Comprehensive Optimization Study Research Articles

A Comprehensive Multi-Objective Optimization Study on the Thermodynamic Performance of a Supercritical CO2 Brayton Cycle Incorporating Multi-Stage Main Compressor Intermediate Cooling

This study proposes a supercritical carbon dioxide Brayton cycle incorporating multi-stage main compressor intermediate cooling (MMCIC sCO2 Brayton cycle), and conducts an in-depth investigation and discussion on the enhancement of its thermodynamic performance. With the aim of achieving the maximum power cycle thermal efficiency and the maximum specific net work, this study examines the variation of the Pareto frontier with respect to the number of intermediate cooling stages and critical operational parameters. The results indicate that the MMCIC sCO2 Brayton cycle offers significant advantages in improving power cycle thermal efficiency, reducing energy consumption, and mitigating the adverse effects associated with main compressor inlet temperature increasing. Under the investigated operational conditions, the optimal cycle performance is achieved with four intermediate cooling stages, yielding a maximum power cycle thermal efficiency of 67.85% and a maximum specific net work of 0.177 MW·kg−1. Cycles with two or three intermediate cooling stages also deliver competitive cycle performance, and can be regarded as alternative options. Additionally, increasing the turbine inlet temperature proves more effective for enhancing power cycle thermal efficiency, whereas increasing the turbine inlet pressure can substantially improve the specific net work. This study provides a feasible structural layout approach and research framework to improve the thermodynamic performance of the sCO2 Brayton cycle, offering a robust theoretical foundation and technical guidance for its implementation in power engineering.

A comprehensive study on the effects of printing parameters on the mechanical properties of PLA

Purpose This study aims to investigate the effects of five different printing parameters, namely, printing speed (PS), printing temperature/nozzle temperature/extrusion temperature, heated-bed temperature, raster angle (RA) and layer height (LT), on mechanical properties. Design/methodology/approach American Society for Testing and Materials (ASTM) standards were used for the specimen design. Then, the Taguchi method was used for the design of the experiment and an L16 orthogonal array was preferred. Tensile, Shore D and surface roughness tests were conducted on polylactic acid test specimens. The test results were analyzed using the signal-to-noise ratio and analysis of variance (ANOVA). Findings As a result of the study, it was seen that RA is the most important parameter for the tensile strength, PS is for the hardness and LT is for the surface roughness. According to the ANOVA results, the effects of the RA, PS and LT on the maximum tensile strength, hardness and surface roughness were 41.59%, 69.51% and 44.6%, respectively. Originality/value To the best of the authors’ knowledge, this study is one of the most comprehensive parameter optimization studies for additive manufacturing in the literature because it includes five different printing parameters and three mechanical test procedures.

Finite element stress analysis and topological optimization of a commercial aircraft seat structure

In recent years, the Finite Element Method (FEM) has emerged as a cornerstone in the field of seating design, particularly within the aircraft industry. Over the past decade, significant advancements in Finite Element (FE) analysis techniques have revolutionized the seat industry, enabling the creation of safer and more cost-effective seat designs. The accuracy of FE analysis plays a pivotal role in this transformation. In the process of constructing a reliable finite element model, the selection and precise manipulation of key parameters are paramount. These crucial parameters encompass element size, time scale, analysis type, and material model. Properly defining and implementing these parameters ensures that the FE model produces accurate results, closely mirroring real-world performance. Verification of Finite Element Analysis (FEA) results is commonly accomplished through experimental methods. Notably, when the parameters are appropriately integrated into the modelling process, FE analysis outcomes closely align with experimental results. This study aims to leverage the power of FEM in performing static stress analysis and topology optimization of aircraft seats using the SOLIDWORKS commercial finite element platform. By simulating loading conditions, this research calculates static stresses and displacements experienced by the aircraft seat. Through a comprehensive topology optimization study, the weight of the airplane seat is remarkably reduced by up to 30%, while still prioritizing passenger safety. The success of this optimization showcases the potential for substantial weight savings in aircraft seat design without compromising safety standards.

