The Experts below are selected from a list of 2310 Experts worldwide ranked by ideXlab platform
Nidal Hilal - One of the best experts on this subject based on the ideXlab platform.
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Humic Substance coagulation artificial neural network simulation
Desalination, 2010Co-Authors: Mohammed Alabri, Khalid Al Anezi, Akram Dakheel, Nidal HilalAbstract:Abstract This paper investigates the use of backpropagation neural network (BPNN) to predict Humic Substance (HS) UV absorbance experimental results. The studied experimental sets include HS and heavy metal agglomeration, HS coagulation using polyelectrolytes and HS and heavy metal coagulation using polyelectrolytes. BPNN simulation showed high prediction accuracy where regression coefficient ( R ) was > 0.95 for all simulations. Lower and higher than optimum training data input reduces BPNN reliability due to under training or over-fitting. The number of neurons study showed that a lower number of neurons led to under training, while a higher number of neurons resulted in the network memorizing the input dataset.
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combined Humic Substance and heavy metals coagulation and membrane filtration under saline conditions
Desalination, 2010Co-Authors: Mohammed Alabri, Akram Dakheel, Chedly Tizaoui, Nidal HilalAbstract:Membrane retention was investigated with the aid of poly diallydimethylammonium chloride and copolymer of dimethyl aminoethyl acrylate polyelectrolyte coagulants. The membranes used were P005F ultrafiltration (UF) and NF270 nanofiltration (NF) membranes and the studied conditions were salinity level, Humic Substances (HS) concentration, heavy metals concentration and polyelectrolytes type and concentration. Results showed that with the aid of polyelectrolytes, HS retention reached a maximum point where further increase in polyelectrolytes degraded membrane retention. This study showed also that at low polyelectrolyte concentration, salinity reduced membrane retention. While at optimum polyelectrolytes dose, membrane performance was not affected by salinity. In addition, HS binding capacity for heavy metals was reduced considerably at high salinity levels.
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combined Humic Substance coagulation and membrane filtration under saline conditions
Desalination and Water Treatment, 2009Co-Authors: Nidal Hilal, Mohammed Alabri, Hilal AlhinaiAbstract:The effects of poly diallydimethylammonium chloride (PDADMAC) and copolymer of dimethyl aminoethyl acrylate (CoAA) coagulants on membrane performance are investigated under different conditions using ultrafiltration (UF) and nanofiltration (NF) membranes. It is evident that PDADMAC performance is better than CoAA in removing Humic Substances (HS) with both membranes, P005F and NF270, having equal fouling potential. Salinity results showed reduction in HS retention and increase in fouling with increasing salinity from 10,000 to 25,000 ppm NaCl using P005F UF membrane. No further reduction in retention or increase in fouling was experienced when salinity was increased to 35,000 ppm NaCl, while NF270 NF membrane experienced reduced retention and increased fouling throughout the studied salinity range. Finally, TMP did neither affect HS retention nor NF270 fouling, but increased P005F fouling.
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artificial neural network simulation of combined Humic Substance coagulation and membrane filtration
Chemical Engineering Journal, 2008Co-Authors: Mohammed Alabri, Nidal HilalAbstract:Backpropagation artificial neural network (BPNN) was utilized to predict membrane performance. The network was used to predict and compare Humic Substance (HS) retention and membrane fouling with previously obtained experimental data. BPNN simulation results show high network reliability, if the network is implemented correctly. The difference between the predicted and experimental data was lower than 5%. Low number of training data input has been shown to hinder the learning process. A high number of training data input has lead to over-fitting or memorization of the training data set, reducing the networks predictability. The number of neurons in the hidden layers needs to be chosen carefully to obtain a reliable network. This paper shows that a lower number of neurons result in low reliability, while a higher number of neurons leads to data over-fitting. The best performance was obtained with 2-10 neurons for HS and heavy metals agglomeration and 5-15 neurons for HS coagulation with and without heavy metals.
