Application of real valued genetic algorithm on prediction of higher heating values of various lignocellulosic materials using lignin and extractive contents

Abstract The higher heating values (HHVs) of 11 non-wood lignocellulosic materials from Turkey were measured experimentally and calculated incorporating various theoretical models with the values of both lignin and extractive contents. Multiple linear regression (MLR) and real valued genetic algorithm (RVGA) were used to derive the theoretical models. A non-linear RVGA6 model was determined as the best non-linear model considering the experimental results with a regression coefficient of 92% coefficient of determination (R2), 0.301 sum of squared errors (SSE), 0.301 mean squared errors (MSE), 0.548 root mean squared errors (RMSE) and 0.0187 mean absolute percentage error (MAPE) and is proposed as a better alternative for theoretical HHV calculations to the multiple linear modellings such as MLR and RVGA1.

[1]  Robert H. White.  EFFECT OF LIGNIN CONTENT AND EXTRACTIVES ON THE HIGHER HEATING VALUE OF WOOD , 1987 .

[2]  Antonio Gutiérrez Lavín,et al.  Spanish biofuels heating value estimation. Part II: Proximate analysis data , 2014 .

[3]  Lluis Belanche Muñoz.  An Application example of the breeder genetic algorithm to function optimization , 1999 .

[4]  Mehrorang Ghaedi,et al.  Back propagation artificial neural network and central composite design modeling of operational parameter impact for sunset yellow and azur (II) adsorption onto MWCNT and MWCNT-Pd-NPs: Isotherm and kinetic study , 2016 .

[5]  Henning Bockhorn,et al.  A comparative kinetic study on the pyrolysis of three different wood species , 2003 .

[6]  Ebru Akkaya,et al.  ANFIS based prediction model for biomass heating value using proximate analysis components , 2016 .

[7]  F. Evrendilek,et al.  Response surface optimization, modeling and uncertainty analysis of mass loss response of co-combustion of sewage sludge and water hyacinth , 2017 .

[8]  Randy L. Haupt,et al.  Practical Genetic Algorithms , 1998 .

[9]  Ayhan Demirbas,et al.  Relationships between lignin contents and heating values of biomass , 2001 .

[10]  F. Evrendilek,et al.  Modeling Efficiency of Dehydrated Sunflower Seed Cake as a Novel Biosorbent to Remove a Toxic Azo Dye , 2015 .

[11]  P. Abdul Salam,et al.  Estimation of higher heating value of biomass from proximate analysis: A new approach , 2012 .

[12]  S. S. Tambe,et al.  Prediction of Higher Heating Value of Solid Biomass Fuels Using Artificial Intelligence Formalisms , 2013, BioEnergy Research.

[13]  F. M. Mahdi,et al.  Novel Linear and Nonlinear Equations for the Higher Heating Values of Municipal Solid Wastes and the Implications of Carbon to Energy Ratios , 2014 .

[14]  Sadriye Küçükbayrak,et al.  Estimation of calorific values of Turkish lignites , 1991 .

[15]  Julio L. Bueno,et al.  Spanish biofuels heating value estimation based on structural analysis , 2015 .

[16]  A. R. Phadke,et al.  A new technique for computation of closest saddle-node bifurcation point of power system using real coded genetic algorithm , 2011 .

[17]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[18]  Musa Buyukada,et al.  Utilization of apricot seed in (co-)combustion of lignite coal blends: Numeric optimization, empirical modeling and uncertainty estimation , 2018 .

[19]  K. Varmuza,et al.  Prediction of heating values of biomass fuel from elemental composition , 2005 .

[20]  A. Barneto,et al.  Use of autocatalytic kinetics to obtain composition of lignocellulosic materials. , 2009, Bioresource technology.

[21]  Farrukh Aslam Khan,et al.  Energy-efficient clustering in mobile ad-hoc networks using multi-objective particle swarm optimization , 2012, Appl. Soft Comput..

[22]  David M. Tate,et al.  A genetic algorithm approach to curve fitting , 1995 .

