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Application of Artificial Intelligence in the Prediction of Thermal Properties of Biomass

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Valorization of Biomass to Value-Added Commodities

Part of the book series: Green Energy and Technology ((GREEN))

Abstract

Biomass has been agreed to be the most sustainable and abundant renewable source which can be used as a replacement for crude oil-based products. Biomass as value-added products in energy generation must be comprehensively characterized in order to determine its properties. However, the experimental procedure for these analyses demands instruments that are very complex, exorbitant and requires a stable electricity supply. The advancement of knowledge in artificial intelligence and blockchain technology is unlocking new potential prediction accuracy for biomass properties. Artificial neural networks (ANNs) have been applied in the prediction and modelling of several processes. Advances in machine learning, rapid development of algorithms and prediction accuracy are the major motivation behind the increasing application of ANN. Therefore, this chapter highlights the methods, which have been applied in the prediction of the properties of biomass. It further discusses the ANN-based prediction models for biomass as regards the thermal properties. The types of models, stages involved in the formulation of prediction models, the paradigm of learning, classification of training algorithm and sensitivity analysis are detailed. The governing principles, applications, merit and challenges associated with this technique are elaborated. Some relevant case studies were reviewed.

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References

  1. C.-Y. Yin, Prediction of higher heating values of biomass from proximate and ultimate analyses. Fuel 90(3), 1128–1132 (2011)

    Article  Google Scholar 

  2. S. Ghugare, S. Tiwary, V. Elangovan, S. Tambe, Prediction of higher heating value of solid biomass fuels using artificial intelligence formalisms. Bioenergy Res. 7(2), 681–692 (2014)

    Article  Google Scholar 

  3. O.O. Olatunji, S.A. Akinlabi, M.P. Mashinini, S.O. Fatoba, O.O. Ajayi, Thermo-gravimetric characterization of biomass properties: A review. IOP Conf. Ser. Mater. Sci. Eng. 423(1), 012175 (2018)

    Article  Google Scholar 

  4. O. Obafemi, A. Stephen, O. Ajayi, P. Mashinini, M. Nkosinathi, Experimental investigation of thermal properties of lignocellulosic biomass: A review. IOP Conf. Ser. Mater. Sci. Eng. 413(1), 012054 (2018)

    Google Scholar 

  5. Harsh, D.S.K. Singal, Integration of renewable energy sources using artificial intelligent system. Int. J. Innov. Res. Sci. Eng. Technol. 03(11), 17291–17305 (2014)

    Google Scholar 

  6. C.H. Dagli, Artificial Neural Networks for Intelligent Manufacturing (Springer, Dordrecht, 2012)

    Google Scholar 

  7. B.-H. Li, B.-C. Hou, W.-T. Yu, X.-B. Lu, C.-W. Yang, Applications of artificial intelligence in intelligent manufacturing: A review. Front. Inf. Technol. Electron. Eng. 18(1), 86–96 (2017)

    Article  Google Scholar 

  8. O. Obafemi, A. Stephen, O. Ajayi, M. Nkosinathi, A survey of artificial neural network-based prediction models for thermal properties of biomass. Proc. Manuf. 33, 184–191 (2019)

    Google Scholar 

  9. E. Akkaya, ANFIS based prediction model for biomass heating value using proximate analysis components. Fuel 180, 687–693 (2016)

    Article  Google Scholar 

  10. F.M. Gray, M. Schmidt, A hybrid approach to thermal building modelling using a combination of Gaussian processes and grey-box models. Energy Build. 165, 56–63 (2018)

    Article  Google Scholar 

  11. International Energy Agency, Digitalization and Energy (2017), http://www.ia.org/digital/. Accessed 12 Sept 2018

  12. S. Finlay, Predictive Analytics, Data Mining and Big Data: Myths, Misconceptions and Methods (Springer, London, 2014)

    Book  Google Scholar 

  13. Technopedia, Artificial Neural Network (2018), https://www.techopedia.com/definition/5967/artificial-neural-network-ann. Accessed 30 Mar 2018

  14. D. Rizopoulos, Max Kuhn and Kjell Johnson. Applied predictive modeling. New York, Springer. Biometrics 74(1), 383–383 (2018)

    Article  Google Scholar 

  15. M. Kuhn, K. Johnson, Applied Predictive Modeling (Springer, New York, 2013)

    Book  MATH  Google Scholar 

  16. S.J. Cranmer, B.A. Desmarais, What can we learn from predictive modeling? Polit. Anal. 25(2), 145–166 (2017)

    Article  Google Scholar 

  17. B. Ratner, Statistical and Machine-Learning Data Mining: Techniques for better Predictive Modeling and Analysis of Big Data (Chapman and Hall/CRC, Boca Raton, 2017)

    MATH  Google Scholar 

  18. E. Siegel, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (Wiley, Hoboken, 2013)

    Google Scholar 

  19. J.I. Ebert, The state of the art in “inductive” predictive modelling: Seven big mistakes (and lots of smaller ones), in Practical Applications of GIS for Archaeologists: A Predictive Modelling Toolkit, (Taylor and Francis, London, 2000), pp. 129–134

    Google Scholar 

  20. E. Vonesh, V.M. Chinchilli, Linear and Nonlinear Models for the Analysis of Repeated Measurements (CRC Press, Boca Raton, 1996)

