Development of a Dynamic Neural Network Model for Multistep ahead Prediction of Exhaust Gas Temperature in Heavy Duty Gas Turbines (original) (raw)

A gas turbine thermal performance prediction method based on dynamic neural network

IOP Conference Series: Materials Science and Engineering, 2021

In order to ensure safety and reliability of energy transportation, it is necessary to understand and predict the performance of the gas turbine components. A prediction frame of the gas turbine compressor isentropic efficiency is established using the neural time series theory based on the Dynamic Neural Network. In order to obtain appropriate parameters for the network, a validation set is introduced to generalize the model. The compressor isentropic efficiency can be predicted based on the suggested model which provides an effective technical mean for the early warning of gas turbine performance. The experiment verified that the performance calculation model and the isentropic entropy efficiency prediction model based on the neural time series are effective.

A PERFORMANCE COMPARISON OF DIFFERENT BACK PROPAGATION NEURAL NETWORKS FOR NITROGEN OXIDES EMISSION PREDICTION IN THERMAL POWER PLANT

IAEME PUBLICATION, 2014

The use of Neural Networks (NN) has been proved to be a cost-effective technique. It is very important to choose a suitable back propagation (BP) algorithm for training a neural network. While these algorithms prove to be very effective and robust in training many types of non-linear multivariable modeling, they suffer from certain disadvantages such as easy entrapment and very slow convergence. The power generating industry is undergoing an unprecedented reform. NN is applied to predict coal properties, economic load dispatch, emission prediction, temperature control, etc. This paper compares the performance of the six neural network methods to predict nitrogen oxides emission from a 500 MW coal fired thermal power plant. The various BP training algorithms used are gradient descent, gradient descent with momentum, variable learning rate with momentum, conjugate gradient back propagation, Quasi-Newton BFGS Algorithm and also LevenbergMarquardt. The parametric field experiment data were used to build neural network. The coal combustion parameters were used as inputs and nitrogen oxides as output of the model. The predicted values of the model for full load condition were verified with the actual values.

Estimating Gas Turbine Internal Cycle Parameters Using a Neural Network

Volume 5: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation; Education; General, 1996

We show that a neural network can be successfully used in place of an actual model to estimate key unmeasured parameters in a gas turbine. As an example we study the combustion reference temperature, a parameter that is currently estimated via a nonlinear model inside the controller and is used in a number of critical mode-setting functions within the controller such as calculating the fuel-split between various manifolds. We show that a feedforward neural network using simple back propagation learning can be built for estimating combustion reference temperature. The neural network matches the accuracy of the current estimate; and it is more robust to errors in its internal parameters. This is advantageous from the point of view of implementation since a number of errors creep in when running the algorithm on a digital controller, and an estimator that is not robust with respect to such errors can degrade the performance of the whole system.

Performance Prediction using Neural Network and Confidence Intervals: a Gas Turbine application

2018 IEEE International Conference on Big Data (Big Data), 2018

The combination of Condition Based monitoring techniques with the predictive capabilities of neural networks represents a topic of central importance when it comes to maximizing production profits and consequently reducing costs and downtime. The ability to plan the best strategy based on the prediction of potential damaging events can represent a significant contribution, especially for the maintenance function. In fact, optimization of the management of the equipment is a fundamental step to guarantee the competitiveness of companies in the current market. In this paper, a tool based on the implementation of Radial Basis Function Neural Networks was developed to support the maintenance function in the decision-making process. In addition to providing an indication of the status of the equipment, the current approach provides an additional level of information in terms of predicting the confidence interval around the prediction of the neural network. The confidence interval combined with the prediction of the future state of the equipment can be of fundamental importance in order to avoid strategic decisions based on a low level knowledge of the system status or prediction performance of the applied algorithm. The developed tool is tested on the prediction of a naval propulsion system gas turbine performance decay, where the statuses of both the turbine and the compressor of the system are predicted as well as predicting their confidence intervals.

Neural Network Monitoring Model for Industrial Gas Turbine

_______________________________________________________________________________________________________________________________________ ABSTRECT-Monitoring and diagnostic faults of industrial gas turbine are not an easy way by using conventional methods due to the nature and complexity of faults. Artificial neural network is considered an efficient tool to monitor and diagnose faults. In this paper, we proposed an efficient neural network model to monitor the gas turbine engine for on-line processing with a twofold advantage. First, the model is able to diagnose the fault in case of uncertainty or corrupted data. Second, it can predict the extent of the deterioration of the performance efficiency of the turbine engine through a simple graphical user interface. The experiment has been done on five faulty conditions and the proposed neural network model tested with new dataset. The results have proven that, the proposed model produced satisfactory results with10-10 mean square error that considered optimal results when compared with training data sets.

Nonlinear Gas Turbine Modelling : A Comparison of Narmax and Neural Network Approaches

2002

In this paper two nonlinear modelling approaches are employed to derive single nonlinear models for a Rolls Royce aircraft gas turbine. The first approach is based on the estimation of a NARMAX model using conventional structure selection and parameter estimation techniques, and the second approach is based on the use of feedforward Multilayer-Perceptron (MLP) neural networks. The performances of the models derived by the two approaches are demonstrated using a range of engine tests and by analysing their static and dynamic behaviours.

Heat Exchanger Performance Prediction Modeling using NARX-type Neural Networks

WSEAS Transactions on Information Science and …, 2007

The Crude Preheat Train (CPT) in a petroleum refinery recovers waste heat from product streams to preheat the crude oil. Due to high fouling nature of the fluids that flow through the exchangers, the performance deteriorates significantly over time as less heat can be transferred through the fouling layers. Prediction of the performance for optimal scheduling of the CPT operations requires a reasonably accurate mathematical model. There are no guidelines for selecting relevant input variables and correct functional forms for building theoretical models for such nonlinear systems. Neural Network (NN) offers the flexibility to model complex and nonlinear systems with good prediction capabilities. In this paper, prediction models using two different types of NNs are developed and compared for a heat exchanger to predict the change in the outlet temperatures over time. The data required for model building were collected from plant historian in a refinery. The data were processed for removal of outliers through Principal Component Analysis (PCA) and the important input variables (predictors) were selected using Projection to Latent Structures (PLS). A nonlinear auto-regression with exogenous inputs (NARX) type neural network model demonstrates its superior prediction capabilities with a root mean square error of less than 2.5 o C in the outlet temperatures and possesses a correct directional change index of more than 90%.

Application of artificial neural network to predict specific fuel consumption and exhaust temperature for a Diesel engine

The ability of an artificial neural network model, using a back propagation learning algorithm, to predict specific fuel consumption and exhaust temperature of a Diesel engine for various injection timings is studied. The proposed new model is compared with experimental results. The comparison showed that the consistence between experimental and the network results are achieved by a mean absolute relative error less than 2%. It is considered that a well-trained neural network model provides fast and consistent results, making it an easy-to-use tool in preliminary studies for such thermal engineering problems.