Bubble size distribution is the basic apparent performance and obvious characteristics in the air dense medium fluidized bed (ADMFB). The approaches of numerical simulation and experimental verification were combined ...Bubble size distribution is the basic apparent performance and obvious characteristics in the air dense medium fluidized bed (ADMFB). The approaches of numerical simulation and experimental verification were combined to conduct the further research on the bubble generation and movement behavior. The results show that ADMFB could display favorable expanded characteristics after steady fluidization. With different particle size distributions of magnetite powder as medium solids, we selected an appropriate prediction model for the mean bubble diameter in ADMFB. The comparison results indicate that the mean bubble diameters along the bed heights are 35 mm < D b < 66 mm and 40 mm < D b < 69 mm with the magnetite powder of 0.3 mm+0.15mm and 0.15mm+0.074mm, respectively. The prediction model provides good agreements with the experimental and simulation data. Based on the optimal operating gas velocity distribution, the mixture of magnetite powder and <1mm fine coal as medium solids were utilized to carry out the separation experiment on 6-50mm raw coal. The results show that an optimal separation density d P of 1.73g/cm 3 with a probable error E of 0.07g/cm 3 and a recovery efficiency of 99.97% is achieved, which indicates good separation performance by applying ADMFB.展开更多
Bed stability, and especially the bed density distribution, is affected by the behavior of bubbles in a gas solid fluidized bed. Bubble rise velocity in a pulsed gas-solid fluidized bed was studied using photographic ...Bed stability, and especially the bed density distribution, is affected by the behavior of bubbles in a gas solid fluidized bed. Bubble rise velocity in a pulsed gas-solid fluidized bed was studied using photographic and computational fluid dynamics methods. The variation in bubble rise velocity was investigated as a function of the periodic pulsed air flow. A predictive model of bubble rise velocity was derived: ub=ψ(Ut+Up-Umf)+kp(gdb)(1/2). The software of Origin was used to fit the empirical coefficients to give ψ = 0.4807 and kp = 0.1305. Experimental verification of the simulations shows that the regular change in bubble rise velocity is accurately described by the model. The correlation coefficient was 0.9905 for the simulations and 0.9706 for the experiments.展开更多
The transparent open box(TOB)learning network algorithm offers an alternative approach to the lack of transparency provided by most machine-learning algorithms.It provides the exact calculations and relationships amon...The transparent open box(TOB)learning network algorithm offers an alternative approach to the lack of transparency provided by most machine-learning algorithms.It provides the exact calculations and relationships among the underlying input variables of the datasets to which it is applied.It also has the capability to achieve credible and auditable levels of prediction accuracy to complex,non-linear datasets,typical of those encountered in the oil and gas sector,highlighting the potential for underfitting and overfitting.The algorithm is applied here to predict bubble-point pressure from a published PVT dataset of 166 data records involving four easy-tomeasure variables(reservoir temperature,gas-oil ratio,oil gravity,gas density relative to air)with uneven,and in parts,sparse data coverage.The TOB network demonstrates high-prediction accuracy for this complex system,although it predictions applied to the full dataset are outperformed by an artificial neural network(ANN).However,the performance of the TOB algorithm reveals the risk of overfitting in the sparse areas of the dataset and achieves a prediction performance that matches the ANN algorithm where the underlying data population is adequate.The high levels of transparency and its inhibitions to overfitting enable the TOB learning network to provide complementary information about the underlying dataset to that provided by traditional machine learning algorithms.This makes them suitable for application in parallel with neural-network algorithms,to overcome their black-box tendencies,and for benchmarking the prediction performance of other machine learning algorithms.展开更多
基金financially supported by the National Natural Science Foundation of China (Nos. 51221462, 51134022,51174203 and 51074156)the National Basic Research Program of China (No. 2012CB214904)China Postdoctoral Science Foundation (No. 2013M531430)
文摘Bubble size distribution is the basic apparent performance and obvious characteristics in the air dense medium fluidized bed (ADMFB). The approaches of numerical simulation and experimental verification were combined to conduct the further research on the bubble generation and movement behavior. The results show that ADMFB could display favorable expanded characteristics after steady fluidization. With different particle size distributions of magnetite powder as medium solids, we selected an appropriate prediction model for the mean bubble diameter in ADMFB. The comparison results indicate that the mean bubble diameters along the bed heights are 35 mm < D b < 66 mm and 40 mm < D b < 69 mm with the magnetite powder of 0.3 mm+0.15mm and 0.15mm+0.074mm, respectively. The prediction model provides good agreements with the experimental and simulation data. Based on the optimal operating gas velocity distribution, the mixture of magnetite powder and <1mm fine coal as medium solids were utilized to carry out the separation experiment on 6-50mm raw coal. The results show that an optimal separation density d P of 1.73g/cm 3 with a probable error E of 0.07g/cm 3 and a recovery efficiency of 99.97% is achieved, which indicates good separation performance by applying ADMFB.
基金financially supported by the National Natural Science Foundation of China for Innovative Research Group (No.51221462)the National Natural Science Foundation of China (Nos.51134022 and 51174203)+2 种基金the State Key Basic Research Program of China (No.2012CB214904)Specialized Research Fund for the Doctoral Program of Higher Education (No.20120095130001)the Fundamental Research Funds for the Central Universities (No.2013DXS02)
文摘Bed stability, and especially the bed density distribution, is affected by the behavior of bubbles in a gas solid fluidized bed. Bubble rise velocity in a pulsed gas-solid fluidized bed was studied using photographic and computational fluid dynamics methods. The variation in bubble rise velocity was investigated as a function of the periodic pulsed air flow. A predictive model of bubble rise velocity was derived: ub=ψ(Ut+Up-Umf)+kp(gdb)(1/2). The software of Origin was used to fit the empirical coefficients to give ψ = 0.4807 and kp = 0.1305. Experimental verification of the simulations shows that the regular change in bubble rise velocity is accurately described by the model. The correlation coefficient was 0.9905 for the simulations and 0.9706 for the experiments.
文摘The transparent open box(TOB)learning network algorithm offers an alternative approach to the lack of transparency provided by most machine-learning algorithms.It provides the exact calculations and relationships among the underlying input variables of the datasets to which it is applied.It also has the capability to achieve credible and auditable levels of prediction accuracy to complex,non-linear datasets,typical of those encountered in the oil and gas sector,highlighting the potential for underfitting and overfitting.The algorithm is applied here to predict bubble-point pressure from a published PVT dataset of 166 data records involving four easy-tomeasure variables(reservoir temperature,gas-oil ratio,oil gravity,gas density relative to air)with uneven,and in parts,sparse data coverage.The TOB network demonstrates high-prediction accuracy for this complex system,although it predictions applied to the full dataset are outperformed by an artificial neural network(ANN).However,the performance of the TOB algorithm reveals the risk of overfitting in the sparse areas of the dataset and achieves a prediction performance that matches the ANN algorithm where the underlying data population is adequate.The high levels of transparency and its inhibitions to overfitting enable the TOB learning network to provide complementary information about the underlying dataset to that provided by traditional machine learning algorithms.This makes them suitable for application in parallel with neural-network algorithms,to overcome their black-box tendencies,and for benchmarking the prediction performance of other machine learning algorithms.