Machine learning approaches to predict adsorption capacity of Azolla pinnata in the removal of methylene blue

Adsorption of aqueous Pb(II) using non-devulcanized and devulcanized tyre rubber powder: a comparative study

This study aimed to compare the adsorption of Pb(II) ions from an aqueous solution using non-devulcanized (NTR) and devulcanized tyre rubber (DTR) powder. Both types of rubber particles were prepared from used truck tyres, with DTR processed through mechano-chemical devulcanization. The adsorption experiments were conducted using 100-200 µm particles, with adsorbent doses ranging from 5 to 15 g/L. Effects of adsorbent dose, initial metal concentration and contact time were investigated. Characterization of both adsorbents was done using SEM-EDS, FTIR, and XRD analysis. Different adsorption isotherm and kinetic models were used to analyse the adsorption mechanisms. The results of the study showed that DTR was significantly more efficient at adsorbing Pb(II) compared to NTR. The maximum adsorption capacities estimated from the Langmuir equation were 75.1 mg/g and 6.61 mg/g for DTR and NTR, respectively. Among the kinetic models tested, pseudo 2nd order kinetic model was found to be the most suitable for tyre rubber adsorbents. The optimal dose and contact time were found to be 5 g/L and 120 min, respectively, for both adsorbents. The superior performance of DTR in Pb(II) adsorption was attributed to the change in the surface morphology of the rubber during the devulcanization process, resulting in increased surface roughness. The adsorption of Pb(II) was accompanied by the leaching of Zn from both types of rubber, suggesting that an ion exchange mechanism might be involved in the adsorption process. In conclusion, devulcanization appears to be a viable method for improving the adsorption properties of tyre rubber.

Green algae Ulva lactuca-derived biochar-sulfur improves the adsorption of methylene blue from water

The present investigation explores the efficacy of green algae Ulva lactuca biochar-sulfur (GABS) modified with H2SO4 and NaHCO3 in adsorbing methylene blue (MB) dye from aqueous solutions. The impact of solution pH, contact duration, GABS dosage, and initial MB dye concentration on the adsorption process are all methodically investigated in this work. To obtain a thorough understanding of the adsorption dynamics, the study makes use of several kinetic models, including pseudo-first order and pseudo-second order models, in addition to isotherm models like Langmuir, Freundlich, Tempkin, and Dubinin-Radushkevich. The findings of the study reveal that the adsorption capacity at equilibrium (qe) reaches 303.78 mg/g for a GABS dose of 0.5 g/L and an initial MB dye concentration of 200 mg/L. Notably, the Langmuir isotherm model consistently fits the experimental data across different GABS doses, suggesting homogeneous adsorption onto a monolayer surface. The potential of GABS as an efficient adsorbent for the extraction of MB dye from aqueous solutions is highlighted by this discovery. The study's use of kinetic and isotherm models provides a robust framework for understanding the intricacies of MB adsorption onto GABS. By elucidating the impact of various variables on the adsorption process, the research contributes valuable insights that can inform the design of efficient wastewater treatment solutions. The comprehensive analysis presented in this study serves as a solid foundation for further research and development in the field of adsorption-based water treatment technologies.

Optimizing Neural Networks for Chemical Reaction Prediction: Insights from Methylene Blue Reduction Reactions

This paper offers a thorough investigation of hyperparameter tuning for neural network architectures using datasets encompassing various combinations of Methylene Blue (MB) Reduction by Ascorbic Acid (AA) reactions with different solvents and concentrations. The aim is to predict coefficients of decay plots for MB absorbance, shedding light on the complex dynamics of chemical reactions. Our findings reveal that the optimal model, determined through our investigation, consists of five hidden layers, each with sixteen neurons and employing the Swish activation function. This model yields an NMSE of 0.05, 0.03, and 0.04 for predicting the coefficients A, B, and C, respectively, in the exponential decay equation A + B · e-x/C. These findings contribute to the realm of drug design based on machine learning, providing valuable insights into optimizing chemical reaction predictions.

Modeling based on machine learning to investigate flue gas desulfurization performance by calcium silicate absorbent in a sand bed reactor

Flue gas desulfurization (FGD) is a critical process for reducing sulfur dioxide (SO2) emissions from industrial sources, particularly power plants. This research uses calcium silicate absorbent in combination with machine learning (ML) to predict SO2 concentration within an FGD process. The collected dataset encompasses four input parameters, specifically relative humidity, absorbent weight, temperature, and time, and incorporates one output parameter, which pertains to the concentration of SO2. Six ML models were developed to estimate the output parameters. Statistical metrics such as the coefficient of determination (R2) and mean squared error (MSE) were employed to identify the most suitable model and assess its fitting effectiveness. The random forest (RF) model emerged as the top-performing model, boasting an R2 of 0.9902 and an MSE of 0.0008. The model's predictions aligned closely with experimental results, confirming its high accuracy. The most suitable hyperparameter values for RF model were found to be 74 for n_estimators, 41 for max_depth, false for bootstrap, sqrt for max_features, 1 for min_samples_leaf, absolute_error for criterion, and 3 for min_samples_split. Three-dimensional surface plots were generated to explore the impact of input variables on SO2 concentration. Global sensitivity analysis (GSA) revealed absorbent weight and time significantly influence SO2 concentration. The integration of ML into FGD modeling offers a novel approach to optimizing the efficiency and effectiveness of this environmentally crucial process.

