A batch experiment was conducted to investigate the adsorption of trivalent chromium （Cr（Ⅲ）） from aqueous solutions by sugarcane pulp residue （SPR） and biochar. The results show that Cr（Ⅲ） adsorption by SPR ...A batch experiment was conducted to investigate the adsorption of trivalent chromium （Cr（Ⅲ）） from aqueous solutions by sugarcane pulp residue （SPR） and biochar. The results show that Cr（Ⅲ） adsorption by SPR and biochar is highly pH-dependent and Cr（Ⅲ） adsorption amount increases with the increase of pH. The adsorption kinetics of Cr（Ⅲ） fits well with the pseudo-second-order model. The maximum Cr（Ⅲ） adsorption capacities of 15.85 mg/g and 3.43 mg/g for biochar and SPR were calculated by Langmuir model. This indicates that biochar has a larger ability for Cr（Ⅲ） adsorption than SPR. The free energy change value （AG） reveals a spontaneous sorption process of Cr（Ⅲ） onto SPR and non-spontaneous sorption process onto biochar. The entropy change （AS） and enthalpy change （AH） are found to be 66.27 J/（mol＇K） and 17.13 kJ/mol for SPR and 91.59 J/（mol-K） and 30.875 kJ/mol for biochar which further reflect an affinity of Cr（Ⅲ） onto SPR and biochar. It is suggested that biochar has potential to be an efficient adsorbent in the removal of Cr（Ⅲ） from industrial wastewater.展开更多
It is generally believed that intelligent management for sewage treatment plants(STPs) is essential to the sustainable engineering of future smart cities.The core of management lies in the precise prediction of daily ...It is generally believed that intelligent management for sewage treatment plants(STPs) is essential to the sustainable engineering of future smart cities.The core of management lies in the precise prediction of daily volumes of sewage.The generation of sewage is the result of multiple factors from the whole social system.Characterized by strong process abstraction ability,data mining techniques have been viewed as promising prediction methods to realize intelligent STP management.However,existing data mining-based methods for this purpose just focus on a single factor such as an economical or meteorological factor and ignore their collaborative effects.To address this challenge,a deep learning-based intelligent management mechanism for STPs is proposed,to predict business volume.Specifically,the grey relation algorithm(GRA) and gated recursive unit network(GRU) are combined into a prediction model(GRAGRU).The GRA is utilized to select the factors that have a significant impact on the sewage business volume,and the GRU is set up to output the prediction results.We conducted a large number of experiments to verify the efficiency of the proposed GRA-GRU model.展开更多
基金Project(50925417) supported by the National Funds for Distinguished Young Scientist,ChinaProject(50830301) supported by the Key Program of National Natural Science Foundation of ChinaProject(51074191) supported by the National Natural Science Foundation of China
文摘A batch experiment was conducted to investigate the adsorption of trivalent chromium （Cr（Ⅲ）） from aqueous solutions by sugarcane pulp residue （SPR） and biochar. The results show that Cr（Ⅲ） adsorption by SPR and biochar is highly pH-dependent and Cr（Ⅲ） adsorption amount increases with the increase of pH. The adsorption kinetics of Cr（Ⅲ） fits well with the pseudo-second-order model. The maximum Cr（Ⅲ） adsorption capacities of 15.85 mg/g and 3.43 mg/g for biochar and SPR were calculated by Langmuir model. This indicates that biochar has a larger ability for Cr（Ⅲ） adsorption than SPR. The free energy change value （AG） reveals a spontaneous sorption process of Cr（Ⅲ） onto SPR and non-spontaneous sorption process onto biochar. The entropy change （AS） and enthalpy change （AH） are found to be 66.27 J/（mol＇K） and 17.13 kJ/mol for SPR and 91.59 J/（mol-K） and 30.875 kJ/mol for biochar which further reflect an affinity of Cr（Ⅲ） onto SPR and biochar. It is suggested that biochar has potential to be an efficient adsorbent in the removal of Cr（Ⅲ） from industrial wastewater.
基金Project(KJZD-M202000801) supported by the Major Project of Chongqing Municipal Education Commission,ChinaProject(2016YFE0205600) supported by the National Key Research&Development Program of China+1 种基金Project(CXQT19023) supported by the Chongqing University Innovation Group Project,ChinaProjects(KFJJ2018069,1853061,1856033) supported by the Key Platform Opening Project of Chongqing Technology and Business University,China。
文摘It is generally believed that intelligent management for sewage treatment plants(STPs) is essential to the sustainable engineering of future smart cities.The core of management lies in the precise prediction of daily volumes of sewage.The generation of sewage is the result of multiple factors from the whole social system.Characterized by strong process abstraction ability,data mining techniques have been viewed as promising prediction methods to realize intelligent STP management.However,existing data mining-based methods for this purpose just focus on a single factor such as an economical or meteorological factor and ignore their collaborative effects.To address this challenge,a deep learning-based intelligent management mechanism for STPs is proposed,to predict business volume.Specifically,the grey relation algorithm(GRA) and gated recursive unit network(GRU) are combined into a prediction model(GRAGRU).The GRA is utilized to select the factors that have a significant impact on the sewage business volume,and the GRU is set up to output the prediction results.We conducted a large number of experiments to verify the efficiency of the proposed GRA-GRU model.
