Forecast of the higher heating value in biomass torrefaction by means of machine learning techniques

Abstract Torrefaction of biomass can be explained as a mild type of pyrolysis at temperatures usually ranging between 200 and 300 °C in lack of oxygen. Torrefaction of biomass enhances properties as the moisture content and calorific value. The objective of this study was to acquire a predictive model of the higher heating value (HHV) in a biomass torrefaction process. This study introduces a new hybrid algorithm, relied on support vector machines (SVMs) combined with the simulated annealing (SA) optimization technique, for predicting the calorific value (HHV) of biomass from operation input parameters determined experimentally during the torrefaction process. Additionally, a multivariate adaptive regression splines (MARS) approach and random forest (RF) technique were fitted to the experimental data for comparison purposes. The results of the present study are two-fold. In the first place, the significance of each physical–chemical variables on the higher heating value (HHV) is presented through the model. Secondly, several models for forecasting the calorific value of torrefied biomass are obtained. Indeed, when this hybrid SVM–SA-based model with RBF kernel function was applied to the experimental dataset with the optimal hyperparameters, a coefficient of determination equal to 0.90 was achieved for the higher heating value estimation of torrefied biomass. Moreover, the results accomplished with the MARS–SA-based approach and RF–SA-based technique are worse than those achieved with the RBF–SVM–SA-based model. The agreement between experimental data and the model demonstrated the good performance of the latter.

[1]  J. J. Chew,et al.  Recent advances in biomass pretreatment – Torrefaction fundamentals and technology , 2011 .

[2]  Bernhard Schölkopf,et al.  New Support Vector Algorithms , 2000, Neural Computation.

[3]  Prabir Basu,et al.  Biomass Gasification, Pyrolysis and Torrefaction: Practical Design and Theory , 2013 .

[4]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[5]  S. Mekhilef,et al.  A review on biomass as a fuel for boilers , 2011 .

[6]  Anthony T. C. Goh,et al.  Multivariate adaptive regression splines for analysis of geotechnical engineering systems , 2013 .

[7]  N. Labbe,et al.  Enhancing the combustible properties of bamboo by torrefaction. , 2011, Bioresource technology.

[8]  A. Ghoniem,et al.  Biomass torrefaction: modeling of volatile and solid product evolution kinetics. , 2012, Bioresource technology.

[9]  M. Clerc,et al.  Particle Swarm Optimization , 2006 .

[10]  Mohammad Reza Nikoo,et al.  Water Quality Zoning Using Probabilistic Support Vector Machines and Self-Organizing Maps , 2013, Water Resources Management.

[11]  Wei Qiao,et al.  Short-term solar power prediction using a support vector machine , 2013 .

[12]  I. M. D. A. Mancisidor,et al.  European Union's renewable energy sources and energy efficiency policy review: The Spanish perspective , 2009 .

[13]  Yuehjen E. Shao,et al.  Mining the breast cancer pattern using artificial neural networks and multivariate adaptive regression splines , 2004, Expert Syst. Appl..

[14]  B. Acharya,et al.  A Comprehensive Review on Biomass Torrefaction , 2014 .

[15]  H. Bi,et al.  Study on Density, Hardness, and Moisture Uptake of Torrefied Wood Pellets , 2013 .

[16]  Ping-Feng Pai,et al.  Support Vector Machines with Simulated Annealing Algorithms in Electricity Load Forecasting , 2005 .

[17]  P. Venkataraman,et al.  Applied Optimization with MATLAB Programming , 2001 .

[18]  Larry Wasserman,et al.  All of Statistics: A Concise Course in Statistical Inference , 2004 .

[19]  Andrea E. Olsson.  Particle Swarm Optimization: Theory, Techniques and Applications , 2010 .

[20]  J. R. Hess,et al.  REVIEW: A review on biomass torrefaction process and product properties for energy applications , 2011 .

[21]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[22]  Emile H. L. Aarts,et al.  Simulated Annealing: Theory and Applications , 1987, Mathematics and Its Applications.

[23]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[24]  Ayhan Demirbas.  Linear equations on thermal degradation products of wood chips in alkaline glycerol , 2004 .

[25]  Hanzade Haykiri-Acma,et al.  Calorific value estimation of biomass from their proximate analyses data , 2010 .

[26]  Mahesh Pal,et al.  Estimation of Discharge and End Depth in Trapezoidal Channel by Support Vector Machines , 2007 .

[27]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[28]  Francisco Javier de Cos Juez,et al.  Battery State-of-Charge Estimator Using the MARS Technique , 2013 .

[29]  F. Manzano-Agugliaro,et al.  Greenhouse crop residues: Energy potential and models for the prediction of their higher heating value , 2011 .

[30]  Sancho Salcedo-Sanz,et al.  Prediction of hourly O3 concentrations using support vector regression algorithms , 2010 .

[31]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[32]  Kj Krzysztof Ptasinski,et al.  Biomass upgrading by torrefaction for the production of biofuels: A review , 2011 .

[33]  Max Kuhn,et al.  Applied Predictive Modeling , 2013 .

[34]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[35]  O. Kisi.  Pan evaporation modeling using least square support vector machine, multivariate adaptive regression splines and M5 model tree , 2015 .

