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Optimize relief loads with dynamic simulation

12.01.2013  |  Xie, C. L.,  CPECC East China Design Branch, Qingdao, ChinaWang, Z. G.,  CPECC East China Design Branch, Qingdao, ChinaQin, Y. F.,  SimTech Beijing Ltd., Beijing, China

Pressure relief analysis is a critical task at the engineering design stage of a grassroots oil refinery project. Relief loads must be determined so that the process units and plant flare systems can be designed.

Keywords: [flare system] [pressure relief] [modeling] [coking]

Pressure relief analysis (PRA) is a critical task at the engineering design stage of a grassroots oil refinery project. During that phase, the relief loads must be determined so that the process units and plant flare systems can be designed.

The key steps of PRA include the determination of individual relief loads and the evaluation/mitigation of each process unit’s overall relief load. Individual relief normally refers to a single piece of equipment or a set of interconnected equipment systems, such as a distillation column protected with a pressure relief valve (hereafter referred to as a “protected system”).

A conventional method based on recommended practices and standards, such as American Petroleum Institute Standard 521 (API 521),1 is normally used for relief load calculation by designers and licensors. However, the conventional method has been widely proved to over-estimate the relief load, leading to the over-design of the flare system. The over-design of the flare system will not only result in unnecessary capital investment, but also lead to design and construction difficulties at very large plants—for example, a refinery with a capacity of several tens of million tons per year (MMtpy).

Many recent reports claim that dynamic simulation is more accurate than the conventional method in predicting relief load.2, 3 The required relief load, as calculated with dynamic simulation, is always far less compared to the relief load calculated with the conventional method. Furthermore, in the latest edition of API 521, dynamic simulation is a recommended method.

Although dynamic simulation would be the best way to predict relief load, building the model for a complete process unit is time-consuming and labor-intensive. Furthermore, this work requires detailed equipment and control system information that would not be available during the early design phase.

An approach combining the conventional method with dynamic simulation is proposed here. By minimizing the inherent drawbacks of both the conventional method and dynamic simulation, this approach can optimize relief load determination for the entire process unit with minimum modeling efforts.

Relief load optimization approach

The relief load determination procedure for a process unit includes several steps, as shown in Fig. 1. Note: During engineering design, an iterative procedure likely will be required due to process modification, control or safety system reconsideration, etc.; this procedure appears to be sequential.

  Fig. 1.  Approach for relief load optimization.

Step 1: Individual load calculation. The conventional method is the simple, fast way to conservatively determine the required relief load and, until now, it has been standard industry practice.

In the first step, the conventional method is applied (Fig. 1). Before the relief load can be calculated, applicable relief cases for a specific protected system must be determined, along with assumptions for these cases based on API 521. These cases and assumptions are used as the basis for later dynamic simulation, since even dynamic simulation should be compliant with the API standard.

The calculation for the distillation column is one of complicated applications. Three approaches have been used in the industry, including flash drum, gross overhead vapor and unbalanced heat load methods. However, the flash drum method can only be used as a rough estimation at an early stage of design. The latter two methods are more realistic, although underestimation of relief load is a common occurrence in the second method.

The last method, unbalanced heat load, is the most complicated and rigorous method among the three, and it is widely accepted as the industry standard. A number of authors have discussed this method in detail,3, 4 and so it is not repeated here; although, a comparison between the second and third methods is shown in Table 1.


The gross overhead vapor method gives a smaller number of figures; therefore, it is not reliable in terms of conservative consideration. Here, the unbalanced heat load method is taken as the standard conventional method for the column calculation.

Step 2: Ranking of loads. When individual relief loads for all cases are worked out, these data are sorted by their values in descending order.

As mentioned, dynamic modeling requires considerable engineering time and effort, and it is unwise to apply dynamic simulation for all cases (with the exception of the critical ones with the largest loads). The purpose of this sorting is to pick out the critical cases for which further dynamic simulation is needed. Note: Relief to different headers must be treated separately.

An example of a 4.2-MMtpy vacuum gasoil (VGO) hydrotreater is given in Table 2. Individual loads of high-pressure and low-pressure heaters are ranked and listed respectively; the largest two or three cases are candidates for dynamic simulation. It is important to keep in mind that the weight relief flowrate is not necessarily the largest relief load, especially for those relief loads with small molecular weights.


Step 3: Matching criteria. In Step 2, critical cases and corresponding protected systems are selected. However, not all protected systems can be modeled properly due to simulation limitations, and, for some cases, it is not worthwhile to model them. As a result, further screening of candidates for dynamic simulation is required. In this step, a judgement will be made vs. a set of criteria. These criteria typically include, but are not limited to, the following:

  • The protected system can be modeled, and the cases can be executed
  • The protected system is a column
  • The protected system is a reactor loop.

Fig. 2 shows a comparison of relief loads estimated by both the conventional method and dynamic simulation for a 3.7-MMtpy grassroots hydrocracking unit. As can be seen, with the same assumptions as the conventional method, dynamic simulation predicts a much smaller peak relief load for most column cases, thereby improving the relief load estimation.2, 3 Similar phenomena is observed for the reactor loop due to its complexity.

