Pub Date : 2024-06-12DOI: 10.55766/sujst-2024-02-e05287
Hoang Dung Nguyen, Sandhya Babel
This study explores the utilization of a cation exchange membrane (CEM) in a microbial fuel cell (MFC) system to isolate nitrogen from wastewater influents. While employing a CEM in an MFC system has drawbacks, such as increased internal resistance and reduced power output, it also provides a means for optimal energy recovery from organics while allowing isolated nitrogen to be treated in subsequent steps. This study evaluated the diffusion of ammonium through CEM in a dual-chamber MFC under different operating conditions. Results indicated that the MFC reactor with CEM as a separator isolated 88-93% of the nitrogen input, demonstrating the feasibility of this approach for nitrogen separation in wastewater treatment applications. Factors affecting nitrogen isolation, including COD input at the anode, dissolved oxygen (DO) at the cathode, and external resistance (ER), are identified. Higher COD input at the anode and the DO at the cathode were found to enhance nitrogen separation, while increased ER had an adverse effect on nitrogen isolation capacity. Additionally, changes in the surface characteristics of the CEM during operation could impact nitrogen isolation, emphasizing the need for careful monitoring and maintenance of the CEM to ensure consistent performance over time. In conclusion, this study highlighted the potential of using a CEM in MFC systems for nitrogen isolation, provided insights into the factors affecting the efficacy of nitrogen separation, and underscored the need for monitoring and maintenance of the CEM. These results could significantly impact the development of more efficient and sustainable wastewater treatment using the MFC system.
本研究探讨了如何在微生物燃料电池（MFC）系统中利用阳离子交换膜（CEM）来分离废水中的氮。虽然在 MFC 系统中使用 CEM 会增加内阻、降低输出功率等缺点，但它也提供了一种从有机物中进行最佳能量回收的方法，同时允许在后续步骤中对分离出的氮进行处理。本研究评估了在不同操作条件下，氨在双室 MFC 中通过 CEM 的扩散情况。结果表明，以 CEM 作为分离器的 MFC 反应器分离了 88-93% 的氮输入，证明了这种方法在废水处理应用中进行氮分离的可行性。确定了影响氮分离的因素，包括阳极的 COD 输入量、阴极的溶解氧 (DO) 和外部电阻 (ER)。研究发现，阳极的 COD 输入量和阴极的溶解氧越高，氮分离效果越好，而 ER 的增加则会对氮分离能力产生不利影响。此外，在运行过程中，CEM 表面特性的变化也会影响氮分离效果，因此需要对 CEM 进行仔细监测和维护，以确保其性能长期稳定。总之，这项研究强调了在 MFC 系统中使用 CEM 进行氮隔离的潜力，深入探讨了影响氮分离效果的因素，并强调了监测和维护 CEM 的必要性。这些结果将对使用 MFC 系统进行更高效、更可持续的废水处理产生重大影响。
{"title":"EXPLORING FACTORS AFFECTING NITROGEN ISOLATION BY CATION EXCHANGE MEMBRANE AND THEIR IMPLICATIONS FOR MICROBIAL FUEL CELL PERFORMANCE IN WASTEWATER TREATMENT","authors":"Hoang Dung Nguyen, Sandhya Babel","doi":"10.55766/sujst-2024-02-e05287","DOIUrl":"https://doi.org/10.55766/sujst-2024-02-e05287","url":null,"abstract":"This study explores the utilization of a cation exchange membrane (CEM) in a microbial fuel cell (MFC) system to isolate nitrogen from wastewater influents. While employing a CEM in an MFC system has drawbacks, such as increased internal resistance and reduced power output, it also provides a means for optimal energy recovery from organics while allowing isolated nitrogen to be treated in subsequent steps. This study evaluated the diffusion of ammonium through CEM in a dual-chamber MFC under different operating conditions. Results indicated that the MFC reactor with CEM as a separator isolated 88-93% of the nitrogen input, demonstrating the feasibility of this approach for nitrogen separation in wastewater treatment applications. Factors affecting nitrogen isolation, including COD input at the anode, dissolved oxygen (DO) at the cathode, and external resistance (ER), are identified. Higher COD input at the anode and the DO at the cathode were found to enhance nitrogen separation, while increased ER had an adverse effect on nitrogen isolation capacity. Additionally, changes in the surface characteristics of the CEM during operation could impact nitrogen isolation, emphasizing the need for careful monitoring and maintenance of the CEM to ensure consistent performance over time. In conclusion, this study highlighted the potential of using a CEM in MFC systems for nitrogen isolation, provided insights into the factors affecting the efficacy of nitrogen separation, and underscored the need for monitoring and maintenance of the CEM. These results could significantly impact the development of more efficient and sustainable wastewater treatment using the MFC system.","PeriodicalId":509211,"journal":{"name":"Suranaree Journal of Science and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141350006","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 : 2024-06-11DOI: 10.55766/sujst-2024-02-e02734
