An improved machine learning approach to estimate hemicellulose, cellulose, and lignin in biomass


Kartal F., Özveren U.

Carbohydrate Polymer Technologies and Applications, vol.2, no.100148, 2021 (Journal Indexed in ESCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 2 Issue: 100148
  • Publication Date: 2021
  • Doi Number: 10.1016/j.carpta.2021.100148
  • Title of Journal : Carbohydrate Polymer Technologies and Applications
  • Keywords: Proximate analysis, Lignocellulosic fuels, Biomass characterization, Biochemical components, Artificial neural network, Machine learning, NEAR-INFRARED SPECTROSCOPY, EMPTY FRUIT BUNCH, PALM KERNEL SHELL, PYROLYSIS KINETICS, LIGNOCELLULOSIC BIOMASS, THERMOCHEMICAL CHARACTERIZATION, THERMAL-BEHAVIOR, PROXIMATE ANALYSIS, ACTIVATED CARBONS, SUGARCANE BAGASSE

Abstract

Biomass consists of predominantly a biochemical mixture of three major lignocellulosic components: hemicellulose, cellulose, and lignin. The amounts of these three major polymeric components in biomass affect the functioning of thermochemical processes and thus affecting the efficiency of the biomass-based energy plants. On the other hand, experimental procedures for the hemicellulose, cellulose, and lignin determination are expensive, time-consuming, and not commercially viable. Recently, researchers are developing computational methods to minimize experimental procedures. Artificial neural network (ANN), which is a subset of machine learning, can develop relationships between input-output for non-linear and complex datasets. In this paper, we reported the first comprehensive study on estimating hemicellulose, cellulose, and lignin fractions of biomass using proximate analysis results on as received basis with a machine learning method. The ANN model performed a satisfactory performance for all the major biochemical components with the determination coefficient R-2 > 0.96.