Pub Date : 2017-04-01DOI: 10.1109/ICMSAO.2017.7934903
Bouslikhane Salim, H. Zied, R. Nidhal
This paper proposes a mathematical model for a manufacturing and remanufacturing production problem which integrates environmental constraint. This study deals a closedloop system composed by a manufacturing and remanufacturing machines subject to random failures, in order to satisfy the random demand. This paper proposed two mathematical model minimizing the total cost of production, inventory and maintenance taking into account the given maintenance plan for the remanufacturing machine and respecting the emission tax constraint. The principle objective is to determine the economical production plans of manufacturing and remanufacturing machines and the quantity of emission carbon for each production period. The key of this study is to take into account the influence of the variation of production rates on the failure rate of manufacturing system.
{"title":"An optimal mathematical modeling for manufacturing/remanufacturing problem under carbon emission constraint","authors":"Bouslikhane Salim, H. Zied, R. Nidhal","doi":"10.1109/ICMSAO.2017.7934903","DOIUrl":"https://doi.org/10.1109/ICMSAO.2017.7934903","url":null,"abstract":"This paper proposes a mathematical model for a manufacturing and remanufacturing production problem which integrates environmental constraint. This study deals a closedloop system composed by a manufacturing and remanufacturing machines subject to random failures, in order to satisfy the random demand. This paper proposed two mathematical model minimizing the total cost of production, inventory and maintenance taking into account the given maintenance plan for the remanufacturing machine and respecting the emission tax constraint. The principle objective is to determine the economical production plans of manufacturing and remanufacturing machines and the quantity of emission carbon for each production period. The key of this study is to take into account the influence of the variation of production rates on the failure rate of manufacturing system.","PeriodicalId":265345,"journal":{"name":"2017 7th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130302271","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 : 2017-04-01DOI: 10.1109/ICMSAO.2017.7934894
M. Nour, P. Thirugnanam
This paper presents a dynamic induction motor (IM) model which incorporates all the power losses. The presented module is entirely built in Simulink to investigate the effect of varying the applied voltage and frequency on IM efficiency for different load applications. The model includes the power losses such as copper losses, core losses, stray load and mechanical. The accurate determination of induction motor efficiency depends on the estimation of all above mentioned power losses which are modeled and presented in this paper. The effect of variation in applied voltage and frequency on induction motor efficiency is investigated at various load conditions and the results are tabulated and evaluated accordingly. The obtained results show that the efficiency of the IM is significantly affected by the voltage and frequency levels especially at low load. Therefore matching the right applied voltage and frequency to the motor terminal based on the load condition will reduce the motor losses and hence increase its efficiency.
{"title":"Investigation of voltage and frequency variation on induction motor core and copper losses","authors":"M. Nour, P. Thirugnanam","doi":"10.1109/ICMSAO.2017.7934894","DOIUrl":"https://doi.org/10.1109/ICMSAO.2017.7934894","url":null,"abstract":"This paper presents a dynamic induction motor (IM) model which incorporates all the power losses. The presented module is entirely built in Simulink to investigate the effect of varying the applied voltage and frequency on IM efficiency for different load applications. The model includes the power losses such as copper losses, core losses, stray load and mechanical. The accurate determination of induction motor efficiency depends on the estimation of all above mentioned power losses which are modeled and presented in this paper. The effect of variation in applied voltage and frequency on induction motor efficiency is investigated at various load conditions and the results are tabulated and evaluated accordingly. The obtained results show that the efficiency of the IM is significantly affected by the voltage and frequency levels especially at low load. Therefore matching the right applied voltage and frequency to the motor terminal based on the load condition will reduce the motor losses and hence increase its efficiency.","PeriodicalId":265345,"journal":{"name":"2017 7th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128407705","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 : 2017-04-01DOI: 10.1109/ICMSAO.2017.7934886
Mohammed H. Albadi, H. Soliman, E. El-Saadany, M. A. Thani, A. Al-Alawi, S. Al-Ismaili, A. Al-Nabhani, H. Baalawi
This manuscript presents a case study to determine the optimal location and size of photovoltaic systems in an electric distribution networks using particle swarm optimization (PSO) such that network voltage profile is improved and losses are minimized. The distribution network of Masirah Island, Oman, is considered as a case study system. The test system is modeled and simulated using MATLAB load flow toolbox.
