A cost and risk analysis report for a proposed methanol refining hydrogen fuel station.

1. Feasibility and Risk Analysis Report of Methanol Reforming Hydrogen Fuel
Stations
Madeline Walbert and Sarah Dent
Executive Summary

2. 1
Our team ran multiple tests and data analysis to determine whether or not this project is
feasible and what risks are associated with it. We based any decisions for change based off of cost,
safety, and protecting the health of workers, the community and the environment.
Each component of the system was evaluated based off of what failures could occur and
the criticality and severity of each of were compiled into a FMECA which can be viewed in the
Appendix. Additionally, a risk matrix was compiled of the individual failure modes of each part.
A reliability analysis of two components of the system, the compressor and the reverse
osmosis filter was performed using a set of provided time to failures of each. This analysis resulted
in the decision to neglect regular maintenance on the reverse osmosis filter, but rather opt for a
scheduled replacement and to run the compressor until it failed.
An analysis of each component was performed using the failure data provided for each
component in order to rank them based off total maintenance cost. The frequency of inspections
was then reduced for the top contributors. The availability, mean time between maintenance
actions, mean maintenance time, and mean time to failure were then calculated for the system.
A disaster analysis was conducted to find the probability of a failure ending in a
catastrophic event and the consequences of this failure. It was found that a catastrophic failure is
extremely unlikely to occur in the system within a five year operating time span.
A Monte Carlo simulation of 2,000 iterations was used to find the net profit of the proposed
system, along with the mean time to failure, and a better understanding of the observed availability
of the system. The system was found to display an average profit of around $1.3 million, and the
median of the data was about $900,000.
The company proposed the installation of three separate detection systems. While alarm
systems are definitely something the facility should have installed, the proposed detectors were
found to be too expensive. We suggest the company finds more suitable detector systems with
increased accuracy, lower installation costs and shorter shutdown periods.
Finally, a set of recommendations for the fuel station were provided. While the proposed
station is profitable, a few changes can be made to increase safety and income.
Table of Contents
I. Introduction
II. FMECA Results

3. 2
III. Reliability Analysis
IV. System Availability Analysis
V. DisasterAnalysis
VI. Simulation Results
VII. Value of Information Analysis
VIII. Recommendations
I. Introduction
We, the Pennsylvania State ENVSE Consulting Team, are proud to be contracted by the
Commonwealth of Pennsylvania to analyze the feasibility and risks associated with the newly
proposed methanol reforming hydrogen fuel stations set to be installed outside the cities of
Pittsburgh, Harrisburg, and Philadelphia.

4. 3
To perform this evaluation, we considered the failure modes and risks of individual parts
throughout the system and their consequences in terms of cost to the company and potential harm
to the public. Our results will be based on failure risks of individual system parts and their costs
as seen in Section II. We then used Weibull Analysis to calculate the reliability of each of these
parts and how often each must be replaced before risking failure. Based on the various risk factors
involved with several parts in the system, we also ran a disaster analysis as well to determine the
specific probability of having a catastrophic part failure or multiple parts failing with an overall
catastrophic result. These results can be seen in section IV and should be considered in depth
before making any decision regarding the overall system as they carry the worst-case scenario
costs for the company.
Following this, the system availability was determined and combined with associated
maintenance costs to decide the optimal amount of maintenance hours necessary in a 24 hr period,
and whether the current 4 hour maintenance window needs to be adjusted. This was then used to
determine a basic maintenance schedule for optimal system functionality with minimal costs. A
monte carlo simulation was used to optimize these results.
Alarm systems were considered for detecting hydrogen, methanol, and methane leaks in
the system to determine whether the system is worth purchasing or not. The cost and probability
of false and positive alarms for these systems was compiled to find the EVII of each. Using all of
this information, a set of recommendations for the system performance and optimal operational
parameters were established.
II. FMECA Results
A FMECA was created by evaluating each part of the system. The results were compiled
into a FMECA table (Appendix Table 1A). The severity of the failure was determined based on
the worst case consequences of the failure, using past experience and knowledge of these types of
failures. A cost value of severity was then calculated attributing replacement costs and the cost of
the failure, as seen below for the methanol storage tank:
Total Cost Replacement = $25,000 + $150 (6 hours) + $250 (6 hours) = $27,400
Total Cost Failure = Total Cost Replacement + $800,000 = $827,400
In this case, due to the chance of a gas explosion resulting in a fatality, the failure cost was
determined to be $800,000. The likelihood was calculated from the Weibull analysis and reliability
of the part over a 5 year period, assuming that the station is continuously running as scheduled and

