Artificial Intelligence-Based Control Strategies for Flow Rate Control of Fluids and Gases in Energy and Process Systems: A Systematic Review
DOI:
https://doi.org/10.22153/kej.2026.12.016Keywords:
Control system; Flow rate; Adaptive control; Fluid; Artificial intelligenceAbstract
Artificial Intelligence (AI)-driven controllers have increased the accuracy, flexibility, and efficiency in flow rate control for gas and liquid flows. In contrast, traditional Proportional–Integral–Derivative (PID) controllers are usually not well trained for nonlinear dynamics and time-varying disturbances, leading to limited stability and control accuracy. Most existing research studies have focused on the design and performance of individual AI-based controllers for individual machines without systematically comparing these devices for various industrial and energy applications. The gap of this study is filled by a review of 34 peer-reviewed studies from 2021 to 2024 detected in Scopus and Web of Science databases, classified on the basis of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) structure. From the quantitative results the AI controllers have a higher performance overall than traditional PID controllers with faster response by 12 to 85%, 15 to 67% reduction in steady-state errors, and 18 to 40% drop in overshoot, which indicates high accuracy and stability of systems. The data indicates that fuzzy and hybrid controllers are highly flexible for dealing with nonlinear and dynamic flow phenomena while model predictive and optimization-based controllers have high accuracy for multivariable processes. Furthermore, AI control technology for energy, hydrogen, and process fluid applications improves operability, reduces energy overhead, and enables real-time flexible management under ever-changing loads. Thus, this review lays the excellent groundwork for next-generation intelligent control frameworks and opens the new paradigm of advanced and data-driven strategies towards subsequent flow regulation and energy system applications.
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[1] G. Filo, “Artificial Intelligence Methods in Hydraulic System Design,” Energies, vol. 16, no. 8, p. 3320, 2023, doi: 10.3390/en16083320.
[2] W. Hu and Y. Jiang, “Intelligent control and optimization of hydraulic systems using reinforcement learning,” Neural Comput. Appl., 2025, doi: 10.1007/s00521-025-11430-z.
[3] M. Alateeq and W. Pedrycz, “Logic-oriented fuzzy neural networks: A survey,” Expert Syst. Appl., vol. 257, p. 125120, 2024, doi: 10.1016/j.eswa.2024.125120.
[4] I. Ahmad, F. M’Zoughi, P. Aboutalebi, I. Garrido, and A. J. Garrido, “Fuzzy logic control of an artificial neural network-based floating offshore wind turbine model integrated with four oscillating water columns,” Ocean Eng., vol. 269, p. 113578, 2023, doi: 10.1016/j.oceaneng.2022.113578.
[5] S. Lu et al., “Exploring the comprehensive integration of artificial intelligence in optimizing HVAC system operations: A review and future outlook,” Results Eng., vol. 25, p. 103765, 2025, doi: 10.1016/j.rineng.2024.103765.
[6] D. Drikakis and F. Sofos, “Can Artificial Intelligence Accelerate Fluid Mechanics Research?,” Fluids, vol. 8, no. 7, p. 212, 2023, doi: 10.3390/fluids8070212.
[7] T. S. Ansari and S. A. A. Taqvi, “State‐of‐the‐Art Review on the Applications of Nonlinear and Artificial Intelligence‐Based Controllers in Petrochemical Processes,” ChemBioEng Rev., vol. 10, no. 6, pp. 884–906, 2023, doi: 10.1002/cben.202300017.
[8] S. Munahar, M. Setiyo, M. M. Saudi, A. Ahmad, and D. Yuvenda, “Modelling Fuel Cut Off Controller on CNG Engines Using Fuzzy Logic: A Prototype,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 12, no. 5, pp. 1857–1865, 2022, doi: 10.18517/ijaseit.12.5.16849.
[9] H. Jung and J. H. Lee, “Flexible operation of Post-combustion CO2 capture process enabled by NARX-MPC using neural network,” Comput. Chem. Eng., vol. 179, 2023, doi: 10.1016/j.compchemeng.2023.108447.
