Application of linear regression algorithm and stochastic gradient descent in a machine‐learning environment for predicting biomass higher heating value. (4th September 2020)

• Record Type: Journal Article
• Authors: Ighalo, Joshua O.; Adeniyi, Adewale George; Marques, Gonçalo
• Is Part Of: Biofuels, bioproducts and biorefining. Volume 14:Number 6(2020)
• Page Start: 1286
• Page End: 1295
• Publication Date: 2020-09-04
• Subjects: HHV -- biomass -- machine learning -- artificial intelligence -- stochastic gradient descent -- linear regression; Biomass energy -- Periodicals; Biological products -- Periodicals; Fuel -- Refining -- Periodicals; 662.8805
• DOI: 10.1002/bbb.2140
• Languages: English
• ISSNs: 1932-104X

Abstract

Abstract: The higher heating value (HHV) provides information about the quantity of energy contained in a fuel such as biomass. Correlations and models can be developed to predict biomass HHV quickly from other analysis data. In this study, a linear regression algorithm (LRA) and stochastic gradient descent (SGD) in a machine‐learning environment were used as novel methods to predict the HHV of biomass. The basis of the model was 78 lines of combined proximate and ultimate analysis data. The LRA model was observed to be more accurate. The testing for both models was done by stratified cross‐validation, stratified shuffle splits, and no sampling tests. The root mean square error (RMSE) of the LRA and SGD models was 8.151 and 21.65 kJ kg −1 and the mean absolute error (MAE) was 6.823 and 13.87 for the stratified shuffle split (ten random samples with 75% data). The coefficient of determination for both models was >0.999 in all cases. The study observed that LRA and SGD are among the most accurate artificial intelligence models for the prediction of biomass HHV. © 2020 Society of Chemical Industry and John Wiley & Sons, Ltd