Reliability, Availability and Maintainability Analysis of Steam Turbines Used in an Oil Refinery

2002 ◽  
Author(s):  
Anwar Khalil Sheikh ◽  
Dahham Matar Al-Anazi ◽  
Muhammad Younas
Keyword(s):  
Oil Refinery ◽  
Maintenance Cost ◽  
Steam Turbines ◽  
Primary Target ◽  
Pareto Analysis ◽  
Equipment Reliability ◽  
Time Between Failures ◽  
Computerized Maintenance Management System ◽  
History Of ◽  
Maintainability Analysis

Weibull reliability and maintainability analysis have been used to analyze the time between failures and time to repair data of a group of steam turbines being used in a large oil refinery. Failure history of a set of steam turbines was obtained from the Computerized Maintenance Management System of the plant. Out of 50 steam turbines in operation, 13 are identified as bad actors which have experienced ≥3 failures in five years. The Pareto analysis performed on this set of turbines further narrowed down the 10 most critical (worst performing) turbines. This group of most critical turbines is the primary target for this Weibull reliability and maintainability analysis. The Weibull reliability and maintainability analysis provides an indication of the equipment reliability and maintainability characteristics including their failure rates and repair rates. In addition to the failure and repair data, the associated maintenance cost for this group of turbines was also collected over a period of five years, and the trends in cost increase with respect to time are plotted.

scholarly journals The Application of Computerised Maintenance Management System to Equipment Failure History

1997 ◽  
Author(s):  
Mohammed E. Samaha
Keyword(s):  
Management System ◽  
Maintenance Management ◽  
Equipment Failure ◽  
Steam Turbines ◽  
Weibull Analysis ◽  
Mean Time Between Failures ◽  
Equipment Reliability ◽  
Time Between Failures ◽  
Random Failure ◽  
Pump Stations

This paper discusses how equipment failure history stored in a computerised maintenance management system has been effectively used through the application of Weibull analysis to give an indication of the component failure mechanism (e.g., infant mortality, random failure, premature wear-out). Weibull analysis was also used to predict plant and equipment reliability by calculating the number of failures expected to occur in the future using Mean Time Between Failures (MTBF). Examples are given for component, equipment, and system failure studies for crude oil pump stations shipper pumps, steam turbines, and natural gas liquidation compression trains.

Read More About The Modernization of the oil refinery of Heydar Aliyev

2021 ◽  
Vol 66 (05) ◽  
pp. 106-108
Author(s):  
Aytac Turab qızı Hüseynova ◽  
Keyword(s):  
Key Words ◽  
Diesel Fuel ◽  
Oil Products ◽  
Oil Refinery ◽  
Fuel Oil ◽  
Oil Refining ◽  
Diesel Fuels ◽  
Foreign Countries ◽  
History Of ◽  
The Republic

The Oil Refinery of Heydar Aliyev was created in July 1953 as a new oil refining plant Baki. The combined atmospheric vacuum plant is the main plant at the oil refining factory and its starting capacity produces 6 million tons of crude oil. In 2010, 43,000 tons A-98, 1.18 tons of A-92 and 19,700 tons of gasoline A-80. At the same time, 600 400t kerosene, 214,000 diesel fuels, 214,000 tons. Liquid gas, 267 500t coke and 220 600t. With this investigation, the history of the oil refinery and the details of modernization were considered. 21 out of 24 types of Azerbaijani oil are processed at the Baku Oil Refinery named after Heydar Aliyev, of which 15 types of oil products, including gasoline, aviation kerosene, diesel fuel, fuel oil, petroleum coke, etc. are produced. The plant fully meets the needs of the republic in oil products. In addition, 45% of oil products are exported to foreign countries. Key words: Azerbaijani, oil, recycling, factory, modernization

Reliability Analysis Using Deep Learning

2018 ◽  
Author(s):  
Chong Chen ◽  
Ying Liu ◽  
Xianfang Sun ◽  
Shixuan Wang ◽  
Carla Di Cairano-Gilfedder ◽  
...  
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Reliability Analysis ◽  
General Principle ◽  
Preventive Maintenance ◽  
Maintenance Cost ◽  
Empirical Results ◽  
Time Between Failures ◽  
Learning Techniques ◽  
Deep Learning Model