[10] K. R. Achu Govind, S. Mahapatra, and S. Ranjan Mahapatro, “Design of a decentralized control law for variable area coupled tank systems using H∞ complimentary sensitivity function,” Asian J. Control, vol. 26, no. 3, pp. 1540–1552, 2024, doi: 10.1002/asjc.3281.
[11] G. M. Bressan and C. M. Agulhari, “Fuzzy System to Control a Liquid Mixing Tank,” Appl. Math. Inf. Sci., vol. 18, no. 3, pp. 595–601, 2024, doi: 10.18576/amis/180311.
[12] X. Chen et al., “Temperature and humidity management of PEM fuel cell power system using multi-input and multi-output fuzzy method,” Appl. Therm. Eng., vol. 203, 2022, doi: 10.1016/j.applthermaleng.2021.117865.
[13] J. Chu, Z. He, W. Wang, B. Li, and G. Yang, “Fuzzy control of temperature in gas flow control system based on mixed cold and hot gases,” Int. Commun. Heat Mass Transf., vol. 148, 2023, doi: 10.1016/j.icheatmasstransfer.2023.107048.
[14] D. Gilbert Chandra, B. Vinoth, U. Srinivasulu Reddy, G. Uma, and M. Umapathy, “Recurrent Neural Network based Soft Sensor for flow estimation in Liquid Rocket Engine Injector calibration,” Flow Meas. Instrum., vol. 83, 2022, doi: 10.1016/j.flowmeasinst.2021.102105.
[15] S. Yahya, A. R. A. Tahtawi, K. Wijayanto, and B. A. Faizah, “Adaptive Fuzzy-PID Controller for Liquid Flow Control in the Heating Tank System,” Int. J. Integr. Eng., vol. 14, no. 1, pp. 173–180, 2022, doi: 10.30880/ijie.14.01.015.
[16] A. F. Quintã, I. Ehtiwesh, N. Martins, and J. A. F. Ferreira, “Gain scheduling model predictive controller design for tankless gas water heaters with time-varying delay,” Appl. Therm. Eng., vol. 213, 2022, doi: 10.1016/j.applthermaleng.2022.118669.
[17] P. Mohindru, “Review on PID, fuzzy and hybrid fuzzy PID controllers for controlling non-linear dynamic behaviour of chemical plants,” Artif. Intell. Rev., vol. 57, no. 4, 2024, doi: 10.1007/s10462-024-10743-0.
[18] J. Fu, B. Qin, Y. Wu, T. He, G. Zhang, and X. Sun, “A control method of proton exchange membrane fuel cell gas supply system based on fuzzy neural network proportion integration differentiation algorithm,” Energy, vol. 315, p. 134355, 2025, doi: 10.1016/j.energy.2024.134355.
[19] J. K. Mezaal and T. Alameri, “DESIGN AND CONSTRUCT UNIT TO CONTROL FLUID ENTERING SOLAR COLLECTORS DURING EFFICIENCY TESTS,” J. Appl. Eng. Sci., vol. 20, no. 4, pp. 1063–1072, 2022, doi: 10.5937/jaes0-35998.
[20] Y. Gou, H. He, H. Li, D. Wang, and M. Zhang, “DESIGN AND EXPERIMENTAL STUDY OF A VEHICLE-MOUNTED FERTILIZING AND SPRAYING MACHINE,” INMATEH - Agric. Eng., vol. 69, no. 1, pp. 55–64, 2023, doi: 10.35633/INMATEH-69-05.
[21] E. M. Solodkiy, A. B. Petrochenkov, D. D. Vishnyakov, and S. V Salnikov, “A Method for Indirect Measurement of the Flow Rate of an Electrically Driven Centrifugal Pump Installation,” Russ. Electr. Eng., vol. 92, no. 11, pp. 663–667, 2021, doi: 10.3103/S1068371221110158.