Optimisation of treatment efficiency and effluent toxicity during carbamazepine degradation via electrochemical oxidation

The commercialisation of electrochemical advanced oxidation processes (eAOPs) as a method for removing recalcitrant pollutants from wastewater is currently limited due to the potential for the formation of toxic by-products. In this regard, this research comprised a comprehensive optimisation study aimed at finding a balance between minimising the toxicity effects observed via phytotoxicity assays and maximising the eAOP degradation efficiency under low energy requirements. To this end, carbamazepine (CBZ) was selected as the target pollutant in synthetic wastewater, and the operating conditions considered for optimisation were the nature of the anolyte (i.e., sulfate- or chloride-based), its concentration, and the current density applied. Together with the identification of the CBZ transformation products and their estimated toxicity based on the ECOSAR model, the optimum treatment was found to correspond to the degradation in 500 mg L−1 of a sulfate-based anolyte at a current density of 10 A m−2. These conditions led to the fewest and least toxic transformation products and favoured plant growth indices in phytotoxicity tests. Even if additional process improvements are required to reduce effluent toxicity, continuing such a multi-target optimisation approach can lead to innocuous electrochemical wastewater treatment.

Optimisation Study of Natural Language Processing Algorithms Based on Deep Learning

This paper presents a comprehensive optimization study of natural language processing (NLP) algorithms based on deep learning techniques. The research explores various strategies to enhance the performance and efficiency of NLP models, aiming to address the challenges posed by large-scale datasets and complex linguistic structures. Through a systematic review of existing literature and methodologies, this study synthesizes insights into key optimization approaches, including model architecture design, parameter tuning, and data preprocessing techniques. Moreover, it investigates the impact of different optimization strategies on NLP tasks such as sentiment analysis, named entity recognition, and machine translation. By elucidating the strengths and limitations of various optimization techniques, this paper offers valuable insights for researchers and practitioners in the field of deep learning-based NLP.

Optimization of electrochromic performance of WO3−x films grown by RF sputtering on gallium-doped zinc oxide

The present paper investigates the sputtered tungsten oxide thin films on gallium-doped zinc oxide-coated glass substrates concerning electrochromic behavior since the most promising candidate of smart glass in the future. Concordantly, each of the deposition parameters, including sputter power, total pressure, and O2/Ar + O2 gas ratio in the plasma, were examined in terms of electrochromic applications to enable to use of these values to be brought to large scale in commercial applications. As a result of comprehensive and systematic optimization studies, the optical modulation values were reached from 63.1 % to 70.8 % and the time needed for coloration and bleaching was observed as 7 and 5 s with final deposition values as 45 W power, 10 mTorr pressure, and 25 % O2 ratio. Additionally, whilst durability is still a problem in electrochromic coatings, reasonable durability cycling was achieved after 900 switching without encapsulation.

Innovative utilization of harvested mushroom substrate for green synthesis of silver nanoparticles: A multi–response optimization approach

In this work, harvested mushroom substrate (HMS) has been explored for the first time through a comprehensive optimization study for the green synthesis of silver nanoparticles (AgNPs). A multiple response central composite design with three parameters: pH of the reaction mixture, temperature, and incubation period at three distinct levels was employed in the optimization study. The particle size of AgNPs, UV absorbance, and the percentage of Ag/Cl elemental ratio were considered as the response parameters. For each response variable examined the model used was found to be significant (P < 0.05). The ideal conditions were: pH 8.9, a temperature of 59.4 °C, and an incubation period of 48.5 h. The UV–visible spectra of AgNPs indicated that the absorption maxima for AgNP–3 were 414 nm, 420 for AgNPs–2, and 457 for AgNPs-1. The XRD analysis of AgNPs-3 and AgNPs-2 show a large diffraction peak at ∼38.2°, ∼44.2°, ∼64.4°, and ∼77.4°, respectively, which relate to the planes of polycrystalline face-centered cubic (fcc) silver. Additionally, the XRD result of AgNPs–1, reveals diffraction characteristics of AgCl planes (111, 200, 220, 311, 222, and 400). The TEM investigations indicated that the smallest particles were synthesized at pH 9 with average diameters of 35 ± 6 nm (AgNPs–3). The zeta potentials of the AgNPs are −36 (AgNPs–3), −28 (AgNPs–2), and −19 (AgNPs–1) mV, respectively. The distinct IR peak at 3400, 1634, and 1383 cm−1 indicated the typical vibration of phenols, proteins, and alkaloids, respectively. The AgNPs were further evaluated against gram (+) strain Bacillus subtilis (MTCC 736) and gram (−) strain Escherichia coli (MTCC 68). All of the NPs tested positive for antibacterial activity against both bacterial strains. The study makes a sustainable alternative to disposing of HMS to achieve the Sustainable Development Goals (SDGs).