R. R, Jaisingh Ebenesar Anna Bagyam
The queueing system with two types of vacation policies and disasters occurring in every state are examined in this study. As soon as the system is empty, the server goes on a short-term vacation. If there are no active clients in the system when the server returns from a short-term vacation, then the server goes on a long-term vacation. Upon seeing at least one client in the queue during a short or long-term vacation, the server will turn on right away. Catastrophes can happen during short or long vacations and also when the server is active. All current clients are removed from the system when a disaster occurs. When the system has been fixed, it starts to work properly once again. After repair, the server immediately switches to working mode(model 1) or takes a short-term vacation (model 2). A steady-state solution is determined by using the PGF (Probability-Generating Function). The above two categories of after-repair will be examined in this paper. Additionally, numerical examples related to performance measures, cost models, and the steady analysis of the provided models are discussed. Management for networking disaster recovery gives suggestions for restarting regular operations and network services following a disaster.
{"title":"COMPREHENSIVE DISCUSSION OF THE REPAIRABLE SINGLE SERVER CATASTROPHE AND MULTIPLE VACATION QUEUEING MODEL","authors":"R. R, Jaisingh Ebenesar Anna Bagyam","doi":"10.55766/sujst-2024-02-e02734","DOIUrl":"https://doi.org/10.55766/sujst-2024-02-e02734","url":null,"abstract":"The queueing system with two types of vacation policies and disasters occurring in every state are examined in this study. As soon as the system is empty, the server goes on a short-term vacation. If there are no active clients in the system when the server returns from a short-term vacation, then the server goes on a long-term vacation. Upon seeing at least one client in the queue during a short or long-term vacation, the server will turn on right away. Catastrophes can happen during short or long vacations and also when the server is active. All current clients are removed from the system when a disaster occurs. When the system has been fixed, it starts to work properly once again. After repair, the server immediately switches to working mode(model 1) or takes a short-term vacation (model 2). A steady-state solution is determined by using the PGF (Probability-Generating Function). The above two categories of after-repair will be examined in this paper. Additionally, numerical examples related to performance measures, cost models, and the steady analysis of the provided models are discussed. Management for networking disaster recovery gives suggestions for restarting regular operations and network services following a disaster.","PeriodicalId":509211,"journal":{"name":"Suranaree Journal of Science and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141355154","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 : 2024-06-11DOI: 10.55766/sujst-2024-02-e03011
Malini Devi Ramanathan, Vaishnavi Kalirajan
Myocardial infarction commonly known as heart attack, is a medical emergency caused by other diseases in humans. In this paper, a mathematical model based on the factors influencing myocardial infarction is generated. The solution of the model is obtained with the help of the Homotopy Perturbation Method. The analytical results are verified with the exact solution by using MATLAB. The behavior of each parameter is discussed. The main aim of this paper is to develop a model for myocardial infarction and discuss the parameters that help in reducing the rate of myocardial infarction.