    Book  MATH  Google Scholar 

  21. J. Pinheiro, D. Bates, S. DebRoy, D. Sarkar, Linear and nonlinear mixed effects models. R Package Version 3, 1–117 (2014)

    Google Scholar 

  22. P.A. Adedeji, S. Akinlabi, O. Ajayi, N. Madushele, Non-linear autoregressive neural network (NARNET) with SSA filtering for a university energy consumption forecast. Proc. Manuf. 33, 176–183 (2019)

    Google Scholar 

  23. G. Guo, H. Wang, D. Bell, Y. Bi, K. Greer, KNN model-based approach in classification, in OTM Confederated International Conferences On the Move to Meaningful Internet Systems, (Springer, Berlin, 2003), pp. 986–996

    Google Scholar 

  24. M. Pal, Multiclass approaches for support vector machine based land cover classification. arXiv preprint arXiv:0802.2411 (2008)

    Google Scholar 

  25. C.-W. Hsu, C.-J. Lin, A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Netw. 13(2), 415–425 (2002)

    Article  Google Scholar 

  26. S. Konishi, G. Kitagawa, Various model evaluation criteria. Inf. Crit. Stat. Model. 2008, 239–254 (2008)

    Google Scholar 

  27. A.Z. Hatem Ksibi, Numerical optimization of biogas production through a 3-steps model of anaerobic digestion: Sensitivity of biokinetic constants values. J Bioremediat. Biodegrad. 06(04), 302 (2015)

    Article  Google Scholar 

  28. R. Bai, H. Jia, P. Cao, Factor sensitivity analysis with neural network simulation based on perturbation system. J. Comput. 6(7), 1402–1407 (2011)

    Article  Google Scholar 

  29. R. Mac Nally, R.P. Duncan, J.R. Thomson, J.D. Yen, Model selection using information criteria, but is the “best” model any good? J. Appl. Ecol. 55(3), 1441–1444 (2018)

    Article  Google Scholar 

  30. C. Huang, L. Han, Z. Yang, X. Liu, Ultimate analysis and heating value prediction of straw by near infrared spectroscopy. Waste Manag. 29(6), 1793–1797 (2009)

    Article  Google Scholar 

  31. L.K. Abidoye, F.M. Mahdi, Novel linear and nonlinear equations for the higher heating values of municipal solid wastes and the implications of carbon to energy ratios. J. Energy Technol. Policy 4(5), 14–27 (2014)

    Google Scholar 

  32. E. Tosun, K. Aydin, M. Bilgili, Comparison of linear regression and artificial neural network model of a diesel engine fueled with biodiesel-alcohol mixtures. Alex. Eng. J. 55(4), 3081–3089 (2016)

    Article  Google Scholar 

  33. A. Abbas, A. Ibrahim, M. Muttalib, M. Aris, Fuel characterization and energy prediction of Malaysian poultry processing. Asian J. Sci. Res. 6(3), 498–507 (2013)

    Article  Google Scholar 

  34. L. Meraz, A. Domı́nguez, I. Kornhauser, F. Rojas, A thermochemical concept-based equation to estimate waste combustion enthalpy from elemental composition☆. Fuel 82(12), 1499–1507 (2003)

    Article  Google Scholar 

  35. P. Thipkhunthod, V. Meeyoo, P. Rangsunvigit, B. Kitiyanan, K. Siemanond, T. Rirksomboon, Predicting the heating value of sewage sludges in Thailand from proximate and ultimate analyses. Fuel 84(7–8), 849–857 (2005)

    Article  Google Scholar 

  36. R. Elneel, S. Anwar, B. Ariwahjoedi, Prediction of heating values of oil palm fronds from ultimate analysis. J. Appl. Sci. 13(3), 491–496 (2013)

    Article  Google Scholar 

  37. D.A. Tillman, Wood as an Energy Resource (Elsevier, Saint Louis, 2012)

    Google Scholar 

  38. A. Demirbas, D. Gullu, A. Caglar, F. Akdeniz, Estimation of calorific values of fuels from lignocellulosics. Energy Sources 19(8), 765–770 (1997)

    Article  Google Scholar 

  39. A. Demirbaş, Relationships between lignin contents and heating values of biomass. Energy Convers. Manag. 42(2), 183–188 (2001)

    Article  Google Scholar 

  40. C. Sheng, J.L.T. Azevedo, Estimating the higher heating value of biomass fuels from basic analysis data. Biomass Bioenergy 28(5), 499–507 (2005)

    Article  Google Scholar 

  41. S. Channiwala, P. Parikh, A unified correlation for estimating HHV of solid, liquid and gaseous fuels. Fuel 81(8), 1051–1063 (2002)

    Article  Google Scholar 

  42. B.M. Jenkins, J.M. Ebeling, Correlation of physical and chemical properties of terrestrial biomass with conversion, in Symposium Papers-Energy from Biomass and Wastes, (Institute of Gas Technology, Chicago, 1985)

    Google Scholar 

  43. W.G. Lloyd, D.A. Davenport, Applying thermodynamics to fossil fuels: Heats of combustion from elemental compositions. J. Chem. Educ. 57(1), 56 (1980)

    Article  Google Scholar 

  44. W. Boie, Fuel technology calculations. Energietechnik 3, 309–316 (1953)

    Google Scholar 

  45. K. Phichai, P. Pragrobpondee, T. Khumpart, S. Hirunpraditkoon, Prediction heating values of lignocellulosics from biomass characteristics. Int. J. Chem. Mol. Nucl. Mater. Metall. Eng. 7(7), 532–535 (2013)

