New insight into impact of humidity on direct air capture performance by SIFSIX-3-Cu MOF.

Removal of CO2 from air is one of the key human challenges in battling global warming. SIFSIX-3-Cu is a promising metal-organic framework (MOF) suggested for carbon capture even at low CO2 concentrations. However, the impact of humidity on its performance in direct air capture (DAC) is poorly understood. To evaluate the MOF performance for DAC application under humid conditions, we investigate the adsorption of H2O, CO2, and N2 using density functional theory (DFT), grand canonical Monte Carlo (GCMC), and molecular dynamics (MD) simulations. The simulation results show a higher tendency of SIFSIX-3-Cu towards H2O adsorption rather than CO2 (and N2). The results agree with the adsorption isotherms for the pure compounds from the Sips model. The extended Sips model shows 1.34 mmol g-1 CO2 adsorption at the atmospheric pressure and 298 K for the CO2/N2 mixture containing 400 ppm CO2, and low CO2 adsorption (less than 0.75 mmol g-1) at a low relative humidity (RH) of 20%. This finding highlights the efficiency of SIFSIX-3-Cu for DAC in dry air and the negative impact of humidity on the CO2 selective adsorption. Therefore, we suggest to consider the impairing of humidity effects when designing a SIFSIX-3-Cu-based CO2 separation process and removal of any water vapor before introduction of the air to SIFSIX-3-Cu.

Improving the estimation accuracy of confined vapor-liquid equilibria by fine-tuning the pure component parameter in the PC-SAFT equation of state.

We propose a thermodynamic model that combines the Young-Laplace equation and perturbed chain-statistical associating fluid theory (PC-SAFT) equation of state to estimate capillary condensation pressure in microporous and mesoporous sorbents. We adjust the PC-SAFT dispersion-energy parameter when the pore size becomes comparable to the molecular dimension. This modelling framework is applied to diverse systems containing associating and non-associating gases, various sorbents, and a wide range of temperatures. Our simulation results show that under extreme confinement, a higher value of the dispersion-energy parameter (ε) is required. Furthermore, using the experimental saturation pressure data for 18 different associating and non-associating confined fluids, we find that the shift in the PC-SAFT dispersion energy correlates with the ratio of the sorbent mean pore size to the PC-SAFT segment size (rp/σ). By fitting to the capillary condensation data, the relative deviation between the confined and bulk PC-SAFT dispersion energy parameter is only 0.1% at rp/σ = 15; however, this deviation starts to increase exponentially as rp/σ decreases. For a sorbent with large pores, when rp/σ > 15, the capillary condensation pressure results from our model are similar to the predictions from the Kelvin equation. Using a dataset containing 235 saturation pressure data points composed of 18 pure gases and 4 binary mixtures, the overall AARD% from our model is 12.26%, which verifies the good accuracy of our model. Because the mean sorbent pore radius (rp), the PC-SAFT energy parameter (ε), and segment size (σ) are known a priori, our model estimates the corrected energy parameter for small pores and, thus, extends its applicability.

Adsorption of copper from water using TiO2-modified activated carbon derived from orange peels and date seeds: Response surface methodology optimization.

