Industrial Domains Research Articles

Simulation and non-similar analysis of magnetized SWCNT-MWCNT hybrid nanofluid flow in porous media using Darcy-Forchheimer-Brinkman model

The field of hybrid nanofluid transport through porous media holds immense promise for optimizing thermal processing and various thermodynamic applications. This study investigates the flow dynamics of a hybrid nanofluid, comprising water and a synergistic combination of single-walled and multi-walled carbon nanotubes (SWCNT-MWCNT), as it traverses a vertically stretched porous surface. The mathematical modeling of this flow scenario considers the influential factors of magnetohydrodynamics (MHD), viscous dissipation, heat sources, and ohmic heating. The Darcy-Forchheimer-Brinkman model is employed to capture the transport of fluid through the porous medium. Through the application of Local Non-Similarity (LNS) technique, the governing equations are converted into a dimensionless system and solved numerically using the robust bvp4c function in MATLAB. Interestingly, higher values of heat source parameter leads to a rising trend in the temperature profile, highlighting the intricate interplay between the thermal and fluid dynamic aspects of the system. This work provides valuable insights into the tailored design of hybrid nanofluids and porous media configurations to harness their enhanced thermal transport capabilities, with potential applications in diverse fields such as energy storage systems, heat exchangers, and thermal management devices. The findings contribute to the broader understanding of hybrid nanofluid transport in porous media and pave the way for the development of innovative thermal management solutions in a wide range of industrial and technological domains.

WTDP-Shapley: Efficient and Effective Incentive Mechanism in Federated Learning for Intelligent Safety Inspection

Artificial Intelligence (AI) has been widely applied for Safety Inspection in a number of industrial domains. However, individual company usually could not provide sufficient data to support well-trained AI models. Federated Learning, as a new AI paradigm, enables a number of participants to contribute training data to co-create high-performance models without compromising data privacy. However, an effective incentive mechanism is essential to encourage participants to contribute high-quality data, and the fundamental of the incentive mechanism is to evaluate participants' contribution fairly. Shapley Value (SV) is a well-known approach to evaluate individual's marginal contribution in a coalition, but the canonical SV calculation and its available variants are very costly. In this paper, we proposed an FL framework to enable a number of natural gas companies from different cities to jointly train an object detection computer vision deep learning model for the purpose of identifying potential hazards, without sharing their confidential inspection photos directly. We improve state-of-the-art SV algorithm by proposing Weighted Truncation (WT) for unnecessary computations, and go further with Dynamic Programming (WTDP), achieving better trade-off between efficiency and accuracy. Based on our proposed WTDP-Shapley participant contribution evaluation approach, an effective end-to-end incentive mechanism is designed by leveraging knowledge of both data scientists and domain experts. According to our experiments, it could encourage participants to contribute scarce photos with potential hazards, and thus co-create a high-performance AI model to identify various hazards accurately for residential natural gas installation safety inspection.

Natural serine proteases and their applications in combating amyloid formation

Background and purpose: Amyloidosis is a group of diseases including diabetes type II and neurological disorders, such as Alzheimer’s disease, Parkinson’s disease, prion disease, etc., where a common trait is observed; accumulation of misfolded protein at different parts of the body, especially the brain which manifests the typical symptoms like dementia, movement disorders, etc. These misfolded proteins, named amyloids, are protease resistant and thus it becomes difficult to manage these diseases in vivo. Enzymes that catalyse the complete breakdown of proteins are known as proteases. The peptide bonds in proteins are degraded by these serine proteases, which cause amyloid disaggregation. Experimental approach: We have searched for related articles using the search engines Google Scholar, PubMed, and Scopus for the past 10 years, selected the relevant articles, and written the outcomes and benefits of protease using the medical topic “serine protease” and the following text phrases -keratinase, lumbrokinase, serratiopeptidase, nattokinase. Key results: Alkaline serine proteases exhibit activity within the neutral to alkaline pH range. They are most capable of degrading host complement proteins, cytokines, and host clotting factors mostly due to their serine centre or metallotype. Because of its potential usage in food, pharmaceutical, and other industrial domains, this category of enzymes has been extensively investigated. Specifically, serine proteases are a group of enzymes that can be consumed orally and are stable in our gastrointestinal tract. Conclusion: In this review, we discussed the role of different serine proteases in amyloid aggregate inhibition and their potential application in treating amyloidosis.

Design-assisted HPLC-UV method for therapeutic drug monitoring of pholcodine, ephedrine, and guaifenesin in biological fluids.

