Introduction
In this case study we present an analysis for designing a chemical plant process, part of an extension project and during the FEED stage. The method and approach presented (as well as the software used - AeROS) is equally applicable in other industries where buffer sizing is necessary (such as Oil & Gas, manufacturing, etc.). A key takeaway from this study, is the importance of considering equipment reliability, repair durations, and production, and their impact on the overall process, when making design decisions.
The Scenario
Figure 1 shows part of the process flow, which includes equipment that can fail (mixers and pumps), together with a buffer tank and the reactor. The purpose of the tank is to ensure uninterrupted flow of liquid to Reactor R1.
In addition to achieving specified availability and production efficiency targets, the main concern is the size of the tank. If the size is too small, it may cause Reactor to shut down. On the flip side, too large of a tank will result in higher project costs (i.e., overdesign).
Given the failure rate and downtime behaviours of the Mixer and Pump, what is the required Tank size so that the risk of process upset is acceptable / minimized?
The Approach
A digital twin of the process is created using System Reliability Model.
System Reliability Model
Upon failure of Mixer or Pump, the production is not affected if it can be restored before the tank is depleted. Otherwise, the reactor has to be shut down, and to restart it, another 8 hours is required.
The reliability performance of both Mixer and Pump are known (from OEM and historical data of similar equipment used in the company).
Inputs
The production rate is 1 unit/hour (normalized), and the value is $1000/unit.
The normal production rates for Mixer M1, and Pump P1 are 1 unit/hour. Their maximum production rates are 2 units/hour.
The initial tank design capacity is 10 units and hence it allows a 10-hour maintenance window for repair activities. This is based on the fact that the average downtime is 8 to 9 hours.
The mixer and pump have the following failure rate and downtime behaviours:
The above information is entered into the corresponding nodes in Figure 2.
Figure 3 shows a 1-year simulation profile.
The Results
The team's objective is to investigate the annual impact of the Tank size on production loss. This is achieved through simulation, using the AeROS software. Specifically, five (5) different scenarios for the Tank Capacity are investigated, with the results of the simulations given in Table 2.
Based on these results, the team is considering increasing the size of the tank from the original size of 10 units to 20 units, and for a cost of $200,000. A financial analysis for this design change / investment (based on future cash flows from the excess production, assuming constant commodity prices and ignoring inflation), returns a NPV of $1,178,520 over a 10 year period, assuming an 8% discount rate.
Conclusion
Performing a reliability-based digital twin simulation during the FEED phase of a projects, which considers equipment availability as well as productivity, is fundamental and necessary for optimizing the design, and avoiding costly (and in some cases irreversible) decisions. In this case study, the use of AeROS software, a tool specifically designed for such modelling and analyses, enabled the team to make informed decisions by avoiding a costly design flaw (under sizing of the tank), and added millions of dollars in future revenues via increased productivity!
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