x, the SEM picture shows masses that are roughly roundish in shape and their size is in between 0.1 to 0.5 microns. The elements detected in these particles are carbon, oxygen, and sulfur (EDS analysis), or in other words, these elements are a typical composition of raw coal. This elemental analysis suggest that fine coal particles come out with usual combustion gases and these emitted particulates are not plastic combustion product. To strengthen this finding, the sample when calcined discarded a significant amount of sulphur oxides species, as determined in the XRF study by noticing a considerable decrease of sulphur content in the calcined particles, suggesting that the particles are actually a coal. The N2 isotherm graph reveals that the light weight flying coal particles has a very low surface area. Additionally, the XRD and TGA studies supports the conclusion that these dark colored particulate emissions are primarily fine coal particles (cenosphere).","PeriodicalId":44836,"journal":{"name":"Mehran University Research Journal of Engineering and Technology","volume":"6 2","pages":""},"PeriodicalIF":0.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139126738","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 : 2024-01-01DOI: 10.22581/muet1982.2401.2710
Fulya Gökşen, Ç. Takva, Yenal Takva, Z. İlerisoy
Earthquakes have great damage potential and importance in risk management and structural engineering, causing fires in buildings such as residences and commercial spaces. Post-earthquake fires (PEF) are secondary disasters that can cause material and moral destruction and loss of life. Similar to natural disasters, they show the time of occurrence and possible scenarios in places. This study aims to analyse and examine what precautions can be taken to prevent or minimize PEF through risk assessment. In this study, a literature review was conducted with the tracking method, focusing on examples from the world where the fires that occur as a secondary effect of the earthquake can cause devastating damages and significant disasters, and inferences are made by classifying the data obtained. Many factors, such as gas leaks due to earthquakes, cracks in pipelines, and short circuits in electrical installations, can cause fires. In addition, flammable liquid or combustible gas emissions and fire protection disturbances create significant fire hazards after earthquakes. In this paper, in which the causes and consequences of fires are analysed, risks, the evaluation process depending on the risks, the precautions that can be taken according to the situations that the risks will cause, and the models developed are emphasized. The research is a reference study with the expectation that there will be an increase in the number of studies examining experimental and physical PEF models.
{"title":"Post-earthquake fires: risk assessment and precautions","authors":"Fulya Gökşen, Ç. Takva, Yenal Takva, Z. İlerisoy","doi":"10.22581/muet1982.2401.2710","DOIUrl":"https://doi.org/10.22581/muet1982.2401.2710","url":null,"abstract":"Earthquakes have great damage potential and importance in risk management and structural engineering, causing fires in buildings such as residences and commercial spaces. Post-earthquake fires (PEF) are secondary disasters that can cause material and moral destruction and loss of life. Similar to natural disasters, they show the time of occurrence and possible scenarios in places. This study aims to analyse and examine what precautions can be taken to prevent or minimize PEF through risk assessment. In this study, a literature review was conducted with the tracking method, focusing on examples from the world where the fires that occur as a secondary effect of the earthquake can cause devastating damages and significant disasters, and inferences are made by classifying the data obtained. Many factors, such as gas leaks due to earthquakes, cracks in pipelines, and short circuits in electrical installations, can cause fires. In addition, flammable liquid or combustible gas emissions and fire protection disturbances create significant fire hazards after earthquakes. In this paper, in which the causes and consequences of fires are analysed, risks, the evaluation process depending on the risks, the precautions that can be taken according to the situations that the risks will cause, and the models developed are emphasized. The research is a reference study with the expectation that there will be an increase in the number of studies examining experimental and physical PEF models.","PeriodicalId":44836,"journal":{"name":"Mehran University Research Journal of Engineering and Technology","volume":"86 9","pages":""},"PeriodicalIF":0.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139128759","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 : 2024-01-01DOI: 10.22581/muet1982.2401.1613
M. Wasim, Ahsan Ali, Muhammad Mateen Afzal Awan, I. Shaikh
This Airships are lighter than air vehicles and due to their growing number of applications, they are becoming attractive for the research community. Most of the applications require an airship autonomous flight controller which needs an accurate model and state information. Usually, airship states are affected by noise and states information can be lost in the case of sensor's faults, while airship model is affected by model inaccuracies and model uncertainties. This paper presents the application of nonlinear and Bayesian estimators for estimating the states and model uncertainties of neutrally buoyant airship. It is considered that minimum sensor measurements are available, and data is corrupted with process and measurement noise. A novel lumped model uncertainty estimation approach is formulated where airship model is augmented with six extra state variables capturing the model uncertainty of the airship. The designed estimator estimates the airship model uncertainty along with its states. Nonlinear estimators, Extended Kalman Filter and Unscented Kalman Filter are designed for estimating airship attitude, linear velocities, angular velocities and model uncertainties. While Particle filter is designed for the estimation of airship attitude, linear velocities and angular velocities. Simulations have been performed using nonlinear 6-DOF simulation model of experimental airship for assessing the estimator performances. 1−𝜎 uncertainty bound and error analysis have been performed for the validation. A comparative study of the estimator's performances is also carried out.
