Predictive Modeling of Product Yields in Microwave-Assisted Co-Pyrolysis of Biomass and Plastic with Enhanced Interpretability Using Explainable AI Approaches

Journal of Analytical and Applied Pyrolysis, Journal Year: 2025, Volume and Issue: unknown, P. 107021 - 107021

Published: Feb. 1, 2025

Language: Английский

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Development of machine learning model for the prediction of selectivity to light olefins from catalytic cracking of hydrocarbons

Fuel, Journal Year: 2024, Volume and Issue: 381, P. 133682 - 133682

Published: Nov. 15, 2024

Language: Английский

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Ensemble Approach Assisted Specific Capacitance Prediction for Heteroatom‐Doped High‐Performance Supercapacitors

International Journal of Energy Research, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

Specific capacitance plays a critical role when assessing the performance of supercapacitor. Hence, its prediction is crucial for evaluating electrochemical electric double‐layer capacitors (EDLCs). Machine learning (ML) offers prospect predicting with nominal investment in synthesis and testing electrode materials. Herein, six ML models: random forest (RF), artificial neural network (ANN), tree (RT), committee (RC), subspace (RS), support vector machine (SVM) regressor are used to analyze effect four hetero atom doping (nitrogen, boron, sulfur, phosphorous) on EDLCs. Amongst all, RF, ANN, RS showed highest correlation values 0.9996, 0.9993 0.9867, respectively, lowest root mean square 0.93, 1.19, 2.31, through selection 12 key input descriptors basis physical, structural, test, operational, parameters. Furthermore, attribute prioritization was introduced identify rank important features within dataset. It highlights that specific surface area, total pore volume, nitrogen most significant among selected features. With fewer iterations, developed models’ estimation accuracy surpassed other state‐of‐art models literature. In perspective, this study considers an extensive dataset extracted from more than 250 research articles heteroatom‐doped carbon electrodes. also provides insights into significance modeling technology.

Language: Английский

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Artificial intelligence in plastic recycling and conversion: A review

Resources Conservation and Recycling, Journal Year: 2024, Volume and Issue: 215, P. 108090 - 108090

Published: Dec. 18, 2024

Language: Английский

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The Use of Nuclear Magnetic Resonance Spectroscopy (NMR) to Characterize Bitumen Used in the Road Pavements Industry: A Review

Molecules, Journal Year: 2024, Volume and Issue: 29(17), P. 4038 - 4038

Published: Aug. 26, 2024

Bitumen, a vital component in road pavement construction, exhibits complex chemo-mechanical properties that necessitate thorough characterization for enhanced understanding and potential modifications. Nuclear Magnetic Resonance (NMR) spectroscopy emerges as valuable technique probing the structural compositional features of bitumen. This review presents an in-depth exploration role NMR bitumen characterization, highlighting its diverse applications determining content, group composition, molecular dynamics, interaction with additives. Various techniques, including free induction decay (FID), Carr-Purcell-Meilboom-Gill (CPMG), Pulsed Field Gradient Stimulated Echo (PFGSE), are discussed context their utility analysis. Case studies, challenges, limitations associated NMR-based critically evaluated, offering insights into future research directions. Overall, this provides comprehensive overview current state-of-the-art identifies avenues further advancement field.

Language: Английский

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Synthesis and Characterization of Biochar Obtained from Microwave-Assisted Copyrolysis of Torrefied Sawdust and Polystyrene

ACS Sustainable Resource Management, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 10, 2024

Language: Английский

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