[HTML][HTML] Machine learning approaches to modeling and optimization of biodiesel production systems: State of art and future outlook

NB Ishola, EI Epelle, E Betiku - Energy Conversion and Management: X, 2024 - Elsevier
One of the main limitations to the economic sustainability of biodiesel production remains
the high feedstock cost. Modeling and optimization are crucial steps to determine if …

[HTML][HTML] Machine learning-aided modeling for predicting freshwater production of a membrane desalination system: A long-short-term memory coupled with election …

M Abd Elaziz, ME Zayed, H Abdelfattah… - Alexandria Engineering …, 2024 - Elsevier
Membrane desalination (MD) is an efficient process for desalinating saltwater, combining
the uniqueness of both thermal and separation distillation configurations. In this context, the …

[HTML][HTML] Experimental investigation and machine learning modeling using LSTM and special relativity search of friction stir processed AA2024/Al2O3 nanocomposites

F Djouider, M Abd Elaziz, A Alhawsawi… - Journal of Materials …, 2023 - Elsevier
In this study, the friction stir technique is proposed to process aluminum nanocomposites
reinforced with alumina nanoparticles. The effects of different processing parameters …

[HTML][HTML] Predictive Models for Biodiesel Performance and Emission Characteristics in Diesel Engines: A Review

W Ai, HM Cho - Energies, 2024 - mdpi.com
With the increasing global demand for renewable energy, biodiesel has become a
promising alternative to fossil fuels with significant environmental benefits. This article …

Machine Learning Accelerated Discovery of Entropy-Stabilized Oxide Catalysts for Catalytic Oxidation

X Duan, Y Li, J Zhao, M Zhang, X Wang… - Journal of the …, 2024 - ACS Publications
The catalytic properties of unary to ternary metal oxides were already well experimentally
explored, and the left space seems like only high entropy metal oxides (HEOs, element …

Investigating the impact of alumina nanoparticles in coconut oil distillate biodiesel to lessen emissions in direct injection diesel engine

K Rajesh, C Bibin, G Soundararajan… - Scientific Reports, 2024 - nature.com
Petroleum fuels are commonly used for automobiles. However, the continuous depletion
and exhaust gas emission causes serious problems. So, there is a need for an alternative …

Machine learning modeling of the capacitive performance of N-doped porous biochar electrodes with experimental verification

X Liu, H Yang, P Xue, Y Tang, C Ye, W Guo - Renewable Energy, 2024 - Elsevier
N-doped porous biochar is considered as a promising carbon material for supercapacitor
electrodes application. However, the intrinsic relations and effect mechanisms of the pore …

Machine Learning Technologies in the Supply Chain Management Research of Biodiesel: A Review

S Kim, J Seo, S Kim - Energies, 2024 - mdpi.com
Biodiesel has received worldwide attention as a renewable energy resource that reduces
greenhouse gas (GHG) emissions. Unlike traditional fossil fuels, such as coal, oil, and …

Preparation of Nanoparticle-Enriched Fuels and Prediction of Cylinder Pressure Through Machine Learning Models

KM Karaoglan, M Çelik - Arabian Journal for Science and Engineering, 2024 - Springer
Precise estimation of cylinder pressure (CP) in internal combustion engines (ICEs) is critical
for optimizing engine performance, increasing fuel efficiency, and controlling emissions. This …

[HTML][HTML] Application of computational technologies for transesterification of waste cooking oil into biodiesel

O Awogbemi, DA Desai - Biomass and Bioenergy, 2025 - Elsevier
Continued depletion of fossil-based energy resources, environmental concerns, and the
necessity to assuage the ever-increasing global energy demand have resulted in efforts to …