Estimation of biomass higher heating value (HHV) based on the proximate analysis: Smart modeling and correlation. (1st December 2019)

• Record Type: Journal Article
• Authors: Dashti, Amir; Noushabadi, Abolfazl Sajadi; Raji, Mojtaba; Razmi, Amir; Ceylan, Selim; Mohammadi, Amir H.
• Is Part Of: Fuel. Volume 257(2019)
• Publication Date: 2019-12-01
• Subjects: Biomass -- Higher heating value (HHV) -- Estimation -- Smart modeling -- Data mining; Fuel -- Periodicals; Coal -- Periodicals; Coal; Fuel; Periodicals; 662.6
• DOI: 10.1016/j.fuel.2019.115931
• Languages: English
• ISSNs: 0016-2361

Abstract

Graphical abstract: Highlights: Accurate models have been developed for estimating HHV of biomass. GA-ANFIS, DEANFIS, ANN, LSSVM, GARBF and an empirical model are proposed. 382 data of HHV of various biomass are categorized. Comparisons with previous models reported in literature show the accuracy of our models. Abstract: In order to evaluate the potential and make a technical assessment of biomass energy, it is crucial to determine the higher heating value (HHV) of biomass fuels. Thus, multilayer perceptron artificial neural network (MLP-ANN) genetic algorithm-adaptive neuro fuzzy inference system (GA-ANFIS) differential evolution-ANFIS (DE-ANFIS), GA-radial basis function (GA-RBF), least square support vector machine (LSSVM) methods and an empirical correlation (multivariate polynomial regression (MPR)) were employed for the estimation of the HHV of biomass fuels. The comparisons of results show that GA-RBF and MPR models have higher accuracy as coefficients of regression (R 2 ) values equal to 0.9591 and 0.9597, respectively. The average Absolute Relative Errors (% AARD) were obtained as 3.9547 for GA-RBF and 3.9791 for MPR models. The results show that proposed techniques are working efficiently in the estimation of HHV of different sources of biomass.