Improve Process Efficiency Research Articles

Improving efficiency in green tea production time using lean manufacturing approach with value stream mapping: A case study at PT Candi Loka [Perbaikan efisiensi waktu produksi teh hijau menggunakan pendekatan produksi ramping dengan value stream mapping: Studi kasus di PT Candi Loka

Green tea production activities at PT Candi Loka indicated a time-consuming, which caused process efficiency to be less than optimal. Process efficiency improvements must be made to increase company competitiveness and consumer satisfaction. This research aimed to analyze the efficiency of green tea production time at PT Candi Loka by identifying the processing time required in each production activity, including the time consumption and formulating recommendations for improvement using a lean manufacturing approach using the VSM (Value Stream Mapping) method. There were 30 activities in 6 processes consisting of receiving raw materials, withering, rolling, drying, and packaging. Identification of value streams using the PAM (Process Activity Mapping) tool produced 9 Value added (VA) activities, 19 Necessary but Non-Value Added (NNVA) activities, and 2 Non-Value Added (NVA) activities in the form of delays in the withering and drying processes. In the current state map analysis, the production lead time value was 315.286 seconds. Recommended improvements to eliminate delays in the withering process were to set up the rotary panner machine earlier and increase the number of supervisors, while to eliminate delays in the drying process were to implement a piece rate system. Increasing efficiency was also carried out by improving NNVA activities by adding weighbridge facilities and using a conveyor system. The future state map proposed by implementing recommended improvements resulted in an increase in production time efficiency to 222.356 seconds and an increase in the process cycle efficiency value from 58.95% to 84.85%.

Benefits of Using Data-Driven Lean Manufacturing in Textile and Apparel Manufacturing Units in Pakistan

The objective of the present study is to investigate the advantages of lean manufacturing in textile and apparel manufacturing facilities in Pakistan. This study examines the influence of lean manufacturing on the time-saving, process efficiency, and industry improvement. Modern organizations are employing lean manufacturing as a critical concept in their operations. There are numerous advantages to employing lean manufacturing; consequently, the textile industry is also transitioning to this methodology. The quantitative methodology employed in this investigation is employed to investigate the current subject matter. The study's data is gathered from the customers and employees of textile and apparel manufacturing units in Pakistan. In order to accumulate data from the respondents of the investigation, questionnaires were implemented. The study's findings suggest that lean manufacturing is significantly positively correlated with industry improvement, process efficiency, and time savings. These three factors are also essential for the success of an organization. This investigation also encompasses practical and managerial implications, future direction, and limitations.

The Effect of Fractionation Temperature Using Spinning Band Distillation on the Myristicin Content of Nutmeg Oil (Myristica fragrans)

Abstract Myristicin is the main compound in nutmeg oil which has potential as an antioxidant, anti-inflammatory and antiproliferative. Miristicin can be obtained through the fractionation distillation process. Fractionated distillation generally requires very high columns to get better results. Spinning band distillation is one of the separation methods built on the principle of simple fractionated distillation with further improved processing efficiency. The purpose of this study was to examine the effect of fractionated temperature on the yield and content of myristicin. This experiment used a pressure of 15 mmHg, a reflux ratio of 5:1, and an equilibration time of 10 minutes. This study used 3 types of experiments with the treatment of differences in fractionated temperature ranges. Each experiment was divided into 4 fractions with different temperature ranges. The results showed that myristicin evaporates at temperatures above 230 °C (AET). The fractionation process under suitable operating conditions can result in high yields and concentrations of myristicin.