  Fig. 2.  Comparison of dynamic simulation
  with the conventional method.

However, for the drum (including the separator, the flash drum, the surge drum, etc.) and the compressor, dynamic simulation cannot make obvious improvements. The reason is that the valve and the drum are simple pieces of equipment and do not leave enough room for more rigorous modeling against manual calculations. Also, the compressor’s overpressure case is normally a “blocked outlet,” in which almost all inlet vapor must be relieved.

For these reasons, only protected systems of the column and/or reactor loop require dynamic study, and only separated modeling of these systems is required. In this way, much engineering time can be saved.

Step 4: Dynamic modeling. As mentioned in Step 3, the dynamic models are built for protected systems with the criteria being matched. Relief cases are executed based on the model, and the peak relief loads are documented.

A key point is that the same assumptions as the conventional method are applied during case studies based on a dynamic model. A typical example is overhead pressure control of the column. When an overpressure case happens, the increasing pressure will push the control valve to open wider, and this will reduce the required relief. However, this conventional instrumentation response should not be assumed when sizing individual process equipment pressure relief, according to API 521.

Therefore, in this case study, the pressure controller is set on manual operation, and the control valve is kept in its last position. Another example is a fire case. Not only is the same heat input model as the conventional method used in the dynamic simulation, but the same assumptions are also applied as the system is isolated and shut down in the occurrence of a fire.

Step 5: Summation of individual loads. Previous steps focused on a single protected system. From these steps, an overall relief load for a process unit is evaluated and altered for general failure cases (GFCs).

GFCs, typically including general power failures (GPFs), general water failures (GWFs) and general instrument air failures (GIAFs), usually are not allowed. The corresponding safety systems, such as the uninterrupted power supply (UPS), dual water supply and dual air supply, must be designed to prevent these failures. However, as far as relief system design is concerned, the extreme cases must still be considered.

Generally speaking, the summation of all individual relief source loads in a GFC should be calculated, yielding a rough and conservative overall load for that case. An example of a 4.2-MMtpy VGO hydrotreater is shown in Table 3, where two protected systems relieve to a low-pressure header in a GPF case. Note: The dynamic result in Step 4 should be used instead of the conventional method, wherever applicable.


Step 6: Depressuring load calculation. The peak load for depressuring is calculated using the conventional method. The timing of depressuring and its relevance to general failures should be carefully evaluated. The engineering judgement should be made and evaluated if depressuring occurs simultaneously in a GFC.

Step 7: Matching criteria. One of the advantages of dynamic simulation is that it considers timing and interconnection of processes. Normally, multiple protected systems relieve in GFCs, although not simultaneously. This scenario provides a good opportunity to mitigate the overall relief load with dynamic simulation.

In practice, one of the typical situations for which dynamic simulation can be implemented is a column series with streams and/or heat interconnections. However, with a simple analysis on the summation table at Step 5 (see Table 3), if the majority of the relief load (i.e., 80% or higher) is found to be contributed by a single column (e.g., the product fractionator in Table 3), then dynamic simulation is not encouraged because no considerable improvement can be expected.

In the same GFC, relief via both the relief valve and the depressuring valve may happen, but they do not peak simultaneously. This is another typical application of dynamic simulation, and a simple summation of those relief loads normally can be mitigated.

Step 8: Dynamic modeling. Dynamic models are built for the systems selected in Step 7, and GFCs are executed. To make the design conservative, the sum of the peak loads of each protected system should be taken as a design reference instead of the dynamic peak summation load. This is further illustrated in the later case study of a delayed coking unit.

Note: The assumptions made for multi-source simulation would differ from those for individual relief analysis. The conservative assumptions for the former simulation are not necessarily the same as in the latter. Therefore, in this step, careful evaluation of the assumption is required to ensure that the total relief load is conservative. It is possible that some models in Step 4 are used as part of the model in this analysis, but the assumptions used in other case studies likely will be different.

Step 9: New relief loads in GFCs. For the relief occurring in GFCs but not included in the analysis at Step 8, individual peak loads should be used to calculate the overall load from Step 8. The summation result is the optimized overall load in the GFC.

Step 10: Optimized loads. The optimized individual relief load in Step 4 can be used to size the relief valve of this protected system. For flare header sizing of this process unit, the maximum individual load, the overall load for the GFC and the depressuring load are collected and sorted, and among them the largest one is taken as the design case. One or more of these loads will be used for the plantwide relief load analysis.

Case study

Relief load optimization for a delayed coking unit with a capacity of 3 MMtpy is performed. The relief of the coker drum is handled separately, and, in this study, only the fractionation section (as shown in Fig. 3) is included.

  Fig. 3.  Process flow of the delayed coking unit.

