Optimal Parameters Research Articles

Robust kernel extreme learning machines with weighted mean of vectors and variational mode decomposition for forecasting total dissolved solids

A stable and accurate forecast of water quality parameters is crucial for planning and managing future investment programs. A well-known water quality indicator is the total dissolved solids (TDS), which measures the number of metals, minerals, and salts dissolved in a particular volume of water. This paper introduces an innovative approach, which combines the kernel extreme learning machine (KELM) with the robust weight mean of vectors (INFO) algorithm. The proposed technique stands out for its strategic integration of Boruta-XGBoost (B-XGB) as an exceptional feature selection method, along with variational mode decomposition (VMD) to decompose the input variables and enhance the accuracy of forecasting. The integrated model is termed V-KELM-INFO. INFO is an essential component that incorporates a complex exploration process. It utilizes a weighted mean of vectors, a vector combining operator to enhance population variety, and a localized search operator to expedite convergence. INFO, a potent tool, is included in the V-KELM training stage. It efficiently extracts optimum parameters and dramatically improves the accuracy of monthly TDS predictions at the Idenak station in southwest Iran. Also, the multi-criteria decision-making (MCDM) approach of weighted aggregated sum product assessment (WASPAS) is used to rank models. The V-KELM-INFO model was chosen based on the WASPAS results and statistical metrics (R = 0.962, RMSE = 57.84, WHD = 7.01, and U95 = 160.74) as the best model to forecast the TDS. In addition, the superiority of the proposed model was assessed against the metaheuristic-based V-KELM models, comprising the V-KELM grey wolf optimizer (V-KELM-GWO), V-KELM slime mold algorithm (V-KELM-SMA), and V-KELM equilibrium optimizer (V-KELM-EO). This work provided insight into improving the accuracy of modeling-based methodologies and spurred water quality modeling technology to develop sustainable and clean practices.

Evaluation of Electro-Fenton Process for Removal of Amoxicillin from Simulated Wastewater

<p>The purpose of this work is to examine the efficiency of the Electro-Fenton process (Fenton oxidation) for the elimination of amoxicillin from simulated wastewater through response surface methodology (RSM). The effect three operating parameters that were selected for this evaluation were electrolysis time, hydrogen peroxide concentration, and the current density. The Electro-Fenton method was assessed by a set of experiments that were designed using the Box-Behnken model. The results conclude optimum parameters conditions, at 30 min electrolysis time, 30 ppm H2O2, and current density 2 amps by fixing the other two parameters; 20 ppm concentration of amoxicillin and pH of 3. The maximum removal efficiency at these conditions was 98.11%. The mathematical model concluded has a high correlation coefficient (R2= 98.3). Depending on the results, the Electro-Fenton process has proven an excellent method for the elimination of antibiotics from wastewater.</p>

Response Surface Methodology: An Optimal Design for Maximising the Efficiency of Microwave-Assisted Extraction of Total Phenolic Compounds from Coriandrum sativum Leaves

The optimization of total phenolic compounds (TPC) extraction yield and maximization of total antioxidant capacity (TAC) from coriander leaves were investigated using response surface methodology. The extraction of TPC was carried out using microwave-assisted extraction. A Box-Behnken design was used to study the effects of the three independent variables, solvent concentration (ethanol/water 20–80%), microwave power (100–500 watt) and irradiation time (30–150 s) on the response. A second-order polynomial model was used to predict the reaction. The regression analysis showed that about 99% of the variations could be explained by the models. The predicted values were 50.97 GAE/g dw and 5.75 mg GAE/g dw for TPC and TAC. The reaction surface analysis showed that the optimum extraction parameters that maximized the extraction of antioxidants yield were 52.62% ethanol, 452.12 watt and 150 s. Under optimal conditions, the experimental values for TPC and TAC were 49.63 ± 0.93 mg GAE/g dw and 5.55 ± 0.07 mg GAE/g dw, respectively. The experimental values are in agreement with the predicted values, indicating the suitability of the model used and the success of the response surface methodology in optimizing the extraction conditions.

