Pub Date : 2021-07-23DOI: 10.36548/JUCCT.2021.2.006
B. Vivekanandam
Data pre-processing is critical for handling classification issues in the field of machine learning and model identification. The processing of big data sets increases the computer processing time and space complexity while decreasing classification model precision. As a result, it is necessary to develop an appropriate method for selecting attributes. This article describes a machine learning technique to solve functional selection by safeguarding the selection and mutation operators of genetic algorithms. During population calculations in the training set, the proposed method is adaptable. Furthermore, for various population sizes, the proposed method gives the best possible probability of resolving function selection difficulties during training process. Furthermore, the proposed work is combined with a better classifier in order to detect the different malware categories. The proposed approach is compared and validated with current techniques by using different datasets. In addition to the test results, this research work utilizes the algorithm for solving a real challenge in Android categorization, and the results show that, the proposed approach is superior. Besides, the proposed algorithm provides a better mean and standard deviation value in the optimization process for leveraging model effectiveness at different datasets.
{"title":"Design an Adaptive Hybrid Approach for Genetic Algorithm to Detect Effective Malware Detection in Android Division","authors":"B. Vivekanandam","doi":"10.36548/JUCCT.2021.2.006","DOIUrl":"https://doi.org/10.36548/JUCCT.2021.2.006","url":null,"abstract":"Data pre-processing is critical for handling classification issues in the field of machine learning and model identification. The processing of big data sets increases the computer processing time and space complexity while decreasing classification model precision. As a result, it is necessary to develop an appropriate method for selecting attributes. This article describes a machine learning technique to solve functional selection by safeguarding the selection and mutation operators of genetic algorithms. During population calculations in the training set, the proposed method is adaptable. Furthermore, for various population sizes, the proposed method gives the best possible probability of resolving function selection difficulties during training process. Furthermore, the proposed work is combined with a better classifier in order to detect the different malware categories. The proposed approach is compared and validated with current techniques by using different datasets. In addition to the test results, this research work utilizes the algorithm for solving a real challenge in Android categorization, and the results show that, the proposed approach is superior. Besides, the proposed algorithm provides a better mean and standard deviation value in the optimization process for leveraging model effectiveness at different datasets.","PeriodicalId":11002,"journal":{"name":"Day 1 Tue, March 23, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78167713","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 : 2021-07-19DOI: 10.36548/JUCCT.2021.2.004
R. Dhaya
The World Health Organization (WHO) considers the COVID-19 Coronavirus to be a global pandemic. The most effective form of protection is to wear a face mask in public places. Moreover, the COVID-19 pandemic prompted all the countries to set up a lockdown to prevent viral transmission. According to a survey study, the use of facemasks at work decreases the chances of fast transmission. If the facemasks are not used or are worn incorrectly, it contributes to the third and fourth waves of the corona virus spreading throughout the world. This motivates us to conduct an efficient investigation of the face mask identification system and monitor people, who use suitable face mask in public places. Deep learning is the most effective approach for detecting whether or not a person is wearing a face mask in a crowded area. Using a multiclass deep learning technique, this research study proposes an efficient two stage identification (ETSI) for face mask detection. Whereas, the binary classification does not offer information about face mask detection and error. The proposed approach employs CNN's "ReLU" activation function to detect the face mask. Furthermore, in the current pandemic crisis, this research article offers a very efficient and precise approach for identifying COVID-19. Precision has increased as a result of the employment of a multi-class abbreviation in the final output.
