scholarly journals Thermodynamic and Economic Evaluation of Gas Turbine Power Plants

2020 ◽  
Vol 7 (1) ◽  
pp. G1-G8
Author(s):  
T. Oyegoke ◽  
I. I. Akanji ◽  
O. O. Ajayi ◽  
E. A. Obajulu ◽  
A. O. Abemi
Keyword(s):  
Power Plant ◽  
Combustion Chamber ◽  
Gas Turbine ◽  
Payback Period ◽  
Operating Conditions ◽  
Plant Performance ◽  
Second Law ◽  
Profitability Analysis ◽  
Second Law Analysis ◽  
Gas Turbine Power Plant

Thermodynamic analysis and economic feasibility of a gas turbine power plant using a theoretical approach are studied here. The operating conditions of Afam Gas Power Plant, Nigeria are utilized. A modern gas turbine power plant is composed of three key components which are the compressor, combustion chamber, and turbine. The plants were analyzed in different control volumes, and plant performance was estimated by component-wise modeling. Mass and energy conservation laws were applied to each component, and a complete energy balance conducted for each component. The lost energy was calculated for each control volume, and cumulative performance indices such as thermal efficiency and power output were also calculated. The profitability of the proposed project was analyzed using the Return on Investment (ROI), Net Present Worth (NPW), Payback Period (PBP), and Internal Rate of Return (IRR). First law analysis reveals that 0.9 % of the energy supplied to the compressor was lost while 99.1 % was adequately utilized. 7.0 % energy was generated within the Combustion Chamber as a result of the combustion reaction, while 33.2 % of the energy input to the Gas Turbine was lost, and 66.8 % was adequately converted to shaft work which drives both compressor and electric generator. Second law analysis shows that the combustion chamber unit recorded lost work of 248.27 MW (56.1 % of the summation), and 77.33 MW (17.5 % of the summation) for Gas Turbine, while air compressor recorded 11.8 MW (2.7 %). Profitability analysis shows that the investment criteria are sensitive to change in the price of natural gas. Selling electricity at the current price set by the Nigerian Electricity Regulation Commission (NERC) at zero subsidies and an exchange rate of 365 NGN/kWh is not profitable, as the analysis of the investment gave an infinite payback period. The investment becomes profitable only at a 45 % subsidy regime. Keywords: energy conversion system, gas turbine, economic analysis, second law analysis, power plant.

A Sequential Approach for Gas Turbine Power Plant Preventative Maintenance Scheduling

2005 ◽  
Vol 128 (4) ◽  
pp. 796-805 ◽  
Author(s):  
Yongjun Zhao ◽  
Vitali Volovoi ◽  
Mark Waters ◽  
Dimitri Mavris
Keyword(s):  
Power Plant ◽  
Gas Turbine ◽  
Preventive Maintenance ◽  
Combined Cycle ◽  
Operating Conditions ◽  
Maintenance Scheduling ◽  
Plant Performance ◽  
Optimization Approach ◽  
Sequential Approach ◽  
Gas Turbine Power Plant

Traditionally, gas turbine power plant preventive maintenance schedules are set with constant intervals based on recommendations from the equipment suppliers. Preventive maintenance is based on fleet-wide experience as a guideline as long as individual unit experience is not available. In reality, the operating conditions for each gas turbine may vary from site to site and from unit to unit. Furthermore, the gas turbine is a repairable deteriorating system, and preventive maintenance usually restores only part of its performance. This suggests a gas turbine needs more frequent inspection and maintenance as it ages. A unit-specific sequential preventive maintenance approach is therefore needed for gas turbine power plant preventive maintenance scheduling. Traditionally, the optimization criteria for preventive maintenance scheduling is usually cost based. However, in the deregulated electric power market, a profit-based optimization approach is expected to be more effective than the cost-based approach. In such an approach, power plant performance, reliability, and the market dynamics are considered in a joint fashion. In this paper, a novel idea that economic factors drive maintenance frequency and expense to more frequent repairs and greater expense as equipment ages is introduced, and a profit-based unit-specific sequential preventive maintenance scheduling methodology is developed. To demonstrate the feasibility of the proposed approach, a conceptual level study is performed using a base load combined cycle power plant with a single gas turbine unit.