Over the last few decades, reliability analysis has gained more and more attention as it can be beneficial in lowering the maintenance cost. Time between failures (TBF) is an essential topic in reliability analysis. If the TBF can be accurately predicted, preventive maintenance can be scheduled in advance in order to avoid critical failures. The purpose of this paper is to research the TBF using deep learning techniques. Deep learning, as a tool capable of capturing the highly complex and nonlinearly patterns, can be a useful tool for TBF prediction. The general principle of how to design deep learning model was introduced. By using a sizeable amount of automobile TBF dataset, we conduct an experiential study on TBF prediction by deep learning and several data mining approaches. The empirical results show the merits of deep learning in performance but comes with cost of high computational load.

scholarly journals A Costing Analysis for Decision Making Grid Model in Failure-Based Maintenance

2011 ◽  
Vol 2011 ◽  
pp. 1-15 ◽  
Author(s):  
Burhanuddin M. A. ◽  
Sami M. Halawani ◽  
A. R. Ahmad
Keyword(s):  
Decision Making ◽  
Production Cost ◽  
Good Control ◽  
Fish Bone ◽  
Maintenance Cost ◽  
Economic Downturn ◽  
Repair Cost ◽  
Grid Model ◽  
Computerized Maintenance Management System ◽  
Costing Analysis

Background. In current economic downturn, industries have to set good control on production cost, to maintain their profit margin. Maintenance department as an imperative unit in industries should attain all maintenance data, process information instantaneously, and subsequently transform it into a useful decision. Then act on the alternative to reduce production cost. Decision Making Grid model is used to identify strategies for maintenance decision. However, the model has limitation as it consider two factors only, that is, downtime and frequency of failures. We consider third factor, cost, in this study for failure-based maintenance. The objective of this paper is to introduce the formulae to estimate maintenance cost. Methods. Fish bone analysis conducted with Ishikawa model and Decision Making Grid methods are used in this study to reveal some underlying risk factors that delay failure-based maintenance. The goal of the study is to estimate the risk factor that is, repair cost to fit in the Decision Making Grid model. Decision Making grid model consider two variables, frequency of failure and downtime in the analysis. This paper introduces third variable, repair cost for Decision Making Grid model. This approaches give better result to categorize the machines, reduce cost, and boost the earning for the manufacturing plant. Results. We collected data from one of the food processing factories in Malaysia. From our empirical result, Machine C, Machine D, Machine F, and Machine I must be in the Decision Making Grid model even though their frequency of failures and downtime are less than Machine B and Machine N, based on the costing analysis. The case study and experimental results show that the cost analysis in Decision Making Grid model gives more promising strategies in failure-based maintenance. Conclusions. The improvement of Decision Making Grid model for decision analysis with costing analysis is our contribution in this paper for computerized maintenance management system.

Optimization of equal-cycle maintenance strategy considering imperfect preventive maintenance

2021 ◽  
pp. 095440892110638
Author(s):  
Xinlong Li ◽  
Yan Ran ◽  
Genbao Zhang
Keyword(s):  
Decision Making ◽  
Preventive Maintenance ◽  
Maintenance Cost ◽  
Maintenance Strategy ◽  
Action Unit ◽  
Cost Rate ◽  
Equipment Reliability ◽  
Optimal Maintenance ◽  
Important Means ◽  
Maintenance Decision

Preventive maintenance is an important means to extend equipment life and improve equipment reliability. Traditional preventive maintenance decision-making is often based on components or the entire system, the granularity is too large and the decision-making is not accurate enough. The meta-action unit is more refined than the component or system, so the maintenance decision-making based on the meta-action unit is more accurate. Therefore, this paper takes the meta-action unit as the research carrier, considers the imperfect preventive maintenance, based on the hybrid hazard rate model, established the imperfect preventive maintenance optimization model of the meta-action unit, and the optimization solution algorithm was given for the maintenance strategy. Finally, through numerical analysis, the validity of the model is verified, and the influence of different maintenance costs on the optimal maintenance strategy and optimal maintenance cost rate is analyzed.

