scholarly journals A Novel Electricity and Freshwater Production System: Performance Analysis from Reliability and Exergoeconomic Viewpoints with Multi-Objective Optimization

2021 ◽  
Vol 13 (11) ◽  
pp. 6448
Author(s):  
Farzad Hamrang ◽  
S. M. Seyed Mahmoudi ◽  
Marc A. Rosen
Keyword(s):  
Gas Turbine ◽  
Unit Cost ◽  
Performance Criteria ◽  
Multi Objective ◽  
Healthy Working ◽  
Combined Cycles ◽  
Optimal Values ◽  
Integrated Gasification ◽  
Artificial Neural Network Ann ◽  
Availability Assessment

Based on the benefits of integrated gasification combined cycles (IGCCs), a cogeneration plant for providing electricity and freshwater is proposed. The main novelties of the devised system are the integration of biomass gasification and a regenerative gas turbine with intercooling and a syngas combustor, where the syngas produced in the gasifier is burned in the combustion chamber and fed to a gas turbine directly. The energy discharged from the gas turbine is utilized for further electricity and freshwater generation via Kalina and MED hybridization. The proposed system is analyzed from energy, exergy, exergoeconomic, and reliability–availability viewpoints. The optimal operating condition and optimum performance criteria are obtained by hybridizing an artificial neural network (ANN), the multi-objective particle swarm optimization (MOPSO) algorithm. According to results obtained, for the fourth scenario of the optimization process, optimal values ​​of , , , and are obtained for the exergy efficiency, freshwater production rate, sum unit cost of products, and net output power, respectively. According to reliability and availability assessment, the probability of the healthy working state of all components and subsystems is the system is shown to be available of the time over the 20-year lifetime.

A comparative study of data-driven and physics-based gas turbine fault recognition approaches

2021 ◽  
pp. 1748006X2198964
Author(s):  
Juan Luis Pérez-Ruiz ◽  
Igor Loboda ◽  
Iván González-Castillo ◽  
Víctor Manuel Pineda-Molina ◽  
Karen Anaid Rendón-Cortés ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Gas Turbine ◽  
Recognition Accuracy ◽  
Data Driven ◽  
Fault Identification ◽  
Fault Recognition ◽  
Data Driven Approach ◽  
Diagnostic Approaches ◽  
Artificial Neural Network Ann ◽  
Two Stages

The present paper compares the fault recognition capabilities of two gas turbine diagnostic approaches: data-driven and physics-based (a.k.a. gas path analysis, GPA). The comparison takes into consideration two differences between the approaches, the type of diagnostic space and diagnostic decision rule. To that end, two stages are proposed. In the first one, a data-driven approach with an artificial neural network (ANN) that recognizes faults in the space of measurement deviations is compared with a hybrid GPA approach that employs the same type of ANN to recognize faults in the space of estimated fault parameter. Different case studies for both anomaly detection and fault identification are proposed to evaluate the diagnostic spaces. They are formed by varying the classification, type of diagnostic analysis, and deviation noise scheme. In the second stage, the original GPA is reconstructed replacing the ANN with a tolerance-based rule to make diagnostic decisions. Here, two aspects are under analysis: the comparison of GPA classification rules and whole approaches. The results reveal that for simple classifications both spaces are equally accurate for anomaly detection and fault identification. However, for complex scenarios, the data-driven approach provides on average slightly better results for fault identification. The use of a hybrid GPA with ANN for a full classification instead of an original GPA with tolerance-based rule causes an increase of 12.49% in recognition accuracy for fault identification and up to 54.39% for anomaly detection. As for the whole approach comparison, the application of a data-driven approach instead of the original GPA can lead to an improvement of 12.14% and 53.26% in recognition accuracy for fault identification and anomaly detection, respectively.

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.