{"title":"MODEL FORMULATION AND COMPUTATION FOR FACTORS INFLUENCING MYOCARDIAL INFARCTION IN HUMANS","authors":"Malini Devi Ramanathan, Vaishnavi Kalirajan","doi":"10.55766/sujst-2024-02-e03011","DOIUrl":"https://doi.org/10.55766/sujst-2024-02-e03011","url":null,"abstract":"Myocardial infarction commonly known as heart attack, is a medical emergency caused by other diseases in humans. In this paper, a mathematical model based on the factors influencing myocardial infarction is generated. The solution of the model is obtained with the help of the Homotopy Perturbation Method. The analytical results are verified with the exact solution by using MATLAB. The behavior of each parameter is discussed. The main aim of this paper is to develop a model for myocardial infarction and discuss the parameters that help in reducing the rate of myocardial infarction.","PeriodicalId":509211,"journal":{"name":"Suranaree Journal of Science and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141358276","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 : 2024-06-11DOI: 10.55766/sujst-2024-02-e02133
S. Agrawal, Yogesh Kumar Gupta
Introduction: Respiratory diseases, particularly pneumonia, pose a significant threat to human life. Pneumonia affects the respiratory function in the human body and is a dangerous lung disease. This study aims to propose a model for detecting pneumonia in chest XR images. By utilizing statistical-based features, relevant and informative features are extracted from lung X-ray images. Objective: The objective is to obtain high accuracy in pneumonia identification; the target of this work is to generate a model that can precisely recognize the presence of pneumonia by evaluating chest X-ray pictures. Method: The Method follows a three-phase approach: preprocessing, categorization, and extraction of features. Preprocessing is the stage when various filters are applied to the chest X-ray images to enhance their eminence and eradicate noise. The feature extraction phase involves extracting statistical-based features from the preprocessed images. These features capture relevant information regarding a pneumonia diagnosis. Finally, in the classification phase, algorithms for machine learning are employed to use the retrieved features to categorize the X-ray pictures as infected or uninfected. Result: The proposed model successfully detects the presence of pneumonia accurately. By leveraging advanced machine learning algorithms, the model achieves accurate X-ray image classification for the chest. Conclusion: This study concludes by presenting a model for detecting pneumonia by examining chest X-ray pictures. To accurately classify infected and non-infected lungs, the proposed model makes use of image dispensation methods and machine learning algorithms. The model's high accuracy in pneumonia detection can significantly contribute to early diagnosis and treatment.
引言呼吸系统疾病，尤其是肺炎，对人类生命构成重大威胁。肺炎影响人体的呼吸功能，是一种危险的肺部疾病。本研究旨在提出一种在胸部 XR 图像中检测肺炎的模型。通过利用基于统计的特征，从肺部 X 光图像中提取相关的信息特征。目标本研究的目标是通过评估胸部 X 光图像，生成一个能准确识别肺炎的模型。方法：该方法分为三个阶段：预处理、分类和提取特征。预处理阶段是对胸部 X 光图像进行各种过滤，以增强图像的清晰度并消除噪音。特征提取阶段包括从预处理图像中提取基于统计的特征。这些特征可捕捉到与肺炎诊断相关的信息。最后，在分类阶段，采用机器学习算法，利用检索到的特征将 X 光图片分为感染和未感染两类。结果：所提出的模型成功地准确检测出肺炎的存在。通过利用先进的机器学习算法，该模型实现了胸部 X 光图像的准确分类。结论本研究最后提出了一种通过检查胸部 X 光图片来检测肺炎的模型。为了准确地对感染和非感染肺部进行分类，所提出的模型利用了图像分配方法和机器学习算法。该模型在肺炎检测方面的高准确率可大大促进早期诊断和治疗。