Optimization of stepped hole drilling for consistent aperture in BCF/PEEK and PEEK stacks

To ensure the aperture consistency of the stepped hole in the drilling of braided carbon fiber reinforced polyether ether ketone (BCF/PEEK) and PEEK stacks, a step variation parameter method is proposed to obtain the optimum process parameters in this study. Initially, the lay-up sequence is determined by analyzing diameter differences in drilled sequences of BCF/PEEK and PEEK stacks. Concurrently, hole-wall surface morphology and damage are observed under traditional drilling conditions to define stepped hole phenomena. Subsequently, optimum step position is identified by analyzing diameter differences at various positions with variable parameters. Moreover, the influence weight of drilling parameters on diameter difference is assessed using Taguchi orthogonal array. An optimization process scheme is proposed for minimum diameter difference control. Results suggest spindle speed of 2000 r/min and feed rate of 40 mm/min for BCF/PEEK, and 5000 r/min and 30 mm/min for PEEK are optimum parameters. This method holds promise for achieving consistent apertures in other laminated composites during drilling.

A Method Based on Artificial Intelligence That Predicts Arabica Coffee Yield by Analyzing Abiotic Factors and the Prevalence of Coffee Leaf Rust

Coffee is a perennial crop that harbors infections throughout the plant, which may worsen illness under favorable circumstances. Coffee leaf rust, a widespread coffee disease and Arabica is susceptible to leaf rust. Disease incidence and severity depend on abiotic variables. Cloudy and constant South-West monsoon weather (June–September) promotes coffee leaf rust growth. In this five-model study, an Extra Tree and Gradient Boosting regression model predicted coffee crop output in Chikamagaluru, Karnataka, with the least error utilizing biotic and abiotic factors. We investigated additional tree, gradient boosting, RF, Decision Tree, and KNN models using biotic and abiotic predictors. Used the independent testing dataset's MSE, MAE, RMSE Root mean square errors, and R-squared errors to compare model performance. The extra tree (R²=0.98 kg/ha ˉ¹ and RMSE = 7.96 kg/ha ˉ¹) and gradient boosting (R²=0.96 kg/ha ˉ¹ and RMSE = 10.96 kg/ha ˉ¹) regression models used Group 1 and 2 characteristics as predictor variables and different parameter fine tuning functions to estimate coffee yield most accurately. Compared to the less precise probabilistic models utilized in this work, such as Random Forest, decision tree, and KNN models, shown in the results section. The optimum weather parameter for coffee production forecasts and biotic-CLR incidence data outperformed random forest, decision tree, and K-Nearest Neighbor models.

Production and Characterization of Activated Carbon Derived from Costus Afer Leaves (C. afer) for the Adsorption of Methylene Blue Dye from an Aqueous Solution

In this work, Costus afer leaves (C. after) was utilized as an agricultural waste material for the synthesis of activated carbon (AC) used for the adsorption of methylene blue dye from its aqueous solution. The raw material was prepared, chemically activated using KHCO3 with an impregnation ratio of 1:3, and later carbonized at a temperature of 700℃ in an inert N2 atmosphere for 1hour to produce activated carbon. The proximate analysis of the biomass revealed the percentage of ash content, moisture content, volatile matter, and fixed carbon present in the waste biomass. The raw and activated carbon produced were characterized using various tests such as: Scanning Electron Microscopy (SEM), Energy Dispersive X-Ray (EDX), Fourier Transform Infrared-(FTIR) Spectrometry, X-Ray Fluorescence (XRF), and Brunauer-Emmett-Teller (BET). The results revealed that the SBET of the biomass increased from 400m2/g to 600m2/g after activation. Adsorption studies were carried out in batch mode to determine the influencing factors of adsorption with varying adsorbent dosage, contact time, and pH values. The adsorption experiment revealed high adsorption efficiency at an optimum parameter of dosage (0.2g), contact time (10 min), and pH (8) with a 70.58% removal of methylene blue dye. An increase in dosage, contact time, and pH above the optimum parameter leads to a decrease in percentage removal. The adsorption capacity was achieved using adsorption isotherm models like Langmuir and Freundlich. Adsorption of methylene blue validated the Langmuir model with an R2 of 0.999 and a Qm of 11.83 mg/g. These results show that activated carbon prepared from Costus afer leaves constitutes an effective low-cost material for the adsorption of Methylene dye from wastewater.

