Application of Artificial Intelligence in the Prediction of Thermal Properties of Biomass

Biomass has been agreed to be the most sustainable and abundant renewable source which can be used as a replacement for crude oil-based products. Biomass as value-added products in energy generation must be comprehensively characterized in order to determine its properties. However, the experimental procedure for these analyses demands instruments that are very complex, exorbitant and requires a stable electricity supply. The advancement of knowledge in artificial intelligence and blockchain technology is unlocking new potential prediction accuracy for biomass properties. Artificial neural networks (ANNs) have been applied in the prediction and modelling of several processes. Advances in machine learning, rapid development of algorithms and prediction accuracy are the major motivation behind the increasing application of ANN. Therefore, this chapter highlights the methods, which have been applied in the prediction of the properties of biomass. It further discusses the ANN-based prediction models for biomass as regards the thermal properties. The types of models, stages involved in the formulation of prediction models, the paradigm of learning, classification of training algorithm and sensitivity analysis are detailed. The governing principles, applications, merit and challenges associated with this technique are elaborated. Some relevant case studies were reviewed.