Drug-drug interactions may amplify or diminish their intended effects, or even produce entirely new effects. Multicomponent mixture HPLC analysis offers a thorough and effective method for comprehending the makeup and behavior of complicated materials, advancing research and development across a range of scientific and industrial domains. A novel experimental design-assisted HPLC methodology for the concurrent investigation of the drug-drug interaction of pholcodine, ephedrine, and guaifenesin in biological fluids has been established. Rather than the routine methodology, the application of the factorial design-HPLC method offers a powerful and efficient tool for the analysis of these compounds. Both mixed and full factorial designs were employed to assess the impact of variable factors on chromatographic results. Utilizing an isocratic elution mode on a C18 column, the chromatographic separation was carried out. 15% Methanol, 5% acetonitrile, and 80% phosphate buffer with 0.1%(v/v) triethylamine set to pH 3 make up the mobile phase flowing at rate 1.0 mL/min. The calibration curves of the drugs show excellent linearity over a concentration ranges: 0.20-13.0µg/mL for PHO, 0.50-20.0µg/mL for EPH and 0.70-20.0µg/mL for GUA with LOQ values of 0.18, 0.38 and 0.50. The fast separation and quantitation in less than 6min is an advantage. Also, the method includes a robust sample preparation protocol for the analysis of complex biological samples, ensuring high selectivity and precision. The ease, speed and cost-effectiveness of the method are ideal for supporting in vitro studies, including drug-drug interaction investigations, especially in bioanalytical labs.

Ground-Based Remote Standoff Laser Spectroscopies and Reflectance Spectral Imaging for Multimodal Analysis of Wall Painting Stratigraphy.

This paper presents a novel multimodal remote sensing setup to analyze the complex stratigraphy of historical wall paintings at distances of order 10 m. The proposed method enables comprehensive investigation of the chemical composition of multilayer paint stratigraphy by combining standoff laser-induced breakdown spectroscopy for elemental profiling with noninvasive standoff Raman spectroscopy and visible and near-infrared (400-900 nm) reflectance spectral imaging for depth-resolved complementary material characterization from a range of distances with instruments and operators located on stable ground. Following proof-of-concept laboratory tests, the feasibility and effectiveness of this standoff analytical approach is demonstrated through field analysis of a whitewashed historical wall painting, successfully identifying at least seven distinct layers from a distance of 7 m. The remote sensing method presented here can also be applied to other scientific and industrial domains to characterize the chemical composition of layered materials at a distance.

Intelligent-based approach for parameters identification and control of fractional order systems

The optimization algorithms for identification and control processes take more and more place in the industrial domain. However, determining a control law for fractional systems remains challenging for researchers today. This paper presents a simple method for using a fractional PID (FOPID) corrector to regulate a class of fractional systems that show a stable step response. The procedure is based on an algorithmic identification to assimilate the fractional system into a simple model. Optimization methods were used. Using techniques like Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Bee Colony (ABC), and Ant Colony Optimization (ACO) to ascertain the parameters of the basic model, a first-order system with delay. Then, the FOPID controller will be calculated using Ziegler–Nichols methods and optimization algorithms to stabilize the real process. Better performance Overshoot and Settling time as well as minimal Energy control effort are benefits of the proposed design. To confirm that the fractional regulator is robust which is optimized by the best-chosen method (PSO), the parameters of the chosen process will be randomly varied. Through two numerical simulations, the efficiency of the suggested method has been confirmed.

Alginate Formulation for Wound Healing Applications.

Significance: Alginate, sourced from seaweed, holds significant importance in industrial and biomedical domains due to its versatile properties. Its chemical composition, primarily comprising β-D-mannuronic acid and α-L-guluronic acid, governs its physical and biological attributes. This polysaccharide, extracted from brown algae and bacteria, offers diverse compositions impacting key factors such as molecular weight, flexibility, solubility, and stability. Recent Advances: Commercial extraction methods yield soluble sodium alginate essential for various biomedical applications. Extraction processes involve chemical treatments converting insoluble alginic acid salts into soluble forms. While biosynthesis pathways in bacteria and algae share similarities, differences in enzyme utilization and product characteristics are noted. Critical Issues: Despite its widespread applicability, challenges persist regarding alginate's stability, biodegradability, and bioactivity. Further understanding of its interactions in complex biological environments and the optimization of extraction and synthesis processes are imperative. Additionally, concerns regarding immune responses to alginate-based implants necessitate thorough investigation. Future Directions: Future research endeavors aim to enhance alginate's stability and bioactivity, facilitating its broader utilization in regenerative medicine and therapeutic interventions. Novel approaches focusing on tailored hydrogel formations, advanced drug delivery systems, and optimized cellular encapsulation techniques hold promise. Continued exploration of alginate's potential in tissue engineering and wound healing, alongside efforts to address critical issues, will drive advancements in biomedical applications.

