A modeling approach for estimating hydrogen sulfide solubility in fifteen different imidazole-based ionic liquids.

Absorption has always been an attractive process for removing hydrogen sulfide (H2S). Posing unique properties and promising removal capacity, ionic liquids (ILs) are potential media for H2S capture. Engineering design of such absorption process needs accurate measurements or reliable estimation of the H2S solubility in ILs. Since experimental measurements are time-consuming and expensive, this study utilizes machine learning methods to monitor H2S solubility in fifteen various ILs accurately. Six robust machine learning methods, including adaptive neuro-fuzzy inference system, least-squares support vector machine (LS-SVM), radial basis function, cascade, multilayer perceptron, and generalized regression neural networks, are implemented/compared. A vast experimental databank comprising 792 datasets was utilized. Temperature, pressure, acentric factor, critical pressure, and critical temperature of investigated ILs are the affecting parameters of our models. Sensitivity and statistical error analysis were utilized to assess the performance and accuracy of the proposed models. The calculated solubility data and the derived models were validated using seven statistical criteria. The obtained results showed that the LS-SVM accurately predicts H2S solubility in ILs and possesses R2, RMSE, MSE, RRSE, RAE, MAE, and AARD of 0.99798, 0.01079, 0.00012, 6.35%, 4.35%, 0.0060, and 4.03, respectively. It was found that the H2S solubility adversely relates to the temperature and directly depends on the pressure. Furthermore, the combination of OMIM+ and Tf2N-, i.e., [OMIM][Tf2N] ionic liquid, is the best choice for H2S capture among the investigated absorbents. The H2S solubility in this ionic liquid can reach more than 0.8 in terms of mole fraction.

Application of sono-electrocoagulation in arsenic removal from aqueous solutions and the related human health risk assessment.

Among the contaminants found in groundwater, arsenic poses a great threat to human health and the ecosystem. Therefore, it is vital to eliminate arsenic from water sources. This study utilizes one of the most efficient and emerging decontamination techniques known as the sono-electrocoagulation method. In recent years, sono-electrocoagulation has attracted many scientists due to its unique features, such as being cost-effective, rapid process, and high efficiency. The required groundwater samples were artificially synthesized in the laboratory, where the anode and cathode were determined to be Fe, Ti/PbO2, and Al, respectively. During the experiment, the impact of pH (5,6,7,8), various initial concentrations (100, 200, 300,400, 500, 600 µg/l), exposure times of 5,10,15,20,25 min, electrode distances of 1.5,2,2.5,3,3.5 cm and different current intensities of 5,10,15,20,25 mA/cm2 were examined. The ambient temperature of the laboratory was kept at 30 and 40 °C. Furthermore, this study showed that the system containing Ti/PbO2 as the anode and Al as the cathode electrodes removed arsenic contamination more effectively in the base environment. The performance of arsenic removal was directly related to current intensity, pH, and time. Nevertheless, time elapse played a negative factor due to the corrosion of the electrodes' surface and the dissolution of floating materials in the solution. With the surge of arsenic concentration from 100 to 300 mg/L, the arsenic removal efficiency increased from 61.9 to 98.5 percent, where the maximum removal efficiency due to the rise of the current intensity was 84.16 percent. The sono-electrocoagulation method reduced the risk of carcinogenic and non-carcinogenicity from 5.15E-03 to 7.73E-05 and 26.71 to 0.40. Accordingly, it was found that a combination of ultrasonic and electrocoagulation processes is a promising approach for arsenic removal.

Estimation of tetracycline antibiotic photodegradation from wastewater by heterogeneous metal-organic frameworks photocatalysts.

