• Hanif Furqon Hidayat Department of Mechanical Engineering, Faculty of Engineering, Universitas Indonesia
  • Rachman Setiawan Department of Mechanical Engineering, Faculty of Engineering, Universitas Indonesia
  • Radon Dhelika Department of Mechanical Engineering, Faculty of Engineering, Universitas Indonesia
  • Adi Surjosatyo Department of Mechanical Engineering, Faculty of Engineering, Universitas Indonesia
  • Hafif Dafiqurrohman Department of Mechanical Engineering, Faculty of Engineering, Universitas Indonesia
Keywords: biomass gasification, artificial neural network, gasification temperature control


Biomass gasification is considered among promising solutions for renewable energy generation. The process converts the biomass, such as rice husk, to synthetic gas (syngas). It produces CO, CO2, CH4, and H2 gas that are useful for internal combustion engines. The process is complicated to control. Hence, a thorough knowledge of this process is needed. One of the approaches to reveal the control parameters of the gasifier is using an artificial neural network (ANN). In this research, an ANN model is deployed from experiments that measure combustion temperature, intake, and discharge airflow rate as input variables. The output of this model is to predict the increase of combustion temperature in the reactor as this parameter is crucial for the design of an automated control system. From the two experiments, the models produce satisfying accuracy (R2 = 0.832 and 0.911) and relatively low errors (RMSE values of 0.250 and 0.098). The neural network itself is used to analyze the significant control parameters by the permutation importance method.


BP, “Statistical Review of World Energy, 2020 | 69th Edition,” Bp, vol. 69, 2020.

Basu, Biomass gasification, pyrolysis and torrefac­tion: Practical design and theory. 2018. doi: 10.1016/ C2016-0-04056-1.

Anis and Z. A. Zainal, “Tar reduction in biomass producer gas via mechanical, catalytic and thermal methods: A review,” Renewable and Sustainable En­ergy Reviews, vol. 15, no. 5. 2011. doi: 10.1016/j. rser.2011.02.018.

M. Jenkins, L. L. Baxter, and J. Koppejan, “Bio­mass Combustion,” in Thermochemical Processing of Biomass, 2019. doi: 10.1002/9781119417637. ch3.

Ramos, E. Monteiro, and A. Rouboa, “Numerical approaches and comprehensive models for gasifi­cation process: A review,” Renewable and Sustain­able Energy Reviews, vol. 110. 2019. doi: 10.1016/j. rser.2019.04.048.

Baruah, D. C. Baruah, and M. K. Hazarika, “Artifi­cial neural network based modeling of biomass gas­ification in fixed bed downdraft gasifiers,” Biomass and Bioenergy, vol. 98, 2017, doi: 10.1016/j.biombi­oe.2017.01.029.

Puig-Arnavat, J. C. Bruno, and A. Coronas, “Mod­ified thermodynamic equilibrium model for biomass gasification: A study of the influence of operating conditions,” Energy and Fuels, vol. 26, no. 2, 2012, doi: 10.1021/ef2019462.

J. Huang and S. Ramaswamy, “Modeling biomass gasification using thermodynamic equilibrium ap­proach,” in Applied Biochemistry and Biotechnology, 2009, vol. 154, no. 1–3. doi: 10.1007/s12010-008- 8483-x.

Adeyemi and I. Janajreh, “Modeling of the en­trained flow gasification: Kinetics-based ASPEN Plus model,” Renewable Energy, vol. 82, 2015, doi: 10.1016/j.renene.2014.10.073.

Halama and H. Spliethoff, “Reaction Kinetics of Pressurized Entrained Flow Coal Gasification: Computational Fluid Dynamics Simulation of a 5MW Siemens Test Gasifier,” Journal of Energy Resources Technology, vol. 138, no. 4, 2016, doi: 10.1115/1.4032620.

Gagliano, F. Nocera, F. Patania, M. Bruno, and D. G. Castaldo, “A robust numerical model for charac­terizing the syngas composition in a downdraft gas­ification process,” Comptes Rendus Chimie, vol. 19, no. 4, 2016, doi: 10.1016/j.crci.2015.09.019.

Wu, J. Beutler, and L. L. Baxter, “Non-catalytic ash effect on char reactivity,” Applied Energy, vol. 260, 2020, doi: 10.1016/j.apenergy.2019.114358.

Xue and R. O. Fox, “Multi-fluid CFD modeling of biomass gasification in polydisperse fluidized-bed gasifiers,” Powder Technology, vol. 254, 2014, doi: 10.1016/j.powtec.2014.01.025.

Klimanek, W. Adamczyk, A. Katelbach-Woźniak, G. Węcel, and A. Szlęk, “Towards a hybrid Euleri­an-Lagrangian CFD modeling of coal gasification in a circulating fluidized bed reactor,” Fuel, vol. 152, 2015, doi: 10.1016/j.fuel.2014.10.058.

Arcotumapathy, F. Alenazey, and A. A. Adesina, “Artificial neural network modeling of forced cycling operation between propane steam reforming and CO2 carbon gasifier,” in Catalysis Today, 2011, vol. 164, no. 1. doi: 10.1016/j.cattod.2010.12.027.

Puig-Arnavat, J. A. Hernández, J. C. Bruno, and A. Coronas, “Artificial neural network models for biomass gasification in fluidized bed gasifiers,” Bio­mass and Bioenergy, vol. 49, 2013, doi: 10.1016/j. biombioe.2012.12.012.

Wang, D. Chaffart, and L. A. Ricardez-Sando­val, “Modelling and optimization of a pilot-scale entrained-flow gasifier using artificial neural net­works,” Energy, vol. 188, 2019, doi: 10.1016/j.ener­gy.2019.116076.

Ozonoh, B. O. Oboirien, A. Higginson, and M. O. Daramola, “Performance evaluation of gasification system efficiency using artificial neural network,” Renewable Energy, vol. 145, 2020, doi: 10.1016/j. renene.2019.07.136.

Y. Mutlu and O. Yucel, “An artificial intelligence based approach to predicting syngas composition for downdraft biomass gasification,” Energy, vol. 165, 2018, doi: 10.1016/

Mikulandrić, D. Lončar, D. Böhning, R. Böhme, and M. Beckmann, “Artificial neural network mod­elling approach for a biomass gasification process in fixed bed gasifiers,” Energy Conversion and Management, vol. 87, 2014, doi: 10.1016/j.encon­man.2014.03.036.

Setiawan, H. F. Hidayat, H. Dafiqurrohman, A. Surjosatyo, and R. Dhelika, “Performance Evalua­tion of a Continuous Downdraft Gasification Reactor Driven by Electric Motors with Manual Mode of Op­eration,” Journal of Engineering and Technological Sciences, 2021. 

Andromeda, A. Surjosatyo, H. Dafiqurrohman, and M. H. Amirullah, “Analysis of fix bed downdraft biomass gasification reactors continues operating characteristics towards synthetic gas quality,” in AIP Conference Proceedings, 2020, vol. 2255. doi: 10.1063/5.0013681.

I. R. Kurnianto, A. G. Setiawan, A. Surjosatyo, H. Dafiqurrohman, and R. Dhelika, “Design and Implementation of a Real-Time Monitoring System Based on Internet of Things in a 10-kW Downdraft Gasifier,” Evergreen, vol. 9, no. 1, pp. 145–149, Mar. 2022, doi: 10.5109/4774230.

Pedregosa et al., “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, 2011.

Elmaz and Ö. Yücel, “Data-driven identification and model predictive control of biomass gasification process for maximum energy production,” Energy, vol. 195, 2020, doi: 10.1016/

Original Scientific Paper