    Google Scholar 

  46. L. Jiménez, F. González, Study of the physical and chemical properties of lignocellulosic residues with a view to the production of fuels. Fuel 70(8), 947–950 (1991)

    Article  Google Scholar 

  47. A. Demirbaş, Calculation of higher heating values of biomass fuels. Fuel 76(5), 431–434 (1997)

    Article  Google Scholar 

  48. T. Cordero, F. Marquez, J. Rodriguez-Mirasol, J. Rodriguez, Predicting heating values of lignocellulosics and carbonaceous materials from proximate analysis. Fuel 80(11), 1567–1571 (2001)

    Article  Google Scholar 

  49. J. Parikh, S. Channiwala, G. Ghosal, A correlation for calculating HHV from proximate analysis of solid fuels. Fuel 84(5), 487–494 (2005)

    Article  Google Scholar 

  50. D.R. Nhuchhen, P. Abdul Salam, Estimation of higher heating value of biomass from proximate analysis: A new approach. Fuel 99, 55–63 (2012)

    Article  Google Scholar 

  51. O.O. Olatunji, S. Akinlabi, N. Madushele, P.A. Adedeji, Estimation of the elemental composition of biomass using hybrid adaptive neuro-fuzzy inference system. Bioenergy Res. 12, 1–11 (2019)

    Article  Google Scholar 

  52. O. Olatunji, S. Akinlabi, N. Madushele, P.A. Adedeji, Estimation of Municipal Solid Waste (MSW) combustion enthalpy for energy recovery. EAI Endorsed Trans. Energy Web 6(23), 159119 (2019)

    Article  Google Scholar 

  53. S.B. Ghugare, S.S. Tambe, Genetic programming based high performing correlations for prediction of higher heating value of coals of different ranks and from diverse geographies. J. Energy Inst. 90(3), 476–484 (2017)

    Article  Google Scholar 

  54. G. Kanat, A. Saral, Estimation of biogas production rate in a thermophilic UASB reactor using artificial neural networks. Environ. Model. Assess. 14(5), 607–614 (2009)

    Article  Google Scholar 

  55. B. Ozkaya, A. Demir, M.S. Bilgili, Neural network prediction model for the methane fraction in biogas from field-scale landfill bioreactors. Environ. Model Softw. 22(6), 815–822 (2007)

    Article  Google Scholar 

  56. J. Wang, W. Wan, Application of desirability function based on neural network for optimizing biohydrogen production process. Int. J. Hydrog. Energy 34(3), 1253–1259 (2009)

    Article  Google Scholar 

  57. H. Abu Qdais, K. Bani Hani, N. Shatnawi, Modeling and optimization of biogas production from a waste digester using artificial neural network and genetic algorithm. Resour. Conserv. Recycl. 54(6), 359–363 (2010)

    Article  Google Scholar 

  58. D.P. Strik, A.M. Domnanovich, L. Zani, R. Braun, P. Holubar, Prediction of trace compounds in biogas from anaerobic digestion using the MATLAB Neural Network Toolbox. Environ. Model Softw. 20(6), 803–810 (2005)

    Article  Google Scholar 

  59. L. Alejo, J. Atkinson, V. Guzmán-Fierro, M. Roeckel, Effluent composition prediction of a two-stage anaerobic digestion process: Machine learning and stoichiometry techniques. Environ. Sci. Pollut. Res. 25, 1–15 (2018)

    Article  Google Scholar 

  60. A. Tardast et al., Use of artificial neural network for the prediction of bioelectricity production in a membrane less microbial fuel cell. Fuel 117, 697–703 (2014)

    Article  Google Scholar 

  61. A. Tardast, M. Rahimnejad, G. Najafpour, K. Pirzade, N. Mokhtarian, Prediction of bioelectricity production by neural network. J. Biotechnol. Pharm. Res. 3(3), 62–68 (2012)

    Google Scholar 

  62. Y. Sewsynker, E.B.G. Kana, A. Lateef, Modelling of biohydrogen generation in microbial electrolysis cells (MECs) using a committee of artificial neural networks (ANNs). Biotechnol. Biotechnol. Equip. 29(6), 1208–1215 (2015)

    Article  Google Scholar 

  63. Y. Sewsynker-Sukai, F. Faloye, E.B.G. Kana, Artificial neural networks: An efficient tool for modelling and optimization of biofuel production (a mini review). Biotechnol. Biotechnol. Equip. 31(2), 221–235 (2017)

    Article  Google Scholar 

  64. V.B. Furlong, R.D. Pereira Filho, A.C. Margarites, P.G. Goularte, J.A.V. Costa, Estimating microalgae Synechococcus nidulans daily biomass concentration using neuro-fuzzy network. Food Sci. Technol. 33, 142–147 (2013)

    Article  Google Scholar 

  65. N. Sarkar, S.K. Ghosh, S. Bannerjee, K. Aikat, Bioethanol production from agricultural wastes: An overview. Renew. Energy 37(1), 19–27 (2012)

    Article  Google Scholar 

  66. E. Betiku, A.E. Taiwo, Modeling and optimization of bioethanol production from breadfruit starch hydrolyzate vis-à-vis response surface methodology and artificial neural network. Renew. Energy 74, 87–94 (2015)