[36]  P. J. García Nieto,et al.  A new data mining methodology applied to the modelling of the influence of diet and lifestyle on the value of bone mineral density in post-menopausal women , 2009, Int. J. Comput. Math..

[37]  Ridha Ziani,et al.  Bearing fault diagnosis using multiclass support vector machines with binary particle swarm optimization and regularized Fisher’s criterion , 2017, J. Intell. Manuf..

[38]  Wei-hsin Chen,et al.  Torrefaction and low temperature carbonization of oil palm fiber and Eucalyptus in nitrogen and air atmospheres. , 2012, Bioresource technology.

[39]  Renato De Leone,et al.  Photovoltaic energy production forecast using support vector regression , 2015, Neural Computing and Applications.

[40]  Kalyani A Motghare,et al.  Comparative study of different waste biomass for energy application. , 2016, Waste management.

[41]  Wei Hsin Chen,et al.  A state-of-the-art review of biomass torrefaction, densification and applications , 2015 .

[42]  R. Dennis Cook,et al.  Cross-Validation of Regression Models , 1984 .

[43]  Tasneem Abbasi,et al.  Biomass energy and the environmental impacts associated with its production and utilization , 2010 .

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

[45]  Xiugang Li,et al.  Predicting motor vehicle crashes using Support Vector Machine models. , 2008, Accident; analysis and prevention.

[46]  Hamit Saruhan,et al.  Modeling of asphalt concrete via simulated annealing , 2010, Adv. Eng. Softw..

[47]  Lingguang Song,et al.  An eco-environmental water demand based model for optimising water resources using hybrid genetic simulated annealing algorithms. Part II. Model application and results. , 2009, Journal of environmental management.

[48]  Kj Krzysztof Ptasinski,et al.  Torrefaction of wood: Part 1. Weight loss kinetics , 2006 .

[49]  O. Nelles,et al.  An Introduction to Optimization , 1996, IEEE Antennas and Propagation Magazine.

[50]  Dervis Karaboga,et al.  A quick artificial bee colony (qABC) algorithm and its performance on optimization problems , 2014, Appl. Soft Comput..

[51]  Zhong-yang Luo,et al.  Influence of torrefaction pretreatment on biomass gasification technology , 2011 .

[52]  J. Friedman.  Multivariate adaptive regression splines , 1990 .

[53]  Thomas Stützle,et al.  Ant Colony Optimization , 2009, EMO.

[54]  Bruce R. Kowalski,et al.  MARS: A tutorial , 1992 .

[55]  S. Mani,et al.  Impact of torrefaction on the grindability and fuel characteristics of forest biomass. , 2011, Bioresource technology.

[56]  Erik Dahlquist,et al.  Biomass as Energy Source : Resources, Systems and Applications , 2013 .

[57]  N. Khabarov,et al.  Global bioenergy scenarios – Future forest development, land-use implications, and trade-offs , 2013 .

[58]  N. Ya'acob,et al.  Lignocellulosic Biomass Solid Fuel Properties Enhancement via Torrefaction , 2016 .

[59]  P. J. García Nieto,et al.  Analysis of lead times of metallic components in the aerospace industry through a supported vector machine model , 2010, Math. Comput. Model..

[60]  Sanjeev R. Kulkarni,et al.  An Elementary Introduction to Statistical Learning Theory , 2011 .

[61]  Charles B. Roosen,et al.  An introduction to multivariate adaptive regression splines , 1995, Statistical methods in medical research.

[62]  H. M. Vinkers,et al.  Multivariate adaptive regression splines—studies of HIV reverse transcriptase inhibitors , 2004 .

[63]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2003, ICTAI.

[64]  C. Cercignani.  The Boltzmann equation and its applications , 1988 .

[65]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[66]  Laure Malherbe,et al.  Sampling design for air quality measurement surveys: An optimization approach , 2011 .

[67]  Min-Yuan Cheng,et al.  Accurately predicting building energy performance using evolutionary multivariate adaptive regression splines , 2014, Appl. Soft Comput..

[68]  Terry Hansen,et al.  Support vector based battery state of charge estimator , 2005 .

[69]  S. Shukla,et al.  Support vector machine based modeling of evapotranspiration using hydro-climatic variables in a sub-tropical environment , 2015 .

[70]  Shengjun Wu,et al.  Assessing the potential of support vector machine for estimating daily solar radiation using sunshine duration , 2013 .

[71]  Andreas Christmann,et al.  Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.

[72]  R. Tibshirani,et al.  Improvements on Cross-Validation: The 632+ Bootstrap Method , 1997 .

[73]  Q. Bach,et al.  Upgrading biomass fuels via wet torrefaction: A review and comparison with dry torrefaction , 2016 .

[74]  C. Yin.  Prediction of higher heating values of biomass from proximate and ultimate analyses , 2011 .

[75]  Wengang Zhang,et al.  Assessment of soil liquefaction based on capacity energy concept and multivariate adaptive regression splines , 2015 .

[76]  Xingbao Gao,et al.  Effect of thermal pretreatment on the physical and chemical properties of municipal biomass waste. , 2012, Waste management.

[77]  A. K. Al-Othman,et al.  Simulated Annealing algorithm for photovoltaic parameters identification , 2012 .