This study evaluated the application and efficiency of modified activated carbon in the removal of copper (Cu) from synthetic aquatic samples. The surface of activated carbon derived from orange peel (AC-OP) and date seeds (AC-DS) have been modified by Titanium dioxide nanoparticles (TiO2 NPs) (1:10 wt% mixing ratio) and used in a series of experiments designed by Response Surface Methodology (RSM) incorporating Central Composite Design (CCD). The Brunauer-Emmett-Teller (BET) test demonstrated that the modification has increased the surface area of AC-OP from 2.40 to 6.06 m2 g-1 and AC-DS from 51.10 to 81.37 m2 g-1. Effects of pH (1-7), ion initial concentration (10-60 mg L-1), adsorbent dose (0.5-8 g L-1), and contact time (0.4-6 h) have been investigated. The results showed that the optimum conditions for TiO2-modified AC-OP (OP-TiO2) are pH 5, initial concentration of 24.6 mg L-1, adsorbent dose of 4.9 g L-1, and contact time of 3.6 h. The optimum conditions for TiO2-modified AC-DS (DS-TiO2) are pH 6.4, initial concentration of 21.2 mg L-1, adsorbent dose of 5 g L-1, and contact time of 3.0 h. The modified quadratic models represented the results well with regression coefficients of 0.91 and 0.99 for OP-TiO2 and DS-TiO2, respectively. The maximum Cu removal for OP-TiO2 and DS-TiO2 were 99.90 % and 97.40 %, and the maximum adsorption capacity was found to be 13.34 and 13.96 mg g-1, respectively. Kinetic data have been fitted to pseudo first-order, pseudo second-order, intra-particle diffusion, and Elovich models. The pseudo second-order showed a better fit to the experimental data (R2 > 98 %). This study demonstrates the successful development of modified activated carbon derived from orange peels and date seeds, modified by TiO2 nanoparticles, for efficient adsorption of copper ions from water. The findings contribute to understanding the adsorption mechanism and provide valuable insights for designing environmentally friendly adsorbents.

Olive stone as an eco-friendly bio-adsorbent for elimination of methylene blue dye from industrial wastewater.

Adsorbents synthesized by activation and nanoparticle surface modifications are expensive and might pose health and ecological risks. Therefore, the interest in raw waste biomass materials as adsorbents is growing. In batch studies, an inexpensive and effective adsorbent is developed from raw olive stone (OS) to remove methylene blue (MB) from an aqueous solution. The OS adsorbent is characterized using scanning electron microscopy (SEM), Fourier Transform Infra-Red (FTIR), and Brunauer-Emmett-Teller (BET) surface area. Four isotherms are used to fit equilibrium adsorption data, and four kinetic models are used to simulate kinetic adsorption behavior. The obtained BET surface area is 0.9 m2 g-1, and the SEM analysis reveals significant pores in the OS sample that might facilitate the uptake of heavy compounds. The Langmuir and Temkin isotherm models best represent the adsorbtion of MB on the OS, with a maximum monolayer adsorption capacity of 44.5 mg g-1. The best dye color removal efficiency by the OS is 93.65% from an aqueous solution of 20 ppm at the OS doses of 0.2 g for 90 min contact time. The OS adsorbent serves in five successive adsorption cycles after a simple filtration-washing-drying process, maintaining MB removal efficiency of 91, 85, 80, and 78% in cycles 2, 3, 4, and 5, respectively. The pseudo second-order model is the best model to represent the adsorption process dynamics. Indeed, the pseudo second-order and the Elovich models are the most appropriate kinetic models, according to the correlation coefficient (R2) values (1.0 and 0.935, respectively) derived from the four kinetic models. The parameters of the surface adsorption are also predicted based on the mass transfer models of intra-particle diffusion and Bangham and Burt. According to the thermodynamic analysis, dye adsorption by the OS is endothermic and spontaneous. As a result, the OS material offers an efficient adsorbent for MB removal from wastewater that is less expensive, more ecologically friendly, and economically viable.

Hybrid Smart Strategies to Predict Amine Thermal Degradation in Industrial CO2 Capture Processes.