    Article  Google Scholar 

  67. M. Esfahanian, M. Nikzad, G. Najafpour, A.A. Ghoreyshi, Modeling and optimization of ethanol fermentation using Saccharomyces cerevisiae: Response surface methodology and artificial neural network. Chem. Ind. Chem. Eng. Q/CICEQ 19(2), 241–252 (2013)

    Article  Google Scholar 

  68. H. Uzun, Z. Yildiz, J.L. Goldfarb, S. Ceylan, Improved prediction of higher heating value of biomass using an artificial neural network model based on proximate analysis. Bioresour. Technol. 234, 122–130 (2017)

    Article  Google Scholar 

  69. G.E. Acquah, B.K. Via, O.O. Fasina, S. Adhikari, N. Billor, L.G. Eckhardt, Chemometric modeling of thermogravimetric data for the compositional analysis of forest biomass. PLoS One 12(3), e0172999 (2017)

    Article  Google Scholar 

  70. R. Guidotti, A. Monreale, S. Ruggieri, F. Turini, F. Giannotti, D. Pedreschi, A survey of methods for explaining black box models. ACM Comput. Surv. (CSUR) 51(5), 93 (2018)

    Google Scholar 

  71. L. Ljung, Black-box models from input-output measurements, in Instrumentation and Measurement Technology Conference, 2001. IMTC 2001. Proceedings of the 18th IEEE, vol. 1, (IEEE, Piscataway, 2001), pp. 138–146

    Google Scholar 

  72. G. Reynders, J. Diriken, D. Saelens, Quality of grey-box models and identified parameters as function of the accuracy of input and observation signals. Energy Build. 82, 263–274 (2014)

    Article  Google Scholar 

  73. C.D. Prada Moraga, D. Hose, G. Gutierrez, J.L. Pitarch, Developing grey-box dynamic process models. IFAC PapersOnLine 51(2), 523–528 (2018)

    Article  Google Scholar 

  74. M.E. Khan, F. Khan, A comparative study of white box, black box and grey box testing techniques. Int. J. Adv. Comput. Sci. Appl. 3(6), 12–15 (2012)

    Google Scholar 

  75. B.C. Bangal, Automatic Generation Control of Interconnected Power Systems Using Artificial Neural Network Techniques, 2009

    Google Scholar 

  76. R. Shariff, Q. Zhang, A. Cudrak, D. Smith, S. Stanley, Real-Time Artificial Intelligence Control and Optimization of a Full-Scale WTP (American Water Works Association, Denver, 2006)

    Google Scholar 

  77. S.J. Stanley, Process Modeling and Control of Enhanced Coagulation (American Water Works Association, Denver, 2000)

    Google Scholar 

  78. S.A. Kalogirou, Artificial Intelligence in Renewable Energy Systems Modelling and Prediction (Proceedings of the World Renewable Energy Congress VII, Cologne, 2002)

    Google Scholar 

  79. S. Haykin, Neural Networks: A Comprehensive Foundation (Prentice Hall PTR, New York, 1994)

    MATH  Google Scholar 

  80. S.S. Haykin, S.S. Haykin, S.S. Haykin, S.S. Haykin, Neural Networks and Learning Machines (Pearson, Upper Saddle River, 2009)

    MATH  Google Scholar 

  81. R. Sathya, A. Abraham, Comparison of supervised and unsupervised learning algorithms for pattern classification. Int. J. Adv. Res. Artif. Intell. 2(2), 34–38 (2013)

    Article  Google Scholar 

  82. C. Crisci, B. Ghattas, G. Perera, A review of supervised machine learning algorithms and their applications to ecological data. Ecol. Model. 240, 113–122 (2012)

    Article  Google Scholar 

  83. G.D. Gillespie, C.D. Everard, C.C. Fagan, K.P. McDonnell, Prediction of quality parameters of biomass pellets from proximate and ultimate analysis. Fuel 111, 771–777 (2013)

    Article  Google Scholar 

  84. A. Özyuğuran, S. Yaman, Prediction of calorific value of biomass from proximate analysis. Energy Procedia 107, 130–136 (2017)

    Article  Google Scholar 

  85. N. Soponpongpipat, D. Sittikul, P. Comsawang, Prediction model of higher heating value of torrefied biomass based on the kinetics of biomass decomposition. J. Energy Inst. 89(3), 425–435 (2016)

    Article  Google Scholar 

  86. S.B. Ghugare, S. Tiwary, S.S. Tambe, Computational intelligence based models for prediction of elemental composition of solid biomass fuels from proximate analysis. Int. J. Syst. Assur. Eng. Manag. 8(4), 2083–2096 (2017)

    Article  Google Scholar 

  87. I. Bayram, I.W. Selesnick, A dual-tree rational-dilation complex wavelet transform. IEEE Trans. Signal Process. 59(12), 6251–6256 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  88. L. Guan, Z. Fan, R. Tibshirani, Supervised learning via the “Hubnet” procedure. Stat. Sin. 28, 1225–1243 (2018)

    MathSciNet  MATH  Google Scholar 

  89. Q. Zhang, Y. Yang, Y. Liu, Y.N. Wu, S.-C. Zhu, Unsupervised learning of neural networks to explain neural networks. arXiv preprint arXiv 1805, 07468 (2018)

