Optimal design of triangular side orifice using multi-objective optimization NSGA-II.

Triangular orifices are widely used in industrial and engineering applications, including fluid metering, flow control, and measurement. Predicting discharge through triangle orifices is critical for correct operation and design optimization in various industrial and engineering applications. Traditional approaches like empirical equations have accuracy and application restrictions, whereas computational fluid dynamics (CFD) simulations can be computationally costly. Alternatively, artificial neural networks (ANNs) have emerged as a successful solution for predicting discharge through orifices. They offer a dependable and efficient alternative to conventional techniques for estimating discharge coefficients, especially in intricate relationships between input parameters and discharge. In this study, ANN models were created to predict discharge through the triangle orifice and velocity at the downstream of the main channel, and their effectiveness was assessed by comparing the performance with the earlier models proposed by researchers. This paper also proposes a novel hybrid multi-objective optimization model (NSGA-II) that uses genetic algorithms to discover the best values for design parameters that maximize discharge and downstream velocity simultaneously.

Enhancement in the efficiency of heat recovery in a Williamson hybrid nanofluid over a vertically thin needle with entropy generation.

The purpose of the present research is to conduct an examination of entropy generation in a 2D magneto Williamson hybrid nanofluid flow that contains cobalt ferrite and titanium oxide nanoparticles and undergoes surface-catalyzed reactions through a thin vertical needle. The consequences of joule heating and viscous dissipation are considered to elaborate the features of heat transport. Further, the influence of thermal stratification, thermal radiation, and homogeneous-heterogeneous reaction is also taken into account. Through the application of appropriate similarity variables, the dimensionless system of coupled ordinary differential equations is achieved. The coupled system of equations is numerically solved by the usage of the bvp4c technique in the MATLAB algorithm. The current investigation also compared the existing outcomes with the available literature, which shows great harmony between the two. The consequences of the physical parameters are discussed graphically and with numerical data. It is worth noting that larger values of homogeneous reaction strength and the surface-catalyzed parameter diminish the concentration field. Further, the velocity distribution and their related momentum boundary layer thickness, diminishes with the enlargement of the Weissenberg parameter.

Evaluating ionic liquids for its potential as eco-friendly solvents for naproxen removal from water sources using COSMO-RS: Computational and experimental validation.

An emerging contaminant of concern in aqueous streams is naproxen. Due to its poor solubility, non-biodegradability, and pharmaceutically active nature, the separation is challenging. Conventional solvents employed for naproxen are toxic and harmful. Ionic liquids (ILs) have attracted great attention as greener solubilizing and separating agent for various pharmaceuticals. ILs have found extensive usage as solvents in nanotechnological processes involving enzymatic reactions and whole cells. The employment of ILs can enhance the effectiveness and productivity of such bioprocesses. To avoid cumbersome experimental screening, in this study, conductor like screening model for real solvents (COSMO-RS) was used to screen ILs. Thirty anions and eight cations from various families were chosen. Activity coefficient at infinite dilution, capacity, selectivity, performance index, molecular interactions using σ-profiles and interaction energies were used to make predictions about solubility. According to the findings, quaternary ammonium cations, highly electronegative, and food-grade anions will form excellent ionic liquid combinations for solubilizing naproxen and hence will be better separating agents. This research will contribute easy designing of ionic liquid-based separation technologies for naproxen. In different separation technologies, ionic liquids can be employed as extractants, carriers, adsorbents, and absorbents.

Support vector regression-based model for phenol adsorption in rotating packed bed adsorber.