CO2 emission reduction is an essential step to achieve the climate change targets. Solvent-based post-combustion CO2 capture (PCC) processes are efficient to be retrofitted to the existing industrial operations/installations. Solvent degradation (and/or loss) is one of the main concerns in the PCC processes. In this study, the thermal degradation of monoethanolamine (MEA) is investigated through the utilization of hybrid connectionist strategies, including an artificial neural network-particle swarm optimization (ANN-PSO), a coupled simulated annealing-least squares support vector machine (CSA-LSSVM), and an adaptive neuro-fuzzy inference system (ANFIS). Moreover, gene expression programming (GEP) is employed to generate a correlation that relates the solvent concentration to the operating variables involved in the adverse phenomenon of solvent thermal degradation. The input variables are the MEA initial concentration, CO2 loading, temperature, and time, and the output variable is the remaining/final MEA concentration after the degradation phenomenon. According to the training and testing phases, the most accurate model is ANFIS, and the reliability/performance of its optimal network is assessed by the coefficient of determination (R2), mean squared error, and average absolute relative error percentage, which are 0.992, 0.066, and 2.745, respectively. This study reveals that the solvent initial concentration has the most significant impact, and temperature plays the second most influential effect on solvent degradation. The developed models can be used to predict the thermal degradation of any solvent in a solvent-based PCC process regardless of the complicated reactions involved in the degradation phenomenon. The models introduced in this study can be employed for the development of more accurate hybrid models to optimize the proposed systems in terms of cost, energy, and environmental prospects.

Applying MCM-48 mesoporous material, equilibrium, isotherm, and mechanism for the effective adsorption of 4-nitroaniline from wastewater.

In this work, the MCM-48 mesoporous material was prepared and characterized to apply it as an active adsorbent for the adsorption of 4-nitroaniline (4-Nitrobenzenamine) from wastewater. The MCM-48 characterizations were specified by implementing various techniques such as; scanning electron microscopy (SEM), Energy dispersive X-ray analysis (EDAX), X-ray diffraction (XRD), Brunauer-Emmett-Teller (BET) surface area, pore size distribution (PSD), and Fourier transform infrared (FTIR). The batch adsorption results showed that the MCM-48 was very active for the 4-nitroaniline adsorption from wastewater. The adsorption equilibrium results were analyzed by applying isotherms like Langmuir, Freundlich, and Temkin. The maximum experimental uptake according to type I Langmuir adsorption was found to be 90 mg g-1 approximately. The Langmuir model with determination coefficient R2 = 0.9965 is superior than the Freundlich model R2 = 0.99628 and Temkin model R2 = 0.9834. The kinetic adsorption was investigated according to pseudo 1st order, pseudo 2nd order, and Intraparticle diffusion model. The kinetic results demonstrated that the regression coefficients are so high R2 = 0.9949, that mean the pseudo 2nd order hypothesis for the adsorption mechanism process appears to be well-supported. The findings of adsorption isotherms and kinetics studies indicate the adsorption mechanism is a chemisorption and physical adsorption process.

Recovery of fuel from real waste oily sludge via a new eco-friendly surfactant material used in a digital baffle batch extraction unit.

This study focused on developing a new cocktail extraction agent (CEA) composed of solvent and a new surfactant material (SM) for enhancing the efficiency of fuel recovery from real waste oil sludge (WSO). The effects of different solvents (e.g. methyl ethyl ketone (MEK), naphtha, petrol and kerosene), SMs (Dowfax and sodium thiosulfate), extraction time (10-20 min), extraction temperatures (20-60 °C) and CEA/sludge ratios (1-4) on the extraction performance were investigated. SMs and DBBE design enhanced the extraction efficiency by increasing the dispersion of solvent in WSO and enhancing the mixing and mass transfer rates. Results proved that Dowfax was the best SM for oil recovery under various conditions. The best CEA (e.g. MEK and Dowfax) provides the maximum fuel recovery rate of 97% at a period of 20 min, temperature of 60 °C and 4:1 CEA/sludge ratio. The produced fuel was analysed and fed to the distillation process to produce diesel oil. The characteristics of diesel oil were measured, and findings showed that it needs treatment processes prior its use as a finished fuel.

Strategies To Improve the Performance of Hydrogen Storage Systems by Liquefaction Methods: A Comprehensive Review.

