Improve Process Efficiency Research Articles

Implementasi SOP Account Payable Pada Pelaporan Penerimaan Barang di Vasa Hotel Surabaya

This research aims to analyze the implementation of Standard Operating Procedures (SOP) for accounts payable in reporting goods receipts at the Vasa Hotel Surabaya. Quantitative methods are used with data collection techniques through observation, interviews and document analysis. The research results show that the implementation of SOP accounts payable still faces several challenges, especially in terms of completeness of Receiving Report documents and coordination between departments. This research provides recommendations for improving process efficiency and compliance with existing SOPs.

Improving processing efficiency through workflow process reengineering, simulation and value stream mapping: a case study of business process reengineering

PurposeBusiness Process Reengineering (BPR) eliminates non-value-added (NVA) and essential non-value-added (ENVA) waste through radical process redesign to improve organizational operations. Comprehensive research integrating BPR tools is needed to understand their benefits for manufacturing firms. This research presents an integrated BPR-simulation framework tailored to the manufacturing sector to maximize process improvements and operational excellence.Design/methodology/approachThe BPR design methodology adopts a systematic, multi-stage approach. The first phase involves identifying a specific improvement process aligned with BPR's core objectives. This phase analyses and redesigns workflows to optimize task sequences, roles, and stakeholder interactions while eliminating redundancies and inefficiencies via Workflow Process Reengineering. Visual process mapping tools, including VSM and simulation, pinpoint areas of waste, delay, and potential enhancement. The second phase follows the workflow analysis and aims to improve efficiency and effectiveness by redefining roles, rearranging tasks, and integrating automation and technology solutions. The redesigned process undergoes evaluation against key performance indicators to ensure measurable improvements are achieved. The final phase validates the proposed changes through simulation models, assesses the impact on key performance metrics, and establishes the necessary infrastructure for successful implementation. The proposed model is empirically validated through a case study of a leading apparel company in Vietnam, confirming its effectiveness.FindingsThe findings reveal that NVA activities are being eliminated, and ENVA activities in key departments are significantly reduced. This yielded a substantial improvement, reducing 25 out of 186 combined ENVA and NVA operations in the sewing facility, involving a decrease of 15 ENVA operations and the removal of 10 NVA operations. Consequently, this led to an 8.5% reduction in the proportion of ENVA operations, accompanied by a complete 100% elimination of NVA activities.Research limitations/implicationsThe single case study limits generalizability; thus, expanded implementation across diverse manufacturing sub-sectors is required to establish validity and broader applicability of the integrated framework.Originality/valueThe experimental results highlight the proposed model's effectiveness in optimizing resource utilization and its practical implementation potential. This structured BPR methodology enables organizations to validate, evaluate, and establish proposed process changes to enhance operational performance and productivity.

Mathematical modeling of the enzymatic hydrolysis of lignocellulosic waste in membrane bioreactor considering transport phenomena

Enzymatic hydrolysis is a crucial step in processing lignocellulosic biomass into biofuels and other valuable products in biorefineries. Applying a membrane reactor to realize this process allows for continuous separation of inhibitory end-products, thus improving process efficiency. Developed so far models have generally been dedicated to describing the reaction kinetics without considering transport phenomena and flux decline prediction. In the present paper, a new mathematical model simulating the hydrolysis process course in membrane bioreactor was established. Compared to previous reports, our study presents a novel approach to modeling the process accounting for the reaction course and permeate flux decline depending on reaction hydrodynamics. A surface renewal theory and empirical reaction kinetic model were implemented to simulate the influences of the operating parameters on the permeate flux and the monosaccharide concentration in the permeate. A procedure to determine the model parameters with experimental data was also presented. The proposed model simulated experimental data with good accuracy (R2>0.97). The total mass of glucose and xylose produced during 10 h of the hydrolysis performed under mixing conditions equaled 2.37 g and 2.39 g for the pressure of 0.4 and 0.6 bar, respectively. They correspond to the high hydrolysis yield, equaled in average to 99.1 %.