The excessive strength of phenol present in industrial wastewater is a major issue of concern to be looked upon. Among the pollutant removal techniques, a novel robust device, the rotating packed bed (RPB) adsorber, offers efficient adsorption of phenol due to its ability to magnify the mass transfer rate. In the present study, support vector regression (SVR) has been applied to predict adsorption of phenol on activated carbon in RPB by taking into account the independent parameters, namely, spray density, gravity factor, concentration, and contact time. The experimental data set of phenol adsorption sample has been randomized and normalized prior to constructing the models. The predictive ability of the SVR model has been compared with other data-driven models like artificial neural network (ANN) and multiple regression (MR) models. Both the SVR-based model and the ANN model have almost similar prediction efficacy; however, the ANN model was found to predict the outputs slightly better. The coefficient of determination (R2) and root mean square error (RMSE) values of test data set for the MR RPB adsorption model were found to be 0.934 and 0.149, while for the SVR and ANN-based models, these values were 0.996 and 0.045 and 0.998 and 0.027, respectively. Thus, it was concluded that the soft computing SVR and ANN models possessed tremendous potential to predict the adsorption process of RPB with remarkable accuracy and were greatly generalized.

gPROMS-driven modeling and simulation of fixed bed adsorption of heavy metals on a biosorbent: benchmarking and case study.

Adsorptive separation of heavy metals from wastewater is a viable approach to reuse it and avoid environmental pollution. The productive employment of adsorptive separation at a commercial scale, however, relies on the optimized conditions of an adsorber bed holding maximum and selective isolation of the heavy metals. The experimental route includes a significant trial and error approach, is time-consuming, involves operating cost, and remains economically unattractive. Contrarily, simulation of a mathematical model mimicking the adsorption system along with experimental validation can significantly minimize optimization efforts and suggests the best conditions of separation. In this work, a convective-dispersive model and adsorption model for fixed bed adsorption of copper (Cu), chromium (Cr), and cadmium (Cd) metals over wheat bran biosorbent are simulated using the gPROMS tool for benchmarking. The influence of feed flow rate, bed height, and metal concentration is studied, and breakthrough profiles of all heavy metals are predicted and matched with the literature. The error values (R2 and RMSE) and Chi-squared values determined from gPROMS simulations matched well with the previously available MATLAB-simulated data. After a successful benchmarking, we modeled pilot-scale adsorption of Cr on coconut coir (or Biosorbent) in a gPROMS simulation environment. A detailed method and algorithm of gPROMS simulation for Cr isolation is provided. The influence of feed flow rate, bed height, and initial metal concentration is studied on the breakthrough curves of the Cr. The optimum operating condition for the pilot-scale isolation of Cr from the water is suggested. The parameters, such as the axial dispersion coefficient and distribution coefficient, are determined.

Adsorptive removal of Pb(II) using nanostructured γ-alumina in a packed bed adsorber: Simulation using gPROMS.

In this work, convective-dispersive and pore volume and surface diffusion models have been used to analyze Pb(II) adsorption from an aqueous solution over a nanostructured γ-alumina adsorbent in a packed bed adsorber. The models encompassing partial differential equation and a linear algebraic equation coupled with isotherm have been simulated in gPROMS using the backward finite difference approach. The predicted breakthrough curves of Pb(II) adsorption concerning flow rate, initial metal concentration, and bed height were matched with the experimental data. The accuracy of model predictions was analyzed through statistical measures such as coefficient of determination (R2), root mean square error, and chi-squared value. The simulation results also predicted the axial dispersion, distribution coefficient, mass transfer coefficient, pore volume, and surface diffusion coefficient, which are, otherwise, difficult to measure experimentally and, in turn, have been used to assess the mass transfer characteristics of continuous Pb(II) adsorption. Additionally, the values of breakthrough time, exhaustion time, adsorption column capacity, and mass transfer zone were determined as a function of flow rate, bed height, and initial metal concentration. Surface and pore volume diffusions (10-11-10-10 m2/s) apparently controlled the continuous adsorption process, with surface diffusion being dominant. The transport parameters evaluated in the current study could be beneficial for the large-scale Pb(II)/nanostructured γ-alumina adsorption system. As evident from the successful simulation, the developed gPROMS program can also be applied to other adsorbate/adsorbent systems with a slight modification concerning the operating parameters.

Functional pleiotropy of calcium binding protein Regucalcin in signaling and diseases.

