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

Abstract

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.

Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy

“…By these processes, carbon dioxide is released into the atmosphere. As a result of this process, carbon absorption and release are brought into equilibrium, which ultimately results in either net-zero emissions or maybe even net-negative emissions when biomass residues are used as fuel by the process [106], [305]. As an additional benefit, biofuels that are generated from biomass, such as biodiesel and bioethanol, provide environmentally friendly alternatives for the transportation and industrial sectors, resulting in a significant reduction in emissions of greenhouse gases.…”

Biomass: A Versatile Resource for Biofuel, Industrial, and Environmental Solution

“…Since then, pyrolysis technologies have gained popularity and are now often used to make charcoal and coke [101], [102]. There are advantages and disadvantages associated with biomass pyrolysis, which is a thermochemical process that involves heating biomass in the absence of oxygen to produce biochar, bio-oil, and syngas [103]- [105].…”

Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms

“…ML and AI have shown to be very helpful in the area of biomassto-energy conversion (García-Nieto et al, 2023;Wang and Yao, 2023). This is attributed to their ability to handle complex, highdimensional data and enhance nonlinear processes (V. G. Nguyen et al, 2024d;Tang et al, 2023). Accurate simulation of conversion processes like as pyrolysis, gasification, and combustion made possible by these technologies enables one to predict and enhance system performance (Alruqi et al, 2024;Ge et al, 2023).…”