Predictive Modeling of Process Parameters in WCO-Based Biodiesel Production Using Advanced Regression Techniques

2025-04-24T06:42:06+00:00

Biodiesel production from waste cooking oil (WCO) presents a compelling opportunity to transform discarded oil into a renewable energy resource. Through the conversion of WCO into biodiesel, not only is waste effectively reduced, but a greener, more sustainable alternative to conventional fossil fuels is provided—furthering the shift towards environmentally conscious energy solutions. The importance of this research cannot be overstated. It plays a crucial role in advancing sustainable energy practices, especially by tapping into WCO as a viable and underutilized feedstock for biodiesel production. Consider the scale of global WCO generation: in Canada alone, 135,000 tons are produced annually, while in Asia, the figures soar to a staggering 5.5 million tons. The vast potential for converting this surplus waste into high-value biofuel not only promises substantial environmental benefits but also unlocks significant economic opportunities. The methodology leveraged three distinct machine learning models: Linear Regression (LR), Random Forest Regression (RFR), and Support Vector Regression (SVR).

These models were rigorously trained and tested on experimental data derived from biodiesel production processes. The study delved into four critical parameters: Free Fatty Acid (FFA) content, fluctuating between 1.7% and 3.5%, moisture percentage ranging from 0.05% to 0.3%, viscosity measured at 35 to 43 cSt, and reaction time spanning 2 to 3.3 hours. The results were striking, underscoring the robust predictive power of all three models. SVR stood out, achieving the highest training accuracy (R² = 0.998), while RFR exhibited a remarkable ability to generalize well on unseen test data (R² = 0.989). The analysis uncovered compelling correlations: notably, a robust negative relationship between FFA content and biodiesel yield (-0.91), alongside a positive correlation between viscosity and yield (0.85).

These findings underline the capacity of machine learning models to accurately predict biodiesel yields from waste cooking oil (WCO). Each model revealed unique strengths, yet even the simpler Linear Regression model, with an impressive R² of 0.979 on test data, pointed to a predominantly linear link between the process parameters and the final yield. Such insights provide invaluable guidance for refining industrial biodiesel production processes, championing the shift towards sustainable energy alternatives and addressing the pressing issues of waste management.

Optimizing Image Processing in OmniView with EDAS Decision-Making

2025-05-06T07:43:25+00:00

Abstract: The evaluation of healthcare management systems is crucial for optimizing patient care and administrative efficiency. This study applies the Evaluation based on Distance from Average Solution (EDAS) methodology to assess six leading healthcare management systems: PointClickCare, MatrixCare, CareCloud, Cerner LTC, Epic MyChart, and Meditech Expanse. The evaluation is based on six key parameters: Scalability, Security & Compliance, Cloud & Integration, Cost & ROI, Technical Support & Training, and Reporting & Analytics. The study aims to provide a comparative analysis to help healthcare providers make data-driven decisions when selecting an optimal management system.

Research Significance: With the increasing adoption of digital solutions in healthcare, selecting a robust and scalable management system is essential. The research highlights the significance of security, interoperability, cost-effectiveness, and analytical capabilities in ensuring operational success. By employing the EDAS methodology, this study provides an objective framework for evaluating healthcare systems, offering valuable insights for stakeholders.

Methodology: EDASThe Evaluation based on Distance from Average Solution (EDAS) is a multi-criteria decision-making (MCDM) approach that evaluates alternatives based on their positive and negative distances from an ideal average solution. The weighted parameters help quantify each system's performance, allowing for a fair and transparent comparison. This methodology ensures an unbiased assessment, prioritizing key performance indicators that influence decision-making in healthcare IT.

Alternative Healthcare Management Systems Point Click Care – A comprehensive cloud-based solution for long-term and post-acute care facilities. Matrix Care – A healthcare management platform tailored for senior living and home care providers. Care Cloud – A flexible, cloud-based EHR and practice management system.Cerner LTC – A long-term care solution offering electronic health records and billing capabilities.Epic MyChart – A patient-centric system that enables real-time access to medical records.Meditech Expanse – A cloud-native EHR system designed for hospitals and clinics.

Evaluation Parameters: Each healthcare system is assessed based on six essential criteria:Scalability – The system's ability to grow with increasing patient volumes and expanding healthcare facilities.Security & Compliance – Adherence to industry regulations such as HIPAA and data protection standards.Cloud & Integration – Compatibility with cloud platforms and interoperability with other healthcare systems.Cost & ROI – The affordability and long-term return on investment of the solution.Technical Support & Training – Availability of customer support, onboarding assistance, and user training programs.Reporting & Analytics – Advanced data analysis and reporting features for better decision-making.

Results: The comparative evaluation using EDAS methodology identified PointClickCare as the top-ranking system, excelling in Technical Support & Training, Security, and Scalability. Cerner LTC and Epic MyChart performed well in Cloud & Integration and Reporting, while Meditech Expanse demonstrated strong cloud-based capabilities. MatrixCare and CareCloud ranked lower, indicating potential limitations in adaptability and security compliance. These results highlight the trade-offs involved in selecting a healthcare management system and emphasize the importance of aligning system capabilities with organizational needs.

Keywords: Healthcare Management System, Electronic Health Records (EHR), Scalability, Security & Compliance, Cloud-Based Integration, Cost-Benefit Analysis, Multi-Criteria Decision-Making (MCDM), Technical Support in Healthcare IT.

Journal of Business Intelligence and Data Analytics

Predictive Modeling of Process Parameters in WCO-Based Biodiesel Production Using Advanced Regression Techniques

Optimizing Image Processing in OmniView with EDAS Decision-Making