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combined Humic Substance and heavy metals agglomeration and membrane filtration under saline conditions
Separation Science and Technology, 2008Co-Authors: Nidal Hilal, Mohammed Alabri, Hilal Alhinai, Chris SomerfieldAbstract:Abstract Humic Substances‐heavy metals complexation combined with membrane filtration is reported. The effects of salinity, Humic Substances (HS) concentration, heavy metals concentration, and trans‐membrane pressure (TMP) on HS and heavy metals retention using two membranes are studied. Membrane fouling is also studied at the aforementioned conditions. NF270 experienced higher fouling. Moreover, salinity tests showed increasing fouling rate and reduction in membrane retention with increasing salinity level. While increasing HS concentration reduced HS retention and increased heavy metals retention and membrane fouling. Heavy metals concentration reduced the NF270 HS retention, but did not affect the P005F HS retention. In addition, TMP did not affect HS and heavy metals retention nor NF270 fouling, but increased P005F fouling.
Mohammed Alabri - One of the best experts on this subject based on the ideXlab platform.
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Humic Substance coagulation artificial neural network simulation
Desalination, 2010Co-Authors: Mohammed Alabri, Khalid Al Anezi, Akram Dakheel, Nidal HilalAbstract:Abstract This paper investigates the use of backpropagation neural network (BPNN) to predict Humic Substance (HS) UV absorbance experimental results. The studied experimental sets include HS and heavy metal agglomeration, HS coagulation using polyelectrolytes and HS and heavy metal coagulation using polyelectrolytes. BPNN simulation showed high prediction accuracy where regression coefficient ( R ) was > 0.95 for all simulations. Lower and higher than optimum training data input reduces BPNN reliability due to under training or over-fitting. The number of neurons study showed that a lower number of neurons led to under training, while a higher number of neurons resulted in the network memorizing the input dataset.
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combined Humic Substance and heavy metals coagulation and membrane filtration under saline conditions
Desalination, 2010Co-Authors: Mohammed Alabri, Akram Dakheel, Chedly Tizaoui, Nidal HilalAbstract:Membrane retention was investigated with the aid of poly diallydimethylammonium chloride and copolymer of dimethyl aminoethyl acrylate polyelectrolyte coagulants. The membranes used were P005F ultrafiltration (UF) and NF270 nanofiltration (NF) membranes and the studied conditions were salinity level, Humic Substances (HS) concentration, heavy metals concentration and polyelectrolytes type and concentration. Results showed that with the aid of polyelectrolytes, HS retention reached a maximum point where further increase in polyelectrolytes degraded membrane retention. This study showed also that at low polyelectrolyte concentration, salinity reduced membrane retention. While at optimum polyelectrolytes dose, membrane performance was not affected by salinity. In addition, HS binding capacity for heavy metals was reduced considerably at high salinity levels.
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combined Humic Substance coagulation and membrane filtration under saline conditions
Desalination and Water Treatment, 2009Co-Authors: Nidal Hilal, Mohammed Alabri, Hilal AlhinaiAbstract:The effects of poly diallydimethylammonium chloride (PDADMAC) and copolymer of dimethyl aminoethyl acrylate (CoAA) coagulants on membrane performance are investigated under different conditions using ultrafiltration (UF) and nanofiltration (NF) membranes. It is evident that PDADMAC performance is better than CoAA in removing Humic Substances (HS) with both membranes, P005F and NF270, having equal fouling potential. Salinity results showed reduction in HS retention and increase in fouling with increasing salinity from 10,000 to 25,000 ppm NaCl using P005F UF membrane. No further reduction in retention or increase in fouling was experienced when salinity was increased to 35,000 ppm NaCl, while NF270 NF membrane experienced reduced retention and increased fouling throughout the studied salinity range. Finally, TMP did neither affect HS retention nor NF270 fouling, but increased P005F fouling.