Effects of the processing parameters and laser textures in friction stir spot welding of Al alloy and polyimide joints

In this paper, friction stir spot welding (FSSW) technology was introduced to join 7075 Al alloy to polyimide (PI) sheets. The effects of the FSSW parameters and laser textures on the microstructure and tensile shear load of the Al/PI joint were studied. The optimum FSSW parameters were the rotation speed of 2400 r/min, the plunge depth of 0.4 mm, the dwell time of 20 s, and the plunge speed of 10 mm/min. The tensile shear load of the joint increased owing to the laser textures in the Al alloy surface. Cross-sections showed that the mechanical locking was found between the Al alloy and PI. The XPS results revealed that no chemical bond were formed at the welding interface after laser textures. The joining mechanism of Al 7075 and PI was main the mechanical locking.

Influence of the Planning Parameters of a New Algorithm on the Dosimetric Quality, Beam-on Time and Delivery Accuracy of Tomotherapy Plans

Background: This work aimed to determine the optimum VOLOTM Ultra algorithm parameters for tomotherapy treatments. Methods: 1056 treatment plans were generated with VOLOTM Ultra for 36 patients and six anatomical locations. The impact of varying four parameters was studied: the accelerated treatment (AT), leaf open/close time (LOT) cutoff, normal tissue objective (NTO) weight, and number of iterations. The beam-on time and dosimetric metrics were quantified for the target volumes and organs at risk (OARs). Delivery quality assurance measurements were obtained for 36 plans to assess the delivery accuracy. Results: The mean beam-on time for the helical tomotherapy and TomoDirect (TD) plans decreased by 26.6 ± 2.8% and 17.4 ± 4.3%, respectively, when the accelerated treatment parameter was increased from 0 to 10, at the expense of the planning target volume (PTV) coverage (2% lower D98%) and OAR dose (up to 15% increase). For TD plans, it seems preferable to systematically use an AT value of 10. Increasing the number of iterations beyond six seems unnecessary. In this study, an NTO weight of approximately 10 appears to be ideal and eliminates the need to use rings in the treatment plan. Finally, no correlation was found between the leaf open/close time cutoff and the delivery accuracy, while a leaf open/close cutoff of 60 ms seemed to degrade dosimetry quality. Conclusion: Optimal values for the AT, LOT cutoff, NTO weight, and number of optimization rounds were identified and should help improve the management of patients whose tomotherapy treatments are planned with VOLOTM Ultra.

A comprehensive analytical and computational assessment of soil water characteristics curves in Atlantic Canada: Application of a novel SelectKbestbased GEP model

For sustainable agriculture, irrigation is one of the most important management practices in a semi-arid climate. Determining the site-specific soil type-based irrigation water requirements requires the development of a soil water characteristics curve (SWCC). This study focuses on exploring the suitability of analytical SWCC models based on the observed data of SWCC developed using the pressure membrane apparatus. A novel SelectKbest gene expression programming (GEP) model is used to create the generalized machine learning SWCC model for Atlantic Canadian soils. The composite soil samples with representative soil textures of loamy sand, loam, and sandy loam are collected from the selected locations in Prince Edward Island and New Brunswick provinces of Canada. The suitability of models at three regions shows that at all sites, including PEI-1 (loam soil) 1, PEI-2 (loamy sand), and NB-3 (sandy loam) if SWCC data has four measured suction potential levels, then McBee & Bomb and Tani exponential models may be selected. Van Genuchten, double exponential, Ruso, Kosuki, and Gardner's four parameters models should be a good choice for five-section potential. Similarly, for six measured suction potential values, the Fredlund & Xing, Omuto, and Van Genuchten models having parameters should be selected for determining the optimum soil hydraulic parameters. The HydroMe program with Brooks and Corey, four, and five parameters Van Genuchten models performed well. The HydroMe program is calibrated and validated with standard RETC for selected regions of Atlantic Canada, which suggests that the HydroMe program is flexible in developing efficient agricultural water management algorithms. Various performance metrics like R2 and RMSE are employed to assess the robustness of the model. A generalized SWCC model (R2=0.976 and RMSE=0.04) is developed using a novel SelectKbest-based GEP modeling approach for Atlantic Canadian soils. These novel approaches can significantly improve site-specific agricultural water management and decision support systems.