Theoretical diversity of the marine biosphere

Paleobiology ◽  
2010 ◽  
Vol 36 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Michał Kowalewski ◽  
Seth Finnegan
Keyword(s):  
Empirical Data ◽  
Fossil Record ◽  
Empirical Studies ◽  
Geological History ◽  
Empirical Knowledge ◽  
Primary Target ◽  
History Of ◽  
Global Biodiversity ◽  
Theoretical Considerations ◽  
Marine Biosphere

In considering the history of biodiversity paleontologists have focused on exploratory investigations of empirical data derived from the fossil record. Starting with the pioneering work of Philips (1860), and continuing at an increasing pace through today, this inductive approach has dominated diversity research. In contrast, deductive theoretical considerations that focus on the expected history of biodiversity, and develop independently of empirical knowledge, have remained under-explored. Appreciating the need for a nomothetic paleobiology (Gould 1980), we here reconsider the history of biodiversity, using deductive models constrained by a few, self-evident parameters. This analysis centers on the marine fossil record, the primary target of most previous empirical studies on the geological history of global biodiversity (e.g., Valentine 1969; Raup 1972, 1976; Sepkoski et al. 1981; Alroy et al. 2008).

Elements of a Successful Waste-to-Energy Boiler Upgrade

2009 ◽  
Author(s):  
Samit J. Pethe ◽  
Chris Dayton ◽  
Marcel D. Berz ◽  
Tim Peterson
Keyword(s):  
Waste To Energy ◽  
Cfd Modeling ◽  
Steam Turbines ◽  
Steam Generation ◽  
Operator Training ◽  
Refuse Derived Fuel ◽  
Energy Plant ◽  
History Of ◽  
Emission Limits ◽  
Combustible Gases

Great River Energy operates a waste-to-energy plant in Elk River, Minnesota. The plant burns 850 tons per day of refuse derived fuel (RDF) in three boilers, and its three steam turbines can produce 32 MW of electricity. In the largest of the three units, the No. 3 Boiler, steam generation was restricted by carbon monoxide (CO) and nitrogen oxides (NOx) emission limits. The plant had an interest in improving the combustion performance of the unit, thereby allowing higher average RDF firing rates while staying within emissions compliance. The project was initiated by an engineering site visit and evaluation. The boiler had a history of unstable burning on the stoker grate, which required periodic natural gas co-firing to reduce CO levels. As an outcome to the evaluation, it was decided to install a new overfire air (OFA) system to improve burnout of combustible gases above the grate. Current and new OFA arrangements were evaluated via Computational Fluid Dynamics (CFD) modeling. The results illustrated the limitations of the original OFA system (comprised of multiple rows of small OFA ports on the front and rear furnace walls), which generated inadequate mixing of air and combustible gases in the middle of the boiler. The modeling illustrated the advantages of large and fewer OFA nozzles placed on the side walls in an interlaced pattern, a configuration that has given excellent performance on over 45 biomass-fired boilers of similar design upgraded by Jansen Combustion and Boiler Technologies, Inc. (JANSEN). Installation of the new OFA system was completed in April of 2008. Subsequent testing of the No. 3 Boiler showed that it could reliably meet the state emission levels for CO and NOx (200 ppm and 250 ppm, respectively, corrected to 7% dry flue gas oxygen) while generating 24% more steam than a representative five month period prior to the upgrade. This paper describes the elements that led to a successful project, including: data collection, engineering analyses, CFD modeling, system design, equipment supply, installation, operator training, and startup assistance.

Enhancement of an Equipment Reliability Program With Smart, Connected Power Plant Assets

2017 ◽  
Author(s):  
Michael Reid ◽  
Tony File
Keyword(s):  
Power Plant ◽  
Resource Constraints ◽  
Electric Utility ◽  
Handheld Devices ◽  
Steam Turbines ◽  
Contributing Factors ◽  
Equipment Reliability ◽  
Balance Of Plant ◽  
Aging Infrastructure ◽  
Substantial Investment

