Improve Process Efficiency Research Articles

A systems perspective on gaps in the person-centered sick leave and rehabilitation process: a Swedish interview study

Background Consensus on priorities to optimize the sick leave and rehabilitation process (SRP) is lacking. Objective To explore perspectives of stakeholders in the SRP on bridging the gap between desired process scope, and actual practice, from a multi-professional, multi-organizational, and interdisciplinary approach. Design and setting Focus group interviews were conducted with various SRP frontline professionals in Region Västra Götaland, Sweden, using purposive sampling to capture a range of experiences. Participants discussed their perceptions of critical changes and priorities needed to meet patients’ SRP needs in a primary care context. All interviews were analyzed using systematic text condensation, as described by Malterud. Subjects General practitioners (n = 6), rehabilitation coordinators and licensed healthcare professionals from primary healthcare (n = 13), administrators from the Social Insurance Agency, the Employment Agency and Social Services (n = 12). Results Through data analysis, the following themes emerged: 1) The need for rules and regulations to enable coherent process governance 2) Challenges and opportunities in person-centered SRP: Professional collaboration, organizational priorities, and the need for enhanced leadership, and 3) Balancing resources and patient needs in the SRP: How to improve care quality and accessibility. In summary, participants mainly discussed how to improve process efficiency and quality of care while balancing available resources and a heavy workload. A main goal was to prevent negative spirals of suboptimal decision-making in individual cases, which could lead to increased work, unfortunate outcomes, and patient suffering. Conclusions This qualitative study indicated that gaps between a desired process scope and actual practice might be bridged by enabling coherent cross-organizational process governance, prioritizing person-centered ways of working, and balancing available resources and workload. The above changes were believed to improve process quality and overall efficiency. Trial registration The study project plan was pre-registered on September 21st, 2020, in the database FOU i VGR (researchweb.org), project number 274941.

Optimisation Studies on Performance Enhancement of Spray Cooling - Machine Learning Approach

The performance optimisation of spray cooling heat transfer systems has been identified as a significant step in improving process efficiency, and the application of machine learning tools is a recent development that has enhanced this. This study aims to maximise the heat transfer coefficient for spray cooling at low heat flux levels. The effects of nozzle inclination angle, water pressure, and spray height on heat transfer coefficient were studied. Taguchi L27 orthogonal array technique was used to perform the experiments. A maximum heat transfer coefficient of 181.4 kW/m2K was obtained at a nozzle inclination angle of 60o, spray height of 4 cm, and water pressure of 15 psi. Analysis of variance was performed to find the significance of each variable and its interactions. The results show that for the maximum heat transfer coefficient (181.4 kW/m2K), the optimum levels of the independent variables were A3H1P3, i.e., the highest level of nozzle inclination angle (60o), the lowest level of spray height (4 cm), and the highest level of water pressure (15 psi). The support vector machine outperformed the Random Forest algorithm and Multiple Regression analysis regarding prediction accuracy with a maximum error of 0.15% and root mean squared error of 0.01.

Acoustic black hole immersed sonoreactor for high-efficiency cavitation treatment

Developing innovative sonoreactors to enhance acoustic processing efficiency holds immense importance in the field of sonochemistry. Traditional immersed sonoreactors (TISs) mainly produce cavitation at the probe tip, with a relatively weak cavitation around the probe, resulting in posing challenges for high-efficiency cavitation treatment. Here we propose an acoustic black hole immersed sonoreactor (ABHIS) in longitudinal-flexural coupled vibration, enabling high-efficiency cavitation treatment by unleashing the cavitation potential of the probe. The symmetrical structure of the probe is altered to introduce a coupling of flexural vibration mode, and an acoustic black hole (ABH) profile is integrated to further enhance both flexural wave number and amplitude. In this paper, we present a systematic theoretical design method for ABHIS and compare its performance with TIS using finite element method (FEM). An ABHIS prototype is fabricated and subjected to experimental tests and cavitation observation. The results demonstrate that our theoretical analysis model accurately predicts the frequency characteristics of ABHIS. The proposed ABHIS exhibits satisfactory dynamic characteristics, with significantly increased vibration displacement and acoustic radiation ability compared to TIS. Importantly, the ABH design significantly expands ultrasonic cavitation regions and enhances acoustic radiation intensity of ABHIS resulting in a substantial improvement in acoustic processing efficiency.