{"title":"Estimation of airship states and model uncertainties using nonlinear estimators","authors":"M. Wasim, Ahsan Ali, Muhammad Mateen Afzal Awan, I. Shaikh","doi":"10.22581/muet1982.2401.1613","DOIUrl":"https://doi.org/10.22581/muet1982.2401.1613","url":null,"abstract":"This Airships are lighter than air vehicles and due to their growing number of applications, they are becoming attractive for the research community. Most of the applications require an airship autonomous flight controller which needs an accurate model and state information. Usually, airship states are affected by noise and states information can be lost in the case of sensor's faults, while airship model is affected by model inaccuracies and model uncertainties. This paper presents the application of nonlinear and Bayesian estimators for estimating the states and model uncertainties of neutrally buoyant airship. It is considered that minimum sensor measurements are available, and data is corrupted with process and measurement noise. A novel lumped model uncertainty estimation approach is formulated where airship model is augmented with six extra state variables capturing the model uncertainty of the airship. The designed estimator estimates the airship model uncertainty along with its states. Nonlinear estimators, Extended Kalman Filter and Unscented Kalman Filter are designed for estimating airship attitude, linear velocities, angular velocities and model uncertainties. While Particle filter is designed for the estimation of airship attitude, linear velocities and angular velocities. Simulations have been performed using nonlinear 6-DOF simulation model of experimental airship for assessing the estimator performances. 1−𝜎 uncertainty bound and error analysis have been performed for the validation. A comparative study of the estimator's performances is also carried out.","PeriodicalId":44836,"journal":{"name":"Mehran University Research Journal of Engineering and Technology","volume":"4 11","pages":""},"PeriodicalIF":0.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139129787","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 : 2024-01-01DOI: 10.22581/muet1982.2401.2917
Muhammad Awais, Feroz Ahmed Soomro, Shreen El-Sapa, Rahim Bux Khokhar, Areej A. Almoneef
The aim of the current study is to investigate the heat transfer performance of 𝐴𝑙2𝑂3 and 𝐶𝑢 nanoparticles suspended based in 𝐻2𝑂 nanofluids inside a partially heated C-shaped enclosure. The governing equations for heat and flow transfer are solved using the Finite Element Method. Heat transmission is affected by the type and form of nanoparticles. To study the improved heat transfer performance, four different shapes of nanoparticles-spherical, cylindrical, column, and lamina-have been used. The investigation showed that among the considered shapes of nanoparticles, the lamina shape of nanoparticles performed best. Considering lamina nanoparticles, in comparison to the simple nanofluids 𝐴𝑙2𝑂3−𝐻2𝑂 and 𝐶𝑢−𝐻2𝑂 the hybrid nanofluid 𝐴𝑙2𝑂3−𝐶𝑢−𝐻2𝑂 provides the enhanced heat transfer rate. The heat transfer is governed by convection at a higher Rayleigh number. On the other hand, the heat transfer rate is decreasing by increasing the impact of the magnetic field. For the increased heat transfer rate, the best choice is lamina nanoparticles and hybrid nanofluid 𝐴𝑙2𝑂3−𝐶𝑢−𝐻2𝑂.
