Bio-inspired, high, and fast adsorption of tetracycline from aqueous media using Fe3O4-g-CN@PEI-β-CD nanocomposite: Modeling by response surface methodology (RSM), boosted regression tree (BRT), and general regression neural network (GRNN)

Status and future trends in wastewater management strategies using artificial intelligence and machine learning techniques

The two main things needed to fulfill the world's impending need for water in the face of the widespread water crisis are collecting water and recycling. To do this, the present study has placed a greater focus on water management strategies used in a variety of contexts areas. To distribute water effectively, save it, and satisfy water quality requirements for a variety of uses, it is imperative to apply intelligent water management mechanisms while keeping in mind the population density index. The present review unveiled the latest trends in water and wastewater recycling, utilizing several Artificial Intelligence (AI) and machine learning (ML) techniques for distribution, rainfall collection, and control of irrigation models. The data collected for these purposes are unique and comes in different forms. An efficient water management system could be developed with the use of AI, Deep Learning (DL), and the Internet of Things (IoT) structure. This study has investigated several water management methodologies using AI, DL and IoT with case studies and sample statistical assessment, to provide an efficient framework for water management.

Artificial neural network modeling for the prediction, estimation, and treatment of diverse wastewaters: A comprehensive review and future perspective

The application of artificial neural networks (ANNs) in the treatment of wastewater has achieved increasing attention, as it enhances the efficiency and sustainability of wastewater treatment plants (WWTPs). This paper explores the application of ANN-based models in WWTPs, focusing on the latest published research work, by presenting the effectiveness of ANNs in predicting, estimating, and treatment of diverse types of wastewater. Furthermore, this review comprehensively examines the applicability of the ANNs in various processes and methods used for wastewater treatment, including membrane and membrane bioreactors, coagulation/flocculation, UV-disinfection processes, and biological treatment systems. Additionally, it provides a detailed analysis of pollutants viz organic and inorganic substances, nutrients, pharmaceuticals, drugs, pesticides, dyes, etc., from wastewater, utilizing both ANN and ANN-based models. Moreover, it assesses the techno-economic value of ANNs, provides cost estimation and energy analysis, and outlines promising future research directions of ANNs in wastewater treatment. AI-based techniques are used to predict parameters such as chemical oxygen demand (COD) and biological oxygen demand (BOD) in WWTP influent. ANNs have been formed for the estimation of the removal efficiency of pollutants such as total nitrogen (TN), total phosphorus (TP), BOD, and total suspended solids (TSS) in the effluent of WWTPs. The literature also discloses the use of AI techniques in WWT is an economical and energy-effective method. AI enhances the efficiency of the pumping system, leading to energy conservation with an impressive average savings of approximately 10%. The system can achieve a maximum energy savings state of 25%, accompanied by a notable reduction in costs of up to 30%.

Polyethyleneimine-functionalized magnetic sugarcane bagasse cellulose film for the efficient adsorption of ibuprofen

Polyethyleneimine-modified magnetic sugarcane bagasse cellulose film (P-SBC/Fe3O4 film) was simply fabricated for the removal of ibuprofen (IBP), a typical emerging organic contaminant. The P-SBC/Fe3O4 film exhibited an equilibrium adsorption amount of 370.52 mg/g for IBP and a corresponding removal efficiency of 92.63 % under following adsorption conditions: 318 K, pH 4, and 0.25 mg/mL dosage. Thermodynamic studies indicated that adsorption of IBP on the P-SBC/Fe3O4 film was spontaneous (∆G < 0) and endothermic (∆H > 0). The adsorption data conformed to the Freundlich isotherm model and multilayer adsorption model (two layers), and an average of 3-4 active sites on the P-SBC/Fe3O4 film share an IBP molecule. Both the EDR-IDR and AOAS models vividly described the dynamic characteristics of adsorption process. Model fitting results, theoretical calculations, and comprehensive characterization revealed that adsorption is driven by electrostatic interactions between the primary amine of P-SBC/Fe3O4 film and the carboxyl group of IBP molecule, while other weak interactions are also non-ignorable. Furthermore, quantitative calculations based on density functional theory (DFT) underscored the importance of PEI functionalization. In conclusion, P-SBC/Fe3O4 film is an environmentally friendly and cost-effective adsorbent with significant potential for effectively removing IBP, while maintaining its efficacy over multiple cycles.