Sandpack flooding of microwave absorbent nanofluids under electromagnetic radiation: an experimental study

AbstractElectromagnetic (EM) radiation has long been recognized as an effective method for enhancing the quality and recovery of heavy and extra-heavy crude oil. The incorporation of EM absorbers, particularly nanoparticles, has demonstrated significant potential to boost efficiency and expand the stimulated reservoir volume. However, the application of simultaneous EM radiation and nanofluid injection in a natural porous medium, which is critical for the successful implementation of this approach in field-scale operations, remains an underexplored frontier. In this context, this research represents a pioneering endeavor, aiming to bridge this knowledge gap through a comprehensive statistical and optimization study. The primary objective was to unravel the intricate interplay between five distinct types of magnetic nanoparticles and their concentrations within the base fluid to improve oil production. Notably, it focused on iron oxide (Fe3O4) magnetic nanoparticles and their innovative hybridization with multi-walled carbon nanotubes (MWCNT) and nickel oxide (NiO) nanomaterial. A newly designed glass sandpack was employed as the porous medium, thus mirroring real reservoir conditions more accurately. Then, a rigorous full factorial design scrutinized the multifaceted effects of nanoparticle type and concentration when introduced into deionized water during this process. The results showed that microwave radiation, applied at 400 W, dramatically improved oil recovery, catapulting it from a baseline of 19% to an impressive 39.5% during water injection. The addition of magnetic nanoparticles to the base fluid enhances efficiency. However, the specific type of nanoparticle exerts varying effects on oil recovery rates. Notably, the synthesis of Fe3O4–MWCNT nanoparticles had a substantial impact on the ultimate oil recovery factor, achieving approximately 69%. Furthermore, the hybridization of Fe3O4 nanoparticles with MWCNT and NiO nanoparticles leads to reduced consumption (using low weight percentages) while achieving the highest oil recovery rates during the injection process. Finally, the optimization analysis demonstrated that employing 0.34 wt.% of Fe3O4–MWCNT nanoparticles under 400 W of microwave radiation represents the optimal condition for achieving the highest oil production in a sandpack porous medium. Under these conditions, the oil recovery factor can increase to 78%.

Reproducible and clinically translatable deep neural networks for cervical screening

Cervical cancer is a leading cause of cancer mortality, with approximately 90% of the 250,000 deaths per year occurring in low- and middle-income countries (LMIC). Secondary prevention with cervical screening involves detecting and treating precursor lesions; however, scaling screening efforts in LMIC has been hampered by infrastructure and cost constraints. Recent work has supported the development of an artificial intelligence (AI) pipeline on digital images of the cervix to achieve an accurate and reliable diagnosis of treatable precancerous lesions. In particular, WHO guidelines emphasize visual triage of women testing positive for human papillomavirus (HPV) as the primary screen, and AI could assist in this triage task. In this work, we implemented a comprehensive deep-learning model selection and optimization study on a large, collated, multi-geography, multi-institution, and multi-device dataset of 9462 women (17,013 images). We evaluated relative portability, repeatability, and classification performance. The top performing model, when combined with HPV type, achieved an area under the Receiver Operating Characteristics (ROC) curve (AUC) of 0.89 within our study population of interest, and a limited total extreme misclassification rate of 3.4%, on held-aside test sets. Our model also produced reliable and consistent predictions, achieving a strong quadratic weighted kappa (QWK) of 0.86 and a minimal %2-class disagreement (% 2-Cl. D.) of 0.69%, between image pairs across women. Our work is among the first efforts at designing a robust, repeatable, accurate and clinically translatable deep-learning model for cervical screening.

Enhancing Anthocyanin Extraction from Wine Lees: A Comprehensive Ultrasound-Assisted Optimization Study.

Wine lees, an important by-product of the wine industry, pose a major environmental problem due to the enormous quantities of solid-liquid waste that are discarded annually without defined applications. In this study, the optimization of a method based on a Box-Behnken design with surface response has been carried out to obtain extracts with high anthocyanin content and potent antioxidant activity. Six variables have been considered: %EtOH, temperature, amplitude, cycle, pH, and ratio. The developed method exhibited important repeatability properties and intermediate precision, with less than 5% CV being achieved. Furthermore, these novel methods were successfully applied to diverse wine lees samples sourced from Cabernet Sauvignon and Syrah varieties (Vitis vinifera), resulting in extracts enriched with significant anthocyanin content and noteworthy antioxidant activity. Additionally, this study evaluated the influence of grape variety, fermentation type (alcoholic or malolactic), and sample treatment on anthocyanin content and antioxidant activity, providing valuable insights for further research and application in various sectors. The potential applications of these high-quality extracts extend beyond the winemaking industry, holding promise for fields like medicine, pharmaceuticals, and nutraceuticals, thus promoting a circular economy and mitigating environmental contamination.