Enhancing lead adsorption capacity prediction in biochar: a comparative study of machine learning models and parameter optimization

Machine learning models for predicting lead adsorption in biochar, based on preparation features, are currently lacking in the environmental field. Existing conventional models suffer from accuracy limitations. This study addresses these challenges by developing back-propagation neural network (BPNN) and random forest (RF) models using selected features: preparation temperature (T), specific surface area (BET), relative carbon content (C), molar ratios of hydrogen to carbon (H/C), oxygen to carbon (O/C), nitrogen to carbon (N/C), and cation exchange capacity (CEC). The RF model outperforms BPNN, improving R2 by 10%. Additional features and particle swarm optimization enhance the RF model's accuracy, resulting in an 8.3% improvement in R2, a decrease in RMSE by up to 56.1%, and a 55.7% reduction in MAE. The importance ranking of features places CEC > C > BET > O/C > H/C > N/C > T, highlighting the significance of CEC in lead adsorption. Strengthening the complexation effect may improve lead removal in biochar. This study contributes valuable insights for predicting and optimizing lead adsorption in biochar, addressing the accuracy gap in existing models. It lays the foundation for future investigations and the development of effective biochar-based solutions for sustainable lead removal in water remediation.

Kinetic studies of the removal of methylene blue from aqueous solution by biochar derived from jackfruit peel

Kinetic studies play an instrumental role in determining the most appropriate reaction rate model for industrial-scale applications. This study focuses on the kinetics of methylene blue (MB) adsorption from aqueous solutions by biochar derived from jackfruit peel. Various kinetic models, including pseudo-first-order (PFO), pseudo-second-order (PSO), intra-diffusion, and Elovich models, were applied to study MB adsorption kinetics of jackfruit peel biochar. The experiments were performed with two initial concentrations of MB (24.23 mg/L and 41.42 mg/L) over a span of 240 min. Our findings emphasized that the Elovich model provided the best fit of the experimental data for MB adsorption. When compared to other materials, biochar from jackfruit peel emerges as an eco-friendly adsorbent for dye decolorization, with potential applications in the treatment of environmental pollution.

Fabrication of titania/calcium alginate nanocomposite matrix for efficient adsorption and photocatalytic degradation of malachite green

This work aims to examine the two techniques' efficiency for the elimination of malachite green (MG) by photocatalytic degradation and adsorption onto synthesized solid nanomaterials. Three solid samples were prepared as calcium alginate (AG), nanotitania (NT), and nanotitania/calcium alginate composite (TG). The morphological and physicochemical characteristics of the solid nanomaterials were investigated by XRD, TGA, DRS, FTIR, pHPZC, nitrogen adsorption/desorption isotherm, SEM, and TEM. The main experimental conditions were determined for sample dose, shaking time, pH, initial malachite green concentration, temperature, ionic strength, and UV lamp power. The resulting data proved that TG attained the higher adsorption capacity (252.52 mg/g) at 40 °C. The adsorption of MG was well fitted by Langmuir, Temkin, Dubinin-Radushkevich, pseudo-second order, intra-particle diffusion, and Elovich models onto all the prepared samples, confirming the endothermic, spontaneous, and favorable adsorption process. The maximum degradation percent (99.6 %) of MG was achieved by using 1.0 g/L as a catalyst dose, 10 mg/L of initial MG concentration, and 33 W for TG. The photodegradation of MG was well fitted by Eyring-Polanyi and Arrhenius models onto the surface of catalyst. The TG reusability resulted in a decrease in the degradation efficiency by 9.8 %, indicating its great capacity as the first nanotitania/calcium alginate nanocomposite used in removing MG from wastewater by two technologies in the same article.