The main challenges of liquid hydrogen (H2) storage as one of the most promising techniques for large-scale transport and long-term storage include its high specific energy consumption (SEC), low exergy efficiency, high total expenses, and boil-off gas losses. This article reviews different approaches to improving H2 liquefaction methods, including the implementation of absorption cooling cycles (ACCs), ejector cooling units, liquid nitrogen/liquid natural gas (LNG)/liquid air cold energy recovery, cascade liquefaction processes, mixed refrigerant systems, integration with other structures, optimization algorithms, combined with renewable energy sources, and the pinch strategy. This review discusses the economic, safety, and environmental aspects of various improvement techniques for H2 liquefaction systems in more detail. Standards and codes for H2 liquefaction technologies are presented, and the current status and future potentials of H2 liquefaction processes are investigated. The cost-efficient H2 liquefaction systems are those with higher production rates (>100 tonne/day), higher efficiency (>40%), lower SEC (<6 kWh/kgLH2), and lower investment costs (1-2 $/kgLH2). Increasing the stages in the conversion of ortho- to para-H2 lowers the SEC and increases the investment costs. Moreover, using low-temperature waste heat from various industries and renewable energy in the ACC for precooling is significantly more efficient than electricity generation in power generation cycles to be utilized in H2 liquefaction cycles. In addition, the substitution of LNG cold recovery for the precooling cycle is associated with the lower SEC and cost compared to its combination with the precooling cycle.

Application of Functionalized Fe3O4 Magnetic Nanoparticles Using CTAB and SDS for Oil Separation from Oil-in-Water Nanoemulsions.

Using magnetic nanoparticles (MNPs) for emulsified oil separation from wastewater is becoming increasingly widespread. This study aims to synthesize MNPs using amphiphilic coatings to stabilize the MNPs and prevent their agglomeration for efficiently breaking oil-in-water nanoemulsions. We coat two different sizes of Fe3O4 nanoparticles (15-20 and 50-100 nm) using cetyltrimethylammonium bromide (CTAB) and sodium dodecyl sulfate (SDS) with surfactant-to-MNP mass ratios of 0.4 and 0.8. We study the effect of various variables on the demulsification performance, including the MNP size and concentration, coating materials, and MNP loading. Based on the oil-water separation analysis, the smaller size MNPs (MNP-S) show a better demulsification performance than the larger ones (MNP-L ) for a 1000 ppm dodecane-in-water emulsion containing nanosized oil droplets (250-300 nm). For smaller MNPs (MNP-S) and at low dosage level of 0.5 g/L, functionalizing with surfactant-to-MNP mass ratio of 0.4, the functionalization increases the separation efficiency (SE) from 57.5% for bare MNP-S to 86.1% and 99.8 for the SDS and CTAB coatings, respectively. The highest SE for MNP-S@CTAB and the zeta potential measurements imply that electrostatic attraction between negatively charged oil droplets (-55.9 ± 2.44 mV) and positively charged MNP-S@CTAB (+35.8 ± 0.34 mV) is the major contributor to a high SE. Furthermore, the reusability tests for MNP-S@CTAB reveal that after 10 cycles, the amount of oil adsorption capacity decreases slightly, from 20 to 19 mg/g, indicating an excellent stability of synthesized nanoparticles. In conclusion, functionalized MNPs with tailored functional groups feature a high oil SE that could be effectively used for oil separation from emulsified oily wastewater streams.

Selective and Continuous Oil Removal from Oil-Water Mixtures Using a Superhydrophobic and Superoleophilic Stainless Steel Mesh Tube.