{"title":"Efficient Two Stage Identification for Face mask detection using Multiclass Deep Learning Approach","authors":"R. Dhaya","doi":"10.36548/JUCCT.2021.2.004","DOIUrl":"https://doi.org/10.36548/JUCCT.2021.2.004","url":null,"abstract":"The World Health Organization (WHO) considers the COVID-19 Coronavirus to be a global pandemic. The most effective form of protection is to wear a face mask in public places. Moreover, the COVID-19 pandemic prompted all the countries to set up a lockdown to prevent viral transmission. According to a survey study, the use of facemasks at work decreases the chances of fast transmission. If the facemasks are not used or are worn incorrectly, it contributes to the third and fourth waves of the corona virus spreading throughout the world. This motivates us to conduct an efficient investigation of the face mask identification system and monitor people, who use suitable face mask in public places. Deep learning is the most effective approach for detecting whether or not a person is wearing a face mask in a crowded area. Using a multiclass deep learning technique, this research study proposes an efficient two stage identification (ETSI) for face mask detection. Whereas, the binary classification does not offer information about face mask detection and error. The proposed approach employs CNN's \"ReLU\" activation function to detect the face mask. Furthermore, in the current pandemic crisis, this research article offers a very efficient and precise approach for identifying COVID-19. Precision has increased as a result of the employment of a multi-class abbreviation in the final output.","PeriodicalId":11002,"journal":{"name":"Day 1 Tue, March 23, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88211594","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 : 2021-07-19DOI: 10.36548/JUCCT.2021.2.005
C. Anand
Several intelligent data mining approaches, including neural networks, have been widely employed by academics during the last decade. In today's rapidly evolving economy, stock market data prediction and analysis play a significant role. Several non-linear models like neural network, generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive conditional heteroscedasticity (ARCH) as well as linear models like Auto-Regressive Integrated Moving Average (ARIMA), Moving Average (MA) and Auto Regressive (AR) may be used for stock forecasting. The deep learning architectures inclusive of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN), Multilayer Perceptron (MLP) and Support Vector Machine (SVM) are used in this paper for stock price prediction of an organization by using the previously available stock prices. The National Stock Exchange (NSE) of India dataset is used for training the model with day-wise closing price. Data prediction is performed for a few sample companies selected on a random basis. Based on the comparison results, it is evident that the existing models are outperformed by CNN. The network can also perform stock predictions for other stock markets despite being trained with single market data as a common inner dynamics that has been shared between certain stock markets. When compared to the existing linear models, the neural network model outperforms them in a significant manner, which can be observed from the comparison results.
{"title":"Comparison of Stock Price Prediction Models using Pre-trained Neural Networks","authors":"C. Anand","doi":"10.36548/JUCCT.2021.2.005","DOIUrl":"https://doi.org/10.36548/JUCCT.2021.2.005","url":null,"abstract":"Several intelligent data mining approaches, including neural networks, have been widely employed by academics during the last decade. In today's rapidly evolving economy, stock market data prediction and analysis play a significant role. Several non-linear models like neural network, generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive conditional heteroscedasticity (ARCH) as well as linear models like Auto-Regressive Integrated Moving Average (ARIMA), Moving Average (MA) and Auto Regressive (AR) may be used for stock forecasting. The deep learning architectures inclusive of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN), Multilayer Perceptron (MLP) and Support Vector Machine (SVM) are used in this paper for stock price prediction of an organization by using the previously available stock prices. The National Stock Exchange (NSE) of India dataset is used for training the model with day-wise closing price. Data prediction is performed for a few sample companies selected on a random basis. Based on the comparison results, it is evident that the existing models are outperformed by CNN. The network can also perform stock predictions for other stock markets despite being trained with single market data as a common inner dynamics that has been shared between certain stock markets. When compared to the existing linear models, the neural network model outperforms them in a significant manner, which can be observed from the comparison results.","PeriodicalId":11002,"journal":{"name":"Day 1 Tue, March 23, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73573717","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 : 2021-07-17DOI: 10.36548/JUCCT.2021.2.003
J. S. Raj
The advent of autonomous vehicles is indeed a potential field of research in today's situation. Connected Vehicles (CV) have received a lot of attention in the last decade, which has resulted in CV as a Service (CVaaS). With the advent of taxi services, there is a need for or demand for robust, seamless, and secure information transmission between the vehicles connected to a vehicular network. Thus, the concept of vehicular networking is transformed into novel concept of autonomous and connected vehicles. These autonomous vehicles will serve as a better experience by providing instant information from the vehicles via congestion reduction. The significant drawback faced by the invention of autonomous vehicles is the malicious floor of intruders, who tend to mislead the communication between the vehicles resulting in the compromised smart devices. To address these concerns, the best methodology that will protect and secure the control system of the autonomous vehicle in real time is blockchain. This research work proposes a blockchain framework in order to address the security challenges in autonomous vehicles. This research work enhances the security of smart vehicles thereby preventing intruders from accessing the vehicular network. To validate the suggested technique, money security criteria such as changing stored user ratings, probabilistic authentication scenarios, smart device compromise, and bogus user requests were employed. The observed findings have been documented and analysed, revealing an 82% success rate.