Review of Patents on Small Grinding Wheel Polishing Technology

Abstract: Grinding disc polishing is an important polishing method that can be used to manufacture various high-precision and high-quality optical components. The smoothing effect of grinding discs is a simple and effective method widely used to suppress MSF errors. Grinding disc technology has good polishing effects on flat, spherical, aspherical, and free form surfaces. The polishing mechanism is to generate chips through the small plastic cutting of abrasive particles, but it only uses a very small amount of abrasive particles to exert force and uses the flow friction between the abrasive particles and the grinding disc and the workpiece to flatten the surface roughness of the workpiece. It can be seen from the relevant literature that the floating tool holder maintains a constant pressure to solve the problem of uneven pressure distribution between optical components. The adjustable tool holder can adjust the amount of spring deformation to adapt to local surface changes of optical parts, ensuring consistent material removal. The force-adaptive tool holder can be adjusted in real time during the machining process to reduce errors generated during machining. The jointed tool holder improves the fit between the grinding wheel and optical components, ensuring a tight fit between the grinding wheel and optical components. This patent paper aims to introduce grinding wheel polishing technology as an essential method for manufacturing high-precision, high-quality optical components. It explores the optimization of abrasive tool design and the improvement of processing efficiency and proposes suggestions and prospects for future research, applications, and development. Through querying literature and patent databases, patent documents related to grinding disc polishing technology were collected and analyzed. The design characteristics of the device structure, technological innovations, as well as the effectiveness and improvements in practical applications were focused. Grinding disc polishing technology effectively removes surface defects and enhances the optical performance of optical components, particularly in suppressing MSF errors. This technology is suitable for flat, spherical, non-spherical, and freeform surfaces, with broad potential applications. Despite the numerous advantages of grinding disc polishing technology, such as simplicity, effectiveness, and wide applicability, critical issues still need to be addressed, including surface morphology control, improving polishing efficiency, and enhancing consistency. Future research should focus on optimizing tool design and developing new polishing materials and processes to improve the manufacturing quality and efficiency of optical components.

Investigation of Tool Cooling and Heat Transfer Using Computational Fluid Dynamics Simulations

Thermal errors remain one of the biggest challenges for the precision of cutting machine tools. Aside from optimizations in the machine tool design and behaviour, optimal cutting process parameters and targeted usage of cutting fluid or alternative methods of tool cooling are required for improved process efficiency with minimal energy demand and maximal tool life. A simulation-based study is presented which compares both different methods of tool cooling, specifically air cooling, flooded cooling and minimum quantity lubrication and also different simulation methods and models. Using a case study, which models an existing thermal test stand comprised of motor spindle, tool holder, tool and coolant nozzle, different cooling scenarios were tested and compared. The simulations were performed in ANSYS CFX. Comparisons were made between simulations with and without buoyancy, with and without tool rotation, transient and steady-state, with laminar flow and with different turbulence models and between the different cooling scenarios. Some insight on different time step sizes and the resulting increase in simulation time and precision was also gained. These results will make future studies on the thermal behaviour of both tool and cutting process easier by showing suitable simulation techniques and viable model simplifications.

Thermal damage diagnosis in micro grinding processes based on raw acoustic emission signals and convolution neural networks

A micro-grinding process is usually the last machining operation among the various processes employed in the manufacturing of metallic components. It is generally the first option when the manufactured products require a combination of low surface roughness values and narrow dimensional tolerances. However, one of the main challenges of a micro-grinding process is avoiding high specific energy and the corresponding intense heat generation, as this can damage the workpiece. The energy consumed during micro-grinding is almost entirely converted into heat, making the component susceptible to thermal damage. As the occurrence of this damage compromises the service life of manufactured components, the implementation of monitoring systems is essential to guarantee the quality of the manufactured products improving process efficiency. In this study, a new approach based on convolution neural networks (CNNs) is proposed to predict the occurrence of thermal damage in the micro-grinding process. This approach uses raw acoustic emission (AE) signals as inputs into CNN, which is a more technically feasible approach to performing real-time monitoring. To obtain the AE data, micro-grinding experiments were conducted on N2711 grade steel (which is susceptible to thermal damage) under different cutting conditions (ae = 5–50 µm). The obtained AE signals were labeled based on the offline analyses performed on the workpieces, which identified the occurrence of thermal damage through visual inspections, microhardness, and microstructural analysis. For the conditions employed in this work, thermal damage was obtained after grinding with depth of cut values > 10 µm. Total 3960 AE signals were obtained of which 2200 of them were associated with thermal damage. The results depict that the proposed CNN model was able to successfully classify normal and thermal damage AE signals reaching an accuracy of 98.6% over the dataset (0.4% false positives and 1.0% false negatives).

