Machine learning for the selection of carbon-based materials for tetracycline and sulfamethoxazole adsorption

Zhu, Xinzhe; Wan, Zhonghao; Tsang, Daniel C.W.; He, Mingjing; Hou, Deyi; Su, Zhishan; Shang, Jin

doi:10.1016/j.cej.2020.126782

Cited by 170 publications

(51 citation statements)

References 55 publications

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“…The Random Forest model was performed using the five selected variables and the importance of the variables is presented in Table 6 . According to this table, the adsorption efficiency is highly affected by initial concentration (0.347), surface area (0.287), and pore volume (0.131), which is in line with previous findings [ 46 , 47 ]. The initial dye concentration has the highest effect on the adsorption capacity [ 47 , 48 ].…”

Section: Resultssupporting

confidence: 91%

“…According to this table, the adsorption efficiency is highly affected by initial concentration (0.347), surface area (0.287), and pore volume (0.131), which is in line with previous findings [ 46 , 47 ]. The initial dye concentration has the highest effect on the adsorption capacity [ 47 , 48 ]. The increase of dye concentration gradients between the aqueous solution and adsorbent surface facilitated the adsorption of dye onto the agro-waste.…”

Section: Resultssupporting

confidence: 91%

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A Study on Machine Learning Methods’ Application for Dye Adsorption Prediction onto Agricultural Waste Activated Carbon

et al. 2021

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The adsorption of dyes using 39 adsorbents (16 kinds of agro-wastes) were modeled using random forest (RF), decision tree (DT), and gradient boosting (GB) models based on 350 sets of adsorption experimental data. In addition, the correlation between variables and their importance was applied. After comprehensive feature selection analysis, five important variables were selected from nine variables. The RF with the highest accuracy (R2 = 0.9) was selected as the best model for prediction of adsorption capacity of agro-waste using the five selected variables. The results suggested that agro-waste characteristics (pore volume, surface area, agro-waste pH, and particle size) accounted for 50.7% contribution for adsorption efficiency. The pore volume and surface area are the most important influencing variables among the agro-waste characteristics, while the role of particle size was inconspicuous. The accurate ability of the developed models’ prediction could significantly reduce experimental screening efforts, such as predicting the dye removal efficiency of agro-waste activated carbon according to agro-waste characteristics. The relative importance of variables could provide a right direction for better treatments of dyes in the real wastewater.

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Section: Resultssupporting

confidence: 91%

Section: Resultssupporting

confidence: 91%

A Study on Machine Learning Methods’ Application for Dye Adsorption Prediction onto Agricultural Waste Activated Carbon

et al. 2021

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“…Different methods for remove antibiotics have been assessed, such as adsorption (Zhu et al 2021), enhanced activated sludge method(Chen et al 2020b), hydrolysis (Mitchell et al 2014), membrane ltration (Yin et al 2021),photocatalysis, photoelectrocatalysis(PEC) and advanced oxidation process(AOPs) (Yang and Wang 2021). Recently, the PEC process has been considered as a novel way to degrade antibiotics that combined with the advantage of photocatalytic (PC) and electrochemistry (EC).…”

Section: Introductionmentioning

confidence: 99%

BiVO4 Photoanode Synergistic with Persulfate to Degrade Antibiotics: Degradation Mechanism and Pathway