{"title":"TO ANALYZE THE LUNGS X-RAY IMAGES USING MACHINE LEARNING ALGORITHM: AN IMPLEMENTATION TO PNEUMONIA DIAGNOSIS","authors":"S. Agrawal, Yogesh Kumar Gupta","doi":"10.55766/sujst-2024-02-e02133","DOIUrl":"https://doi.org/10.55766/sujst-2024-02-e02133","url":null,"abstract":"Introduction: Respiratory diseases, particularly pneumonia, pose a significant threat to human life. Pneumonia affects the respiratory function in the human body and is a dangerous lung disease. This study aims to propose a model for detecting pneumonia in chest XR images. By utilizing statistical-based features, relevant and informative features are extracted from lung X-ray images. Objective: The objective is to obtain high accuracy in pneumonia identification; the target of this work is to generate a model that can precisely recognize the presence of pneumonia by evaluating chest X-ray pictures. Method: The Method follows a three-phase approach: preprocessing, categorization, and extraction of features. Preprocessing is the stage when various filters are applied to the chest X-ray images to enhance their eminence and eradicate noise. The feature extraction phase involves extracting statistical-based features from the preprocessed images. These features capture relevant information regarding a pneumonia diagnosis. Finally, in the classification phase, algorithms for machine learning are employed to use the retrieved features to categorize the X-ray pictures as infected or uninfected. Result: The proposed model successfully detects the presence of pneumonia accurately. By leveraging advanced machine learning algorithms, the model achieves accurate X-ray image classification for the chest. Conclusion: This study concludes by presenting a model for detecting pneumonia by examining chest X-ray pictures. To accurately classify infected and non-infected lungs, the proposed model makes use of image dispensation methods and machine learning algorithms. The model's high accuracy in pneumonia detection can significantly contribute to early diagnosis and treatment.","PeriodicalId":509211,"journal":{"name":"Suranaree Journal of Science and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141358488","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}
One of the most prevalent hormonal disorders affecting women who are of reproductive age is PCOS, a serious public health concern. Insulin resistance is significantly more common together with hirsutism, polycystic ovaries, and oligo/anovulatory periods as typical symptoms and other multitude of chronic health issues that impact quality of life are linked to PCOS.PCOS patient and control group (non-PCOS) hormonal profiles are compared in this study.TSH, FSH, LH, and AMH are measured in this research. The results clarify the hormonal irregularities and consequences of PCOS.The data shows that women with PCOS had significantly greater AMH than controls, with a sensitivity and specificity of 93%.AMH has an NPV and PPV of 83% and 97%, respectively.The majority of PCOS patients reported higher LH levels than the control group, which had 86% sensitivity and 88% specificity. The LH level is impacted by PCOS, as seen by the 95% of PPV and 69%, of NPV.The FSH levels of controls and PCOS patients are comparable. Since FSH levels are comparable, the primary hormonal imbalance in PCOS is probably elevated LH and AMH. PCOS patients showed higher TSH levels than the control group, which may cause thyroid issues since 43% of PCOS patients had elevated TSH.These findings illuminate PCOS hormone abnormalities. Furthermore, people with PCOS are more prone than the general population to suffering from the morbidity related to metabolic and cardiovascular diseases.Understanding the hormonal imbalances linked to PCOS makes future research easier, which is required to understand the etiology of PCOS and hormone-regulated PCOS treatment.