Physics-guided neural network for grinding temperature prediction

Creep-feed grinding is a high-efficiency, high-precision grinding process widely used in the manufacturing of aviation engines. However, the workpiece burn and other quality issues caused by high processing temperature limit the yield of grinding. Therefore, the accurate model of grinding temperature has become the key to improving processing efficiency and quality. Different from the traditional physical or data-driven models, this paper attempts to combine both perspectives based on Physics-Guided Neural Networks (PGNN) to accurately predict grinding temperature with a small number of experiments. At the level of data acquisition, real grinding experiment data was obtained and a data augmentation method had been proposed. At the level of neural network structure, optimisation processes were implemented to enhance prediction performance, and a physics-guided loss function was inserted to guide network training. The experiment results shows that PGNN had better prediction accuracy than the physical model, while also mitigating the limitations of data-driven models on small sample sets. PGNN also performed better with noisy data and predictions out of the training data range, this reveals the benefits of PGNN for small sample problems in processing scenarios.

Application of Parallel Computing in the Analysis of Usability Tests, Under the Mouse Tracking Approach

Mouse tracking tests play a crucial role in evaluating software usability, but their efficient image processing remains challenging, especially with high-resolution images and numerous participants. This article introduces a novel method, leveraging parallel computing, for the efficient analysis of interaction zones in mouse tracking test images. Following Pratt's iterative research pattern, the proposed method is implemented using Python libraries Dask and OpenCV, validated through a proof of concept on Eclipse software. Results demonstrate the parallel approach's remarkable efficiency, being 252 times faster than the sequential method across various executions. The method's potential impact is discussed, providing a valuable reference for developing tools in usability and other application contexts. The open-source tools employed, Dask and OpenCV, prove suitable for parallel image analysis, offering versatility for broader application in diverse fields. This work contributes to advancing the field of mouse tracking analysis by significantly improving processing efficiency and lays the groundwork for future tools and methodologies.

Upgrading of dichloromethane to olefins by hydrodechlorination: Improving process efficiency by the addition of Fe to carbon nanotubes-supported Pd catalyst

Upgrading of waste dichloromethane to produce olefins has been performed by hydrodechlorination (HDC) using bimetallic catalysts consisting of Pd-Fe/CNT, Pd-Sn/CNT and Pd-Ag/CNT. Pd/CNT was also studied. Pd-Fe/CNT showed the best performance in terms of activity (X≈81 %) and stability, with the highest selectivity to olefins (>60 %, mainly C2H4), at 350 °C, which could be mainly attributed to the existence of stable Pd-Fe interaction. Characterization results and density functional theory calculations suggest that incorporating Fe atoms into the Pd lattice changes its electronic and geometrical properties. Fe atoms show stronger dissociative adsorption of CH2Cl2 than Pd ones, leading to lower activation energies for the dechlorination of CH2Cl2 on the studied Pd-Fe(111), which also favors the formation of C-C bond between two adjacent CH2*, promoting the formation of C2H4. Pd-Sn and Pd-Ag catalysts hinder olefins formation, due to the non-reactive Sn atoms and significant metal segregation in case of Pd-Ag.

Query Optimization by Quantifier Elimination

Query optimizers have a limited arsenal of techniques for optimizing nested queries. In this paper, we develop a new approach for query optimization based on quantifier elimination. Quantifier elimination is a well-established tool for proving the decidability of logical theories. Here, however, we show that it can be turned into an effective query optimization technique that may yield asymptotic improvements in query processing efficiency. In addition, the technique establishes a foundation for certain well-known but previously little-understood aggregation based techniques for optimizing nested queries.

Advancements in Motion Detection Within Video Streams Through the Integration of Optical Flow Estimation and 3D-Convolutional Neural Network Architectures

