Application of computational technologies for transesterification of waste cooking oil into biodiesel

Biomass and Bioenergy, Journal Year: 2025, Volume and Issue: 194, P. 107620 - 107620

Published: Jan. 18, 2025

Language: Английский

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Current status of research on hydrogen generation, storage and transportation technologies: A state-of-the-art review towards sustainable energy

Process Safety and Environmental Protection, Journal Year: 2024, Volume and Issue: 191, P. 1445 - 1460

Published: Aug. 31, 2024

Language: Английский

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K-means clustering optimization of various quantum dots and nanoparticles-added biofuels for engine performance, emission, vibration, and noise characteristics

Thermal Science and Engineering Progress, Journal Year: 2024, Volume and Issue: 54, P. 102815 - 102815

Published: Aug. 23, 2024

Language: Английский

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Feasibility of waste-to-hydrogen generation system based on gasification/pyrolysis: a comprehensive review of experimental studies

Journal of Thermal Analysis and Calorimetry, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 19, 2024

Language: Английский

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Applications of Artificial Intelligence and Cell Balancing Techniques for Battery Management System (BMS) in Electric Vehicles: A Comprehensive Review

Process Safety and Environmental Protection, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 1, 2024

Language: Английский

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A Case Study on Integrating an AI System into the Fuel Blending Process in a Chemical Refinery

ChemEngineering, Journal Year: 2025, Volume and Issue: 9(1), P. 4 - 4

Published: Jan. 3, 2025

Fuel blending plays a very important role in petroleum refineries, because it directly affects the quality of end products, as well overall profitability refinery. This process involves combination various hydrocarbon streams to make fuels that meet specific performance standards and comply with regulatory guidelines. For many decades, most refineries have been dependent on linear programming (LP) models for developing recipes optimization. However, LP normally fail capture complex nonlinear interaction blend components fuel properties, leading off-specification products may necessitate re-blending. work discusses case study hybrid artificial intelligence (AI)-based method gasoline based genetic algorithm (GA) combined an neural network (ANN). AI-based systems are more flexible will enable product specifications regularly result cost reduction owing fall giveaways. The AI-powered discussed can predict, much better accuracy, critical combustion properties such Research Octane Number (RON), Motor (MON), Antiknock Index (AKI), compared classical models, added advantage optimization ratio real time. results showed AI-integrated system was able produce mean absolute error (MAE) 1.4 AKI. obtained MAE is close experimental uncertainty 0.5 octane. A high coefficient determination (R2) 0.99 also when validated new set 57 comprising primary reference blends. highlights potential transforming traditional practices towards sustainable economically viable refinery operations.

Language: Английский

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A comprehensive review on forward osmosis mass transfer and fouling: Mathematical modeling, mechanism, prediction and optimization

Journal of Water Process Engineering, Journal Year: 2025, Volume and Issue: 72, P. 107677 - 107677

Published: April 1, 2025

Language: Английский

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Modeling and Simulation of Biodiesel Production in the Supercritical Process Using Response Surface Methodology and Machine Learning Techniques

Published: Jan. 1, 2025

Language: Английский

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Optimization of Thermal Performance in Lauric Acid‐Based Phase Change Materials Using a Priority Clustering Approach

Energy Storage, Journal Year: 2024, Volume and Issue: 6(6)

Published: Sept. 1, 2024

ABSTRACT This study investigates the thermal properties of lauric acid (LA) as a phase change material (PCM) using K ‐Means clustering method to analyze melting characteristics. focuses on optimization PCMs hybrid methodology analytic hierarchy process (AHP) and clustering. LA, enhanced with zinc oxide (ZnO) nanoparticles, was evaluated for its performance. LA's suitability PCM is based initial temperature, heating rate, final time melt. AHP employed determine weightage three critical outcomes: latent heat, point, conductivity. The weightages assigned were 59%, 31%, 11%, respectively, reflecting relative importance each outcome in assessing efficiency LA PCM. Furthermore, then applied categorize data these weighted outcomes. utilized input parameters, assigning 27% 15% 22% underscoring their significance analysis. optimal conditions identified an temperature 24.8°C, ieating rate 5.6°C/min, 81.4°C, melt 10.6 min. These resulted outcomes 208 J/g point 80.9°C, conductivity 0.21 W/m·K. approach provides robust framework optimizing performance, facilitating energy storage release practical applications.

Language: Английский

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A novel intensified anaerobic co-digestion process for high-quality biomethane production using ultrasound energy: Optimization and robustness tests with ML-RSM-TLBO algorithm

Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 325, P. 119408 - 119408

Published: Dec. 17, 2024

Language: Английский

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