Preparation of amino functionalized magnetic oyster shell powder adsorbent for selective removal of anionic dyes and Pb (II) from wastewater

A kind of new magnetic oyster shell (OS) composite (OS-Fe3O4@SiO2@NH2@PEI) was synthesized and used as an adsorbent to remove carmine (CM), sunset yellow (SY) and Pb (II) from water. Firstly, Fe3O4 magnetic nanoparticles were introduced on the surface of waste oyster shell powder, then amino silanization was used to improve the stability of the material, and finally polyethylenimide (PEI) was grafted by Schiff base reaction. The composite was characterized by FT-IR, SEM, EDS, XPS, VSM, BET, TEM and zeta potential. The effects of adsorbent dosage and initial solution pH on the three samples were investigated by adsorption experiments. The adsorption kinetics and isotherms were investigated in depth under the best experimental conditions. The composite adsorbent not only selectively removed anionic azo dyes, but also had good recycling. In addition, OS-Fe3O4@SiO2@NH2@PEI still had good performance in mixed samples. These results indicated that OS-Fe3O4@SiO2@NH2@PEI was successfully used for the removal of a wide range of anionic dyes and heavy metal ions from the environment, and provided a new strategy for recycling waste.

Adsorption of tetracycline by polycationic straw: Density functional theory calculation for mechanism and machine learning prediction for tetracyclines' remediation

The abuse of antibiotics causes serious environmental pollution, whose removal has become a hot topic. The adsorption of tetracycline (TC) on a prepared polycationic straw (MMS) was investigated. The kinetic, thermodynamic and adsorption isotherm models showed that adsorption of TC by MMS was a spontaneous, monolayer reaction with coexistence of physical and chemical process. Density functional theory indicated that the adsorption of TC resulted from electrostatic interaction and hydrogen bonds, which proved the mechanism of TC by macromolecular biomass for the first time. The expected and empirical values of TC adsorption showed a high fit degree, through predication of machine learning, indicating the feasibility and avoiding lots of experiments. Further, the adsorption ability of MMS to other TCs was predicted, founding that the highest removal efficiency was doxycycline, which provides a novel strategy for removal of other pollution and reduce of economic and time cost in practical application.

Exploring Optimization of Zeolites as Adsorbents for Rare Earth Elements in Continuous Flow by Machine Learning Techniques

Unsupervised machine learning (ML) techniques are applied to the characterization of the adsorption of rare earth elements (REEs) by zeolites in continuous flow. The successful application of principal component analysis (PCA) and K-Means algorithms from ML allowed for a wide range assessment of the adsorption results. This global approach permits the evaluation of the different stages of the sorption cycles and their optimization and improvement. The results from ML are also used for the definition of a regression model to estimate other REEs' recoveries based on the known values of the tested REEs. Overall, it was possible to remove more than 70% of all REEs from aqueous solutions during the adsorption assays and to recover over 80% of the REEs entrapped on the zeolites using an optimized desorption cycle.

Nanotechnology-assisted treatment of pharmaceuticals contaminated water

The presence of pharmaceutical compounds in wastewater due to an increase in industrialization and urbanization is a serious health concern. The demand for diverse types of pharmaceutical compounds is expected to grow as there is continuous improvement in the global human health standards. Discharge of domestic pharmaceutical personal care products and hospital waste has aggravated the burden on wastewater management. Further, the pharmaceutical water is toxic not only to the aquatic organism but also to terrestrial animals coming in contact directly or indirectly. The pharmaceutical wastes can be removed by adsorption and/or degradation approach. Nanoparticles (NPs), such as 2D layers materials, metal-organic frameworks (MOFs), and carbonaceous nanomaterials are proven to be more efficient for adsorption and/or degradation of pharmaceutical waste. In addition, inclusion of NPs to form various composites leads to improvement in the waste treatment efficacy to a greater extent. Overall, carbonaceous nanocomposites have advantage in the form of being produced from renewable resources and the nanocomposite material is biodegradable either completely or to a great extent. A comprehensive literature survey on the recent advancement of pharmaceutical wastewater is the focus of the present article.

