بحوث سكوبس — احمد صلاح فاهم العجميين

The present work numerically demonstrates the uniform magnetohydrodynamics Newtonian, laminar natural convection in elliptical cold enclosure with inner hot circular body considering the influence of its vertical movement on the fluid flow and heat transfer. The gap area between the elliptical enclosure and the inner body had been filled by the Al2O3-water nanofluid in the upper layer, whereas lower layer has been filled by the porous medium that has been saturated by an identical nanofluid. The local thermal equilibrium model had been implemented to model the nanofluid and porous media. Additionally, Darcy–Brinkman model considered in the representation of the porous media. The three governing equations of heat and fluid flow like energy and momentum of fluid, in addition to the continuity equation, had been solved numerically utilising finite element formulation. The parameters under investigation are the Rayleigh number value (Formula presented.), Darcy number (Formula presented.), and Hartmann number (Formula presented.). Additionally, two geometrical parameters had been selected, which are the three different locations of inner cylinder (top, middle, bottom), as well as the four different values of the enclosure’s orientation angle (Formula presented.). The results have been presented to reflect the influence of the abovementioned parameters on isotherms and streamlines, besides Nusselt’s number. It has been proved that to improve the heat transfer rate, it is better to locate inner body in bottom region. The Nusselt number increases by 17.44% when it is moved from the top to the bottom. Additionally, the Nusselt number along the elliptical enclosure attached to the nanofluid layer when rotating the elliptical body from (Formula presented.) into (Formula presented.) leads to lower the Nusselt number by 32.3372%. This result is exactly inverse considering Nusselt number along nanofluid–porous layer which proved that increasing the orientation angle from (Formula presented.) to (Formula presented.) contributed to enhancing Nusselt number by 24.7009%. © 2024 Engineers Australia.

Abstract: Metals’ potential hazards have drawn greater attention to the influence of metal pollution on water, making it a crucial subject of study in recent environmental research. This research aligns with the Sustainable Development Goals (SDGs), that aim to protect the world by addressing environmental concerns. As a consequence, understanding the impact of metal pollution on water is an essential aspect of the SDGs’ efforts to improve environmental preservation. This study provides insight into the removal of zinc ions from industrial wastewater using emulsion liquid membrane (ELM) technology. A study was conducted to investigate the use of ELM technology for removing zinc ions from industrial wastewater. Previous studies have shown that ELM can easily remove metals in their ionic form, but the presence of other organic or inorganic compounds like sulfates, phosphates, and carbonates in industrial wastewater increases their solubility and complexity of the removal. To develop the liquid membrane, a surfactant called Sorbitan monooleate (Span 80), an extractant called bis-2-ethylhexyl phosphoric acid (D2EHPA), hydrogen chloride as a reagent, and kerosene as a diluent were used. The study investigated the impact of surfactant concentration, homogenizer speed, extractant concentration, and external phase pH on zinc ion removal using a Box-Behnken design based on Response Surface Methodology (RSM). The results showed that surfactant concentration and pH had the greatest impact on removal efficiency, while homogenizer speed and surfactant extractant had a lower impact on zinc removal. The investigation adjusted numerous parameters to achieve a zinc recovery rate of more than 93% from the bioleaching solution. The most beneficial conditions were a stirring speed of 250 rpm for 10 min, 4.75% v/v Span 80, a homogenizer speed of 11 212 rpm for 8 min, a feed phase pH of 5 or 4.9, and 6% v/v D2EHPA in kerosene. © Pleiades Publishing, Ltd. 2025.

The current research compares a geothermal-driven combined cooling, heating, and power generation cycle (B–CCHP) and a modified version using turbine bleeding and regeneration process named the TBR-CCHP cycle. These cycles incorporate organic Rankine systems, an ejector cooling system, and a heat pump system. The procedure of this study entails (i) introduction of an innovative CCHP setup, (ii) structural modification of the devised cycle, (iii) evaluation based on thermodynamic laws, (iv) optimization through GA, (v) sensitivity (vi) evaluation of the design parameters, Profitability assessment. The results indicate that the TBR-CCHP system achieves the most significant energy and exergy efficiencies with values of 87.83 % and 70.29 %, respectively. The system demonstrates heating load, cooling load, net electricity production, and total exergy destruction values of 80.38 kW, 24.26 kW, 34.44 kW, and 22.32 kW, respectively. Through optimization using genetic algorithm, improvements in energetic efficiency, exergetic efficiency, and overall energy destruction of 7.93 %, 25.53 %, and 34.83 % are seen in the B–CCHP system, and 7.37 %, 19.87 %, and 33.43 % in the TBR-CCHP system. The study reveals that in the TBR-CCHP system, the compressor is identified as the primary source of irreversibility, with reduced irreversibility during optimization. A comprehensive examination of critical parameters of the cycles indicates the significance of optimizing the generator pressure. Also, the payback period in the modified system is reduced to 6.72 years compared to the base cycle, which has a value of 8.43 years. © 2024 The Authors