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artificial neural network simulation of combined Humic Substance coagulation and membrane filtration
Chemical Engineering Journal, 2008Co-Authors: Mohammed Alabri, Nidal HilalAbstract:Backpropagation artificial neural network (BPNN) was utilized to predict membrane performance. The network was used to predict and compare Humic Substance (HS) retention and membrane fouling with previously obtained experimental data. BPNN simulation results show high network reliability, if the network is implemented correctly. The difference between the predicted and experimental data was lower than 5%. Low number of training data input has been shown to hinder the learning process. A high number of training data input has lead to over-fitting or memorization of the training data set, reducing the networks predictability. The number of neurons in the hidden layers needs to be chosen carefully to obtain a reliable network. This paper shows that a lower number of neurons result in low reliability, while a higher number of neurons leads to data over-fitting. The best performance was obtained with 2-10 neurons for HS and heavy metals agglomeration and 5-15 neurons for HS coagulation with and without heavy metals.
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combined Humic Substance and heavy metals agglomeration and membrane filtration under saline conditions
Separation Science and Technology, 2008Co-Authors: Nidal Hilal, Mohammed Alabri, Hilal Alhinai, Chris SomerfieldAbstract:Abstract Humic Substances‐heavy metals complexation combined with membrane filtration is reported. The effects of salinity, Humic Substances (HS) concentration, heavy metals concentration, and trans‐membrane pressure (TMP) on HS and heavy metals retention using two membranes are studied. Membrane fouling is also studied at the aforementioned conditions. NF270 experienced higher fouling. Moreover, salinity tests showed increasing fouling rate and reduction in membrane retention with increasing salinity level. While increasing HS concentration reduced HS retention and increased heavy metals retention and membrane fouling. Heavy metals concentration reduced the NF270 HS retention, but did not affect the P005F HS retention. In addition, TMP did not affect HS and heavy metals retention nor NF270 fouling, but increased P005F fouling.
Bart Van Der Bruggen - One of the best experts on this subject based on the ideXlab platform.
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sustainable management of landfill leachate concentrate through recovering Humic Substance as liquid fertilizer by loose nanofiltration
Water Research, 2019Co-Authors: Hongwei Liu, Mei Jiang, Jiuyang Lin, Shengqiong Fang, Shuaifei Zhao, Bart Van Der BruggenAbstract:Abstract The hybrid membrane bioreactor - nanofiltration treatment process can be an effective approach for treating the landfill leachate, but the residual leachate concentrate highly loaded with the Humic Substance and salts remains an environmental concern. Herein, a loose nanofiltration membrane (molecular weight cut-off of 860 Da) was used to recover the Humic Substance, which can act as a key component of organic fertilizer, from the leachate concentrate. The loose nanofiltration membrane showed the high permeation fluxes and high transmissions (>94.7%) for most inorganic ions (i.e., Na+, K+, Cl−, and NO3−), while retaining 95.7 ± 0.3% of the Humic Substance, demonstrating its great potential in effective fractionation of Humic Substance from inorganic salts in the leachate concentrate. The operation conditions, i.e., cross-flow rates and temperatures, had more pronounced impacts on the filtration performance of the loose nanofiltration membrane. Increasing cross-flow rates from 60 to 260 L h−1 resulted in an improvement of ca. 7.3% in the Humic Substance rejection, mainly due to the reduced concentration polarization effect. In contrast, the solute rejection of the nanofiltration membrane was negatively dependent on the temperature. The rejection of Humic Substance decreased from 96.3 ± 0.3% to 92.0 ± 0.4% with increasing the temperature from 23 to 35 °C, likely due to the enlargement of the membrane pore size and enhancement in solute diffusivity. The Humic Substance was enriched from 1735 to 15,287 mg L−1, yielding a 91.2% recovery ratio with 85.7% desalination efficiency at a concentration factor of 9.6. The recovered HS had significantly stimulated the seed germination and growth of the green mungbean plants with no obvious phytotoxicity. These results demonstrate that loose nanofiltration can be an effective promising technology to recover the Humic Substance as a valuable fertilizer component towards sustainable management of the landfill leachate concentrate.
Changle Qing - One of the best experts on this subject based on the ideXlab platform.
