https://jbid.sciforce.org/JBID/issue/feedJournal of Business Intelligence and Data Analytics2025-04-24T06:42:06+00:00Suryakiran Navath Ph. D.editor@sciforce.netOpen Journal Systems<p>Navigating the Data-Driven World: Journal of Business Intelligence and Data Analytics (JBID) by Sciforce Publications</p> <p>Unlock the power of data and analytics with the Journal of Business Intelligence and Data Analytics (JBID), a premier publication by Sciforce Publications. JBID serves as a compass for the latest research and innovations in the fields of business intelligence, data analytics, and data-driven decision-making. In this web content, we will explore the significance of JBID, its contributions to the scientific community, and the dynamic realm of business intelligence and data analytics.</p>https://jbid.sciforce.org/JBID/article/view/247Predictive Modeling of Process Parameters in WCO-Based Biodiesel Production Using Advanced Regression Techniques2025-04-24T06:42:06+00:00Tejasvi GorreTejasvigorre@gmail.com<p>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).</p> <p>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).</p> <p>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.</p>2025-03-29T00:00:00+00:00Copyright (c) 2025 Journal of Business Intelligence and Data Analytics