Chlorothiazide, with the brand name Diuril, is used as a diuretic as well as an antihypertensive drug. This medicine with very slight solubility in water and low permeability is categorized in the class IV of Biopharmaceutical Classification System (BCS) and possess low bioavailability. Therefore, enhancement of the drug solubility would be of great importance to reduce its dosage and consequently side effects. Decrement of Chlorothiazide particles size to micro/nano scale using a supercritical carbon dioxide (scCO2)-based method can be an efficient approach to enhance its bioavailability and therapeutic efficiency. To select and design a proper supercritical micronization/nanonization method, solubility of Chlorothiazide in scCO2 should be determined which is conducted in this work by gravimetric method. In this study, solubility of Chlorothiazide in scCO2 was obtained at various operating conditions (308-338K and 130-290 bar). It was found between 0.417 × 10-5 to 1.012 × 10-5 mol mol-1 (mole fraction) for Chlorothiazide. Moreover, the obtained values were correlated through five empirical models (Chrastil, Mendez-Santiago and Teja (MST), Kumar-Johnston (K-J), Bartle, and Garlapati-Madras), as well as SRK and PR equations of state. All of the mentioned models have shown satisfactory correlation accuracy for the drug solubility. Meanwhile, the K-J model with the minimum AARD% value of 3.15 and the PR-EoS with the mean AARD% value of 6.51 have the highest precision to fit the experimental data. Also, the extrapolative ability of the mentioned empirical models to predict the Chlorothiazide solubility outside the considered range of operating conditions was investigated. © 2022 The Authors.

A computational method was proposed in this work for prediction of drug solubility in supercritical solvent for the drug desoxycorticosterone acetate as a case study. The main focus was on the assessment of drug candidacy for nanomedicine production via green chemistry process which does not use organic solvent. We developed the models on the solubility of desoxycorticosterone acetate (DA) drug considering two inputs: temperature (Kelvin unit) and pressure (Megapascal unit). For the modeling, the only output is drug solubility in the solvent which was considered to be mole fraction. This dataset contains 45 data rows that were collected at 5 temperatures and 9 pressure levels. We employed Decision Tree (DT), Theil-Sen (TS), and Gaussian Process Regression (GPR) core models coupled with Adaboost ensemble method and EPO for model tuning. Final generated models, namely EPA-DT, EPA-TS, and EPA-GPR have R-squared scores of 0.924, 0.882, and 0.997, respectively. Based on this fact and other analysis the EPA-GPR model is selected as the best model of this work which has RMSE error rate of 1.59 × 10−1 and MAE error of 1.16 × 10−1. © 2023 Elsevier B.V.

Background: In the present paper, the natural convection of phase change material (PCM) in a T-shaped cavity is investigated by the lattice Boltzmann method (LBM). In recent years, LBM has become a powerful method for computational modeling of a broad variety of complex fluid flow concerns, including the simulation of the melting process in PCMs. Methods: The enclosure contains CuO nanoparticles and two constant temperature heat sources on the sidewalls of the cavity. The obtained results are presented in different Rayleigh numbers (Ra=103–105), cavity angles (θ = 0 − 90), the volume fraction of nanoparticles (ϕ = 0 − 0.03), and aspect ratios. Results show that the PCM melting speed is lower for low Rayleigh numbers compared to when the number is equal to 105 by 50%. To alter melting time in a specific Ra number, adding nanoparticles, changing cavity slope, and aspect ratio are investigated. Results show that the melting rate is little affected by the addition of nanoparticles but, generally adding nanoparticles delays PCM melting. Significant findings: Raising the distance between the battery and the top of the cavity is known to delay melting by 70% when the distance ratio increase from H1/H = 0.25 to 075, whereas increasing or lowering the distance between the batteries does not affect the melting time. Such a study can be used to design battery thermal management systems (BTMS). © 2023 Taiwan Institute of Chemical Engineers