The development of continuous oil-water separation processes has applications in the treatment of industrial oily wastewater and effective management of oil spills. In this research, the performance of a superhydrophobic-superoleophilic (SHSO) membrane in oil-water separation is investigated through dynamic tests. We investigate the effects of the total flow rate and oil concentration on the separation efficiency using an as-fabricated SHSO mesh tube. To construct the SHSO membrane, a tubular stainless steel mesh is dip-coated into a solution, containing a long-chain alkyl silane (Dynasylan F8261) and functionalized silica nanoparticles (AEROSIL R812). The as-prepared SHSO mesh tube illustrates a water contact angle of 164° and an oil contact angle of zero for hexane. A maximum oil separation efficiency (SE) of 97% is obtained when the inlet oil-water mixture has the lowest flow rate (5 mL/min) with an oil concentration of 10 vol %, while the minimum oil SE (86%) is achieved for the scenario with the highest total flow rate (e.g., 15 mL/min) and the highest oil concentration (e.g., 50 vol %). The water SE of about 100% in the tests indicates that the water separation is not affected by the total flow rate and oil concentration, due to the superhydrophobic state of the fabricated mesh. The clear color of water and oil output streams also reveals the high SE of both phases in dynamic tests. The outlet oil flux increases from 314 to 790 (L/m2·h) by increasing the oil permeate flow rate from 0.5 to 7.5 (mL/min). The linear behavior of the cumulative amounts of collected oil and water with time demonstrates the high separation performance of a single SHSO mesh, implying no pore blocking during dynamic tests. The significant oil SE (97%) of the fabricated SHSO membrane with robust chemical stability shows its promising potential for industrial-scale oil-water separation applications.

CFD modeling and performance evaluation of an open-aperture partially evacuated receiver with internal twisted inserts in solar PTCs: energy and exergy analysis.

The solar flux distribution on the walls of the receivers in solar parabolic and circular collectors (PTCs and CTCs) is highly non-uniform. To prevent major problems caused by flux non-uniformity in such devices, a partially open aperture evacuated receiver (OAER) with an internal twisted insert is introduced, and its performance is compared to that of conventional evacuated receivers (CERs). A comprehensive computational fluid dynamic (CFD) model is developed for the receivers. The simulation results show that the proposed receiver has a more uniform solar flux distribution, and consequently greater energy and exergy efficiencies compared to CERs. The proposed OAER acts as a fluid turbulator, absorbs solar energy more efficiently, distributes the temperature gradient inside the absorber more uniformly, shifts the maximum temperature on the exterior surface of the CERs to the interior surface, and reduces radiative losses. The enhancement in total heat absorbed by OAER with 10 blades (OAER_10) compared to the CER_0 is 7.69%. The simulation results reveal that the average daily exergy efficiency of an OAER_10 is about 27.8% higher than a CER_0. The average daily exergy efficiency of a CER_0 is obtained to be about 7.55%, while it decreases to 7.42% for a CER_6 due to larger exergy destruction caused by air pressure drop. The introduced design offers a higher potential for efficient absorption of solar energy (proper sensible heat storage device) and provides a more useful contact area.

Modeling Approach to Determine Static Rivulet Height in Regular Polygonal Capillary Tubes.

The rise of partially wetting liquids along the corners of noncircular capillary tubes is observed in many practical science and engineering applications such as wastewater treatment using membranes, remediation, oil recovery from petroleum reservoirs, and blood flow. In this paper, rivulet rise at the corners of polygonal capillary tubes is studied for partially wetting liquids with contact angles below the critical value. The presence of corners changes the distribution of a liquid in an incomplete wetting condition. In this study, geometrical models are proposed to better understand the capillary rise and flow behavior at the corners. A geometrical solution for the capillary rivulet height and profile is derived under gravity in triangular, square, and pentagonal capillary tubes. The effects of several factors including contact angle, number of polygon sides, and liquid properties on the capillary rivulet height are examined. It was found that the ratio of liquid surface tension to density directly affects the corner rise, while it has an inverse relationship with other factors. The maximum rivulet height of 91.6 mm is obtained in the triangular capillary tube with a side length of 1 mm and a contact angle of 30° for polydimethylsiloxane (PDMS-20)-air fluid pair. The minimum capillary rivulet height of 6.2 mm, on the other hand, is achieved in the pentagonal capillary tube, with a side length of 3 mm and a contact angle of 30°. To validate the developed analytical approach, comparisons are made between the model results, literature predictions, and experimental data. In addition, the geometrical model for a square capillary tube is compared with previous published studies, revealing a good agreement. This study provides quantitative results for the influence of capillary tube shape on the flow behavior of fluids in noncircular tubes that can be useful for control and optimization of transport phenomena in corresponding systems.