Regucalcin (Mr âˆ¼ 33.38 kDa) is a calcium binding protein, discovered in rat liver. In humans, gene for regucalcin is located on chromosome-11 (p11.3-q11.2) consisting of seven exons and six introns. The protein differs from other calcium binding protein in the way that it lacks EF-hand motif of calcium binding domain. It is also called as Senescence Marker Protein-30 (SMP-30) as previously its weight assumes to be 30 kDa and expression of this protein decreases with aging in androgen independent manner. Among vertebrates, it is a highly conserved protein showing gene homology in Drosophila, Xenopus, fireflies and others too. It is primarily expressed in liver and kidney in addition to brain, lungs, and skeletal muscles. Regucalcin acts as a Ca2+ regulatory protein and controls various cellular functions in liver and other organs. It suppresses protein phosphatase, protein kinase, DNA and RNA synthesis. Published evidences suggest regucalcin to be a reliable biomarker in various disorders of liver, kidney, brain and ocular. In over expressed state, it subdues apoptosis in cloned rat hepatoma cells and also induces hyperlipidemia and osteoblastogenesis by regulating various factors. Owing to the multi-functionality of regucalcin this review is presented to elaborate its importance in order to understand its involvement in cellular signaling during various pathologies.

Modelling and optimization study to improve the filtration performance of fibrous filter.

Fibrous filter made up of non-woven material was utilized in many industrial applications for increasing the collection efficiency and the quality factor. But there exists a competing effect among the fibre diameter, filtration efficiency, pressure drop, and sometime type of aerosol (liquid or solid) plays a crucial role in the performance of the fibrous filter. To avoid overdesigning of the filter along with better performance, optimum set of parameters are to be decided before the manufacturing process. In the current effort, the desirability approach and along with the "Response Surface Methodology (RSM)" were considered to optimize filtration efficiency and pressure drop simultaneously. In this perspective, the impact of Filtration velocity (v), Basis weight (φ), Particle diameter (dp), and Packing fraction (α) on filtration efficiency (η) and pressure drop (Pd) was studied. Based on the outcome, the predicted values lie within experimental data through smart agreement. The maximum percentage (%) error was only 3% and 6% filtration efficiency (η) and pressure drop (Pd), which determine the effectiveness of this useful model. The most dominant factor which affects the filtration efficiency (η) was found to be the Basis weight (φ), followed by packing fraction. However, in the case of pressure drop, the most dominant factors were filtration speed followed by the pachining fraction. Moreover, artificial neural network (ANN) models are developed for the prediction of filtration efficiency and pressure drop. The model accuracy has been estimated by calculating "Mean Square Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R2)". Both models show promising results when compared with experimental data with the R2 value of 98.50-99.86. The optimized values of the maximum filtration efficiency and minimum pressure drop simultaneously were obtained for v = 5, φ = 59.60, dp = 52.23, α = 0.24 according to desirability approach.

Multi-objective optimization of thermophysical properties of multiwalled carbon nanotubes based nanofluids.

The experimental determination of thermophysical properties of nanofluid (NF) is time-consuming and costly, leading to the use of soft computing methods such as response surface methodology (RSM) and artificial neural network (ANN) to estimate these properties. The present study involves modelling and optimization of thermal conductivity and viscosity of NF, which comprises multi-walled carbon nanotubes (MWCNTs) and thermal oil. The modelling is performed to predict the thermal conductivity and viscosity of NF by using Response Surface Methodology (RSM) and Artificial Neural Network (ANN). Both models were tested and validated, which showed promising results. In addition, a detailed optimization study was conducted to investigate the optimum thermal conductivity and viscosity by varying temperature and NF weight per cent. Four case studies were explored using different objective functions based on NF application in various industries. The first case study aimed to maximize thermal conductivity (0.15985 W/m oC) while minimizing viscosity (0.03501 Pa s) obtained at 57.86 °C and 0.85 NF wt%. The goal of the second case study was to minimize thermal conductivity (0.13949 W/m °C) and viscosity (0.02526 Pa s) obtained at 55.88 °C and 0.15 NF wt%. The third case study targeted maximizing thermal conductivity (0.15797 W/m °C) and viscosity (0.07611 Pa s), and the optimum temperature and NF wt% were 30.64 °C and 0.0.85,' respectively. The last case study explored the minimum thermal conductivity (0.13735) and maximum viscosity (0.05263 Pa s) obtained at 30.64 °C and 0.15 NF wt%.