    Google Scholar 

  90. X. Zhu, Semi-supervised learning literature survey. Comput. Sci. Univ. Wisconsin-Madison 2(3), 4 (2006)

    Google Scholar 

  91. P. Tóth, A. Garami, B. Csordás, Image-based deep neural network prediction of the heat output of a step-grate biomass boiler. Appl. Energy 200, 155–169 (2017)

    Article  Google Scholar 

  92. A.Y. Sendjaja et al., Regression based state space adaptive model of two-phase anaerobic reactor. Chemosphere 140, 159–166 (2015)

    Article  Google Scholar 

  93. J. Mascaro et al., A tale of two “forests”: Random forest machine learning aids tropical forest carbon mapping. PLoS One 9(1), e85993 (2014)

    Article  Google Scholar 

  94. Y. Liu, H. Wang, H. Zhang, K. Liber, A comprehensive support vector machine-based classification model for soil quality assessment. Soil Tillage Res. 155, 19–26 (2016)

    Article  Google Scholar 

  95. T. Kohonen, E. Oja, O. Simula, A. Visa, J. Kangas, Engineering applications of the self-organizing map. Proc. IEEE 84(10), 1358–1384 (1996)

    Article  Google Scholar 

  96. A.S. Hess, J.R. Hess, Principal component analysis. Transfusion 58, 1580 (2018)

    Article  Google Scholar 

  97. S. Hosseinpour, M. Aghbashlo, M. Tabatabaei, Biomass higher heating value (HHV) modeling on the basis of proximate analysis using iterative network-based fuzzy partial least squares coupled with principle component analysis (PCA-INFPLS). Fuel 222, 1–10 (2018)

    Article  Google Scholar 

  98. A. Ahmad, L. Dey, A k-mean clustering algorithm for mixed numeric and categorical data. Data Knowl. Eng. 63(2), 503–527 (2007)

    Article  Google Scholar 

  99. V. Prasath, H.A.A. Alfeilat, O. Lasassmeh, A. Hassanat, Distance and similarity measures effect on the performance of K-nearest neighbor classifier-a review. arXiv preprint arXiv:1708.04321 (2017)

    Google Scholar 

  100. K. Teknomo, K-means clustering tutorial. Medicine 100(4), 3 (2006)

    Google Scholar 

  101. P. Vora, B. Oza, A survey on k-mean clustering and particle swarm optimization. Int. J. Sci. Mod. Eng. 1(3), 1–14 (2013)

    Google Scholar 

  102. A.K. Jain, M.N. Murty, P.J. Flynn, Data clustering: A review. ACM Comput. Surv. (CSUR) 31(3), 264–323 (1999)

    Article  Google Scholar 

  103. J.M. Lattin, J.D. Carroll, P.E. Green, Analyzing Multivariate Data (Thomson Brooks/Cole, Pacific Grove, 2003)

    Google Scholar 

  104. A.K. Jain, Data clustering: 50 years beyond K-means. Pattern Recognit. Lett. 31(8), 651–666 (2010)

    Article  Google Scholar 

  105. S. Azizpour, K. Giesecke, G. Schwenkler, Exploring the sources of default clustering. J. Financ. Econ. 129(1), 154–183 (2018)

    Article  Google Scholar 

  106. Z.K. Baker, V.K. Prasanna, Efficient hardware data mining with the Apriori algorithm on FPGAs, in Field-Programmable Custom Computing Machines, 2005. FCCM 2005. 13th Annual IEEE Symposium on, (IEEE, Piscataway, 2005), pp. 3–12

    Google Scholar 

  107. R.S. Sutton, A.G. Barto, Introduction to Reinforcement Learning (MIT Press, Cambridge, 1998)

    Book  MATH  Google Scholar 

  108. L.P. Kaelbling, M.L. Littman, A.W. Moore, Reinforcement learning: A survey. J. Artif. Intell. Res. 4, 237–285 (1996)

    Article  Google Scholar 

  109. J. Schmidhuber, A general method for multi-agent learning and incremental selfimprovement in unrestricted environments, in Evolutionary Computation: Theory and Applications, (Scientific Publishing Corporation, Singapore, 1996)

    Google Scholar 

  110. P. Van Wesel, A.E. Goodloe, Challenges in the Verification of Reinforcement Learning Algorithms (NASA Langely Research Center, Hampton, 2017)

    Google Scholar 

  111. R.S. Sutton, A.G. Barto, Reinforcement Learning: An Introduction (MIT Press, Cambridge, 1998)

    MATH  Google Scholar 

  112. A.L. Strehl, L. Li, E. Wiewiora, J. Langford, M.L. Littman, PAC model-free reinforcement learning, in Proceedings of the 23rd International Conference on Machine Learning, (ACM, New York, 2006), pp. 881–888

    Google Scholar 

  113. K. Arulkumaran, M.P. Deisenroth, M. Brundage, A.A. Bharath, A brief survey of deep reinforcement learning. arXiv preprint arXiv:1708.05866 (2017)

    Google Scholar 

  114. D. Lu et al., Aboveground forest biomass estimation with Landsat and LiDAR data and uncertainty analysis of the estimates. Int. J. For. Res. 2012, 1–16 (2012)

    Google Scholar 

  115. N.R. Jachowski, M.S. Quak, D.A. Friess, D. Duangnamon, E.L. Webb, A.D. Ziegler, Mangrove biomass estimation in Southwest Thailand using machine learning. Appl. Geogr. 45, 311–321 (2013)

