scholarly journals Identification of Two-shaft Gas Turbine Variables Using a Decoupled Multi-model Approach With Genetic Algorithm

2021 ◽  
Author(s):  
Sidali Aissat ◽  
Ahmed Hafaifa ◽  
Abdelhamid Iratni ◽  
Mouloud Guemana
Keyword(s):  
Genetic Algorithm ◽  
Gas Turbine ◽  
Input Output ◽  
Identification Procedure ◽  
Output Data ◽  
Modes Of Operation ◽  
Industrial Practice ◽  
Operating Variables ◽  
Weighted Combination ◽  
Model Approach

In industrial practice, the representation of the dynamics of nonlinear systems by models linking their different operating variables requires an identification procedure to characterize their behavior from experimental data. This article proposes the identification of the variables of a two-shafts gas turbine based on a decoupled multi-model approach with genetic algorithm. Hence the multi-model is determined in the form of a weighted combination of the decoupled linear local state space sub-models, with optimization of an objective cost function in different modes of operation of this machine. This makes it possible to have robust and reliable models using input / output data collected on the examined system, limiting the influence of errors and identification noises.

Dynamic model linearization of two shafts gas turbine via their input/output data around the equilibrium points

Energy ◽  
2017 ◽  
Vol 120 ◽  
pp. 488-497 ◽  
Author(s):  
Nadji Hadroug ◽  
Ahmed Hafaifa ◽  
Abdellah Kouzou ◽  
Ahmed Chaibet
Keyword(s):  
Gas Turbine ◽  
Dynamic Model ◽  
Equilibrium Points ◽  
Input Output ◽  
Output Data ◽  
Model Linearization

scholarly journals A Genetic Algorithm Approach for Prediction of Linear Dynamical Systems

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Za'er Abo-Hammour ◽  
Othman Alsmadi ◽  
Shaher Momani ◽  
Omar Abu Arqub
Keyword(s):  
Genetic Algorithm ◽  
Dynamical Systems ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Model Parameters ◽  
Input Output ◽  
Data Sequence ◽  
Output Data ◽  
Linear Dynamical Systems ◽  
Genetic Algorithm Approach

Modelling of linear dynamical systems is very important issue in science and engineering. The modelling process might be achieved by either the application of the governing laws describing the process or by using the input-output data sequence of the process. Most of the modelling algorithms reported in the literature focus on either determining the order or estimating the model parameters. In this paper, the authors present a new method for modelling. Given the input-output data sequence of the model in the absence of any information about the order, the correct order of the model as well as the correct parameters is determined simultaneously using genetic algorithm. The algorithm used in this paper has several advantages; first, it does not use complex mathematical procedures in detecting the order and the parameters; second, it can be used for low as well as high order systems; third, it can be applied to any linear dynamical system including the autoregressive, moving-average, and autoregressive moving-average models; fourth, it determines the order and the parameters in a simultaneous manner with a very high accuracy. Results presented in this paper show the potentiality, the generality, and the superiority of our method as compared with other well-known methods.

Commodity-Based Truck Origin–Destination Matrix Estimation Using Input–Output Data and Genetic Algorithms

2005 ◽  
Vol 1923 (1) ◽  
pp. 37-45 ◽  
Author(s):  
Omar Al-Battaineh ◽  
Isam A. Kaysi
Keyword(s):  
Genetic Algorithm ◽  
Population Data ◽  
Future Research ◽  
Generation Model ◽  
Input Output ◽  
Output Data ◽  
Commodity Flow ◽  
Matrix Estimation ◽  
Genetic Algorithm Model

A commodity-based model to estimate a truck origin–destination (O-D) matrix is presented. The model takes advantage of the genetic algorithm global search method to find the best O-D matrix that when assigned to the network gives the minimum deviation between observed and estimated data. The model is flexible with respect to the type of data used in estimating the O-D matrix; however, the case study presented in this paper takes into consideration only two sets of information: commodity flow on specific links and column and row sums of the O-D matrix. Flows are treated as commodity dollar value; therefore, the estimated O-D matrix entries consist of the value of the commodity shipped by truck from the origin zone to the destination zone. The method is composed of two submodels. The first submodel, the trip generation model, uses input–output data with employment and population data to estimate the zonal level of commodity attraction and production. The second submodel, the genetic algorithm model, searches globally for the optimum O-D matrix. The model and its application to a case study of a region in Ontario, Canada, are presented. Directions for future research are provided.

scholarly journals Detection of Vibrations Defects in Gas Transportation Plant Based on Input / Output Data Analysis: Gas Turbine Investigations

2020 ◽  
Vol 25 (4) ◽  
pp. 42-58
Author(s):  
B. Djaidir ◽  
A. Hafaifa ◽  
M. Guemana ◽  
A Kouzou
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Oil And Gas ◽  
Input Output ◽  
Output Data ◽  
Fault Monitoring ◽  
Main Components ◽  
And Control ◽  
Detection And Localization ◽  
Proper Operation