LFL-COBC: Lightweight Federated Learning on Blockchain-Based Device Contribution Allocation

In the distributed cyber-physical systems (CPSs) within the industrial domain, the volume of data produced by interconnected devices is escalating at an unprecedented pace, presenting novel opportunities to enhance service quality through data sharing. Nevertheless, data privacy protection emerges as a significant challenge for data providers in wireless networks. This paper puts forward a solution integrating blockchain and lightweight federated learning, designated as LFL-COBC, which aims to tackle the issues related to data privacy and device performance optimization. We initially analyze multiple dimensions influencing the performance of computing devices, such as mining capacity, data quality, computational efficiency and local device deviation, which are crucial for augmenting user engagement. Based on these dimensions, we deduce a set of cooperation strategies for selecting the optimal committee members and rewarding the contributions of node devices equitably, thereby stimulating cooperation between users and servers. To intelligently and automatically detect device anomalies and alleviate the operational burden, a convolutional neural network (CNN) model is employed. Additionally, to address the escalating cost of customer participation and the potential data explosion issue, a near-optimal model pruning algorithm is designed. This algorithm can make the model obtained from the training of node equipment lightweight, thereby reducing the load of federated learning and the blockchain, as well as enhancing the overall efficiency of the system. The efficacy of our approach is demonstrated through numerical experiments on the HDFS and BGL public data sets. Experimental results indicate that the LFL-COBC scheme can effectively safeguard data privacy and optimize device performance concurrently, providing an effective solution for device anomaly detection in CPSs.

Enhancing data representation in forging processes: Investigating discretization and R-adaptivity strategies with Proper Orthogonal Decomposition reduction

Effective data reduction techniques are crucial for enhancing computational efficiency in complex industrial processes such as forging. In this study, we investigate various discretization and mesh adaptivity strategies using Proper Orthogonal Decomposition (POD) to optimize data reduction fidelity in forging simulations. We focus particularly on r-adaptivity techniques, which ensure a consistent number of elements throughout the field representation, filling a gap in existing research that predominantly concentrates on h-adaptivity. Our investigation compares isotropic mesh approaches with anisotropic mesh adaptations, including gradient-based, isolines-based, and spring-energy-based methods. Through numerical simulations and analysis, we demonstrate that these anisotropic techniques provide superior fidelity in representing deformation fields compared to isotropic meshes. These improvements are achieved while maintaining a similar level of model reduction efficiency. This enhancement in representation leads to improved data reduction quality, forming the foundation for data-driven models. This research contributes to advancing the understanding of mesh adaptivity approaches and their potential applications in data-driven modeling across various industrial domains.

Fiber-Optic Sensor Spectrum Noise Reduction Based on a Generative Adversarial Network

In the field of fiber-optic sensing, effectively reducing the noise of sensing spectra and achieving a high signal-to-noise ratio (SNR) has consistently been a focal point of research. This study proposes a deep-learning-based denoising method for fiber-optic sensors, which involves pre-processing the sensor spectrum into a 2D image and training with a cycle-consistent generative adversarial network (Cycle-GAN) model. The pre-trained algorithm demonstrates the ability to effectively denoise various spectrum types and noise profiles. This study evaluates the denoising performance of simulated spectra obtained from four different types of fiber-optic sensors: fiber Fabry–Perot interferometer (FPI), regular fiber Bragg grating (FBG), chirped FBG, and FBG pair. Compared to traditional denoising algorithms such as wavelet transform (WT) and empirical mode decomposition (EMD), the proposed method achieves an SNR improvement of up to 13.71 dB, an RMSE that is up to three times smaller, and a minimum correlation coefficient (R2) of no less than 99.70% with the original high-SNR signals. Additionally, the proposed algorithm was tested for multimode noise reduction, demonstrating an excellent linearity in temperature response with a R2 of 99.95% for its linear fitting and 99.74% for the temperature response obtained from single-mode fiber sensors. The proposed denoising approach effectively reduces the impact of various noises from the sensing system, enhancing the practicality of fiber-optic sensing, especially for specialized fiber applications in research and industrial domains.

