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Optimization of Biodiesel Production from Waste Cooking Oil by Box Behnken Design Using Response Surface Methodology
Interest in Biodiesel production has grown over the years due to concerns related to the environment, and the solutions include deriving energy from waste as the replacement for diesel, a petroleum-derived fuel. Biodiesel has been accepted as a "green fuel" as it is a renewable, non-toxic, safe and biodegradable energy material. The utilisation of waste cooking oil (WCO) by converting it into biodiesel is one of the promising alternatives to diesel. An attempt to optimise the biodiesel production from WCO (a waste material) has been made via this study. The process adopted was Trans-esterification of pretreated WCO, and the optimization of biodiesel production was carried out by Box-Behnken method using a response surface methodology. The variations between the analytical and experimental results were within acceptable limits. The response surface methodology resulted in an optimum yield of 96.88% (analytical), which was validated through an experiment within an acceptable error of 0.58%. 2021,International Journal Of Renewable Energy Research.All rights reserved. -
Optimization of cutting parameters and prediction of surface roughness during hard turning of H13 steel with minimal vegetable oil based cutting fluid application using response surface methodology
The manufacturing industries in modern era are competing to reduce cost of production by employing innovative techniques, one being hard turning. In hard turning process, the work piece is heat treated to the required hardness in the initial stage itself and near net shape is arrived directly by hard turning process. Hard turning reduces manufacturing lead time by excluding the normal cost incurring processes such as, turning, heat treatment, finish grinding etc. In this experimental investigation hard turning process is assisted with minimal cutting fluid application technique, which reduces cutting fluid usage to a minimum of 6-8 ml/min. Soya bean oil based emulsion was used to make the hard turning environment friendly. The oil was prepared by adding additives, which will enhance the desirable properties of the oil for hard turning. Response surface methodology was used for optimization of cutting parameters and for the prediction of surface roughness. A central composite design was implemented to estimate the second-degree polynomial model. The cutting parameters considered for experimentation were cutting speed, feed rate and depth of cut. The surface roughness was considered parameter for prediction. Surface roughness predicted by the response Surface Methodology matched well with the experimental results. Published under licence by IOP Publishing Ltd. -
Optimization of Flexible Manufacturing Production Line System Based on Digital Twin
This research presents a revolutionary Digital Twin (DT)driven method aimed at quick customization of computerized industrial processes. The DT includes dual components, the semi-physical replication that transfers system information and gives data input to the subsequent clause, which is enhanced. The outcomes of the optimum section are returned directly to the semi-physical replication used for validation. The term Open-Architecture Machine Tool (OAMT) led to a fundamental class of machine tools that consists of a basic unified platform and many individually designed modules that may be quickly added or replaced away. Designers can dynamically modify the production system for responding to process planning by inserting personalized components into its OAMTs. Major enabling approaches, along with how to identical virtual and substantial systems and how to instantly bi-level program the invention size and efficiency of developed structures to accommodate sudden variations of goods, are explained. A real execution is done to demonstrate the efficacy of the method to achieve increased enactment of the system by minimizing the overhead cost of the recompose method by systematizing and quickly enhancing it. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
Optimization of Friction Stir Welding of AlCu Butt Joint Using Taguchi Method
In this work, the 5mm thickness of base metals AA6101 and C11000 was welded using a hardened OHNS steel tool by FSW mechanism. The Taguchi method involves the optimization of welding mechanism variables tool rotation speed (rpm), feed rate (mm/min), and tool offset (mm) to gain extremely rigid joints. The ANOVA reveals the percentage contribution of the three welding mechanism variables can be examined. From the Taguchi design of optimization technique, at 1000rpm, 40mm/min, andtool offset towards softer metal will possess maximum impact load. The tools rotating speed produced the greatest contribution to the impact load. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Optimization of friction stir welding parameters during joining of AA3103 and AA7075 aluminium alloys using Taguchi method
