The efficiency and precision of parameter calibration in discrete element method （DEM） are not satisfactory, and parameter calibration for granular heat transfer is rarely involved. Accordingly, parameter calibratio...The efficiency and precision of parameter calibration in discrete element method （DEM） are not satisfactory, and parameter calibration for granular heat transfer is rarely involved. Accordingly, parameter calibration for granular heat transfer with the DEM is studied. The heat transfer in granular assemblies is simulated with DEM, and the effective thermal conductivity （ETC） of these granular assemblies is measured with the transient method in simulations. The measurement testbed is designed to test the ETC of the granular assemblies under normal pressure and a vacuum based on the steady method. Central composite design （CCD） is used to simulate the impact of the DEM parameters on the ETC of granular assemblies, and the heat transfer parameters are calibrated and compared with experimental data. The results show that, within the scope of the considered parameters, the ETC of the granular assemblies increases with an increasing particle thermal conductivity and decreases with an increasing particle shear modulus and particle diameter. The particle thermal conductivity has the greatest impact on the ETC of granular assemblies followed by the particle shear modulus and then the particle diameter. The calibration results show good agreement with the experimental results. The error is less than 4%, which is within a reasonable range for the scope of the CCD parameters. The proposed research provides high efficiency and high accuracy parameter calibration for granular heat transfer in DEM.展开更多
This study examines the efficacy of Avicennia marina(AM)leaves as an environmentally sustainable biosorbent for the extraction of methylene blue(MB)dye from wastewater.A hybrid approach of Response Surface Methodology...This study examines the efficacy of Avicennia marina(AM)leaves as an environmentally sustainable biosorbent for the extraction of methylene blue(MB)dye from wastewater.A hybrid approach of Response Surface Methodology(RSM)and Artificial Neural Networks(ANN)was implemented to assess,optimize,and forecast biosorption effectiveness across different operating parameters.The experimental design employed a Central Composite Design(CCD)methodology,focusing on critical parameters including pH,initial dye concentration,temperature,and biosorbent dosage.The ideal biosorption parameters were identified as a temperature of 44.3℃,pH 7.1,a biosorbent dosage of 0.3 grams,and an initial dye concentration of 48.4 mg/L,resulting in a maximum removal efficiency of 84.26%.The ANN model exhibited significant prediction accuracy,so confirming its appropriateness for predicting and enhancing intricate biosorption processes.The findings underscore that AM leaves constitute a cost-efficient,plentiful,and ecologically sustainable resource for wastewater treatment purposes.Furthermore,the amalgamation of RSM and ANN shown significant efficacy in process optimization and forecasting.These findings provide significant insights into the advancement of eco-friendly solutions for the treatment of dye-contaminated water.Subsequent study must prioritize the amplification of the procedure for industrial applications,the execution of ongoing system assessments,and the evaluation of the enduring environmental and economic ramifications of utilizing AM leaves as a biosorbent.展开更多
基金Supported by National Natural Science Foundation of China(Grant Nos.51105092,61403106)International Science and Technology Cooperation Program of China(Grant No.2014DFR50250)the 111 Project,China(Grant No.B07018)
文摘The efficiency and precision of parameter calibration in discrete element method （DEM） are not satisfactory, and parameter calibration for granular heat transfer is rarely involved. Accordingly, parameter calibration for granular heat transfer with the DEM is studied. The heat transfer in granular assemblies is simulated with DEM, and the effective thermal conductivity （ETC） of these granular assemblies is measured with the transient method in simulations. The measurement testbed is designed to test the ETC of the granular assemblies under normal pressure and a vacuum based on the steady method. Central composite design （CCD） is used to simulate the impact of the DEM parameters on the ETC of granular assemblies, and the heat transfer parameters are calibrated and compared with experimental data. The results show that, within the scope of the considered parameters, the ETC of the granular assemblies increases with an increasing particle thermal conductivity and decreases with an increasing particle shear modulus and particle diameter. The particle thermal conductivity has the greatest impact on the ETC of granular assemblies followed by the particle shear modulus and then the particle diameter. The calibration results show good agreement with the experimental results. The error is less than 4%, which is within a reasonable range for the scope of the CCD parameters. The proposed research provides high efficiency and high accuracy parameter calibration for granular heat transfer in DEM.
文摘This study examines the efficacy of Avicennia marina(AM)leaves as an environmentally sustainable biosorbent for the extraction of methylene blue(MB)dye from wastewater.A hybrid approach of Response Surface Methodology(RSM)and Artificial Neural Networks(ANN)was implemented to assess,optimize,and forecast biosorption effectiveness across different operating parameters.The experimental design employed a Central Composite Design(CCD)methodology,focusing on critical parameters including pH,initial dye concentration,temperature,and biosorbent dosage.The ideal biosorption parameters were identified as a temperature of 44.3℃,pH 7.1,a biosorbent dosage of 0.3 grams,and an initial dye concentration of 48.4 mg/L,resulting in a maximum removal efficiency of 84.26%.The ANN model exhibited significant prediction accuracy,so confirming its appropriateness for predicting and enhancing intricate biosorption processes.The findings underscore that AM leaves constitute a cost-efficient,plentiful,and ecologically sustainable resource for wastewater treatment purposes.Furthermore,the amalgamation of RSM and ANN shown significant efficacy in process optimization and forecasting.These findings provide significant insights into the advancement of eco-friendly solutions for the treatment of dye-contaminated water.Subsequent study must prioritize the amplification of the procedure for industrial applications,the execution of ongoing system assessments,and the evaluation of the enduring environmental and economic ramifications of utilizing AM leaves as a biosorbent.