Electrocoagulation (EC) can be defined a method utilized to remove pollutants from wastewater by applying an electric current to sacrificial electrodes. Many experimental variables like NaCl content (0–4 g/l), current density (5–25 mA/cm2), time (30–90 mins), and pH (4–10) that influence the removal efficiency regarding COD were considered. In the presented research, three distinct configurations related to electrodes, i.e. Al-Al, Fe-Al, and Fe-Fe, have been utilized to determine which was the most effective. RSM depending on BBD was utilized for optimizing various operational parameters with regard to HWW by use of EC. Maximum COD removal (97.9%) was reached at Fe-Al electrodes, NaCl (3.2 g/l), current density (24.7 mA/cm2), time (81.7 mins), and pH (7.4). COD removal (91.3%) was achieved at the Al-Al electrodes, NaCl (3.8 g/l), current density(23.5 mA/cm2), time-86.3 min, and Ph (7.7). At the Fe-Fe electrodes, the removal of COD (89.5%) was obtained at NaCl (2.3 g/l), current density (24.6 mA/cm2), pH 8.5, and time (86.9 min). This indicates that EC could remove pollutants from different types of wastewaters under many operating parameters and with arrangements of electrodes © 2023,Journal of Ecological Engineering.All Rights Reserved.

Background: In this article, the impinging jets (IJ) with the swirling flow coming out of the nozzle, which is created by twisted tape, were studied. Methods: Twisted tapes (TT) are considered in two models, in the T1 model, one twisted tape is in the center of the nozzle, and the T2 model, four TTs at the edge of the nozzle were placed at the same distance. The results show that this type of IJ creates an asymmetric distribution in heat transfer (HT) on the surface and increases the HT in certain directions when the nozzle is close to the impingement surface compared to normal IJ. At a low Reynolds number (Re), the swirling flow almost reduces the HT. However, when Re increased, the swirling flow's effects on Nusselt number (Nu) became higher, and at relatively close proximity to the impingement surface, it increased the HT in comparison to the comparable normal IJ. The effect of the whirling flow is less and the Nu distribution becomes more symmetrical as the nozzle's distance from the impingement surface increases. Significant findings: The effect of the swirling flow of the T1 model on HT is greater than that of the T2 model. In the T2 model, a slight decrease in HT was generally observed. © 2023 Taiwan Institute of Chemical Engineers

Preparation of clean fuels from petroleum need decent removal of sulfur compounds from the petroleum which can be done using the well-known hydrodesulfurization (HDS) method. The removal of sulfur compounds from crude oil would be essential in order to control the emission of gas pollutants such as SO2. However, due to the complexity of the sulfur separation process, predictive modeling and computations are required to improve the separation efficiency. In this research, we have collected some experimental data for optimization of separation process. Each data point has four input features: temperature, pressure, initial sulfur content, and dose. On the other hand, the outputs included left sulfur amount in the feed, value of SO2 emission, and process cost ($). Adaboost technique integrated with three core models of DT, Lasso, and KNN is used for modeling. The models are tuned using LOA method on the available dataset, thereby the optimum models’ parameters were determined for the best fitting. For sulfur concentration and emission parameters, the ADA-DT technique is the best one among other methods, but for the HDS cost, the ADA-LASSO framework works the best. Using these models, the R2-score for outputs is, respectively, 0.940, 0.923, and 0.999. This work makes significant contributions by providing accurate predictive models that would enhance the understanding and optimization of sulfur separation process. These models pave the way for more efficient and environmentally-friendly production of clean fuels from petroleum, contributing to reduced gas pollutants emissions. © 2023 The Authors

In this study, we developed a comprehensive modeling framework for simulation of ozonation process using combination of artificial intelligence and computational fluid dynamics (CFD). The process is carried out in a hollow-fiber membrane contactor in which the concentration data of ozone was obtained by solution of mass transfer equations, and then the results were used for artificial intelligence modeling. We used three different machine learning models to predict concentration of ozone (C) in a system based on its coordinates, i.e., r and z. The models were optimized using the Bat Algorithm (BA) and were trained on a dataset consisting of over 10,000 data points. The three models developed were Support Vector Regression (SVR), Decision Tree Regressor, and Orthogonal Matching Pursuit (OMP). These models were evaluated using three common metrics — Mean Squared Error (MSE), R-squared (R2), and Mean Absolute Error (MAE). Our results indicated that the SVR model overperformed the other two models in terms of all evaluation metrics. Specifically, the SVR model achieved an MSE of 0.003, an R2 of 0.998, and an MAE of 0.046. The Decision Tree Regressor and OMP models achieved less favorable results with MSEs of 0.007 and 0.221, R2 scores of 0.996 and 0.878, and MAEs of 0.056 and 0.359, respectively. © 2023 Elsevier Ltd