Exploring and understanding the microwave-assisted pyrolysis of waste lignocellulose biomass using gradient boosting regression machine learning model

The production of bio-oil is a complex process influenced by various parameters. Optimizing these parameters can significantly enhance bio-oil yield, thus improving process efficiency. This study aims to develop a predictive model for bio-oil yield using the Gradient Boosting Regression (GBR) technique. It also seeks to identify the key factors affecting bio-oil yield and determine the optimal conditions for maximizing production. The GBR model was constructed using data collected from the literature. The model's performance was evaluated based on its determination coefficients for training and testing datasets. Optimization studies were conducted to identify the best conditions for bio-oil production. The GBR model demonstrated high precision, with determination coefficients of 0.983 and 0.913 for the training and testing datasets, respectively, indicating its effectiveness in predicting bio-oil yield. The optimal conditions for maximizing bio-oil yield were identified as 20 min of pyrolysis time, a temperature of 771 °C, and 524W of microwave power. The two-way PDP analysis provided valuable insights into the interactive effects of temperature with other factors, enhancing the understanding of the dynamics of the bio-oil production process. This study not only identifies the most impactful variables for bio-oil yield but also offers critical guidance for optimizing the production process.

Design framework of a computer-generated hologram that performs volumetric beam shaping for advanced laser processing

Axial beam shaping is very effective for material laser processing, typically laser cutting, drilling, and grooving. We demonstrate a framework for designing a computer-generated hologram (CGH) that performs volumetric beam shaping. The procedure performs axial beam shaping with a continuous intensity distribution, unlike our previous research in which only discrete focal points were arranged three-dimensionally. This research is the more general approach for volumetric beam shaping. An important point in this research is finding an optimal interval in the optical axis direction and in calculating the CGH design. The design interval is half of the focusing length (the full width at half-maximum of the laser beam profile in the axial direction) given by the diffraction limit of the optical system. The optimal value is obtained using an axially shaped beam that is the reconstruction of the CGH calculated from Zernike polynomials. We also demonstrate that the optimal interval for evaluating the axially shaped beam is also half of the beam length. Following the CGH design procedure, we demonstrate CGHs that generate long-focus beams with an arbitrary axially shaped beam. We found a tradeoff relation between the focusing length and the intensity of the long-focus beam, suggesting that the use of a focused beam with an appropriate length according to the purpose will lead to improved processing efficiency.

Integrating Change Management with a Knowledge Management Framework: A Methodological Proposal

This study proposes the integration of change management with a knowledge management framework to address knowledge retention and successful change management in the context of Industry 5.0. Using the ADKAR model, it is suggested to implement strategies for training and user acceptance testing. The research highlights the importance of applying the human capital life cycle in knowledge and change management, demonstrating the effectiveness of this approach in adapting to Industry 5.0. The methodology includes a review of the state of the art in intangible asset management, change management models, and the integration of change and knowledge management. In addition, a case study is presented in a food production company that validates the effectiveness of the ADKAR model in implementing digital technologies, improving process efficiency and increasing employee acceptance of new technologies. The results show a significant improvement in process efficiency and a reduction in resistance to change. The originality of the study lies in the combination of the ADKAR model with intangible asset and knowledge management, providing a holistic solution for change management in the Industry 5.0 era. Future implications suggest the need to explore the applicability of the ADKAR model in different industries and cultures, as well as its long-term effects on organisational sustainability and innovation. This comprehensive approach can serve as a guide for other organisations seeking to implement successful digital transformations.

Enhancing monolignol ferulate conjugate levels in poplar lignin via OsFMT1

BackgroundThe phenolic polymer lignin is one of the primary chemical constituents of the plant secondary cell wall. Due to the inherent plasticity of lignin biosynthesis, several phenolic monomers have been shown to be incorporated into the polymer, as long as the monomer can undergo radicalization so it can participate in coupling reactions. In this study, we significantly enhance the level of incorporation of monolignol ferulate conjugates into the lignin polymer to improve the digestibility of lignocellulosic biomass.ResultsOverexpression of a rice Feruloyl-CoA Monolignol Transferase (FMT), OsFMT1, in hybrid poplar (Populus alba x grandidentata) produced transgenic trees clearly displaying increased cell wall-bound ester-linked ferulate, p-hydroxybenzoate, and p-coumarate, all of which are in the lignin cell wall fraction, as shown by NMR and DFRC. We also demonstrate the use of a novel UV–Vis spectroscopic technique to rapidly screen plants for the presence of both ferulate and p-hydroxybenzoate esters. Lastly we show, via saccharification assays, that the OsFMT1 transgenic p oplars have significantly improved processing efficiency compared to wild-type and Angelica sinensis-FMT-expressing poplars.ConclusionsThe findings demonstrate that OsFMT1 has a broad substrate specificity and a higher catalytic efficiency compared to the previously published FMT from Angelica sinensis (AsFMT). Importantly, enhanced wood processability makes OsFMT1 a promising gene to optimize the composition of lignocellulosic biomass.