Optimization of fluoride adsorption from aqueous solution over mesoporous titania-alumina composites using Taguchi method.

The optimization of fluoride removal from aqueous media was studied over the mesoporous titania-alumina composites using Taguchi method-based L25 orthogonal array experimental design. The chemical structure, surface chemistry, and morphology of as-prepared composite adsorbents were studied utilizing various analytical methods. The findings of the characterization demonstrated that the produced composites have high textural qualities, which are conducive to enhanced fluoride adsorption. The optimum conditions for maximum percentage removal of fluoride from aqueous solution were found as adsorbent type as TA75, adsorbent dose 4 g L-1 , initial concentration of fluoride 40 ppm, solution pH 3 with a treatment time of 60 min. Under the optimum conditions, 98% of fluoride adsorption was achieved. Analysis of variance revealed that the solution pH followed by the adsorbent dose was the most significant for fluoride adsorption. The Langmuir model and pseudo-second-order kinetic model fit the adsorption data well, and the TA75 adsorbent had a maximum Langmuir fluoride adsorption capacity of 34.48 mg g-1 at pH = 3. The thermodynamic information suggests that the adsorption was spontaneous and endothermic under the given operating conditions. The synergic combination of Ti-Al nanoparticles demonstrated a high percentage removal of fluoride under the optimized operating conditions. PRACTITIONER POINTS: The Taguchi method-based design of the experimental approach was implemented in the fluoride adsorption process. Mesoporous titania-alumina composites with 0 to 100 wt.% of alumina in titania were prepared and applied to remove fluoride from an aqueous solution. Solution pH was the most influential parameter for the fluoride adsorption process, while the synergistic combination of 75 wt.% alumina in titania showed the maximum adsorption capacity.

Comparative Study of CO2 Capture by Adsorption in Sustainable Date Pits-Derived Porous Activated Carbon and Molecular Sieve.

The rising CO2 concentration has prompted the quest of innovative tools to reduce its effect on the environment. A comparative adsorption study using sustainable low-cost date pits-derived activated carbon and molecular sieve has been carried out for CO2 separation. The adsorb ents were characterized for surface area and morphological properties. The outcomes of flow rate, temperature and initial adsorbate concentration on adsorption performance were examined. The process effectiveness was investigated by breakthrough time, adsorbate loading, efficiency, utilized bed height, mass transfer zone and utilization factor. The immensely steep adsorption response curves demonstrate acceptable utilization of adsorbent capability under breakthrough condition. The adsorbate loading 73.08 mg/g is achieved with an 0.938 column efficiency for developed porous activated carbon at 298 K. The reduced 1.20 cm length of mass transfer zone with enhanced capacity utilization factor equal 0.97 at 298 K with Cin = 5% signifies better adsorption performance for date pits-derived adsorbent. The findings recommend that produced activated carbon is greatly promising to adsorb CO2 in fixed bed column under continuous mode.

CO2 Capture by Low-Cost Date Pits-Based Activated Carbon and Silica Gel.

The rising levels of CO2 in the atmosphere are causing escalating average global temperatures. The capture of CO2 by adsorption has been carried out using silica gel type III and prepared activated carbon. The date pits-based activated carbon was synthesized using a tubular furnace by physical activation. The temperature of the sample was increased at 10 °C/min and the biomass was carbonized under N2 flow maintained continuously for 2 h at 600 °C. The activation was performed with the CO2 flow maintained constantly for 2 h at 600 °C. The temperature, feed flow and adsorbate volume were the parameters considered for CO2 adsorption. The success of CO2 capture was analyzed by CO2 uptake, efficiency based on column capacity, utilization factors and the mass transfer zone. The massively steep profiles of the breakthrough response of the AC demonstrate the satisfactory exploitation of CO2 uptake under the conditions of the breakthrough. The SG contributed to a maximal CO2 uptake of 8.61 mg/g at 298 K and Co = 5% with F = 5 lpm. The enhanced CO2 uptake of 73.1 mg/g was achieved with a column efficiency of 0.94 for the activated carbon produced from date pits at 298 K. The AC demonstrated an improved performance with a decreased mass transfer zone of 1.20 cm with an enhanced utilization factor f = 0.97 at 298 K. This finding suggests that a date pits-based activated carbon is suitable for CO2 separation by adsorption from the feed mixture.