In this work, the potential ability of various modern and powerful machine learning methods such as Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Gradient-Boosted Decision Trees (GBDT), Extra Tree (ET), Decision Trees (DT), and Random Forest (RF) were investigated to estimate tetracycline (TC) photodegradation from wastewater by 10 different metal-organic frameworks (MOFs). A comprehensive databank was gathered, including 374 data points from the photodegradation percentage of MOFs in various practical conditions. The inputs of the employed models were chosen as catalyst dosage, antibiotic concentration, Illumination time, solution pH, and specific surface area and pore volume of the investigated MOFs, and the output was TC degradation efficiency. Different statistical criteria were calculated for the validation of the developed models. Average absolute percent relative error (AAPRE) and standard deviation error (STD) values of 1.19% and 0.0431, 3.07% and 0.0628, 2.88% and 0.0751, 2.86% and 0.1304, 8.73% and 0.2751, 4.24% and 0.1024, 2.83% and 0.0934, and 11.56% and 0.4459 were obtained for CatBoost, LightGBM, XGBoost, AdaBoost, GBDT, ET, DT, and RF approaches, respectively. Among all implemented models, the CatBoost was found to be the most trustable model. Moreover, this model followed the expected trends of the TC degradation process with variation of catalyst dosage, initial TC concentration, and reaction pH. The developed CatBoost model predicted the removal of TC by MOFs accurately, which proved the capability of this approach in solving complex problems with numerous data points and its straightforwardness and cost-effectiveness for environmental applications.

State of the art on the ultrasonic-assisted removal of environmental pollutants using metal-organic frameworks.

The environmental and health issues of drinking water and effluents released into nature are among the major area of contention in the past few decades. With the growth of ultrasound-based approaches in water and wastewater treatment, promising materials have also been considered to employ their advantages. Metal-organic frameworks (MOFs) are among the porous materials that have received great attention from researchers in recent years. Features such as high porosity, large specific surface area, electronic properties like semi-conductivity, and the capacity to coordinate with the organic matter have resulted in a substantial increase in scientific researches. This work deals with a comprehensive review of the application of MOFs for ultrasonic-assisted pollutant removal from wastewater. In this regard, after considering features and synthesis methods of MOFs, the mechanisms of several ultrasound-based approaches including sonocatalysis, sonophotocatalysis, and sono-adsorption are well assessed for removal of different organic compounds by MOFs. These methods are compared with some other water treatment processes with the application of MOFs in the absence of ultrasound. Also, the main concern about MOFs including environmental hazards and water stability is fully discussed and some techniques are proposed to reduce hazardous effects of MOFs and improve stability in humid/aqueous environments. Economic aspects for the preparation of MOFs are evaluated and cost estimates for ultrasonic-assisted AOP approaches were provided. Finally, the future outlooks and the new frontiers of ultrasonic-assisted methods with the help of MOFs in global environmental pollutant removal are presented.

Modeling of CO2 adsorption capacity by porous metal organic frameworks using advanced decision tree-based models.

In recent years, metal organic frameworks (MOFs) have been distinguished as a very promising and efficient group of materials which can be used in carbon capture and storage (CCS) projects. In the present study, the potential ability of modern and powerful decision tree-based methods such as Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) was investigated to predict carbon dioxide adsorption by 19 different MOFs. Reviewing the literature, a comprehensive databank was gathered including 1191 data points related to the adsorption capacity of different MOFs in various conditions. The inputs of the implemented models were selected as temperature (K), pressure (bar), specific surface area (m2/g) and pore volume (cm3/g) of the MOFs and the output was CO2 uptake capacity (mmol/g). Root mean square error (RMSE) values of 0.5682, 1.5712, 1.0853, and 1.9667 were obtained for XGBoost, CatBoost, LightGBM, and RF models, respectively. The sensitivity analysis showed that among all investigated parameters, only the temperature negatively impacts the CO2 adsorption capacity and the pressure and specific surface area of the MOFs had the most significant effects. Among all implemented models, the XGBoost was found to be the most trustable model. Moreover, this model showed well-fitting with experimental data in comparison with different isotherm models. The accurate prediction of CO2 adsorption capacity by MOFs using the XGBoost approach confirmed that it is capable of handling a wide range of data, cost-efficient and straightforward to apply in environmental applications.