Effects of different post-harvest processing methods on changes in the active ingredients of licorice based on LC-MS and plant metabolomics.

Licorice, the dried roots and rhizomes of the Glycyrrhiza uralensis Fisch., holds a prominent status in various formulations within the realm of Chinese medicinal practices. The traditional processing methods of licorice hinder quality assurance, thus prompting Chinese medicine researchers to focus on the fresh processing methods to enhancing processing efficiency and quality. This study aimed to identify the differential compounds of licorice between traditional and fresh processing methods and provide a scientific basis for the fresh processing of licorice and for further research on the processing mechanism. A methodology integrating ultra-performance liquid chromatography with quadrupole-time-of-flight tandem mass spectrometry combined with multivariate statistical analysis was employed to characterize the differential compounds present in licorice between traditional processing and fresh processing. The results derived from principal component analysis and heat map analyses underscored significant differences in the content of bioactive compounds between the two processing methods. By applying conditions of VIP > 1.5 and p < 0.05, a total of 38 differential compounds were identified through t tests, and the transformation mechanisms of select compounds were illustrated. The adoption of fresh processing techniques not only improved processing efficiency but also significantly enhanced the preservation of bioactive compounds within licorice. This research has established a rapid and efficient analytical method for the identification of differential compounds present in differently processed licorice products.

Sustainable Strategies for Crystalline Solar Cell Recycling: A Review on Recycling Techniques, Companies, and Environmental Impact Analysis

Solar PV is gaining increasing importance in the worldwide energy industry. Consequently, the global expansion of crystalline photovoltaic power plants has resulted in a rise in PV waste generation. However, disposing of PV waste is challenging and can pose harmful chemical effects on the environment. Therefore, developing technologies for recycling crystalline silicon solar modules is imperative to improve process efficiency, economics, recovery, and recycling rates. This review offers a comprehensive analysis of PV waste management, specifically focusing on crystalline solar cell recycling. The classification of PV recycling companies based on various components, including solar panels, PV glass, aluminum frames, silicon solar cells, junction boxes, plastic, back sheets, and cables, is explored. Additionally, the survey includes an in-depth literature review concentrating on chemical treatment for crystalline solar cell recycling. Furthermore, this study provides constructive suggestions for PV power plants on how to promote solar cell recycling at the end of their life cycles, thereby reducing their environmental impact. Moreover, the techno-economic and environmental dimensions of solar cell recycling techniques are investigated in detail. Overall, this review offers valuable insights into the challenges and opportunities associated with crystalline solar cell recycling, emphasizing the importance of economically feasible and environmentally sustainable PV waste management solutions in the constantly evolving solar energy market.

Removal of acetaminophen from wastewater using a novel bimetallic La/Th metal-organic framework: Kinetics, thermodynamics, isotherms, and optimization through Box-Behnken design

Acetaminophen (ACT), a common drug pollutant, has been effectively removed from wastewater by a novel adsorbent called La/Th-MOF. This adsorbent is made of stacked nanorods of 2-methyl imidazole with lanthanum and thorium. La/Th-MOF, which is characterized by sophisticated techniques including SEM, XRD, and BET analysis, has a mesoporous assembly with a 1.23 nm pore size and a capacious surface area of 2226.93 m2/g, which facilitates effectual adsorption. The adsorption tests, which were impacted by factors such as pH levels, ACT concentration, and contact time, fit into a pseudo-second-order chemisorption kinetic model and with the Langmuir isotherm, suggesting homogenous monolayer adsorption. The material in question demonstrated a noteworthy maximum adsorption capability of 339.75 mg/g for ACT, indicating its probable as a feasible and economical adsorbent for the mitigation of pharmaceutical contamination in wastewater. Our results designate that pH levels have an important influence on the efficacy of ACT absorption by La/Th-MOF, with an acidic pH 5 environment being most favorable for maximal adsorption at a material dosage of 0.02 g. It was discovered that the adsorption procedure was endothermic. Using pseudo-second-order kinetic models and the Langmuir isotherm, which both fit the experimental data very well, to explain the adsorption kinetics. A chemisorption mechanism was found to be predominant throughout the process, as specified by the predicted adsorption energy of 22.62 kJ/mol. In addition, optimized the influencing parameters by using the Box-Behnken design (BBD) and response surface methodology (RSM), which improved process efficiency. La/Th-MOF, has a remarkable half-life, as demonstrated by six rounds of adsorption and desorption without a discernible decline in performance. Research endeavors aimed at elucidating the interplay between La/Th-MOF and ACT hint to several plausible points of contact that could contribute to the adsorption procedure, such as hydrogen bonding, π-π interactions, pore filling or electrostatic forces. This study, which is innovative in its use, reveals the exceptional efficacy of La/Th-MOF in wastewater purification using ACT extraction. An astounding 339.75 mg/g of optimal adsorption capability was attained at an acidic pH of 5. This highlights how the La/Th-MOF can be used in practice to treat pharmaceutical pollutants found in water sources.

