Neural Network Based Predictive Emission Monitoring Module for a GE LM2500 Gas Turbine

2010 ◽  
Author(s):  
K. K. Botros ◽  
M. Cheung
Keyword(s):  
Neural Network ◽  
Gas Turbine ◽  
Operating Conditions ◽  
Training Data ◽  
Pipeline System ◽  
Emission Data ◽  
Ambient Temperatures ◽  
Emission Monitoring ◽  
Data Points ◽  
Monitoring Module

A Predictive Emission Monitoring (PEM) model has been developed for a non-DLE GE LM2500 gas turbine used on a natural gas compressor station on the TransCanada Pipeline System in Alberta. The PEM model is based on an optimized Neural Network (NN) architecture which takes four fundamental engine parameters as input variables. The model predicts NOx emission in ppmv-dry-O2 corrected and in kg/hr as NO2. The NN was trained using Continuous Emission Monitoring (CEM) measurements comprising two sets of actual emission data collected over two different dates in 2009, when the ambient ambient temperatures were vastly different (∼1° C and 24 °C), respectively. These training data were supplemented by other emission data generated by GE ‘Cycle-Deck’ tool to generate emission data at different ambient temperatures ranging from −30 to +30 °C. The outcome is a total of 1872 emission data of engine emissions at different operating conditions covering the range of the engine operating parameters (402 data points from CEM and 1470 data points from GE Cycle-Deck). The PEM model comprises a simple single hidden layer perceptron type NN with only two neurons in it. The performance of the NN-based model showed a correlation coefficient greater than 0.99, and error standard deviation of 4.5 ppmv of NOx and 1.4 kg/hr as NO2. Uncertainty analysis was conducted to assess the effects of uncertainties in the engine parameters on the NOx predictions by PEM. It was shown that for uncertainty in the ambient temperature of ±1 °C, the uncertainty in the NOx prediction is ± 0.9 to ±3.5%. Uncertainties of the order of ±1% in the other three input parameters results in uncertainties in NOx predictions by ±2.5 to ±6%. Finally, the PEM model was implemented in the station CEHM (Compressor Equipment Health Monitoring) system and NOx prediction were reported online on a minutely basis. These data are presented here over the first three months since implementation.

Verification of a Neural Network Based Predictive Emission Monitoring Module for an RB211-24C Gas Turbine

2009 ◽  
Author(s):  
K. K. Botros ◽  
C. Selinger ◽  
L. Siarkowski
Keyword(s):  
Neural Network ◽  
Gas Turbine ◽  
Operating Conditions ◽  
Pipeline System ◽  
Emission Data ◽  
Emission Monitoring ◽  
Different Seasons ◽  
Input Variables ◽  
Monitoring Module ◽  
Good Agreement

This paper presents a verification of a Predictive Emission Monitoring (PEM) model developed for a non-DLE RB211-24C gas turbine used at a natural gas compressor station on the TransCanada Pipeline System in Alberta, Canada. The basis and methodology of the PEM model is first described, and its predictions were compared to recent Continuous Emission Monitoring (CEM) data obtained at different engine load conditions varying from 10 to 19 MW (site condition). The PEM model is based on an optimized Neural Network (NN) architecture which takes 6 fundamental engine parameters as input variables. The model predicts NOx (dry) as an output variable. The NN was trained using CEM measurements comprising four sets of actual emission data collected over four different dates in four different seasons during 2000, and at different operating conditions covering the range of the engine operating parameters. The PEM model was then implemented in the station Compressor Equipment Health Monitoring (CEHM) system and NOx predictions were reported online on a minutely basis for several months and NOx emission trends were captured and analyzed. Comparison between predictions and stack measurements shows a fairly good agreement between the PEM and CEM data within ±10 ppm (dry).

Analysis of Fin-Tube Evaporator Performance With Limited Experimental Data Using Artificial Neural Networks

2000 ◽  
Author(s):  
Arturo Pacheco-Vega ◽  
Mihir Sen ◽  
Rodney L. McClain
Keyword(s):  
Neural Network ◽  
Heat Rate ◽  
Network Models ◽  
Activation Function ◽  
Operating Conditions ◽  
Training Data ◽  
Neural Network Models ◽  
The Neural Network ◽  
Artificial Neural ◽  
Fin Tube