{"title":"Blockchain Framework for Communication between Vehicle through IoT Devices and Sensors","authors":"J. S. Raj","doi":"10.36548/JUCCT.2021.2.003","DOIUrl":"https://doi.org/10.36548/JUCCT.2021.2.003","url":null,"abstract":"The advent of autonomous vehicles is indeed a potential field of research in today's situation. Connected Vehicles (CV) have received a lot of attention in the last decade, which has resulted in CV as a Service (CVaaS). With the advent of taxi services, there is a need for or demand for robust, seamless, and secure information transmission between the vehicles connected to a vehicular network. Thus, the concept of vehicular networking is transformed into novel concept of autonomous and connected vehicles. These autonomous vehicles will serve as a better experience by providing instant information from the vehicles via congestion reduction. The significant drawback faced by the invention of autonomous vehicles is the malicious floor of intruders, who tend to mislead the communication between the vehicles resulting in the compromised smart devices. To address these concerns, the best methodology that will protect and secure the control system of the autonomous vehicle in real time is blockchain. This research work proposes a blockchain framework in order to address the security challenges in autonomous vehicles. This research work enhances the security of smart vehicles thereby preventing intruders from accessing the vehicular network. To validate the suggested technique, money security criteria such as changing stored user ratings, probabilistic authentication scenarios, smart device compromise, and bogus user requests were employed. The observed findings have been documented and analysed, revealing an 82% success rate.","PeriodicalId":11002,"journal":{"name":"Day 1 Tue, March 23, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90535795","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 : 2021-07-14DOI: 10.36548/JUCCT.2021.2.002
J. Chen, Kong-Long Lai
The design of an analogue IC layout is a time-consuming and manual process. Despite several studies in the sector, some geometric restrictions have resulted in disadvantages in the process of automated analogue IC layout design. As a result, analogue design has a performance lag when compared to manual design. This prevents the deployment of a large range of automated tools. With the recent technical developments, this challenge is resolved using machine learning techniques. This study investigates performance-driven placement in the VLSI IC design process, as well as analogue IC performance prediction by utilizing various machine learning approaches. Further, several amplifier designs are simulated. From the simulation results, it is evident that, when compared to the manual layout, an improved performance is obtained by using the proposed approach.
{"title":"Machine Learning Algorithms Performance Analysis for VLSI IC Design","authors":"J. Chen, Kong-Long Lai","doi":"10.36548/JUCCT.2021.2.002","DOIUrl":"https://doi.org/10.36548/JUCCT.2021.2.002","url":null,"abstract":"The design of an analogue IC layout is a time-consuming and manual process. Despite several studies in the sector, some geometric restrictions have resulted in disadvantages in the process of automated analogue IC layout design. As a result, analogue design has a performance lag when compared to manual design. This prevents the deployment of a large range of automated tools. With the recent technical developments, this challenge is resolved using machine learning techniques. This study investigates performance-driven placement in the VLSI IC design process, as well as analogue IC performance prediction by utilizing various machine learning approaches. Further, several amplifier designs are simulated. From the simulation results, it is evident that, when compared to the manual layout, an improved performance is obtained by using the proposed approach.","PeriodicalId":11002,"journal":{"name":"Day 1 Tue, March 23, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83812739","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 : 2021-05-25DOI: 10.36548/JEEA.2021.1.004
Nayana
Often, coalitions are formed by the hierarchical integrated energy systems (HIESs) and their evolutionary process which is driven by the benefits of stakeholders and consolidate energy consumers and producers. Several literature have failed to analyze the operation of HIES under the impact of multiple coalitions. At the lower level, multiple users, in the middle level, the multiple distributed energy stations (DESs) and at the upper level, one natural gas and one electricity utility company structure is used for analyzing the HIES operation with a trading scheme. The Lagrange function is used for deriving the optimal operation strategy based analytical function for each probable coalition and each market participant comprising of users and the DESs. It is evident from the results that in a single coalition, the profits linked to other DESs will decrease while increasing the profit of one DES with technological enhancements, users show an aversion towards DESs with high generation coefficient while they are attracted to the ones that enable reduction of heat and electricity price. Maintaining their isolation is preferred by high heat and electricity consuming DESs at the same energy price. Other coalitions and their operations are not affected by the change in parameters of one coalition.
