Prediction of the physicochemical properties of woody biomass using linear prediction and artificial neural networks

Hao Li; Shuangjun Yang; Weiqi Zhao; Zhihan Xu; Shiyu Zhao; Xifeng Liu

doi:10.1080/15567036.2014.934412

Prediction of the physicochemical properties of woody biomass using linear prediction and artificial neural networks

Hao Li, Shuangjun Yang, Weiqi Zhao, Zhihan Xu, Shiyu Zhao, Xifeng Liu

Research output: Contribution to journal › Article › peer-review

8 Citations (Scopus)

Abstract

ABSTRACT: This article aims at using Artificial Neural Networks (ANNs) and linear prediction to predict the physicochemical properties of woody biomass, including gross calorific value, carbon content, and oxygen content. By analyzing 43 data groups, it was found that Multilayer Feedforward Neural Network (MLFN) with 11 nodes is the best model for predicting the gross calorific value, with a root mean square (RMS) error of 0.85; General Regression Neural Network (GRNN) is the best model for predicting the carbon content, with an RMS error of 1.66; and linear prediction is the best model for predicting the oxygen content, with an RMS error of 2.11.

Original language	English
Pages (from-to)	1569-1573
Number of pages	5
Journal	Energy Sources, Part A: Recovery, Utilization and Environmental Effects
Volume	38
Issue number	11
DOIs	https://doi.org/10.1080/15567036.2014.934412
Publication status	Published - 2016 Jun 2
Externally published	Yes

Keywords

Artificial neural network
carbon content
gross calorific value
linear prediction
oxygen content
physicochemical properties
woody biomass

ASJC Scopus subject areas

Renewable Energy, Sustainability and the Environment
Nuclear Energy and Engineering
Fuel Technology
Energy Engineering and Power Technology

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1080/15567036.2014.934412

Cite this

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title = "Prediction of the physicochemical properties of woody biomass using linear prediction and artificial neural networks",

abstract = "ABSTRACT: This article aims at using Artificial Neural Networks (ANNs) and linear prediction to predict the physicochemical properties of woody biomass, including gross calorific value, carbon content, and oxygen content. By analyzing 43 data groups, it was found that Multilayer Feedforward Neural Network (MLFN) with 11 nodes is the best model for predicting the gross calorific value, with a root mean square (RMS) error of 0.85; General Regression Neural Network (GRNN) is the best model for predicting the carbon content, with an RMS error of 1.66; and linear prediction is the best model for predicting the oxygen content, with an RMS error of 2.11.",

keywords = "Artificial neural network, carbon content, gross calorific value, linear prediction, oxygen content, physicochemical properties, woody biomass",

author = "Hao Li and Shuangjun Yang and Weiqi Zhao and Zhihan Xu and Shiyu Zhao and Xifeng Liu",

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doi = "10.1080/15567036.2014.934412",

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T1 - Prediction of the physicochemical properties of woody biomass using linear prediction and artificial neural networks

AU - Li, Hao

AU - Yang, Shuangjun

AU - Zhao, Weiqi

AU - Xu, Zhihan

AU - Zhao, Shiyu

AU - Liu, Xifeng

PY - 2016/6/2

Y1 - 2016/6/2

N2 - ABSTRACT: This article aims at using Artificial Neural Networks (ANNs) and linear prediction to predict the physicochemical properties of woody biomass, including gross calorific value, carbon content, and oxygen content. By analyzing 43 data groups, it was found that Multilayer Feedforward Neural Network (MLFN) with 11 nodes is the best model for predicting the gross calorific value, with a root mean square (RMS) error of 0.85; General Regression Neural Network (GRNN) is the best model for predicting the carbon content, with an RMS error of 1.66; and linear prediction is the best model for predicting the oxygen content, with an RMS error of 2.11.

AB - ABSTRACT: This article aims at using Artificial Neural Networks (ANNs) and linear prediction to predict the physicochemical properties of woody biomass, including gross calorific value, carbon content, and oxygen content. By analyzing 43 data groups, it was found that Multilayer Feedforward Neural Network (MLFN) with 11 nodes is the best model for predicting the gross calorific value, with a root mean square (RMS) error of 0.85; General Regression Neural Network (GRNN) is the best model for predicting the carbon content, with an RMS error of 1.66; and linear prediction is the best model for predicting the oxygen content, with an RMS error of 2.11.

KW - Artificial neural network

KW - carbon content

KW - gross calorific value

KW - linear prediction

KW - oxygen content

KW - physicochemical properties

KW - woody biomass

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Prediction of the physicochemical properties of woody biomass using linear prediction and artificial neural networks

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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