Determination of parameters generating the reference pressure in the primary level dynamic pressure measurement system based on the drop weight method

For some industries such as automotive, defence, aerospace, pharmaceutical manufacturing, dynamic pressure measurement is an important requirement. In a primary level dynamic pressure measurement system with a drop weight method, the dynamic pressure value is calculated using parameters such as the effective area value depending on the piston cylinder unit, the maximum acceleration value measured by a laser interferometer. On the other hand, the type of liquid used in the measuring head is another important factor affecting repeatability and providing ease of measurement. In this study, a new measurement head, piston and cylinders were designed, manufactured and the Taguchi method was used to accurately determine some parameters affecting the measurements in a dynamic primary pressure measurement system operating with the drop weight method. In the studies carried out, four pistons, four cylinders, four sampling frequency values and two liquid types were considered. By using the Taguchi method, the optimum parameters of the dynamic pressure measurement system with drop weight method were determined with only sixteen experiments instead of one hundred and twenty-eight.

Optimizing FDM process parameters: predictive insights through taguchi, regression, and neural networks

Fused deposition modelling (FDM) is a popular additive manufacturing process used for rapid prototyping and the production of complex geometries. Despite its popularity, FDM’s susceptibility to variations in numerous process parameters can significantly impact the quality, design, functionality, and mechanical properties of 3D printed parts. This study explores thirteen FDM process parameters and their influence on the mechanical properties of polylactic acid (PLA) polymer, encompassing surface roughness, warpage, tensile and bending strength, elongation at break, deformation, and microhardness. The optimum parameters were identified alongside key contributors by applying the Taguchi method, signal-to-noise ratios, and analysis of variances (ANOVA). Notably, specific FDM parameters significantly affect the surface profile, with layer thickness contributing 32.65% and fan speed contributing 8.59% to the observed variations. Similarly, warping values show notable influence from nozzle temperature (29.53%), wall thickness (16.74%), layer thickness (16.56%), and retraction distance (12.80%). Tensile strength is primarily determined by wall thickness (31.83%), followed by infill percentage (26.73%) and infill pattern (16.18%). Elongation at break predominantly correlates with wall thickness (44.82%), with a supplementary contribution from nozzle temperature (10.90%). Microhardness lacks a dominant parameter. Bending strength variations primarily arise from layer thickness (38%), wall thickness (37.6%), and infill percentage (9.17%). Deformation tendencies are influenced by layer thickness (19.20%), print speed (11.37%), wall thickness, and fan speed (10.9% each). The optimized dataset of FDM process parameters was then employed in two prediction models: multiple-regression and artificial neural network (ANN). Evaluation based on the correlation coefficient (R2) and root mean squared error (RMSE) indicates that the ANN model outperforms the multiple-regression approach. The results indicate that precise control of FDM parameters, coupled with ANN predictions, facilitates the fabrication of 3D printed parts with the desired mechanical characteristics.

