Within the petrochemical industry, fired heater
tubes and boilers experience high tube metal temperatures (TMTs) for extended
periods of time. The dominant failure mechanism is often creep damage, which
can cause a tube rupture, even when operating under design limits. Since creep
is a function of stress, temperature and time, it must be evaluated at regular
intervals to predict the equipment’s expected end of life (EOL).
Engineering calculations are performed to
estimate the current creep life consumed and to predict the future creep
consumption rate based on expected future operating conditions. The objective
is to make risk-based decisions on when to proactively replace the heater or
boiler tubes, and to determine if the current TMT limit can be extended for
future operation to maximize benefit.
Remaining creep life evaluations can be
performed using commercially available software or internal tools developed by
the end user, such as an Excel spreadsheet. However, these methods require
large amounts of data processing and formatting for input to the tools. Another issue is that this evaluation is typically
performed before a planned turnaround, potentially without adequate time to
make proper decisions. If operating conditions have changed since the
evaluation, then using the results to estimate creep consumption between the time
of the evaluation and the turnaround could result in the overestimation of
creep life consumed, leading to either a premature re-tubing or leaving margin
incentives on the table. Conversely, if the creep consumption rate is underestimated
due to inaccurate assumptions or different operating conditions, a premature
tube failure could occur.
To effectively address these shortcomings, an advanced
web application has been implemented that eliminates the need for manual data
processing and iterations of creep calculations. It is possible to monitor the
remaining creep life of existing tubes in real time and to predict future creep
consumption at various operating scenarios as desired.
Background. While design codes [e.g., American
Petroleum Institute (API) 530] address creep rupture, the methodology to
evaluate creep life consumption for tubes in fired heaters and boilers once placed
in service is detailed in Part 10 of API 579-1/American Society of Mechanical
Engineers (ASME) FFS-1. In these procedures, which are widely accepted and used
throughout the industry, the user must develop a load history consisting of the
tube thicknesses or corrosion rate, TMTs (or infrared scan results), operating pressures,
and number of operating cycles. For each segment (nt) of a
particular cycle in the load history, a creep damage rate is calculated and
summed together to determine the total creep life consumed. This is shown in
Eq. 1, and is from API 579 Part 10:
The two common methodologies for calculating remaining creep life, represented by nL in Eq. 1, are MPC Project Omega and the Larson-Miller parameter (LMP). Either of these methodologies can be implemented into the web application tool. Once the total creep life consumed (mDc) for a cycle is determined, the user must sum all cycles’ creep damage and verify that the following equation is satisfied, where Dcallow is typically set to 1, unless an alternative value can be justified (Eq. 2):
This process is a summary of the Level 2 assessment
in API 579-1 Part 10.
Developing the load history for the
calculations is frequently the most time-intensive portion of the analysis. This
usually involves reviewing inspection history to determine the corrosion rates,
obtaining the measured TMT values (or infrared scan results) over long periods
of time, and either obtaining or calculating the operating pressure in the
tubes being evaluated.
If there is missing data in the load history, the user must make assumptions and manually insert values based on engineering judgment. Improper assumptions can significantly affect the accuracy of the results, leading to either underestimation or overestimation of creep failure risk. It is also important to check for bad or erroneous data, and to manually change the values as appropriate to increase the accuracy of the analysis.
Once the data for the load history has been formatted and inputted into the calculation tool, the creep life consumed is calculated, as shown in the previous Eqs. 1 and 2. These results can be used to estimate what the future creep life consumed will be if the operating conditions and assumptions are still applicable. However, the tubes will lose thickness due to corrosion or oxidation, and the TMTs can increase due to fouling/coking, resulting in an increased rate of creep life consumption. Therefore, if the tubes are approaching the end of their creep life, calculations may be required on a more frequent basis. This requires updating the load history and pulling more recent temperature and pressure data. The intent of re-running the creep life calculation near EOL is to determine if the tubes can survive an extra turnaround cycle or if replacement is required at the next turnaround. It is also important to note that creep life consumed grows exponentially as corrosion increases and/or TMTs increase.