{"title":"DECIPHERING THE INTRICATE NETWORK OF POLY CYSTIC OVARIAN SYNDROME: A THOROUGH EXAMINATION OF HORMONAL AND DEMOGRAPHIC INFLUENCES","authors":"Janvi Varma, Aparna Pandey, Arun Kumar Kulshrestha, Vijay Jagdish Upadhye","doi":"10.55766/sujst-2024-02-e03383","DOIUrl":"https://doi.org/10.55766/sujst-2024-02-e03383","url":null,"abstract":"One of the most prevalent hormonal disorders affecting women who are of reproductive age is PCOS, a serious public health concern. Insulin resistance is significantly more common together with hirsutism, polycystic ovaries, and oligo/anovulatory periods as typical symptoms and other multitude of chronic health issues that impact quality of life are linked to PCOS.PCOS patient and control group (non-PCOS) hormonal profiles are compared in this study.TSH, FSH, LH, and AMH are measured in this research. The results clarify the hormonal irregularities and consequences of PCOS.The data shows that women with PCOS had significantly greater AMH than controls, with a sensitivity and specificity of 93%.AMH has an NPV and PPV of 83% and 97%, respectively.The majority of PCOS patients reported higher LH levels than the control group, which had 86% sensitivity and 88% specificity. The LH level is impacted by PCOS, as seen by the 95% of PPV and 69%, of NPV.The FSH levels of controls and PCOS patients are comparable. Since FSH levels are comparable, the primary hormonal imbalance in PCOS is probably elevated LH and AMH. PCOS patients showed higher TSH levels than the control group, which may cause thyroid issues since 43% of PCOS patients had elevated TSH.These findings illuminate PCOS hormone abnormalities. Furthermore, people with PCOS are more prone than the general population to suffering from the morbidity related to metabolic and cardiovascular diseases.Understanding the hormonal imbalances linked to PCOS makes future research easier, which is required to understand the etiology of PCOS and hormone-regulated PCOS treatment.","PeriodicalId":509211,"journal":{"name":"Suranaree Journal of Science and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141363090","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 : 2024-06-07DOI: 10.55766/sujst-2024-02-e01595
Kittisak Sriwongsa
In this work, the g-ray and thermal neutron shielding properties of high entropy alloys in formula Wx(TaVZr)100-x, where (x = 5, 10, 15, 20 and 25%wt) have been studied. The mass attenuation coefficients (µm) for g-ray of these high entropy alloys have been obtained at energy range 59.6-1332 keV using Phy-X software and FLUKA Monte Carlo code simulation. The obtained results are found to be in good agreement. The W25 for high entropy alloys sample showed the highest µm value and the lowest HVL, MFP and transmission factor compared with the other ones in these high entropy alloys samples. As for the case of thermal neutron shielding properties, the FLUKA Monte Carlo code was used for simulated. It was found that W5 high entropy alloy sample shown the highest mass attenuation coefficients (µm) value. These results indicated that the high entropy alloys sample, with high W content possesses excellent g-ray shielding properties, while alloys with low W content possesses superb thermal neutron shielding.
这项工作研究了 Wx(TaVZr)100-x 式（其中 x = 5、10、15、20 和 25%重量）高熵合金的 g 射线和热中子屏蔽特性。利用 Phy-X 软件和 FLUKA 蒙特卡罗代码模拟，获得了这些高熵合金在 59.6-1332 keV 能量范围内的 g 射线质量衰减系数（µm）。结果发现两者非常吻合。与其他高熵合金样品相比，高熵合金样品的 W25 显示出最高的 µm 值和最低的 HVL、MFP 和透射系数。至于热中子屏蔽性能，则使用了 FLUKA Monte Carlo 代码进行模拟。结果发现，W5 高熵合金样品的质量衰减系数（µm）值最高。这些结果表明，含 W 量高的高熵合金样品具有极佳的 g 射线屏蔽性能，而含 W 量低的合金则具有极佳的热中子屏蔽性能。
{"title":"THE MONTE CARLO FLUKA SIMULATION OF GAMMA-RAY AND NEUTRON ATTENUATION CHARACTERISTICS OF REFRACTORY HIGH ENTROPY ALLOYS","authors":"Kittisak Sriwongsa","doi":"10.55766/sujst-2024-02-e01595","DOIUrl":"https://doi.org/10.55766/sujst-2024-02-e01595","url":null,"abstract":"In this work, the g-ray and thermal neutron shielding properties of high entropy alloys in formula Wx(TaVZr)100-x, where (x = 5, 10, 15, 20 and 25%wt) have been studied. The mass attenuation coefficients (µm) for g-ray of these high entropy alloys have been obtained at energy range 59.6-1332 keV using Phy-X software and FLUKA Monte Carlo code simulation. The obtained results are found to be in good agreement. The W25 for high entropy alloys sample showed the highest µm value and the lowest HVL, MFP and transmission factor compared with the other ones in these high entropy alloys samples. As for the case of thermal neutron shielding properties, the FLUKA Monte Carlo code was used for simulated. It was found that W5 high entropy alloy sample shown the highest mass attenuation coefficients (µm) value. These results indicated that the high entropy alloys sample, with high W content possesses excellent g-ray shielding properties, while alloys with low W content possesses superb thermal neutron shielding.","PeriodicalId":509211,"journal":{"name":"Suranaree Journal of Science and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141373316","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 : 2024-06-07DOI: 10.55766/sujst-2024-02-e03172
