Recruitment of long short-term memory for envisaging the higher heating value of valorized lignocellulosic solid biofuel: A new approach

Abstract

The valorization of lignocellulosic wastes via the concept of bio-based circular economy to achieve the sustainable development goals of clean energy, safe life on land, and climate change mitigation is a worldwide scope nowadays. Lignocellulosic wastes are considered sustainable energy resources; consequently, it is crucial to find a cost-effective and time-saving method for predicting its higher heating value (HHV) to qualify its feasibility as a solid biofuel. In this study, the long short-term memory (LSTM) algorithm as a deep-learning (DL) approach has been applied in a pioneering step to calculate the HHV from 623 proximate analyses of various lignocellulosic biomasses. The relatively high value of the correlation coefficent of determination (R2 0.8567) and low values of mean square error (MSE 0.67), root-mean-square error (RMSE 0.819), mean absolute error (MAE 0.597), and average absolute error (AAE 0.0319) confirmed the exceptional accuracy of the suggested LSTM model. Thus, recommending applying DL-LSTM as a new approach for building models since it provides an accurate prediction of HHV without the need for time-consuming and complicated experimental measurements or the conventional regression analysis and statistical modeling.