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
Recognizing the drawbacks of stand-alone computer-aided tools in engineering, several hybrid systems are suggested with varying degree of success. In transforming the design concept to a finished product, in particula...Recognizing the drawbacks of stand-alone computer-aided tools in engineering, several hybrid systems are suggested with varying degree of success. In transforming the design concept to a finished product, in particular, smooth interfacing of the design data is crucial to reduce product cost and time to market. Having a product model that contains the complete product description and computer-aided tools that can understand each other are the primary requirements to achieve the interfacing goal. This article discusses the development methodology of hybrid engineering software systems with particular focus on application of soft computing tools such as genetic algorithms and neural networks. Forms of hybridization options are discussed and the applications are elaborated using two case studies. The forefront aims to develop hybrid systems that combine the strong side of each tool, such as, the learning, pattern recognition and classification power of neural networks with the powerful capacity of genetic algorithms in global search and optimization. While most optimization tasks need a certain form of model, there are many processes in the mechanical engineering field that are difficult to model using conventional modeling techniques. The proposed hybrid system solves such difficult-to-model processes and contributes to the effort of smooth interfacing design data to other downstream processes.展开更多
A numerical analysis of the thermohydraulics of an enhanced geothermal system project is presented. The rock structures are modelled as porous medium, based on the computationally obtained hydraulic fracturing data of...A numerical analysis of the thermohydraulics of an enhanced geothermal system project is presented. The rock structures are modelled as porous medium, based on the computationally obtained hydraulic fracturing data of other authors. The influence of the domain size, grid resolution, temporal resolution and the discretization scheme is assessed to obtain a highly accurate numerical solution under the prevailing modelling assumptions. Based on the numerical model, different production scenarios are investigated and discussed. The relative positioning of the injection and production ports is also analyzed. It is shown that there is a considerable potential for optimizing the production rate and the port configuration to obtain the most favorable results for the production temperature, investment costs and operation costs.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Recognizing the drawbacks of stand-alone computer-aided tools in engineering, several hybrid systems are suggested with varying degree of success. In transforming the design concept to a finished product, in particular, smooth interfacing of the design data is crucial to reduce product cost and time to market. Having a product model that contains the complete product description and computer-aided tools that can understand each other are the primary requirements to achieve the interfacing goal. This article discusses the development methodology of hybrid engineering software systems with particular focus on application of soft computing tools such as genetic algorithms and neural networks. Forms of hybridization options are discussed and the applications are elaborated using two case studies. The forefront aims to develop hybrid systems that combine the strong side of each tool, such as, the learning, pattern recognition and classification power of neural networks with the powerful capacity of genetic algorithms in global search and optimization. While most optimization tasks need a certain form of model, there are many processes in the mechanical engineering field that are difficult to model using conventional modeling techniques. The proposed hybrid system solves such difficult-to-model processes and contributes to the effort of smooth interfacing design data to other downstream processes.
文摘A numerical analysis of the thermohydraulics of an enhanced geothermal system project is presented. The rock structures are modelled as porous medium, based on the computationally obtained hydraulic fracturing data of other authors. The influence of the domain size, grid resolution, temporal resolution and the discretization scheme is assessed to obtain a highly accurate numerical solution under the prevailing modelling assumptions. Based on the numerical model, different production scenarios are investigated and discussed. The relative positioning of the injection and production ports is also analyzed. It is shown that there is a considerable potential for optimizing the production rate and the port configuration to obtain the most favorable results for the production temperature, investment costs and operation costs.