On the adsorption characteristics and mechanism of methylene blue by ball mill modified biochar

In this study, modified biochar (BRB) was prepared from rice straw by ball milling technique and used for the adsorption of methylene blue (MB) in wastewater. The BRB was characterized by SEM, FTIR and XPS, and the adsorption model and Box-Behnken design were used to optimize the five influencing factors. The results showed that the ball milling technique could increase the content of functional groups (-OH, C=C and C-O, etc.) and aromatic structures on the surface of biochar, thus facilitating the removal of MB. The isotherm model was consistent with the Langmuir adsorption model (R2 = 0.947) and the maximum adsorption capacity was 50.27 mg/g. The adsorption kinetics was consistent with the pseudo-second-order kinetic model (R2 = 1) and the adsorption rate was mainly controlled by chemisorption. The thermodynamic model confirmed that the adsorption process was a spontaneous heat absorption reaction. The maximum adsorption efficiency was 99.78% under the optimal conditions (40℃, pH 8, reaction time = 90 min, dosing amount = 0.1 mg), and the adsorption efficiency could be improved by increasing the pH and BRB dosing amount. The surface functional groups and crystal structure properties of BRB were the main determinants of adsorption, and it was clarified that physical adsorption, electrostatic attraction and π-π interaction were the main mechanisms for the adsorption of MB by BRB. The main mechanisms were clarified. Therefore, BRB is an economic, efficient and green adsorption material with good potential for the removal of dye pollutants in the aqueous environment.

Preparation of graphene oxide-doped silica aerogel using supercritical method for efficient removal of emerging pollutants from wastewater

Emerging pollutants and a large volume of unused dyes from the textile industry have been contaminating water bodies. This work introduces a scalable approach to purifying water by the adsorption of Acid green 25 (AG), Crystal Violet (CV), and Sulfamethoxazole (SMA) from an aqueous solution by graphene oxide (GO) doped modified silica aerogel (GO-SA) with supercritical fluid deposition (SFD) method. Characterization of GO-SA using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), high-resolution scanning electron microscopy (HR-SEM), thermogravimetric analysis (TGA), and Brunauer-Emmett-Teller (BET) adsorption isotherms revealed the improvement in the adsorbent surface area, and its textural properties. The high removal percentages observed in most of the experimental runs provide evidence of the excellent performance of the adsorbent towards the anionic and cationic dyes along with the antibiotic. The adsorption isotherm and kinetics showed that the Langmuir isotherm and pseudo-second-order kinetic models could explain adsorption. The adsorbent holds a higher adsorption capacity for SMA (67.07 mg g-1) than for CV (41.46 mg g-1) and AG (20.56 mg g-1) due to the higher hydrophobicity that interacts with the hydrophobic adsorbent. The GO-SA successfully removed AG, CV, and SMA with removal percentages of 98.23%, 98.71%, and 94.46%, respectively. The parameters were optimized using Central Composite Design (RSM-CCD). The prepared aerogel showed excellent reusability with a removal efficiency of > 85% even after 5 cycles. This study shows the potential of GO-SA adsorbent in textile and other wastewater purification.

Time series analyses based on the joint lagged effect analysis of pollution and meteorological factors of hemorrhagic fever with renal syndrome and the construction of prediction model

BACKGROUND

Hemorrhagic fever with renal syndrome (HFRS) is a rodent-related zoonotic disease induced by hantavirus. Previous studies have identified the influence of meteorological factors on the onset of HFRS, but few studies have focused on the stratified analysis of the lagged effects and interactions of pollution and meteorological factors on HFRS.

METHODS

We collected meteorological, contaminant and epidemiological data on cases of HFRS in Shenyang from 2005-2019. A seasonal autoregressive integrated moving average (SARIMA) model was used to predict the incidence of HFRS and compared with Holt-Winters three-parameter exponential smoothing model. A distributed lag nonlinear model (DLNM) with a maximum lag period of 16 weeks was applied to assess the lag, stratification and extreme effects of pollution and meteorological factors on HFRS cases, followed by a generalized additive model (GAM) to explore the interaction of SO2 and two other meteorological factors on HFRS cases.

RESULTS

The SARIMA monthly model has better fit and forecasting power than its own quarterly model and the Holt-Winters model, with an optimal model of (1,1,0) (2,1,0)12. Overall, environmental factors including humidity, wind speed and SO2 were correlated with the onset of HFRS and there was a non-linear exposure-lag-response association. Extremely high SO2 increased the risk of HFRS incidence, with the maximum RR values: 2.583 (95%CI:1.145,5.827). Extremely low windy and low SO2 played a significant protective role on HFRS infection, with the minimum RR values: 0.487 (95%CI:0.260,0.912) and 0.577 (95%CI:0.370,0.898), respectively. Interaction indicated that the risk of HFRS infection reached its highest when increasing daily SO2 and decreasing humidity.