AbstractIn oil and gas industrial production and transportation plants, gas turbines are considered to be the major pieces of equipment exposed to several unstable phenomena presenting a serious danger to their proper operation and to their exploitation. The main objective of this work is to improve the competitiveness performance of this type of equipment by analyses and control of the dynamic behaviors and to develop a fault monitoring system for the equipment exposed and subject to certain eventual anomalies related to the main components, namely the shaft and the rotors. This study will allow the detection and localization of vibration phenomena in the studied gas turbine based on the input / output data.

scholarly journals Method of value chain mapping with international input–output data: application to the agricultural value chain in three Greater Mekong Subregion countries

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Ikuo Kuroiwa
Keyword(s):  
Value Chain ◽  
Value Added ◽  
Input Output ◽  
Output Data ◽  
Primary Input ◽  
Goods And Services ◽  
Unit Structure ◽  
Data Application ◽  
Final Output ◽  
Greater Mekong Subregion

AbstractExtending the technique of unit structure analysis, which was originally developed by Ozaki (J Econ 73(5):720–748, 1980), this study introduces a method of value chain mapping that uses international input–output data and reveals both the upstream and downstream transactions of goods and services, as well as primary input (value added) and final output (final demand) transactions, which emerge along the entire value chain. This method is then applied to the agricultural value chain of three Greater Mekong Subregion countries: Thailand, Vietnam, and Cambodia. The results show that the agricultural value chain has been increasingly internationalized, although there is still room to benefit from participating in global value chains, especially in a country such as Cambodia. Although there are some constraints regarding the methodology and data, the method proves useful in tracing the entire value chain.

scholarly journals AOIO: Application Oriented I/O Optimization for Buffer Management

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 573
Author(s):  
Xiaochang Li ◽  
Zhengjun Zhai ◽  
Xin Ye
Keyword(s):  
System Performance ◽  
Simulation Experiment ◽  
Buffer Management ◽  
Input Output ◽  
Output Data ◽  
Program Counter ◽  
Efficient Management ◽  
Management Scheme ◽  
Buffer Cache ◽  
Management Schemes

Emerging scale-out I/O intensive applications are broadly used now, which process a large amount of data in buffer/cache for reorganization or analysis and their performances are greatly affected by the speed of the I/O system. Efficient management scheme of the limited kernel buffer plays a key role in improving I/O system performance, such as caching hinted data for reuse in future, prefetching hinted data, and expelling data not to be accessed again from a buffer, which are called proactive mechanisms in buffer management. However, most of the existing buffer management schemes cannot identify data reference regularities (i.e., sequential or looping patterns) that can benefit proactive mechanisms, and they also cannot perform in the application level for managing specified applications. In this paper, we present an A pplication Oriented I/O Optimization (AOIO) technique automatically benefiting the kernel buffer/cache by exploring the I/O regularities of applications based on program counter technique. In our design, the input/output data and the looping pattern are in strict symmetry. According to AOIO, each application can provide more appropriate predictions to operating system which achieve significantly better accuracy than other buffer management schemes. The trace-driven simulation experiment results show that the hit ratios are improved by an average of 25.9% and the execution times are reduced by as much as 20.2% compared to other schemes for the workloads we used.

Multi-objective optimization of a recuperative gas turbine cycle using non-dominated sorting genetic algorithm

2011 ◽  
Vol 225 (8) ◽  
pp. 1041-1051 ◽  
Author(s):  
H Sayyaadi ◽  
H R Aminian
Keyword(s):  
Genetic Algorithm ◽  
Gas Turbine ◽  
Heat Exchangers ◽  
Mixed Integer ◽  
Design Stage ◽  
Tube Length ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Gas Turbine Cycle

A regenerative gas turbine cycle with two particular tubular recuperative heat exchangers in parallel is considered for multi-objective optimization. It is assumed that tubular recuperative heat exchangers and its corresponding gas cycle are in design stage simultaneously. Three objective functions including the purchased equipment cost of recuperators, the unit cost rate of the generated power, and the exergetic efficiency of the gas cycle are considered simultaneously. Geometric specifications of the recuperator including tube length, tube outside/inside diameters, tube pitch, inside shell diameter, outer and inner tube limits of the tube bundle and the total number of disc and doughnut baffles, and main operating parameters of the gas cycle including the compressor pressure ratio, exhaust temperature of the combustion chamber and the air mass flowrate are considered as decision variables. Combination of these objectives anddecision variables with suitable engineering and physical constraints (including NO x and CO emission limitations) comprises a set of mixed integer non-linear problems. Optimization programming in MATLAB is performed using one of the most powerful and robust multi-objective optimization algorithms, namely non-dominated sorting genetic algorithm. This approach is applied to find a set of Pareto optimal solutions. Pareto optimal frontier is obtained, and a final optimal solution is selected in a decision-making process.

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