    Article  Google Scholar 

  116. I. Ali, F. Greifeneder, J. Stamenkovic, M. Neumann, C. Notarnicola, Review of machine learning approaches for biomass and soil moisture retrievals from remote sensing data. Remote Sens. 7(12), 16398–16421 (2015)

    Article  Google Scholar 

  117. Y. Li, J. Yuan, Prediction of key state variables using support vector machines in bioprocesses. Chem. Eng. Technol. 29(3), 313–319 (2006)

    Article  Google Scholar 

  118. D.A. Saldana, L. Starck, P. Mougin, B. Rousseau, N. Ferrando, B. Creton, Prediction of density and viscosity of biofuel compounds using machine learning methods. Energy Fuel 26(4), 2416–2426 (2012)

    Article  Google Scholar 

  119. A.Y. Mutlu, O. Yucel, An artificial intelligence based approach to predicting syngas composition for downdraft biomass gasification. Energy 165, 895 (2018)

    Article  Google Scholar 

  120. B. Najafi, M.A. Fakhr, S. Jamali, Prediction of heating value of vegetable oil-based ethyl esters biodiesel using artificial neural network. Tarım Makinaları Bilimi Dergisi 7(4), 20140807 (2011)

    Google Scholar 

  121. Z. Yildiz, H. Uzun, S. Ceylan, Y. Topcu, Application of artificial neural networks to co-combustion of hazelnut husk-lignite coal blends. Bioresour. Technol. 200, 42–47 (2016)

    Article  Google Scholar 

  122. X. Zhu, X. Wu, Class noise vs. attribute noise: A quantitative study. Artif. Intell. Rev. 22(3), 177–210 (2004)

    Article  MATH  Google Scholar 

  123. C. Yuan, C. Wang, Performance of deterministic learning in noisy environments. Neurocomputing 78(1), 72–82 (2012)

    Article  Google Scholar 

  124. D. Gamberger, N. Lavrac, S. Dzeroski, Noise detection and elimination in data preprocessing: Experiments in medical domains. Appl. Artif. Intell. 14(2), 205–223 (2000)

    Article  Google Scholar 

  125. G. An, The effects of adding noise during backpropagation training on a generalization performance. Neural Comput. 8(3), 643–674 (1996)

    Article  MathSciNet  Google Scholar 

  126. T.C. Redman, The impact of poor data quality on the typical enterprise. Commun. ACM 41(2), 79–82 (1998)

    Article  Google Scholar 

  127. S. García, J. Luengo, F. Herrera, Data preprocessing in data mining (Springer, Berlin, 2015)

    Book  Google Scholar 

  128. J.I. Maletic, A. Marcus, Data cleansing: Beyond integrity analysis, in Iq, (Citeseer, Boston, 2000), pp. 200–209

    Google Scholar 

  129. X. Zhu, X. Wu, Q. Chen, Eliminating class noise in large datasets, in Proceedings of the 20th International Conference on Machine Learning (ICML-03), 2003, pp. 920–927

    Google Scholar 

  130. X. Zhu, X. Wu, Q. Chen, Bridging local and global data cleansing: Identifying class noise in large, distributed data datasets. Data Min. Knowl. Disc. 12(2–3), 275–308 (2006)

    Article  MathSciNet  Google Scholar 

  131. X. Wu, Knowledge Acquisition from Databases (Intellect Books, Norwood, 1995)

    Google Scholar 

  132. M. Leshno, V.Y. Lin, A. Pinkus, S. Schocken, Multilayer feedforward networks with a nonpolynomial activation function can approximate any function. Neural Netw. 6(6), 861–867 (1993)

    Article  Google Scholar 

  133. B. Karlik, A.V. Olgac, Performance analysis of various activation functions in generalized MLP architectures of neural networks. Int. J. Artif. Intell. Expert Syst. 1(4), 111–122 (2011)

    Google Scholar 

  134. F. Mazhar, A.M. Khan, I.A. Chaudhry, M. Ahsan, On using neural networks in UAV structural design for CFD data fitting and classification. Aerosp. Sci. Technol. 30(1), 210–225 (2013)

    Article  Google Scholar 

  135. P. Sibi, S.A. Jones, P. Siddarth, Analysis of different activation functions using back propagation neural networks. J. Theor. Appl. Inf. Technol. 47(3), 1264–1268 (2013)

    Google Scholar 

  136. S. Gong, J. Sasanipour, M.R. Shayesteh, M. Eslami, A. Baghban, Radial basis function artificial neural network model to estimate higher heating value of solid wastes. Energy Sources A 39(16), 1778–1784 (2017)

    Article  Google Scholar 

  137. E.B. Gueguim Kana, J.K. Oloke, A. Lateef, M.O. Adesiyan, Modeling and optimization of biogas production on saw dust and other co-substrates using artificial neural network and genetic algorithm. Renew. Energy 46, 276–281 (2012)

    Article  Google Scholar 

  138. S. Jacob, R. Banerjee, Modeling and optimization of anaerobic codigestion of potato waste and aquatic weed by response surface methodology and artificial neural network coupled genetic algorithm. Bioresour. Technol. 214, 386–395 (2016)

    Article  Google Scholar 

  139. K. Nath, D. Das, Modeling and optimization of fermentative hydrogen production. Bioresour. Technol. 102(18), 8569–8581 (2011)

    Article  Google Scholar 

  140. R. Setiono, L.C.K. Hui, Use of a quasi-Newton method in a feedforward neural network construction algorithm. IEEE Trans. Neural Netw. 6(1), 273–277 (1995)