Hybrid composites (HC) refer to a type of material that combines aluminum (Al), titanium carbide (TiC), and graphite (Gr) at the nano level. These HC have shown promise in applications requiring high strength, wear resistance (WR), and tribological performance, such as automotive, aerospace, and industrial sectors. In this study, these HC are made using a combination of Powder metallurgy (PM) and accumulative press bonding (APB) processes have been developed. This is the first time that the wear resistance of a hybrid metal matrix composite fabricated with Gr as a solid lubricant has been done and thid is the novelty of this study. In fact, the presence of TiC nanoparticles (NP) provides improved mechanical properties, such as hardness (Hr), strength, and WR for HC. On the other hand, Gr acts as a solid nano-lubricant (NLU) in HC, reducing friction and WR during sliding contact. The presence of Gr-NP also helps to form a durable Gr-nanolayer on tribo surfaces and further improves the WR of HC. This study used a scanning electron microscope (SEM). The results demonstrated that incorporating TiC- NP reduced the WR rate and promoted NL development at extended sliding distances, creating a durable TiC/Gr HC on the TS. Finally, the improved WR of Al/TiC/Gr-HC can be attributed to the stability of the Gr-NL on the TS. © 2023 The Author(s)

Heat pipes are a practical and powerful tool for recovering thermal energy and conserving energy sources. Thermosiphon is one of the most widely used devices that can transfer large amounts of heat at high rates between hot and cold sources without the use of external energy. The amount of vacuum in the pipe, the percentage of fluid filling, the type of operating fluid, the pipe's length and the quantity of heat flux are the factors affecting the efficiency and effectiveness of the thermosiphon heat pipe. In this paper, the effects of different variables in the construction of heat pipes such as working fluid, pipe length, the use of mesh screen wick structure and the use of internal adiabatic wall on the thermosiphon heat pipes performance are investigated. The results show that using of an internal adiabatic wall eliminates and reduces limitations such as boiling, evaporator drying, thermosiphon flooding and vapor pressure and significantly improves the heat pipe's performance. So that, the effective thermal conductivity (K) is increased up to 350% using the internal adiabatic wall. However, in some nanofluids, such as water/multi-walled carbon nanotubes (MWCNT), with increasing the nanofluid's mass fraction, the startup speed in heat pipes with internal adiabatic wall is reduced by up to 20%. © 2023 The Author(s)

The Reduced Crude Residue R.C.R it's product by atmospheric distillation unit from crude oil in Al-Diwaniya refinery, where product from the bottom of distillation tower at 300 °C in atmospheric distillation unit. For Production of Light Petroleum fractions from Reduced Crude Residue R.C.R by Cracking Reaction. Thermal cracking of residual crude residue oil (R.C.R) was conducted in a high-pressure batch reactor was carried out in an autoclave under different process conditions, temperature about 350-450 °C, thermal cracking time 60-120 minute and pressure 1-5 bar in the existence of Nitrogen. Statistical program design of testing its Response Surface Methodology (RSM)byBox-Behnken(BB) was used to predict the effect of necessary changing in the thermal cracking of residual crude residue (R.C.R), and to gain the process conditions best. Based on the three level factorial design, cubist model was progressing between the thermal cracking conditions to total production, the more influential operator for all testing design response was selected. The predictedconversion and yields of total distillate liquid, gas, coke and residue were found to agree satisfactory with the experimental value. When the reaction pressure was increased, the heavy distillate yield decreased with increasing yields of lighter distillates, another interpretation and when increased pressure will improved the quality while decreased the yield. At temperature 400 °C, pressure 1 bar and longer residence time(2 h) the best test to get the maximumyield of the liquid phase product and gas phase product. The better run for quality when temperature 400 °C, pressure 5 bar and 120 minute as running time depended on API, sp.gr, density, viscosity and flash point. The optimum condition the pressure 3.3 bar, temperature 442.92 °C and cracking time its 112.72 min. © 2023 Author(s).