Predictive Modelling and Multi-Objective Optimization of Surface Integrity Parameters in Sustainable Machining Processes of Magnesium Alloy.

Magnesium alloys are widely used in numerous engineering applications owing to their superior structural characteristics. However, the machining of magnesium alloy is challenging because of its poor machinability characteristics. Therefore, this paper investigates the machining of magnesium alloys under different sustainable cooling conditions. The machining was performed by varying cutting velocity, feed rate, and depth of cut under dry and cryogenic cooling conditions. The primary focus of the paper is to develop a predictive model for surface roughness under different machining environments. The models developed were found to be in excellent agreement with experimental results, with only 0.3 to 1.6% error. Multi-objective optimization were also performed so that the best surface finish together with high material removal rate could be achieved. Furthermore, the various parameters of surface integrity (i.e., surface roughness, micro-hardness, micro-structures, crystallite size, and lattice strain) were also investigated.

Investigation of Coatings, Corrosion and Wear Characteristics of Machined Biomaterials through Hydroxyapatite Mixed-EDM Process: A Review.

Together, 316L steel, magnesium-alloy, Ni-Ti, titanium-alloy, and cobalt-alloy are commonly employed biomaterials for biomedical applications due to their excellent mechanical characteristics and resistance to corrosion, even though at times they can be incompatible with the body. This is attributed to their poor biofunction, whereby they tend to release contaminants from their attenuated surfaces. Coating of the surface is therefore required to mitigate the release of contaminants. The coating of biomaterials can be achieved through either physical or chemical deposition techniques. However, a newly developed manufacturing process, known as powder mixed-electro discharge machining (PM-EDM), is enabling these biomaterials to be concurrently machined and coated. Thermoelectrical processes allow the migration and removal of the materials from the machined surface caused by melting and chemical reactions during the machining. Hydroxyapatite powder (HAp), yielding Ca, P, and O, is widely used to form biocompatible coatings. The HAp added-EDM process has been reported to significantly improve the coating properties, corrosion, and wear resistance, and biofunctions of biomaterials. This article extensively explores the current development of bio-coatings and the wear and corrosion characteristics of biomaterials through the HAp mixed-EDM process, including the importance of these for biomaterial performance. This review presents a comparative analysis of machined surface properties using the existing deposition methods and the EDM technique employing HAp. The dominance of the process factors over the performance is discussed thoroughly. This study also discusses challenges and areas for future research.

Investigations on Surface Roughness and Tool Wear Characteristics in Micro-Turning of Ti-6Al-4V Alloy.

Micro-turning is a micro-mechanical cutting method used to produce small diameter cylindrical parts. Since the diameter of the part is usually small, it may be a little difficult to improve the surface quality by a second operation, such as grinding. Therefore, it is important to obtain the good surface finish in micro turning process using the ideal cutting parameters. Here, the multi-objective optimization of micro-turning process parameters such as cutting speed, feed rate and depth of cut were performed by response surface method (RSM). Two important machining indices, such as surface roughness and material removal rate, were simultaneously optimized in the micro-turning of a Ti6Al4V alloy. Further, the scanning electron microscope (SEM) analysis was done on the cutting tools. The overall results depict that the feed rate is the prominent factor that significantly affects the responses in micro-turning operation. Moreover, the SEM results confirmed that abrasion and crater wear mechanism were observed during the micro-turning of a Ti6Al4V alloy.

Phytochemical characterization, and antioxidant and antimicrobial activities of essential oil from leaves of the common sage Salvia officinalis L. from Abha, Saudi Arabia.