Large Language Model Agents as Prognostics and Health Management Copilots

Amid concerns of an aging or diminishing industrial workforce, the recent advancement of large language models (LLMs) presents an opportunity to alleviate potential experience gaps. In this context, we present a practical Prognostics and Health Management (PHM) workflow and self-evaluation framework that leverages LLMs as specialized in-the-loop agents to enhance operational efficiency without subverting human subject matter expertise. Specifically, we automate maintenance recommendations triggered by PHM alerts for monitoring the health of physical assets, using LLM agents to execute structured components of the standard maintenance recommendation protocol, including data processing, failure mode discovery, and evaluation. To illustrate this framework, we provide a case study based on historical data derived from PHM model alerts. We discuss requirements for the design and evaluation of such “PHM Copilots” and formalize key considerations for integrating LLMs into industrial domain applications. Refined deployment of our proposed end-to-end integrated system may enable less experienced and professionals to back-fill existing personnel at reduced costs.

Artificial neural network-based computational heat transfer analysis of Carreau fluid over a rotating cone

Heat transport in a dynamically rotating cone immersed in a Carreau fluid is the subject of this investigation. The fluid is non-Newtonian, admired for its characteristics, and extensively utilized in numerous industrial domains. The study investigates the interplay between buoyancy and centrifugal forces within an analytical framework. The study employs sophisticated mathematical methods, including similarity transformations, to convert governing partial differential equations into nonlinear ordinary differential equations. These equations are then solved using the shooting method, a numerical technique that solves a boundary value problem by iteratively adjusting the initial conditions until the boundary conditions are satisfied. We employ an artificial neural network algorithm with backpropagation Levenberg–Marquardt scheme to analyze the heat transfer mechanism quantitatively. In conjunction with the shooting mechanism, we will use numerical simulation with an artificial neural network algorithm, namely the backpropagation Levenberg–Marquardt scheme. The results prove the enormous influence of centrifugation and buoyancy on complex fluid dynamics and heat exchange processes. Some critical parameters that govern the convective heat transport process are the Nusselt number, the Reynolds number, the Grashof number, and the fluid and cone rotational velocities. The research validates the requirement of considering non-Newtonian complexity and viscous dissipation when investigating heat transfer dynamics and fluid flow, facilitating more accurate expectations and improved efficiency in various industrial processes.

Edge-Cloud Continuum: Integrating Edge Computing and Cloud Computing for IOT Applications

Abstract: This research will use a mixed-method approach with qualitative understanding and the quantitative analysis of data to examine how edge and cloud computing blend into the frame of an edge-cloud continuum for IoT applications. It adopts an exploratory and descriptive approach to research in carrying out a holistic assessment of the edge-cloud continuum in efficiency, scalability, and performance. The data required for collection was obtained through simulations using the IoTSim-Osmosis framework and also through the use of other case studies and literature published elsewhere before preparing this document. The data is usual to what has been used in several IoT deployments, including smart cities, the health sector, and industrial automation. Based on those KPIs - latency, 70.83%; bandwidth utilization, 40% reduced; energy consumption, 25% reduced; and task completion rate improved by 18.75% - the edge-cloud continuum significantly outperforms traditional cloud-only systems. Especially a very big reduction in latency is significantly important for real-time applications as it would drive the potential ability to improve responsiveness in those applications, like autonomous cars and smart healthcare. The current study demonstrates that the edge-cloud continuum can efficiently enhance resource allocation and enhance the effectiveness of complex IoT systems. This has wide implications for designing and implementing IoT solutions across industry domains

Quality assurance strategies for machine learning applications in big data analytics: an overview

Machine learning (ML) models have gained significant attention in a variety of applications, from computer vision to natural language processing, and are almost always based on big data. There are a growing number of applications and products with built-in machine learning models, and this is the area where software engineering, artificial intelligence and data science meet. The requirement for a system to operate in a real-world environment poses many challenges, such as how to design for wrong predictions the model may make; How to assure safety and security despite possible mistakes; which qualities matter beyond a model’s prediction accuracy; How can we identify and measure important quality requirements, including learning and inference latency, scalability, explainability, fairness, privacy, robustness, and safety. It has become crucial to test thoroughly these models to assess their capabilities and potential errors. Existing software testing methods have been adapted and refined to discover faults in machine learning and deep learning models. This paper covers a taxonomy, a methodologically uniform presentation of all presented solutions to the aforementioned issues, as well as conclusions about possible future development trends. The main contributions of this paper are a classification that closely follows the structure of the ML-pipeline, a precisely defined role of each team member within that pipeline, an overview of trends and challenges in the combination of ML and big data analytics, with uses in the domains of industry and education.