The purpose from testing studies to discovery out the efficiency of CKD from Al-Muthana city Iraq government to removal Methylene Blue Dye (MB) in simulated textile wastewater generated from Al-Diwaniya textile factory in Al-Diwaniya city Iraq government. The wastewater discharge from textile industries that contain methylene blue dye is removed by adsorption through cement kiln dust, therefore, was studied this case. The best electrostatic action is the basic action between methylene blue and cement kiln dust. As cement kiln dust has especially nanostructure properties and the negative charge for his plane, the positive charge for methylene blue compound ability for simply adsorb above it. © 2023 Author(s).

We scrutinise the adsorption of cyclohexylamine (CHA) on pure and Cu-doped BN-nanotube (Cu@BN-NT) through density functional theory calculations. CHA had a weak interaction with the pristine BN-NT, making BN-NT not suitable to be used as a sensor. However, there was a substantial rise in the reactivity and sensitivity of the BN-NT after replacing the B with metal Cu, based on the standard Gibbs free energy of formation. There is a reduction in the energy gap of the HOMO–LUMO of Cu@BN-NT from 2.28 to 1.42 eV (∼ −37.7%) when CHA was adsorbed, which substantially increased the electrical conductivity. Hence, converting the substantial change in the electrical conductivity into an electronic signal was possible, which demonstrated that the Cu@BN-NT was an encouraging sensor to detect CHA. The computed recovery time for the Cu@BN-NT was 26.8 s, which is short. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Aluminum-based composites (AMMCs) have become a popular topic in industrial progress. The aluminum (Al) structure is such that, while it is very light, it also has significant strength. This capability has increased the use of Al in various industries, especially the aerospace and marine industries, even more. Other properties of Al include the favorable plasticity of these structures. It is worth mentioning that many methods can be used to produce Al metal matrix composite (AMMC). One of these methods is Accumulated Press Bonding (APB). APB is one of the most powerful processes as a solid welding method for making MMCs. This method can be called a complex technology that has many advantages. One of the main advantages of this method is that it has a high potential to refine the nanostructures that make up a composite, making it possible to design, produce and refine composites consisting of several layers. In addition to advantages, this process also has disadvantages. Actually, in this process, the bond strength (BS) is weak. This study uses Sn particles to improve the BS of Al laminates as filler metal. So, AA1060 bars with different wt% of Sn particles (interlayer filler material) were manufactured at various pressing temperatures (Temp's) and APB steps. The peeling test was used to evaluate the bonding strength. It was found that the pressing Temp increased APB number of steps and Sn wt%, popular bonds with upper strength were shaped. Also, to illustrate the peeling surface of AA1060/Sn samples, scanning electron microscope (SEM) was used. © 2023 Elsevier Ltd

Since fossil fuels are slowly depleting, bio and renewable energies are now given more attention. The main purpose of this research is to investigate and optimize the influencing parameters of bioenergy production through transesterification process. The application of artificial intelligence (AI) in bioenergy production studies has become increasingly popular due to its capability of interpreting nonlinear relationships between inputs and outputs for complex systems. Here, after conducting library studies and carefully reviewing the existing methods, the multi-layer perceptron (MLP), K-nearest neighbors (KNN), Artificial neural network (ANN), and Gaussian processes regression (GPR) models were selected for simulation and prediction of the efficiency of fatty acid methyl ester (FAME) production. The main effective transesterification parameters on production of biodiesel including the temperature of reaction (°C), catalyst mass to oil mass ratio (wt.%), and the molar ratio of methanol to oil were set as the input variables in all studied models. For reaction between oil and short chain alcohols, wollastonite (a calcium metasilicate, CaSiO3) was utilized as a phase boundary catalyst. By carefully selecting the execution conditions of the algorithms in the model selection phase, all three models reached a result above 0.99 and close to 1 with the square R criterion. Also, the RMSE values for the studied models were 3.95 for MLP, 1.09 for KNN, 0.13 for ANN and 3.60 for GPR models. Therefore, it can be concluded that although the ANN model was to be a better model in process efficiency prediction in terms of error, but all three algorithms had high accuracy because of different generality types. The optimum yield of 97.8% for FAME production was observed at optimum methanol to oil molar ratio, reaction temperature, and catalyst mass to oil mass ratio 65°C, 15, and 9.21 wt%, respectively. © 2022 The Author(s)