Background: The composition and activities of essential oil of common sage from Saudi Arabia have not yet been reported. Objectives: To analyze the composition and antibacterial and antioxidant activities of essential oil from leaves of the common sage Salvia officinalis L. from Abha, Saudi Arabia. Methods: Essential oil was extracted from the leaves of S. officinalis by hydrodistillation, and its composition was analyzed using gas chromatography and mass spectrometry. Phenolics and flavonoids were determined using gallic acid and quercetin standards. Antioxidant activity was determined using a 2,2-diphenyl-1-picrylhydrazyl radical scavenging method. Activity against various gram-positive and gram-negative bacteria was determined by disk diffusion and microdilution. Results: The yield of essential oil was 3.24 ± 0.55% (w/dry weight). Major compounds identified were camphor (20.3%), 1,8-cineole (15.0%), α-thujone (14.9%), viridiflorol (9.9%), carvone (6.2%), and ß-thujone (5.7%). Phenolic content was 134.3 ± 17.61 µg/mL and flavonoid content was 119.5 ± 18.75 µg/mL. Antioxidant IC50 was 970 ± 5.5 µg/mL. The highest gram-positive antibacterial activity was for Bacillus subtilis and the highest gram-negative activity was for Escherichia coli. Minimum inhibitory concentrations ranged from 62.2 ± 3.9 to 1398.1 ± 50.7 µg/mL for gram-positive bacteria and from 323.4 ± 69.5 to 968.4 ± 120.6 µg/mL for gram-negative bacteria. Minimum bactericidal concentrations ranged from 120.3 ± 7.6 to 1387.4 ± 161.8 µg/mL for gram-positive bacteria and from 386 ± 8.3 to 1225.2 ± 100.9 µg/mL for gram-negative bacteria. Conclusions: Essential oil of S. officinalis L. from Abha, Saudi Arabia, showed compositional, antioxidant, and antibacterial properties generally consistent with essential oil of S. officinalis L. from other locations as reported in the literature.

Study on the Optimum Cutting Parameters of an Aluminum Mold for Effective Bonding Strength of a PDMS Microfluidic Device.

Master mold fabricated using micro milling is an easy way to develop the polydimethylsiloxane (PDMS) based microfluidic device. Achieving high-quality micro-milled surface is important for excellent bonding strength between PDMS and glass slide. The aim of our experiment is to study the optimal cutting parameters for micro milling an aluminum mold insert for the production of a fine resolution microstructure with the minimum surface roughness using conventional computer numerical control (CNC) machine systems; we also aim to measure the bonding strength of PDMS with different surface roughnesses. Response surface methodology was employed to optimize the cutting parameters in order to obtain high surface smoothness. The cutting parameters were demonstrated with the following combinations: 20,000 rpm spindle speed, 50 mm/min feed rate, depth of cut 5 µm with tool size 200 µm or less; this gives a fine resolution microstructure with the minimum surface roughness and strong bonding strength between PDMS⁻PDMS and PDMS⁻glass.

Evaluation of the toxic potential of calcium carbide in the third instar larvae of transgenic Drosophila melanogaster (hsp70-lacZ)Bg(9).

In the present study the toxic potential of calcium carbide (CaC2) was studied on the third instar larvae of transgenic Drosophila melanogaster (hsp70-lacZ)Bg(9). The third instar larvae were exposed to 2, 4, 8, 16 and 32×10(-3)g/ml of CaC2 in diet for 24h. The results reveal that the dose 2×10(-3)g/ml was not toxic but the remaining doses showed a dose dependent significant increase in the hsp70 expression, ß-galactosidase activity, tissue damage, oxidative stress markers (lipid peroxidation and protein carbonyl content), glutathione-S-transferase activity, expression of Caspase 3 and 9, apoptotic index and DNA damage (midgut cells). A significant reduction as compared to control group in total protein, glutathione content and acetylcholinesterase activity was also observed. The Inductively Coupled Plasma Atomic Emission Spectroscopy analysis (ICPAES) reveals the presence of copper, iron, sodium, aluminium, manganese, calcium, nickel and mercury. The toxic effects of CaC2 in the present study may be attributed to the impurities present in it.