Accommodation of Multi-Shaft Gas Turbine Switching Control Gain Tuning Problem to IGV Position

2021 ◽  
Author(s):  
Yujia Ma ◽  
Liu Jinfu ◽  
Linhai Zhu ◽  
Qi Li ◽  
Huanpeng Liu ◽  
...  
Keyword(s):  
Gas Turbine ◽  
Guide Vane ◽  
Optimization Procedure ◽  
Switching Control ◽  
Inlet Guide Vane ◽  
Multi Objective ◽  
Control Gain ◽  
Controller Gain ◽  
Compressor Inlet ◽  
Comprehensive Indicator

Abstract This article aims to discuss the influence of compressor Inlet Guide Vane (IGV) position on gas turbine switching control system gain tuning problem. The distinction between IGV and normally reckoned working conditions is differentiated, and an improved double-layer LPV model is proposed to estimate the protected parameters under various IGV positions. Controller gain tuning is conducted with single and multi-objective intellectual optimization algorithms. Simulation results reveal that normally used multi-objective optimization procedure is unnecessary and time-consuming. While with the comprehensive indicator introduced in this paper, the calculation burden can be greatly eased. This improvement is especially advantageous when tuning work is carried out under multiple IGV positions.

Optimization of Advanced Liquid Natural Gas-Fuelled Combined Cycle Machinery Systems for a High-Speed Ferry

2012 ◽  
Author(s):  
Kari Anne Tveitaskog ◽  
Fredrik Haglind
Keyword(s):  
Gas Turbine ◽  
High Speed ◽  
Dynamic Network ◽  
Combined Cycle ◽  
Working Fluid ◽  
Power Requirement ◽  
Pressure Steam ◽  
Combined Cycles ◽  
Liquid Natural Gas ◽  
Steam Cycle

This paper is aimed at designing and optimizing combined cycles for marine applications. For this purpose, an in-house numerical simulation tool called DNA (Dynamic Network Analysis) and a genetic algorithm-based optimization routine are used. The top cycle is modeled as the aero-derivative gas turbine LM2500, while four options for bottoming cycles are modeled. Firstly, a single pressure steam cycle, secondly a dual-pressure steam cycle, thirdly an ORC using toluene as the working fluid and an intermediate oil loop as the heat carrier, and lastly an ABC with inter-cooling are modeled. Furthermore, practical and operational aspects of using these three machinery systems for a high-speed ferry are discussed. Two scenarios are evaluated. The first scenario evaluates the combined cycles with a given power requirement, optimizing the combined cycle while operating the gas turbine at part load. The second scenario evaluates the combined cycle with the gas turbine operated at full load. For the first scenario, the results suggest that the thermal efficiencies of the combined gas and steam cycles are 46.3% and 48.2% for the single pressure and dual pressure steam cycles, respectively. The gas ORC and gas ABC combined cycles obtained thermal efficiencies of 45.6% and 41.9%, respectively. For the second scenario, the results suggest that the thermal efficiencies of the combined gas and steam cycles are 53.5% and 55.3% for the single pressure and dual pressure steam cycles, respectively. The gas ORC and gas ABC combined cycles obtained thermal efficiencies of 51.0% and 47.8%, respectively.

scholarly journals Simulation and optimization integrated gasification combined cycle by used aspen hysys and aspen plus

2015 ◽  
Vol 3 (1) ◽  
pp. 178
Author(s):  
Mohsen Darabi ◽  
Mohammad Mohammadiun ◽  
Hamid Mohammadiun ◽  
Saeed Mortazavi ◽  
Mostafa Montazeri
Keyword(s):  
Gas Turbine ◽  
Coal Gasification ◽  
Aspen Plus ◽  
Combined Cycle ◽  
Parameters Estimation ◽  
Integrated Gasification Combined Cycle ◽  
Support Tool ◽  
Simulation And Optimization ◽  
Aspen Hysys ◽  
Integrated Gasification

<p>Electricity is an indispensable amenity in present society. Among all those energy resources, coal is readily available all over the world and has risen only moderately in price compared with other fuel sources. As a result, coal-fired power plant remains to be a fundamental element of the world's energy supply. IGCC, abbreviation of Integrated Gasification Combined Cycle, is one of the primary designs for the power-generation market from coal-gasification. This work presents a in the proposed process, diluted hydrogen is combusted in a gas turbine. Heat integration is central to the design. Thus far, the SGR process and the HGD unit are not commercially available. To establish a benchmark. Some thermodynamic inefficiencies were found to shift from the gas turbine to the steam cycle and redox system, while the net efficiency remained almost the same. A process simulation was undertaken, using Aspen Plus and the engineering equation solver (EES).The The model has been developed using Aspen Hysys® and Aspen Plus®. Parts of it have been developed in Matlab, which is mainly used for artificial neural network (ANN) training and parameters estimation. Predicted results of clean gas composition and generated power present a good agreement with industrial data. This study is aimed at obtaining a support tool for optimal solutions assessment of different gasification plant configurations, under different input data sets.</p>

scholarly journals A Thermodynamic Transient Model for Performance Analysis of a Twin Shaft Industrial Gas Turbine