The current research is focused on development of machine learning model for estimation of pharmaceutical solubility in supercritical CO2 as the green solvent. The main aim is to assess the suitability of supercritical processing for preparation of nanomedicine. Oxaprozin was taken as model drug for the solubility measurements, and its solubility was determined at different operational conditions by variation of temperature and pressure of the process. Artificial Neural Network (ANN) model was implemented for simulation of the drug solubility, and the best model was obtained with R2 greater than 0.99 for the training and validation as well. The tested model was then exploited to understand the process, and it turned out that both pressure and temperature had major and considerable influence on the solubility of Oxaprozin in supercritical carbon dioxide as solvent. However, the effect of pressure was shown to be more significant on the solubility compared to the effect of pressure, which was attributed to the effect of pressure on the density of the supercritical solvent. The developed ANN model was indicated to be robust in estimating the values of drug solubility in wide range of conditions which can save time and cost of the measurements. © 2022 The Author(s)

We studied the possibility of development of a novel green methodology for preparation of nanomedicine. The designed process does not use organic solvent in the manufacturing the drug and supercritical gas is used as solvent in the process. The method is efficient to enhance the drug efficacy for the patients. We used a dataset of solubility data in our study, which includes two inputs, pressure, and temperature, as well as one output, solubility, in order to carry out the study. In fact, the solubility of a drug namely Tolmetin has been predicted in supercritical carbon dioxide as the solvent using machine learning based models. As part of this research, a heap-based optimizer (HBO) was used on three selected machine learning models to obtain optimal estimators that can be used in the future. Machine learning models are multilayer perceptron (MLP), polynomial support vector regression (PSVR), and ridge regression. PSVR, Ridge, and MLP each have R2-scores of 0.976, 0.749, and 0.957, and MSEs of 1.81 × 10−8, 1.40 × 10−7, and 4.18 × 10−8, respectively. So, PSVR was selected as the most accurate model among all models assessed in this work for description of Tolmetin solubility in supercritical CO2. The results indicated that the developed models are robust and accurate enough for prediction of the pharmaceutical solubility in different solvents and operational ranges. © 2022 Elsevier B.V.

Preparation of nanodrug has been a subject of great interest in pharmaceutical manufacturing due to the inherent high solubility of drug nanoparticles in aqueous media. For the solid dosage oral formulations, the size of drug particles plays fundamental role in the solubility values of drug in aqueous media due to the enhanced surface energy associated with the nanoparticles. To develop the drug production at nanoscale, the solubility of drugs in the solvents must be determined prior to the operational processing. In this study, the solubility of Tolmetin (an anti-inflammatory drug) in supercritical carbon dioxide (SC-CO2) is modeled and analyzed using tree-based models. Temperature and pressure are the two features of the input, and the solubility of the Tolmetin is the target output of this modeling. The studied models are CART (Regression Tree), Extra Tree (ET), and Gradient Tree Boosting (GBRT). Their hyper-parameters were optimized based on multiple statistical criteria and three final models was obtained. CART, ET, and GBRT models had error values of 9.79E-08, 5.53E-08, and 1.24E-08 based on MSE parameter. Based on this fact and other metrics, we finally introduced the Gradient Tree Boosting (GBRT) model as the strongest and most accurate model developed in this research compared to other two models. The developed machine learning models for the solubility prediction, indicated that these models are robust enough to be considered for prediction of drug solubility in supercritical solvents. © 2022 Elsevier B.V.

We have developed computational modeling based on machine learning and computational fluid dynamics for purification and isolation of Ibuprofen from aqueous solutions. The considered separation and purification system is a membrane-based process which is used to selectively remove Ibuprofen from liquid phase. The computational fluid dynamics (CFD) method was performed to obtain the concentration of the drug in the membrane system, and then the concentration values were used as inputs to a number of machine learning models to build the hybrid model of process. Indeed, we dealt with a dataset of 8K data points generated from a CFD simulation. As core models, Multilayer Perceptron (MLP), Lasso, and Support Vector Regression (SVR) were used. To improve efficiency, a technique known as bagging has been added to these models. We optimized the models to find optimal hyper-parameters. With R2 metric, all three models have scores above 0.995 so all models have acceptable performances. When we consider the MAE metric, the lowest error is related to the BAGGING+MLP model with a value of 5.805 × 101, and the BAGGING+LASSO and BAGGING+SVR models have an error rate of 1.401 × 102 and 1.055 × 102, respectively. Finally BAGGING+MLP can be introduced as the most accurate model. © 2022 The Author(s)

