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Öğe Hydrothermal carbonization of food waste as sustainable energy conversion path(Elsevier Sci Ltd, 2022) Le, Huu Son; Chen, Wei-Hsin; Ahmed, Shams Forruque; Said, Zafar; Rafa, Nazifa; Le, Anh Tuan; Ağbulut, ÜmitEvery day, a large amount of food waste (FW) is released into the environment, causing financial loss and un-predictable consequences in the world, highlighting the urgency of finding a suitable approach to treating FW. As moisture content makes up 75% of the FW, hydrothermal carbonization (HTC) is a beneficial process for the treatment of FW since it does not require extensive drying. Moreover, the process is considered favorable for carbon sequestration to mitigate climate change in comparison with other processes because the majority of the carbon in FW is integrated into hydrochar. In this work, the reaction mechanism and factors affecting the HTC of FW are scrutinized. Moreover, the physicochemical properties of products after the HTC of FW are critically presented. In general, HTC of FW is considered a promising approach aiming to attain simultaneously-two core benefits on economy and energy in the sustainable development strategy.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.