Motion detection in video streams is important for many applications, such as autonomous navigation, human-computer interaction, and spying. Conventional techniques, which depend on backdrop removal or frame differencing, frequently break down when dealing with intricate motion patterns and occlusions. Current techniques struggle to handle occlusions and correctly capture complex motion patterns. Furthermore, these techniques could be computationally expensive, especially when dealing with big amounts of video data. An integrated strategy combining optical flow estimation with 3D convolutional neural network (3D-CNN) architectures is presented to address these limitations and boost motion detection systems' accuracy and efficiency. The suggested technique is unusual as it combines 3D CNNs with optical flow estimates. Motion vectors representing dynamic scene changes are produced by optical flow estimates, and spatiotemporal characteristics are extracted by 3D CNNs processing together with optical flow information. By using the complementing capabilities of both approaches, the method performs better in motion detection applications such as object tracking, action recognition, and anomaly detection in video streams. The efficiency of the strategy is assessed by experiments carried out on benchmark datasets. The results show that it is more accurate, resistant against occlusions, and computationally efficient than existing approaches. The suggested approach offers a viable way to improve motion detection capabilities for a range of practical uses. The results show a 98% improvement in processing efficiency and motion detection accuracy when compared to baseline methods. More research and advancement in this area are made possible by the attainable way that MATLAB's suggested approach offers to enhance motion detection skills in a variety of real-world applications.

Tax compliance auditing as a tool for detecting and preventing tax risks for the detection and prevention of tax risks

Tax auditing is a fundamental practice in financial management, evolving from its origins in Great Britain to become an essential global tool. Its objective is to evaluate and verify financial information to ensure regulatory compliance and transparency. This study analyzed good practices in tax audits, highlighting the importance of planning, impartiality and regulatory compliance. Innovative methodologies, such as data analysis and risk-based auditing, were also explored to improve process efficiency. The results highlighted the need for effective tax management and the prevention of economic crises in organizations. In conclusion, tax auditing is crucial to ensure compliance with tax obligations and improve the financial management of organizations, promoting transparency and efficiency in the management of financial resources.

Simulation study of the electrothermal field in a chloride electrolysis cell with 16 electrode pairs

AbstractThe spatial distribution of the electrothermal field within molten salt electrolytic cells significantly influences the efficiency of the electrolysis process, the quality of the end products, and the overall stability of the system. Enhancing the design of chloride electrolysis cells can lead to marked improvements in both process efficiency and system stability, thereby yielding increased economic benefits. This study introduces a design for a chloride electrolysis cell and examines the thermal budget under elevated electrical current intensities across 16 electrode pairs. It further analyzes the degree to which various parameters affect thermal equilibrium. The findings indicate that the electrolytic cell maintains a small thermal budget discrepancy at a current intensity of 124 kA and is less than 5%, with substantial heat dissipation observed at the surface of the top area of anode exposure and considerable energy consumption at both the anode and the electrolyte. Dimensionless treatment of the different parameterized simulation results has been conducted to develop a nondimensional equation, describing the thermal equilibrium conditions within the electrolytic cell. This foundational work paves the way for further investigations into the interactions between the electrothermal field and other physical fields, as well as for the optimization of the electrolytic cell's geometry and design.

Enhancing Regular Expression Processing through Field-Programmable Gate Array-Based Multi-Character Non-Deterministic Finite Automata

This work investigates the advantages of FPGA-based Multi-Character Non-Deterministic Finite Automata (MC-NFA) for enhancing regular expression processing over traditional software-based methods. By integrating Field-Programmable Gate Arrays (FPGAs) within a data processing framework, our study showcases significant improvements in processing efficiency, accuracy, and resource utilization for complex pattern matching tasks. We present a novel approach that not only accelerates database and network security applications, but also contributes to the evolving landscape of computational efficiency and hardware acceleration. The findings illustrate that FPGA’s coherent access to main memory and the efficient use of resources lead to considerable gains in processing times and throughput for handling regular expressions, unaffected by expression complexity and driven primarily by dataset size and match location. Our research further introduces a phase shift compensation technique that elevates match accuracy to optimal levels, highlighting FPGA’s potential for real-time, accurate data processing. The study confirms that the benefits of using FPGA for these tasks do not linearly correlate with an increase in resource consumption, underscoring the technology’s efficiency. This paper not only solidifies the case for adopting FPGA technology in complex data processing tasks, but also lays the groundwork for future explorations into optimizing hardware accelerators for broader applications.

