Machine learning based prediction and experimental validation of arsenite and arsenate sorption on biochars.

Arsenic (As) contamination in water is a significant environmental concern with profound implications for human health. Accurate prediction of the adsorption capacity of arsenite [As(III)] and arsenate [As(V)] on biochar is vital for the reclamation and recycling of polluted water resources. However, comprehending the intricate mechanisms that govern arsenic accumulation on biochar remains a formidable challenge. Data from the literature on As adsorption to biochar was compiled and fed into machine learning (ML) based modelling algorithms, including AdaBoost, LGBoost, and XGBoost, in order to build models to predict the adsorption efficiency of As(III) and As(V) to biochar, based on the compositional and structural properties. The XGBoost model showed superior accuracy and performance for prediction of As adsorption efficiency (for As(III): coefficient of determination (R2) = 0.93 and root mean square error (RMSE) = 1.29; for As(V), R2 = 0.99, RMSE = 0.62). The initial concentrations of As(III) and As(V) as well as the dosage of the adsorbent were the most significant factors influencing adsorption, explaining 48 % and 66 % of the variability for As(III) and As(V), respectively. The structural properties and composition of the biochar explained 12 % and 40 %, respectively, of the variability of As(III) adsorption, and 13 % and 21 % of that of As(V). The XGBoost models were validated using experimental data. R2 values were 0.9 and 0.84, and RMSE values 6.5 and 8.90 for As(III) and As(V), respectively. The ML approach can be a valuable tool for improving the treatment of inorganic As in aqueous environments as it can help estimate the optimal adsorption conditions of As in biochar-amended water, and serve as an early warning for As-contaminated water.

Enhancing EDM Machining Precision through Deep Cryogenically Treated Electrodes and ANN Modelling Approach.

The critical applications of difficult-to-machine Inconel 617 (IN617) compel the process to be accurate enough that the requirement of tight tolerances can be met. Electric discharge machining (EDM) is commonly engaged in its machining. However, the intrinsic issue of over/undercut in EDM complicates the achievement of accurately machined profiles. Therefore, the proficiency of deep cryogenically treated (DCT) copper (Cu) and brass electrodes under modified dielectrics has been thoroughly investigated to address the issue. A complete factorial design was implemented to machine a 300 µm deep impression on IN617. The machining ability of DCT electrodes averagely gave better dimensional accuracy as compared to non-DCT electrodes by 13.5% in various modified dielectric mediums. The performance of DCT brass is 29.7% better overall compared to the average value of overcut (OC) given by DCT electrodes. Among the non-treated (NT) electrodes, the performance of Cu stands out when employing a Kerosene-Span-20 modified dielectric. In comparison to Kerosene-Tween-80, the value of OC is 33.3% less if Kerosene-Span-20 is used as a dielectric against the aforementioned NT electrode. Finally, OC's nonlinear and complex phenomena are effectively modeled by an artificial neural network (ANN) with good prediction accuracy, thereby eliminating the need for experiments.

Hydrogen Production Using TiO2-Based Photocatalysts: A Comprehensive Review.

Titanium dioxide (TiO2) is one of the most widely used photocatalysts due to its physical and chemical properties. In this study, hydrogen energy production using TiO2- and titanate-based photocatalysts is discussed along with the pros and cons. The mechanism of the photocatalysis has been elaborated to pinpoint the photocatalyst for better performance. The chief characteristics and limitations of the TiO2 photocatalysts have been assessed. Further, TiO2-based photocatalysts modified with a transition metal, transition metal oxide, noble metal, graphitic carbon nitride, graphene, etc. have been reviewed. This study will provide a basic understanding to beginners and detailed knowledge to experts in the field to optimize the TiO2-based photocatalysts for hydrogen production.

Artificial Intelligence Modeling-Based Optimization of an Industrial-Scale Steam Turbine for Moving toward Net-Zero in the Energy Sector.