CONCLUSIONS

The SARIMA model may help to enhance the forecast of monthly HFRS incidence based on a long-range dataset. Our study had shown that environmental factors such as humidity and SO2 have a delayed effect on the occurrence of HFRS and that the effect of humidity can be influenced by SO2 and wind speed. Public health professionals should take greater care in controlling HFRS in low humidity, low windy conditions and 2-3 days after SO2 levels above 200 μg/m3.

Ag NPs decorated on the magnetic rod-like hydroxyapatite/MIL-101(Fe) nanocomposite as an efficient catalyst for the reduction of some nitroaromatic compounds and as an effective antimicrobial agent

A rod-like magnetic nanocomposite was successfully synthesized in this work by loading Ag and Fe₃O₄ nanoparticles onto the surface of the hydroxyapatite/MIL-101(Fe) metal-organic framework. Various techniques were used to investigate the crystalline nature, size, morphology, and magnetic and structural properties of the HAP/MIL-101(Fe)/Ag/Fe₃O₄ nanocomposite, including X-ray diffraction (XRD), Fourier transform infrared (FT-IR) spectroscopy, Raman spectroscopy, field-emission scanning electron microscopy (FE-SEM), energy-dispersive X-ray spectroscopy (EDX), transmission electron microscopy (TEM), vibrating sample magnetometry (VSM), BET surface area measurements, and zeta potential analysis. The results indicate that the nanocomposite sample is composed of Ag and Fe₃O₄ nanoparticles adhered to rod-like hydroxyapatite/MIL-101(Fe). The catalytic and antibacterial abilities of the as-prepared HAP/MIL-101(Fe)/Ag/Fe₃O₄ were studied. This nanocomposite was utilized as a heterogeneous catalyst for the catalytic reduction of toxic pollutants, including 4-nitrophenol (4-NP), 2-nitrophenol (2-NP), 2,4-dinitrophenol (2,4-NP), 4-nitroaniline (4-NA), and 2-nitroaniline (2-NA) by NaBH₄ in water and at room temperature. These compounds were converted to their amine derivatives within 8-18 min with rate constant values equal to 0.2, 0.3, 0.33, and 0.47 min^-1, respectively. This quaternary magnetic catalyst can be easily separated from the reaction medium using an external magnetic field and reused. The synthesized nanocomposite maintained its efficiency in reducing nitroaromatic compounds after 5 runs, showing the high stability of the catalyst. Besides, the antibacterial activity of the nanocomposite against Gram-negative and Gram-positive bacteria was evaluated using the disk diffusion method. The inhibition zone diameter of the nanocomposite against Staphylococcus aureus, Staphylococcus saprophyticus, and Escherichia coli was measured to be 17, 14, and 12 mm, respectively.

Mesoporous Activated Biochar from Crab Shell with Enhanced Adsorption Performance for Tetracycline

In this study, three mesoporous-activated crab shell biochars were prepared by carbonation and chemical activation with KOH (K-CSB), H₃PO₄ (P-CSB), and KMnO₄ (M-CSB) to evaluate their tetracycline (TC) adsorption capacities. Characterization by SEM and a porosity analysis revealed that the K-CSB, P-CSB, and M-CSB possessed a puffy, mesoporous structure, with K-CSB exhibiting a larger specific surface area (1738 m²/g). FT-IR analysis revealed that abundant, surface ox-containing functional groups possessed by K-CSB, P-CSB, and M-CSB, such as -OH, C-O, and C=O, enhanced adsorption for TC, thereby enhancing their adsorption efficiency for TC. The maximum TC adsorption capacities of the K-CSB, P-CSB, and M-CSB were 380.92, 331.53, and 281.38 mg/g, respectively. The adsorption isotherms and kinetics data of the three TC adsorbents fit the Langmuir and pseudo-second-order model. The adsorption mechanism involved aperture filling, hydrogen bonding, electrostatic action, π-π EDA action, and complexation. As a low-cost and highly effective adsorbent for antibiotic wastewater treatment, activated crab shell biochar has enormous application potential.