Optimizing Neurosurgery Clinic Operations: A Comparative Study of Interventions in Finland's Public Healthcare System.

The Finnish public healthcare system aims to ensure equal access to health services for all but faces challenges in meeting the demand for specialized care, such as neurosurgery, due to resource constraints. This study investigates interventions to increase resources at a neurosurgery outpatient clinic to improve patient care without compromising waiting times for diagnoses and treatments, leveraging Finland's unique healthcare landscape. The study was conducted at Kuopio University Hospital's Department of Neurosurgery, the sole provider of neurosurgical care in Eastern Finland. Two interventions were designed to optimize clinic operations: one focusing on dynamic resource allocation through continuous monitoring and the other on establishing a fixed additional neurosurgeon slot. Process capability and regression analysis were employed to evaluate the effects of these interventions on the number of outpatient visits and the variability in daily patient numbers. The preliminary analysis showed an average of 9.3 outpatient visits per day (SD 5.2). The introduction of an additional neurosurgeon led to an increase of 5.014 visits per day, according to the regression analysis performed before the interventions. Following the interventions, the clinic observed an increase in the average number of daily outpatient visits to 9.8 after the first intervention and 11.6 after the second, with corresponding improvements in the number of neurosurgeons present. The second intervention, which established a predictable additional resource, resulted in a more significant improvement in process efficiency and stability. After the interventions, the number of new neurosurgical first patient visits increased by 7% (97 patients). This study demonstrates the importance of structured and predictable resource allocation in enhancing the efficiency of specialized healthcare services, particularly in neurosurgery. It also underscores the potential of planned interventions to manage and improve patient care in a publicly funded healthcare system, despite the challenges posed by limited resources and the need for prioritization. Moreover, the findings highlight the necessity of ongoing measurement and analysis of development projects to ensure sustained improvement and avoid regression in process quality.

The Role of AI in Data Engineering and Integration in Cloud Computing

This article presents a comprehensive analysis of the transformative role of Artificial Intelligence (AI) in revolutionizing data engineering and integration processes within cloud computing environments. The article examines the implementation of AI-driven solutions across multiple dimensions, including automated pipeline generation, intelligent schema matching, anomaly detection, and real-time data integration. Through a mixed-methods approach incorporating both quantitative and qualitative analyses, the article demonstrates significant improvements in data processing efficiency, with organizations achieving up to 67% reduction in processing time and 89% enhancement in accuracy. The article encompasses case studies from financial services, healthcare, and e-commerce sectors, providing concrete evidence of practical applications and scalability. Key findings reveal that AI-powered systems substantially outperform traditional approaches in cost efficiency, scalability, and data quality management, while simultaneously reducing operational overhead. The article also addresses implementation challenges, including legacy system integration and initial deployment complexities, offering strategic insights for organizations pursuing AI integration in their data engineering workflows. These article contribute to the broader understanding of how AI technologies can be effectively leveraged to address the growing challenges of data management in cloud computing environments, while providing a framework for future developments in this rapidly evolving field.

Control system and process optimization for electroless nickel plating

A novel method based on mid-frequency vibration is proposed to eliminate coating defects such as bubbles during electroless nickel plating. An automated control system for the plating, enabling precise and stable measurements and adjustments of critical parameters such as plating solution temperature, pH, and nickel ion concentration, is also established, which significantly improves process efficiency and coating quality. Experimental results indicate that the system is capable of realizing stable operation over extended periods. A nonporous nickel–phosphorus coating with a thickness greater than 200 μm is successfully obtained, with high phosphorus content, robust adhesion, and superior machinability.

Design and implementation of an edge embedded intelligent electronic nose system based on 1D convolutional neural network and online passive-aggressive algorithms (1DCNN-OPA)