Effects of Antifoaming Agents on Manufacturing Silver Dendrites Through Fluoride-Assisted Galvanic Replacement Reaction

Abstract In fluoride-assisted galvanic replacement reaction (FAGRR), metallic dendrites are formed simultaneously with hydrogen gas. However, the presence of hydrogen bubbles impedes the reduction of metallic ions to form metallic dendrites. This study investigates the FAGRR approach to manufacturing Ag dendrites where ethanol is incorporated into an AgNO3 reaction solution. The findings of this study demonstrate the efficacy of ethanol as an antifoaming agent in enhancing the deposition of the Ag dendrites during the FAGRR process. The antifoaming effect of ethanol becomes more intense at higher concentrations of AgNO3. The introduction of ethanol into FAGRR can significantly improve processing efficiency and yield in the limited time for manufacturing science and engineering.

Lean and sustainability assessment in the sawmill industry using Sus-VSM: A case study in the context of Fiji

Waste from the perspective of lean manufacturing and sustainability has a huge influence on economic, environmental, and societal aspects. The inefficiency of the current manufacturing systems poses a huge risk to small and medium size sawmills going out of business as current prices of fossil fuel and associated emissions continue to rise at an alarming rate. In this case study, a local sawmill has been investigated to improve process efficiency using sustainable value stream mapping in the context of Fiji. The produced timber with the highest demand is selected as the product and a complete assessment is done on the downstream operation to compile data and develop the current state map. The current state map incorporates sustainability pillars to address the lacking factors such as energy and water consumption, raw material usage, labor physical load, and workspace noise level along with standard lean manufacturing time-based metrics. Sustainable value stream mapping allowed the research team along with a panel of experts to identify processor 2 as the root cause of bottlenecking, unnecessary material movement/transportation, and high physical load index score. Implementing a multi-blade horizontal bandsaw in place of processor 2 reduces; energy consumption for transport, energy consumption for processing, VAT, NVAT, process water consumption, raw material usage, and carbon emission by; 34.9%, 8.72%, 6.37%, 20%, 24.71%, 3.05%, and 10.33% respectively without compromising productivity. The findings of this research suggest evaluating and assessing sustainability performance using Sus-VSM in the sawmill industry unveils potential economic, environmental, and societal benefits.

Improving process efficiency to reduce cost-of-goods per dose in manufacturing of recombinant AAVs

Improving process efficiency to reduce cost-of-goods per dose in manufacturing of recombinant AAVs

Effect of abnormal grain refinement on synchronous strengthening behavior of Fe50Mn30Co10Cr10 high entropy alloys

Improving processing efficiency while obtaining excellent material formation and mechanical properties is the primary concern of the manufacturing industry. This study aims to refine the grains of Fe50Mn30Co10Cr10 high entropy alloy during friction stir processing, achieving exceptional tensile properties in the stir zone by increasing the rotational speed. The key driver behind grain refinement lies in the alloy's inherently low stacking fault energy, with the primary recrystallization mechanism being discontinuous dynamic recrystallization. The freshly recrystallized fine grains share analogous microstructures and energy levels during severe plastic deformation, which engenders a state of increased competition among them. This robust competitive dynamic impedes grain growth, thereby promoting the development of refined grains. This microstructure proves instrumental in avoiding premature microcracking during tensile loading by hindering martensite formation, consequently improving the UTS and EL of the material. Nevertheless, the high heat input and substantial strain result in an increase of temperature and strain energy in the system, eventually disrupting the high energy system and causing grain coarsening as the rotational speed continues to increase. This coarsened grain and high martensite fraction lead to premature initiation of cracks under tensile loading, which is not conducive to further improvement of tensile properties.