    Article  Google Scholar 

  141. G.-B. Huang, Q.-Y. Zhu, C.-K. Siew, Extreme learning machine: a new learning scheme of feedforward neural networks, in Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on, vol. 2, (IEEE, Piscataway, 2004), pp. 985–990

    Google Scholar 

  142. M.T. Hagan, M.B. Menhaj, Training feedforward networks with the Marquardt algorithm. IEEE Trans. Neural Netw. 5(6), 989–993 (1994)

    Article  Google Scholar 

  143. B. Ghobadian, H. Rahimi, A. Nikbakht, G. Najafi, T. Yusaf, Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network. Renew. Energy 34(4), 976–982 (2009)

    Article  Google Scholar 

  144. G. Karatas, O.K. Sahingoz, Neural network based intrusion detection systems with different training functions, in Digital Forensic and Security (ISDFS), 2018 6th International Symposium on, (IEEE, Piscataway, 2018), pp. 1–6

    Google Scholar 

  145. M.F. Møller, A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw. 6(4), 525–533 (1993)

    Article  Google Scholar 

  146. A.A. Suratgar, M.B. Tavakoli, A. Hoseinabadi, Modified Levenberg-Marquardt method for neural networks training. World Acad. Sci. Eng. Technol. 6(1), 46–48 (2005)

    Google Scholar 

  147. N.M. Nawi, A. Khan, M.Z. Rehman, A new Levenberg Marquardt based back propagation algorithm trained with cuckoo search. Procedia Technol. 11, 18–23 (2013)

    Article  Google Scholar 

  148. E.P. Maillard, D. Gueriot, RBF neural network, basis functions and genetic algorithm, in Neural Networks, 1997., International Conference on, vol. 4, (IEEE, Piscataway, 1997), pp. 2187–2192

    Google Scholar 

  149. F. Günther, S. Fritsch, Neuralnet: Training of neural networks. R. J. 2(1), 30–38 (2010)

    Article  Google Scholar 

  150. Ö. Kişi, E. Uncuoğlu, Comparison of three back-propagation training algorithms for two case studies. Indian J. Eng. Mater. Sci. 12(5), 434–442 (2005)

    Google Scholar 

  151. J.-S. Jang, ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)

    Article  Google Scholar 

  152. A. Saxena et al., A review of clustering techniques and developments. Neurocomputing 267, 664–681 (2017)

    Article  Google Scholar 

  153. L. Rokach, O. Maimon, Clustering Methods, in Data Mining and Knowledge Discovery Handbook, (Springer, Boston, 2005), pp. 321–352

    Chapter  MATH  Google Scholar 

  154. C. Fraley, A.E. Raftery, How many clusters? Which clustering method? Answers via model-based cluster analysis. Comput. J. 41(8), 578–588 (1998)

    Article  MATH  Google Scholar 

  155. P. Domingos, A few useful things to know about machine learning. Commun. ACM 55(10), 78–87 (2012)

    Article  Google Scholar 

  156. Z. You, J. Ye, K. Li, P. Wang, Adversarial Noise Layer: Regularize Neural Network By Adding Noise. arXiv preprint arXiv 1805, 08000 (2018)

    Google Scholar 

  157. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

  158. R. Kocjančič, J. Zupan, Modelling of the river flowrate: the influence of the training set selection. Chemom. Intell. Lab. Syst. 54(1), 21–34 (2000)

    Article  Google Scholar 

  159. G.J. Bowden, G.C. Dandy, H.R. Maier, Data transformation for neural network models in water resources applications. J. Hydroinf. 5(4), 245–258 (2003)

    Article  Google Scholar 

  160. H.R. Maier, A. Jain, G.C. Dandy, K.P. Sudheer, Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions. Environ. Model. Softw. 25(8), 891–909 (2010)

    Article  Google Scholar 

  161. I. Flood, N. Kartam, Neural networks in civil engineering. I: Principles and understanding. J. Comput. Civ. Eng. 8(2), 131–148 (1994)

    Article  Google Scholar 

  162. K. Rajer-Kanduč, J. Zupan, N. Majcen, Separation of data on the training and test set for modelling: A case study for modelling of five colour properties of a white pigment. Chemom. Intell. Lab. Syst. 65(2), 221–229 (2003)

    Article  Google Scholar 

  163. A. Atkinson, Beyond response surfaces: Recent developments in optimum experimental design. Chemom. Intell. Lab. Syst. 28(1), 35–47 (1995)

    Article  Google Scholar 

  164. R.K.H. Galvao, M.C.U. Araujo, G.E. Jose, M.J.C. Pontes, E.C. Silva, T.C.B. Saldanha, A method for calibration and validation subset partitioning. Talanta 67(4), 736–740 (2005)

    Article  Google Scholar 

  165. R.W. Kennard, L.A. Stone, Computer aided design of experiments. Technometrics 11(1), 137–148 (1969)

    Article  MATH  Google Scholar 

  166. A. Saptoro, H.M. Yao, M. Tadé, H. Vuthaluru, Prediction of coal hydrogen content for combustion control in power utility using neural network approach. Chemom. Intell. Lab. Syst. 94(2), 149–159 (2008)

    Article  Google Scholar 

  167. A. Saptoro, M.O. Tadé, H. Vuthaluru, A modified Kennard-Stone algorithm for optimal division of data for developing artificial neural network models. Chem. Prod. Process. Model. 7(1), 1–16 (2012)