[22] A. Routh, S. Ghosh, I. Dey, M. Rahaman, and A. Ghosh, “Optimization of PEMFC pressure control using fractional PI/D controller with non-integer order: design and experimental evaluation,” Eng. Res. Express, vol. 6, no. 2, 2024, doi: 10.1088/2631-8695/ad33ff.
[23] S. Yelagandula and P. R. Ginuga, “Control of a Waste Water Treatment Plant Using Fuzzy Logic Controller,” J. Inst. Eng. Ser. E, vol. 103, no. 2, pp. 167–177, 2022, doi: 10.1007/s40034-022-00241-9.
[24] S. Manna, D. K. Singh, Y. Y. Ghadi, A. Yousef, H. Kotb, and K. M. Aboras, “Probabilistic Bi-Level Assessment and Adaptive Control Mechanism for Two-Tank Interacting System,” IEEE Access, vol. 11, pp. 118268–118280, 2023, doi: 10.1109/ACCESS.2023.3326727.
[25] C. Li, V. S. K. Adi, and J. Sunarso, “Control design for throughput improvement of fuel cell-integrated solar heated membrane desalination system,” Chem. Eng. Process. - Process Intensif., vol. 174, 2022, doi: 10.1016/j.cep.2022.108868.
[26] C. Zhang, Y. Hu, X. Gong, Y. Huang, and H. Chen, “Design and experimental verification of model-free adaptive sliding controller for air supply system of PEMFCs,” Control Eng. Pract., vol. 128, 2022, doi: 10.1016/j.conengprac.2022.105336.
[27] Z. Ren, J. Lin, Z. Wu, and S. Xie, “Dynamic optimal control of flow front position in injection molding process: A control parameterization-based method,” J. Process Control, vol. 132, 2023, doi: 10.1016/j.jprocont.2023.103125.
[28] I. Khurram Faridi, E. Tsotsas, W. Heineken, M. Koegler, and A. Kharaghani, “Development of a neural network model predictive controller for the fluidized bed biomass gasification process,” Chem. Eng. Sci., vol. 293, 2024, doi: 10.1016/j.ces.2024.120000.
[29] W.-Q. Wang, M.-J. Li, J.-Q. Guo, and W.-Q. Tao, “A feedforward-feedback control strategy based on artificial neural network for solar receivers,” Appl. Therm. Eng., vol. 224, 2023, doi: 10.1016/j.applthermaleng.2023.120069.
[30] F. Amiri, A. Shiani, and M. Mirzaei, “To Be or Not to Be: Addressing of PRISMA Checklist for Reporting Systematic Reviews and Meta-Analyses,” Iran. J. Public Health, 2023, doi: 10.18502/ijph.v52i10.13862.
[31] A. Boaye Belle and Y. Zhao, “Evidence-based decision-making: On the use of systematicity cases to check the compliance of reviews with reporting guidelines such as PRISMA 2020,” Expert Syst. Appl., vol. 217, p. 119569, 2023, doi: 10.1016/j.eswa.2023.119569.
[32] F. Xie, X. Guan, X. Peng, Y. Zeng, Z. Wang, and T. Qin, “Application of Fuzzy Control and Neural Network Control in the Commercial Development of Sustainable Energy System,” Sustainability, vol. 16, no. 9, p. 3823, 2024, doi: 10.3390/su16093823.
[33] C. De Koning and A. Jamshidnejad, “Hierarchical Integration of Model Predictive and Fuzzy Logic Control for Combined Coverage and Target-Oriented Search-and-Rescue via Robots with Imperfect Sensors,” J. Intell. & Robot. Syst., vol. 107, no. 3, 2023, doi: 10.1007/s10846-023-01833-2.
[34] S. B. Javed, A. A. Uppal, R. Samar, and A. I. Bhatti, “Design and implementation of multi-variable H∞ robust control for the underground coal gasification project Thar,” Energy, vol. 216, 2021, doi: 10.1016/j.energy.2020.119000.
