Heavy duty gas turbine monitoring based on adaptive neuro-fuzzy inference system: speed and exhaust temperature control

Hadroug, Nadji; Hafaifa, Ahmed; Guemana, Mouloud; Kouzou, Abdellah; Salam, Abudura; Chaibet, Ahmed

doi:10.1186/s40929-017-0017-8

Cited by 6 publications

(3 citation statements)

References 15 publications

(21 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using an adaptive neuro-fuzzy inference system (ANFIS) to maintain turbine operation at optimum performance. The results obtained, based on the use of the Rowen model, show the effectiveness of the proposed system (pure fuzzy) 2017 [16] Design of an Interval Fuzzy Type-2 PID Controller for a Gas Turbine Power Plant…”

Section: Paper Title Main Achievement Publication Yearmentioning

confidence: 83%

Fuzzy Controller Structures Investigation for Future Gas Turbine Aero-Engines

Fard

Jafari

2021

IJTPP

View full text Add to dashboard Cite

The Advisory Council for Aeronautics Research in Europe (ACARE) Flight Path 2050 focuses on ambitious and severe targets for the next generation of air travel systems (e.g., 75% reduction in CO2 emissions per passenger kilometer, a 90% reduction in NOx emissions, and 65% reduction in noise emission of flying aircraft relative to the capabilities of typical new aircraft in 2000). In order to meet these requirements, aircraft engines should work very close to their operating limits. Therefore, the importance of advanced control strategies to satisfy all engine control modes simultaneously while protecting them from malfunctions and physical damages is being more crucial these days. In the last three decades, fuzzy controllers (FCs) have been proposed as a high potential solution for performance improvement of the next generation of aircraft engines. Based on an analytic review, this paper divides the trend of FCs design into two main lines including pure FCs (PFC) and min–max FCs (MMFC). These two main architectures are then designed, implemented on hardware, and applied in a case study to analyze the advantages and disadvantages of each structure. The analysis of hardware-in-the-loop (HIL) simulation results shows that the pure FC structure would be a high potential candidate for maneuverability and response time indices improvement (e.g., military applications); while min–max FC architecture has a great potential for future civil aero-engines where the fuel consumption and steady-state responses are more important. The simulation results are also compared with those of industrial min–max controllers to confirm the feasibility and reliability of the fuzzy controllers for real-world application. The results of this paper propose a general roadmap for fuzzy controllers’ structure selection for new and next generation of aircraft engines.

show abstract

Section: Paper Title Main Achievement Publication Yearmentioning

confidence: 83%

Fuzzy Controller Structures Investigation for Future Gas Turbine Aero-Engines

Fard

Jafari

2021

IJTPP

View full text Add to dashboard Cite

show abstract

“…In the literature on gas turbines, several different models have been presented for the dynamic analysis of gas turbines, such as those of Benrahmoune et al (2021) and Hadroug et al (2017). The mathematical model presented in this paper can be defined as an aero-thermal model with a global representation of the components of the gas turbine.…”

Section: Solar Titan 130 Turbine Modelsmentioning

confidence: 99%

Gas Turbine Speed Monitoring Using a Generalized Predictive Adaptive Control Algorithm

ALAOUI¹,

Alshammari²,

Iratni³

et al. 2022

STUD INFORM CONTROL

Self Cite

View full text Add to dashboard Cite

The outstanding performance of modern gas turbine power plants requires an efficient and robust control strategy in order to recover the losses caused by the undesirable effects and the instabilities related to their operation. Also, these losses can be the cause of their unstable dynamic behaviour during full-load and partial load operation in the unplanned shutdown state. Hence, choosing the right control strategy can improve the operation of this type of machine by increasing its efficiency by up to 60%. This paper proposes the implementation of an innovative control strategy based on a generalized predictive adaptive control algorithm for monitoring the rotation speed of a Solar Titan 130 gas turbine, with the purpose of considering the constraints related to the nonlinear behavior of a turbine during large variations in machine load. The aim is to automatically adjust the parameters of the regulator of the turbine's control loop in real time, with a recursive estimation of these parameters. Using the experimental input/output measurements in order to study its behavior, by integrating the predictive control estimators, ensures the superior performance of the analyzed gas turbine in terms of energy, efficiency and robustness regarding the parametric uncertainties and the variation of the turbine load.

show abstract

“…In countries with natural gas deposits, it is currently a common energy producing method. 9,10 Using the Roen model, the turbine was kept running at peak efficiency. The efficiency of the proposed hybrid technique in managing the exhaust temperature and speed of gas turbines was convincingly established.…”

Section: Introductionmentioning

confidence: 99%

Real world evaluation and control of vehicle engine exhaust air pollution using adaptive neural fuzzy inference system

Abouel-Seoud

Shiba

Abdallah

2022

International Journal of Engine Research

View full text Add to dashboard Cite

Internal combustion engines are considered primarily responsible for air pollution. Accordingly, a concentrated effort has been made to reduce these pollutants which are emitted from the exhaust system of these engines while preserving energy and fuel consumption. There are also other sources besides internal combustion engines, such as power stations and those released from industry and home consumption. Industrial and household fuel consumers are also adding pollution. The industrial development requires the establishment of effective control systems to deal with its equipment to increase its operational life and increase its productivity with lower maintenance costs. Therefore, the aim of the study in this research is exposed to be used of adaptive neural fuzzy inference system to control the components of the emission of exhaust pollutants for gasoline vehicle. Exhaust pollutants consist of unburned carbon monoxide, hydrocarbons, carbon dioxide, water vapor nitrogen oxides, and energy produced from the combustion process which emit into atmosphere through the exhaust tailpipe. Also, there are hydrocarbons produced from gasoline vaporization and from the crankcase of the vehicle. The results indicate that the adaptive neural fuzzy inference system proves to be a useful tool for simulating and controlling vehicle engine exhaust emissions.

show abstract

Heavy duty gas turbine monitoring based on adaptive neuro-fuzzy inference system: speed and exhaust temperature control

Cited by 6 publications

References 15 publications

Fuzzy Controller Structures Investigation for Future Gas Turbine Aero-Engines

Fuzzy Controller Structures Investigation for Future Gas Turbine Aero-Engines

Gas Turbine Speed Monitoring Using a Generalized Predictive Adaptive Control Algorithm

Real world evaluation and control of vehicle engine exhaust air pollution using adaptive neural fuzzy inference system

Contact Info

Product

Resources

About