Demand forecasting model for time-series pharmaceutical data using shallow and deep neural network model

Demand forecasting is a scientific and methodical assessment of future demand for a critical product.The effective Demand Forecast Model (DFM) enables pharmaceutical companies to be successful in the global market. The purpose of this research paper is to validate various shallow and deep neural network methods for demand forecasting, with the aim of recommending sales and marketing strategies based on the trend/seasonal effects of eight different groups of pharmaceutical products with different characteristics. The root mean squared error (RMSE) is used as the predictive accuracy of DFMs. This study also found that the mean RMSE value of the shallow neural network-based DFMs was 6.27 for all drug categories, which was lower than deep neural network models. According to the findings, DFMs based on shallow neural networks can effectively estimate future demand for pharmaceutical products.

Synthesis, characterization, and application of graphene oxide/layered double hydroxide /poly acrylic acid nanocomposite (LDH-rGO-PAA NC) for tetracycline removal: A comprehensive chemometric study

Tetracycline (TC), as the second produced and used antibiotic worldwide, is difficult to be entirely metabolized not only in the body, but also in the treatment processes of water and/or wastewater. Therefore, special attention needs to be paid on defining or developing new options for removing such contaminant. Herein, a reduced graphene oxide (GO) was integrated with Ni-Al layered double hydroxide (LDH) as well poly acrylic acid (LDH-rGO-PAA) and examined to reduce TC -as a model antibiotic-in water media under different operational parameters of TC initial concentration, pH, NC dose, and time. The governed behaviour in the adsorption process was investigated using three model methods of response surface methodology (RSM), artificial neural networks (ANN), and general regression neural network (GRNN) after confirming the physico-chemical properties of LDH-rGO-PAA nanocomposite (NC) using different techniques. The LDH-rGO-PAA NC displayed a good performance as either removal efficiency (R = 94.87 ± 0.25%) or adsorption capacity (q_e = 887.5 mg/g) with the respective values of 110 mg/L, 6.3, 20 mg, and 18.50 min for the mentioned factors (TC initial concentration, pH, NC dose, and time, respectively), which was higher than that of reported for the similar adsorbents until now.

Polydopamine-mediated modification of ZIF-8 onto magnetic nanoparticles for enhanced tetracycline adsorption from wastewater

Magnetic nanoparticle materials which could be used to remove tetracycline were confined seriously due to their poor stability and unsatisfactory reusability. Here, we facilely prepared novel zeolitic imidazolate framework-8 (ZIF-8) functionalized magnetic nanoparticles (Fe₃O₄@PDA-ZIF-8) adsorbent utilizing polydopamine as a bond to establish a connection between zeolitic imidazolate framework-8 and Fe₃O₄, which could improve the stability of magnetic nanoparticles and enhance the tetracycline adsorption capacity simultaneously. The prepared nanocomposites were characterized and their TC adsorption abilities under various experiment conditions (contact time, TC initial concentration and pH values) were also investigated. Experimental results proved that the prepared adsorbent showed superior TC adsorption capacities (92.01 mg/g at pH = 7). Further, the adsorption mechanisms were comprehensively studied and the prepared adsorbent showed satisfactory stability and reusability during the cycle experiment. Altogether, our findings provided a feasible way to design and construct functional magnetic MOF materials for enhancing tetracycline adsorption from wastewater.

Adsorptive removal of tetracycline and ciprofloxacin drugs from water by using a magnetic rod-like hydroxyapatite and MIL-101(Fe) metal-organic framework nanocomposite

A novel porous nanocomposite composed of hydroxyapatite nanorods (HAP), a MIL-101(Fe) metal-organic framework, and Fe₃O₄ nanoparticles was successfully fabricated in this work. The magnetic HAP/MIL-101(Fe)/Fe₃O₄ ternary nanocomposite was identified by various techniques, namely FT-IR spectroscopy, XRD, Raman spectroscopy, SEM, EDX, TEM, BET specific surface area, zeta potential, and VSM measurements. Tetracycline (TC) and ciprofloxacin (CIP) aqueous solutions were used to evaluate the adsorption performance of the resulting HAP/MIL-101(Fe)/Fe₃O₄ composite. The adsorption rate and capacity of HAP/MIL-101(Fe)/Fe₃O₄ were increased as compared with HAP, MIL-101(Fe), and HAP/MIL-101(Fe) samples due to the increased attraction. The influence of initial drug concentration, adsorbent dosage, temperature, and pH on the adsorption process was investigated. The results showed that the removal efficiencies of HAP/MIL-101(Fe)/Fe₃O₄ for TC and CIP were 95% and 93%, under the determined optimum conditions: pH of 7, drug concentration of 50 mg L^-1, adsorbent dosage of 30 mg, and temperature of 25 °C. The maximum adsorption capacities of HAP/MIL-101(Fe)/Fe₃O₄ for TC and CIP were 120.48 mg g^-1 and 112.35 mg g^-1, respectively. Reusability of the prepared nanocomposite was easily achieved up to three times without significant change in its structure. As a result, the synthesized magnetic nanocomposite can be reused as a suitable absorbent for TC and CIP removal from aqueous solutions.