How Does a Microfluidic Platform Tune the Morphological Properties of Polybenzimidazole Nanoparticles?

Microfluidic synthesis methods are among the most promising approaches for controlling the size and morphology of polymeric nanoparticles (NPs). In this work, for the first time, atomistic mechanisms involved in morphological changes of polybenzimidazole (PBI) NPs in microfluidic media are investigated. The multiscale molecular dynamic (MD) simulations are validated with the literature modeling and experimental data. A good agreement is obtained between the molecular modeling results and experimental data. The effects of mixing time, solvent type, dopant, and simulation box size at the molecular level are investigated. Mixing time has a positive impact on the morphology of the PBI NPs. Microfluidic technology can control the mixing time well and engineer the morphology of the NPs. In the process of morphological changes, at the optimum time (about 11.5 ms), the attraction energy between the polymer molecules is at the highest level (-37.65 kJ/mol). The size of the polymer NPs is minimal (2.3 nm), and the aspect ratio and entropy are at the lowest level, equal to 1.07 and 11.024 kJ/mol·K, respectively. It was found that the presence of water leads to the precipitation of polymeric NPs owing to the dominance of hydrophobic forces. Both dimethylacetamide (DMA) and phosphoric acid (PA) improve the control of the size and morphology of NPs. However, the addition of PA has a greater impact; PA acts as a cross-linker, making PBI NPs finer and more spherical. In addition, MD simulation reveals that PA increases the proton diffusion coefficient in PBI and enhances its efficiency in fuel cells. This study paves a new efficient way for morphological engineering of polymeric NPs using microfluidic technology.

A Connectionist Model for Dynamic Economic Risk Analysis of Hydrocarbons Production Systems.

This study presents a connectionist model for dynamic economic risk evaluation of reservoir production systems. The proposed dynamic economic risk modeling strategy combines evidence-based outcomes from a Bayesian network (BN) model with the dynamic risks-based results produced from an adaptive loss function model for reservoir production losses/dynamic economic risks assessments. The methodology employs a multilayer-perceptron (MLP) model, a loss function model; it integrates an early warning index system (EWIS) of oilfield block with a BN model for process modeling. The model evaluates the evidence-based economic consequences of the production losses and analyzes the statistical disparities of production predictions using an EWIS-assisted BN model and the loss function model at the same time. The proposed methodology introduces an innovative approach that effectively minimizes the potential for dynamic economic risks. The model predicts real-time daily production/dynamic economic losses. The connectionist model yields an encouraging overall predictive performance with average errors of 1.954% and 1.957% for the two case studies: cases 1 and 2, respectively. The model can determine transitional/threshold production values for adequate reservoir management toward minimal losses. The results show minimum average daily dynamic economic losses of $267,463 and $146,770 for cases 1 and 2, respectively. It is a multipurpose tool that can be recommended for the field operators in petroleum reservoir production management related decision making.

Application of decision tree-based ensemble learning in the classification of breast cancer.

As a common screening and diagnostic tool, Fine Needle Aspiration Biopsy (FNAB) of the suspicious breast lumps can be used to distinguish between malignant and benign breast cytology. In this study, we first review published works on the classification of breast cancer where the machine learning and data mining algorithms have been applied by using the Wisconsin Breast Cancer Database (WBCD). This work then introduces useful new tools, based on Random Forest (RF) and Extremely Randomized Trees or Extra Trees (ET) algorithms to classify breast cancer. The RF and ET strategies use the decision trees as proper classifiers to attain the ultimate classification. The RF and ET approaches include four main stages: input identification, determination of the optimal number of trees, voting analysis, and final decision. The models implemented in this research consider important factors such as uniformity of cell size, bland chromatin, mitoses, and clump thickness as the input parameters. According to the statistical analysis, the proposed methods are able to classify the type of breast cancer accurately. The error analysis results reveal that the designed RF and ET models offer easy-to-use outcomes and the highest diagnostic performance, compared to previous tools/models in the literature for the WBCD classification. The highest and lowest magnitudes of relative importance are attributed to the uniformity of cell size and mitoses among the factors. It is expected that the RF and ET algorithms play an important role in medicine and health systems for screening and diagnosis in the near future.