Graphene quantum dots-polyfluorene hybrid nanobiosensor for mitomycin C-DNA interaction sensing

A novel graphene quantum dots (GQD) / polyfluorene (PF) nanocomposite was deposited on the disposable pencil tip graphite electrode (PGE) and proven to be an efficient nanosensor for analysis of the electrochemical interaction between the antitumor compoundmitomycinC(MC) with double stranded DNA (ds-DNA). This modified electrode (GQD@PF-PGE) was prepared in four steps: hydrothermal, chemical oxidation, ultrasonication and electro-oxidation processes. GQD, PF, GQD@PF and GQD@PF-PGE have been characterized by different analytical techniques such as SEM, TEM, XRD, FTIR, UV–Vis, EIS. Compared to bare PGE, GQD@PF modified PGE performed approximately 56 times more sensitive analysis when evaluating the guanine oxidation signals measured by DPV. CV and EIS measurements also showed that GQD@PF-PGE possesses a fast electron transfer as compared to bare electrode and exhibit a remarkable electrocatalytic activity towards both guanine and MC electrooxidation. Comprehensive optimization studies have also been carried out for the developed new nanobiosensor.

A comprehensive optimization study of personal cooling radiant desks integrated to HVAC system for energy efficiency and thermal comfort in office buildings

Personal comfort systems (PCS) maintain the occupant's preferred thermal environment and expand his thermal comfort experience under varying environmental conditions. Among PCSs, radiant-based systems are popular due to their comfort and energy efficiency. A personal cooling radiant desk (PCRD) in conjunction with conventional heating, ventilation, and air conditioning (HVAC) has proven to reduce energy costs. A system's energy and thermal performance, however, depends on various factors making it important to identify the optimal design parameters of a PCRD coupled with HVAC system in an office space. To optimize the energy and thermal performance of the PCRD-HVAC system, a numerical/mathematical model is developed simulating different design parameters. The model uses an artificial neural network (ANN), combined with a multi-objective genetic algorithm (MOGA) to identify the optimal design parameters. The decision variables include the supply temperature, the temperature, and the flow rate of chilled water flowing inside the desk. The study's findings show that the optimal design parameters achieved a balance between thermal comfort and energy efficiency. The recommended design parameters result in an annual energy consumption of 3880 kWh and a predicted percentage of dissatisfied individuals (PPD) of 6 %. The experiment is carried out to validate the model showed a maximum relative error of only 12.54 %. The results of this study have important implications for designing sustainable cooling solutions for office spaces in hot climates. The successful optimization of the PCRD system highlights the importance of balancing energy efficiency and thermal comfort in designing sustainable cooling solutions.

Enhancing thermal performance of latent heat storage unit for solar cooling: A hybrid approach with C-shaped fins and nano-additives

The performance of solar cooling systems is significantly affected by the intermittent nature of solar energy. To address this problem, thermal energy storage systems such as sensible and latent heat storage systems can be a viable option. Among these, the latent heat storage systems are promising due to their high energy storage density and nearly isothermal charging/discharging characteristics. However, the low thermal conductivity of the phase change material used within the storage unit hinders heat transfer, leading to longer charging and discharging periods. To overcome this problem, a hybrid approach that uses fins and nano additives is proposed in this study. A novel C-shaped fin design is introduced that effectively utilises the natural convection current in the molten phase change material. A comprehensive geometrical and material optimisation study is performed to determine the optimal shape, position, and material of the fins. The study also investigates the effect of the inclusion of graphene nanoplatelets (GnP) on the latent heat storage units. An experimental study is conducted to characterise the composite phase change material, and empirical correlations for viscosity and thermal conductivity are developed. The performance of an optimally designed LHS stacked with nano PCM composite of varying mass fractions is also evaluated. The study reveals that the C-shaped fins can reduce melting time by up to 59% compared to conventional fin designs, while PCM dispersed with 1% GnP reduces melting time by 27% when compared to the base PCM. Finally, a two-bed solar vapour adsorption cooling system with a 500 W capacity is considered to conduct a case study for demonstrating the performance of the proposed LHS. The result shows that the solar adsorption system integrated with the proposed heat storage unit can operate up to 3.3 h longer than it currently does. Thus the study presents a promising solution to overcome the obstacles limiting the widespread utilisation of solar cooling systems.

