In this research,the thermal performance of a single U-tube vertical ground heat exchanger is evaluated numerically as a function of the most influential flow parameters,namely,the soil porosity,volumetric heat capaci...In this research,the thermal performance of a single U-tube vertical ground heat exchanger is evaluated numerically as a function of the most influential flow parameters,namely,the soil porosity,volumetric heat capacity,and thermal conductivity of the backfill material,inlet volume flow rate,and inlet fluid temperature.The results are discussed in terms of the variations of the heat exchange rate,the effective thermal resistance,and the effectiveness of the ground heat exchanger.They show that the inlet volume flow rate,inlet fluid temperature,and backfill material thermal conductivity have significant effects on the thermal performance of the ground heat exchanger,such that by decreasing the inlet volume flow rate and increasing the backfill material thermal conductivity and inlet fluid temperature,the outlet fluid temperature decreases considerably.On the contrary,the soil porosity and backfill material volumetric heat capacity have negligible effects on the studied ground heat exchanger’s thermal performance.The lowest inlet fluid temperature reaches a the maximum effective thermal resistance of borehole and soil,and consequently the minimum heat transfer rate and effectiveness.Also,multilinear regression analyses are performed to determine the most feasible models able to predict the thermal properties of the single U-tube ground heat exchanger.展开更多
A novel method for developing a reliable data driven soft sensor to improve the prediction accuracy of sulfur content in hydrodesulfurization(HDS) process was proposed. Therefore, an integrated approach using support ...A novel method for developing a reliable data driven soft sensor to improve the prediction accuracy of sulfur content in hydrodesulfurization(HDS) process was proposed. Therefore, an integrated approach using support vector regression(SVR) based on wavelet transform(WT) and principal component analysis(PCA) was used. Experimental data from the HDS setup were employed to validate the proposed model. The results reveal that the integrated WT-PCA with SVR model was able to increase the prediction accuracy of SVR model. Implementation of the proposed model delivers the best satisfactory predicting performance(EAARE=0.058 and R2=0.97) in comparison with SVR. The obtained results indicate that the proposed model is more reliable and more precise than the multiple linear regression(MLR), SVR and PCA-SVR.展开更多
文摘In this research,the thermal performance of a single U-tube vertical ground heat exchanger is evaluated numerically as a function of the most influential flow parameters,namely,the soil porosity,volumetric heat capacity,and thermal conductivity of the backfill material,inlet volume flow rate,and inlet fluid temperature.The results are discussed in terms of the variations of the heat exchange rate,the effective thermal resistance,and the effectiveness of the ground heat exchanger.They show that the inlet volume flow rate,inlet fluid temperature,and backfill material thermal conductivity have significant effects on the thermal performance of the ground heat exchanger,such that by decreasing the inlet volume flow rate and increasing the backfill material thermal conductivity and inlet fluid temperature,the outlet fluid temperature decreases considerably.On the contrary,the soil porosity and backfill material volumetric heat capacity have negligible effects on the studied ground heat exchanger’s thermal performance.The lowest inlet fluid temperature reaches a the maximum effective thermal resistance of borehole and soil,and consequently the minimum heat transfer rate and effectiveness.Also,multilinear regression analyses are performed to determine the most feasible models able to predict the thermal properties of the single U-tube ground heat exchanger.
文摘A novel method for developing a reliable data driven soft sensor to improve the prediction accuracy of sulfur content in hydrodesulfurization(HDS) process was proposed. Therefore, an integrated approach using support vector regression(SVR) based on wavelet transform(WT) and principal component analysis(PCA) was used. Experimental data from the HDS setup were employed to validate the proposed model. The results reveal that the integrated WT-PCA with SVR model was able to increase the prediction accuracy of SVR model. Implementation of the proposed model delivers the best satisfactory predicting performance(EAARE=0.058 and R2=0.97) in comparison with SVR. The obtained results indicate that the proposed model is more reliable and more precise than the multiple linear regression(MLR), SVR and PCA-SVR.
