Applying Dynamic Process Simulations Toward Flaring Reduction

Transcription

1 Applying Dynamic Process Simulations Toward Flaring Reduction White Paper Siemens AG 2018, All rights reserved 1

2 Dynamic process simulation has traditionally been a tool of process engineers and process control engineers for design and control of unit operations. The main objectives of this paper are to highlight specific opportunities to apply this tool toward reduction of both routine and non-routine flaring. Application of dynamic process simulation for operational surveillance, optimization of start-up and shutdown procedures, and pressure relief and flare load modeling will be discussed. Proper application of this tool could have significant benefits in terms of equipment availability, plant performance, capital and operating expenditures, and emission reduction. Siemens AG 2018, All rights reserved 2

3 Applying Dynamic Process Simulations Toward Flaring Reduction Drivers and Methods for Flaring Reduction As refineries and chemical processing plants strive to improve operational efficiency and ensure compliance with environmental regulations, increasing attention has been given to various initiatives to reduce flaring. For example, recent regulations such as 40 CFR 60 Subpart Ja (Ref. 1) are intended to reduce flaring at petroleum refineries during routine and nonroutine operations. Such initiatives have primarily focused on installing flare gas recovery systems, improved operational, monitoring, and plant equipment maintenance practices. Such efforts have generally yielded meaningful results. Dynamic process simulation could be applied to supplement and support such initiatives. Applications may include enhancing operational surveillance, optimizing start-up and shutdown procedures, and modeling pressure relief and flare loads more accurately. Steady-state versus Dynamic Process Simulations Process operations are dynamic by nature, but steady-state simulations are commonly employed by assuming relatively stable operating conditions. In addition, mass and energy transfer limitations based on equipment and piping design or sizing may not necessarily be included in such models, depending on the objectives of the analysis. In practice, plant operating conditions are constantly changing. Fluctuations in process conditions, flow rates, feed and product stream properties, utility conditions, and ambient conditions may change. The main differences between steady-state and dynamic process simulation are noted in Table 1. Table 1 - Comparison of Steady-State and Dynamic Process Simulation Parameters Design Parameters Steady-State Process Simulation Dynamic Process Simulation Equipment and piping design and sizing details Basic or limited inputs required Detailed inputs required Process conditions Production flow rates Fluid properties Constant basis Dynamic basis to account for process or operational changes Equipment liquid hold-up Equipment performance Thermal and hydraulic models Not required for modeling or constant basis Constant basis Required to account for system capacity and process control Dynamic basis Process control design Not required for modeling Required to validate dynamic system responses Siemens AG 2018, All rights reserved 3

4 Process Gas Compressor Example A simplified example based on a typical process gas compressor is discussed below, as illustrated in Figure 1. When performing a steady-state simulation, limited design, sizing, and performance characteristics of the equipment are usually included in the model. Conditions, flow rates, and fluid properties for the major streams would be defined or calculated based on implicit assumptions for the equipment performance, i.e. such as cooler duties, separation efficiencies of scrubbers, compressor duty, etc. Results would then be compared against equipment design ratings for validation. Figure 1. Simplified diagram of process gas compressor When performing a dynamic process simulation, more details of the equipment performance characteristics are included in the model. Examples of such details include heat transfer and geometrical details (scrubbers/ coolers), performance curves (compressor), scrubber carryover/entrainment and heat loss models, control valve characteristic curves, and piping dimensional and elevation data. Initial boundary and system characteristics are specified or determined, and the process model is initialized based on the starting conditions. A steady-state condition could serve as the basis of the initialization process. Details such as initial scrubber liquid level, control valve opening position, process control logic, setpoints, and tuning parameters are added. Significant effort is typically required to ensure that the initial modeling results match as closely as possible the actual or expected system performance. Plant operational experience and historical operating data may serve as good basis for finetuning the model. Once the initial model is fixed, the relevant changes in operational parameters could then be modeled by specifying a timed sequence of events. For example, the compressor could be modeled dynamically to ensure that the operating point stays within the performance limits of the compressor during start-up. A typical compressor operating window is shown in Figure 2. An acceptable path during start-up is selected to avoid operating too close to the surge and stonewall regions, as indicated in the figure. The setpoints for compressor speed, flow and head are adjusted during the start-up sequence to account for both equipment performance as well as process limitations. Compressor recycle flow is commonly maintained during start-up as the initial production gas flow rate from upstream equipment may not be sufficient. As shown in Figure 3, the compressor recycle flow may be decreased as the production (or process) gas flow rate builds up. Siemens AG 2018, All rights reserved 4