Optimizing Security Systems with an Optimum Design of a Hybrid Renewable Energy System

Security systems play a crucial role in protecting individuals and assets within diverse infrastructures, including seaports. The uninterrupted operation of these systems heavily relies on a continuous power supply, as any disruptions can lead to severe consequences. Therefore, security systems are classified as critical loads requiring uninterrupted power availability. This study focuses on the investigation of an optimal hybrid energy system (HES) to ensure a reliable power supply for security systems in two seaports located in Turkiye. Through the utilization of the HOMER software, optimization analyses were conducted, considering both conventional sources such as grid-generator or grid-generator-battery configurations, as well as off-grid and on-grid HES solutions integrating photovoltaic (PV) and wind turbine technologies. The findings reveal that on-grid HES solutions incorporating PV and wind technologies offer a more cost-effective and dependable energy supply for security systems in seaports, surpassing traditional alternatives. This study represents a significant contribution to the existing literature, as it presents the first comprehensive optimization study on the design of HES for security systems. The outcomes serve as a valuable reference for future research endeavors in this field.

Beyond traditional boundaries: Exergo-economic and thermo-mechanical optimization of segmented thermoelectric generators with varied cross-sections

In this work, we present a comprehensive optimization study of a concentrating solar segmented thermoelectric generator (CSSTEG) with variable area leg geometry. We consider not just geometrical parameters but also concentrated solar heat flux and convective cooling film coefficients, pushing the boundaries of traditional studies. Performance is evaluated on multiple fronts: power output, exergy efficiency, entropy generation, CO2 savings, dollar per watt, and thermal stress. Uniquely, we establish that an optimal skutterudite content of 6.17% provides maximum power output (47.47 W) and exergy efficiency (10.95%) at a solar heat flux of 50 kWm−2. This optimized configuration also delivers maximal annual CO2 savings (22.5 kgyr−1). Additionally, we pinpoint that a semiconductor height of 0.2 mm minimizes the dollar per watt (0.0048 $W−1) at 100 kWm−2 solar irradiances and reduces thermal stress (0.25 GPa) at a cooling heat transfer coefficient of 4 kWm−2 K−1. Our research is pioneering in its holistic approach to CSSTEG optimization, providing valuable insights for creating efficient, sustainable energy systems.

A comprehensive multi-objective optimization study for the aerodynamic noise mitigation of a small wind turbine

The noise emitted by small wind turbines is one of the main reasons for their poor public perception for use in urban areas. An optimization study was performed to mitigate the aerodynamic noise of a 750 W turbine through altering the geometry of solid and more importantly hollow blades for the first time. A validated in-house code simultaneously minimizes the aerodynamic noise and maximizes the output power. The inertia of the blades is another important design parameter for small turbines as it influences the starting performance. The minimization of starting time was added to the above-mentioned goals and another optimization was done. The outcomes highlight the great significance of two ends of the blade; the tip section has a key role in alleviation of the noise as well as the improvement of the power coefficient while the root part could speed up the starting. Quantitatively, the optimization of solid blades shows that a 1.9 dB reduction in aerodynamic noise is attainable at the expense of a 1.6% loss in the power coefficient. Compared to solid blades, the aerodynamic noise and the starting time were reduced for hollow ones by 1 dB and 30% respectively. The latter is very attractive for use of small turbines in low-wind areas.

Fabrication of metal–organic framework based electrochemical Leishmania immunosensor

Leishmaniasis is a disease with a high impact on public health in many countries. In this study, an electrochemical immunosensor for Leishmania major surface protease (Gp63) antibody detection was developed using copper metal organic framework (Cu-(NH2-BDC)-MOF) modified gold screen printed electrode (AuSPE). For this purpose, after modification step of AuSPE with Cu-(NH2-BDC)-MOF, recombinant L.donovani antigen was immobilized on to electrode surface via crosslinker where change in electrochemical signal was monitored with electrochemical impedance spectroscopy technique. After conducting comprehensive optimization studies on experimental parameters of developed Leishmania immunosensor, analytical characteristics were searched. As a result, it was observed that anti-gp63 solution diluted up to the ratio of 1:1500 could be detected with the developed sensor. Meanwhile, relative standard deviation value was found as 4.67% for anti-gp63 diluted in the ratio of 1:80 (n = 3). Accordingly, real sample analysis was also performed by using Leishmania parasite crude antigen and rabbit serum which were positive and negative control samples, respectively. According to the results obtained, it can be concluded that a practical immunosensor that can allow the early diagnosis of leishmaniasis was developed.