5. 4
that the part has been in operation that entire time. This length of time was chosen to ensure that
long term probabilities were accounted for. A risk matrix of these results was compiled in Figure
1B in the appendix.
Table 2B: The results of the reliability of the system parts and their failure rates.
Based on these figures, it was found that the compressor carries the greatest risk of failure
but that catalytic reactors reaching too high of a temperature and exploding carries the highest
consequence of failure. The likelihood and severity of each failure can be seen in Table 1D in the
appendix. Following the reactors, other parts with notable failure consequences included the
methanol storage tank, the hydrogen storage tank, the fired heater, the compressor, and the gas
separator all of which had failure costs greater than $800,000. Thus, there is a significant need to
reduce the occurence of these failures.
III. Reliability Analysis Results
A reliability analysis was performed on two parts within the system: the compressor and
the reverse osmosis filter. This analysis resulted in the choice to not perform maintenance on the
osmosis filter, but to schedule a replacement every 9 months. The compressor was determined to
have a slower growing failure rate after about 1200 hours, so we decided to run the compressor
until failure rather than schedule replacements to get the maximum life out of the part.
Osmosis Filter: For the reverse osmosis filter, the recommended maintenance action is to clean it
every 1,000 hours. This takes half an hour each time, resulting in a maintenance cost of $75.
Table 3A: The results of the reliability analysis on the reverse osmosis filter.
Reverse Osmosis Filter With Maintenance Without Maintenance
Shape Factor (β) 4.662726 4.14931
Characteristic Life (η) (hours) 4948.98 5017.35
Failure Rate (t=2000 hours) 0.014525 0.021768

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Failure Rate (t=5000 hours) 0.6497 0.6286833
Time to Reach Critical Failure
Probability (0.77)
5,400 hours 5,520 hours
At the start of the filter’s life, regular maintenance results in a lower probability of failure.
This is seen in Table 3A with the failure rate of the component with maintenance at 2,000 hours
being around 1% lower than without maintenance. However, regular maintenance takes its toll on
the part rather than improving it. This is seen at around 5,000 hours that the part without
maintenance is less likely to fail. It would be wise to replace the filter in intervals based on the
decaying reliability of the component.
The replacement interval for the filter was decided by finding when the expected value of
a filter failure would surpass the cost of replacement. The failure cost of the filter was assumed to
be around $2,000 for any time it caused a decreased output of the system, or any contaminated
product that would have to be discarded:
Total Cost Replacement = $5,000 + $150 (5 hours) + $250 (5 hours) = $7,000
Total Cost Failure = Total Cost Replacement + $2,000 = $9,000
Based on this information, the part should be replaced when the failure rate is around 77%.
In Table 3A, this is seen at around 5,500 hours of operation, so a replacement period of every 9
months was chosen.
Compressor: The Weibull analysis of the compressor showed a change in distribution after a
certain time. This is evident in Figure 3A below, the slope of the line on the graph changes at the
points corresponding to a known TTF of around 1200 hours.
Figure 3A: The Weibull distribution of the failures of the compressor used in the system.