{"title":"Comparative heat transfer analysis of 𝑨𝒍𝟐𝑶𝟑 and 𝑪𝒖 nanoparticles based in 𝑯𝟐𝑶 nanofluids flow inside a C-shaped partially heated rectangular cavity","authors":"Muhammad Awais, Feroz Ahmed Soomro, Shreen El-Sapa, Rahim Bux Khokhar, Areej A. Almoneef","doi":"10.22581/muet1982.2401.2917","DOIUrl":"https://doi.org/10.22581/muet1982.2401.2917","url":null,"abstract":"The aim of the current study is to investigate the heat transfer performance of 𝐴𝑙2𝑂3 and 𝐶𝑢 nanoparticles suspended based in 𝐻2𝑂 nanofluids inside a partially heated C-shaped enclosure. The governing equations for heat and flow transfer are solved using the Finite Element Method. Heat transmission is affected by the type and form of nanoparticles. To study the improved heat transfer performance, four different shapes of nanoparticles-spherical, cylindrical, column, and lamina-have been used. The investigation showed that among the considered shapes of nanoparticles, the lamina shape of nanoparticles performed best. Considering lamina nanoparticles, in comparison to the simple nanofluids 𝐴𝑙2𝑂3−𝐻2𝑂 and 𝐶𝑢−𝐻2𝑂 the hybrid nanofluid 𝐴𝑙2𝑂3−𝐶𝑢−𝐻2𝑂 provides the enhanced heat transfer rate. The heat transfer is governed by convection at a higher Rayleigh number. On the other hand, the heat transfer rate is decreasing by increasing the impact of the magnetic field. For the increased heat transfer rate, the best choice is lamina nanoparticles and hybrid nanofluid 𝐴𝑙2𝑂3−𝐶𝑢−𝐻2𝑂.","PeriodicalId":44836,"journal":{"name":"Mehran University Research Journal of Engineering and Technology","volume":"109 10","pages":""},"PeriodicalIF":0.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139128643","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 : 2024-01-01DOI: 10.22581/muet1982.2401.2175
Sara Qadeer Rajput, Khuhed Memon, G. H. Palli
Artificial Intelligence (AI) has brought about a profound transformation in the realm of technology, with Machine Learning (ML) within AI playing a crucial role in today's healthcare systems. Advanced systems with intellectual abilities resembling those of humans are being created and utilized to carry out intricate tasks. Applications like Object recognition, classification, Optical Character Recognition (OCR), Natural Language processing (NLP), among others, have started producing magnificent results with algorithms trained on humongous data readily available these days. Keeping in view the socio-economic implications of the pandemic threat posed to the world by COVID-19, this research aims at improving the quality of life of people suffering from mild depression by timely diagnosing the symptoms using AI in android devices, especially phones. In cases of severe depression, which is highly likely to lead to suicide, valuable lives can also be saved if adequate help can be dispatched to such patients within time. This can be achieved using automatic analysis of users’ data including text messages, emails, voice calls and internet search history, among other mobile phone activities, using Text mining/ text analytics which is the process of deriving meaningful information from natural language text. Machine Learning models analyse the users’ behaviour continuously from text and voice communications and data, thereby identifying if there are any negative tendencies in the behaviour over a certain period of time, and by using this information make inferences about the mental health state of the patient and instantly request appropriate healthcare before it is too late. In this research, an android application capable of performing the aforementioned tasks in real-time has been developed and tested for various performance features with an average accuracy of 95%.
{"title":"Predicting depression and suicidal tendencies by analyzing online activities using machine learning in android devices","authors":"Sara Qadeer Rajput, Khuhed Memon, G. H. Palli","doi":"10.22581/muet1982.2401.2175","DOIUrl":"https://doi.org/10.22581/muet1982.2401.2175","url":null,"abstract":"Artificial Intelligence (AI) has brought about a profound transformation in the realm of technology, with Machine Learning (ML) within AI playing a crucial role in today's healthcare systems. Advanced systems with intellectual abilities resembling those of humans are being created and utilized to carry out intricate tasks. Applications like Object recognition, classification, Optical Character Recognition (OCR), Natural Language processing (NLP), among others, have started producing magnificent results with algorithms trained on humongous data readily available these days. Keeping in view the socio-economic implications of the pandemic threat posed to the world by COVID-19, this research aims at improving the quality of life of people suffering from mild depression by timely diagnosing the symptoms using AI in android devices, especially phones. In cases of severe depression, which is highly likely to lead to suicide, valuable lives can also be saved if adequate help can be dispatched to such patients within time. This can be achieved using automatic analysis of users’ data including text messages, emails, voice calls and internet search history, among other mobile phone activities, using Text mining/ text analytics which is the process of deriving meaningful information from natural language text. Machine Learning models analyse the users’ behaviour continuously from text and voice communications and data, thereby identifying if there are any negative tendencies in the behaviour over a certain period of time, and by using this information make inferences about the mental health state of the patient and instantly request appropriate healthcare before it is too late. In this research, an android application capable of performing the aforementioned tasks in real-time has been developed and tested for various performance features with an average accuracy of 95%.","PeriodicalId":44836,"journal":{"name":"Mehran University Research Journal of Engineering and Technology","volume":"79 7","pages":""},"PeriodicalIF":0.6,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139127606","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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