Estimation of the Elemental Composition of Biomass Using Hybrid Adaptive Neuro-Fuzzy Inference System
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Estimation of the Elemental Composition of Biomass Using Hybrid Adaptive Neuro-Fuzzy Inference System Obafemi O. Olatunji 1
&
Stephen Akinlabi 2 & Nkosinathi Madushele 1 & Paul A. Adedeji 1
# Springer Science+Business Media, LLC, part of Springer Nature 2019
Abstract The application of evolutionary algorithms could be a rapid and acceptable means of predicting the elemental composition of biomass applicable in power generation. This article introduces a hybrid model comprising of adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) called PSO-ANFIS model. The model is based on the proximate values of biomass materials to predict their corresponding elemental compositions. This intelligent hybrid model was trained with 581 biomass data and further tested with a new 249 biomass data. The carbon, hydrogen, and oxygen contents of solid biomass were predicted based on the ash content (Ash), fixed carbon (FC), and volatile matter (VM) as the inputs. The model was evaluated based on some known performance criteria. A root mean squared error (RMSE), mean absolute deviation (MAD), mean absolute percentage error (MAPE), log of accuracy ratio (LAR), coefficient of correlation (CC), mean absolute error (MAE) value of 3.5816, 2.3279, 4.8521, 0.0012, 0.9329, 0.0227 at computation time (CT) of 47.04 secs for Carbon (C); 0.6383, 0.4047, 10.2187, 0.002, 0.7986, 0.0261 at CT of 35.2 secs for Hydrogen (H); 4.2042, 2.7417, 10.7612,0.0016, 0.9137 at CT of 28.16 secs for Oxygen (O) respectively was obtained. A regression analysis was also carried out to determine the level of correlation between actual and predicted values. The reported indices show that PSO-ANFIS can be used as a novel computation approach for the prediction of elemental compositions of biomass, which is vital to the combustion, thermochemical, gasification, and pyrolysis process toward energy production. Keywords Biomass energy . Elemental composition . Hybrid model . Proximate properties . PSO-ANFIS
Introduction The evolution of the world economy is largely dependent on sustainable, effective, and efficient methods of energy generation, strategic resources management, and intelligent decision-making process. The conventional energy generated from fossil fuel has enormous side effects on the global climate. The report about global warming across the globe is an eye-opener. According to the UN Intergovernmental Panel on Climate Change (IPCC), by 2030, global warming is likely to reach a critical threshold of 1.5 °C more than the pre-industrial level [1]. This means that global net emission of CO2 will
* Obafemi O. Olatunji [email protected] 1
Department of Mechanical Engineering Science, University of Johannesburg, Johannesburg, South Africa
2
Department of Mechanical and Industrial Engineering, University of Johannesburg, Johannesburg, South Africa
have to fall by 45% from 2010 levels by 2030 and reach a zero value by approximately 2050 in order to keep global warming at 1.5 °C [2]. Presently, laudable progress has been made
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