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Table 3B: A summary of theWeibull Parameters for the compressor, notice thechange in values at 1200 hours
Compressor Before 1200 hours After 1200 hours
Shape Factor (β) 1.28706 0.51604
Characteristic Life (η) (hours) 3,277.206 29,041.14
After 1,200 hours, the shape factor decreases to a value below 1 which extends the life of
the component significantly. This lead us to decide that it is best to run the compressor until a
failure is reached. Thus regular, scheduled, replacements would actually result in a loss of money
as the cost of a replacement is high at $75,300.
IV. System Availability Analysis
The cost of maintenance actions and possible failures were summarised and prioritized to
determine where maintenance can be reduced. This reduction was then analysed to see if it was
beneficial with the failure rate penalty in mind. After reduction, the inherent and actual availability
were found along with the mean maintenance time, mean time to failure, mean time to repair and
the mean time between maintenance actions.
Each of the components of the system have a recommended set of maintenance actions that
need to be performed. The total cost of each of these components relies on the cost of preventative
maintenance, PM, as well as the cost of replacement given a failure. This information is
summarized in Table 4A below which ranks each of the parts of the system based on their overall
costs.

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Table 4A: A summary of the cost of maintaining and replacing the components of the system.
Table 4B: A summary of the reductions made to the highest contributing PM costs.
In Table 4B above, the components that contributed the most to maintenance cost were
analysed to see if a reduction in maintenance would lead to a greater cost in failures. After reducing
the frequency of inspections and tests for each of the parts, the total maintenance cost decreased
significantly.
Table 4C:A summaryof all of thepreventative maintenanceactions on each component,note the compressor has no suggested maintenance.

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Based off the sum of the total amount of PM time needed per day, 4 hours set aside each
day should be plenty for scheduled maintenance. It should not be reduced, however, because
even though less time is needed per day on average, some actions will take the entire 4 hours,
and thus the set PM time should not change. Maintenance should however, leave the premises
after performing necessary tasks for that day, resulting in some days having a smaller
maintenance period.
Table 4D: A summaryof thedifferent maintenancedata ofthe systemas well as the overall failure rate andthe availability
Mean Time to Failure 354.18 hours
Mean Time to Repair 4.99 hours
Mean Time between Maintenance Actions 25.77 hours
Mean Maintenance Time 1.72 hours
Inherent Availability 0.9861057
Achieved Availability 0.93732488
Overall System Failure Rate 0.002823432 failures/hour
Finally, after reducing the amount of maintenance hours for the gas separator, cooler and
reactors, the following data combined in Table 4D. The overall system failure rate was found to

10. 9
be around 0.003 failures/hour. A simulation regarding the availability and each parts contribution
to failures will be listed later in the report.
V. Disaster Analysis
Due to the high consequences involved with certain failures of the various parts, a disaster
analysis was conducted to determine how likely it was that these specific scenarios would occur.
The analysis was conducted by obtaining the probability of a catastrophic event failure by
multiplying the failure probability of the part by the catastrophic event probability to determine
how often that part would fail in a way that lead to a catastrophic event. As it was determined in
section I that the worst consequences are associated with the methanol storage tank, the analysis
was started with that part. In the case of the methanol tank the disaster probability was as follows:
Probability of a catastrophic event = 0.36014 * 0.00001 = 3.6*10-6
Table 5A: The likelihoodandconsequences of a catastrophic failure occurringfor theparts ofthe system.
The results of this analysis proved that although catastrophic events may arise from failure
of the system, most are extremely unlikely to occur. Out of these events, the most likely to occur
during a five year time period was found to be the hydrogen gas storage tank, with a probability
of 1.945 x 10-6.Considering these results, a catastrophic failure is unlikely to occur if the company
remains vigilant and continues maintenance on the system. However, it should be advised that the
company have forms of damage control in place in the case of an emergency and follow all
necessary safety precautions. The best option in this scenario is to implement a system shutdown
procedure which can limit other system parts from adding to the damages.
VI. Simulation Results
A Monte Carlo Simulation of 2,000 iterations was performed on each component of the
system using their Weibull parameters for failure. This data was then used to further determine the
availability of the system, the system TTF, the number of each type of spare component that should