{"title":"Energy Management Scheme in Hierarchical Integrated Energy Systems with Coalition","authors":"Nayana","doi":"10.36548/JEEA.2021.1.004","DOIUrl":"https://doi.org/10.36548/JEEA.2021.1.004","url":null,"abstract":"Often, coalitions are formed by the hierarchical integrated energy systems (HIESs) and their evolutionary process which is driven by the benefits of stakeholders and consolidate energy consumers and producers. Several literature have failed to analyze the operation of HIES under the impact of multiple coalitions. At the lower level, multiple users, in the middle level, the multiple distributed energy stations (DESs) and at the upper level, one natural gas and one electricity utility company structure is used for analyzing the HIES operation with a trading scheme. The Lagrange function is used for deriving the optimal operation strategy based analytical function for each probable coalition and each market participant comprising of users and the DESs. It is evident from the results that in a single coalition, the profits linked to other DESs will decrease while increasing the profit of one DES with technological enhancements, users show an aversion towards DESs with high generation coefficient while they are attracted to the ones that enable reduction of heat and electricity price. Maintaining their isolation is preferred by high heat and electricity consuming DESs at the same energy price. Other coalitions and their operations are not affected by the change in parameters of one coalition.","PeriodicalId":11002,"journal":{"name":"Day 1 Tue, March 23, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89463063","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}
Thomas Amanuel, Amanuel Ghirmay, Huruy Ghebremeskel, Robel Ghebrehiwet, Weldekidan Bahlibi
Signal processing is considered as an efficient technique to detect the faults in three-phase induction motors. Detection of different varieties of faults in the rotor of the motor are widely studied at the industrial level. To extend further, this research article presents the analysis on various signal processing techniques for fault detection in three-phase induction motor due to the damages in rotor bar. Usually, Fast Fourier Transform (FFT) and STFT are used to analyze the healthy and faulty motor conditions based on the signal characteristics. The proposed study covers the advantages and limitations of the proposed wavelet transform (WT) and each technique for detecting the broken bar of induction motors. The good frequency information can be collected from FFT techniques for handling multiple faults identification in three-phase induction motor. Despite the hype, the detection accuracy gets reduced during the dynamic condition of the machine because the frequency information on sudden time changes cannot be employed by FFT. The WT method signal analysis is compared with FFT to propose fault detection method for induction motor. The WT method is proving better accuracy when compared to all existing methods for signal information analysis. The proposed research work has simulated the proposed method with MATLAB / SIMULINK and it helps to effectively detect the healthy and faulty conditions of the motor.