Selective Oxidation by TS-1 coupled with in-situ Synthesised H2O2

Feedstock valorisation with preformed H2O2 in conjunction with titanosilicate zeotypes is particularly attractive due to the increased selectivity, lower operating temperatures and improved process efficiency (particularly lower energy usage and enhanced atom economy) compared to aerobic pathways or alternative stoichiometric oxidants. However, numerous environmental and financial concerns associated with the application of commercial H2O2 remain. These concerns have led to a growing interest in the application of the in-situ synthesized oxidant for a range of chemical transformations. Herein, we provide an overview of the key contributions to this emerging field of oxidative catalysis, with a particular focus on propene epoxidation and ketone ammoximation, which are currently two crucial industrial-scale processes that utilise preformed H2O2 with the titanosilicate catalyst TS-1 for chemical synthesis. This contribution will highlight the potential for in-situ H2O2-mediated feedstock upgrading to replace existing technologies and contribute to the declared sustainability goals of the chemical synthesis sector.

Automatic recognition of Rice Plant leaf diseases detection using deep neural network with improved threshold neural network

The farming industry widely requires automatic detection and analysis of rice diseases to avoid wasting financial and other resources, reduce yield loss, improve processing efficiency, and obtain healthy crop yields. The progress made in deep learning techniques has significantly impacted agricultural disease diagnostics. Rice Plant leaf diseases can have severe adverse effects on crop yield, and proper diagnosis of Rice Plant diseases is vital to avoid these effects. Most of the existing methods do not adequately detect the leaf images and cannot find the condition accurately. Previously, there were some problems with leaf segmentation. Complex disease stages are efficient but computationally time-consuming, and segmentation needs to be more accurate, affordable, and reliable. To overcome the issues, the proposed method of Deep Spectral Generative Adversarial Neural Network (DSGAN2) with Improved Artificial Plant Optimization for Rice Plant leaf disease detection. Initially, we fed into the input of healthy and non-healthy leaves from the collected dataset. Then, we apply an Improved Threshold Neural Network (ITNN) method to enhance the image quality. Next, it uses a Segmentation using a Segment Multiscale Neural Slicing (SMNS) algorithm to identify the support-intensive color saturation based on the enhanced image. After that, the Spectral Scaled Absolute Feature Selection (S2AFS) method is applied to select optimal features and the Closest Weight from segmented Rice Plant leaves. Social Spider Optimization to select the Feature with the Closest Weight (S2O-FCW) algorithm for analysis of the feature weight values. Finally, the proposed Soft-Max Logistic Activation Function with Deep Spectral Generative Adversarial Neural Network (DSGAN2) algorithm detects Rice Plant disease based on selected features. The proposed system Deep Spectral Generative Adversarial Neural Network (DSGAN2) produces a decreasing false rate compared to the existing system of ACPSOSVM-Dual Channels Convolutional Neural Network (APS-DCCNN) is 55.2 %, Alex Net is 50.4 %, and Convolutional Neural Network (CNN) is 49.5 %.

Enhancing Hospital Pharmacy Operations Through Lean and Six Sigma Strategies: A Systematic Review.