This paper investigates the optimization of input parameters for the friction stir welding of AA3103 and AA7075 aluminium alloys. The properties of base materials AA3103 are non-heat-treatable alloy, which is having good weldability while AA 7075 is having higher strength. Therefore, the welding of these aluminium alloys will produce superior mechanical properties. Friction stir welding is a rapidly growing welding process which is being widely used in marine, automobile and aerospace industries. Rather than its widespread use, this type of welding has several advantages over normal welding processes like low production of fume, no consumable electrodes are used and can be used in any position. In this paper, optimization of input parameters were conducted based on Taguchi method using the L9 orthogonal array. There were nine experimental runs in total after creating the L9 orthogonal array table in MINITAB software. The input parameters selected for optimization are tool rotation speed, feed rate, tool pin profile the output parameters which are optimized hardness, tensile strength, impact strength. The ANOVA analysis was carried out in the Qualitek 4 software to find out the percentage influence of input parameters on the output parameters. This research work was carried out to find the optimized condition to carry out friction stir welding of above mentioned aluminium alloys. 2021 Elsevier Ltd. All rights reserved. -
Optimization of Friction Stir Welding Parameters for the Optimum Hardness of AlCu Butt Joints Using the Taguchi Method
In the present study, the base plates made of alloys AA6101 and C11000 (each 5 mm thick) were welded bythe FSW technique using a hardened OHNS steel weld tool. The percentage contribution of the input process parameters, such as tool rotational speed in rpm, feed rate in mm/min, and tool pin offset in mm, on the output parameter joint hardness, were examined using the experimental design Taguchi L9 and ANOVA numerical tool analysis. From the optimization method, at 1000rpm tool rotational speed, 40mm/min feed rate and weld tool pin toward AA6101 alloy side will have the highest hardness. The tool rotational speed experiences a maximum significant impact on the joint hardness. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Optimization of Friction Stir Welding Parameters Using Taguchi Method for Aerospace Applications
The current research work investigated the optimization of the input parameters for the friction stir welding of AA3103 and AA7075 aluminum alloys for its applications in aerospace components. Friction stir welding is rapidly growing welding process which is being widely used in aerospace industries due to the added advantage of strong strengths without any residual stresses and minimal weld defects, in addition to its flexibility with respect to the position and direction of welding. Thus, the demand for this type of welding is very high; however, the welding of aluminum alloys is a key aspect for its use in aircraft components, particularly with respect to bracket mounting frames, braces and wing components. Henceforth in the current work, research is focused on optimization of welding of aluminum alloys, viz. AA 3103 and AA 7075; AA 3103 is a non-heat treatable alloy which is having good weldability, while AA 7075 is having higher strength. Therefore, the welding of these aluminum alloys will produce superior mechanical properties. The optimization of input parameters was accomplished in this work based on L9 orthogonal array designed in accordance with Taguchi methodusing which the friction stir welding experiment was conducted. There were nine experimental runs in total after formulating the L9 orthogonal array table in Minitab software. The input parameters which were selected for optimization weretool rotation speed, feed rate, tool pin profile. The output parameters which were optimized were hardness, tensile strength and impact strength. In addition, the microstructure of the fractured surfaces of the friction stir welded joint was analyzed. It was found from the optimization of the process parameters that strong friction stir welded joints for aerospace applications can be produced at an optimized set of parameters of tool rotational speed of 1100rpm, traverse speed of 15mm/min with a FSW tool of triangular pin profile of H13 tool steel material. 2020, Springer Nature Singapore Pte Ltd. -
