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Kinetic and Artificial neural network modelling of marabú (Dichrostachys cinerea) pyrolysis based on thermogravimetric data

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Abstract

Marabú (Dichrostachys cinerea), a fast-growing shrub species, has garnered interest as a potential energy crop due to its properties. In developing thermochemical processes for utilising D. cinerea, specifically through pyrolysis, precise prediction of its behaviour is essential for optimising process efficiency and understanding the underlying mechanisms. This study focuses on comparing the effectiveness of kinetic and artificial neural network (ANN) modelling methods in predicting the pyrolysis of D. cinerea. Utilising thermogravimetric data at four different heating rates (5, 10, 20 and 40 °C/min), a kinetic model based on three independent parallel reactions was developed. In the ANN model, the input variables (heating rate (°C/min), temperature (°C) and time (min)) were used to predict the output variable: weight loss (%). To optimise a backpropagation neural network (BPNN), 4-fold cross-validation and Bayesian optimisation were employed. The findings demonstrate that both methods effectively predict weight loss, with the ANN model achieving superior accuracy in capturing experimental data, particularly at local maxima of weight loss, reflected by R2 values exceeding 0.99. The ANN method excels without the need for predetermined kinetic reaction mechanisms, showcasing its ability to adapt to complex, non-linear types of behaviour more accurately than traditional models. This study not only provides valuable insights into the pyrolytic behaviour of D. cinerea but also establishes a benchmark for future research in the predictive modelling of pyrolysis for diverse types of lignocellulosic biomass.

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Data availability

Data will be made available on request.

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The code will be made available on request.

Abbreviations

ANN:

Artificial neutral network

BPNN:

Backpropagation neural network

DTG:

Derivative thermogravimetry

GPU:

Graphics processing unit

GRG:

Generalised reduced gradient

h:

Time step size in Runge-Kutta method

 k 1,  k 2,  k 3, and k 4 :

Intermediate slope calculations in Runge-Kutta method

MSE:

Mean square error

OF:

Objective function

ReLU:

Rectified linear unit

R² :

Coefficient of determination

Tanh:

Hyperbolic tangent function

TGA:

Thermogravimetric analysis

α :

Learning rate 

β 1, β 2 :

Decay factors

ϵ:

Small number to prevent division by zero

m t , v t :

Estimates of the first and second moments

∇J(θ) :

Gradient of the objective with respect to parameters

References

  1. Eksi G, Karaosmanoglu F (2017) Combined bioheat and biopower: a technology review and an assessment for Turkey. Renew Sustain Energy Rev 73:1313–1332. https://doi.org/10.1016/j.rser.2017.01.093

    Article  Google Scholar 

  2. Reyes YA, Pérez M, Barrera EL, Martínez Y, Cheng KK (2022) Thermochemical conversion processes of Dichrostachys cinerea as a biofuel: a review of the Cuban case. Renew Sustain Energy Rev 160:112322. https://doi.org/10.1016/j.rser.2022.112322

    Article  Google Scholar 

  3. Mortezaeikia V, Tavakoli O, Khodaparasti MS (2021) A review on kinetic study approach for pyrolysis of plastic wastes using thermogravimetric analysis. J Anal Appl Pyrol 160:105340. https://doi.org/10.1016/j.jaap.2021.105340

    Article  Google Scholar 

  4. Hakeem IG, Halder P, Dike CC, Chiang K, Sharma A, Paz-Ferreiro J et al (2022) Advances in biosolids pyrolysis: roles of pre-treatments, catalysts, and co-feeding on products distribution and high-value chemical production. J Anal Appl Pyrol 166:105608. https://doi.org/10.1016/j.jaap.2022.105608

    Article  Google Scholar 

  5. Ding Y, Zhang J, He Q, Huang B, Mao S (2019) The application and validity of various reaction kinetic models on woody biomass pyrolysis. Energy 179:784–791. https://doi.org/10.1016/j.energy.2019.05.021