Augmentation of energy efficiency in the power generation systems can aid in decarbonizing the energy sector, which is also recognized by the International Energy Agency (IEA) as a solution to attain net-zero from the energy sector. With this reference, this article presents a framework incorporating artificial intelligence (AI) for improving the isentropic efficiency of a high-pressure (HP) steam turbine installed at a supercritical power plant. The data of the operating parameters taken from a supercritical 660 MW coal-fired power plant is well-distributed in the input and output spaces of the operating parameters. Based on hyperparameter tuning, two advanced AI modeling algorithms, i.e., artificial neural network (ANN) and support vector machine (SVM), are trained and, subsequently, validated. ANN, as turned out to be a better-performing model, is utilized to conduct the Monte Carlo technique-based sensitivity analysis toward the high-pressure (HP) turbine efficiency. Subsequently, the ANN model is deployed for evaluating the impact of individual or combination of operating parameters on the HP turbine efficiency under three real-power generation capacities of the power plant. The parametric study and nonlinear programming-based optimization techniques are applied to optimize the HP turbine efficiency. It is estimated that the HP turbine efficiency can be improved by 1.43, 5.09, and 3.40% as compared to that of the average values of input parameters for half-load, mid-load, and full-load power generation modes, respectively. The annual reduction in CO2 measuring 58.3, 123.5, and 70.8 kilo ton/year (kt/y) corresponds to half-load, mid-load, and full load, respectively, and noticeable mitigation of SO2, CH4, N2O, and Hg emissions is estimated for the three power generation modes of the power plant. The AI-based modeling and optimization analysis is conducted to enhance the operation excellence of the industrial-scale steam turbine that promotes higher-energy efficiency and contributes to the net-zero target from the energy sector.

Acetone-Gasoline Blend as an Alternative Fuel in SI Engines: A Novel Comparison of Performance, Emission, and Lube Oil Degradation.

The disproportionate use of petroleum products and stringent exhaust emissions has emphasized the need for alternative green fuels. Although several studies have been conducted to ascertain the performance of acetone-gasoline blends in spark-ignition (SI) engines, limited work has been done to determine the influence of fuel on lubricant oil deterioration. The current study fills the gap through lubricant oil testing by running the engine for 120 h on pure gasoline (G) and gasoline with 10% by volume acetone (A10). Compared to gasoline, A10 produced better results in 11.74 and 12.05% higher brake power (BP) and brake thermal efficiency (BTE), respectively, at a 6.72% lower brake-specific fuel consumption (BSFC). The blended fuel A10 produced 56.54, 33.67, and 50% lower CO, CO2, and HC emissions. However, gasoline remained competitive due to lower oil deterioration than A10. The flash-point and kinematic viscosity, compared to fresh oil, decreased by 19.63 and 27.43% for G and 15.73 and 20.57% for A10, respectively. Similarly, G and A10 showed a decrease in total base number (TBN) by 17.98 and 31.46%, respectively. However, A10 is more detrimental to lubricating oil due to a 12, 5, 15, and 30% increase in metallic particles like aluminum, chromium, copper, and iron, respectively, compared to fresh oil. Performance additives like calcium and phosphorous in lubricant oil for A10 decreased by 10.04 and 4.04% in comparison to gasoline, respectively. The concentration of zinc was found to be 18.78% higher in A10 when compared with gasoline. A higher proportion of water molecules and metal particles were found in lubricant oil for A10.

Cadmium cyanide complexes with heterocyclic thiones: solid state and solution NMR studies.

Cadmium(II)cyanide complexes of various thiones (imidazolidine-2-thione, diazinane-2-thione and their derivatives) have been prepared and characterized by elemental analysis, IR and solid as well as solution NMR spectroscopy. It appears from the IR data that all complexes are non-ionic [(>C=S)(2)Cd(CN)(2)]. An upfield shift in the (13)C NMR and downfield shifts in the (1)H NMR are consistent with the sulfur coordination to cadmium(II). The solid (113)Cd NMR data show the presence of different coordination numbers in some complexes.

Levels of selected heavy metals in black tea varieties consumed in Saudi Arabia.