Abstract In the current study we consider the problem of accuracy in heat rate estimations from artificial neural network models of heat exchangers used for refrigeration applications. The network configuration is of the feedforward type with a sigmoid activation function and a backpropagation algorithm. Limited experimental measurements from a manufacturer are used to show the capability of the neural network technique in modeling the heat transfer in these systems. Results from this exercise show that a well-trained network correlates the data with errors of the same order as the uncertainty of the measurements. It is also shown that the number and distribution of the training data are linked to the performance of the network when estimating the heat rates under different operating conditions, and that networks trained from few tests may give large errors. A methodology based on the cross-validation technique is presented to find regions where not enough data are available to construct a reliable neural network. The results from three tests show that the proposed methodology gives an upper bound of the estimated error in the heat rates.

scholarly journals METHOD OF FORMULATING INPUT PARAMETERS OF NEURAL NETWORK FOR DIAGNOSING GAS-TURBINE ENGINES

Aviation ◽  
2013 ◽  
Vol 17 (2) ◽  
pp. 52-56 ◽  
Author(s):  
Mykola Kulyk ◽  
Sergiy Dmitriev ◽  
Oleksandr Yakushenko ◽  
Oleksandr Popov
Keyword(s):  
Neural Network ◽  
Gas Turbine ◽  
Gas Turbine Engine ◽  
Training Data ◽  
Turbine Engines ◽  
Data Sets ◽  
Gas Turbine Engines ◽  
Turbine Engine ◽  
Wide Range ◽  
And Training

A method of obtaining test and training data sets has been developed. These sets are intended for training a static neural network to recognise individual and double defects in the air-gas path units of a gas-turbine engine. These data are obtained by using operational process parameters of the air-gas path of a bypass turbofan engine. The method allows sets that can project some changes in the technical conditions of a gas-turbine engine to be received, taking into account errors that occur in the measurement of the gas-dynamic parameters of the air-gas path. The operation of the engine in a wide range of modes should also be taken into account.

Predictive Emission Monitoring Model for LM1600 Gas Turbines Based on Neural Network Architecture Trained on Field Measurements and CFD Data

2001 ◽  
Author(s):  
K. K. Botros ◽  
G. R. Price ◽  
G. Kibrya
Keyword(s):  
Neural Network ◽  
Ambient Temperature ◽  
Gas Turbines ◽  
Network Architecture ◽  
Absolute Error ◽  
Operating Conditions ◽  
Maximum Absolute Error ◽  
Flamelet Model ◽  
Emission Monitoring ◽  
One Year

A Predictive Emission Monitoring (PEM) model has been developed based on an optimized Neural Network (NN) architecture which takes 8 fundamental parameters as input variables. The model predicts both NO and NOx as output variables. The NN is initially trained using a combination of two sets of data: a) measured data at various loads from an LM1600 gas turbine installed at one of the compressor stations on TransCanada Transmission system in Alberta, Canada, b) data generated by a Computational Fluid Dynamics (CFD) at different operating conditions covering the range of the engine operating parameters spanned over one year. The predictions of NOx by CFD employed the ‘flamelet’ model and a set of 8 reactions including the Zeldovich mechanism for thermal NOx along with an empirical correlation for prompt NOx formation. It was found that a Multi Layer Perceptron type Neural Network with two hidden layers was the optimum architecture for predicting NO levels with a maximum absolute error of around 7%, mean absolute error of 2.3% and standard deviation of 1.97%. The model is easy to implement on the station PLC. A set of one year data consisting of 2804 cases was submitted to the above optimized NN architecture with varying ambient temperature from –29.9 °C to 35.7 °C and output power from 570 kW to 16.955 MW. This gave consistent contours of NO levels. As expected, NN architecture shows that NO increases with increasing power or ambient temperature.

Gas Turbine Power Augmentation: Parametric Study Relating to Fog Droplet Size and Its Influence on Evaporative Efficiency

2011 ◽  
Vol 133 (9) ◽  
Author(s):  
Mustapha Chaker ◽  
Cyrus B. Meher-Homji
Keyword(s):  
Gas Turbine ◽  
Droplet Size ◽  
Operating Conditions ◽  
Comprehensive Treatment ◽  
Ambient Temperatures ◽  
Numerical Studies ◽  
Spray Plume ◽  
Augmentation Techniques ◽  
Wet Compression ◽  
Nozzle Type

Several gas turbine power augmentation techniques are available to counter the detrimental drop in power and thermal efficiency that occur at high ambient temperatures. Inlet fogging and wet compression are two common and relatively simple techniques. This paper addresses the influence and importance of droplet size on evaporative cooling performance and efficiency. Spray nozzles used for inlet fogging and wet compression include impaction pin, swirl jet, air assisted, and swirl flash nozzle designs. The evaporation efficiency of the atomized droplets from these nozzles depends on the droplet size, size distribution, and spray plume shape. Droplets size varies with nozzle type, configuration, operating conditions, and nozzle manifold location in the gas turbine inlet duct and are affected by airflow velocity, residence time, coalescence effects, and water carryover. The proper selection of nozzle type, nozzle manifold location, and nozzle distribution are of cardinal importance to avoid large droplets and under-/oversaturated areas, which would affect compressor mechanical and aerodynamic efficiency. Analytical and numerical studies are compared with experimental results. This paper provides a comprehensive treatment of parameters affecting droplet size and will be of value to gas turbine fog system designers and users.