M. Patel, Anant Parghi, M. H. Lunagaria
In this study, the behavior of square and circular reinforced concrete (RC) columns confined with fiber reinforced polymer (FRP) composites were compared using the finite element model (FEM) under axial compressive load. The different parameters are considered as the type of FRP confinement such as carbon and glass, grade of concrete, and aspect ratios of the columns. The verified model was used to study the different parameters to investigate the different geometric and materials properties on the axial capacity of the columns. The results revealed a significant increase in the strength and ductility of both the square and circular columns due to FRP confinement. The carbon FRP was slightly more effective in enhancing column strength due to their higher modulus of elasticity compared to the glass FRP. The study also found that higher grades of concrete and lower aspect ratios led to higher strength and ductility in confined columns. Overall, the study suggests that the use of FRP composites for the confinement of RC columns is a viable method for enhancing their strength and ductility. These findings can be valuable for designing and retrofitting RC columns with FRP composites in practical applications.
{"title":"NUMERICAL INVESTIGATION OF FRP CONFINED RC SQUARE AND CIRCULAR COLUMNS UNDER CONCENTRIC COMPRESSION LOAD","authors":"M. Patel, Anant Parghi, M. H. Lunagaria","doi":"10.55766/sujst-2024-02-e03172","DOIUrl":"https://doi.org/10.55766/sujst-2024-02-e03172","url":null,"abstract":"In this study, the behavior of square and circular reinforced concrete (RC) columns confined with fiber reinforced polymer (FRP) composites were compared using the finite element model (FEM) under axial compressive load. The different parameters are considered as the type of FRP confinement such as carbon and glass, grade of concrete, and aspect ratios of the columns. The verified model was used to study the different parameters to investigate the different geometric and materials properties on the axial capacity of the columns. The results revealed a significant increase in the strength and ductility of both the square and circular columns due to FRP confinement. The carbon FRP was slightly more effective in enhancing column strength due to their higher modulus of elasticity compared to the glass FRP. The study also found that higher grades of concrete and lower aspect ratios led to higher strength and ductility in confined columns. Overall, the study suggests that the use of FRP composites for the confinement of RC columns is a viable method for enhancing their strength and ductility. These findings can be valuable for designing and retrofitting RC columns with FRP composites in practical applications.","PeriodicalId":509211,"journal":{"name":"Suranaree Journal of Science and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141372549","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 : 2024-06-06DOI: 10.55766/sujst-2024-02-e02972
Rajnish, Sumit Saroha, Manish Saini
Solar energy has vast potential in India which is a rapidly growing economy with diverse geographical features. Solar energy has intermittent behaviour and depends on geographical and weather conditions. Therefore, the reliability of the solar depends on the seamless operation of solar plants with the latest technologies. The main objective of power operator is to facilitate the renewable power sources intergeration for maintaining an uninterrupted power supply. To achieve this objective, researchers have employed various Deep Learning methods of machine learning, such as RNN, LSTM, CNN and SVM for accurate solar power forecasting with higher relibaility. In this paper, a GA-CNN deep learning algorithm is employed with an optimized hyperparameters technique for PV energy forecasting. This technique outperforms when compared with the other methods such as LSTM, KNN-SVM, and CNN-RNN techniques in terms of RMSE, MAE, MSE and R-Square performance indices. This method provides a better and more robust method of deep learning for solar PV energy forecasting.