Optimizing leachate treatment with titanium oxide-impregnated activated carbon (TiO2@ASC) in a fixed-bed column: characterization, modeling, and prediction study

This research focused on the application of a fixed bed column filled with immobilized titanium oxide-loaded almond shell carbon (TiO2@ASC) for the treatment of leachate. The adsorption performance of synthesized TiO2@ASC in fixed bed column is analyzed using adsorption experiments and modeling study. The characteristics of synthesized materials are determined by several instrumental techniques like BET, XRD, FTIR, and FESEM-EDX. The flow rate, initial concentration of COD and NH3-N, and bed height were optimized to determine the effectiveness of leachate treatment. The linear bed depth service time (BDST) plots equations with a correlation coefficient of greater than 0.98 confirmed the model's accuracy for COD and NH3-N adsorption in column structure. The adsorption process was found to be well predicted by an artificial neural network (ANN) model with a root mean square error of 0.0172 and 0.0167 for COD and NH3-N reduction, respectively. The immobilized adsorbent was regenerated using HCl and was found to be reusable for up to three cycles, promoting material sustainability. This study is aimed to contribute towards SDG 6 and SDG11 by United Nations Sustainable Development Goals.

Meta-analysis of engineered nanoparticles dynamic aggregation in freshwater-like systems using machine learning techniques

Predictive algorithms for exposure characterization of engineered nanoparticles (ENPs) in the ecosystems are essential to improve the development of robust nano-safety frameworks. Here, machine learning (ML) techniques were utilised for data mining and prediction of the dynamic aggregation transformation process in aqueous environments using case studies of nZnO and nTiO₂. Supervised ML models using input variables of natural organic matter, ionic strength, size, and ENPs concentration showed poor prediction performance based on statistical metric values of root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R²), and Nash-Sutcliffe efficiency (NSE) for both types of ENP. On the contrary, algorithms developed using model input parameters of zeta potential, pH, and time had good generalisation and high prediction accuracy. Among the five developed ML algorithms, random forest regression, support vector regression, and artificial neural network generated good prediction accuracy for both data sets. Therefore, the use of ML can be valuable in the development of robust nano-safety frameworks to optimise societal benefits, and for proactive long-term ecological protection.

Remediation performance of As-contaminated water and soil using a novel Fe-Mn bimetallic (oxyhydr)oxide coated on natural magnetite

It is challenging to separate the materials for treating arsenic contamination of soil and water from systems. The natural magnetite covered with Fe-Mn bimetallic (oxyhydr)oxide (Fe-Mn MSM) was effectively created in this study, and its potential use in removing As from water and soil was investigated. Batch adsorption studies showed that the As(V) adsorption on Fe-Mn MSM could achieve equilibrium after 120 min when the initial As(V) concentration was 39.85 mg/L. The calculated maximum adsorption of Fe-Mn MSM for As(V) was 17.94 mg/g at 20 °C. The mechanism of As(V) adsorption was confirmed to be a combination of ligand exchange and electrostatic attraction by the outcomes of FTIR analysis, SEM, and batch adsorption tests. Fe-Mn MSM can also be a successful amendment for cleaning up As-polluted soil. The 5% Fe-Mn MSM treatment group had the lowest exchangeable fraction of As (EX-As) concentration, 0.039 mg/kg (8.3% of initial EX-As), after 40 days. Magnetic separation could be used to quickly and completely recover the used Fe-Mn MSM from the soil. EX-As was present in higher concentrations on Fe-Mn MSM than that of the original soil. As a result, this work offers a strategy that may be put into practice to cheaply remove As from soil and water while also encouraging the reuse of natural magnetite.

Aqueous Pb(II) Removal Using ZIF-60: Adsorption Studies, Response Surface Methodology and Machine Learning Predictions

Zeolitic imidazolate frameworks (ZIFs) are increasingly gaining attention in many application fields due to their outstanding porosity and thermal stability, among other exceptional characteristics. However, in the domain of water purification via adsorption, scientists have mainly focused on ZIF-8 and, to a lesser extent, ZIF-67. The performance of other ZIFs as water decontaminants is yet to be explored. Hence, this study applied ZIF-60 for the removal of lead from aqueous solutions; this is the first time ZIF-60 has been used in any water treatment adsorption study. The synthesized ZIF-60 was subjected to characterization using FTIR, XRD and TGA. A multivariate approach was used to investigate the effect of adsorption parameters on lead removal and the findings revealed that ZIF-60 dose and lead concentration are the most significant factors affecting the response (i.e., lead removal efficiency). Further, response surface methodology-based regression models were generated. To further explore the adsorption performance of ZIF-60 in removing lead from contaminated water samples, adsorption kinetics, isotherm and thermodynamic investigations were conducted. The findings revealed that the obtained data were well-fitted by the Avrami and pseudo-first-order kinetic models, suggesting that the process is complex. The maximum adsorption capacity (q_max) was predicted to be 1905 mg/g. Thermodynamic studies revealed an endothermic and spontaneous adsorption process. Finally, the experimental data were aggregated and used for machine learning predictions using several algorithms. The model generated by the random forest algorithm proved to be the most effective on the basis of its significant correlation coefficient and minimal root mean square error (RMSE).