Optimization of graded catalyst layer to enhance uniformity of current density and performance of high temperature-polymer electrolyte membrane fuel cell
The optimal use of catalyst materials is essential to improve the performance, durability and reduce the overall cost of the fuel cell. The present study is related to spatial distributions of current and overpotential for various graded catalyst structures in a high temperature-polymer electrolyte membrane fuel cell (HT-PEMFC). The effect of catalyst gradient across the catalytic layer (CL) thickness and along the channel and their combination on cell performance and catalyst utilization is investigated. The graded catalytic structure comprises two, three, or multiple layers of catalyst distribution. For a total cathode catalyst loading of 0.35 mg/cm2, higher loading near the membrane presents improved cell performance and catalyst utilization due to reduced limitations caused by oxygen and ion diffusions. However, non-uniformity in the current distribution is significantly increased. The increase in the catalyst loading along the reactant flow provides a substantially uniform current density but lower cell performance. The synergy of varying catalytic profiles across the CL thickness and along the cathode flow direction is investigated. The results emphasize the importance of a rational design of cathode structure and mathematical functions as a strategic tool for functional grading of a CL towards improved uniform current distribution and catalyst utilization. 2021 Hydrogen Energy Publications LLC -
Optimization of heat transfer in the thermal Marangoni convective flow of a hybrid nanomaterial with sensitivity analysis
The heat transfer rate of the thermal Marangoni convective flow of a hybrid nanomaterial is optimized by using the response surface methodology (RSM). The thermal phenomenon is modeled in the presence of a variable inclined magnetic field, thermal radiation, and an exponential heat source. Experimentally estimated values of the thermal conductivity and viscosity of the hybrid nanomaterial are utilized in the calculation. The governing intricate nonlinear problem is treated numerically, and a parametric analysis is carried out by using graphical visualizations. A finite difference-based numerical scheme is utilized in conjunction with the 4-stage Lobatto IIIa formula to solve the nonlinear governing problem. The interactive effects of the pertinent parameters on the heat transfer rate are presented by plotting the response surfaces and the contours obtained from the RSM. The mono and hybrid nanomaterial flow fields are compared. The hybrid nanomaterial possesses enhanced thermal fields for nanoparticle volume fractions less than 2%. The irregular heat source and the thermal radiation enhance the temperature profiles. The high level of the thermal radiation and the low levels of the exponential heat source and the angle of inclination (of the magnetic field) lead to the optimized heat transfer rate (Nux = 7.462 75). 2021, Shanghai University. -
Optimization of multiple responses using overlaid contour plot and steepest methods analysis on hydroxyapatite coated magnesium via cold spray deposition
In this work, sequential optimization strategy based statistical design was employed to enhance the mechanical properties of hydroxyapatite coatings onto a pure magnesium substrate using a cold spray technique. A fractional factorial design (24-1) was applied to elucidate the process parameters that significantly affected the mechanical properties of the coating samples. Standoff distance, surface roughness, and substrate heating temperature were identified as important process parameters affecting thickness, nanohardness, and the elastic modulus of the coating sample. The overlaid method analysis was employed to determine tradeoff optimal values from multiple regressive equations. Then, finally, steepest method analysis was used to reconfirm and relocate the optimal domain from which the factor levels for maximum mechanical properties of the coating were determined at 49.77mm standoff distance, 926.4grit surface roughness, and 456C substrate heating temperature, which can accommodate the optimum requirements for the cold spray process with a coating of 49.77?m thickness, 462.61MPa nanohardness, and 45.69GPa elastic modulus. Scanning electron microscopy revealed that a short standoff distance, high surface roughness, and high substrate temperatures improved the bond between the coated layers and substrates. 2015 Elsevier B.V. -
Optimization of rGO-MoO3 nanocomposite electrode to fabricate an aqueous symmetric supercapacitor device with enhanced electrochemical performance