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relationships between Humic Substance bound mercury contents and soil properties in subtropical zone
Journal of Environmental Sciences-china, 2006Co-Authors: Y U Guifen, Xin Jiang, W U Hongtao, H E Wenxiang, Changle QingAbstract:The bioavailability of Humic Substance-bound mercury (HS-Hg) has been established, while the distribution of HS-Hg in soils in relation to soil properties remains obscure. Path analysis and principal component analysis were employed in present study to investigate how soil factors influence the contents of HS-Hg in soils. Results showed that HS-Hg ranged from 0.0192 to 0.2051 mg/kg in soils. The two fractions existed in soils as Humic acid-bound mercury (HA-Hg) > fulvic acid-bound mercury (FA-Hg) and the ratio of HA-Hg/FA-Hg was 1.61 on the average. Soil organic carbon (OC) and HS favorably determined soil HS-Hg and the two fractions. The mercury source forming HS-Hg derived from soil total mercury and HS-Hg. FA-Hg and HA-Hg served as mercury source for each other. In acidic soils, FA-Hg and HA-Hg consistently rose with the increase of OC, and generally HA-Hg increased more dramatically. Soils with lower pH and lighter texture contained more HS-Hg, particularly fraction of FA-Hg. Among all influencing factors, organic material source showed the strongest effect, followed by other soil properties and soil mercury source.
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bioavailability of Humic Substance bound mercury to lettuce and its relationship with soil properties
Communications in Soil Science and Plant Analysis, 2004Co-Authors: Changle Qing, Xin Jiang, Jiaoyan ZhangAbstract:The bioavailability of Humic Substance-bound mercury (HS-Hg) in relation to soil properties was investigated using lettuce (Lactuca sativa var. Angustana irish). Results showed fulvic acid-bound Hg (FA-Hg) was an important source of Hg for lettuce. The availability of added Hg to the lettuce depends on the supply power of added Hg and the absorption capacity of the lettuce. Acidic soil, light soil texture, and the amount of Humic Substances and available nutrients enhance Hg availability. This suggests that Humic Substance-bound Hg poses greater risk to human health through food chain in sandy, acidic, and fertile soils.
Hongwei Liu - One of the best experts on this subject based on the ideXlab platform.
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sustainable management of landfill leachate concentrate through recovering Humic Substance as liquid fertilizer by loose nanofiltration
Water Research, 2019Co-Authors: Hongwei Liu, Mei Jiang, Jiuyang Lin, Shengqiong Fang, Shuaifei Zhao, Bart Van Der BruggenAbstract:Abstract The hybrid membrane bioreactor - nanofiltration treatment process can be an effective approach for treating the landfill leachate, but the residual leachate concentrate highly loaded with the Humic Substance and salts remains an environmental concern. Herein, a loose nanofiltration membrane (molecular weight cut-off of 860 Da) was used to recover the Humic Substance, which can act as a key component of organic fertilizer, from the leachate concentrate. The loose nanofiltration membrane showed the high permeation fluxes and high transmissions (>94.7%) for most inorganic ions (i.e., Na+, K+, Cl−, and NO3−), while retaining 95.7 ± 0.3% of the Humic Substance, demonstrating its great potential in effective fractionation of Humic Substance from inorganic salts in the leachate concentrate. The operation conditions, i.e., cross-flow rates and temperatures, had more pronounced impacts on the filtration performance of the loose nanofiltration membrane. Increasing cross-flow rates from 60 to 260 L h−1 resulted in an improvement of ca. 7.3% in the Humic Substance rejection, mainly due to the reduced concentration polarization effect. In contrast, the solute rejection of the nanofiltration membrane was negatively dependent on the temperature. The rejection of Humic Substance decreased from 96.3 ± 0.3% to 92.0 ± 0.4% with increasing the temperature from 23 to 35 °C, likely due to the enlargement of the membrane pore size and enhancement in solute diffusivity. The Humic Substance was enriched from 1735 to 15,287 mg L−1, yielding a 91.2% recovery ratio with 85.7% desalination efficiency at a concentration factor of 9.6. The recovered HS had significantly stimulated the seed germination and growth of the green mungbean plants with no obvious phytotoxicity. These results demonstrate that loose nanofiltration can be an effective promising technology to recover the Humic Substance as a valuable fertilizer component towards sustainable management of the landfill leachate concentrate.