As an artificial olfactory bionic technology, the electronic nose finds extensive applications in healthcare, food, and environmental monitoring areas. It monitors the concentration, level or composition of the measured gas/odor based on the response of a sensor array, combined with artificial intelligence technologies like pattern recognition and deep learning. To effectively address the growing demands for system intelligence, processing efficiency, and recognition accuracy in electronic nose systems, this study proposes an intelligent edge-embedded electronic nose system based on online learning. The system is developed based on an array composed of 7 commercial gas sensors, and cooperated by a single chip micro-controller and an embedded GPU platform. A gas/odor recognition algorithm based on the one-dimensional convolutional neural network (1DCNN) incorporated with an Online Passive-Aggressive (OPA) algorithm, named as 1DCNN-OPA is proposed and embedded into the system to enhance the gas/odor recognition accuracy with online transfer learning and incremental learning scheme. Meanwhile, the algorithm model is optimized for inference acceleration to achieve efficient deployment at the embedded edge. Experimental results demonstrate that the designed intelligent electronic nose system performs well on different platforms. In the task of gas/odor concentration estimation, the system achieves a classification accuracy exceeding 95%, with a coefficient of determination (R2) of 0.82 for peracetic acid concentration quantification. Compared to PC platforms, the inference speed on embedded edge devices is increased by 30 times, effectively improving processing efficiency and portability while ensuring accuracy, thus meeting the application requirements for detection/monitoring of gas environments in various scenarios.

Optimization of Business Processes Through BPM Methodology: A Case Study on Data Analysis and Performance Improvement

This study explores the application of the BPM lifecycle to optimize the market analysis process within the market intelligence department of a major energy company. The semi-structured, virtual nature of the process necessitated careful adaptation of BPM methodology, starting with process discovery through data collection, modeling, and validation. Qualitative analysis, including value-added and root-cause analysis, revealed inefficiencies. The redesign strategy focused on selective automation using Python 3.10 scripts and Power BI dashboards, incorporating techniques such as linear programming and forecasting to improve process efficiency and quality while maintaining flexibility. Post-implementation, monitoring through a questionnaire showed positive results, though ongoing interviews were recommended for sustained performance evaluation. This study highlights the value of BPM methodology in enhancing decision-critical processes and offers a model for adaptable, value-driven process improvements in complex organizational environments.

Cloud-Native ETL: Integrating Databricks and Azure Data Factory for Scalable Data Processing in Enterprise Environments

This article examines the implementation of cloud-native ETL solutions leveraging Databricks and Azure Data Factory (ADF) for scalable data processing in enterprise environments. The article presents a comprehensive analysis of the architectural design, integration strategies, and performance optimization techniques for combining Databricks' powerful data transformation capabilities with ADF's robust workflow orchestration. Through a series of case studies and empirical evaluations, we demonstrate how this integrated approach addresses the challenges of big data processing, including scalability, flexibility, and cost-effectiveness. Our findings reveal significant improvements in processing efficiency and resource utilization, with observed reductions in ETL pipeline execution times by up to 40% and overall cloud infrastructure costs by 25%. The article also highlights best practices for data governance, security, and quality management within this framework. These insights provide valuable guidance for data engineers and IT professionals seeking to modernize their data processing infrastructure and harness the full potential of cloud-native ETL solutions.

Research on machining technology of stepped boring for intersecting holes in aircraft

Abstract During the assembly process of a large-scale aircraft fuselage, critical bearing areas require the drilling of intersecting holes, which are commonly processed using traditional single-edged reamers by most domestic manufacturers. To improve the efficiency and accuracy of the finishing process for these holes, enhance the quality of the assembly, and further shorten the construction cycle for precision processing, improvements need to be made to traditional single-edge reamer machining methods. This paper proposes a new approach that uses stepped reamers to machine intersecting holes. By setting up an experimental platform to simulate actual processing conditions and optimize processing parameters, the study analyzes and compares the quality and efficiency of traditional reaming and stepped reaming modes. The results show that stepped reaming can improve processing efficiency by 50.6% compared to traditional machining while also reducing human intervention and minimizing quality risks. The findings from this research project can be extended to various aircraft fuselages or wings for precise drilling of intersecting holes, providing significant guidance for efficient and high-quality delivery of aircraft.

A critical Review on Advanced Membrane Bioreactors for Wastewater Treatment: Fouling Reduction and Energy Demand