Layered Porous Graphitic Carbon Nitride Stabilized Effective Co2SnO4 Inverse Spinel as a Bifunctional Electrocatalyst for Overall Water Splitting

Developing an efficient, low-cost, and non-noble metal oxide-based nanohybrid material for overall water splitting is a highly desirable approach to promote clean energy harnessing and to minimize environmental issues. Accordingly, we proposed an interfacial engineering approach to construct layered porous graphitic carbon nitride (g-C₃N₄)-stabilized Co₂SnO₄ inverse spinel nanohybrid materials as highly active bifunctional electrocatalysts for hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) in an alkaline medium. Here, a Co₂SnO₄/g-C₃N₄ nanohybrid with a layered porous g-C₃N₄ stabilized cubelike inverse spinel has been synthesized with an enhanced surface area via a simple one-pot hydrothermal method. Besides, detailed structural and morphological characterizations were carried out using X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), field emission-scanning electron microscopy (FE-SEM), high-resolution transmission electron microscopy (HR-TEM), Fourier transform infrared (FT-IR), and Brunauer-Emmett-Teller (BET) analysis. Briefly, XPS analysis has revealed the existence of a strong coupling bond at the interface between a definite proportion of g-C₃N₄ nanosheets and the inverse spinel, which act as an electron transport channel to explore the exceptional performances for HER and OER. Compared to the Co₂SnO₄ inverse spinel lattice or g-C₃N₄ nanosheets, the prepared Co₂SnO₄/g-C₃N₄ nanohybrid-loaded 316 SSL mesh electrode showed excellent and stable electrocatalytic performances with very low overpotentials of 41 mV for HER and 260 mV for OER to reach the current density of 10 mA cm^-2. To understand the electrocatalytic phenomena, the faradic efficiency was calculated for the prepared bifunctional electrocatalyst as 96%, which effectively would favor water electrolysis. Accordingly, the Co₂SnO₄/g-C₃N₄ nanohybrid-loaded electrodes were constructed, and the minimum cell voltage was found to be 1.52 V to reach the current density of 10 mA cm^-2, which is comparable to the standard RuO₂∥Pt/C in two-electrode systems. Thus, the developed nanohybrid-based electrocatalyst could be an alternative to noble metal-centered systems for highly efficient overall water splitting.

Machine Learning-Based Sine-Cosine Algorithm for Wastewater Quality Assessment Using Activated Carbon

Activated carbon is one of the most highly proven adsorbents for organic chemicals from wastewater. It acts as a filter and adsorbs various chemicals from the wastewater. It has large pore size and strong adsorptive capacity. The quality of wastewater is generally determined by chemical oxygen demand (COD), biochemical oxygen demand (BOD5), total suspended solids (TSSs), total phosphorus (TP), and total nitrogen (TN). Wastewater contaminant measurement is significant for saving aquatic life and reusing treated water. Adsorption of contaminants that contribute for wastewater quality indicators uses machine learning algorithm for prediction. Many research works have been done, and the issues are inefficiency and time consuming in the adsorption of contaminants by activated carbon in wastewater management. To overcome these issues, this paper introduces hybrid technique of Voting-Based Extreme Learning Machine with sine cosine algorithm (VELM-SCA). The accuracy of VELM-SCA algorithm in classification of water quality status produced improved accuracy is 0.97.