Online
monitoring. Developing the load history for creep life consumption calculations can
be very time consuming, as it requires downloading and processing large amounts
of operational data. When using commercially available software, the data also must
be formatted for entry.
Another limitation in the commercially
available software is the lack of flexibility in simulating various future operating
scenarios (e.g., pressures, TMT profiles, corrosion rates). Developing and
using a manual internal tool may reduce this burden, but the user must still format
data from the operational history to input into the creep life evaluation tool.
Making this manual internal tool user friendly can be complex and challenging.
To provide real-time creep life monitoring, a
C#-based web application has been developed utilizing Microsoft technologies,
such as the Blazor app and the Azure cloud. Users input tube properties and
corrosion rates, which are calculated or estimated based on inspection history like
other creep evaluations. Specific TMT sensor data tags are selected to
represent temperature and pressure values over time. The time-series sensor
data is stored in a data repository and provided to the program by an
application programming interface. With these inputs, an automated tag can be
generated to continuously calculate the creep life consumed.
Users can override the measured values
from the program to correct erroneous data, and warnings are provided to help
users know when it is necessary to correct invalid or inappropriate conditions.
As the user changes inputs (FIG. 1), the program recalculates and displays creep life in
real time and provides instant feedback for analysis (FIG. 2). Since this
tag continuously displays the creep life consumed, engineers can trend the
creep rate growth to better predict EOL for the tubes. This allows operators to
visually see how fast creep life is consumed when adjusting the firing rate or to
determine when tubes start to experience significant fouling or coking.
In addition to monitoring creep life in real time,
an additional feature of the tool allows the functionality to develop different
future TMT and pressure profiles for simulating future operations. These
simulations can help provide a planning basis of when to re-tube the heater or
boiler. If creep life calculations indicate that the tubes will reach EOL
between the next two turnarounds, the TMT targets can be pushed to a higher
temperature to squeeze additional margin before the tubes are replaced at the
next turnaround. Alternatively, TMT targets could be lowered to extend the life
of the tubes to the second turnaround, depending on the economics. In
conclusion, heater or boiler operations can be optimized between margin
incentives and reduced creep life when modeling future operations.
Takeaway. Numerous benefits for linking measured
operating data to a continuous creep life calculation can be realized through a
web application program. One benefit is that operators and process engineers
can visually monitor how increased firing and higher TMTs affect the rate of
creep life consumed in real time. Optimizing equipment operation is feasible,
considering increased unit incentives and costs associated with reduced tube
life.
Another advantage is that the average creep consumption rate can be used over certain periods of time to predict when the heater or boiler will require a re-tubing. While manual methods also work, having a real-time tag effectively optimizes when the re-tubing procedure is planned to eliminate the financial risk of prematurely replacing tubes. This tool can considerably help users strike the correct balance of optimizing tube life and minimizing safety/reliability risks prior to the fired heater and boiler tubes’ EOL. HP
Matt Lindblade is currently a fired equipment engineer at ExxonMobil Technology & Engineering Company. He has 8+ years of industry experience, mostly in static equipment engineer roles at manufacturing sites all within ExxonMobil. He holds a BS degree in Mechanical Engineering from Texas A&M University.
HS Lee is a fired equipment specialist who has worked on capital projects and troubleshooting of Fired Heaters, Boilers, Flares, and Incinerators. Before joining ExxonMobil, he worked on experimental and theoretical combustion and heat transfer research. He earned a Ph.D. degree in Mechanical Engineering from the Pennsylvania State University with chemical kinetic and laser diagnostic studies of rocket propellants.
Christopher Ray is a senior software developer with 18 years’ experience creating and supporting engineering applications for companies in the oil and gas industry. Christopher earned a BS degree in software engineering from the University of Phoenix. He can be reached at cray@yirgainc.com