11. 10
be kept on hand, total net income of the fuel stations. The results of the simulation are compiled
in the CDF graphs below (Figure 6A).
Since all parts of the system, with the exception of the two reactors, run in series, the TTF
for the system was the TTF of the first part to fail. The simulation results showed that the part that
is most likely the cause to failure prior to it’s replacement interval was the osmosis filter. The other
likely components that contributed to failures in the simulation are summarised in Table 6A below.
Table 6A: A summary ofthe simulation results regardingeach parts contributiontofailures in the2000iteration simulation.
Component Times Failed First % Contribution to Failure
Fired Heater 85 4.25
Reactors 1 and 2 0 0
Gas Separator 0 0

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Cooler 2 0.1
Fluid Mixer 0 0
Hydrogen Storage Tank 63 3.15
Methanol Storage Tank 190 9.50
Water Storage Tank 49 2.45
Compressor 694 34.7
Reverse Osmosis Filter 916 45.8
With these contributions in mind, regardless of the replacement interval, spare osmosis
filters should be kept on site due to their tendency to fail. Additionally, a spare compressor
should be kept on site in order to reduce downtime when it fails. The other parts that made major
contributions to the TTF of the system were the fuel tanks and the fired heater, while these can’t
necessary have “spares” kept nearby, extra note should be taken during inspections to look for
possible signs of upcoming failure.
The simulation also provided information on the TTF, availability of the system. The
availability was found using the TTR of the first component to fail, along with the time until the
first part failed. The net profit of the system was found by subtracting the cost of the replacement
of the first part to fail as well as the cost of the downtime from replacement from the profit of the
system prior to failure (TTF x hourly profit).
Table 6B: A summary ofsimulation results.
Statistic 10th Percentile 90th Percentile
TTF 261.497 hours 9,345.04 hours
TTR 2 hours 6 hours
Availability 0.987666 0.999556
Net Profit $64,445.07 $2,335,457.56
In the rare case that an expensive part fails quite early, a negative net profit is seen.
However, the system is still shown to be quite profitable as even the a lower percentile of the net
profit distribution is a positive value (Table 6B). Additionally, the median profit is around
$900,000 and the average is roughly $1.3 million.

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VII. Value of Information Analysis
Due to the high cost of a possible leak from damages, government fees, and possible
injuries, an alarm system would be beneficial to the fuel stations. After analysis of the proposed
alarm systems, however, the cost of shutdowns would far outweigh the expected value of a leak
from either the methanol, hydrogen, or methane storage tanks. This is due to the low probability
of a leak itself, and also the high probability of a false alarm occurring from the provided systems.
The alarm system, when triggered, results in a day long shutdown of the system as well as
the cost for maintenance to perform inspections. Additionally, if a leak does occur and the alarm
goes off, there is a cost for replacing the equipment as well the time for maintenance to perform
the replacement.
Taking into account that the fuel station is expected to produce an hourly profit of $250, a
day long shutdown will result in a loss of $5000.
Table 7A: A summary of thecosts that result from a detected and undetected leak in addition to theloss of profit from a shutdown.
Event Cost Hydrogen Tank Methanol Tank Fired Heater
Inspection by Maintenance $150 $150 $300
Repair Cost (Replacement +
Maintenance Cost)
$53,000 $259,000 $51,200
Cost of Leak (Undetected) $450,000 $300,000 $450,000
Using the provided data of the detector systems, the probability of an alarm going off was
calculated for each system. This was done using the probability of a leak as “improbable” or
1/1000. Additionally, the probability of the alarm going off in the event of a leak, the probability
of a “false alarm”, and the probability of the alarm failing to detect a leak were found.
Table 7B: A summary of the probabilities that result from the imperfect information given by the alarm systems. Since the
probability of a leak is so small, the accuracies of the systems are far from perfect.
Probability Hydrogen Detector Methanol Detector Methane Detector
Leak 0.001 0.001 0.001
No Leak 0.999 0.999 0.999
Alarm 0.02096 0.04086 0.10086
No Alarm 0.046755725 0.95914 0.89914