[35] S. Shi, Z. Li, Y. Zhu, H. Wang, and J. Chen, “Multivariable non-singular terminal composite sliding mode control of gas-water-coal mixture lifting system,” ISA Trans., vol. 144, pp. 385–397, 2024, doi: 10.1016/j.isatra.2023.10.018.
[36] M.-F. R. Lee, “A Review on Intelligent Control Theory and Applications in Process Optimization and Smart Manufacturing,” Processes, vol. 11, no. 11, p. 3171, 2023, doi: 10.3390/pr11113171.
[37] S. M. Zanoli, C. Pepe, and L. Orlietti, “Synergic Combination of Hardware and Software Innovations for Energy Efficiency and Process Control Improvement: A Steel Industry Application,” Energies, vol. 16, no. 10, 2023, doi: 10.3390/en16104183.
[38] J. Zhou, Y. Liao, J. Liu, Y. Xue, and S. Xue, “Deep Reinforcement Learning Guided Cascade Control for Air Supply of Polymer Exchange Membrane Fuel Cell,” Energy Technol., vol. 9, no. 9, 2021, doi: 10.1002/ente.202100149.
[39] M. Fang, X. Wan, and J. Zou, “Development of a fuel cell humidification system and dynamic control of humidity,” Int. J. Energy Res., vol. 46, no. 15, pp. 22421–22438, 2022, doi: 10.1002/er.8547.
[40] Z. Meng, L. Zhang, H. Wang, X. Ma, H. Li, and F. Zhu, “Research and Design of Precision Fertilizer Application Control System Based on PSO-BP-PID Algorithm,” Agric., vol. 12, no. 9, 2022, doi: 10.3390/agriculture12091395.
[41] X. Chen et al., “Temperature and voltage dynamic control of PEMFC Stack using MPC method,” Energy Reports, vol. 8, pp. 798–808, 2022, doi: 10.1016/j.egyr.2021.11.271.
[42] M. Gambini, F. Guarnaccia, M. Manno, and M. Vellini, “Hydrogen flow rate control in a liquid organic hydrogen carrier batch reactor for hydrogen storage,” Int. J. Hydrogen Energy, vol. 51, pp. 329–339, 2024, doi: 10.1016/j.ijhydene.2023.05.153.
[43] G. Y. Kim, H. J. Lee, and H. Huh, “Experimental study on flow control system of an electric pump-fed cycle for thrust control,” Acta Astronaut., vol. 216, pp. 44–54, 2024, doi: 10.1016/j.actaastro.2023.11.039.
[44] K. Frick and S. Bragg-Sitton, “Development of the NuScale Power Module in the INL Modelica Ecosystem,” Nucl. Technol., vol. 207, no. 4, pp. 521–542, 2021, doi: 10.1080/00295450.2020.1781497.
[45] O. Sabbagh, M. A. Fanaei, A. Arjomand, and M. H. Ahmadi, “Plantwide control and dynamic assessment of a novel NGL/LNG integrated scheme,” Sustain. Energy Technol. Assessments, vol. 52, 2022, doi: 10.1016/j.seta.2022.102226.
[46] V. T. Skjervold, G. Mondino, L. Riboldi, and L. O. Nord, “Investigation of control strategies for adsorption-based CO2 capture from a thermal power plant under variable load operation,” Energy, vol. 268, 2023, doi: 10.1016/j.energy.2023.126728.
[47] S. Almutairi, F. Anayi, M. Packianather, and M. Shouran, “An Optimal Two-Stage Tuned PIDF + Fuzzy Controller for Enhanced LFC in Hybrid Power Systems,” Sustainability, vol. 17, no. 20, p. 9109, 2025, doi: 10.3390/su17209109.
[48] A. B. Wazir, S. Alghamdi, A. Alobaidi, A. A. Alhussainy, and A. H. Milyani, “Efficient Frequency Management for Hybrid AC/DC Power Systems Based on an Optimized Fuzzy Cascaded PI−PD Controller,” Energies, vol. 17, no. 24, p. 6402, 2024, doi: 10.3390/en17246402.