Nonlinear Autoregressive Neural Network for Antimicrobial Waste Water Treatment

Antibiotics become an emerging contaminant and receive more interests due to its ecotoxicological and strong stability in water ecosystems. Antibiotic adsorption onto carbon materials are biochars among the wastewater mechanisms. This research used machine learning (ML) techniques to generate general adsorption forecasting model for sulfamethoxazole (SMX) and tetracycline (TC) on CBM. Dirichlet design parameters and a combined combination of Neumann and Dirichlet boundary situation are applied to the system of differential equations. In addition, the proposed method use the learning under supervision technique of a nonlinear autoregressive for estimating the CO2 concentration and flows in units of rate of a reaction characteristics, an exogenous (NARX) neural network model with two activation functions was used (Log-sigmoid and hyperbolic tangent) and for both the findings of a TC and SMX absorption simulations showed the random forest performed support vector tree and nonlinear autoregressive exogenous neural networks and machine learning methods. Their relevance and complete dependency graph evaluation lead reasonable CBM uses for antimicrobial wastewater treatment. Also, machine learning forecasting model with good generalization capability is useful for building effective CBMs with few empirical screens. It evaluates the accuracy, precision, recall, false positive rate (FPR), and false negative rate (FNR) and also reduces the experimental screening.

Three porous shapeable three-component hydrogen-bonded covalent-organic aerogels as backfill materials in a simulated permeable reactive barrier (PRB) for trapping levofloxacin

Levofloxacin (LEV) infiltrated in groundwater has threatened the safety of drinking water. For in-situ remediation of LEV-contaminated groundwater, there exists a main challenge of exploiting proper high efficient backfill medium in utilizing charming permeable reactive barriers (PRBs). Herein, three porous shapeable three-component hydrogen-bonded covalent organic aerogels (H_COA-1, H_COA-2 and H_COA-3) were fabricated based on a multiple-linking-site strategy to evaluate for adsorptive removal of LEV. The three H_COAs exhibited satisfactory performance in LEV adsorption that could integrate high adsorption capacity, good antiion interference, excellent recyclability and wide pH tolerance. The different regularity of kinetics and isotherms of three H_COAs signified that electrostatic effect, pore preservation, hydrogen bonding probably govern the adsorption process in combination, coupling with π-π electron-donor-acceptor (EDA), dipole-dipole and hydrophobic-hydrophobic interaction besides. In addition, the response surface methodology (RSM) was employed for studying the single and synergetic effects of selected variables and optimizing operation conditions. Furthermore, a laboratory PRB column packed with processable H_COA-2 was set up to investigate the LEV removal, and the breakthrough data was explained by Adams-Bohart, Thomas, BDST and Yoon-Nelson models. We believe could hopefully bring H_COAs into the real in-situ remediation of such challenging and persistent LEV-polluted groundwater with further massive-scale efficiently.

Construction of high-performance g-C3N4-based photo-Fenton catalysts by ferrate-induced defect engineering

A highly-efficient Fe–g-C3N4 photo-Fenton catalyst was prepared using ferrate as a critical iron source and defect control additive. The complementary synergistic effects of Fe species and defect sites promote photoelectron transfer.

Supramolecular Assembly of β-Cyclodextrin-Modified Polymer by Electrospinning with Sustained Antibacterial Activity

Supramolecular assembly loading drug as biomedical materials is a research hotspot. Herein, we reported a supramolecular electrospun assembly constructed via the hydrophobic and hydrogen bonding interaction. The obtained results showed that the assembly by supramolecular electrospinning not only increased the interactions of multiple antibacterial active species including antibiotics, cationic polymers, and silver to form a flexible membrane with good mechanical strength but also indicated the dual effects of rapid doxycycline and polyethyleneimine release as well as a sustained Ag release. Interestingly, the assembly showed not only good degradability but also a high bacteriostatic efficacy toward Escherichia coli (E. coli) up to 99.9%. More importantly, the in vivo wound healing assay indicated that the assembly could promote the healing of uninfected, E. coli-infected, and even methicillin-resistant staphylococcus aureus-infected wounds. The current research provides a novel approach to construct a supramolecular assembly by electrospinning mechanically induced strong noncovalent interaction.

Application of Magnetic Composites in Removal of Tetracycline through Adsorption and Advanced Oxidation Processes (AOPs): A Review

Water pollution induced by the tetracycline (TC) has caused global increasing attention owing to its extensive use, environmental persistence, and potential harm for human health. Adsorption and advanced oxidation processes (AOPs) have been promising techniques for TC removal due to ideal effectiveness and efficiency. Magnetic composites (MCs) which exploit the combined advantages of nano scale, alternative sources, easy preparation, and separation from wastewater are widely used for catalysis and adsorption. Herein, we intensively reviewed the available literature in order to provide comprehensive insight into the applications and mechanisms of MCs for removal of TC by adsorption and AOPs. The synthesis methods of MCs, the TC adsorption, and removal mechanisms are fully discussed. MCs serve as efficient adsorbents and photocatalysts with superior performance of photocatalytic performance in TC degradation. In addition, the TC can be effectively decomposed by the Fenton-based and SO4•− mediated oxidation under catalysis of the reported MCs with excellent catalytic performance. Based on the existing literature, we further discuss the challenge and future perspectives in MCs-based adsorption and AOPs in removing TC.