{"title":"Optimal allocation of PV systems in distribution networks using PSO","authors":"Mohammed H. Albadi, H. Soliman, E. El-Saadany, M. A. Thani, A. Al-Alawi, S. Al-Ismaili, A. Al-Nabhani, H. Baalawi","doi":"10.1109/ICMSAO.2017.7934886","DOIUrl":"https://doi.org/10.1109/ICMSAO.2017.7934886","url":null,"abstract":"This manuscript presents a case study to determine the optimal location and size of photovoltaic systems in an electric distribution networks using particle swarm optimization (PSO) such that network voltage profile is improved and losses are minimized. The distribution network of Masirah Island, Oman, is considered as a case study system. The test system is modeled and simulated using MATLAB load flow toolbox.","PeriodicalId":265345,"journal":{"name":"2017 7th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124109188","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 : 2017-04-01DOI: 10.1109/ICMSAO.2017.7934918
M. Qaraqe
This paper presents a patient-specific seizure onset detector based on the fusion of classification decisions from a common spatial pattern (CSP)-enhanced energy based detector and a neural synchronization based detector. Specifically, one level of the detector evaluates the amount of neural synchrony present within the electroencephalography (EEG) channels by calculating the condition number (CN) of the EEG matrix. On a parallel level, the detector first enhances the EEG via CSP and then evaluates the energy contained in four EEG frequency subbands. The information is then fed into two independent and parallel classification units based on support vector machines to determine the electrographic onset of a seizure event. The decisions from the two classifiers are then coupled according to two fusion techniques to determine a global decision. Experimental results demonstrate a sensitivity of 100%, detection latency of 1.75 seconds, and a false alarm rate of 3.14 per hour for the detector based on the AND fusion technique. The OR fusion technique achieves a sensitivity of 100%, and significantly improves delay latency (0.61 seconds), yet it achieves 14.26 false alarms per hour.
{"title":"Patient-specific seizure onset detection based on CSP-enhanced energy and neural synchronization decision fusion","authors":"M. Qaraqe","doi":"10.1109/ICMSAO.2017.7934918","DOIUrl":"https://doi.org/10.1109/ICMSAO.2017.7934918","url":null,"abstract":"This paper presents a patient-specific seizure onset detector based on the fusion of classification decisions from a common spatial pattern (CSP)-enhanced energy based detector and a neural synchronization based detector. Specifically, one level of the detector evaluates the amount of neural synchrony present within the electroencephalography (EEG) channels by calculating the condition number (CN) of the EEG matrix. On a parallel level, the detector first enhances the EEG via CSP and then evaluates the energy contained in four EEG frequency subbands. The information is then fed into two independent and parallel classification units based on support vector machines to determine the electrographic onset of a seizure event. The decisions from the two classifiers are then coupled according to two fusion techniques to determine a global decision. Experimental results demonstrate a sensitivity of 100%, detection latency of 1.75 seconds, and a false alarm rate of 3.14 per hour for the detector based on the AND fusion technique. The OR fusion technique achieves a sensitivity of 100%, and significantly improves delay latency (0.61 seconds), yet it achieves 14.26 false alarms per hour.","PeriodicalId":265345,"journal":{"name":"2017 7th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128524390","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 : 2017-01-19DOI: 10.1109/ICMSAO.2017.7934921
Q. Guan, Y. Yap, A. Goharzadeh, J. Chai, F. Vargas, W. Chapman, M. Zhang
Asphaltene deposition in wellbores/pipelines causes serious production losses in the oil and gas industry. This work presents a numerical model to predict asphaltene deposition in wellbores/pipelines. This model consists of two modules: a Thermodynamic Module and a Transport Module. The Thermodynamic Module models asphaltene precipitation using the Peng-Robinson Equation of State with Peneloux volume translation (PR-Peneloux EOS). The Transport Module covers the modeling of fluid transport, asphaltene particle transport and asphaltene deposition. These modules are combined via a thermodynamic properties lookup-table generated by the Thermodynamic Module prior to simulation. In this work, the Transport Module and the Thermodynamic Module are first verified and validated separately. Then, the integrated model is applied to an oilfield case with asphaltene deposition problem where a reasonably accurate prediction of asphaltene deposit profile is achieved.
{"title":"Integrated one-dimensional modeling of asphaltene deposition in wellbores/pipelines","authors":"Q. Guan, Y. Yap, A. Goharzadeh, J. Chai, F. Vargas, W. Chapman, M. Zhang","doi":"10.1109/ICMSAO.2017.7934921","DOIUrl":"https://doi.org/10.1109/ICMSAO.2017.7934921","url":null,"abstract":"Asphaltene deposition in wellbores/pipelines causes serious production losses in the oil and gas industry. This work presents a numerical model to predict asphaltene deposition in wellbores/pipelines. This model consists of two modules: a Thermodynamic Module and a Transport Module. The Thermodynamic Module models asphaltene precipitation using the Peng-Robinson Equation of State with Peneloux volume translation (PR-Peneloux EOS). The Transport Module covers the modeling of fluid transport, asphaltene particle transport and asphaltene deposition. These modules are combined via a thermodynamic properties lookup-table generated by the Thermodynamic Module prior to simulation. In this work, the Transport Module and the Thermodynamic Module are first verified and validated separately. Then, the integrated model is applied to an oilfield case with asphaltene deposition problem where a reasonably accurate prediction of asphaltene deposit profile is achieved.","PeriodicalId":265345,"journal":{"name":"2017 7th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130140334","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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