{"title":"Comparative Analysis of Signal Processing Techniques for Fault Detection in Three Phase Induction Motor","authors":"Thomas Amanuel, Amanuel Ghirmay, Huruy Ghebremeskel, Robel Ghebrehiwet, Weldekidan Bahlibi","doi":"10.36548/JEI.2021.1.006","DOIUrl":"https://doi.org/10.36548/JEI.2021.1.006","url":null,"abstract":"Signal processing is considered as an efficient technique to detect the faults in three-phase induction motors. Detection of different varieties of faults in the rotor of the motor are widely studied at the industrial level. To extend further, this research article presents the analysis on various signal processing techniques for fault detection in three-phase induction motor due to the damages in rotor bar. Usually, Fast Fourier Transform (FFT) and STFT are used to analyze the healthy and faulty motor conditions based on the signal characteristics. The proposed study covers the advantages and limitations of the proposed wavelet transform (WT) and each technique for detecting the broken bar of induction motors. The good frequency information can be collected from FFT techniques for handling multiple faults identification in three-phase induction motor. Despite the hype, the detection accuracy gets reduced during the dynamic condition of the machine because the frequency information on sudden time changes cannot be employed by FFT. The WT method signal analysis is compared with FFT to propose fault detection method for induction motor. The WT method is proving better accuracy when compared to all existing methods for signal information analysis. The proposed research work has simulated the proposed method with MATLAB / SIMULINK and it helps to effectively detect the healthy and faulty conditions of the motor.","PeriodicalId":11002,"journal":{"name":"Day 1 Tue, March 23, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91392932","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 : 2021-04-10DOI: 10.36548/JTCSST.2021.1.002
Vijesh Joe C, Jennifer S. Raj
As the technology revolving around IoT sensors develops in a rapid manner, the subsequent social networks that are essential for the growth of the system will be utilized as a means to filter the objects that are preferred by the consumers. The ultimate purpose of the system is to give the customers personalized recommendations based on their preference. Similarly, the location and orientation will also play a crucial role in identifying the preference of the customer is a more efficient manner. Almost all social networks make use of location information to provide better services to the users based on the research performed. Hence there is a need for developing a recommender system that is dependent on location. In this paper, we have incorporated a recommender system that makes use of recommender algorithm that is personalized to take into consideration the context of the user. The preference of the user is analysed with the help of IoT smart devices like the smart watches, Google home, smart phones, ipads etc. The user preferences are obtained from these devices and will enable the recommender system to gauge the best resources. The results based on evaluation are compared with that of the content-based recommender algorithm and collaborative filtering to enable the recommendation engine’s power.
{"title":"Location-based Orientation Context Dependent Recommender System for Users","authors":"Vijesh Joe C, Jennifer S. Raj","doi":"10.36548/JTCSST.2021.1.002","DOIUrl":"https://doi.org/10.36548/JTCSST.2021.1.002","url":null,"abstract":"As the technology revolving around IoT sensors develops in a rapid manner, the subsequent social networks that are essential for the growth of the system will be utilized as a means to filter the objects that are preferred by the consumers. The ultimate purpose of the system is to give the customers personalized recommendations based on their preference. Similarly, the location and orientation will also play a crucial role in identifying the preference of the customer is a more efficient manner. Almost all social networks make use of location information to provide better services to the users based on the research performed. Hence there is a need for developing a recommender system that is dependent on location. In this paper, we have incorporated a recommender system that makes use of recommender algorithm that is personalized to take into consideration the context of the user. The preference of the user is analysed with the help of IoT smart devices like the smart watches, Google home, smart phones, ipads etc. The user preferences are obtained from these devices and will enable the recommender system to gauge the best resources. The results based on evaluation are compared with that of the content-based recommender algorithm and collaborative filtering to enable the recommendation engine’s power.","PeriodicalId":11002,"journal":{"name":"Day 1 Tue, March 23, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80742312","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}
Ashoka Ghosh, P. Hart, Adele Panek, T. Nguyen, Meredith Dooley
Grease and cheese contamination of used pizza boxes has led to misunderstanding and controversy about the recyclability of pizza boxes. Some collection facilities accept pizza boxes while others do not. The purpose of this study is to determine whether typical grease or cheese contamination levels associated with pizza boxes impact finished product quality. Grease (from vegetable oil) and cheese are essentially hydrophobic and in sufficiently high concentration could interfere with interfiber bonding, resulting in paper strength loss. Findings from this study will be used to determine the viability of recycling pizza boxes at current and future concentrations in old corrugated containers (OCC) recovered fiber streams. These findings will also be used to inform the acceptability of pizza boxes in the recycle stream and educate consumers about acceptable levels of grease or cheese residue found on these recycled boxes.