Modeling of photolytic degradation of sulfamethoxazole using boosted regression tree (BRT), artificial neural network (ANN) and response surface methodology (RSM); energy consumption and intermediates study

This study explores the boosted regression trees (BRT), artificial neural network (ANN) and response surface methodology (RSM) to model and optimize the operational variables for the simulation of the Photolytic degradation of Sulfamethoxazole (SMX) and concurrent total organic carbon (TOC) removal, based on the experimental data set. Four candidate variables involving initial pH (2-11), initial SMX concentration (50-200 mg L^-1), temperature (15-45 °C) and time (6-120 min) were considered for simultaneous optimization of SMX and TOC degradation. The result revealed that all the three models are statistically considerable as the values of R, R², adj-R² are >0.85, thus be deemed to work well in data fitting, prediction, and optimization, nevertheless, the values of R, R², adj-R², RMSE, MAE and AAD are far better for ANN and BRT than RSM method. The ∼100% SMX degradation conditions were found to be as follows: treatment time: 25 min, pH: 2.0, temperature: 35 °C and SMX concentration: 50 mg L^-1, while the maximum possible removal of TOC under the given conditions was ∼25%. The percentage contribution (PC) of each variable was deduced by ANOVA analysis of proposed quadratic models which indicated that time and pH are important factors than temperature and SMX concentration. The photolytic intermediates and inorganic ions of SMX, were identified and a potential route of transformation was also proposed.

Chemical Characterization of Marrubium vulgare Volatiles from Serbia

Marrubium vulgare is a cosmopolitan medicinal plant from the Lamiaceae family, which produces structurally highly diverse groups of secondary metabolites. A total of 160 compounds were determined in the volatiles from Serbia during two investigated years (2019 and 2020). The main components were E-caryophyllene, followed by germacrene D, α-humulene and α-copaene. All these compounds are from sesquiterpene hydrocarbons class which was dominant in both investigated years. This variation in volatiles composition could be a consequence of weather conditions, as in the case of other aromatic plants. According to the unrooted cluster tree with 37 samples of Marrubium sp. volatiles from literature and average values from this study, it could be said that there are several chemotypes: E-caryophyllene, β-bisabolene, α-pinene, β-farnesene, E-caryophyllene + caryophyllene oxide chemotype, and diverse (unclassified) chemotypes. However, occurring polymorphism could be consequence of adaptation to grow in different environment, especially ecological conditions such as humidity, temperature and altitude, as well as hybridization strongly affected the chemotypes. In addition, this paper aimed to obtain validated models for prediction of retention indices (RIs) of compounds isolated from M. vulgare volatiles. A total of 160 experimentally obtained RIs of volatile compounds was used to build the prediction models. The coefficients of determination were 0.956 and 0.964, demonstrating that these models could be used for predicting RIs, due to low prediction error and high r².

Machine Learning Based PEVs Load Extraction and Analysis

Transformation of the energy sector due to the appearance of plug-in electric vehicles (PEVs) has faced the researchers with challenges in recent years. The foremost challenge is uncertain behavior of a PEV that hinders operators determining a deterministic load profile. Load forecasting of PEVs is so crucial in both operating and planning of the energy systems. PEV load demand mainly depends on traveling behavior of them. This paper tries to present an accurate model to forecast PEVs’ traveling behavior in order to extract the PEV load profile. The presented model is based on machine-learning techniques; namely, a generalized regression neural network (GRNN) that correlates between PEVs’ arrival/departure times and traveling behavior is considered in the model. The results show the ability of the GRNN to communicate between arrival/departure times of PEVs and the distance traveled by them with a correlation coefficient (R) of 99.49% for training and 98.99% for tests. Therefore, the trained and saved GRNN model is ready to forecast PEVs’ trip length based on training and testing with historical data. Finally, the results indicate the importance of implementing more accurate methods to predict PEVs to gain the significant advantages in the importance of electrical energy in vehicles in the years to come.

A review on tetracycline removal from aqueous systems by advanced treatment techniques

Tetracycline occurrence and advanced treatment techniques.