[49] L. A. Aloo, P. K. Kihato, S. I. Kamau, and R. S. Orenge, “Modeling and control of a photovoltaic-wind hybrid microgrid system using GA-ANFIS,” Heliyon, vol. 9, no. 4, p. e14678, 2023, doi: 10.1016/j.heliyon.2023.e14678.
[50] A. S. Bahedh, I. A. Kheioon, B. S. Munahi, and R. Al-Sabur, “Modelling and controlling of modified robotic gripper mechanism using intelligent technique scheme,” in 2022 Iraqi International Conference on Communication and Information Technologies (IICCIT), IEEE, 2022. doi: 10.1109/iiccit55816.2022.10010666.
[51] M. Kuang, Q. Hou, J. Wang, J. Guo, and Z. Wei, “GA-Synthesized Training Framework for Adaptive Neuro-Fuzzy PID Control in High-Precision SPAD Thermal Management,” Machines, vol. 13, no. 7, p. 624, 2025, doi: 10.3390/machines13070624.
[52] K. Kumar, M. Das, and A. K. Karn, “ANFIS robust control application and analysis for load frequency control with nonlinearity,” J. Electr. Syst. Inf. Technol., vol. 11, no. 1, 2024, doi: 10.1186/s43067-024-00175-9.
[53] I. A. Kheioon, K. B. Saleem, and H. S. Sultan, “Analysis of Natural Convection and Radiation from a Solid Rod Under Vacuum Conditions with the Aiding of ANFIS,” Exp. Tech., vol. 47, no. 1, pp. 139–152, 2023, doi: 10.1007/s40799-022-00596-z.
[54] B. Panimathi, K. Deepa, S. V. T. Sangeetha, T. Porselvi, and M. L. Kolhe, “Adaptive neuro-fuzzy control for dual boost converter in fuel cell electric vehicles,” AIMS Energy, vol. 13, no. 5, pp. 1195–1218, 2025, doi: 10.3934/energy.2025044.
[55] L. Kaltoum, Y. Mouloudi, A. Hazzab, and A. Ben Abdelkader, “A comparative analysis of ANFIS and fuzzy controllers for a dynamic hybrid model,” Int. J. Appl. Power Eng., vol. 14, no. 1, p. 244, 2025, doi: 10.11591/ijape.v14.i1.pp244-254.
[56] I. O. Bachi, A. S. Bahedh, and I. A. Kheioon, “Design of control system for steel strip-rolling mill using NARMA-L2,” J. Mech. Sci. Technol., vol. 35, no. 4, pp. 1429–1436, 2021, doi: 10.1007/s12206-021-0308-7.
[57] B. Maroua, Z. Laid, H. Benbouhenni, Z. M. S. Elbarbary, I. Colak, and M. M. Alammar, “Genetic algorithm type 2 fuzzy logic controller of microgrid system with a fractional-order technique,” Sci. Rep., vol. 15, no. 1, 2025, doi: 10.1038/s41598-025-90239-1.
[58] H. Rezk, A. G. Olabi, M. A. Abdelkareem, A. H. Alami, and E. T. Sayed, “Optimal Parameter Determination of Membrane Bioreactor to Boost Biohydrogen Production-Based Integration of ANFIS Modeling and Honey Badger Algorithm,” Sustainability, vol. 15, no. 2, p. 1589, 2023, doi: 10.3390/su15021589.
[59] H. I. Khalaf, K. B. Saleem, K. Al-Farhany, and W. Al-Kouz, “Double-diffusive Air-CO2 mixture flow in a staggered cavity with numerous concave lower wall aspect ratios,” Eur. Phys. J. Plus, vol. 136, no. 5, 2021, doi: 10.1140/epjp/s13360-021-01486-w.
