Prediction of Biomass Composition in Fluidized Matrix Biomass Gasifier

Biomass prediction plays a critical role in sustainable forestry management which means that it encompasses the social and environmental aspects of the conservation and use of forests. It can also be used in carbon sequestration is a process of removing carbon dioxide from the atmosphere and in ecosystem health monitoring. The reason for using the machine learning models for predicting biomass is because when we use traditional methods often rely on time-consuming and labor-intensive field measurements. We collected a comprehensive dataset consisting of various natural features of biomass composition and environmental variables such as soil type and climate data. This rich dataset is used to train and evaluate a range of machine learning models like Random Forest, XG boost, and weighted ensemble. Our results demonstrate which machine learning model can predict biomass accurately and efficiently. By comparative analysis, a voting ensemble combined with logistic regression is used for enhanced insights into biomass prediction. Because this model has more features like diverse model overfitting, reducing overfitting, and Hyperparameter tuning. To assess the scalability and generalization of our approach, we applied the trained model to estimate biomass in different forest regions which consist of different types of gasses and environmental features, and achieved consistent performance. Our study demonstrates the potential of machine learning as a valuable tool for precise and efficient biomass prediction, with implications for sustainable forest management, climate change mitigation, and biodiversity conservation. This developed model can serve as a valuable resource for forestry management, urban planning, and ecosystem health monitoring. This biomass prediction process is based on the moreover steps like gasification which means converting the carbon materials into fuel or gas.