Retrieval of carbon and inorganic phosphorus during hydrothermal carbonization: ANN and RSM modeling.

In this study, modeling and optimization of Hydrothermal Carbonization (HTC) of Poultry litter were conducted to convert it into high-value materials. The aim was to understand the process and predict the effect of the influencing parameters on the product properties. The recovery of Inorganic Phosphorous (IP) and Carbon (C) was regarded as the model's response, although temperature and reaction time were thought to be important variables.

The potential of TiCT-KHPO and TiCT-chitosan in the efficient removal of cesium from nuclear wastewater.

TiCT is synthesized from TiAlC by two common methods, HF and HF in situ. The synthesis approach is very practical regarding the structure, morphology, space between layers, type and number of surface-active sites and its specific surface. XRD, SEM, EDS, FTIR and BET analyzes were used to investigate the structure, morphology, type and number of surface-active sites.

Efficient CO adsorption by deoiled flaxseed hydrochar.

This study optimizes CO adsorption using hydrochar from de-oiled flaxseed (FDOP), a waste byproduct of the oil extraction industry, through hydrothermal carbonization (HTC). The aim is to enhance CO capture sustainably and cost-effectively. Using Response Surface Methodology (RSM), in optimal conditions achieved 1153.

Experimental exploring of TiCT MXene for efficient and deep removal of magnesium in water sample.

In this work, the mechanism and behaviour of magnesium adsorption with TiCT adsorbent is investigated. TiCT was synthesized by selective exfoliation of Al layer from TiAlC using acidic solutions of HF 40% and 12 M LiF/ 9 M HCl. The effect of the synthesis method on the structure, the interlayer distance, the type and abundance of the functional groups, the bonds formed, the surface area and the volume of the formed cavities were evaluated by X-ray diffraction, scanning electron microscopy, Energy-dispersive X-ray spectroscopy, Brunauer-Emmett-Teller and fourier transform infrared analyses.

Predictive modeling of membrane reactor efficiency using advanced artificial neural networks for green hydrogen production.

The imperative to decarbonize the energy sector has prompted substantial advancements in clean electricity generation, with hydrogen emerging as a promising low-carbon energy carrier. While hydrogen synthesis from renewable sources is crucial, challenges persist, necessitating innovative approaches for efficient and sustainable production. This study leverages diverse artificial neural network (ANN) models to assess and predict system efficiency based on key operational variables in membrane reactor systems.

Modeling of carbon dioxide absorption into aqueous alkanolamines using machine learning and response surface methodology.

This research focuses on modeling CO absorption into alkanolamine solvents using multilayer perceptron (MLP), radial basis function network (RBF), Support Vector Machine (SVM), networks, and response surface methodology (RSM). The parameters, including solvent density, mass fraction, temperature, liquid phase equilibrium constant, CO loading, and partial pressure of CO, were used as input factors in the models. In addition, the value of CO mass flux was considered as output in the models.

Modification of TiCT nanostructure with KHPO and chitosan for effective removal of strontium from nuclear waste.

Nanostructure titanium carbide MXene (TiCTx) was modified with KHPO and chitosan to effectively remove strontium from nuclear wastewater. Nuclear waste includes radionuclides of uranium, thorium, strontium, and cesium, which are classified depending on the concentration of radionuclides. Nuclear waste with a high strontium concentration is the production waste of radiopharmaceutical production centers.

Analysis of effective area and mass transfer in a structure packing column using machine learning and response surface methodology.

The study examined mass transfer coefficients in a structured CO absorption column using machine learning (ML) and response surface methodology (RSM). Three correlations for the fractional effective area (a), gas phase mass transfer coefficient (k), and liquid phase mass transfer coefficient (k) were derived with coefficient of determination (R) values of 0.9717, 0.

Improving CO adsorption efficiency of an amine-modified MOF-808 through the synthesis of its graphene oxide composites.

This research developed a novel composite of MOF-NH and graphene oxide (GO) for enhanced CO capture. Employing the response surface methodology-central composite design (RSM-CCD) for experiments design, various MOF-NH/GO samples with GO loadings from 0 to 30 wt% were synthesized. The results of SEM, XRD, EDS, and BET analysis revealed that the materials maintained their MOF crystal structure, confirmed by X-ray diffraction, and exhibited unique texture, high porosity, and oxygen-enriched surface chemistry.

Evaluation of hydrogen production via steam reforming and partial oxidation of dimethyl ether using response surface methodology and artificial neural network.

This study aims to develop two models for thermodynamic data on hydrogen generation from the combined processes of dimethyl ether steam reforming and partial oxidation, applying artificial neural networks (ANN) and response surface methodology (RSM). Three factors are recognized as important determinants for the hydrogen and carbon monoxide mole fractions. The RSM used the quadratic model to formulate two correlations for the outcomes.

Impregnation of Silica Gel with Choline Chloride-MEA as an eco-friendly adsorbent for CO capture.

Deep eutectic solvents (DES) are a generation of ionic liquids that benefit from low cost, good stability, and environmental-friendly features. In this research, a porous silica gel was impregnated with a eutectic Choline Chloride-Monoethanolamine solvent (ChCl-MEA) to greatly improve its CO capture performance. In the impregnation, the weight percentages of ChCl-MEA were used in the range of 10-60 wt% at a temperature of 25 °C.

Using NaOH@Graphene oxide-FeO as a magnetic heterogeneous catalyst for ultrasonic transesterification; experimental and modelling.

Burning fossil fuels causes toxic gas emissions to increase, therefore, scientists are trying to find alternative green fuels. One of the important alternative fuels is biodiesel. However, using eco-friendly primary materials is a main factor.