Using Natural Language Processing to Visualize Narrative Feedback in a Medical Student Performance Dashboard.

Clinical competency committees rely on narrative feedback for important insight into learner performance, but reviewing comments can be time-consuming. Techniques such as natural language processing (NLP) could create efficiencies in narrative feedback review. In this study, the authors explored whether using NLP to create a visual dashboard of narrative feedback to preclerkship medical students would improve the competency review efficiency. Preclerkship competency review data collected at the Northwestern University Feinberg School of Medicine from 2014 to 2021 were used to identify relevant features of narrative data associated with review outcome (ready or not ready) and draft visual summary reports of the findings. A user needs analysis was held with experienced reviewers to better understand work processes in December 2019. Dashboards were designed based on this input to help reviewers efficiently navigate large amounts of narrative data. The dashboards displayed the model's prediction of the review outcome along with visualizations of how narratives in a student's portfolio compared with previous students' narratives. Excerpts of the most relevant comments were also provided. Six faculty reviewers who comprised the competency committee in spring 2023 were surveyed on the dashboard's utility. Reviewers found the predictive component of the dashboard most useful. Only 1 of 6 reviewers (17%) agreed that the dashboard improved process efficiency. However, 3 (50%) thought the visuals made them more confident in decisions about competence, and 3 (50%) thought they would use the visual summaries for future reviews. The outcomes highlight limitations of visualizing and summarizing narrative feedback in a comprehensive assessment system. Future work will explore how to optimize the dashboards to meet reviewer needs. Ongoing advancements in large language models may facilitate these efforts. Opportunities to collaborate with other institutions to apply the model to an external context will also be sought.

Development of conjugate heat- and moisture-transfer model for pineapple drying using OpenFOAM

The kinetic model of drying is important for food preservation, particularly for pineapples, which have a high moisture content and are highly perishable. This paper presents a novel approach utilizing OpenFOAM, which is an open-source computational fluid dynamics (CFD) software, to model the hot-air drying kinetics of pineapple slices. The developed conjugate heat- and moisture-transfer model integrates forced-convection phenomena with moisture-diffusion dynamics. Owing to enhancements to the original OpenFOAM solver, C++ programming facilitates the appropriate representation of heat and moisture transfer based on the finite-volume method. Experimental data are utilized to derive a moisture diffusivity model, which achieved high statistical quality with an average R2 of 0.9443. Additionally, the SST k–ω turbulence model accurately describes the airflow around the pineapple-slice model. The developed model agrees well with the experimental results, with an average error of less than 1.29 %. Further analysis using the model provides insights into the effects of variations in the pineapple-slice geometry, where increased inner diameter and reduced thickness result in enhancement indices of 1.002 and 1.005, respectively. This novel CFD solver is promising for simulating the drying processes of various agricultural products, including pineapples, thus contributing to improvements in process optimization and efficiency.

Is Boundary Annotation Necessary? Evaluating Boundary-Free Approaches to Improve Clinical Named Entity Annotation Efficiency: Case Study.