    Google Scholar 

  168. S.O. Giwa, S.O. Adekomaya, K.O. Adama, M.O. Mukaila, Prediction of selected biodiesel fuel properties using artificial neural network. Front. Energy 9(4), 433–445 (2015)

    Article  Google Scholar 

  169. I. Estiati, F.B. Freire, J.T. Freire, R. Aguado, M. Olazar, Fitting performance of artificial neural networks and empirical correlations to estimate higher heating values of biomass. Fuel 180, 377–383 (2016)

    Article  Google Scholar 

  170. S. Hosseinpour, M. Aghbashlo, M. Tabatabaei, M. Mehrpooya, Estimation of biomass higher heating value (HHV) based on the proximate analysis by using iterative neural network-adapted partial least squares (INNPLS). Energy 138, 473–479 (2017)

    Article  Google Scholar 

  171. J. Chen et al., Investigation of co-combustion characteristics of sewage sludge and coffee grounds mixtures using thermogravimetric analysis coupled to artificial neural networks modeling. Bioresour. Technol. 225, 234–245 (2017)

    Article  Google Scholar 

  172. U. Ozveren, An artificial intelligence approach to predict gross heating value of lignocellulosic fuels. J. Energy Inst. 90(3), 397–407 (2017)

    Article  Google Scholar 

  173. A.V. Fiacco, Introduction to Sensitivity and Stability Analysis in Nonlinear Programming (Elsevier, New York, 1983)

    MATH  Google Scholar 

  174. D.J. Pannell, Sensitivity analysis: Strategies, methods, concepts, examples. Agric. Econ. 16, 139–152 (1997)

    Article  Google Scholar 

  175. G.D. Garson, Interpreting neural-network connection weights. AI Expert 6(4), 46–51 (1991)

    Google Scholar 

  176. M.H. Shojaeefard, M. Akbari, M. Tahani, F. Farhani, Sensitivity analysis of the artificial neural network outputs in friction stir lap joining of aluminum to brass. Adv. Mater. Sci. Eng. 2013, 1 (2013)

    Article  Google Scholar 

  177. D. Hamby, A review of techniques for parameter sensitivity analysis of environmental models. Environ. Monit. Assess. 32(2), 135–154 (1994)

    Article  Google Scholar 

  178. J. Kim, M.J. Realff, J.H. Lee, Optimal design and global sensitivity analysis of biomass supply chain networks for biofuels under uncertainty. Comput. Chem. Eng. 35(9), 1738–1751 (2011)

    Article  Google Scholar 

  179. J.F. Pérez, P.N. Benjumea, A. Melgar, Sensitivity analysis of a biomass gasification model in fixed bed downdraft reactors: Effect of model and process parameters on reaction front. Biomass Bioenergy 83, 403–421 (2015)

    Article  Google Scholar 

  180. K. Janocha, W.M. Czarnecki, On loss functions for deep neural networks in classification. arXiv preprint arXiv:1702.05659 (2017)

    Google Scholar 

  181. T. Hastie, R. Tibshirani, J. Friedman, Unsupervised learning, in The Elements of Statistical Learning, (Springer, New York, 2009), pp. 485–585

    Chapter  MATH  Google Scholar 

  182. A.E. Eiben, J.E. Smith, Introduction to Evolutionary Computing (Springer, Berlin, 2003)

    Book  MATH  Google Scholar 

  183. N.M. Al-Salami, Evolutionary algorithm definition. Am. J. Eng. Appl. Sci. 2(4), 789–795 (2009)

    Article  Google Scholar 

  184. P.A. Vikhar, Evolutionary algorithms: A critical review and its future prospects, in Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), 2016 International Conference on, (IEEE, New York, 2016), pp. 261–265

    Google Scholar 

  185. R. Martí, P. Pardalos, M. Resende, Handbook of Heuristics (Springer, Cham, 2018)

    Book  MATH  Google Scholar 

  186. C.A.C. Coello, G.B. Lamont, D.A. Van Veldhuizen, Evolutionary Algorithms for Solving Multi-Objective Problems (Springer, Boston, 2007)

    MATH  Google Scholar 

  187. M. Suleymani, A. Bemani, Application of ANFIS-PSO algorithm as a novel method for estimation of higher heating value of biomass. Energy Sources A 40(3), 288–293 (2018)

    Article  Google Scholar 

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  1. Mechanical Engineering Science, University of Johannesburg, Johannesburg, South Africa

    O. Olatunji & N. Madushele

  2. Department of Mechanical Engineering, Faculty of Engineering & Technology, Walter Sisulu University, Eastern Cape, South Africa

    S. Akinlabi

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  1. Department of Chemical Engineering, Faculty of Engineering, Built Environment and Information Technology, University of Pretoria, Hatfield, Pretoria, South Africa

    Michael O. Daramola

  2. Department of Chemical Engineering, Covenant University, Ota, Nigeria

    Augustine O. Ayeni

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Olatunji, O., Akinlabi, S., Madushele, N. (2020). Application of Artificial Intelligence in the Prediction of Thermal Properties of Biomass. In: Daramola, M., Ayeni, A. (eds) Valorization of Biomass to Value-Added Commodities. Green Energy and Technology. Springer, Cham. https://doi.org/10.1007/978-3-030-38032-8_4

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