Surfactant-assisted electrochemical sensor for phytocannabinoids

The Cannabis sativa plant is known to be a complex source of phytocannabinoids with diverse applications across medicinal, recreational, and industrial domains. However, the proliferation of cannabidiol (CBD)-based or low- Δ9-tetrahydrocannabinol (Δ9-THC) content products has posed challenges for law enforcement agencies due to the complexity of distinguishing lawful from illicit usage. As legislation around cannabis evolves, precise and reliable analytical methods are urgently needed. Traditional techniques, while accurate, are time-consuming and resource intensive. Portable field tests lack specificity and often need follow-up confirmatory tests. Herein, an innovative electrochemical sensor for accurate and efficient identification of phytocannabinoids is presented. The study focuses on the detection of tetrahydrocannabinolic acid (THCA), Δ9-THC, cannabidiolic acid (CBDA), CBD, cannabichromene (CBC) and cannabinol (CBN), employing unmodified carbon screen-printed electrodes (SPEs) as the platform. Additionally, the integration of a cationic surfactant, hexadecyltrimethylammonium bromide (CTAB), is investigated to enhance the sensitivity. This electrochemical sensor demonstrates successful qualitative and semi-quantitative identification of the main phytocannabinoids in seizures. Ultimately, our sensor benefits harm and supply reduction agencies by facilitating quality control and addressing the complexities of varied cannabis products.

Research on Whether Artificial Intelligence Affects Industrial Carbon Emission Intensity Based on the Perspective of Industrial Structure and Government Intervention

Artificial intelligence serves as the fundamental catalyst for a new wave of technological innovation and industrial transformation. It holds vital importance in reaching carbon reduction targets and the objectives of “carbon peak and neutrality”. This factor contributes significantly to the reduction in carbon emissions in the industrial domain. This article utilizes panel data from 30 provinces in China, covering the years 2013 to 2021, to develop an evaluation framework for assessing the progress of artificial intelligence development. Through the use of double fixed-effect models, mediation effect models, and threshold effect models, the empirical analysis examines the industrial carbon reduction effects of artificial intelligence and its operating mechanisms. Research indicates that the advancement of AI can significantly reduce carbon emission intensity within the industrial sector. This conclusion remains valid following comprehensive robustness tests. Furthermore, there exists temporal and regional variability in AI’s impact on industrial carbon reduction, particularly more pronounced after 2016 and in central and western regions. AI influences carbon emission reduction in China’s industrial sector through the advancement and optimization of industrial structures. Here, the increase in senior-level operations acts as a partial masking effect, while optimization serves as a partial mediator. The relationship between AI and industrial carbon emission intensity is non-linear, being influenced by the threshold of government intervention; minimal intervention weakens AI’s effect on carbon intensity reduction. These findings enhance our understanding of the factors influencing industrial carbon emissions and contribute to AI-related research. They also lay a solid empirical groundwork for promoting carbon emission reduction in the industrial domain via AI. Additionally, the results offer valuable insights for formulating policies aimed at the green transformation of industry.

CoFiNet: Unveiling camouflaged objects with multi-scale finesse

Camouflaged Object Detection (COD) is a critical aspect of computer vision aimed at identifying concealed objects, with applications spanning military, industrial, medical and monitoring domains. To address the problem of poor detail segmentation effect, we introduce a novel method for camouflaged object detection, named CoFiNet. Our approach primarily focuses on multi-scale feature fusion and extraction, with special attention to the model’s segmentation effectiveness for detailed features, enhancing its ability to effectively detect camouflaged objects. CoFiNet adopts a coarse-to-fine strategy. A multi-scale feature integration module is laveraged to enhance the model’s capability of fusing context feature. A multi-activation selective kernel module is leveraged to grant the model the ability to autonomously alter its receptive field, enabling it to selectively choose an appropriate receptive field for camouflaged objects of different sizes. During mask generation, we employ the dual-mask strategy for image segmentation, separating the reconstruction of coarse and fine masks, which significantly enhances the model’s learning capacity for details. Comprehensive experiments were conducted on four different datasets, demonstrating that CoFiNet achieves state-of-the-art performance across all datasets. The experiment results of CoFiNet underscore its effectiveness in camouflaged object detection and highlight its potential in various practical application scenarios.