    Article  Google Scholar 

  6. Purnomo DMJ, Richter F, Bonner M, Vaidyanathan R, Rein G (2020) Role of optimisation method on kinetic inverse modelling of biomass pyrolysis at the microscale. Fuel 262:116251. https://doi.org/10.1016/j.fuel.2019.116251

    Article  Google Scholar 

  7. Ding Y, Huang B, Li K, Du W, Lu K, Zhang Y (2020) Thermal interaction analysis of isolated hemicellulose and cellulose by kinetic parameters during biomass pyrolysis. Energy 195:117010. https://doi.org/10.1016/j.energy.2020.117010

    Article  Google Scholar 

  8. González Rivera JE, Oliva Merencio D, Romero Vistín AS, Ledesma Acosta RD, Coronel Espinoza BD, Abreu-Naranjo R (2022) Thermogravimetric characteristics and kinetic modeling of Piptocoma discolor pyrolysis and combustion processes to contribute to its use as a renewable energy source in the Ecuadorian Amazon region. Biomass Convers Biorefinery :1–8

  9. Conesa JA (2021) Numerical integration of weight loss curves for kinetic analysis. Thermo 1(1):32–44

    Article  Google Scholar 

  10. Zhong Y, Ding Y, Jiang G, Lu K, Li C (2023) Comparison of artificial neural networks and kinetic inverse modeling to predict biomass pyrolysis behavior. J Anal Appl Pyrol 169:105802

    Article  Google Scholar 

  11. Nawaz A, Kumar P (2022) Pyrolysis behavior of low value biomass (Sesbania bispinosa) to elucidate its bioenergy potential: kinetic, thermodynamic and prediction modelling using artificial neural network. Renewable Energy 200:257–270. https://doi.org/10.1016/j.renene.2022.09.110

    Article  Google Scholar 

  12. Chen Z, Hu M, Zhu X, Guo D, Liu S, Hu Z et al (2015) Characteristics and kinetic study on pyrolysis of five lignocellulosic biomass via thermogravimetric analysis. Bioresour Technol 192:441–450

    Article  Google Scholar 

  13. Çepelioğullar Ö, Mutlu İ, Yaman S, Haykiri-Acma H (2018) Activation energy prediction of biomass wastes based on different neural network topologies. Fuel 220:535–545. https://doi.org/10.1016/j.fuel.2018.02.045

    Article  Google Scholar 

  14. Guo B, Li D, Cheng C, Lü Z-a, Shen Y (2001) Simulation of biomass gasification with a hybrid neural network model. Bioresour Technol 76(2):77–83. https://doi.org/10.1016/S0960-8524(00)00106-1

    Article  Google Scholar 

  15. Sunphorka S, Chalermsinsuwan B, Piumsomboon P (2017) Application of artificial neural network for kinetic parameters prediction of biomass oxidation from biomass properties. J Energy Inst 90(1):51–61. https://doi.org/10.1016/j.joei.2015.10.007

    Article  Google Scholar 

  16. ASTM-E1757-01 (2007) Standard practice for preparation of biomass for compositional analysis. ASTM International, West Conshohocken, PA

    Google Scholar 

  17. Abreu-Naranjo R, Foppa-Pedretti E, Romero-Romero O, Riva G (2010) Caracterización energética Del MARABÚ. Dyna 85(7):581–586

    Article  Google Scholar 

  18. Abreu-Naranjo R, Conesa JA, Pedretti EF, Romero OR (2012) Kinetic analysis: simultaneous modelling of pyrolysis and combustion processes of dichrostachys cinerea. Biomass Bioenergy 36(0):170–175. https://doi.org/10.1016/j.biombioe.2011.10.032

    Article  Google Scholar 

  19. J PG, V NK (2021) Google colaboratory: tool for deep learning and machine learning applications. Indian J Computer Sci 6(3–4). https://doi.org/10.17010/ijcs/2021/v6/i3-4/165408

