PID control mechanism for the impact of pyrolysis temperature on bio-oil yield from biomass feedstock
DOI:
https://doi.org/10.15282/jceib.v12i1.12149Keywords:
Bio-oil, Pyrolysis, MATLAB, PID Controller, SimulinkAbstract
The global population is expected to increase on a yearly basis. To meet the energy demands of this rapid expansion, researchers must identify new alternative energy sources and replace depleted fossil fuels with other sources, such as biomass. Biomass can be thermochemically converted into bio-oil through pyrolysis; however, the conventional pyrolysis method lacks adequate process control. This study aims to analyze the effects of temperature on the effectiveness of disturbance in pyrolysis. The temperature was adjusted to determine the bio-oil yield as the primary process variable. MATLAB was employed to simulate data from the preceding case study using the Proportional Integral Derivative (PID) controller method. Transfer functions were developed during system identification to assess the efficiency of the order systems adopted. The PID controller was also used to evaluate the impact of disturbances on the process control system. The experiment was divided into four groups: PID controller with 0 zeros, 0 zeros with disturbance, 1 zero, and 1 zero with disturbance. Various transfer functions were applied to convert each group into first-, second-, and third-order systems. The results indicated that the first-order system produced the most stable process and met the target yield of 72%. This dataset can be used to advance current research on bio-oil yield optimization using the PID control mechanism as a component of the process control system.
References
[1] Hoel M, Kverndokk S. Depletion of fossil fuels and the impacts of global warming. Resource and Energy Economics. 1996;18(2):115-36. https://doi.org/10.1016/0928-7655(96)00005-X
[2] Sakulkit P, Palamanit A, Dejchanchaiwong R, Reubroycharoen P. Characteristics of pyrolysis products from pyrolysis and co-pyrolysis of rubber wood and oil palm trunk biomass for biofuel and value-added applications. Journal of Environmental Chemical Engineering. 2020;8(6):104561. https://doi.org/10.1016/j.jece.2020.104561
[3] Roman A, Bucura F, Botoran OR, Radu GL, Constantinescu M. Pyrolysis and gasification of energy crops for phytoremediation in Romania’s coal mining region. International Journal of Green Energy. 2025;22(12):2645-62. https://doi.org/10.1080/15435075.2025.2469139
[4] McKendry P. Energy production from biomass (part 1): Overview of biomass. Bioresource Technology. 2002; 83(1):37-46. https://doi.org/10.1016/S0960-8524(01)00118-3
[5] Singh Y, Singh NK, Sharma A, Lim WH, Palamanit A, Alhussan AA, El-kenawy ES. Bio-oil yield maximization and characteristics of neem based biomass at optimum conditions along with feasibility of biochar through pyrolysis. AIP Advances. 2024;14(8):085104. https://doi.org/10.1063/5.0214438
[6] Shrivastava P, Kumar A, Tekasakul P, Lam SS, Palamanit A. Comparative investigation of yield and quality of bio-oil and biochar from pyrolysis of woody and non-woody biomasses. Energies. 2021; 14(4):1092. https://doi.org/10.3390/en14041092
[7] Zhang X, Zhang Y, Zhang S, Yao L, Hao Y. Lignocellulosic biomass pyrolysis: A review on the pretreatment and catalysts. Fuel Processing Technology 2025;279: 108352. https://doi.org/10.1016/j.fuproc.2025.108352
[8] Bouchaib R, Abdelkhalak EH. Introduction to MATLAB. New York: John Wiley & Sons, Inc.; 2018.
[9] Muharto B, Saputro FR, Prabowo W, Anggoro T, Adiprabowo AB, Masfuri I, et al. Pyrolysis process control: temperature control design and application for optimum process operation. International Journal of Electrical & Computer Engineering. 2024;14(2): 1473-1485. https://doi.org/10.11591/ijece.v14i2.pp1473-1485
[10] Sánchez-López C, Carbajal-Gómez VH, Carrasco-Aguilar MA, Morales-López FE. PID controller design based on memductor. AEU-International Journal of Electronics and Communications. 2019;101:9-14. https://doi.org/10.1016/j.aeue.2019.01.019
[11] Faridi IK, Tsotsas E, Heineken W, Koegler M, Kharaghani A. Development of a neural network model predictive controller for the fluidized bed biomass gasification process. Chemical Engineering Science. 2024;293:120000. https://doi.org/10.1016/j.ces.2024.120000
[12] Bu Q, Cai J, Liu Y, Cao M, Dong L, Ruan R, Mao H. The effect of fuzzy PID temperature control on thermal behavior analysis and kinetics study of biomass microwave pyrolysis. Journal of Analytical and Applied Pyrolysis. 2021;158:105176. https://doi.org/10.1016/j.jaap.2021.105176
[13] Gómez-Vásquez RD, Marenco-Porto CA, Riveros-Almanza LG, Palacio M, Espinosa-Corrales DE. Predictive modelling of biomass pyrolysis: Product estimation using thermogravimetry, mass balance, and empirical correlations. Results in Engineering. 2025;25:104071. https://doi.org/10.1016/j.rineng.2025.104071
[14] Naidu S, Pandey H, Passalacqua A, Hameed S, Joshi J, Sharma A. Advancements in modeling and simulation of biomass pyrolysis: A comprehensive review. Journal of Analytical and Applied Pyrolysis. 2025;188:107030. https://doi.org/10.1016/j.jaap.2025.107030
[15] Seborg DE, Edgar TF, Mellichamp DA, Doyle III FJ. Process dynamics and control. New York: John Wiley & Sons; 2016.
[16] Ellens CJ, Brown RC. Optimization of a free-fall reactor for the production of fast pyrolysis bio-oil. Bioresource Technology. 2012;103(1):374-80. https://doi.org/10.1016/j.biortech.2011.09.087
[17] Bajpai P. Biermann's Handbook of Pulp and Paper: Volume 2: Paper and Board Making. Elsevier; 2018.
[18] Corrigan D. Characterizing the response of a closed loop system. Electronic and Electrical Engineering. 2012;2102.
[19] Kumar VB, Sampath D, Praneeth VS, Kumar YP. Error performance index based PID tuning methods for temperature control of heat exchanger system. In: IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS); 2021; Canada: pp. 1-6. https://doi.org/10.1109/IEMTRONICS52119.2021.9422613
[20] Yang Y. Application and optimization of PID control in modern industrial systems. Applied and Computational Engineering. 2025;195:83–89.
[21] Zhang S. Research on the application of PID control algorithm and fuzzy control theory in the field of temperature control. Applied and Computational Engineering. 2025;117:16-22. https://doi.org/10.54254/2755-2721/2025.19948
[22] MathWorks. Control System Toolbox™ User’s Guide. Natick (MA): MathWorks; 2018.
[23] Ridzuan A, Rahman HA. Real time comparison between PID and fuzzy logic controller for DC motor speed control. Evolution in Electrical and Electronic Engineering. 2024;5(1):512-20. https://publisher.uthm.edu.my/periodicals/index.php/eeee
Downloads
Published
Issue
Section
License
Copyright (c) 2026 The Author(s)

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