Effect of Water on Molecular and Transport Phenomena Behaviors of [Bmim][Ac]/Water/CO2, Using Molecular Dynamics Strategy.

Mixing ionic liquids (ILs) with water proposes an effective way of designing IL solutions with engineered properties that have applications in various processes/industries. Understanding molecular interactions of ILs, water, and carbon dioxide (CO2) is of great importance in the selection of ILs with high selectivity and solubility for CO2 capture application. We perform molecular dynamics simulations to investigate the effects of water concentration on excess energy, molecular distribution, and dynamic behaviors of mixtures of 1-butyl-3-methylimidazolium acetate ([Bmim][Ac]), water (W), and CO2 at different water concentrations. The results of this study include radial distribution functions, coordination numbers, water cluster size distribution, hydrogen bonding, and diffusivity coefficients in IL/W and CO2/IL/W systems, using [Bmim][Ac] as the IL, at various process conditions. Analysis of the water clusters in the IL/W system reveals that the water clusters are connected mainly through hydrogen bonds. The presence of water in the IL solutions increases the diffusivity of cations, anions, water, and CO2 molecules in the mixture because of the reduced viscosity of the solution and the hydrophilicity of [Bmim][Ac]. This study highlights the effect of water (as an additive to IL) on key parameters such as diffusion coefficient and cluster formation in optimal design and operation of carbon capture processes.

Artificial Intelligence Based Methods for Asphaltenes Adsorption by Nanocomposites: Application of Group Method of Data Handling, Least Squares Support Vector Machine, and Artificial Neural Networks.

Asphaltenes deposition is considered a serious production problem. The literature does not include enough comprehensive studies on adsorption phenomenon involved in asphaltenes deposition utilizing inhibitors. In addition, effective protocols on handling asphaltenes deposition are still lacking. In this study, three efficient artificial intelligent models including group method of data handling (GMDH), least squares support vector machine (LSSVM), and artificial neural network (ANN) are proposed for estimating asphaltenes adsorption onto NiO/SAPO-5, NiO/ZSM-5, and NiO/AlPO-5 nanocomposites based on a databank of 252 points. Variables influencing asphaltenes adsorption include pH, temperature, amount of nanocomposites over asphaltenes initial concentration (D/C0), and nanocomposites characteristics such as BET surface area and volume of micropores. The models are also optimized using nine optimization techniques, namely coupled simulated annealing (CSA), genetic algorithm (GA), Bayesian regularization (BR), scaled conjugate gradient (SCG), ant colony optimization (ACO), Levenberg-Marquardt (LM), imperialistic competitive algorithm (ICA), Fletcher-Reeves update (CGF), and particle swarm optimization (PSO). According to the statistical analysis, the proposed RBF-ACO and LSSVM-CSA are the most accurate approaches that can predict asphaltenes adsorption with average absolute percent relative errors of 0.892 % and 0.94%, respectively. The sensitivity analysis shows that temperature has the most impact on asphaltenes adsorption from model oil solutions.

Decision tree-based diagnosis of coronary artery disease: CART model.