A Sequential Approach for Gas Turbine Power Plant Preventive Maintenance Scheduling

2005 ◽  
Author(s):  
Yongjun Zhao ◽  
Vitali Volovoi ◽  
Mark Waters ◽  
Dimitri Mavris
Keyword(s):  
Power Plant ◽  
Gas Turbine ◽  
Preventive Maintenance ◽  
Gas Turbines ◽  
Combined Cycle ◽  
Operating Conditions ◽  
Maintenance Scheduling ◽  
Plant Performance ◽  
Optimization Approach ◽  
Gas Turbine Power Plant

Traditionally the gas turbine power plant preventive maintenances are scheduled with constant maintenance intervals based on recommendations from the equipment suppliers. The preventive maintenances are based on fleet wide experiences, and they are scheduled in a one-size-fit-all fashion. However, in reality, the operating conditions for each gas turbine may vary from site to site, and from unit to unit. Furthermore, the gas turbine is a repairable deteriorating system, and preventive maintenance usually restores only part of its performance. This suggests the gas turbines need more frequent inspection and maintenance as it ages. A unit specific sequential preventive maintenance approach is therefore needed for gas turbine power plants preventive maintenance scheduling. Traditionally the optimization criteria for preventive maintenance scheduling is usually cost based. In the deregulated electric power market, a profit based optimization approach is expected to be more effective than the cost based approach. In such an approach, power plant performance, reliability, and the market dynamics are considered in a joint fashion. In this paper, a novel idea that economics drive maintenance expense and frequency to more frequent repairs and greater expense as the equipment and components age is introduced, and a profit based unit specific sequential preventive maintenance scheduling methodology is developed. To demonstrate the feasibility of the proposed approach, this methodology is implemented using a base load combined cycle power plant with single gas turbine unit.

Combustion Tuning for a Gas Turbine Power Plant Using Data-Driven and Machine Learning Approach

2021 ◽  
Vol 143 (3) ◽  
Author(s):  
Suhui Li ◽  
Huaxin Zhu ◽  
Min Zhu ◽  
Gang Zhao ◽  
Xiaofeng Wei
Keyword(s):  
Machine Learning ◽  
Power Plant ◽  
Gas Turbine ◽  
Operating Conditions ◽  
Data Driven ◽  
Ann Model ◽  
Promising Alternative ◽  
Combustion Performance ◽  
Wide Range ◽  
Gas Turbine Power Plant

Abstract Conventional physics-based or experimental-based approaches for gas turbine combustion tuning are time consuming and cost intensive. Recent advances in data analytics provide an alternative method. In this paper, we present a cross-disciplinary study on the combustion tuning of an F-class gas turbine that combines machine learning with physics understanding. An artificial-neural-network-based (ANN) model is developed to predict the combustion performance (outputs), including NOx emissions, combustion dynamics, combustor vibrational acceleration, and turbine exhaust temperature. The inputs of the ANN model are identified by analyzing the key operating variables that impact the combustion performance, such as the pilot and the premixed fuel flow, and the inlet guide vane angle. The ANN model is trained by field data from an F-class gas turbine power plant. The trained model is able to describe the combustion performance at an acceptable accuracy in a wide range of operating conditions. In combination with the genetic algorithm, the model is applied to optimize the combustion performance of the gas turbine. Results demonstrate that the data-driven method offers a promising alternative for combustion tuning at a low cost and fast turn-around.

