Deep learning-based analysis of process parameters of biodiesel production using biochar as a catalyst

Garg, Dhruv; Dashmana, Ashish; Verma, Chirag; Garg, Aditi; Jain, Siddharth

doi:10.1016/j.matpr.2023.01.356

Cited by 1 publication

(2 citation statements)

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“…Researchers need to determine significant traits and delete any that are redundant or unnecessary to increase the performance of the model. 172 Domain knowledge can also be used to select the most informative characteristics that will add the most to the classification process. The size and quality of the dataset are critical factors in the performance of classification models; to train a precise and resilient model, a large, diverse dataset is required with an adequate number of samples representing each type of biomass source.…”

Section: Classification Models For Biomass Source Identificationmentioning

confidence: 99%

“…To build efficient classification models for the identification of biomass sources, careful feature selection and engineering are required. Researchers need to determine significant traits and delete any that are redundant or unnecessary to increase the performance of the model 172 . Domain knowledge can also be used to select the most informative characteristics that will add the most to the classification process.…”

Section: Applications Of ML In the Prediction Of Biochar Yieldmentioning

confidence: 99%

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Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy

Nguyen,

Sharma,

Ağbulut

et al. 2024

Biofuels Bioprod Bioref

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Biochar is emerging as a potential solution for biomass conversion to meet the ever increasing demand for sustainable energy. Efficient management systems are needed in order to exploit fully the potential of biochar. Modern machine learning (ML) techniques, and in particular ensemble approaches and explainable AI methods, are valuable for forecasting the properties and efficiency of biochar properly. Machine‐learning‐based forecasts, optimization, and feature selection are critical for improving biomass management techniques. In this research, we explore the influences of these techniques on the accurate forecasting of biochar yield and properties for a range of biomass sources. We emphasize the importance of the interpretability of a model, as this improves human comprehension and trust in ML predictions. Sensitivity analysis is shown to be an effective technique for finding crucial biomass characteristics that influence the synthesis of biochar. Precision prognostics have far‐reaching ramifications, influencing industries such as biomass logistics, conversion technologies, and the successful use of biomass as renewable energy. These advances can make a substantial contribution to a greener future and can encourage the development of a circular biobased economy. This work emphasizes the importance of using sophisticated data‐driven methodologies such as ML in biochar synthesis, to usher in ecologically friendly energy solutions. These breakthroughs hold the key to a more sustainable and environmentally friendly future.

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Section: Classification Models For Biomass Source Identificationmentioning

confidence: 99%

Section: Applications Of ML In the Prediction Of Biochar Yieldmentioning

confidence: 99%