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Öğe Improving the prediction of biochar production from various biomass sources through the implementation of eXplainable machine learning approaches(Taylor & Francis Inc, 2024) Nguyen, Van Giao; Sharma, Prabhakar; Agbulut, Uemit; Le, Huu Son; Cao, Dao Nam; Dzida, Marek; Osman, Sameh M.Examining the game-changing possibilities of explainable machine learning techniques, this study explores the fast-growing area of biochar production prediction. The paper demonstrates how recent advances in sensitivity analysis methodology, optimization of training hyperparameters, and state-of-the-art ensemble techniques have greatly simplified and enhanced the forecasting of biochar output and composition from various biomass sources. The study argues that white-box models, which are more open and comprehensible, are crucial for biochar prediction in light of the increasing suspicion of black-box models. Accurate forecasts are guaranteed by these explainable AI systems, which also give detailed explanations of the mechanisms generating the outcomes. For prediction models to gain confidence and for biochar production processes to enable informed decision-making, there must be an emphasis on interpretability and openness. The paper comprehensively synthesizes the most critical features of biochar prediction by a rigorous assessment of current literature and relies on the authors' own experience. Explainable machine learning techniques encourage ecologically responsible decision-making by improving forecast accuracy and transparency. Biochar is positioned as a crucial participant in solving global concerns connected to soil health and climate change, and this ultimately contributes to the wider aims of environmental sustainability and renewable energy consumption.Öğe Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy(Wiley, 2024) Nguyen, Van Giao; Sharma, Prabhakar; Agbulut, Uemit; Le, Huu Son; Truong, Thanh Hai; Dzida, Marek; Tran, Minh HoBiochar 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.Öğe Precise prognostics of biochar yield from various biomass sources by Bayesian approach with supervised machine learning and ensemble methods(Taylor & Francis Inc, 2024) Nguyen, Van Giao; Sharma, Prabhakar; Agbulut, Uemit; Le, Huu Son; Tran, Viet Dung; Cao, Dao NamBiomass pyrolysis is a sustainable process for generating biochar from agricultural waste, though it is generally energy-intensive and time-consuming. To address this issue, the researchers gathered data from published literature on various biomass types and employed ensemble methods (LSBoost) and supervised machine learning (Gaussian process regression) to construct predictive models. The results reveal that both models can predict well, with excellent correlations between expected and actual values. In comparison to the LSBoost model (0.9783 for training and 0.9879 for testing), the Gaussian process regression (GPR) model had higher R values for training (0.9883) and testing (0.9969). Likewise, the R2 values during training (0.9767) and testing (0.9938) were greater in the case of the GPR model than for the LSBoost model (0.9571 for training). Nash-Sutcliffe efficiency (NSE) revealed that both models captured the data precisely. However, the GPR model outperformed the LSBoost model in both during training as well as model test stages, providing higher (0.9766 for training and 0.9933 for testing) values. The GPR model outperforms the others due to superior correlation, improved variability capture, and lower errors. These findings offer useful insights for sustainable biomass utilization and provide valuable insights for optimizing pyrolysis operations.Öğe Recent advances in hydrogen production from biomass waste with a focus on pyrolysis and gasification(Pergamon-Elsevier Science Ltd, 2024) Nguyen, Van Giao; Nguyen-Thi, Thanh Xuan; Nguyen, Phuoc Quy Phong; Tran, Viet Dung; Agbulut, Umit; Nguyen, Lan Huong; Balasubramanian, DhineshEnergy consumption was skyrocketing along with fast economic development as well as continuous global population growth. Furthermore, environmental concerns about greenhouse gas emissions were also increasing, which indicated that these issues could be resolved by the development and utilization of renewable and clean energy. Among many renewable kinds of energy, hydrogen was considered the cleanest generating water because it was the only combustion product, allowing for truly zero pollutant emissions. As a result, developing efficient hydrogen generation technologies that utilized biomass feedstock, and ensured clean energy produced with low-carbon emissions was critical in helping fight against global warming as well as obtain waste recovery. In this paper, recent investigation advances in the generation of hydrogen from biomass pyrolysis and gasification were comprehensively reviewed in this regard. Also, the most recent studies on biomass pyrolysis and gasification in the use of biomass waste to produce hydrogen, with an emphasis on technical problems, efficiency, and mechanism were summarized. Following that, challenges and opportunities were presented for enhancing the efficiencies of the process and the quality of the products, which was significant to obtain sustainable and green development. Importantly, even though several advances and innovations in generating hydrogen from biomass were made using existing technologies, more scientific advances were needed to make it economically competitive as well as environmentally friendly for industrial production on large scale. Overall, the entire article gives a consolidated overview of the optimal condition suggested for superior H2 yield, and %volume from various biomass.(c) 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
