The Convergence of Squeeze: With Respectable Speed, a New Gas Turbine Power Plant Rises at an Arizona Mine

2012 ◽  
Author(s):  
John Baker ◽  
Marshall Ralph
Keyword(s):  
Power Plant ◽  
Gas Turbine ◽  
Power Supply ◽  
Market Forces ◽  
Market Opportunity ◽  
Mine Site ◽  
Gray Market ◽  
Capital Development ◽  
Schedule Change ◽  
Gas Turbine Power Plant

When muscular market forces and juicy resource opportunities fall into alignment, the usual pace of power plant capital development can give way to a literal sprint. The 2010 development by Mercator Minerals of a new 45 MW gas turbine power plant at the Mineral Park Mine in Arizona is an example of the respectable speed at which an LM6000 PF Sprint plant can be bought, fueled, built and fired up. In this case, a grand market opportunity dropped into the in-basket of a mine CEO prepared to pounce: Mercator Minerals got the opportunity to sell, in a short delivery window, a great amount of copper/molybdenum ore concentrate. The opportunity was blocked by a shortage of electricity needed to mine it and concentrate the ore. A long-planned 220kV transmission line could not be permitted and built in time. Mercator recognized that a gas pipeline could be built, however, and was within the capabilities of Mercator’s construction resources. Solution: a gas-fired mine-site power plant. On Christmas Eve, 2009, Mercator summoned its power supply consultant to the mine. Power plant engineers earn part of their keep by inserting a moderating element into these spirited discussions. But when the engineers met with Mercator’s CEO on Christmas Eve, they found themselves pressed “vigorously” on the spot for a review of plant and equipment options, and an AFE-level cost of electricity estimate. The mad pace continued: the final consultant report, and Mercator’s command to proceed, came before New Year’s Day. After a multi-month scramble to find financing and an investigation into used, gray-market and new turbine availability, the engineers located a new LM6000 high in GE’s queue and temporarily homeless due to a schedule change. All parties agreed that EPC would be too slow. Mercator undertook to procure and build the entire project, employing the consultant as the design engineer. The plant’s completion and entry into service in 2010 was no surprise to Mercator. For others involved, the project seemed to finish before it had a chance to start. The project, now running productively, is a vivid testimony to Mercator’s ability to move decisively to develop a power plant crucial to the Mineral Park Mine’s production commitments.

scholarly journals A Proper Shape of the Trailing Edge Modification to Solve a Housing Damage Problem in a Gas Turbine Power Plant

Processes ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 705
Author(s):  
Thodsaphon Jansaengsuk ◽  
Mongkol Kaewbumrung ◽  
Wutthikrai Busayaporn ◽  
Jatuporn Thongsri
Keyword(s):  
Power Plant ◽  
Gas Turbine ◽  
Structural Dynamics ◽  
Pressure Ratio ◽  
Equivalent Stress ◽  
Total Deformation ◽  
Trailing Edge ◽  
Housing Damage ◽  
The Difference ◽  
Gas Turbine Power Plant

To solve the housing damage problem of a fractured compressor blade (CB) caused by an impact on the inner casing of a gas turbine in the seventh stage (from 15 stages), modifications of the trailing edge (TE) of the CB have been proposed, namely 6.5 mm curved cutting and a combination of 4 mm straight cutting with 6.5 mm curved cutting. The simulation results of the modifications in both aerodynamics variables Cl and Cd and the pressure ratio, including structural dynamics such as a normalized power spectrum, frequency, total deformation, equivalent stress, and the safety factor, found that 6.5 mm curved cutting could deliver the aerodynamics and structural dynamics similar to the original CB. This result also overcomes the previous work that proposed 5.0 mm straight cutting. This work also indicates that the operation of a CB gives uneven pressure and temperature, which get higher in the TE area. The slightly modified CB can present the difference in the properties of both the aerodynamics and the structural dynamics. Therefore, any modifications of the TE should be investigated for both properties simultaneously. Finally, the results from this work can be very useful information for the modification of the CB in the housing damage problem of the other rotating types of machinery in a gas turbine power plant.

Thermal performance of gas turbine power plant based on exergy analysis

2017 ◽  
Vol 115 ◽  
pp. 977-985 ◽  
Author(s):  
Thamir K. Ibrahim ◽  
Firdaus Basrawi ◽  
Omar I. Awad ◽  
Ahmed N. Abdullah ◽  
G. Najafi ◽  
...  
Keyword(s):  
Power Plant ◽  
Gas Turbine ◽  
Thermal Performance ◽  
Exergy Analysis ◽  
Gas Turbine Power Plant

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.

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