Two-Dimensional Hydration and Triple-Interlayer Lattice Structures in Sulfate-Intercalated Graphene Oxide Nanosheets

Sulfate anions (SO42−) are pivotal in various scientific and industrial domains, including mineralogy, biology, and materials science. While extensive research has elucidated sulfate hydration in bulk solids, liquids, and gaseous clusters, a significant gap persists in understanding sulfate interactions within two-dimensional materials, particularly graphene oxide (GO) nanosheets. This study investigates the intricate hydration phenomena and novel triple-interlayer lattice configurations that emerge from sulfate intercalation in GO nanosheets. Utilizing a straightforward methodology for obtaining precise X-ray measurements of confined nanospaces, we analyzed the temperature-dependent behavior and structural characteristics of these systems. Our findings reveal how sulfate ions modulate interlayer spacing, the dynamics of GO layers, and phase transitions. This research offers an atomic-scale understanding of hybrid hydration behaviors within confined SO4-H2O nano-environments, advancing our knowledge of sulfate interactions in two-dimensional materials.

Analytical Hierarchy Process for Construction Safety Management and Resource Allocation

The construction industry plays a crucial role in contributing to the economy and developing sustainable infrastructures. However, it is known as one of the most dangerous industrial domains. Over the years, special attention has been paid to developing models for managing and planning construction safety. Many research studies have been carried out to analyze the root causes of fatal accidents in construction sites to develop models for preventing them and mitigating their consequences. Root cause identification and analysis are essential for effective risk mitigation. However, implementing mitigation activities is usually limited to the project’s safety budget. The construction sector suffers from a lack of allocation of appropriate safety resources triggered by a dynamic and complex project environment. This study aims to address the gap in safety resource allocation through a comprehensive root cause analysis of construction work accidents. In this paper, we present a comprehensive review of work accident-related research, categorized according to the 5M model into five root factors: medium, mission, man, management, and machinery. A novel methodology for construction safety resource allocation is proposed to mitigate risks analyzed by the 5M model with the aid of advanced technological solutions. Safety resource allocation alternatives are formulated, and their priorities are established based on an analysis of structured criteria that integrate both risk and cost considerations. The Analytical Hierarchy Process (AHP) is employed to select the optimal alternative for safety resource allocation, with the objective of effective risk mitigation. The proposed model underwent validation through two different case studies. The findings indicate that risk aversion is a critical factor in the optimal allocation of safety resources. Furthermore, the results suggest that regulatory measures should prioritize the stimulation of risk motivation in the safety decision-making processes of construction firms.

Optimising nanoparticles: harnessing quality by design through 3-level factorial design and box-behnken methodology

Nanoparticles (NPs) have become essential elements in a number of scientific and industrial domains, such as materials research, drug delivery, and diagnostics, because of their special qualities and potential for focused uses. An ideal formulation procedure is essential to maximising the effectiveness and efficiency of nanoparticles. In order to optimise the formulation and production processes for nanoparticles, this research investigates the application of Quality by Design (QbD) principles in conjunction with sophisticated statistical approaches, namely the 3-level factorial design and the Box-Behnken methodology. The Quality by Design methodology is a methodical approach that prioritises predetermined goals, in-depth process comprehension, and careful control grounded in reliable science and quality risk management. When QbD is incorporated into nanoparticle optimisation, reliable and repeatable formulations that adhere to specifications with little variation are guaranteed. To look into how different factors interact and affect the properties of nanoparticles, the 3-level factorial design is used. With this design approach, a thorough understanding of the process variables can be obtained by exploring a broad variety of component values. Critical quality attributes (CQAs) include important parameters such drug encapsulation efficiency, zeta potential, polydispersity index (PDI), and particle size. These parameters are methodically examined to determine the ideal amounts of each. To further refine the 3-level factorial design, the Box-Behnken methodology is applied. Without the requirement for extensive combination testing, this response surface methodology (RSM) is especially useful for building second-order polynomial models and investigating quadratic response surfaces. By enabling a more accurate comprehension of the interactions between inputs and responses, the Box-Behnken design improves the optimisation process and makes it easier to identify the ideal circumstances for nanoparticle synthesis. By utilising both approaches in tandem, this work methodically determines ideal formulation parameters that minimise nanoparticle size and polydispersity while optimising stability and drug loading efficiency. The outcomes highlight how important a QbD framework is for directing the creation of reliable nanoparticle systems that function consistently and predictably.