Modeling and Exergy and Exergoeconomic Optimization of a Gas Turbine Power Plant Using a Genetic Algorithm

2010 ◽  
Author(s):  
Soheil Fouladi ◽  
Hamid Saffari
Keyword(s):  
Genetic Algorithm ◽  
Power Plant ◽  
Combustion Chamber ◽  
Gas Turbine ◽  
Exergy Efficiency ◽  
Design Parameters ◽  
Working Fluid ◽  
Exergy Destruction ◽  
The Cost ◽  
Gas Turbine Power Plant

In this paper, the thermodynamic modelling of a gas turbine power plant in Iran is performed. Also, a computer code has been developed based on Matlab software. Moreover, both exergy and exergoeconomic analysis of this power plant have been conducted. To have a good insight into this study, the effects of key parameters such as compressor pressure ratio, gas turbine inlet temperature (TIT), compressor and turbine isentropic efficiency on the total exergy destruction, total exergy efficiency as well as total cost of exergy destruction have been performed. The modelling results have been compared with an actual running power plant located in Yazd city, Iran. The results of developed code have shown reasonable agreement between the simulation code results and experimental data obtained from power plant. The exergy analysis revealed that the combustion chamber is the must exergy destructor in comparison with other components. Also, its exergy efficiency is less than other components. This is due to the high temperature difference between working fluid and burner temperature. In addition, it was found that by the increase of TIT, the exergy destruction of this component can be reduced. On the other hand, the cost of exergy destruction is high for the combustion chamber. The effects of design parameters on exergy efficiency have shown that increase in the air compressor ratio and TIT, increases the total exergy efficiency of the cycle. Furthermore, the results have revealed that by the increase of TIT by 350°C, the cost of exergy destruction is decreased about 22%. Therefore, TIT is the best option to improve the cycle losses. In addition, an optimization using a genetic algorithm has been conducted to find the optimal solution of the plant.

Using Machine Learning to Increase Model Performance for a Gas Turbine System

2020 ◽  
Author(s):  
Samuel M. Hipple ◽  
Zachary T. Reinhart ◽  
Harry Bonilla-Alvarado ◽  
Paolo Pezzini ◽  
Kenneth Mark Bryden
Keyword(s):  
Machine Learning ◽  
Power Plant ◽  
Gas Turbine ◽  
Clean Energy ◽  
Learning Model ◽  
Operating Conditions ◽  
Plant Performance ◽  
Outlet Temperature ◽  
Machine Learning Model ◽  
Critical Components

Abstract With increasing regulation and the push for clean energy, the operation of power plants is becoming increasingly complex. This complexity combined with the need to optimize performance at base load and off-design condition means that predicting power plant performance with computational modeling is more important than ever. However, traditional modeling approaches such as physics-based models do not capture the true performance of power plant critical components. The complexity of factors such as coupling, noise, and off-design operating conditions makes the performance prediction of critical components such as turbomachinery difficult to model. In a complex system, such as a gas turbine power plant, this creates significant disparities between models and actual system performance that limits the detection of abnormal operations. This study compares machine learning tools to predict gas turbine performance over traditional physics-based models. A long short-term memory (LSTM) model, a form of a recurrent neural network, was trained using operational datasets from a 100 kW recuperated gas turbine power system designed for hybrid configuration. The LSTM turbine model was trained to predict shaft speed, outlet pressure, and outlet temperature. The performance of both the machine learning model and a physics-based model were compared against experimental data of the gas turbine system. Results show that the machine learning model has significant advantages in prediction accuracy and precision compared to a traditional physics-based model when fed facility data as an input. This advantage of predicting performance by machine learning models can be used to detect abnormal operations.

Combustion Tuning for a Gas Turbine Power Plant Using Data-Driven and Machine Learning Approach

2020 ◽  
Author(s):  
Suhui Li ◽  
Huaxin Zhu ◽  
Min Zhu ◽  
Gang Zhao ◽  
Xiaofeng Wei
Keyword(s):  
Machine Learning ◽  
Power Plant ◽  
Gas Turbine ◽  
Operating Conditions ◽  
Data Driven ◽  
Ann Model ◽  
Promising Alternative ◽  
Combustion Performance ◽  
Wide Range ◽  
Gas Turbine Power Plant

Abstract In this paper, we present a cross-disciplinary study on the combustion tuning of an F-class gas turbine that combines machine learning with physics understanding. An artificial-neural-network-based (ANN) model is developed to predict the combustion performance (outputs), including NOx emissions, combustion dynamics, combustor acceleration, and turbine exhaust temperature. The inputs of the ANN model are identified by analyzing the key operating variables that impact the combustion performance, such as the pilot and the premixed fuel flow, and the inlet guide vane angle. The ANN model is trained by field data from an F-class gas turbine power plant. The trained model is able to describe the combustion performance at an acceptable accuracy in a wide range of operating conditions. In combination with the genetic algorithm, the model is applied to optimize the combustion performance of the gas turbine. Results demonstrate that the data-driven method offers a promising alternative for combustion tuning at a low cost and fast turn-around.