Named entity recognition (NER) is a fundamental task in natural language processing. However, it is typically preceded by named entity annotation, which poses several challenges, especially in the clinical domain. For instance, determining entity boundaries is one of the most common sources of disagreements between annotators due to questions such as whether modifiers or peripheral words should be annotated. If unresolved, these can induce inconsistency in the produced corpora, yet, on the other hand, strict guidelines or adjudication sessions can further prolong an already slow and convoluted process. The aim of this study is to address these challenges by evaluating 2 novel annotation methodologies, lenient span and point annotation, aiming to mitigate the difficulty of precisely determining entity boundaries. We evaluate their effects through an annotation case study on a Japanese medical case report data set. We compare annotation time, annotator agreement, and the quality of the produced labeling and assess the impact on the performance of an NER system trained on the annotated corpus. We saw significant improvements in the labeling process efficiency, with up to a 25% reduction in overall annotation time and even a 10% improvement in annotator agreement compared to the traditional boundary-strict approach. However, even the best-achieved NER model presented some drop in performance compared to the traditional annotation methodology. Our findings demonstrate a balance between annotation speed and model performance. Although disregarding boundary information affects model performance to some extent, this is counterbalanced by significant reductions in the annotator's workload and notable improvements in the speed of the annotation process. These benefits may prove valuable in various applications, offering an attractive compromise for developers and researchers.

Application and performance analysis of advanced materials in engine lightweight

This paper discusses the application and performance analysis of advanced materials in engine lightening. Firstly, we analyze the properties of high performance aluminum alloy, titanium alloy and composite materials, including their composition, mechanical properties and high temperature resistance. Then, the practical application effects of these materials in key components such as automobile and aero-engine are analyzed through concrete cases. For example, the introduction of micronutrient and improvements in heat treatment processes have significantly increased the strength and corrosion resistance of high-performance aluminium alloys. Titanium alloy is widely used in the key parts of aero-engine because of its high strength and light weight, which greatly improves the performance of engine. The low density and high strength of the composite make it an ideal choice for reducing engine weight. Finally, we discuss the current challenges and future directions, and explore solutions to reduce costs, improve processing efficiency and develop new alloys, in order to provide guidance and reference for the further development of engine lightweight technology.

Optimizing Continuous‐Flow Biocatalysis with 3D‐Printing and Inline IR Monitoring

AbstractEnzymatic biocatalysis typically generates less waste, uses less water, and minimizes energy consumption compared to traditional chemical methods. Efficient, cell‐free biosynthesis relies on the reuse of its valuable biocatalysts. Immobilization of enzymes on solid supports, such as enzyme carrier resins (ECRs), offers a reliable and widely deployed approach to maximize enzyme turnover in cell‐free biosynthesis. We focus on two major bottlenecks associated with optimizing cell‐free biocatalysis. First, we apply our lab's 3D‐printed labware to screen ECRs in 96‐well mini‐reactors to optimize enzyme immobilization conditions. Second, we introduce inline infrared spectroscopy to monitor bioreactor output and maximize enzyme productivity. Urease provides a model system for examining immobilization conditions and continuous assessment of biocatalyst performance. As required for the high substrate concentrations to improve process efficiency and minimize waste, urease was studied in unusually high concentrations of its substrate – molar concentrations of urea. The optimized reactor processed 3.24 L of 4.00 M urea at an average volumetric productivity of 13 g ⋅ L−1 ⋅ h−1 over 18 h and achieved an estimated productivity number of &gt;17.4 kg urea processed per g of immobilized urease Type‐IX. This workflow can be generalized to most biocatalytic processes and could accelerate adoption of cell‐free biosynthesis for greater chemical sustainability.

Optimizing Video Queries with Declarative Clues

Video Database Management Systems (VDBMS) leverage advancements in computer vision and deep learning for efficient video data analysis and retrieval. This paper introduces the concept of user-specified Clues, allowing users to incorporate domain-specific knowledge, referred to as Clues, into query optimization. Clues are expressed as Clue types, each associated with optimization rules, and applied to queries through Clue instances. The extensible ClueVQS system we present to incorporate these ideas, optimizes queries automatically, utilizing Clues to improve processing efficiency. We also introduce algorithms to optimize queries using Clues allowing for trade-offs between speed and query accuracy. Our proposals and system address challenges such as data-dependent Clue effectiveness, limiting search space, and accuracy-efficiency trade-offs. Detailed experimental results demonstrate query speedups of up to two orders of magnitude compared to other applicable approaches, and a reduction of the query optimizer time by up to 95% while respecting user-specified accuracy constraints, showcasing the effectiveness of the proposed framework.