Investigation of water treatment sludge for the treatment of saline water: Batch studies

In this study, water treatment sludge was investigated through batch modes for the treatment of saline water to meet livestock drinking consents. The water quality was assessed using water quality index (WQI).The kinetic data was best described by the Pseudo - Second - Order model and the equilibrium isotherm by the Freundlich model. The maximum removal efficiency was 51.5 ± 0.65%, 22.6 ± 0.5% and 100% for Sulphates (SO₄ ^2-), Chloride (Cl^-), and Nitrates (NO₃ ^-), respectively. The maximum removal efficiency of sodium (Na) and nickel (Ni) was 100% each at 0.4g dose and that of manganese (Mn) was 87.5% at 1.2g dose. The effect of temperature revealed that the adsorption capacity for anions, decreased with increasing temperature, whereas for the cations the adsorption capacities increased with increasing temperature. The water treatment sludge reduced the total dissolved solids (TDS) and electrical conductivity (EC) from the initial values of 19600 mgL^-1 and 38900 μScm^-1 to 98 mgL^-1 and 1728 μScm^-1,respectively. The Water Quality Indices were 37.35 and 7.57, before and after treatment, respectively. Water treatment sludge can be used for the pre-treatment process before using conventional treatment technologies. Pilot scale investigations should be conducted before field trials.

Sargassum@magnetite Composite EDTA-Functionalized for the Potential Removal of Mercury

Sargassum spp. affects the Caribbean shores; thus, its remotion or valorization is a priority. This work aimed to synthesize a low-cost magnetically retrievable Hg+2 adsorbent functionalized with ethylenediaminetetraacetic acid (EDTA) based on Sargassum. The Sargassum was solubilized to synthesize by co-precipitation a magnetic composite. A central composite design was assessed to maximize the adsorption of Hg+2. The solids yield magnetically attracted mass, and the saturation magnetizations of the functionalized composite were 60.1 ± 17.2%, 75.9 ± 6.6%, and 1.4 emu g−1. The functionalized magnetic composite yielded 29.8 ± 0.75 mg Hg+2 g−1 of chemisorption after 12 h, pH 5, and 25 °C achieving 75% Hg+2 adsorption after four reuse cycles. Crosslinking and functionalization with Fe3O4 and EDTA created differences in surface roughness as well as the thermal events of the composites. The Fe3O4@Sargassum@EDTA composite was a magnetically recovered biosorbent of Hg2+.

Machine learning modelling of removal of reactive orange RO16 by chemical activated carbon in textile wastewater

This study develops machine learning model of removal of reactive orange dye (Azo) RO16 from textile wastewater by chemical activated carbon CAC. The study addresses the contamination removal efficiency with respect to changing dynamics of concentration, temperature, time, pH and dose, respectively. Machine learning based learning multiple polynomial regression is implemented to fit a model on the experimental observed data. The machine learns from the data and fit the multiple polynomial regression model for the data. The observed and predicted data are in close agreement with the R-squared value of 92%. The results show that the baseline efficiency of using chemical activated carbon adsorbent for removing RO16 is 76.5%. The most significant input parameter increasing the efficiency by a constant value of 35 units out of 100 is the second order response of the dose. Moreover, four input parameters can considerably increase the efficiency. Furthermore, six input parameters can considerably decrease the efficiency. It is investigated, that the second order response with respect to time has the minute decreasing effect on the removal efficiency. The superior abilities of the modeling are two fold. Firstly, the contamination removal of reactive orange dye (Azo) RO16 with chemical activated carbon adsorbent is studied with respect to five multiple parameters. Secondly, the model exploits the machine learning capability of the renowned Python machine learning module sklearn to fit a multiple polynomial regression model. Thus a robust model is fitted giving twenty-one inputs/output interactions and responses. From the input-target correlation analysis it is clear that the removal efficiency has a strong correlation with the time. It has considerably significant relationship with dose of the CAC and the temperature with values of 18% and 17%, respectively. Moreover, the removal efficiency has inverse relations with pH and Ci, with values of 15% and 12%, respectively.