Objectives:Rapid industrialization has highlighted the importance of addressing environmental issues and the increasing demand for water supply. As a result, innovative solutions to improve water circulation management in both public and industrial sectors have been proposed, one of which is the introduction of Membrane Bioreactor (MBR) technology. This study aims to understand the mechanisms and characteristics of membrane fouling in MBR processes and compare the associated energy consumption, with the goal of improving process efficiency and energy performance.Methods:In this study, various factors influencing the MBR process were considered to analyze the mechanisms and characteristics of membrane fouling. The impact of fouling on the process was assessed, and energy consumption was compared. Specifically, the power consumption of flat-sheet and hollow-fiber MBRs was analyzed, focusing on the proportion of energy used for microbial treatment and membrane cleaning. The Specific Energy Demand (SED) was calculated to evaluate the energy consumption efficiency of different fouling control methods in MBRs.Results and Discussion:In typical MBR processes, 68.0% (flat-sheet) and 53.0% (hollow-fiber) of the power consumption is attributed to air scouring for microbial treatment and membrane cleaning. Notably, 57.0% (flat-sheet) and 36.0% (hollow-fiber) of the power is consumed to supply air for detaching adhered foulants to maintain filtration performance. These results indicate that aeration is a major energy consumer in MBR processes. SED is a critical indicator for assessing the energy consumption of specific fouling control methods, with typical MBR processes having an SED of 0.2-0.3kWh/m3. In contrast, using a non-aerated membrane module with reciprocating inertial force for fouling control reduces SED to 0.005-0.01 kWh/m3, representing a 20-60 fold decrease in energy consumption, highlighting the need for energy reduction.Conclusion:This study provides insights into fouling reduction characteristics in MBR processes, suggesting a new paradigm for enhancing the economic feasibility of MBR technology. Applying non-aerated membrane modules to reduce energy consumption can significantly improve MBR efficiency, contributing to energy reduction and environmentally friendly technology development. Therefore, strategies to maximize energy efficiency and minimize environmental impact in MBR processes are essential.

Capacity Constraint Analysis Using Object Detection for Smart Manufacturing

The increasing adoption of Deep Learning (DL)-based Object Detection (OD) models in smart manufacturing has opened up new avenues for optimizing production processes. Traditional industries facing capacity constraints require noninvasive methods for in-depth operations analysis to optimize processes and increase revenue. In this study, we propose a novel framework for capacity constraint analysis that identifies bottlenecks in production facilities and conducts cycle time studies using an end-to-end pipeline. This pipeline employs a Convolutional Neural Network (CNN)-based OD model to accurately identify potential objects on the production floor, followed by a CNN-based tracker to monitor their lifecycle in each workstation. The extracted metadata are further processed through the proposed framework. Our analysis of a real-world manufacturing facility over six months revealed that the bottleneck station operated at only 73.1% productivity, falling to less than 40% on certain days; additionally, the processing time of each item increased by 53% during certain weeks due to critical labor and materials shortages. These findings highlight significant opportunities for process optimization and efficiency improvements. The proposed pipeline can be extended to other production facilities where manual labor is used to assemble parts, and can be used to analyze and manage labor and materials over time as well as to conduct audits and improve overall yields, potentially transforming capacity management in smart manufacturing environments.

Application of Non-Parametric and Forecasting Models for the Sustainable Development of Energy Resources in Brazil

To achieve Sustainable Development Goal 7 (SDG7) and improve energy management efficiency, it is essential to develop models and methods to forecast and enhance the process accurately. These tools are crucial in shaping the national policymakers’ strategies and planning decisions. This study utilizes data envelopment analysis (DEA) and bootstrap computational methods to evaluate Brazil’s energy efficiency from 2004 to 2023. Additionally, it compares seasonal autoregressive integrated moving average (SARIMA) models and autoregressive integrated moving average (ARIMA) forecasting models to predict the variables’ trends for 2030. One significant contribution of this study is the development of a methodology to assess Brazil’s energy efficiency, considering environmental and economic factors to formulate results. These results can help create policies to make SDG7 a reality and advance Brazil’s energy strategies. According to the study results, the annual energy consumption rate is projected to increase by an average of 2.1% by 2030, which is accompanied by a trend of GDP growth. By utilizing existing technologies in the country, it is possible to reduce electricity consumption costs by an average of 30.58% while still maintaining the same GDP value. This demonstrates that sustainable development and adopting alternatives to minimize the increase in energy consumption can substantially impact Brazil’s energy sector, improving process efficiency and the profitability of the Brazilian industry.

Otomatisasi Penyemprotan Polyester Menggunakan Kawasaki Cobot dengan Human Machine Interface (HMI) Berbasis Web