2017 ◽  
Author(s):  
Samuel Cruz-Manzo ◽  
Vili Panov ◽  
Yu Zhang ◽  
Anthony Latimer ◽  
Festus Agbonzikilo
Keyword(s):  
Gas Turbine ◽  
System Performance ◽  
Computational Tool ◽  
Ambient Conditions ◽  
Transient Model ◽  
Simulink Model ◽  
Artificial Neural Network Ann ◽  
Industrial Gas Turbine ◽  
Scientific Engineering ◽  
Steady State Performance

In this study, a Simulink model based on fundamental thermodynamic principles to predict the dynamic and steady state performance in a twin shaft Industrial Gas Turbine (IGT) has been developed. The components comprising the IGT have been implemented in the modelling architecture using a thermodynamic commercial toolbox (Thermolib, EUtech Scientific Engineering GmbH) and Simulink environment. Measured air pressure and air temperature discharged by compressor allowed the validation of the modelling architecture. The model assisted the development of a computational tool based on Artificial Neural Network (ANN) for compressor fault diagnostics in IGTs. It has been demonstrated that modelling plays an important role to predict and monitor gas turbine system performance at different operating and ambient conditions.

Advanced and Transformational Hydrogen Turbines for Integrated Gasification Combined Cycles

2016 ◽  
Author(s):  
Walter W. Shelton ◽  
Robin W. Ames ◽  
Richard A. Dennis ◽  
Charles W. White ◽  
John E. Plunkett ◽  
...  
Keyword(s):  
Gas Turbine ◽  
Carbon Capture ◽  
Combined Cycle ◽  
Plant Performance ◽  
System Level ◽  
Inlet Temperature ◽  
Air Cooling ◽  
Reference Case ◽  
Advanced Technologies ◽  
Integrated Gasification

The U.S. Department of Energy’s (DOE) provides a worldwide leadership role in the development of advanced fossil fuel-based energy conversion technologies, with a focus on electric power generation with carbon capture and storage (CCS). As part of DOE’s Office of Fossil Energy, the National Energy Technology Laboratory (NETL) implements research, development, and demonstration (RD&D) programs that address the challenges of reducing greenhouse gas emissions. To meet these challenges, NETL evaluates advanced power cycles that will maximize system efficiency and performance, while minimizing CO2 emissions and the costs of CCS. NETL’s Hydrogen Turbine Program has sponsored numerous R&D projects in support of Advanced Hydrogen Turbines (AHT). Turbine systems and components targeted for development include combustor technology, materials research, enhanced cooling technology, coatings development, and more. The R&D builds on existing gas turbine technologies and is intended to develop and test the component technologies and subsystems needed to validate the ability to meet the Turbine Program goals. These technologies are key components of AHTs, which enable overall plant efficiency and cost of electricity (COE) improvements relative to an F-frame turbine-based Integrated Gasification Combined Cycle (IGCC) reference plant equipped with carbon capture (today’s state-of-the-art). This work has also provided the basis for estimating future IGCC plant performance based on a Transformational Hydrogen Turbine (THT) with a higher turbine inlet temperature, enhanced material capabilities, reduced air cooling and leakage, and higher pressure ratios than the AHT. IGCC cases from using system-level AHT and THT gas turbine models were developed for comparisons with an F-frame turbine-based IGCC reference case and for an IGCC pathway study. The IGCC pathway is presented in which the reference case (i.e. includes F-frame turbine) is sequentially-modified through the incorporation of advanced technologies. Advanced technologies are considered to be either 2nd Generation or Transformational, if they are anticipated to be ready for demonstration by 2025 and 2030, respectively. The current results included the THT, additional potential transformational technologies related to IGCC plant sections (e.g. air separation, gasification, gas cleanup, carbon capture, NOx reduction) are being considered by NETL and are topics for inclusion in future reports.

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