Recall the approach procedure in Fig. 1. Individual relief loads are calculated in Step 1 using the conventional method, and the three largest loads are listed in Table 4. As can be seen, the largest loads come from the coker fractionator in this case, which matches the criteria in Step 3. Then, a dynamic model is built for the fractionators, and its relief cases are executed. The new relief loads are reported in Table 4 for comparison.


As expected, the relief loads are considerably mitigated by dynamic simulation for the first two cases, which are only around 60% of the relief loads obtained with the conventional method. No relief occurs for the single power failure case, but the column pressure increases; however, its peak value of 0.23 megapascal gauge (MPaG) is lower than the relief valve set pressure of 0.35 MPaG. At this point, Step 4 is finished.

Step 4 is followed by a summation of individual loads at the GFCs. In this study, the GPF and GWF cases are examined. The relieving equipment in the GPF includes the coker fractionator, the stripper feed drum and the debutanizer, while only the coker fractionator will relieve in the GWF case. Therefore, as per the criteria in Step 7, the GPF case would be further analyzed with dynamic simulation. A summary of the results is listed in Table 5.


As shown in the table, a single coker fractionator contributes 80% of the total relief load in the GPF case. For this reason, it is not a practical candidate for dynamic studies for multiple protected systems; however, it is still implemented in this study to illustrate Step 8.

As shown in Fig. 4, four protected systems will relieve in the GPF case, and the dynamic summation (black curve) peaks at 275,000 kg/hr. However, to make a conservative estimate, the summation of peak loads of 355,000 kg/hr for four individual sources is taken as the design reference. A large improvement is not seen compared with 376,000 kg/hr at Step 5, due to the reasons mentioned above. This summarizes Step 8.

  Fig. 4.  Dynamic relief curve for multiple
  protected systems in the GPF case.

Note: An additional relieving protected system—a compressor interstage drum—was found in the dynamic simulation. It splits the relief flow from other sources and does not increase the overall load; however, this phenomenon can only be observed and evaluated through dynamic simulation.

Finally, the optimized individual relief loads are collected and summarized. The results of the conventional method are replaced by the dynamic simulation results, where applicable. These data provide sizing references for individual relief valves. The required orifice size of a relief valve can always be reduced through relief load mitigation. With the coker fractionator, for example, the GPF is the controlling case, and the required relief valve size is reduced from 1,010 cm2 to 623 cm2 (based on the API relief valve sizing method). It can be determined that 355,000 kg/hr is the largest relief load for this delayed coking unit at the GPF case, which is only 63% of the load without optimization (565,000 kg/hr, as shown in Table 5), and this value is used to set the flare header size of the unit.

It is also apparent that modeling the entire flowsheet at Step 8 is unnecessary, since only a slight improvement can be obtained compared to the individual load summations. Therefore, Step 8 is bypassed in real engineering design, leading to considerable savings of modeling time and effort.


The relief load estimation derived by combining the conventional method with dynamic simulation was optimized as expected, with minimum dynamic modeling efforts. This estimation was successfully applied to a relief load case study of a large-scale delayed coking unit. Although a refinery process is studied here, the approach is also applicable to other oil, gas and petrochemical processes. HP


The authors thank Xiaobo Liu and Xiuwen Zhao from China Petroleum Engineering & Construction Corp.’s East China Design Branch; Ximei Lv and Zengfu Zhang from SimTech Beijing Ltd.; and Professor Enxi Lu from South China University of Technology for their contributions to this article.


1 American Petroleum Institute, “Guide for pressure relieving and depressuring systems,” API Standard 521, 5th Ed., January 2007.
2 Depew, C. and J. Dessing, “Dynamic simulation improves column relief load estimates,” Hydrocarbon Processing, December 1999.
3 Nazami, P. L., “Distillation column relief loads—Part 1, 2,” Hydrocarbon Processing, April 2008/May 2008.
4 Rahimi Mofrad, S., “Tower pressure relief calculation,” Hydrocarbon Processing, September 2008.

The authors

Chong Liang Xie is the chief engineer for China Petroleum Engineering & Construction Corp.’s (CPECC’s) East China Design Branch. He has 27 years of experience in refinery design, with specialized expertise in the delayed coking process and in general configuration design. Mr. Xie holds a BS degree from Northeast Petroleum University in Heilongjiang, China.

Zhi Gang Wang is the director for the general design division of CPECC’s East China Design Branch. He has 20 years of experience in refinery design and is an expert in general configuration design and in the fluid catalytic cracking process. Mr. Wang holds a BS degree from China University of Petroleum in Beijing, China.
Yun Feng Qin is the technical manager for SimTech Beijing Ltd. He has more than 10 years of experience in process simulation. Before joining SimTech, Mr. Qin worked with Invensys/SimSci-Esscor China for eight years as a technical support specialist and consultant. He holds an MS degree in chemical engineering from South China University of Technology in Guangdong, China, and a BS degree in chemical engineering from the Beijing Institute of Light Industry in Beijing, China. Mr. Qin can be reached at yunfeng.qin@sim-tech.com.cn

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May i inquire which software was used to model this depressuring study?

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