Optimization of viscosity of titania nanotubes ethylene glycol/water-based nanofluids using response surface methodology

Nanofluids have gained significant importance in heat transfer applications due to their unique thermal properties, such as higher thermal conductivity, higher convective heat transfer coefficients, and improved thermal stability. They offer the potential for more efficient heat transfer in various engineering applications, including cooling systems, heat exchangers, and thermal energy storage systems. Additionally, their use can lead to reduced energy consumption and improved performance in various industrial processes, making them an attractive option for researchers and engineers in heat transfer. Their thermophysical properties must be known to gain the maximum benefits of using nanofluids as process fluids. Despite the significance of viscosity as a characteristic of nanofluids, adequate research on viscosity modeling cannot offer accurate predictions. The current work aims to use Response Surface Methodology to develop a prediction model for the viscosity of a nanofluid comprised of Titanium (TiO2) nanotubes dissolved in ethylene glycol and water. Different mass concentrations of nanotubes ranged from 0% to 1%, the shear rate was set to 150–500 s−1, and the temperature range was kept from 25 to 65 °C. Moreover, a comprehensive optimization study was conducted using the proposed model. The significance of process input parameters was assessed, and the findings showed that the nanofluid concentration is the most significant input, followed by temperature and shear rate. Two case studies were explored for both maximization and minimization of viscosity. The optimization results showed that the optimum condition, for viscosity maximization (0.00371 Pa·s), of input parameters such as nanofluid concentration, temperature, and shear rate are 0.675%, 205 1/s, and 25 °C for the maximum viscosity 0.00371 Pa·s. For viscosity minimization, the optimum input parameters were nanofluid concentration, temperature, and shear rate are 0.04%, 179 1/s, and 63.4 °C, respectively.

Discovery of the First-in-Class Intestinal Restricted FXR and FABP1 Dual Modulator ZLY28 for the Treatment of Nonalcoholic Fatty Liver Disease.

The prevalence of nonalcoholic steatohepatitis (NASH) is increasing rapidly worldwide, and NASH has become a serious problem for human health. Recently, the selective activation of the intestinal farnesoid X receptor (FXR) was considered as a more promising strategy for the treatment of NASH with lesser side effects due to reduced systemic exposure. Moreover, the inhibition of intestinal fatty acid binding protein 1 (FABP1) alleviated obesity and NASH by reducing dietary fatty acid uptake. In this study, the first-in-class intestinal restricted FXR and FABP1 dual-target modulator ZLY28 was discovered by comprehensive multiparameter optimization studies. The reduced systemic exposure of ZLY28 might provide better safety by decreasing the on- and off-target side effects in vivo. In NASH mice, ZLY28 exerted robust anti-NASH effects by inhibiting FABP1 and activating the FXR-FGF15 signaling pathway in the ileum. With the above attractive efficacy and preliminary safety profiles, ZLY28 is worthy of further evaluation as a novel anti-NASH agent.

A comprehensive process optimization study of the mixing assisted oxidative desulfurization of diesel oil

In the aspect of oils derived from fossil fuel, it is vital to remove high sulfur concentrations via the desulfurization technique before its combustion. In this study, a comprehensive process optimization analysis for the mixing assisted oxidative desulfurization (MAOD) that undertakes a process intensification method by a high shear mixer and the simultaneous application of the sodium phosphotungstate/hydrogen peroxide (NaPW/H2O2) system were examined for the treatment of a raw diesel sample. The response surface methodology via Box–Behnken design for the method of MAOD was employed to examine the parametric influences of sulfur conversion by the independent variables of mixing time (6 min to 30 min), NaPW dose (10 mg to 500 mg), H2O2 concentration (30 %v/v to 50 %v/v), and reaction temperature (30 °C to 70 °C). The investigation of the level of significance in the individual process variable and its respective interactive effects were investigated through an analysis of variance. The results indicated that the process variables of mixing time, NaPW dosage, and reaction temperature has high statistical significance (p-value < 0.0001). The mean optimum sulfur conversion of 96.6 % for the raw diesel samples has been attained after three confirmatory runs at 29.6 min, 471.4 mg NaPW, 44.4 %v/v H2O2, and 62.8 °C. Therefore, the results obtained in this research demonstrated the novel application of MAOD as the desulfurization technique to treat raw diesel. This validates a promising result for the application of MAOD as a prospective methodology for future applications.