MgO-La2O3 mixed metal oxides heterostructure catalysts for photodegradation of dyes pollutant: synthesis, characterization and artificial intelligence modelling

In the present work, we prepared MgO-La₂O₃-mixed-metal oxides (MMO) as efficient photocatalysts for degradation of organic pollutants. First, a series of MgAl-%La-CO₃-layered double hydroxide (LDH) precursors with different contents of La (5, 10, and 20 wt%) were synthesized by the co-precipitation process and then calcined at 600 °C. The prepared materials were characterized by XRD, SEM-EDX, FTIR, TGA, ICP, and UV-vis diffuse reflectance spectroscopy. XRD indicated that MgO, La₂O₃, and MgAl₂O₄ phases were found to coexist in the calcined materials. Also, XRD confirms the orthorhombic-tetragonal phases of MgO-La₂O₃. The samples exhibited a small band gap of 3.0-3.22 eV based on DRS. The photocatalytic activity of the catalysts was assessed for the degradation of two dyes, namely, tartrazine (TZ) and patent blue (PB) as model organic pollutants in aqueous mediums under UV-visible light. Detailed photocatalytic tests that focused on the impacts of dopant amount of La, catalyst dose, initial pH of the solution, irradiation time, dye concentration, and reuse were carried out and discussed in this research. The experimental findings reveal that the highest photocatalytic activity was achieved with the MgO-La₂O₃-10% MMO with photocatalysts with a degradation efficiency of 97.4% and 93.87% for TZ and PB, respectively, within 150 min of irradiation. The addition of La to the sample was responsible for its highest photocatalytic activity. Response surface methodology (RSM) and gradient boosting regressor (GBR), as artificial intelligence techniques, were employed to assess individual and interactive influences of initial dye concentration, catalyst dose, initial pH, and irradiation time on the degradation performance. The GBR technique predicts the degradation efficiency results with R² = 0.98 for both TZ and PB. Moreover, ANOVA analysis employing CCD-RSM reveals a high agreement between the quadratic model predictions and the experimental results for TZ and PB (R² = 0.9327 and Adj-R² = 0.8699, R² = 0.9574 and Adj-R² = 0.8704, respectively). Optimization outcomes indicated that maximum degradation efficiency was attained under the following optimum conditions: catalyst dose 0.3 g/L, initial dye concentration 20 mg/L, pH 4, and reaction time 150 min. On the whole, this study confirms that the proposed artificial intelligence (AI) techniques constituted reliable and robust computer techniques for monitoring and modeling the photodegradation of organic pollutants from aqueous mediums by MgO-La₂O₃-MMO heterostructure catalysts.

Sustainable Synthesis of Iron-Zinc Nanocomposites by Azadirachta indica Leaves Extract for RSM-Optimized Sono-Adsorptive Removal of Crystal Violet Dye

Environmental pollution has exacerbated the availability of clean water to mankind. In this study, Azadirachta indica leaf extract was used for sustainable synthesis of Fe-Zn nanocomposites (IZNC). The instrumental techniques of Fourier transformed infrared (FTIR) spectroscopy, energy dispersive X-ray spectroscopy (EDS), and scanning electron microscopy (SEM) were used to determine the structural and chemical composition. The overall surface was mildly acidic in nature, as the pH_PZC was observed to be 6.00. The ultrasonicated adsorption experiments were designed by central composite design (CCD). The best responses, which proposed a contaminants removal of 80.39%, were assessed using the response surface methodology (RSM). By repeating experimental runs at the expected optimum operating parameters (OOP), the method was experimentally affirmed with the %mean error and %RSD₉ being 2.695% and 1.648%, respectively. The interaction of CV dye and the nanocomposite showed tremendous adsorption efficiency towards crystal violet (CV) dye, as revealed by isotherm studies. Fitting kinetics and isotherm models were affirmed by root mean square error (RMSE), χ2, and a Pearson regression coefficient. Thermodynamic studies proved spontaneity of the CV dye adsorption over the nanocomposites. The values for ΔG^o, ΔH^o, and ΔS^o were observed to be -1.089 kJ/mol, 28.59 kJ/mol, and -3.546 kJ/mol, respectively. Recovery of CV dye was carried out in a variety of media, including NaOH, NaCl, and CH₃COOH. The maximum CV recovery was achieved in an acidic media. The robustness of adsorption was affirmed by the interference of various matrix ions, including KCl, LiCl, NaCl, and MgCl₂, which did not significantly affect the adsorption process. The maximum adsorption capacity was obtained at a low concentration of LiCl. The results show that a green synthesis approach for nanocomposite synthesis might be an effective and economical way to remove organic contaminants from wastewater. Moreover, it is also effective for effluent treatment plants (ETP) for waste management purposes, in which it may be coupled with chlorine as a disinfectant to purify water that can be used for domestic and irrigation purposes.