Application of Blockchain Technology in Coal Mine Waste Treatment

With the sustainable development of coal mine industry, coal mine garbage disposal has become a key factor restricting the sustainable development of the industry. In order to meet this challenge, this paper analyzes how blockchain technology provides a new solution for coal mine garbage disposal. Through the introduction of blockchain technology, the comprehensive monitoring and transparent management of coal mine garbage disposal process are realized, and the treatment efficiency and credibility are significantly improved. Specifically, blockchain technology is used for data recording and traceability, ensuring the authenticity and traceability of garbage disposal information; The application of intelligent contract realizes the automation and intelligence of processing flow, reduces human intervention and improves processing efficiency; At the same time, combined with the incentive mechanism, the enthusiasm of all parties to participate in garbage disposal has been stimulated. In the future, blockchain technology will be integrated with other advanced technologies to promote the intelligence and efficiency of coal mine garbage disposal and contribute to environmental protection and sustainable development.

Influence of Anode Immersion Speed on Current and Power in Plasma Electrolytic Polishing.

Plasma electrolytic polishing (PeP) is mainly used to improve the surface quality and thus the performance of electrically conductive parts. It is usually used as an anodic process, i.e., the workpiece is positively charged. However, the process is susceptible to high current peaks during the formation of the vapour-gaseous envelope, especially when polishing workpieces with a large surface area. In this study, the influence of the anode immersion speed on the current peaks and the average power during the initialisation of the PeP process is investigated for an anode the size of a microreactor mould insert. Through systematic experimentation and analysis, this work provides insights into the control of the initialisation process by modulating the anode immersion speed. The results clarify the relationship between immersion speed, peak current, and average power and provide a novel approach to improve process efficiency in PeP. The highest peak current and average power occur when the electrolyte splashes over the top of the anode and not, as expected, when the anode touches the electrolyte. By immersion of the anode while the voltage is applied to the anode and counterelectrode, the reduction of both parameters is over 80%.

Bed Stability Control in Pulsed Fluidized-Bed Agglomeration of Instant Riceberry Powder Using an Image-Processing Technique.

The problematic cohesiveness of food powders can commonly be solved using pulsed fluidized-bed agglomeration. However, progressively larger granules may result in unstable fluidization. The aims of this research study were to investigate fluid bed expansion as affected by particle enlargement and to control its stability using an image-processing technique. Instant riceberry powder (IRP) was agglomerated using varied air pulsation frequencies (1, 2.5, and 4 Hz). Bed expansion captured by image processing revealed that expanded bed height decreased with agglomeration time. The results showed an enlargement of agglomerated IRP, expressed in D10, D50, and D90, with narrower distribution presented by span, and an improvement in bulk and reconstitution properties. The reduced Carr index (22-27%) and Hausner ratio (1.28-1.38) presented fair flowability and intermediate cohesiveness, respectively. Additionally, airflow during agglomerate growth was progressively adjusted using the image-processing method to enhance bed hydrodynamic stability, leading to improved process efficiency and product quality. This proposed approach has potential applications in the food powder manufacturing industry, particularly by enhancing the fluidization of cohesive particles with cracks and channels.

Effects of multi-spring wires on hydrothermal performance of double tube heat exchanger

Double-tube heat exchangers play a vital role in industries by facilitating efficient heat transfer between two fluids while keeping them physically separated. Their design offers versatility, reliability, and ease of maintenance, contributing to improved process efficiency, energy conservation, and cost savings across a wide range of applications. This study investigates the influence of multi-spring wires on the performance of double-tube heat exchangers using A CFD turbulence model. The analysis covers friction factor, Nusselt number, and exergy efficiency across Reynolds numbers spanning from 4500 to 7500. Employing a steel spring wire of 2 mm diameter and 1000 mm length in three configurations (1, 2, and 3 springs), the results reveal a consistent increase in Nusselt number with rising Reynolds number. Notably, a 3-wire configuration demonstrates a 112 % enhancement in Nusselt number compared to plain tubes, with a maximum friction factor increase of 134 %. Exergy efficiency experiences significant improvement, rising by 61 % with the 3-wire setup compared to plain tubes. Additionally, the study predicts temperature, pressure, and velocity distributions along the tube through numerical analysis of the results. This study improves the energy efficiency and sustainability of double-tube heat exchangers using multi-spring inserts, lowering emissions and costs while advancing thermal management systems.