A Computationally Efficient Methodology for Generating Training Data for a Transient Neural Network of a Tip-Jet Reaction Drive System

2011 ◽  
Vol 133 (12) ◽  
Author(s):  
Brian K. Kestner ◽  
Jimmy C.M. Tai ◽  
Dimitri N. Mavris
Keyword(s):  
Neural Network ◽  
Steady State ◽  
Network Model ◽  
Neural Network Model ◽  
Operating Conditions ◽  
Drive System ◽  
Training Data ◽  
Computationally Efficient ◽  
Model Based Control ◽  
Training Methodology

This paper presents a computationally efficient methodology for generating training data for a transient neural network model of a tip-jet reaction drive system for potential use as an onboard model in a model based control application. This methodology significantly reduces the number of training points required to capture the transient performance of the system. The challenge in developing an onboard model for a tip-jet reaction drive system is that the model has to operate over the whole flight envelope, to account for the different dynamics present in the system, and to adjust to system degradation or potential faults. In addition, the onboard model must execute in less time than the update interval of the controller. To address these issues, a computationally efficient training methodology and neural network surrogate model have been developed that captures the transient performance of the tip-jet reaction system. As the number of inputs to a neural network becomes large, the computational time needed to generate the number of training points required to accurately represent the range of operating conditions of the system may become quite large also. A challenge for the tip-jet reaction drive system is to minimize the number of neural network training points, while maintaining the high accuracy. To address this issue, a novel training methodology is presented which first trains a steady-state neural network model and uses deviations from steady-state operating conditions to define the transient portion of the training data. The combined results from both the transient and the steady-state training data can then be used to create a single transient neural network of the system. The results in this paper demonstrate that a transient neural network using this new computationally efficient training methodology has the potential to be a feasible option for use as an onboard real-time model for model based control of a tip-jet reaction drive system.

scholarly journals An Explainable Intelligent Framework for Anomaly Mitigation in Cyber-Physical Inverter-based Systems

2022 ◽  
Author(s):  
Asad Ali Khan ◽  
Omar A Beg ◽  
Yufang Jin ◽  
Sara Ahmed
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Control Level ◽  
Operating Conditions ◽  
Training Data ◽  
Control Technique ◽  
Normal Operation ◽  
Secondary Control ◽  
False Data Injection ◽  
The Neural Network

An explainable intelligent framework for cyber anomaly mitigation of cyber-physical inverter-based systems is presented.<div><br></div><div>Smart inverter-based microgrids essentially constitute an extensive communication layer that makes them vulnerable to cyber anomalies. The distributed cooperative controllers implemented at the secondary control level of such systems exchange information among physical nodes using the cyber layer to meet the control objectives. The cyber anomalies targeting the communication network may distort the normal operation therefore, an effective cyber anomaly mitigation technique using an artificial neural network (ANN) is proposed in this paper. The intelligent anomaly mitigation control is modeled using adynamic recurrent neural network that employs a nonlinear autoregressive network with exogenous inputs. The effects of false data injection to the distributed cooperative controller at the secondary control level are considered. The training data for designing the neural network are generated by multiple simulations of the designed microgrid under various operating conditions using MATLAB/Simulink. The neural network is trained offline and tested online in the simulated microgrid. The proposed technique is applied as secondary voltage and frequency control of distributed cooperative control-based microgrid to regulate the voltage under various operating conditions. The performance of the proposed control technique is verified by injecting various types of false data injection-based cyber anomalies. The proposed ANN-based secondary controller maintained the normal operation of microgrid in the presence of cyber anomalies as demonstrated by real-time simulations on a real-time digital simulator OPAL-RT.<br></div>

scholarly journals Fault Identification by Extreme Pressure in Oil Pipelines Using Artificial Neural Network

2019 ◽  
Vol 8 (6) ◽  
pp. 90-96
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Contextual Information ◽  
Training Data ◽  
Fault Identification ◽  
Pipeline System ◽  
Extreme Pressure ◽  
Feed Forward ◽  
Oil Pipeline ◽  
Artificial Neural