The U.S. electric utility industry continues to undergo dramatic and accelerating transformation. Reliability and resiliency are a key focus. A number of important issues including cyber and physical security challenges, aging infrastructure, and low natural gas prices continue to be of concern. Significant advances in technology, and prolonged regulatory uncertainty are also contributing factors. Electric utilities are now making substantial investment in renewable resources and other technologies needed for renewables integration. This means a reduction in investment in generation assets and an increase in the transmission and distribution grids. There is also increased investment in providing customers with solutions to lower their costs, reduce their carbon footprint and provide control over their energy management. The transformation ultimately demands significant increases in power plant generation operating capabilities and higher levels of equipment reliability while reducing O&M and capital budgets. Achieving higher levels of equipment reliability, with such tightening budget and resource constraints, requires a very disciplined approach to maintenance and an optimized mix of the following maintenance practices: • Preventative (time-based) • Predictive (condition-based) • Reactive (run-to-failure) • Proactive (combination of 1, 2 and 3 + root cause failure analysis) Preventive maintenance (PM) is planned maintenance actions taken to ensure equipment is capable of performing its required functions. PM tasks are generally time-based, depending on the availability of condition monitoring data through a predictive maintenance (PdM) program. Traditionally, PdM is largely performed by maintenance technicians in the field with handheld devices. Resource constraints usually mean that often weeks or even months elapsed between readings on the same piece of equipment. This approach has limitations with data volume, velocity, variety, and veracity. Significant recent advances in sensor and technology associated with the Industrial Internet of Things (IIoT) have enabled the transformation of critical power plant assets such as steam turbines, combustion turbines, generators, and large balance-of-plant equipment into smart, connected power plant assets. These enhanced assets, in conjunction with analysis and visualization software, provide a comprehensive on-line conditioning monitoring solution that enables both a reduction in time-based PM tasks and also automation of PdM tasks. This paper describes an approach by Duke Energy to apply smart, connected power plant assets to greatly enhance its fossil generation equipment reliability program and processes. It will outline the value that is currently being realized and will also examine additional opportunities.

Maintainability Evaluation Based on the Product Disassembly Analysis

2009 ◽  
Author(s):  
Nan Wu ◽  
Qingjin Peng
Keyword(s):  
Product Life Cycle ◽  
Life Time ◽  
Life Cycle Management ◽  
Maintenance Cost ◽  
Product Life ◽  
Time Operation ◽  
Materials Recycling ◽  
Maintainability Analysis ◽  
The Cost ◽  
Product Disassembly

Maintainability is an important product characteristic related to the product life-time operation and the operation cost. Maintainability increases product’s serviceability and decreases product’s maintenance cost. Product disassembability is one of the key factors of product maintainability. A disassembly is normally required for product repairing, product remanufacturing, components reusing, and materials recycling in the product life-cycle management. A better disassembly plan will improve efficiency of these processes for product maintainability. This paper introduces the product maintainability analysis based on the disassembly sequence evaluation. An AND/OR graph based disassembly analysis and the cost-based evaluation are introduced for the evaluation of product maintainability. The proposed data structure and methods are discussed. The system developed is demonstrated using a case study.

scholarly journals Forecasting Machine Failure Using DMG and Weibull Analysis in an Automotive Industry: A Case Study

2021 ◽  
Vol 40 (2) ◽  
pp. 435-442
Author(s):  
Kamran Shah ◽  
Hassan Khurshid ◽  
Izhar Ul Haq ◽  
Shaukat Ali Shah ◽  
Zeeshan Ali
Keyword(s):  
Decision Making ◽  
Manufacturing Industry ◽  
Management Strategies ◽  
Fixed Time ◽  
Maintenance Management ◽  
Maintenance Cost ◽  
Weibull Analysis ◽  
Effect Analysis ◽  
Machine Failure ◽  
Computerized Maintenance Management System

In manufacturing or production setup, maintenance cost is one of the major portions of overall operating expenditure. It can vary between 15 to 60 percentage of overall cost for various industries including food related industries, iron, steel and other heavy industries. Such a high cost directly impacts manufacturing setup, profitability and sustainability in long run. In most of the industries, ineffective maintenance management can result in loss of capital and inefficient human resource deployment. This in turn affects the plants’ ability to manufacture quality products that are competitive in the market. Various maintenance management strategies including Operate to Failure (OTF), Design Out Maintenance (DOM), Skill Level Upgrade (SLU), ConditionBased Monitoring (CBM) and Fixed Time Maintenance (FTM) are used in industries for maximizing productivity. In recent years, Computerized Maintenance Management System (CMMS) has become an integral part of most of the industries so its importance and characteristics cannot be understated. While CMMS cannot live standalone, it requires some decision-making techniques to be equipped with. These techniques range from Failure Mode and Effect Analysis (FMEA) to Decision Making Grid (DMG). In this paper, concept of DMG has been applied to an automotive parts Manufacturing Industry in conjunction with Weibull analysis. Parallels are drawn between the results of DMG and Weibull analysis.

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