  20. Kimm H, Paik I, Kimm H (2021) Performance Comparision of TPU, GPU, CPU on Google Colaboratory over distributed deep learning. In: 2021 IEEE 14th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC)

  21. Hecht-Nielsen R III (1989) 3 - Theory of the backpropagation neural network**based on nonindent by Robert Hecht-Nielsen, which appeared in Proceedings of the International Joint Conference on Neural Networks 1, 593–611, June © 1989 IEEE. In: Wechsler H, editor. Neural Networks for Perception: Academic Press; 1992. pp. 65–93

  22. Ciuparu A, Nagy-Dăbâcan A, Mureşan RC (2020) Soft++, a multi-parametric non-saturating non-linearity that improves convergence in deep neural architectures. Neurocomputing 384:376–388. https://doi.org/10.1016/j.neucom.2019.12.014

    Article  Google Scholar 

  23. Hu Z, Zhang J, Ge Y (2021) Handling vanishing gradient problem using artificial derivative. IEEE Access 9:22371–22377. https://doi.org/10.1109/ACCESS.2021.3054915

    Article  Google Scholar 

  24. Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. InProceedings of the fourteenth international conference on artificial intelligence and statistics (pp. 315–323). JMLR Workshop and Conference Proceedings

  25. Liu M, Yao D, Liu Z, Guo J, Chen J (2023) An improved Adam optimization algorithm combining adaptive coefficients and composite gradients based on randomized block coordinate descent. Comput Intell Neurosci 2023:4765891. https://doi.org/10.1155/2023/4765891

    Article  Google Scholar 

  26. Mehmood F, Ahmad S, Whangbo TK (2023) An efficient optimization technique for training deep neural networks. Mathematics 11(6):1360

    Article  Google Scholar 

  27. Arunsuriyasak P, Boonme P, Phasukkit P (2019) Investigation of deep learning optimizer for water pipe leaking detection. 2019 16th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)

  28. Zhang C, Shao Y, Sun H, Xing L, Zhao Q, Zhang L (2024) The WuC-Adam algorithm based on joint improvement of warmup and cosine annealing algorithms. Math Biosci Eng 21(1):1270–1285. https://doi.org/10.3934/mbe.2024054

    Article  Google Scholar 

  29. Jierula A, Wang S, OH T-M, Wang P (2021) Study on accuracy metrics for evaluating the predictions of damage locations in deep piles using artificial neural networks with acoustic emission data. Appl Sci 11(5):2314

    Article  Google Scholar 

  30. Hastie T, Tibshirani R, Friedman JH, Friedman JH (2009) The elements of statistical learning: data mining, inference, and prediction. Springer

    Book  Google Scholar 

  31. Alruqi M, Sharma P, Ağbulut Ü (2023) Investigations on biomass gasification derived producer gas and algal biodiesel to power a dual-fuel engines: application of neural networks optimized with bayesian approach and K-cross Fold. Energy 282:128336. https://doi.org/10.1016/j.energy.2023.128336

    Article  Google Scholar 

  32. Theckel Joy T, Rana S, Gupta S, Venkatesh S (2019) A flexible transfer learning framework for bayesian optimization with convergence guarantee. Expert Syst Appl 115:656–72. https://doi.org/10.1016/j.eswa.2018.08.023

    Article  Google Scholar 

  33. Xu L, Jiang Y, Wang L (2017) Thermal decomposition of rape straw: pyrolysis modeling and kinetic study via particle swarm optimization. Energy Conv Manag 146. https://doi.org/10.1016/j.enconman.2017.05.020

  34. Di Blasi C (2008) Modeling chemical and physical processes of wood and biomass pyrolysis. Prog Energy Combust Sci 34:47–90. https://doi.org/10.1016/j.pecs.2006.12.001