Comparative adsorption of cationic and anionic dye by using non-activated Black Plum seed biochar for aquatic phase: Isotherm, kinetic and thermodynamic studies

Industrial dye effluents play a major role in water pollution. Textile and leather industries are one of the important sectors that pollute the water by discharging many harmful cationic and anionic dyes during the dyeing process. This study aims to compare the dye removal efficiency of Black Plum seed biochar for the cationic and anionic dyes. The non-activated biochar was prepared through pyrolysis of Black Plum seed powder at 5700 C. The sample's surface chemistry was understood using various characterization techniques like Field Emission Scanning Electron Microscopy (FESEM), Energy-dispersive X-ray spectroscopy (EDS), Brunauer-Emmett-Teller (BET), X-ray diffraction (XRD), Raman-Spectroscopy and Zeta-potential. The experiments were successfully performed by conducting a batch adsorption study at different parameters such as pH, concentration, contact time and temperature. The coefficients of different isotherms and kinetics models were evaluated for non-linear study and the thermodynamic model was also applied. The maximum adsorption capacity was obtained as 42.39 mg/g for Crystal Violet (CV) dye and 6.22 mg/g for Eosin Blue (EB) dye by using the Langmuir isotherm model. Hence, the Black Plum seed biochar (BPSB) has superior adsorption capacity for the cationic dye CV ( removal - 98%) as compared to the anionic dye EB ( removal - 65%) at optimum parameters. Therefore, it is concluded that the BPSB is a natural toxin-free biosorbent and it can be used for the remediation of dyes from industrial wastewater.

Optimal design of chevron braced friction damper for mainshock–aftershock vulnerability control

This study proposes the optimum design of chevron braced friction dampers (CBFDs) for the vulnerability control of steel moment–resisting frames (SMRFs) under mainshock-aftershock (MS-AS) sequences. First, the optimum parameters of the CBFD systems installed in the stories of an inelastic SMRF are found through minimizing the maximum damage index of stories averaged over seven scaled earthquake excitations. The uniform distribution of the story damage along the height of the controlled SMRF is adopted as constraint in the optimization procedure. The damage index is calculated based on the Park-Ang damage model which is expressed based on a linear combination of deformation, moment, and absorbed hysteretic energy of structural elements imposed by an earthquake excitation. Results reveal that the optimized CBFDs-equipped SMRF exhibits better distribution of the story damage than that of the uncontrolled SMRF. Finally, the vulnerability assessment of the optimized CBFDs-equipped SMRF under MS and MS-AS ground motions is investigated by the fragility curves. The fragility assessment emphasize that the optimized CBFDs effectively mitigate the seismic vulnerability of the controlled SMRF under MS-AS sequences at different damage states.

Development of trench-shaped microstructure thermal neutron detector using α-Al2O3:C based on optically stimulated luminescence

Optical stimulated luminescence technique is increasingly used in neutron personal dosimetry, due to its capability of fast readout and potential for re-estimation of absorbed dose. The design configuration and performance of the microstructure optically stimulated luminescence neutron detector (MOSLND) using α-Al2O3:C was reported. To improve thermal neutron response, the MOSLND is structured with a matrix of microstructure trenches into the α-Al2O3:C substrate and backfilled with 6LiF. To obtain an optimum trench geometric parameter, several Monte Carlo simulations were performed using Geant4 code. The simulation results show that relatively high energy deposition can be realized for small cell dimensions with deep trenches, and is significantly increased compared with that of simple planar designs. Based on the results of calculation and practical limitation, the device was produced and tested using D2O-modrated 252Cf. The optimized minimal detectable dose (MDD) corresponding to signal integration time of 2 s is 7.2 μSv. The experimental results indicate that MOSLND can be a potential solution for personal neutron dosimetry.

Optimization of Process Parameters for Microwave Drying of Yellow- and Purple-Fleshed Potatoes

AbstractThe main objective of the present work was to study the optimization of microwave drying of potatoes that have different flesh colors. The effects of independent variables of microwave power (300, 450, 600 W), slice thickness (2–4, 6 mm), and steam blanching time (2, 5, 8 min) on the color, total phenolic content (TPC), antioxidant activity, starch ratio, and total monomeric anthocyanin content (TMA) were investigated by using the Response Surface Methodology (RSM). Before drying, potato slices that had different thicknesses were blanched in steam at 90 °C for indicated times. Optimization was applied to improve bioactive compounds, starch ratio, and color. The optimum drying parameters were determined as 300 W, 6 mm, and 8 min for purple-fleshed potatoes, and 450 W, 6 mm, and 2 min for yellow-fleshed potatoes. This study is beneficial to the development of the processing of potatoes in the food industry and provides more insights into the application of microwave drying technology.

