Abstract
This paper presents a critical review of modern control methods applied to industrial gas-fired furnaces. Conventional PID controllers and their advanced modifications, including Advanced Process Control (APC) and cascade control schemes, are analyzed alongside Model Predictive Control (MPC) approaches. In addition, intelligent control algorithms—such as fuzzy logic and neuro-fuzzy systems (Fuzzy, ANFIS)—are examined. The study also considers advanced sensing technologies, including Tunable Diode Laser Absorption Spectroscopy (TDLAS), acoustic pyrometry, and infrared pyrometers, as well as digital twins and CFD-based modeling techniques. Particular attention is given to a comparative evaluation of control strategies based on key performance criteria, including energy efficiency, product quality, and emission reduction.
First Page
94
Last Page
102
References
- Zhao, J., et al. (2021). Industrial reheating furnaces: A review of energy efficiency assessments, waste heat recovery potentials, heating process characteristics and perspectives for steel industry. Energy. https://doi.org/10.1016/j.energy.2021.119608 ResearchGate
- Bisht, P.S., et al. (2024). Parametric Energy Efficiency Impact Analysis for Industrial Process Heating Furnaces. Processes, 12(4), 737. https://doi.org/10.3390/pr12040737 MDPI
- Sun, K., Sur, R., Chao, X., Jeffries, J.B., Hanson, R.K. (2013). Wavelength-modulation diode laser absorption spectroscopy for real-time sensing of combustion gases. Proceedings of the Combustion Institute, 34(2), 3593-3601. https://doi.org/10.1016/j.proci.2012.06.155
- Li, X., et al. (2025). Review on performance enhancement methods of tunable diode laser absorption spectroscopy for in situ gas measurement. Applied Spectroscopy Reviews, 60(6), 1150-1183. https://doi.org/10.1080/05704928.2025.2592732
- Liu, X., et al. (2022). TDLAS-based gas detection advances for combustion. Sensors, 22(16), 6095.
- Sepman, A., et al. (2019). Tunable diode laser absorption spectroscopy diagnostics of potassium, carbon monoxide, and soot in oxygen-enriched biomass combustion close to stoichiometry. Energy & Fuels. https://doi.org/10.1021/acs.energyfuels.9b02257
- Wang, Y., Liu, Q., Zhang, J., Yan, J. (2019). Acoustic pyrometry–based temperature field reconstruction in industrial furnaces. Applied Thermal Engineering, 153, 457-466. https://doi.org/10.1016/j.applthermaleng.2019.03.018
- Di Capaci, R. B., et al. (2024). Model-based control of a glass melting furnace. IFAC-PapersOnLine. https://doi.org/10.1016/j.ifacol.2024.07.123 onlinelibrary.wiley.com
- Aborisade, D.O., Adewuyi, P.A. (2014). Evaluation of PID tuning methods on direct gas-fired oven. International Journal of Engineering Research and Applications, 4(3), 1-9.
- Mirjalili, S., Lewis, A., Sadiq, A.S. (2014). Autonomous particles groups for PID controller tuning. Engineering Applications of Artificial Intelligence, 36, 24-33.https://doi.org/10.1016/j.engappai.2014.07.006
- Åström, K.J., Hägglund, T. (2006). Advanced PID Control. Research Triangle Park, NC, USA: ISA – The Instrumentation, Systems, and Automation Society, 352 p.