[23]  Haren B. Gosai,et al.  Bioengineering for polycyclic aromatic hydrocarbon degradation by Mycobacterium litorale: Statistical and artificial neural network (ANN) approach , 2016 .

[24]  A. Çaǧlar,et al.  The pyrolysis process verification of hydrogen rich gas (H–rG) production by artificial neural network (ANN) , 2016 .

[25]  José Lousada,et al.  The explained variation by lignin and extractive contents on higher heating value of wood , 2011 .

[26]  Heinz Mühlenbein,et al.  Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization , 1993, Evolutionary Computation.

[27]  Musa Buyukada.  Prediction of Photocatalytic Degradation and Mineralization Efficiencies of Basic Blue 3 Using $${{\rm TiO}_{2}}$$TiO2 by Nonlinear Modeling Based on Box–Behnken Design , 2016 .

[28]  F. Kačík,et al.  Chemical Profiles of Wood Components of Poplar Clones for Their Energy Utilization , 2012 .

[29]  J. Azevedo,et al.  Estimating the higher heating value of biomass fuels from basic analysis data , 2005 .

[30]  Ugur Ozveren,et al.  An artificial intelligence approach to predict gross heating value of lignocellulosic fuels , 2017 .

[31]  Musa Buyukada.  Modeling of decolorization of synthetic reactive dyestuff solutions with response surface methodology by a rapid and efficient process of ultrasound-assisted ozone oxidation , 2016 .

[32]  Modelling stochastic variability and uncertainty in aroma active compounds of PEF-treated peach nectar as a function of physical and sensory properties, and treatment time. , 2016, Food chemistry.

[33]  Ahmet Demir,et al.  Predicting the Heating Value of Municipal Solid Waste-based Materials: An Artificial Neural Network Model , 2010 .

[34]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[35]  Abdelmalik Taleb-Ahmed,et al.  Social spiders optimization and flower pollination algorithm for multilevel image thresholding: A performance study , 2016, Expert Syst. Appl..

[36]  S. Dayana Priyadharshini,et al.  Optimization of phenol degradation by the microalga Chlorella pyrenoidosa using Plackett-Burman Design and Response Surface Methodology. , 2016, Bioresource technology.

[37]  Erdal Aydemir,et al.  Breeder hybrid algorithm approach for natural gas demand forecasting model , 2017 .

[38]  Musa Buyukada.  Data-driven nonlinear modeling studies on removal of Acid Yellow 59 using Si-doped multi-walled carbon nanotubes , 2017, International Journal of Environmental Science and Technology.

[39]  E. T. Howard.  Heat of combustion of various southern pine materials , 1973 .

[40]  Musa Buyukada,et al.  Probabilistic uncertainty analysis based on Monte Carlo simulations of co-combustion of hazelnut hull and coal blends: Data-driven modeling and response surface optimization. , 2017, Bioresource technology.

[41]  Musa Buyukada,et al.  Uncertainty estimation by Bayesian approach in thermochemical conversion of walnut hull and lignite coal blends. , 2017, Bioresource technology.

[42]  Borja Velázquez-Martí,et al.  A review of the mathematical models for predicting the heating value of biomass materials , 2012 .

[43]  Musa Buyukada,et al.  Co-combustion of peanut hull and coal blends: Artificial neural networks modeling, particle swarm optimization and Monte Carlo simulation. , 2016, Bioresource technology.

[44]  W. Klauditz,et al.  Cellulose und Cellulosederivate , 1933 .

[45]  Theodor Curtius,et al.  Cellulose und Cellulosederivate , 1933 .

[46]  S. Channiwala,et al.  A correlation for calculating HHV from proximate analysis of solid fuels , 2005 .

[47]  Indranil Pan,et al.  Incorporating uncertainty in data driven regression models of fluidized bed gasification: A Bayesian approach , 2016 .

[48]  B. Velázquez‐Martí,et al.  Prediction models for higher heating value based on the structural analysis of the biomass of plant remains from the greenhouses of Almería (Spain) , 2014 .