Bayesian Calibration for Power Splitting in Single-Shaft Combined Cycle Plant Diagnostics

2015 ◽  
Vol 138 (5) ◽  
Author(s):  
Xiaomo Jiang ◽  
TsungPo Lin ◽  
Eduardo Mendoza
Keyword(s):  
Bayesian Inference ◽  
Power Plant ◽  
Gas Turbine ◽  
Combined Cycle ◽  
Operating Conditions ◽  
Plant Performance ◽  
Sensor Data ◽  
Power Splitting ◽  
Plant Level ◽  
Operational Data

Condition monitoring and diagnostics of a combined cycle gas turbine (CCGT) power plant has become an important tool to improve its availability, reliability, and performance. However, there are two major challenges in the diagnostics of performance degradation and anomaly in a single-shaft combined cycle (CC) power plant. First, since the gas turbine (GT) and steam turbine (ST) in such a plant share a common generator, each turbine's contribution to the total plant power output is not directly measured, but must be accurately estimated to identify the possible causes of plant level degradation. Second, multivariate operational data instrumented from a power plant need to be used in the plant model calibration, power splitting, and degradation diagnostics. Sensor data always contain some degree of uncertainty. This adds to the difficulty of both estimation of GT to ST power split (PS) and degradation diagnostics. This paper presents an integrated probabilistic methodology for accurate power splitting and the degradation diagnostics of a single-shaft CC plant, accounting for uncertainties in the measured data. The method integrates the Bayesian inference approach, thermodynamic physics modeling, and sensed operational data seamlessly. The physics-based thermodynamic heat balance model is first established to model the power plant components and their thermodynamic relationships. The model is calibrated to model the plant performance at the design conditions of its main components. The calibrated model is then employed to simulate the plant performance at various operating conditions. A Bayesian inference method is next developed to determine the PS between the GT and the ST by comparing the measured and expected power outputs at different operation conditions, considering uncertainties in multiple measured variables. The calibrated model and calculated PS are further applied to pinpoint the possible causes at individual components resulting in the plant level degradation. The proposed methodology is demonstrated using operational data from a real-world single-shaft CC power plant with a known degradation issue. This study provides an effective probabilistic methodology to accurately split the power for degradation diagnostics of a single-shaft CC plant, addressing the uncertainties in multiple measured variables.

Modeling of a SOFC-GT Hybrid Power Plant With Anode Recycling

2010 ◽  
Author(s):  
Denver Cheddie ◽  
Renique Murray
Keyword(s):  
Power Plant ◽  
Gas Turbine ◽  
Power Plants ◽  
Hybrid Plant ◽  
Second Law ◽  
Steam Generation ◽  
Hybrid Power Plant ◽  
External Water ◽  
Gas Turbine Power Plant ◽  
Generation Unit

Power generation using gas turbine power plants operating on the Brayton cycle suffers from low efficiencies and high irreversibilities. In this work, a solid oxide fuel cell (SOFC) is proposed for integration into a 10 MW gas turbine power plant, operating at 30% electrical efficiency (13.7% second law efficiency). The SOFC system entails anode recycling to enable self sustaining reformation reactions, thus alleviating the need for an external water supply and steam generation unit. It also utilizes turbine outlet heat recovery to ensure a sufficiently high SOFC operating temperature. The power output of the hybrid plant is 26.2 MW at 63.4% efficiency (35.3% second law efficiency). The hybrid plant performs best when 70–80% anode recycling is used. A thermo-economic model predicts a payback period of 4.6 years, based on future projected SOFC cost estimates.

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