We proposed a methodology based on machine learning approach for estimation of ion separation via adsorption technique. The case study considered in this work is removal of two water pollutants including Hg or Ni from water using a metal organic framework (MOF) material, with the formula of UiO-66-(Zr)-(COOH)2. A set of data was collected from literature and then used for training and validation of the model. The used data have two outputs for Qe and Ce which are the adsorption capacity and the equilibrium concentration, respectively. The modeling takes two inputs: ion type (Hg or Ni) and initial ion concentration (C0). We analyzed and modeled the data employing three different regression models, including multilayer perceptron (MLP), linear support vector regression (LSVR), and Gaussian process regression (GPR), to make regression on this data. Implementation and testing of the final models followed the tuning of hyper-parameters using SMA algorithm. With R2 criterion, three models were shown the score of more than 0.92 for both Ce and Qe. Despite the fact that all models have acceptable performances, GPR has been shown to have the largest generality and accuracy for both outputs. As a result, it was selected as the main model in our study. For Ce and Qe, the RMSE metrics calculated using GPR are 4.22E + 00 and 4.98E-01, respectively, based on the GPR. © 2022 Elsevier B.V.

ANFIS (Adaptive neuro fuzzy inference system) modeling of CO2 capture using chemical absorbent was carried out in this study to correlate the solubility of CO2 to the solvent and operational parameters. In the ANFIS model, the input parameters including temperature, pressure, and physio-chemical properties of the solvent were considered, while the loading of CO2 in the absorbent was considered as the sole target output to be predicted by the model. Indeed, we developed a machine learning based model for predicting the CO2 loading capacity in amino acid salt solutions as the chemical absorbent of carbon dioxide. This model uses a metaheuristic optimized ANFIS based on a wide range of amino acids. This study's novel part is the use of Differential Evolution (DE) and Firefly Algorithm (FA) metaheuristics in order to solve hyper-parameter tuning of ANFIS as an optimization problem based on differential evolution. Accordingly, the optimized ANFIS model has an R2 score of 0.9520 for the test data and a score of 0.9841 for the training data. This indicates that the proposed model is both general and accurate in terms of its predictions for CO2 loading in amino acid salt solutions. The MAPE and RMSE error rates are also 1.17E-01, respectively, while the MAPE error rate is 1.14E-01. © 2022 The Author(s)

In this study, we developed a novel methodology for isolation and separation of pharmaceutical compounds from aqueous solutions. Pharmaceutical pollutants are an important group of contaminants of emerging concern (CECs), which their discharge in aquatic environment may result in severe ecological impacts. Penicillin G (Pen G) is a common type of antibiotic, which its presence in drinkable water sources can increase the probability of drug resistance in bacteria. This paper aims to study the removal efficiency of Pen G antibiotic from wastewater (as the aqueous solution) using novel Amberlite LA-2-contained tributyl phosphate solvent (as the organic solution) inside a porous hollow fiber membrane contactor (HFMC). To reach this aim, a comprehensive numerical simulation has been developed based on the computational fluid dynamics (CFD) technique to solve the principal transport equations in shell, membrane and tube sections of HFMC. Evaluation of the results shows the fact that the Amberlite LA-2-contained tributyl phosphate as the organic solution removed very high percentage of existed Pen G in aqueous solution. It is perceived from the results that increase in some parameters such as the concentration of organic solution improved the dimensionless concentration (C/C0) of Pen G in the HFMC and consequently enhances its removal efficiency. Also, increase in the membrane parameters like porosity, packing density and the number of fibers have positive effect on the removal efficiency of Pen G. The method developed in this study is robust and can be employed in development of advanced pharmaceutical industry with focus on green technology. © 2022 The Author(s)

Amylase is a significant industrial enzyme that is used in a variety of industries, including scarification of starchy materials, pharmaceuticals, food, textiles and detergents. This research work is concerned with the optimization increase enzyme efficiency of dissolution unit from Etihad Food Industries Company (Sugar plant), Babylon, Iraq. Effects of operating parameters such as amylase concentration (0-50 ppm), time (0-15 min) and temperature (25-85 C◦) on the starch’s removal efficiency were investigated. Also, the results indicated that the temperature has the main effect on the amylase efficiency. Under optimized operating conditions of initial temperature =85 C, amylase concentration =25 ppm, and time for reaction=15 min the removal efficiency of starch was found to be 60% which is relatively higher than the previous works. © 2022 National Information and Documentation Center (NIDOC).