Destruction of n-hexane from the air stream by pulsed discharge plasma: Modelling and key process parameters optimization by CCD-RSM

In this study, a Dielectric-Barrier Discharge (DBD) plasma reactor was used for the degradation of n-hexane from an air stream. a Central Composite Design (CCD) based Response Surface Methodology (RSM), (CCD-RSM) was utilized to explore the effects of experimental parameters including discharge voltage, initial concentration, gas flow rate, relative humidity and their interactions on the reactor performance. The proposed optimization model showed a satisfactory correlation between the predicted and actual results. According to the results, discharge voltage was the most significant parameter affecting the removal efficiency, CO2 selectivity and the level of O3 and NOx formation, whereas energy yield was mainly influenced by gas flow rate and initial concentration. At optimized conditions (discharge voltage of 8.5 kV, inlet concentration of 94.3 ppm, relative humidity of 55 % and gas flow rate of 733.16 ml/min with combined desirability of 0.921), removal efficiency of 92.57 %, CO2 selectivity of 58.87 %, energy efficiency of 0.25 g/kWh, O3 formation of 140.88 mg/m3 and NOx formation of 122.46 ppm were obtained. Water vapour significantly increased the removal efficiency and CO2 selectivity as well as reduced O3 and NOx. The results revealed that the CCD-RSM provided the optimum process parameters for the Non-Thermal Plasma (NTP) system.

Trajectory Tracking Algorithm Study of Coal Mine Water Detector Drilling Bar Installation

Mechanical water detection is recognized as the most reliable and safe production technology for coal mines, mainly for the detection of water hazards in pre-mining operations. Intelligent water detectors are currently the main research direction in mechanical water detection, and the automatic installation of drilling bars is the key to achieving intelligent water detection. Improving the connection accuracy in the process of installing drilling bars is an important research topic for the improvement of control links. To improve the connection accuracy of the drilling bars at the time of supplying material, we used the modified Denavit–Hartenberg method to analyze the motion gestures of the supplied material device and the Lagrange equation to establish a dynamic analysis model. We aimed at better control precision by improving the sliding mode control algorithm and at increasing the convergence rate of tracking errors with a sliding controller based on an exponential approximation law and using saturated functions instead of the symbol functions in the reaching law to weaken the vibration in the control process. We then used particle swarm optimization (PSO) to find the optimum combination parameters of the sliding mode controllers and test the performance of the sliding mode controllers before and after PSO with MATLAB/Simulink. The results showed that the optimized controller has a strong resistance to parameter fluctuations, and the system responds quickly, achieves a good performance, and improves the convergence rate of tracking errors.

Improvement of the oxidative stability of edible oils through enzymatic esterification with hydroxytyrosol‐rich extract

