A computational mass transfer model is proposed for predicting the concentration profile and Murphree efficiency of sieve tray distillation column. The proposed model is based on using modified c＇2 -εc＇ two equatio...A computational mass transfer model is proposed for predicting the concentration profile and Murphree efficiency of sieve tray distillation column. The proposed model is based on using modified c＇2 -εc＇ two equations formulation for closing the differential turbulent mass transfer equation with improvement by considering the vapor injected from the sieve hole to be three dimensional. The predicted concentration distributions by using proposed model were checked by experimental work conducted on a sieve tray simulator of 1.2 meters in diameter for desorbing the dissolved oxygen in the feed water by blowing air. The model predictions were confirmed by the experimental measurement. The validation of the proposed model was further tested by comparing the simulated result with the performance of an industrial scale sieve tray distillation column reported by Kunesh et al. for the stripping of toluene from its water solution. The predicted outlet concentration of each tray and the Murphree tray efficiencies under different operating conditions were in agreement with the published data. The simulated turbulent mass transfer diffusivity on each tray was within the range of the experimental result in the same sieve column reported by Cai et al. In addition, the prediction of the influence of sieve tray structure on the tray efficiency by using the proposed model was demonstrated.展开更多
This research paper describes an SEE （Structural Engineering Encounter） Lab project. The paper reports on the development of a single-story, single-bay portal frame model as part of the AIMS2 （attract, inspire, men...This research paper describes an SEE （Structural Engineering Encounter） Lab project. The paper reports on the development of a single-story, single-bay portal frame model as part of the AIMS2 （attract, inspire, mentor and support students） grant supported through the US DOE （Department of Education） summer research program at California State University, Northridge. This research effort is part of a comprehensive program to develop laboratory models of structures commonly encountered in civil engineering practice, which can serve the dual purpose of accomplishing engineering education and research in the areas of structural and earthquake engineering. The objective of the present study was to construct a physical model of the aforementioned frame to experimentally collect data due to the application of vertical and lateral loadings through instrumentation such as strain gages and an LVDT （linear variable differential transformer） displacement transducer, and also to make comparisons with theoretical and numerical predictions.展开更多
By using the method of least square linear fitting to analyze data do not exist errors under certain conditions, in order to make the linear data fitting method that can more accurately solve the relationship expressi...By using the method of least square linear fitting to analyze data do not exist errors under certain conditions, in order to make the linear data fitting method that can more accurately solve the relationship expression between the volume and quantity in scientific experiments and engineering practice, this article analyzed data error by commonly linear data fitting method, and proposed improved process of the least distance squ^re method based on least squares method. Finally, the paper discussed the advantages and disadvantages through the example analysis of two kinds of linear data fitting method, and given reasonable control conditions for its application.展开更多
This article presents an Artificial Neural Network （ANN） architecture to model the Electrical Discharge Machining （EDM） process. It is aimed to develop the ANN model using an input-output pattern of raw data colle...This article presents an Artificial Neural Network （ANN） architecture to model the Electrical Discharge Machining （EDM） process. It is aimed to develop the ANN model using an input-output pattern of raw data collected from an experimental of EDM process, whereas several research objectives have been outlined such as experimenting machining material for selected gap current, identifying machining parameters for ANN variables and selecting appropriate size of data selection. The experimental data （input variables） of copper-electrode and steel-workpiece is based on a selected gap current where pulse on time, pulse off time and sparking frequency have been chosen at optimum value of Material Removal Rate （MRR）. In this paper, the result has significantly demonstrated that the ANN model is capable of predicting the MRR with low percentage prediction error when compared with the experimental result.展开更多
基金Supported by the National lqatural Science Foundation of China （20736005）.
文摘A computational mass transfer model is proposed for predicting the concentration profile and Murphree efficiency of sieve tray distillation column. The proposed model is based on using modified c＇2 -εc＇ two equations formulation for closing the differential turbulent mass transfer equation with improvement by considering the vapor injected from the sieve hole to be three dimensional. The predicted concentration distributions by using proposed model were checked by experimental work conducted on a sieve tray simulator of 1.2 meters in diameter for desorbing the dissolved oxygen in the feed water by blowing air. The model predictions were confirmed by the experimental measurement. The validation of the proposed model was further tested by comparing the simulated result with the performance of an industrial scale sieve tray distillation column reported by Kunesh et al. for the stripping of toluene from its water solution. The predicted outlet concentration of each tray and the Murphree tray efficiencies under different operating conditions were in agreement with the published data. The simulated turbulent mass transfer diffusivity on each tray was within the range of the experimental result in the same sieve column reported by Cai et al. In addition, the prediction of the influence of sieve tray structure on the tray efficiency by using the proposed model was demonstrated.
文摘This research paper describes an SEE （Structural Engineering Encounter） Lab project. The paper reports on the development of a single-story, single-bay portal frame model as part of the AIMS2 （attract, inspire, mentor and support students） grant supported through the US DOE （Department of Education） summer research program at California State University, Northridge. This research effort is part of a comprehensive program to develop laboratory models of structures commonly encountered in civil engineering practice, which can serve the dual purpose of accomplishing engineering education and research in the areas of structural and earthquake engineering. The objective of the present study was to construct a physical model of the aforementioned frame to experimentally collect data due to the application of vertical and lateral loadings through instrumentation such as strain gages and an LVDT （linear variable differential transformer） displacement transducer, and also to make comparisons with theoretical and numerical predictions.
文摘By using the method of least square linear fitting to analyze data do not exist errors under certain conditions, in order to make the linear data fitting method that can more accurately solve the relationship expression between the volume and quantity in scientific experiments and engineering practice, this article analyzed data error by commonly linear data fitting method, and proposed improved process of the least distance squ^re method based on least squares method. Finally, the paper discussed the advantages and disadvantages through the example analysis of two kinds of linear data fitting method, and given reasonable control conditions for its application.
文摘This article presents an Artificial Neural Network （ANN） architecture to model the Electrical Discharge Machining （EDM） process. It is aimed to develop the ANN model using an input-output pattern of raw data collected from an experimental of EDM process, whereas several research objectives have been outlined such as experimenting machining material for selected gap current, identifying machining parameters for ANN variables and selecting appropriate size of data selection. The experimental data （input variables） of copper-electrode and steel-workpiece is based on a selected gap current where pulse on time, pulse off time and sparking frequency have been chosen at optimum value of Material Removal Rate （MRR）. In this paper, the result has significantly demonstrated that the ANN model is capable of predicting the MRR with low percentage prediction error when compared with the experimental result.