Brago Luchttechniek BV is a company specializing in the production of PIR (Polyisocyanurate) air ducts, facing challenges in maintaining accuracy and consistency in the polyester insulation spraying process on its products using Kawasaki collaborative robots (cobot) for automation. To address these challenges, this study designed a web-based Human Machine Interface (HMI) to automate the polyester insulation spraying process on PIR air ducts using the Kawasaki Cobot. A web-based HMI was chosen for its ease of development, flexibility, and integration capabilities with modern technology. This HMI uses the TCP/IP protocol to send real-time command signals to automation devices such as the Kawasaki Cobot and devices connected to the Programmable Logic Controller (PLC). Testing results demonstrated an improvement in the spraying process efficiency, reducing the time from 15–18 minutes conventionally to 5–7 minutes with the web-based HMI automation. Additionally, the web-based HMI successfully communicated with other devices connected to the robot, such as the conveyor belt and rotating table, via TCP/IP protocols, with each device having its own ID and IP address to facilitate linear message delivery, prevent collisions, and minimize errors. After eight successful automation tests on four different products, the system operated smoothly and systematically through the available HMI controls. The system was also evaluated by four respondents, with an overall score of 4.33 out of 5, indicating a high level of satisfaction.

Enabling data-driven process dynamic modeling for extractive leaching and chemical precipitation

To address the limitations of static models and gain insight into the processes of extractive leaching and chemical precipitation, a data-driven dynamic modeling strategy is proposed using a Lithium-ion battery recycling case study. The data correlations among pH, temperature, redox potential, conductivity and system state are investigated. Predictive models are then developed to describe the system state online and are employed as surrogate models for time-intensive offline chemical analyses. This enables further process optimization, such as time-saving measures and improved process efficiency through dynamic parameter studies. The proposed strategy serves as a guideline for dynamic modeling and integrates big data methodologies into chemical engineering.

Fisher-informed continual learning for remaining useful life prediction of machining tools under varying operating conditions

Accurate prediction of remaining useful life (RUL) of equipment has become an essential task in manufacturing. It not only helps prevent unexpected failures but also enables maximal utilization of available life, thus improving process efficiency. In practice, however, the use of multiple operating conditions that vary by time impedes efficient data-driven RUL prediction. Unlike conventional supervised learning setups, varying operating conditions generate heterogeneous data with time-varying generating distributions. Thus, existing approaches cannot be effectively applied due to increasing modeling and memory costs. One of the domains that suffer from this issue is machining, where RUL prediction of cutting tools is crucial for productivity. Considering realistic circumstances with varying operating conditions, this work proposes a method named Fisher-informed continual learning (FICL), which enables efficient tool RUL prediction that adaptively learns as conditions change without storing previous data and models. FICL uses Fisher information to improve generalization via sharpness-aware minimization and transfer knowledge between operating conditions through structural regularization. Experiments using datasets from real-world machining processes under five distinct operating conditions prove FICL’s efficacy, indicating its superior prediction performance to existing methods for all operating conditions. Particularly, FICL manifests the least catastrophic forgetting, implying it effectively retains informative knowledge from varying operating conditions.

Fast biodiesel production via potassium ferrate catalysis at room temperature using refined coconut oil and vegetable oil

Abstract Traditionally, the transesterification reaction for biodiesel production takes place at elevated temperatures, around 60-65°C, for up to two hours of reaction time, entailing a high energy consumption. As the University of the Philippines Los Baños – Interdisciplinary Biofuels Research Studies Center (UPLB-IBRSC) continuously aims to conduct studies on improving process efficiencies in biofuels production, this research investigated the potential of potassium ferrate as a catalyst for fast biodiesel production at room temperature. Particularly, the catalytic activity of potassium ferrate in the transesterification of refined coconut oil and vegetable oil was examined. The effects of the parameters such as methanol-to-oil molar ratio, catalyst loading, and reaction time on biodiesel yield and purity were studied. Thin layer chromatography, with the aid of ImageJ software, was applied to analyze the purity of the biodiesel through the developed spots’ area estimation. Characterization experiments in terms of free fatty acid (FFA) content and acid value were performed to confirm the low FFA content of the feedstocks prior to transesterification. Results of the parametric and optimization study revealed that a biodiesel yield of 87.67% using vegetable oil was experimentally achieved at the optimum conditions of 6.75 wt% catalyst loading, 6:1 methanol-to-oil molar ratio, and 30 minutes reaction time. Using coconut oil, on the other hand, resulted in a higher biodiesel yield of 94.90% at a catalyst loading of 6 wt%, a methanol-to-oil molar ratio of 6:1, and a reaction time of 60 minutes. Generally, findings show that catalyst loading has a positive effect on biodiesel yield and purity only up to a certain optimum amount. Among all the parameters analyzed, the methanol-to-oil molar ratio was found to have the most significant effect as an individual factor. Given the results of this study, it can be concluded that the potassium ferrate-catalyzed transesterification reaction at room temperature can potentially reduce the energy consumption in biodiesel production and the production cost in general.