{"title":"PV ENERGY FORECASTING USING DEEP LEARNING ALGORITHM","authors":"Rajnish, Sumit Saroha, Manish Saini","doi":"10.55766/sujst-2024-02-e02972","DOIUrl":"https://doi.org/10.55766/sujst-2024-02-e02972","url":null,"abstract":"Solar energy has vast potential in India which is a rapidly growing economy with diverse geographical features. Solar energy has intermittent behaviour and depends on geographical and weather conditions. Therefore, the reliability of the solar depends on the seamless operation of solar plants with the latest technologies. The main objective of power operator is to facilitate the renewable power sources intergeration for maintaining an uninterrupted power supply. To achieve this objective, researchers have employed various Deep Learning methods of machine learning, such as RNN, LSTM, CNN and SVM for accurate solar power forecasting with higher relibaility. In this paper, a GA-CNN deep learning algorithm is employed with an optimized hyperparameters technique for PV energy forecasting. This technique outperforms when compared with the other methods such as LSTM, KNN-SVM, and CNN-RNN techniques in terms of RMSE, MAE, MSE and R-Square performance indices. This method provides a better and more robust method of deep learning for solar PV energy forecasting.","PeriodicalId":509211,"journal":{"name":"Suranaree Journal of Science and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141378352","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 : 2024-04-19DOI: 10.55766/sujst-2024-01-e02530
A. R. Alfian, Nindi Octaviani, Aken Puti Nandi Nanti, Muhammad Varrel Anandito, Shirly Rizky, Trianda Nurlia Hidayat
The use of Alum in water treatment has a negative impact on human health if used in the long term. The potential of Opuntia ficus indica (OFI) to be used as an organic coagulant that comes directly from nature can minimize metal contamination. Hence, OFI could be used as a new alternative in water treatment. The purpose of this study was to determine the effectiveness and efficiency of OFI in eliminating Fe, Mn, and As contamination. This research was conducted for four months at the University of Andalas. The research method was done by making metal-contaminated water samples, making OFI extract in the shape of powder form, then carrying out the FTIR test, providing treatment in the Laboratory of Organic Chemistry of Natural Materials Department of Chemistry Faculty of Mathematics and Natural Sciences, Universitas Andalas, then conducting Atomic Absorption Spectroscopy (AAS) testing for Fe metals and Mn metals and UV-Vis spectrophotometer test for metals as metals in the West Sumatra Regional Health Laboratory. Analysis of the results of the AAS test and UV-Vis spectrophotometer test showed that OFI powder is more effective and efficient than alum in removing metal contamination. OFI powder is optimal at a mass of 50 mg for removing Mn metal contamination and 100 mg for removing Fe metal contamination, while As metal cannot be identified because the before and after values are smaller than 0.01 mg/L. OFI powder is also more efficient because it is cheaper, easier to cultivate and use by the public, and does not cause side effects on health. It is hoped that the use of OFI powder as a natural coagulant can be used massively by the community.