Experimental, RSM modelling, and DFT simulation of CO adsorption on Modified activated carbon with LiOH.

This research investigates the enhancement of CO adsorption capacity through the use of modified activated carbon (AC) with LiOH, focusing on operational conditions and adsorbent properties. Response Surface Methodology (RSM) is employed to optimize process parameters for maximizing CO adsorption capacity. The study considers temperature, pressure, LiOH concentration for modification, and adsorbent weight as independent variables across five levels.

Optimization of simultaneous adsorption of nickel, copper, cadmium and zinc from sulfuric solutions using weakly acidic resins.

In this research, the adsorption of nickel (Ni), copper (Cu), cadmium (Cd), and zinc (Zn) from real sulfuric leaching solution with weakly acidic resins has been studied using response surface methodology (RSM). The adsorption process on two weakly acidic resins has been investigated as a function of pH, time, temperature, and resin dosage. The experimental results indicate that the amino phosphoric acid resin removed Ni, Cu, Cd, and Zn from an acidic solution very efficiently.

Facile synthesis of Persian gum-graphene oxide composite as a novel adsorbent for CO capture: characterization and optimization.

Burning fossil fuels releases toxic gases into the environment and has negative effects on it. In this study, Persian gum@Graphene oxide (Pg@GO) was synthesized and used as a novel adsorbent for CO capture. The characterization of materials was determined through XRD, FTIR, FE-SEM, and TGA analysis.

Comprehensive investigation of isotherm, RSM, and ANN modeling of CO capture by multi-walled carbon nanotube.

Chemical vapor deposition was used to produce multi-walled carbon nanotubes (MWCNTs), which were modified by Fe-Ni/AC catalysts to enhance CO adsorption. In this study, a new realm of possibilities and potential advancements in CO capture technology is unveiled through the unique combination of cutting-edge modeling techniques and utilization of the recently synthesized Fe-Ni/AC catalyst adsorbent. SEM, BET, and FTIR were used to analyze their structure and morphology.

Hypercrosslinked waste polycarbonate to remove heavy metal contaminants from wastewater.

In this research, the waste polycarbonate was hypercrosslinked during the Friedel-Crafts reaction to eliminate metallic ions from the wastewater solution. The experiments for inspecting the adsorption behavior of lead and cadmium ions were conducted at the initial concentration of 20-100 mg/L, contact time of 10-80 min, temperature of 20-80 °C, and pH of 6-11. The isotherm, kinetic, and thermodynamic models have been used to explain the behavior of the metal ions removal process.

Efficient CO adsorption using chitosan, graphene oxide, and zinc oxide composite.

This study was deeply focused on developing a novel CTS/GO/ZnO composite as an efficient adsorbent for CO adsorption process. To do so, design of experiment (DOE) was done based on RSM-BBD technique and according to the DOE runs, various CTS/GO/ZnO samples were synthesized with different GO loading (in the range of 0 wt% to 20 wt%) and different ZnO nanoparticle's loading (in the range of 0 wt% to 20 wt%). A volumetric adsorption setup was used to investigate the effect of temperature (in the range of 25-65 °C) and pressure (in the range of 1-9 bar) on the obtained samples CO uptake capability.

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 (SO) emissions from industrial sources, particularly power plants. This research uses calcium silicate absorbent in combination with machine learning (ML) to predict SO 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 SO.

Investigating the effect of textural properties on CO adsorption in porous carbons via deep neural networks using various training algorithms.

The adsorption of carbon dioxide (CO) on porous carbon materials offers a promising avenue for cost-effective CO emissions mitigation. This study investigates the impact of textural properties, particularly micropores, on CO adsorption capacity. Multilayer perceptron (MLP) neural networks were employed and trained with various algorithms to simulate CO adsorption.

Enhanced CO capture potential of UiO-66-NH synthesized by sonochemical method: experimental findings and performance evaluation.

The excessive release of greenhouse gases, especially carbon dioxide (CO) pollution, has resulted in significant environmental problems all over the world. CO capture technologies offer a very effective means of combating global warming, climate change, and promoting sustainable economic growth. In this work, UiO-66-NH was synthesized by the novel sonochemical method in only one hour.

Improved structure of Zr-BTC metal organic framework using NH to enhance CO adsorption performance.

Modified mesoporous NH-Zr-BTC mixed ligand MOF nanocomposites were synthesized via the hydrothermal method as a novel adsorbent for CO capture. The newly modified MOF-808 with NH demonstrated a similar mesoporous morphology as MOF-808, whereas the specific surface area, pore volume, and average particle size, respectively, increased by 15%, 6%, and 46% compared to those of MOF-808. The characterization analyses exhibited the formation of more active groups on the adsorbent surface after modification.

Improving the efficiency of 4A-zeolite synthesized from kaolin by amine functionalization for CO capture.

This study focuses on optimizing the CO adsorption capacity of 4A-zeolite synthesized from kaolin by employing structural modifications through impregnation with tetraethylenepentamine (TEPA) and diethanolamine (DEA). Various analytical techniques were utilized to evaluate the effectiveness of these modifications. Design expert software and response surface methodology (RSM) was employed for data analysis and operational variable optimization, leading to improved CO adsorption performance of the modified zeolites.

Improving adsorption performance of L-ascorbic acid from aqueous solution using magnetic rice husk as an adsorbent: experimental and RSM modeling.

In this research, rice husk (RH) was utilized to prepare a magnetic adsorbent for adsorption of ascorbic acid (AA). The magnetic agent is iron(III) chloride (FeCl). The impact of acid concentration in the range of 400-800 ppm, adsorbent dosage in the range of 0.