- Li, D., Wang, J., Zhao, Y. (2018). Intelligent PID control for temperature regulation in industrial heating furnaces. ISA Transactions, 79, 181-192. https://doi.org/10.1016/j.isatra.2018.04.014
- Zanoli, S.M., et al. (2018). MPC-based energy efficiency improvement in a pusher-type billets reheating furnace. Advances in Science, Technology and Engineering Systems Journal (ASTESJ), 3(2). https://doi.org/10.25046/aj030239 astesj.com
- Zanoli, S.M., et al. (2023). Multi-Mode MPC for walking-beam reheating furnace. Sensors, 23(8), 3966. https://doi.org/10.3390/s23083966
- Byrski, W., Drapała, M., Byrski, J., Noack, M., Reger, J. (2024). Comparison of LQR with MPC in the adaptive stabilization of a glass conditioning process using soft-sensors for parameter identification and state observation. Control Engineering Practice, 146, 105884. https://doi.org/10.1016/j.conengprac.2024.105884
- Cho, M., Ban, J., Seo, M., & Kim, S. W. (2023). Neural network MPC for heating section of annealing furnace. Expert Systems with Applications, 223, 119869. https://doi.org/10.1016/j.eswa.2023.119869
- Krzywański, J., et al. (2024). Fuzzy modeling of reheating and scale formation in steel furnaces. Materials, 17(5), 1132. https://doi.org/10.3390/ma17051132
- Huang, C.-W., Li, C.-H., Pan, C.-H. (2014). A sliding-mode control scheme for the precision positioning of a compliant stage. The Scientific World Journal, 349162. https://doi.org/10.1155/2014/349162
- Raič, J., et al. (2021). Validation of coupled 3D CFD model for oxy-fuel cross-fired furnace with electric boosting. Applied Thermal Engineering, 194, 117026. https://doi.org/10.1016/j.applthermaleng.2021.117026 MDPI
- Yu, W., et al. (2022). Energy digital twin technology for industrial systems: Review and perspectives. Applied Energy, 314, 118879. https://doi.org/10.1016/j.apenergy.2022.118879 ResearchGate
- Ba, L., et al. (2025). Analysis of Digital Twin Applications in Energy Efficiency: A systematic review. Sustainability, 17(8), 3560. https://doi.org/10.3390/su17083560 MDPI
- Norouzi, P., Maalej, S., Mora, R. (2023). Applicability of Deep Learning Algorithms for Predicting Indoor Temperatures: Towards the Development of Digital Twin HVAC Systems. Buildings, 13(6), 1542. https://doi.org/10.3390/buildings13061542 MDPI
- Hafeez, M.A., Procacci, A., Coussement, A., Parente, A. (2024). Challenges and opportunities for the application of digital twins in hard-to-abate industries: A review. Resources, Conservation & Recycling, 209, 107796. https://doi.org/10.1016/j.resconrec.2024.107796
- Viskanta, R., Mengüç, M.P. (2016). Radiative heat transfer in glass manufacturing furnaces: Modeling and simulation. Journal of Quantitative Spectroscopy and Radiative Transfer, 182, 229-244. https://doi.org/10.1016/j.jqsrt.2016.05.012
- Raič, J., Wachter, P., Hödl, P., et al. (2022). CFD simulation aided glass quality and energy efficiency analysis of an oxy-fuel glass melting furnace with electric boosting. Energy Conversion and Management: X, 15, 100252. https://doi.org/10.1016/j.ecmx.2022.100252
- Daurer, G., Raič, J., Demuth, M., Gaber, C., Hochenauer, C. (2023). Detailed comparison of physical fining methods in an industrial glass melting furnace using coupled CFD simulations. Applied Thermal Engineering, 121022. https://doi.org/10.1016/j.applthermaleng.
- Cravero, C., et al. (2023). Numerical Simulation of Melted Glass Flow Structures. Energies, 16(10), 4187. https://doi.org/10.3390/en16104187
- Qi, Y., Wang, C., Zhang, M., Zhao, L., Wang, M., Zhang, W., Chen, X., Ge, P. (2024). Comparative analysis of combustion characteristics and economy between air assisted combustion and oxy-fuel combustion in glass furnaces. Thermal Science and Engineering Progress, 53, 102699. https://doi.org/10.1016/j.tsep.2024.102699
Recommended Citation
Kholmanov, Utkir Uktam o'gli
(2025)
"CURRENT TRENDS IN INDUSTRIAL GAS FURNACE CONTROL AND MONITORING SYSTEMS,"
Chemical Technology, Control and Management: Vol. 2025:
Iss.
6, Article 10.
DOI: https://doi.org/10.59048/2181-1105.1744
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Complex Fluids Commons, Controls and Control Theory Commons, Industrial Technology Commons, Process Control and Systems Commons