Artificial neural networks for the prediction of biochar yield: A comparative study of metaheuristic algorithms

Khan, Muzammil; Ullah, Zahid; Mašek, Ondřej; Naqvi, Salman Raza; Khan, Muhammad Nouman Aslam

doi:10.1016/j.biortech.2022.127215

Cited by 70 publications

(13 citation statements)

References 39 publications

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“…Furthermore, owing to their capacity to learn complicated patterns from big datasets, neural networks have grown in prominence in recent years. 170,171 Deep learning models, as a subtype of neural networks, employ several hidden layers to identify subtle correlations between data, which makes them ideal for biomarker detection and for discriminating between different biomass sources. To build efficient classification models for the identification of biomass sources, careful feature selection and engineering are required.…”

Section: Classification Models For Biomass Source Identificationmentioning

confidence: 99%

“…This adaptability expands the use of biochar to a variety of sectors, thereby increasing its economic worth and overall impact. 170,216 In addition to its direct uses in biochar synthesis, the information acquired from applying ML models to the conversion of biomass may have wider ramifications for biomass usage and projects related to the circular economy. The ideas and approaches developed in this context may be applied to other renewable energy and bio-based product industries, thus encouraging work towards global sustainability objectives.…”

Section: Sustainable Biochar Productionmentioning

confidence: 99%

“…It performs satisfactorily with high‐dimensional information and can handle complicated datasets effectively, making it useful for determining the source of biochar by considering numerous factors. Furthermore, owing to their capacity to learn complicated patterns from big datasets, neural networks have grown in prominence in recent years 170,171 …”

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: Sustainable Biochar Productionmentioning

confidence: 99%

Section: Applications Of ML In the Prediction Of Biochar Yieldmentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

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“…For better patient outcomes, personalized pharmacokinetic models aid in the optimization of drug doses and treatment plans. 147,148 Machine learning plays a crucial role in predicting the blood-brain barrier (BBB) permeability of chemotherapeutics, expediting drug development specifically for glioblastoma. 149 Experimental validation for theranostic agents crossing the BBB in glioblastoma is time-consuming, often taking a decade with low success rates.…”

Section: Role Of Ai In Glioblastoma Using Nanocarriersmentioning

confidence: 99%

Nanomedicine facilitated cell signaling blockade: difficulties and strategies to overcome glioblastoma

Habeeb,

Vengateswaran,

You

et al. 2024

J. Mater. Chem. B

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“…Taguchi method was applied to set multi-factor and horizontal experimental parameters as input variables, and the improved ANN exhibited satisfactory performance in predicting the maximum yield of biochar, indicating that the ANN combined with fast propagation algorithm is an appropriate method to predict the yield of biochar. It should be pointed out that metaheuristic algorithm was reported to be integrated into ANN models, significantly improving prediction accuracy (Khan et al 2022). Further, combining the ANFIS-SSO and PSO algorithm, the prediction performance of model could be improved (Abd El Aziz et al 2017).…”

Section: Prediction Of Biochar Yieldmentioning

confidence: 99%

Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning

Zhang

Chen

et al. 2023

Biochar

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Due to large specific surface area, abundant functional groups and low cost, biochar is widely used for pollutant removal. The adsorption performance of biochar is related to biochar synthesis and adsorption parameters. But the influence factor is numerous, the traditional experimental enumeration is powerless. In recent years, machine learning has been gradually employed for biochar, but there is no comprehensive review on the whole process regulation of biochar adsorbents, covering synthesis optimization and adsorption modeling. This review article systematically summarized the application of machine learning in biochar adsorbents from the perspective of all-round regulation for the first time, including the synthesis optimization and adsorption modeling of biochar adsorbents. Firstly, the overview of machine learning was introduced. Then, the latest advances of machine learning in biochar synthesis for pollutant removal were summarized, including prediction of biochar yield and physicochemical properties, optimal synthetic conditions and economic cost. And the application of machine learning in pollutant adsorption by biochar was reviewed, covering prediction of adsorption efficiency, optimization of experimental conditions and revelation of adsorption mechanism. General guidelines for the application of machine learning in whole-process optimization of biochar from synthesis to adsorption were presented. Finally, the existing problems and future perspectives of machine learning for biochar adsorbents were put forward. We hope that this review can promote the integration of machine learning and biochar, and thus light up the industrialization of biochar. Graphical Abstract

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Artificial neural networks for the prediction of biochar yield: A comparative study of metaheuristic algorithms

Cited by 70 publications

References 39 publications

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

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

Nanomedicine facilitated cell signaling blockade: difficulties and strategies to overcome glioblastoma

Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning

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