Optimization of biodiesel production from rice bran oil by ultrasound and infrared radiation using ANN-GWO

Sebayang, Abdi Hanra; Kusumo, Fitranto; Milano, Jassinnee; Shamsuddin, Abd Halim; Silitonga, A.S.; Ideris, Fazril; Siswantoro, Joko; Veza, Ibham; Mofijur, M.; Chia, Shir Reen

doi:10.1016/j.fuel.2023.128404

Cited by 27 publications

(7 citation statements)

References 48 publications

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“…Furthermore, raising the ultrasonic frequency from 1 MHz to 3 MHz results in higher conversion rates (48.7 to 79.5%) for the same reaction time [217]. A few studies revealed that the combination of ultrasound and microwave irradiation was efficient and the yield of methyl esters from rice bran oil was around 97.74% [218].…”

Section: Transesterification Using Ultrasoundmentioning

confidence: 99%

Kernel-Based Biodiesel Production from Non-Edible Oil Seeds: Techniques, Optimization, and Environmental Implications

Sambasivam,

Kuppan,

Laila

et al. 2023

Energies

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Biodiesel represents a renewable alternative to conventional diesel, offering comparable potential. This paper delves into the production of biodiesel from non-edible oil seeds, emphasizing kernel-based feedstocks for their sustainable qualities. We discuss the critical stages of kernel separation and degumming, offering an in-depth examination of seed distribution, attributes, pretreatment, and oil extraction methodologies. Additionally, the paper considers the status of life cycle assessment (LCA) associated with biodiesel. Furthermore, it outlines the necessary steps toward sustainable biodiesel production and underscores the importance of integrating a sustainable circular bioeconomy in biodiesel synthesis.

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Section: Transesterification Using Ultrasoundmentioning

confidence: 99%

Kernel-Based Biodiesel Production from Non-Edible Oil Seeds: Techniques, Optimization, and Environmental Implications

Sambasivam,

Kuppan,

Laila

et al. 2023

Energies

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“…The training is carried out for the period specified in the requirements. To choose the appropriate training technique and training epochs, a neural network will take into account the values of the input parameter and then make predictions about the values of the output parameter based on those predictions [144], [145]. In the event that the error value is lower than the permissible value, the trained neural network that employs that particular combination of training algorithms has the possibility of being selected as the ideal neural network with the most effective training algorithm.…”

Section: A Artificial Neural Networkmentioning

confidence: 99%

Artificial intelligence applications in solar energy

Le,

et al. 2024

JOIV : Int. J. Inform. Visualization

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Renewable energy research has become significant in the modern period owing to escalating prices of fossil fuels and the pressing need to reduce greenhouse gas emissions. Solar energy stands out among these sources due to its abundance and global accessibility. However, its weather-dependent and cyclical nature add inherent risks, making effective planning and management difficult. Soft computing technologies provide attractive solutions for modeling such systems, while machine learning and optimization techniques are gaining popularity in the solar energy industry. The current literature highlights the growing use of soft computing technologies, emphasizing their potential to address difficult challenges in solar energy systems. To effectively reap the benefits, these strategies must be seamlessly connected with emerging technologies like the Internet of Things (IoT), big data analytics, and cloud computing. This integration provides a unique opportunity to improve the scalability, flexibility, and efficiency of solar energy systems. Researchers can use these synergies to create intelligent, linked solar energy ecosystems capable of real-time optimization of energy production, delivery, and consumption. These technologies have the potential to transform the renewable energy environment, allowing for more resilient and sustainable energy infrastructures. Furthermore, as these technologies improve, there is a growing demand for trained experts to address associated cybersecurity problems, assuring the integrity and security of these sophisticated systems. Researchers may pave the road for a more sustainable and energy-efficient future by working collaboratively and using interdisciplinary methodologies.

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“…Optimization techniques, including linear and non-linear equations, are highly recommended for this purpose. These techniques are advantageous because they are easy to analyze and capable of resolving complex conditions effectively [21].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Biodiesel Production Process Using MoO3 Catalysts and Residual Oil: A Comprehensive Experimental 23 Study

Silva,

Pereira,

Sales

et al. 2024

Molecules

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The study aimed to utilize MoO3 catalysts, produced on a pilot scale via combustion reaction, to produce biodiesel from residual oil. Optimization of the process was conducted using a 23 experimental design. Structural characterization of the catalysts was performed through X-ray diffraction, fluorescence, Raman spectroscopy, and particle size distribution analyses. At the same time, thermal properties were examined via thermogravimetry and differential thermal analysis. Catalytic performance was assessed following process optimization. α-MoO3 exhibited a monophasic structure with orthorhombic phase, whereas α/h-MoO3 showed a biphasic structure. α-MoO3 had a larger crystallite size and higher crystallinity, with thermal stability observed up to certain temperatures. X-ray fluorescence confirmed molybdenum oxide predominance in the catalysts, with traces of iron oxide. Particle size distribution analyses revealed polymodal distributions attributed to structural differences. Both catalysts demonstrated activity under all conditions tested, with ester conversions ranging from 93% to 99%. The single-phase catalyst had a long life cycle and was reusable for six biodiesel production cycles. The experimental design proved to be predictive and significant, with the type of catalyst being the most influential variable. Optimal conditions included α-MoO3 catalyst, oil/alcohol ratio of 1/15, and a reaction time of 60 min, resulting in high biodiesel conversion rates and showcasing the viability of MoO3 catalysts in residual oil biodiesel production.

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Optimization of biodiesel production from rice bran oil by ultrasound and infrared radiation using ANN-GWO

Cited by 27 publications

References 48 publications

Kernel-Based Biodiesel Production from Non-Edible Oil Seeds: Techniques, Optimization, and Environmental Implications

Kernel-Based Biodiesel Production from Non-Edible Oil Seeds: Techniques, Optimization, and Environmental Implications

Artificial intelligence applications in solar energy

Optimization of Biodiesel Production Process Using MoO3 Catalysts and Residual Oil: A Comprehensive Experimental 23 Study

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