    Article  Google Scholar 

  35. Caballero JA, Conesa JA (2005) Mathematical considerations for nonisothermal kinetics in thermal decomposition. J Anal Appl Pyrol 73(1):85–100

    Article  Google Scholar 

  36. Mood SH, Golfeshan AH, Tabatabaei M, Jouzani GS, Najafi GH, Gholami M et al (2013) Lignocellulosic biomass to bioethanol, a comprehensive review with a focus on pretreatment. Renew Sustain Energy Rev 27:77–93

    Article  Google Scholar 

  37. Kanthasamy R, Almatrafi E, Ali I, Hussain Sait H, Zwawi M, Abnisa F et al (2023) Bayesian optimized multilayer perceptron neural network modelling of biochar and syngas production from pyrolysis of biomass-derived wastes. Fuel 350:128832. https://doi.org/10.1016/j.fuel.2023.128832

    Article  Google Scholar 

  38. Adil M, Ullah R, Noor S, Gohar N (2022) Effect of number of neurons and layers in an artificial neural network for generalized concrete mix design. Neural Comput Appl 34(11):8355–8363. https://doi.org/10.1007/s00521-020-05305-8

    Article  Google Scholar 

  39. Altriki A, Ali I, Razzak SA, Ahmad I, Farooq W (2022) Assessment of CO2 biofixation and bioenergy potential of microalga Gonium pectorale through its biomass pyrolysis, and elucidation of pyrolysis reaction via kinetics modeling and artificial neural network. Front Bioeng Biotechnol 10. https://doi.org/10.3389/fbioe.2022.925391

  40. Sahoo A, Gautam R, Kumar S, Mohanty K (2021) Energy optimization from a binary mixture of non-edible oilseeds pyrolysis: Kinetic triplets analysis using Thermogravimetric Analyser and prediction modeling by Artificial Neural Network. J Environ Educ 297:113253. https://doi.org/10.1016/j.jenvman.2021.113253

  41. Carvalho RFd, Pasolini VdH, Fraga Breciani JG, Silva Costa AB, Sousa RCd (2024) Poultry manure combustion parameters to produce bioenergy: a thermogravimetric analysis by isoconventional models and machine learning. Case Stud Therm Eng 53:103757. https://doi.org/10.1016/j.csite.2023.103757

    Article  Google Scholar 

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Acknowledgements

The authors are indebted to Ms. Helen Pugh for her extensive proofreading of the manuscript.

Funding

This study was funded by Universidad Estatal Amazónica in Puyo.

Author information

Authors and Affiliations

  1. Facultad de Ciencias de la Vida, Universidad Estatal Amazónica (UEA), Vía Tena km 2½, Puyo, Pastaza, Ecuador

    Reinier Abreu-Naranjo & Amaury Pérez-Martínez

  2. Faculty of Engineering, China University of Geosciences, Wuhan, 430074, China

    Yu Zhong & Yanming Ding

  3. Institute for Natural Disaster Risk Prevention and Emergency Management, China University of Geosciences, Wuhan, 430074, China

    Yu Zhong & Yanming Ding

Authors
  1. Reinier Abreu-Naranjo
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  2. Yu Zhong
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  4. Yanming Ding
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by R.A.-N., Y.Z., A.P.M. and Y.D. The first draft of the manuscript was written by R.A.-N., and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Reinier Abreu-Naranjo.

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Highlights

Kinetic modelling yielded R2 > 0.96 for D. cinerea pyrolysis.

ANN models achieved R2 > 0.99, outperforming kinetic models.

ANN captured local maxima of weight loss in a more competent way.

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Abreu-Naranjo, R., Zhong, Y., Pérez-Martínez, A. et al. Kinetic and Artificial neural network modelling of marabú (Dichrostachys cinerea) pyrolysis based on thermogravimetric data. Biomass Conv. Bioref. (2024). https://doi.org/10.1007/s13399-024-05759-z

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