This study presents the first investigation into the effect of reduced graphene oxide (rGO) on the electrochemical properties of rGO-MoO? nanocomposites, synthesized via the hydrothermal method. The nanocomposites were prepared with varying rGO concentrations, and their structural, morphological, elemental, electrical, optical, and surface characteristics were analyzed. Structural analysis confirmed the presence of an orthorhombic MoO? phase, while the morphological analysis revealed MoO? nanobars anchored onto rGO nanosheets. The electrochemical performance of the nanocomposites was evaluated using a three-electrode configuration. The electrode demonstrating superior performance was selected to fabricate a prototype symmetric device. This device exhibited a specific capacitance of 369 F g?1 at a current density of 1 A g?1 and an energy density of 51 W h kg?1. Moreover, the device demonstrated a stability of 91% over 1000 cycles with a coulombic efficiency of 104%. 2025 Hydrogen Energy Publications LLC -
Optimization of sustainable portfolios considering behavioral biases: ESG risk management
As the role of sustainability is gaining importance among investors, they are more focused on adopting ESG principles into their portfolios. Despite this, bringing a balance between financial returns and sustainability objectives is frequently challenged by the behavioral biases affecting investor's decision- making. Biases like herd behavior, overconfidence, and loss aversion disrupt the investor's investment decisions, weakening the effectiveness of ESG strategies and negatively affecting portfolio returns. Therefore, it is essential to embed behavioral finance concepts into optimizing sustainable portfolios. This research explores the optimization of sustainable portfolios by addressing behavioral biases and the application of effective risk management techniques. The research identifies the significant cognitive biases that shape investor's behavior in the context of ESG investing. The research then investigates how these biases influence financial returns and ESG goals. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Optimization procedure for multilayer heat transfer in nanoliquid with Joule heating using response surface methodology
In this chapter, magnetohydrodynamic flow (MHD) and heat transfer in a multilayer vertical channel are studied with one phase containing pure water and the other phase containing oil-based Cu nanofluid. The effects of viscous dissipation and Joule heating are included in the energy equation. The modeled equations are coupled and nonlinear; they are solved using the regular perturbation method (RPM) and the differential transform method (DTM). The analysis examines the impact of the Hartmann number, thermal Grashof number, nanoparticle volume fraction (NVF), and Brinkman number on the Nusselt number, velocity, and temperature distributions. Furthermore, an optimization of the Nusselt number is performed for three different levels of the Hartmann number (5?M?6), the Brinkman number (0.1?Br?0.3), and the NVF (1%???3%) using the Response Surface Methodology (RSM). The Hartmann number and NVF were found to suppress flow, while the thermal Grashof number and the Brinkman number increase the flow field. Sensitivity computations reveal that the Nusselt number on the left wall is more sensitive to the Hartmann number and the NVF, while the Nusselt number of the right wall is more sensitive to the Brinkman number and NVF. 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Optimization-Based Cash Management Model for Microfinance Applications Using GSA and PSO
Banks and businesses use cash as a means for exchange in finance on a regular basis to please customers. Making decisions about cash management can be challenging because banks must keep significant sums of cash in order to sustain high levels of client satisfaction. In this paper, linear PSO and GSA models are given for estimating the daily cash demand of a bank by taking into account the variables Year of Reference (RY), Years Month (My), Months Day (Dm), Days Week (Dw), Payday Effect Salary (Se), and Holiday Effect (He). Using PSO and GSA in MATLAB, the algorithms for estimating both the model coefficients for short term are implemented from the real data of a specific bank branch. The proposed system's overall cost is minimized using a fitness function. It was discovered that the results are in good accord with the observed data and that the PSO-based cash management model outperformed other models with superior accuracy. The models are then used for future cash management for validation. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Optimized deep maxout for breast cancer detection: consideration of pre-treatment and in-treatment aspect