{"title":"Incorporation of post-consumer pizza boxes in the recovered fiber stream: Impacts of grease on finished product quality","authors":"Ashoka Ghosh, P. Hart, Adele Panek, T. Nguyen, Meredith Dooley","doi":"10.32964/tj20.3.161","DOIUrl":"https://doi.org/10.32964/tj20.3.161","url":null,"abstract":"Grease and cheese contamination of used pizza boxes has led to misunderstanding and controversy about the recyclability of pizza boxes. Some collection facilities accept pizza boxes while others do not. \u0000The purpose of this study is to determine whether typical grease or cheese contamination levels associated with pizza boxes impact finished product quality. Grease (from vegetable oil) and cheese are essentially hydrophobic and in sufficiently high concentration could interfere with interfiber bonding, resulting in paper strength loss.\u0000Findings from this study will be used to determine the viability of recycling pizza boxes at current and future concentrations in old corrugated containers (OCC) recovered fiber streams. These findings will also be used to inform the acceptability of pizza boxes in the recycle stream and educate consumers about acceptable levels of grease or cheese residue found on these recycled boxes.","PeriodicalId":11002,"journal":{"name":"Day 1 Tue, March 23, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90635618","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}
Black liquor evaporation is generally the most energy intensive unit operation in a pulp and paper manufacturing facility. The black liquor evaporators can represent a third or more of the total mill steam usage, followed by the paper machine and digester. Evaporator steam economy is defined as the unit mass of steam required to evaporate a unit mass of water from black liquor (i.e., lb/lb or kg/kg.) The economy is determined by the number of effects in an evaporator train and the system configuration. Older systems use four to six effects, most of which are the long tube vertical rising film type. Newer systems may be designed with seven or even eight effects using falling film and forced circulation crystallization technology for high product solids. The median age of all North American evaporator systems is 44 years. Roughly 25% of the current North American operating systems are 54 years or older. Older systems require more periodic maintenance and have a higher risk of unplanned downtime. Also, older systems have chronic issues with persistent liquor and vapor leaks, shell wall thinning, corrosion, and plugged tubes. Often these issues worsen to the point of requiring rebuild or replacement. When considering the age, technology, and lower efficiency of older systems, a major rebuild or new system may be warranted. The intent of this paper is to review the current state of black liquor evaporator systems in North America and present a basic method for determining whether a major rebuild or new installation is warranted using total life cycle cost analysis (LCCA).
{"title":"Black liquor evaporator upgrades— life cycle cost analysis","authors":"J. Cantrell","doi":"10.32964/tj20.3.208","DOIUrl":"https://doi.org/10.32964/tj20.3.208","url":null,"abstract":"Black liquor evaporation is generally the most energy intensive unit operation in a pulp and paper manufacturing facility. The black liquor evaporators can represent a third or more of the total mill steam usage, followed by the paper machine and digester. Evaporator steam economy is defined as the unit mass of steam required to evaporate a unit mass of water from black liquor (i.e., lb/lb or kg/kg.) The economy is determined by the number of effects in an evaporator train and the system configuration. Older systems use four to six effects, most of which are the long tube vertical rising film type. Newer systems may be designed with seven or even eight effects using falling film and forced circulation crystallization technology for high product solids. \u0000The median age of all North American evaporator systems is 44 years. Roughly 25% of the current North American operating systems are 54 years or older. Older systems require more periodic maintenance and have a higher risk of unplanned downtime. Also, older systems have chronic issues with persistent liquor and vapor leaks, shell wall thinning, corrosion, and plugged tubes. Often these issues worsen to the point of requiring rebuild or replacement. When considering the age, technology, and lower efficiency of older systems, a major rebuild or new system may be warranted. The intent of this paper is to review the current state of black liquor evaporator systems in North America and present a basic method for determining whether a major rebuild or new installation is warranted using total life cycle cost analysis (LCCA).","PeriodicalId":11002,"journal":{"name":"Day 1 Tue, March 23, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74562689","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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