Biosorption of zinc (II) from synthetic wastewater by using Inula Viscosa leaves as a low-cost biosorbent: Experimental and molecular modeling studies

The use of biosorption as a strategy for lowering the amount of pollution caused by heavy metals is particularly encouraging. In this investigation, a low-cost and efficient biosorbent, Inula Viscosa leaves were used to remove zinc ions (Zn²⁺) from synthetic wastewater. A Fourier transform infrared spectroscopy experiment, a scanning electron microscopy experiment, and an energy dispersive X-ray spectroscopy experiment were used to describe the support. Several different physicochemical factors, such as the beginning pH value, contact duration, initial zinc concentration, biosorbent dose, and temperature, were investigated in this study. When the Langmuir, Freundlich, Temkin, Toth, and Redlich-Peterson models were used to match the data from the Inula Viscosa leaves biosorption isotherms, it was found that the biosorption isotherms correspond most closely with the Langmuir isotherm. On the other hand, the kinetic biosorption process was investigated using pseudo-first-order, pseudo-second-order (PS2), and Elovich models. The PS2 model was the one that provided the most accurate description of the biosorption kinetics. The thermodynamics process shows the spontaneous and endothermic character of Zn²⁺ sorption on Inula Viscosa leaves, which also entails the participation of physical interactions. In addition, the atom-in-molecule analysis, density functional theory, and the conductor like screening model for real solvents, were used to investigate the relationship that exists between quantum calculations and experimental outcomes.

Modeling, optimization and understanding of adsorption process for pollutant removal via machine learning: Recent progress and future perspectives

It is crucial to reduce the concentration of pollutants in water environment to below safe levels. Some cost-effective pollutant removal technologies have been developed, among which adsorption technology is considered as a promising solution. However, the batch experiments and adsorption isotherms widely employed at present are inefficient and time-consuming to some extent, which limits the development of adsorption technology. As a new research paradigm, machine learning (ML) is expected to innovate traditional adsorption models. This reviews summarized the general workflow of ML and commonly employed ML algorithms for pollutant adsorption. Then, the latest progress of ML for pollutant adsorption was reviewed from the perspective of all-round regulation of adsorption process, including adsorption efficiency, operating conditions and adsorption mechanism. General guidelines of ML for pollutant adsorption were presented. Finally, the existing problems and future perspectives of ML for pollutant adsorption were put forward. We highly expect that this review will promote the application of ML in pollutant adsorption and improve the interpretability of ML.

Bamboo cellulose-derived activated carbon aerogel with controllable mesoporous structure as an effective adsorbent for tetracycline hydrochloride

Activated carbon has been widespread applied in the removal of pollutants in wastewater. However, many biomass-derived activated carbon suffer from the challenge of controllable pore size regulation, hindering their efficient adsorption of pollutants. Herein, bamboo-derived activated carbon aerogel (BACA) has been successfully prepared through KOH high-temperature activation of cellulose aerogel which was prepared using cellulose extracted from bamboo. Bamboo cellulose aerogel provides sufficient reaction sites for KOH, which is conducive to the formation of a mass of mesoporous structures on the pore walls of the activated carbon aerogel. The optimal BACA adsorbent shows high specific surface area (2503.80 m²/g), and maximum adsorption capability for tetracycline hydrochloride (TCH) reaches 863.8 mg/g at 30 ℃. The removal efficiencies of TCH are 100% and 98.4% at 40 ℃ when the initial concentrations are 500 and 700 mg/L, respectively. Adsorption kinetics and isotherm indicate that the adsorption of BACA for TCH is monolayer adsorption based on chemical adsorption. Spontaneous and endothermic adsorption processes are proved by adsorption thermodynamic studies. Additionally, coexisting ions have insignificant effect on TCH adsorption, and the BACA sample displays excellent adsorption property for five reuse cycles with a removal efficiency of 80.95%, indicating the outstanding adsorption capacity of BACA in practical application. The excellent adsorption performance provides BACA with a promising perspective to remove TCH from wastewater, and the prepared method of BACA can be widely extended to other biomass materials.

Nitrates Removal from Simulated Groundwater Using Nano Zerovalent Iron Supported by Polystyrenic Gel

The main objectives of this present paper were to indicate the immobilization of nano zerovalent iron (nZVI) onto a polymeric material (Purolite A400) and the synthesis of the polymeric material (A400-nZVI) through sodium borohydride (NaBH4) reduction. The obtained polymeric material (A400-nZVI) was used for the nitrate ions removal from a simulated groundwater at different conditions. The polymeric materials, without and with nano zerovalent iron (A400 and A400-nZVI), were characterized trough the FTIR, SEM-EDAX, XRD, and TGA analysis. The analysis confirmed the presence of nano zerovalent iron (nZVI) onto the polymeric material (A400). The adsorption capacity of A400-nZVI, used as polymeric adsorbent, was evaluated by kinetic and thermodynamic studies. The obtained experimental results indicated that the nitrate ions reduction was fitted well by models: pseudo-second-order kinetic and Freundlich isotherm. According to the kinetic model results, a reaction mechanism could exist in the stage of reactions. The higher value of removal nitrate (>80%) was obtained under acidic condition. The results indicated that the obtained polymeric material (A400-nZVI) can be considered as a potential polymeric adsorbent for different pollutants from groundwater and wastewater.