Breast cancer is one of the deadliest diseases, accounting for the second-highest rate of cancer mortality among females. Breast tissue begins to develop cancerous, malignant lumps as the disease progresses. Self-examinations and routine clinical checks aid in early diagnosis, which considerably increases the likelihood of survival. Because of this, we have created a revolutionary method for finding breast cancer that has the following four steps. Fuzzy filters are used in the initial pre-processing stage to reduce noise and improve outcomes from the incoming data. In the second stage, we have presented an Improved Hierarchical DBSCAN (Density-based clustering algorithm) for the segmentation of anomalous areas. Feature extraction will be carried out following segmentation. We have also developed a better kurtosis-based feature to complement traditional statistical and shape-based features and deliver better results. The Optimized Deep Maxout Neural Network is used for classification in the final step, with the suggested Shark Smell Indulged Shuffled Shepherd Optimization used to optimize the weight parameter (SSISSO). At 90% the learning percentage of the proposed model SSISSO model has achieved 0.984391 accuracy, which is superior to 22.54%, 28.46%, 17.44%, 17%, 15.04%, 13.28%, 29.45%, 28.59%, 21.58%, and 30.72% as compared to other methods like SVM-BS1, CNN-BS7, LSTM, NN, Bi-GRU, RNN, ARCHO, AOA, HGS, CMBO, SSOA, and SSO. Finally, the results of the proposed breast cancer detection technique are compared with conventional techniques. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
Optimized Fake News Detection in Social Networks Using Boosting Algorithms andMachine Learning Classifiers
Rising incidence of fake news on social media has turned verifying information into an imperative issue; hence, fact-checking information is becoming an important task. The traditional machine learning-based models like Logistic Regression, Nae Bayes, Support Vector Machines, and Random Forest suffer from the high-dimensional textual data, and the model may not yield optimal results in fake news detection classification. This paper suggests a better detection framework incorporating Gradient Boosting, CatBoost, and AdaBoost, along with Multinomial Nae Bayes for comparative study. This research uses TF-IDF vectorization and advanced text preprocessing, such as stopword removal, tokenization, and feature engineering,are done for better classification accuracy. The research was carried out on public dataset, including the Fake Job Posting dataset of Kaggle, to ensure model flexibility. The findings show remarkable performance enhancement with CatBoost posting the best accuracy of 98.23% and an ROC-AUC score of 0.9739, surpassing traditional models. A statistical significance test (t-test) validates the improvements as significant. Results have shown that ensemble-based approaches perform well in handling imbalanced and high-dimensional text data, and they should be generalizable to real-world fake news detection tasks. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Optimized Feature Selection for Kidney Ultrasound Image Classification Using Binary Coati Weighted Mean Vector Algorithm
The analysis of medical images presents many challenges, especially when making precise diagnoses. In pediatric Chronic Kidney Disease (CKD), early identification is critical because of its gradual progression to significant kidney failure. This study proposes a diagnostic framework for pediatric ultrasound image classification that incorporated machine learning and advanced feature selection methods. This approach is divided into four stages: Preprocessing, feature extraction, feature selection, and classification. Initially, pediatric kidney ultrasound images are enhanced using gaussian median filter. Radiomics features were then extracted, including Gray Level Co-Occurrence Matrix (GLCM), Gray Level Size Zone Matrix (GLSZM), Gray Level Run Length Matrix (GLRLM), Neighboring Gray Tone Difference Matrix (NGTDM), Gray Level Dependence Matrix (GLDM), and first-order statistics. To optimize this feature space, we introduce the Binary Coati Weighted Mean Vector (BinCoWmv) optimization algorithm, which uses a customized fitness function. Herein, the selected features were evaluated using different classifiers: Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Nae Bayes (NB), K-Nearest Neighbor (KNN), and XG-Boost. Comparative evaluations with existing optimizers, such as the Coati Optimization Algorithm (COA), weighted average vector (INFO), Firefly Algorithm (FFA), and Harris Hawk Optimization (HHO), showed that BinCoWmv achieved a higher classification accuracy. Our framework improves diagnostic reliability and assists radiologist and nephrologist in the early detection of chronic kidney disease in children. 2025 Fizhan Kausar and Ramamurthy B. -