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Leak Given Alarm 0.953244275 0.022026432 0.009518144
No Leak Given Alarm 2.04282 x 10-5 0.977973568 0.990481856
Leak Given No Alarm 0.999979572 0.00010426 4.4487 x 10-5
No Leak Given Alarm 0.046755725 0.99989574 0.999955513
The EVII for the system was found using the decision tree below (Figure 7A) by evaluating
the expected value of the alarm as well as a system without an alarm:
Figure 7A: A decision tree that applies to all three alarm systems, note that the values that differ between each system aren't
depicted numerically. The decision is based on a daily basis as the alarms can result in a day's shutdown.
In order for the alarm system to be worth investing, the EVII of the system must be more
than the cost of the system so that the company essentially gets back what they invested in the
alarm by avoiding other costs.
Table 7C:A summaryof theexpectedvalues as well as the cost ofthe systems forcomparison. The EVII is significantly lower than the costs.
Value Hydrogen Detector Methanol Detector Methane Detector
Cost of the Detector 4920 13500 6280
System without Alarm 5544 5694 5544
System with Alarm 5684.276 5449.641 4692.03

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EVII 140.276 -244.359 -851.97
Since the EVII for all three of the systems were found to be far less than their cost, they
were deemed not worthy of investment. After investigating the effects of a more accurate system,
as well as the EVII of the same systems but with varying costs of leaks, they were still found to be
unworthy of investment as the EVII never surpassed the cost of the system.
Figure 7B: A graph of the EVII of each detector system at varying accuracies of the systems probability of an alarm.
Figure 7C: A graph of theEVII of each detector systemat varyingcosts of aleak. Notethehigher cost of aleak, themore beneficial
the system becomes.
From the graph, you can see by increasing the probability of the alarm going off given a
leak, the EVII does not increase significantly. Additionally, when increasing the accuracy values
to their extremes (Table NUMBER), where the probability of the alarm going off when there is a
leak is 0.9999 and the probability of a false alarm is 0.0000001, the EVII of the system does not
increase.
If the probability of a leak were to increase, an acceptable EVII of the systems could be
obtained, especially if the systems were to be accurate. Table 4 also includes the EVII of the
detector systems with the probability of a leak increasing by a factor of 10 (1/50 instead of 1/1000).

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In this case, though this system would be undesirable if a leak occurs several times a year, the
hydrogen and methane detectors would be worthy of investment.
Table 7D: The EVII of the systems given that the systems are almost perfect (nearly no false alarms and nearly every leak is
detected), as well as if the leak were more probable.
Hydrogen Detector Methanol Detector Methane Detector
Cost of the Detector 4920 13500 6280
EVII of an “Extreme”
Accurate System
267 390.834 392.5
EVII if leaks were more
probable. (1/50)
7422.52 4346.82 6330.6
Even if the cost of the leak is significantly larger, resulting in the alarm saving the company
from a significant loss, the EVII of the system never surpasses the cost of the alarms. This is seen
in Figure 7C.
Overall, since the alarm results in a day long shutdown, and the probability of a leak itself
is so small, the alarm systems are not worth the investment given their cost.
VIII. Recommendations
This section summarises the recommendations that we give due to our findings from the
previous sections of the report. Overall, the fuel station was found to be profitable and well worth
the investment, but the list below details changes we suggest:
● An alarm system that is less likely to set off false alarms or perhaps does not result in a day
long shutdown would be beneficial as it would show that the company cares for the safety
of the public and the environment, as well as workers. Though a leak is rare, the outcome
could be catastrophic and if the company were to be found to not have something as simple
as a detection system it could look very bad to the public.
● Reverse osmosis filter should be refurbished every 9 months rather than cleaned for
maintenance.
● Compressor should be ran until failure while keeping spare compressors on site rather than
setting up replacement intervals
● Maintenance can be reduced slightly as suggested in Section IV. After the station has been
functioning for several months, further investigation on maintenance needs could be
performed.

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● Implementing a system shutdown procedure during emergency situations and following all
safety protocols.

18. 17
Appendix
Table 1A: FMECA Table

19. 18

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Figure 1C: Risk Matrix of system parts based on severity and likelihood
*Please note that the pump failures (Ei and Eii), osmosis filter failures (Ai and Aii), and failures Bi, Di, Jii, and Jiii
are not plotted within the parameters.
Table 1D: Failure Likelihoods and Severity

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