Hospital pharmacies are integral to the healthcare system, and evaluating the factors influencing their efficiency and service standards is imperative. This analysis offers global insights to assist in developing strategies for future enhancements. The objective is to identify the optimal Lean Six Sigmamethodologies to improve workflow and quality of hospital pharmacy services. A strategic search, aligned with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, encompassed an extensive range of academic databases, including Scopus, PubMed/Medline, Web of Science, and other sources for relevant studies published from 2009 to 2023. The focus was on management tactics and those examining outcomes, prioritizing publications reflecting pharmacy operations management's state. The quality of the selected articles was assessed, and the results were combined and analyzed. The search yielded 1,447 studies, of which 73 met the inclusion criteria. The systematic review found a low to moderate overall risk of bias. The number of publications roseduring the coronavirus disease (COVID-19) outbreak. Among studies, research output in the United States of Americarepresented26% of the total. Other countries such asIndonesia, Spain, Canada, China, Saudi Arabia, the United Arab Emirates, and the United Kingdom also made significant contributions. Each country accounted for 12%, 8%, 7%, 5%, 5%, 5%, and 5%, respectively. The pharmacy journals led with26 publications, and healthcare/medical with 14. The quality category came next with 12 articles, whileseven journals represented engineering.Studies used empirical and observational methods, focusing on practice quality enhancement. The process control plan had 26 instances, and the define, measure, analyze, improve, and control (DMAIC) was identified 13 times. The sort, set in order, shine, standardize, and sustain (5S)ranked third, totaling seven occurrences. Failure mode and effects analysis (FMEA) and root cause analysiswere moderately utilized, with six and four instances, respectively. Poka-Yoke (mistake-proofing measures) and value stream mappingwere each counted three times.Quality improvement and workflow optimization dominated managerial strategies in 22 (30.14%) studies each, followed by technology integration in 15 (20.55%). Cost, patient care, and staffing each featured in three (4.11%) studies, while two (2.74%) focused on inventory management. One (1.37%) study each highlighted continuing education, collaboration, and policy changes. Analysis of the 73 studies on Lean and Six Sigma in hospital pharmacy operations showed significant impacts, with 26% of studies reporting decreased medication turnaround time, 15% showing process efficiency improvements, and 11% each for enhanced inventory management and bottleneck/failure mode reduction. Additionally, 9% of studies observed decreased medication errors, 8% noted increased satisfaction and cost savings, 6% identified enhancements in clinical activities, 3% improved prescription accuracy, 2% reduced workflow interruptions, and 1% reported increased knowledge.Also, this study has identified key strategies for service delivery improvement and the importance of quality practices and lean leadership. To the best of the author's knowledge, this research is believed to be the first in-depth analysis of Lean and Six Sigma in the hospital pharmacy domain, spanning 15 years from 2009 to 2023.

A POCUS-first pathway to streamline care for children with suspected ileocolic intussusception.

As point-of-care ultrasound (POCUS) has emerged as a valuable tool for intussusception screening, this quality improvement study aimed to implement a "POCUS-first" pathway in a Pediatric Emergency Department (ED) to streamline workflow and expedite care for children with suspected intussusception. This was a prospective analysis of children diagnosed with ileocolic intussusception in a Pediatric ED between June 2022 and June 2023. The study compared the "POCUS-first" cohort with the group receiving only radiology-performed ultrasound. Key outcomes included physician initial assessment to radiology-performed US time and physician initial assessment to reduction time. Continuous improvement efforts incorporated pediatric emergency medicine physician training, education, and pathway dissemination through plan-do-study-act cycles. The study included 29 patients in the "POCUS-first" pathway group and 70 patients in the non-POCUS group. The "POCUS-first" pathway demonstrated a significantly shorter physician initial assessment to reduction time compared to the non-POCUS group (170.7min vs. 240.6min, p = 0.02). Among non-transferred patients, the "POCUS-first" group also had a significantly shorter emergency department length of stay (386min vs. 544min, p = 0.047). Implementation of a "POCUS-first" pathway for managing ileocolic intussusception led to notable improvements in process efficiency. The shorter physician initial assessment to reduction time highlights the potential for expedited decision-making and intervention. These study findings support the potential of this pathway to optimize the management and outcomes of children with ileocolic intussusception.

Simulation and quantitative analysis of Raman spectra in chemical processes with autoencoders

Raman spectroscopy represents an advanced process analytical technology to monitor and control chemical and biochemical processes. This study presents an autoencoder-based methodology that simulates Raman spectra from process variables and predicts the concentrations of different chemicals. The methodology accurately predicts concentrations from the spectra, even considering the temperature influences, and can work as an anomaly detector in process monitoring. The proposed methodology has significant implications for the optimization of industrial processes, improving process efficiency, reducing waste, and minimizing costs. It can also be extended to other industrial processes and imaging spectroscopy techniques, making it a valuable tool for process monitoring. This study highlights the effectiveness of autoencoders in simulating spectra and quantitative analysis, contributing significantly to the field of process monitoring. It has the potential to revolutionize industrial process monitoring and optimization, leading to substantial improvements in productivity and sustainability.