Conventional Artificial Neural Network approaches such as Feed-Forward Networks has been used in diverse applications but are not naturally predictive and also require supervised learning. Feed-forward Artificial Neural Network also trained by backpropagation poses the problem of varnishing gradient. Algorithm using Gaussian membership function with a context-decision gate for detection operations has been proposed as an alternative to the traditional Feed Forward Architecture. The AI monitoring System shows promising results in solving many recurrent problems, particularly those requiring long-term storage dependencies - the Vanishing Gradient problem (VGP) and has the ability to use contextual information when mapping between input and output sequences. The Oil monitoring system employs dynamic data flow modeling to simulate the behavior of probably militant behaviors. The contextual information (context data) includes such context as Pressure from the lab scale experimental setup of oil pipeline system. In this approach, not only the networks are trained to adapt to the given training data, the training data (the expected outputs of fault indices) is also updated to adapt to the neural network. During the training procedure, both the neural networks and training data are updated interactively. Dynamic simulations were performed using a real-time data obtained from the Radial Bias Kernel Network. The data is tested using AI system in MATLAB-SIMULINK environment to verify the performance of the proposed system. The results were promising indicating the real state of fault identification in oil pipeline system caused by extreme pressure during transportation.

scholarly journals State of Charge Estimation of Power Battery Using Improved Back Propagation Neural Network

Batteries ◽  
2018 ◽  
Vol 4 (4) ◽  
pp. 69 ◽  
Author(s):  
Chuan-Wei Zhang ◽  
Shang-Rui Chen ◽  
Huai-Bin Gao ◽  
Ke-Jun Xu ◽  
Meng-Yue Yang
Keyword(s):  
Neural Network ◽  
Electric Vehicles ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Lithium Ion ◽  
Operating Conditions ◽  
State Of Charge ◽  
Training Data ◽  
Particle Swarm Algorithm ◽  
Power Battery

Accurately estimating the state of charge (SOC) of power batteries in electric vehicles is of great significance to the measurement of the endurance mileage of electric vehicles, as well as the safety protection of the power battery. In view of lithium ion batteries’ nonlinear relation between SOC estimation and current, voltage, and temperature, the improved Back Propagation (BP) neural network method is proposed to accurately estimate the SOC of power batteries. To address the inherent limitations of BP neural network, particle swarm algorithm is adopted to modify the relevant weighting coefficients. In this paper, the lithium iron phosphate battery (3.2 V/20 Amper-Hour) was studied. Charge and discharge experiments were conducted under a constant temperature. The training data were used to construct the surrogate model using the improved BP neural network. It is noted that the accuracy of the developed algorithm is increased by 2% as compared to that of conventional BP. Finally, an actual vehicle condition experiment was designed to further verify the accuracy of these two algorithms. The experimental results show that the improved algorithm is more suitable for real vehicle operating conditions than the traditional algorithm, and the estimation accuracy can meet the industry standards to a greater extent.

scholarly journals A Reliable Acoustic EMISSION Based Technique for the Detection of a Small Leak in a Pipeline System

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1472 ◽  
Author(s):  
Thang Bui Quy ◽  
Sohaib Muhammad ◽  
Jong-Myon Kim
Keyword(s):  
Acoustic Emission ◽  
Nearest Neighbor ◽  
Transformation Method ◽  
Operating Conditions ◽  
Training Data ◽  
Pipeline System ◽  
K Nearest Neighbor ◽  
Knn Classifier ◽  
Water Pipelines ◽  
Ae Signals

This paper proposes a reliable leak detection method for water pipelines under different operating conditions. This approach segments acoustic emission (AE) signals into short frames based on the Hanning window, with an overlap of 50%. After segmentation from each frame, an intermediate quantity, which contains the symptoms of a leak and keeps its characteristic adequately stable even when the environmental conditions change, is calculated. Finally, a k-nearest neighbor (KNN) classifier is trained using features extracted from the transformed signals to identify leaks in the pipeline. Experiments are conducted under different conditions to confirm the effectiveness of the proposed method. The results of the study indicate that this method offers better quality and more reliability than using features extracted directly from the AE signals to train the KNN classifier. Moreover, the proposed method requires less training data than existing techniques. The transformation method is highly accurate and works well even when only a small amount of data is used to train the classifier, whereas the direct AE-based method returns misclassifications in some cases. In addition, robustness is also tested by adding Gaussian noise to the AE signals. The proposed method is more resistant to noise than the direct AE-based method.

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