{"title":"THE EFFECTIVENESS AND EFFICIENCY OF OPUNTIA FICUS-INDICA (OFI) POWDER IN REMOVING Fe, Mn, AND As METAL CONTAMINATION IN WATER TREATMENT","authors":"A. R. Alfian, Nindi Octaviani, Aken Puti Nandi Nanti, Muhammad Varrel Anandito, Shirly Rizky, Trianda Nurlia Hidayat","doi":"10.55766/sujst-2024-01-e02530","DOIUrl":"https://doi.org/10.55766/sujst-2024-01-e02530","url":null,"abstract":"The use of Alum in water treatment has a negative impact on human health if used in the long term. The potential of Opuntia ficus indica (OFI) to be used as an organic coagulant that comes directly from nature can minimize metal contamination. Hence, OFI could be used as a new alternative in water treatment. The purpose of this study was to determine the effectiveness and efficiency of OFI in eliminating Fe, Mn, and As contamination. This research was conducted for four months at the University of Andalas. The research method was done by making metal-contaminated water samples, making OFI extract in the shape of powder form, then carrying out the FTIR test, providing treatment in the Laboratory of Organic Chemistry of Natural Materials Department of Chemistry Faculty of Mathematics and Natural Sciences, Universitas Andalas, then conducting Atomic Absorption Spectroscopy (AAS) testing for Fe metals and Mn metals and UV-Vis spectrophotometer test for metals as metals in the West Sumatra Regional Health Laboratory. Analysis of the results of the AAS test and UV-Vis spectrophotometer test showed that OFI powder is more effective and efficient than alum in removing metal contamination. OFI powder is optimal at a mass of 50 mg for removing Mn metal contamination and 100 mg for removing Fe metal contamination, while As metal cannot be identified because the before and after values are smaller than 0.01 mg/L. OFI powder is also more efficient because it is cheaper, easier to cultivate and use by the public, and does not cause side effects on health. It is hoped that the use of OFI powder as a natural coagulant can be used massively by the community.","PeriodicalId":509211,"journal":{"name":"Suranaree Journal of Science and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140684524","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 : 2024-04-19DOI: 10.55766/sujst-2024-01-e03075
Lenin Kanagasabai
Reminiscence inspired optimization algorithm (RIA) and Approximation based Measurement of Mount Kailash optimization (MMK) algorithm are applied to solve the true power loss reduction problem. Reminiscence inspired optimization algorithm is scientifically designed based on the reminiscence of human beings around the globe. In the procedure poor solutions are replaced by the present solutions consecutively. In the Approximation Measurement of the Mount Kailash optimization algorithm, the measurement of Mount Kailash is done by approximation of comparison with triangles in the four faces of the Mount Kailash. The first measurements on the east face of Mount Kailash are done through the approximation of the triangle and from this population created in the search space. The key objectives of the paper are true power loss reduction, voltage deviation minimization, and voltage stability enhancement. Validity RIA and MMK are verified in 23 Benchmarking functions and IEEE 30, 354 systems.
{"title":"NOVEL REMINISCENCE INSPIRED AND APPROXIMATION BASED MEASUREMENT OF MOUNT KAILASH OPTIMIZATION ALGORITHMS","authors":"Lenin Kanagasabai","doi":"10.55766/sujst-2024-01-e03075","DOIUrl":"https://doi.org/10.55766/sujst-2024-01-e03075","url":null,"abstract":"Reminiscence inspired optimization algorithm (RIA) and Approximation based Measurement of Mount Kailash optimization (MMK) algorithm are applied to solve the true power loss reduction problem. Reminiscence inspired optimization algorithm is scientifically designed based on the reminiscence of human beings around the globe. In the procedure poor solutions are replaced by the present solutions consecutively. In the Approximation Measurement of the Mount Kailash optimization algorithm, the measurement of Mount Kailash is done by approximation of comparison with triangles in the four faces of the Mount Kailash. The first measurements on the east face of Mount Kailash are done through the approximation of the triangle and from this population created in the search space. The key objectives of the paper are true power loss reduction, voltage deviation minimization, and voltage stability enhancement. Validity RIA and MMK are verified in 23 Benchmarking functions and IEEE 30, 354 systems.","PeriodicalId":509211,"journal":{"name":"Suranaree Journal of Science and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140685389","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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