Optimized Fuzzy SVM with Chaotic Henry Gas Solubility Algorithm for Fault Identification in Rotating Machinery
Reliable and accurate fault diagnosis in rotating machinery is vital for minimizing unplanned downtime, reducing maintenance costs, and ensuring operational safety in industrial environments. Traditional diagnostic approaches depend heavily on manual feature extraction from vibration signals, which can be time-consuming, expertise-dependent, and prone to missing subtle fault patterns. This study presents a novel hybrid frameworkIDL-OFSVMthat combines Intelligent Deep Learning (IDL) with an Optimized Fuzzy Support Vector Machine (OFSVM) for automated fault classification. Vibration signals are first transformed using the Continuous Wavelet Transform (CWT), and deep features are extracted via the lightweight MobileNet architecture. The Chaotic Henry Gas Solubility Optimization (CHGSO) algorithm significantly enhances the classification model's performance, which effectively tunes the FSVM parameters. Experimental evaluations on benchmark datasets show that the proposed method achieves 99.8% training and 99.7% testing accuracy, outperforming several state-of-the-art approaches. Beyond technical accuracy, the framework offers practical advantages, including reduced dependency on domain expertise, suitability for real-time monitoring, and potential integration into predictive maintenance systems. These benefits make the IDL-OFSVM model a promising solution for industrial fault diagnosis applications, where reliability, speed, and scalability are crucial. 2025 by the Dr. Mohan S B, Dr. Prajith Prabhakar, Dr. Yokesh V, M Bharathi, Dr. Gayathry S Warrier, and Dr Mahalakshmi J. -
Optimized gateway oriented unicast and multicast routing for multi hop communication network
Networking and communication in an infrastructure less environment had brought interest in the development of mobile ad hoc networks (MANET). Growth of technologies in years has increased the size of the network and its applications. In larger MANET, it becomes important to maintain the quality of performance for increased overhead scenarios. Grouping of network optimizes the workload with reduced overhead for routing and maintenance in larger MANET. Mobile nodes are grouped by the use of clustering algorithms. Once the MANET environment is formed, the network needs appropriate architecture and methods to have efficient and effective transactions. Mobility, energy, selection of cluster head and gateway are parallel related with efficiency metrics of the network optimizing these parameter helps to increase performance of network in terms of higher packet delivery ratio, less energy consumption and jitter. This work proposes a routing architectural algorithm especially for very large network to get high-quality performance. The proposed method uses unicasting and multicasting approaches in an optimized way for routing and maintenance of the network. Analysis results prove that the proposed model has performed with higher packet delivery ration of 1.17% with a reduced jitter of 0.0014 s. Springer Nature Singapore Pte Ltd 2017. -
Optimized green synthesis of ZnO nanoparticles: evaluation of structural, morphological, vibrational and optical properties
In this study, leaf extracts of Aloe vera (AV), Azadirachta indica (AI), and Amaranthus dubius (AD) were used to synthesize zinc oxide nanoparticles utilizing a simple green synthesis process. The structural, optical, band energy, size, surface area, and shape of as-prepared nanoparticles were studied using analytical techniques. The hexagonal phase was revealed by XRD studies for all three samples: AV-ZnO, AI-ZnO, and AD-ZnO, with crystallite sizes of 35.8nm, 30.83nm, and 33.1nm, respectively. The UVVisible spectra of AV-ZnO, AI-ZnO, and AD-ZnO exhibit the characteristic absorption in the range of 200 to 450nm, and the band gap energy was found to be 3.10eV, 3.12eV, and 3.07eV, respectively. FESEM and TEM studies revealed that the ZnO NPs are rod-shaped with a roughly spherical appearance. EDAX analysis confirmed the presence of zinc and oxygen and indicates that the formed product is a pure phase of ZnO NPs. Increased antibacterial activity was noted for AV-ZnO, AI-ZnO, and AD-ZnO against gram-negative (Klebsiella pneumonia, Shigella dysenteriae), gram positive (Staphylococcus aureus, and Bacillus) bacterial strain. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.