BACKGROUND AND OBJECTIVE: As the most common cardiovascular defect, coronary artery disease (CAD), also called ischemic heart disease, is one of the substantial causes of death globally. Several diagnosis approaches such as baseline electrocardiography, echocardiography, magnetic resonance imaging, and coronary angiography are suggested for screening the suspected patients that may suffer from CAD. However, applying such methods may have health side effects and/or expensive costs. METHODS: As an alternative to the available diagnosis tools/methods, this research involves a decision tree learning algorithm called classification and regression tree (CART) for a simple and reliable diagnosis of CAD. Several CART models are developed based on the recently CAD dataset published in the literature. RESULTS: Utilizing all the features of the dataset (55 independent parameters), it was found that only 40 independent parameters influence the CAD diagnosis and consequently development of the predictive model. Based on the feature importance obtained from the first CART model, three new CART models are then developed using 18, 10, and 5 selected features. Except for the five-feature CART model, the outcomes of developed CART models demonstrate the maximum achievable accuracy, sensitivity, and specificity for CAD diagnosis (100%), while comparing the predictions with the reported targets. The error analysis reveals that the literature models including sequential minimal optimization (SMO), bagging SMO, Naïve Bayes (NB), artificial neural network (ANN), C4.5, J48, Bagging, and ANN in conjunction with the genetic algorithm (GA) do not outperform the CART methodology in classifying patients as normal or CAD. CONCLUSIONS: Hence, the robustness of the tree-based algorithm in accurate and fast predictions is confirmed, implying the proposed classification technique can be successfully utilized to develop a coherent decision-making system for the CAD diagnosis.

Central-Moments-Based Lattice Boltzmann for Associating Fluids: A New Integrated Approach.

Dynamic and thermodynamic behaviors of associating fluids play a crucial role in various science and engineering disciplines. Cubic plus association equation of state (CPA EOS) is implemented in a central-moments-based lattice Boltzmann method (LBM) in order to mimic the thermodynamic behavior of associating fluids. The pseudopotential approach is selected to model the multiphase thermodynamic characteristics such as reduced density of associating fluids. The priority of central-moments-based approach over multiple-relaxation-time collision operator is highlighted by performing double shear layers. The integration of central-moments-based LBM and CPA EOS is useful to simulate the dynamic and thermodynamic characteristics of associating fluids at high flow rate conditions, which is extended to high-density ratio scenarios by increasing the anisotropy order of gradient operator. In order to increase the stability of the model, a higher anisotropy order of the gradient operator is implemented; about 34 present reduction in spurious velocities is noticed in some cases. The type of gradient operator considerably affects the model thermodynamic consistency. Finally, the model is validated by observing a straight line in the Laplace law test. Prediction of thermodynamic behaviors of associating fluids is of significance in various applications including biological processes as well as fluid flow in porous media.

Hybridized method of pseudopotential lattice Boltzmann and cubic-plus-association equation of state assesses thermodynamic characteristics of associating fluids.

It is crucial to properly describe the associating fluids in terms of phase equilibrium behaviors, which are needed for design, operation, and optimization of various chemical and energy processes. Pseudopotential lattice Boltzmann method (LBM) appears to be a reliable and efficient approach to study thermodynamic behaviors and phase transition of complex fluid systems. However, when cubic equations of state (EOSs) are incorporated into single-component multiphase LBM, simulation results are not well matched with experimental data. This study presents the utilization of cubic-plus-association (CPA) EOS in the LBM structure to obtain more accurate modeling results for associating fluids. An approach based on the global search optimization algorithm is introduced to find the optimal association parameters of CPA EOS for water and primary alcohols in the lattice units. The thermodynamic consistency is verified by the Maxwell construction and is also improved by the forcing scheme of [Q. Li, K. H. Luo, and X. J. Li, Phys. Rev. E 86, 016709 (2012)10.1103/PhysRevE.86.016709]. The spurious velocity is reduced with increasing isotropy in the gradient operator. Furthermore, an extended version of CPA EOS is introduced, which increases the system stability at low reduced temperatures. There is a very good match between the LBM results and experimental data, confirming the reliability of the model developed in the present study. The introduced approach has potential to be employed for simulating transport phenomena and interfacial characteristics of associating fluids in porous systems.