[60] T. L. Sime, P. Aluvada, S. Habtamu, and Z. Tolosa, “Modeling of genetic algorithm tuned adaptive fuzzy fractional order PID speed control of permanent magnet synchronous motor for electric vehicle,” Discov. Appl. Sci., vol. 6, no. 10, 2024, doi: 10.1007/s42452-024-06183-8.
[61] N. Kabasawa and Y. Noda, “Model-based flow rate control with online model parameters identification in automatic pouring machine,” Robotics, vol. 10, no. 1, 2021, doi: 10.3390/robotics10010039.
[62] S. A. C. Giraldo et al., “Model predictive control with dead-time compensation applied to a gas compression system,” J. Pet. Sci. Eng., vol. 203, 2021, doi: 10.1016/j.petrol.2021.108580.
[63] T. Castiglione, D. Perrone, L. Strafella, A. Ficarella, and S. Bova, “Linear Model of a Turboshaft Aero-Engine Including Components Degradation for Control-Oriented Applications,” Energies, vol. 16, no. 6, 2023, doi: 10.3390/en16062634.
[64] H. H. Manap, A. K. Abdul Wahab, and F. Mohamed Zuki, “Control for Carbon Dioxide Exchange Process in a Membrane Oxygenator Using Online Self-Tuning Fuzzy-PID Controller,” Biomed. Signal Process. Control, vol. 64, 2021, doi: 10.1016/j.bspc.2020.102300.
[65] T. W. Wu and I. L. Chien, “Novel control strategy of intensified hybrid reactive-extractive distillation process for the separation of water-containing ternary mixtures,” Sep. Purif. Technol., 2022, doi: 10.1016/j.seppur.2022.121159.
[66] A. H. Bashiri, “Empirical study of robust/developed PID control for nonlinear time-delayed dynamical system in discrete time domain,” Heliyon, vol. 10, no. 9, p. e29749, 2024, doi: 10.1016/j.heliyon.2024.e29749.
[67] Y. Ding, X. Ren, X. Zhang, X. Liu, and X. Wang, “Multi-Phase Focused PID Adaptive Tuning with Reinforcement Learning,” Electronics, vol. 12, no. 18, p. 3925, 2023, doi: 10.3390/electronics12183925.
[68] K. N. Kamaludin, L. Abdullah, S. N. S. Salim, and A. S. N. Chairat, “ACCURATE AND ROBUST ADAPTIVE HYPERBOLIC-PID CONTROL STRATEGY FOR A SERVO PNEUMATIC SYSTEM,” J. Adv. Manuf. Technol., vol. 19, no. 2 SE-Articles, Aug. 2025, [Online]. Available: https://jamt.utem.edu.my/jamt/article/view/6872
[69] G. K. Costa and N. Sepehri, “Energy management in pump-controlled actuators,” Front. Mech. Eng., vol. 10, 2024, doi: 10.3389/fmech.2024.1453739.
[70] S. Yuan, W. Yi, and G. Yang, “Adaptive robust control of electromagnetic actuators with friction nonlinearity and uncertainty compensation,” Math. Model. Eng., vol. 10, no. 2, pp. 75–86, 2024, doi: 10.21595/mme.2024.23935.
[71] D. Bordeasu, O. Prostean, I. Filip, and C. Vasar, “Adaptive Control Strategy for a Pumping System Using a Variable Frequency Drive,” Machines, vol. 11, no. 7, p. 688, 2023, doi: 10.3390/machines11070688.
[72] H. L. Kang, H. J. Park, and S. H. Han, “Evaluation of cavitation phenomena in three-way globe valve through computational analysis and visualization test,” Sci. Rep., vol. 14, no. 1, 2024, doi: 10.1038/s41598-024-72585-8.
[73] P. Ferretti, C. Pagliari, A. Montalti, and A. Liverani, “Design and development of a peristaltic pump for constant flow applications,” Front. Mech. Eng., vol. 9, 2023, doi: 10.3389/fmech.2023.1207464.
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