Analysis of the Effects of Online and Offline Washing on Degradation and Life Cycle Costs in Gas Turbine Combined Cycle Power Plants: A Comparative Study
Bibliographic record
Abstract
Abstract Gas turbine degradation poses a significant challenge for power plant operators, as it diminishes the overall efficiency and Power output of gas turbine based combined cycle power plants. While certain aspects of degradation can be mitigated through maintenance and cleaning procedures such as compressor washing, some degradation cannot be recovered. This paper focuses on the recoverable degradation aspect, specifically through the process of compressor washing. The paper delves into the two primary types of washing techniques employed by operators: online and offline washing. It provides a comprehensive overview of these methods, highlighting their operational procedures and the impact on Gas turbine based combined cycle power plant performance. A key aspect discussed is the availability factor during offline washing and its implications on the overall cost analysis of Gas turbine based combined cycle power plant. The first section of the paper offers a foundational understanding of online and offline washing, detailing the processes and their respective effects on Gas turbine availability. It also provides information about recovery of performance loss gained through online and offline washing. The subsequent section presents a quantitative analysis of the benefits derived from both washing techniques, examining their influence on the life cycle costs of gas turbine-based combined cycle power plants. The life cycle cost analysis encompasses the calculation of the Levelized Cost of Electricity (LCOE) and the Payback Period (PBP) over a 20-year span for a gas turbine-based combined cycle power plant. This analysis is performed using the PV tool, an exclusive Siemens Energy’s in-house software. The analysis considers relevant expenses such as fuel, capital maintenance, and electricity costs. The life cycle cost analysis presented in this paper provides valuable insights for both customers and Original Equipment Manufacturers (OEMs), enabling them to quantify the benefits of online and offline washing while considering availability factors. Additionally, the paper explores the impact of these washing techniques on decarbonization efforts, offering guidance on how to optimize washing practices to support environmental sustainability. By presenting a detailed examination of washing techniques and their economic and environmental implications, this paper aims to contribute to the ongoing efforts to enhance the efficiency and sustainability of gas turbine operations in power plants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".