Magnetic Luffa-Leaf-Derived Hierarchical Porous Biochar for Efficient Removal of Rhodamine B and Tetracycline Hydrochloride

Luffa leaf (LL) is an agricultural waste produced by loofah. In this work, LL was used as biomass carbon source for biochars for the first time. After carbonization, activation, and chemical co-precipitation treatments, a magnetic lignocellulose-derived hierarchical porous biochar was obtained. The specific surface area and total pore volume were 2565.4 m²/g and 1.4643 cm³/g, and the surface was rich in carbon and oxygen functional groups. The synthetic dye rhodamine B (RhB) and the antibiotic tetracycline hydrochloride (TH) were selected as organic pollutant models to explore the ability to remove organic pollutants, and the results showed good adsorption performances. The maximum adsorption capacities were 1701.7 mg/g for RhB and 1755.9 mg/g for TH, which were higher than most carbon-based adsorbents. After 10 cycles of use, the removal efficiencies were still maintained at more than 70%, showing good stability. This work not only verified the feasibility of lignocellulose LL as a carbon source to prepare biochar but also prepared a magnetic hierarchical porous adsorbent with good performances that can better treat RhB and TH, which provided a new idea and direction for the efficient removal of organic pollutants in water.

Surface Characteristics of Electrospun p-Sulphonated Calix[4]Arene Functionalized Cellulose Acetate Nanofiber and Its Behaviour towards Methylene Blue Adsorption

p-sulphonated calix[4]arene (p-SOCX) functionalized cellulose acetate/graphene oxide (CA/GO) nanofibers were successfully prepared via the electrospinning technique. The presence of p-SOCX within the fiber matrix was ascertained using Fourier Transform Infrared spectroscopy and Carbon-Hydrogen-Nitrogen-Sulphur elemental analyses. Apparent changes in morphology of the samples were observed under scanning electron microscope where fiber diameters increased with increasing p-SOCX content. Preliminary dye removal test at different pH, dosage, and temperature of functionalized CA/GO nanofiber demonstrates enhanced adsorption capacity of methylene blue in presence of p-SOCX compared to pristine CA nanofiber and CA/GO nanofiber at an optimum pH 8. The highest removal efficiency obtained was 88.84% with initial methylene blue dye concentration of 10 mg/L, adsorbent dosage of 20 mg/10 mL, contact time of 30 min at room temperature ca. 293 K.

Exploring the Biosorption of Methylene Blue Dye onto Agricultural Products: A Critical Review

Due to their higher specific area and, in most cases, higher adsorption capacity, nanomaterials are noteworthy and attractive adsorbents. Agricultural products that are locally available are the best option for removing methylene blue (MB) dye from aqueous solutions. Because it is self-anionic, FT-IR and SEM investigations of biosorption have confirmed the role of the functional group and its contribution to the formation of pores that bind cationic dye. It is endothermic if the adsorption of MB by an adsorbent is high as the temperature increases; on the other hand, exothermic if it is high as the temperature decreases. A basic medium facilitates adsorption with respect to pH; adsorption is proportional to the initial concentration at a certain level before equilibrium; after equilibrium, adsorption decreases. A pseudo-second-order model applies for certain agricultural products. As per plotted graph for the solid-phase concentration against the liquid-phase concentration, the Langmuir adsorption isotherm model is favored; this model describes a situation in which a number of molecules are adsorbed by an equal number of available surface sites, and there is no interaction between adsorbate molecules once all sites are occupied. In contrast, the Freundlich model depicts non-ideal multi-layer sorption onto heterogeneous surfaces via numerical analysis; with a value of n = 1, the result is a linear isotherm. If the value of n < 1 or n > 1, then it is chemical or physical adsorption, respectively. Based on an EDX analysis, relevant elements are confirmed. BET analysis confirms the surface area. Nanoproducts categorized as agricultural products exhibit the aforementioned tendency. Even though nanoparticles show positive outcomes in terms of higher adsorption, a high specific area for the targeted pollutant is needed in real-world applications. In the relevant sections herein, the behavior of thermodynamic parameters, such as enthalpy, entropy, and Gibbs free energy, are examined. There is some question as to which form of agricultural waste is the most effective adsorption medium. There is no direct answer because every form of agricultural waste has its own distinct chemical and physical characteristics, such as porosity, surface area, and strength.