The metal contents (Fe, Cu, Zn, Mn, Cr, Pb, Ni, Cd, Co) of 17 black tea samples were analyzed by using Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-AES). Among the investigated metals Mn was the highest (1,071.7 microg/g), whereas Pb showed minimum levels (0.30 microg/g). Iron was the second highest element found in black tea samples. Lowest Pb concentration (0.30 microg/g) was found in Abu Jabal tea; whereas maximum (2.2 microg/g) was found in Manasul tea. Lowest Cd concentration was found in Lipton whereas maximum level was observed for Al-Diafa tea. The concentrations of Co and Zn in the analyzed samples were in the range of 4.5-17.4 and 23.7-122.4 microg/g, respectively. Metal-to-metal correlation studies showed strong correlations between iron-chromium, iron-cadmium and lead-copper pairs. The data obtained in the present work compared well with the counterpart data reported internationally. Based upon the present safety standards, the tea versions selected in the present study were found to be safe for human consumption.

Levels of selected metals in canned fish consumed in Kingdom of Saudi Arabia.

In the present paper, seven heavy metals (Pb, Cd, Ni, Cu, Zn, Cr and Fe) in canned salmon, sardine and tuna fish were determined by using atomic absorption spectroscopy. Cadmium and lead levels were determined by graphite tube AAS whereas Ni, Cu, Cr and Fe were determined by flame AAS. Analytical results were validated by spiking the samples with various concentrations of these metals for recovery. The metal contents, expressed in microg/g, wet weight, varied depending upon the specie studied. The levels of Pb ranged from 0.03-1.20 microg-g(-1) with an average of 0.313 microg-g(-1) for salmon; 0.03-0.51 microg-g(-1) with an average of 0.233 microg-g(-1) for tuna and 0.13-1.97 microg-g(-1) with an average of 0.835 microg-g(-1) for sardines. The levels of Cd ranged from 0.02-0.38 microg-g(-1) with an average of 0.161 microg-g(-1) for salmon; 0.07-0.64 microg-g(-1) with an average of 0.227 microg-g(-1) for tuna and 0.010-0.690 microg-g(-1) with an average of 0.183 microg-g(-1) for sardines. Comparative evaluation of these metals in three varieties of fish showed that average concentration of lead in sardines is about 4 times and Ni about 3 times higher as compared to tuna. Generally, the levels of these metals follow the order sardine > salmon > tuna. The data generated in the present study compared well with the similar studies carried out in different parts of the world. The results indicate that canned fish, in general and tuna in particular, have concentrations within permissible limits of WHO/FAO levels for these heavy metals. Therefore, their contribution to the total body burden of these metals can be considered as negligibly small.

Accumulation of heavy metals in kidney and heart tissues of Epinephelus microdon fish from the Arabian Gulf.

Levels of Pb, Co, Cu, Ni, Zn, Mn and Cd in the kidney and heart tissues of Epinephelus Microdon collected from the Arabian Gulf, Eastern province of Saudi Arabia, were determined by wet digestion-based atomic absorption method. The results indicated that accumulation pattern of analyzed metals in the kidney tissues followed the order; Zn > Cu > Pb > Ni > Co > Mn > Cd, with Zn at 47.73 +/- 13.26 ppm and Cd at 0.41 +/- 0.16 ppm. Cu, Mn, Co and Ni levels in the kidney tissues were significantly lower or within the ranges reported previously. In the heart tissue the analyzed metals followed almost the same pattern of metal accumulation; Zn > Cu > Pb > Co > Ni > Mn > Cd. The average lead (3.19 +/- 2.03 ppm), nickel (1.69 +/- 0.52 ppm), cobalt (1.75 +/- 0.44 ppm), copper (3.96 +/- 0.98 ppm) and cadmium (0.34 +/- 0.23 ppm) concentrations were found high in the heart tissues whereas zinc and manganese levels were found high in kidney tissues. In general, the data indicated that marine fish from the sampling site of Arabian Gulf are comparatively clean and unpolluted.