AbstractOxidative degradation is a common issue of commercial edible oils that results in rancidity, downgrading the organoleptic characteristics of food matrices. The aim of this study was the enzymatic modification of common vegetable oils (pomace olive and sunflower oil) with polyphenolic extract to prepare functional oils enriched with bioactive phenolipids. Under that scope, an agro‐industrial effluent, olive mill wastewater, was used as source to recover phenolic compounds. The recovery of hydroxytyrosol and tyrosol reached up to 81.2% ± 0.3% and 98.3% ± 0.2%, respectively. Further, immobilized lipase was used to catalyze the one‐step acylation of these phenolic antioxidants into the oils with the aim of enhancing their stability under accelerated oxidation conditions. The optimum parameters of the bioconversion were found to be 0.1% (w/w) immobilized lipase, 24h of incubation at 50°C under orbital shaking reaching high conversion yields for both phenolic compounds (≥94%). The results also highlighted that the extract‐modified oils presented notable thermooxidative stability at 60°C for 28 days compared to the control and synthetic antioxidant‐enriched oils, as demonstrated by several analytical (conjugated dienes, thiobarbituric reactive substances), chromatographic (solid‐phase microextraction gas chromatography/mass spectroscopy), and spectroscopic (fluorescence, attenuated total reflectance) techniques, giving new insights regarding the preservation of edible oils.Practical Application: Htyr has recently gained much attention as an active ingredient of food matrices for industrial applications due to its high antioxidant/antiradical activity. A major drawback of Htyr exploitation is the high market price and its solubility. Meanwhile, several edible oils are prone to autooxidation during transport, stock management, and household storage. The proposed one‐step biocatalytic process aims to enhance the lipophilicity and bioactivity of Htyr in bulk oil systems, making the use of this potent antioxidant compound feasible. This approach shows promise in extending the shelf life of common oils and highlights the advantages of by‐product management. Moreover, these functional oils can be applied for the preparation of sauces, bakery products, food supplements, and so on, as the bioactive ingredients that are present in them can delay the oxidative cascade reactions and, thus, the spoilage of the products, whereas the whole process meets the requirements of circular economy and green chemistry.

Optimization of Hypericum Perforatum Microencapsulation Process by Spray Drying Method

Hypericum perforatum (HP) contains valuable and beneficial bioactive compounds that have been used to treat or prevent several illnesses. Encapsulation technology offers protection of the active compounds and facilitates to expose of the biologically active compounds in a controlled mechanism. Microcapsulation of the hydroalcoholic gum arabic and maltodextrin have hot been used as wall materials in the encapsulation of HP extract. Therefore, the optimum microencapsulation parameters of Hypericum perforatum (HP) hydroalcoholic extract were determined using response surface methodology (RSM) for the evaluation of HP extract. Three levels of three independent variables were screened using the one-way ANOVA. Five responses were monitored, including total phenolic content (TPC), 2,2-Diphenyl-1-picrylhydrazyl (DPPH), carr index (CI), hausner ratio (HR), and solubility. Optimum drying conditions for Hypericum perforatum microcapsules (HPMs) were determined: 180 °C for inlet air temperature, 1.04/1 for ratio of maltodextrin to gum arabic (w/w), and 1.98/1 for coating to core material ratio (w/w). TPC, antioxidant activity, CI, HR, and solubility values were specified as 316.531 (mg/g GAE), 81.912%, 6.074, 1.066, and 35.017%, respectively, under the optimized conditions. The major compounds of Hypericum perforatum (hypericin and pseudohypericin) extract were determined as 4.19 μg/g microcapsule and 15.09 μg/g microcapsule, respectively. Scanning electron microscope (SEM) analysis revealed that the mean particle diameter of the HPMs was 20.36 µm. Based on these results, microencapsulation of HPMs by spray drying is a viable technique which protects the bioactive compounds of HP leaves, facilitating its application in the pharmaceutical, cosmetic, and food industries.Graphical

Parametric optimisation of laser welding of stainless steel 316L

This paper presents numerical and experimental studies focused on the optimisation of laser welding parameters for AISI 316L stainless steel. The focus of the numerical studies was to obtain the mathematical model with the least time complexity and the highest fidelity. Based on the comparison of different mathematical models, a combination of two models, double ellipsoidal and conical models, was found to be optimal for the numerical simulation of the laser welding process. The studies were also complemented by material characterization studies for validation purpose. A pulse duration of 8 milliseconds and a current of 400 amperes with an average power of 380W were found to be the optimum parameters for laser welding of standard gauge 12 sheet of stainless steel AISI 316L. In addition, the effect of duty factor of the pulsed laser beam on the weld profile was also investigated and was found to be a major contributor to the optimisation process. The properties of the sample welded with the optimised set of parameters were also compared with the base metal, and based on the mechanical characterisation studies, it was found that the yield strength and hardness of the welded sample were improved, but the overall ductility was slightly reduced as compared to the base metal. The average weld zone size was also reduced by increasing the power density due to multiple reflections of the beam.