Modeling of Remora Optimization with Deep Learning Enabled Heavy Metal Sorption Efficiency Prediction onto Biochar

Environmental distresses linked to heavy metal (HM) impurity in the water received significant attention among research communities. Recently, advancements in industrial sectors like paper industries, mining, non-ferrous metallurgy, electroplating, mineral paint production, etc. have resulted in massive heavy metals in wastewater. In contrast to organic pollutants, HMs are not recyclable and can be simply engrossed by living organisms. Recently, different solutions have been employed for removing HMs from water and wastewater, like membrane filtration, chemical precipitation, adsorption, ion-exchange, flotation, flocculation, etc. Sorption can be considered one of the efficient solutions for eradicating HMs from waste water. With this motivation, this article concentrates on the design of Remora Optimization with Deep Learning Enabled Heavy Metal Sorption Efficiency Prediction (RODL-HMSEP) model onto Biochar. The proposed RODL-HMSEP technique intends to determine the sorption performance of HMs of various biochar features. Initially, the density based clustering (DBSCAN) technique is applied to simulating the features of metal adsorption data and splitting them into clusters of identical features. Besides, deep belief network (DBN) model was employed for prediction and the efficiency of the DBN model is optimally adjusted with utilize of RO technique. The experimental validation of the RODL-HMSEP technique ensured the promising performance of the RODL-HMSEP technique on the prediction of sorption efficiency onto biochar over other methods The experimental validation of the RODL-HMSEP technique ensured the promising performance of the RODL-HMSEP technique on the prediction of sorption efficiency onto biochar over other methods.

Machine Learning and Multiple Imputation Approach to Predict Chlorophyll-a Concentration in the Coastal Zone of Korea

The concentration of chlorophyll-a (Chl-a) is an integrative bio-indicator of aquatic ecosystems and a direct indicator that evaluates the ecological status of water bodies. In this study, we focused on predicting the Chl-a concentration in seawater using machine learning (after replacing missing values). To replace the missing values among marine environment observation data, a comparison experiment was performed using multiple built-in imputation methods (i.e., pmm, cart, rf, norm, norm.nob, norm.boot, and norm.predict) of the mice package in R. The cart method was selected as the most suitable. We generated each regression model using six machine learning algorithms (regression tree, support vector regression (SVR), bagging, random forest, gradient boosting machine (GBM), and extreme gradient boosting (XGBoost)) to predict the Chl-a concentration based on the complete imputed dataset. The prediction performance of the models was evaluated by four evaluation criteria using 10-fold cross-validation tests. XGBoost, an ensemble learning approach, outperformed other models in predicting the Chl-a concentration; SVR, a single model, also showed a good performance. The most important environmental factor in predicting the Chl-a concentration was an organic carbon particulate; however, dissolved oxygen also showed potential. This study was conducted with field observations in the spring and summer in the coastal zone of Korea. There exists a limit in machine learning applications, which excludes temporal and spatial factors. However, extensions to time series forecasting for deep learning or machine learning can lead to meaningful regional and seasonal analysis. It can also improve prediction performance as a result of the long-term data accumulation of field observations of more varied features (such as meteorological and hydrodynamic) besides water quality.

Predicting Carbon Residual in Biomass Wastes Using Soft Computing Techniques

In recent decades, the development of complex materials developed a class of biomass waste-derived porous carbons (BWDPCs), which are used for carbon capture and sustainable waste management. It is difficult in understanding the adsorption mechanism of CO2 in the air as it has a wide range of properties associated with its diverse textures, functional group existence, pressure, and temperature of varying range. These properties influence diversely the adsorption mechanism of CO2 and pose serious challenges in the process. To resolve this multiobjective formulation, we use a machine learning classifier that maps systematically the CO2 adsorption as a function of compositional and textural properties and adsorption parameters. The machine learning classifier helps in the classification of various porous carbon materials during the time of training and testing. The results of the simulation show that the proposed method is more efficient in classifying the porous nature of the CO2 adsorption materials than other methods.

The Use of Gigantochloa Bamboo-Derived Biochar for the Removal of Methylene Blue from Aqueous Solution

In this study, locally grown bamboo (Gigantochloa spp.) was used as feedstock for pyrolysis production of biochar under various pyrolysis temperatures (400–800°C). The resultant biochars were tested for their performance in adsorptive removal of the methylene blue (MB) dye. The scope of the adsorption experiment includes the effects of adsorbent dosage, solution pH, initial adsorbate concentration, and contact time. The adsorption data confirmed that pyrolysis temperature has a significant effect on adsorptive performance, whereas biochar pyrolysed at 500°C (BC500) has the highest adsorptive performance with the maximum adsorption capacity (derived from the Langmuir model) being 86.6 mg g-1. Basic characterisations (SEM, EDX, XRD, FTIR, and BET) were carried out for BC500 where FTIR and SEM confirmed the adsorption of MB onto the biochar, while the BET data showed the reduction of the BET surface area, total pore volume, and pore diameter after the adsorption process.