Chen¹,

Wan²,

Ma³

et al. 2021

Preprint

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Antibiotics pose a great threat to ecological environment and human health, which should remove with an efficient innovative treatment method. Here in, a photoelectrocatalytic strategy for persulfate activation by BiVO4 photoanode had been systematically researched in this work. In the presence of PS, the degradation efficiency of sulfamethoxazole (SMX) reached to 97.3% within 100 min, whereas the kinetic rate constant was 0.0388 min− 1, which was about 7.05 times higher than no addition of PS (0.0055 min− 1). Open circuit potential (OCP), electrochemical impedance spectroscopy (EIS) and linear sweep voltammetry (LSV) were measured, suggesting that PS played a photoelectron acceptor for improving the separation of photogenerated holes and electrons and accelerating charge transfer. The radical quenching experiments and EPR tests indicated that holes and superoxide were dominant reactive oxidation species by directly oxidation pollutants. Indirect transfer pathway of S2O82− for producing hydroxyl radicals played a minor role, while only a small amount of sulfate radical was produced. Mixed pollutants with three antibiotics (ciprofloxacin (CIP), tetracycline (TC) and SMX) had been further degraded. Results showed that the degradation of SMX was inhibited in a content compared to a single pollutant, but both TC and CIP showed satisfactory removal effects, especially in natural water. Liquid chromatography-mass spectroscopy (LC-MS) was used to determine the intermediates of SMX (with 7 intermediates), CIP (with 11 intermediates) and TC (with 19 intermediates) in the PEC system. This work provides an eco-friendly and efficient method for antibiotic removal, which has good application prospect in the degradation of mixed pollutants.

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“…A literature survey shows that the removal of the TCs family from wastewater has been reported, utilizing a variety of approaches, e.g., adsorption, photocatalysis, microbial degradation, membrane filtration, electrocatalytic oxidation, etc. [ 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. Yet, most of these reports, if not all, to the best of our knowledge, were focused on the elder members of the family with almost no effort being made for the removal of TIGC.…”

Section: Introductionmentioning

confidence: 99%

“…Magnetization of the BC offers an extra advantage where the existence of magnetic nanoparticles with their tiny particle size, high surface-area-to-volume ration, rapid removal kinetics, possible recovery, and most significantly magnetism help in developing a unique wastewater treatment system [ 39 , 40 , 41 ]. Several reports on the utilization of BC (pristine and magnetic “MBC”) in the removal of antibiotics could be found in literature [ 20 , 21 , 38 , 39 ].…”

Section: Introductionmentioning

confidence: 99%

Eco-Structured Adsorptive Removal of Tigecycline from Wastewater: Date Pits’ Biochar versus the Magnetic Biochar

et al. 2020

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Non-magnetic and magnetic low-cost biochar (BC) from date pits (DP) were applied to remove tigecycline (TIGC) from TIGC-artificially contaminated water samples. Pristine biochar from DP (BCDP) and magnetite-decorated biochar (MBC-DP) were therefore prepared. Morphologies and surface chemistries of BCDP and MBC-DP were explored using FT-IR, Raman, SEM, EDX, TEM, and BET analyses. The obtained IR and Raman spectra confirmed the presence of magnetite on the surface of the MBC-DP. SEM results showed mesoporous surface for both adsorbents. BET analysis indicated higher amount of mesopores in MBC-DP. Box–Behnken (BB) design was utilized to optimize the treatment variables (pH, dose of the adsorbent (AD), concentration of TIGC [TIGC], and the contact time (CT)) and maximize the adsorptive power of both adsorbents. Higher % removal (%R), hitting 99.91%, was observed using MBC-DP compared to BCDP (77.31%). Maximum removal of TIGC (99.91%) was obtained using 120 mg/15 mL of MBC-DP for 10 min at pH 10. Equilibrium studies showed that Langmuir and Freundlich isotherms could best describe the adsorption of TIGC onto BCDP and MBC-DP, respectively, with a maximum adsorption capacity (qmax) of 57.14 mg/g using MBC-DP. Kinetics investigation showed that adsorption of TIGC onto both adsorbents could be best-fitted to a pseudo-second-order (PSO) model.

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Machine learning for the selection of carbon-based materials for tetracycline and sulfamethoxazole adsorption

Cited by 170 publications

References 55 publications

A Study on Machine Learning Methods’ Application for Dye Adsorption Prediction onto Agricultural Waste Activated Carbon

A Study on Machine Learning Methods’ Application for Dye Adsorption Prediction onto Agricultural Waste Activated Carbon

BiVO4 Photoanode Synergistic with Persulfate to Degrade Antibiotics: Degradation Mechanism and Pathway

Eco-Structured Adsorptive Removal of Tigecycline from Wastewater: Date Pits’ Biochar versus the Magnetic Biochar

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