5 Figure 2 - Typical Compressor Operating Envelope Figure 3 Compressor Start-up Flow Rates By allowing plant personnel to validate key parameters, identify limitations and weaknesses, and test potential solutions to resolve them, dynamic process simulation provides a credible basis to enhance system design, process control, and operating procedures, thereby reduce process upsets which could lead to relief or blowdown to the flare. For example, dynamic simulation could be used to determine the available time for an operator to respond or for a safety instrumented system to isolate a system upon detection of an initiating event that could eventually lead to a severe consequence. Siemens AG 2018, All rights reserved 5

6 Operational surveillance Traditionally, dynamic process simulation has been used to study process design, equipment sizing, process control, and system operability. Such applications are typically performed offline. Examples include modeling compressor surge, compressor settleout, process unit start-up/shutdown, chemical reactions, depressurization or blowdown, water hammer, etc. They have also been used for operator training purposes and what-if analysis. Process upsets and trips may be accompanied by flaring by design to ensure process safety. Deploying dynamic process simulation for operator surveillance could help minimize such events by providing or supplementing early warning of significant process deviations, thereby alerting the operator to intervene as needed to avoid or mitigate the upset or trip. An application of dynamic process simulation for operational surveillance is shown in Figure 4. The horizontal axis shows expected compressor performance indicator values based on dynamic process simulation results, whereas the vertical axis shows the measured or derived values for the same indicators based on process instrumentation readings. Ideally, the values per the two bases should track each other closely. If one or a few outliers are indicated, this could signify isolated case(s) of sensor drift or failure. However, if significant deviations are observed across multiple indicators, this could imply a systemic problem and provide an early warning to operators that a more serious issue is developing. Figure 4 - Tracking Compressor Performance Indicators Another way to visualize deviations between measured and expected values is to track their ratios over time, as shown in Figure 5. In this example, a process upset or disturbance is assumed to result in significant deviations across multiple indicators. As the process control system responds or as the operator takes corrective actions, the surveillance system would indicate that the system is returning to more stable operating conditions. It should be noted that such a surveillance system should be separate from the plant process control system and emergency shutdown system. The dynamic process simulator may rely on the same process sensors to collect input data, but should not provide any outputs to affect the operation of such systems directly. This real-time surveillance system could be used to track the performance of the process control system, cross-check or supplement available plant instrumentation, monitor equipment condition and performance, and provide data to identify Siemens AG 2018, All rights reserved 6

7 developing problem areas. The system could serve as virtual sensors to provide process information at locations wherein there are no physical sensors or where the physical sensors are failing. Figure 5 - Trended Data for Compressor Performance Indicators The input parameters of the dynamic model should be mapped to the relevant process sensors. The initial model is typically over-determined based on the available instrumentation. Sensor weights could be allocated to ensure best overall match of model outputs against operational data. Multiple calculation modes (e.g. by specifying different sets of inputs for the simulation) could be compared to cross-check reliability of process sensors and validate the model. The dynamic process model should be calibrated or tuned using actual plant operating data, once such data become available, covering the expected range of operating conditions. While plant operating data based on normal conditions are useful, data based on transient conditions such as start-up, upsets, and turndown could provide valuable insight for model validation. It is important to get the inputs of the experienced plant operators and engineers in the interpretation of plant operating data. Periodic model re-calibration is needed to maintain model fidelity over time as equipment and sensor performance may drift and significant plant design and operational changes may occur over the life of the plant. Moreover, key performance indicators for the entire unit or facility could be developed and tracked based on the dynamic process simulation application for operational surveillance. This could provide indication of overall health as well as signal impending or actual equipment or instrumentation failure, for example, and thus serve as basis for enhanced maintenance program or activities. Start-up and Shutdown During plant start-ups, there is usually a higher risk of process trips than during normal operations as the operating conditions of interconnected equipment may deviate significantly from their normal conditions. In addition, some control and alarm functions may only be valid during normal operations and thus bypassed during start-ups to avoid spurious trips or shutdown. By performing dynamic process simulation prior to start-up and shutdown operations, system weaknesses could be identified more easily, and operating procedures could be enhanced to reduce upsets and trips that could lead to flaring. An example based on compressor start-up sequence is discussed above. In another application, the typical start-up of a distillation column could take several hours to several days, depending on its size and complexity as well as the availability of upstream and downstream process systems. A typical processing unit in a Siemens AG 2018, All rights reserved 7

8 refining or petrochemical environment may consist of a series of distillation columns with potential heat integration between feed and product streams. A simplified arrangement of two distillation columns in series is shown in Figure 6. The level of complexity could dramatically increase as the number of variables involved increases. It is not uncommon to have multiple trips during restarts due to equipment malfunction or process upset. Dynamic process simulation could be used to optimize the start-up sequence, to improve understanding of the key parameters, and to evaluate recycle options to reduce flaring or disposal of off-spec products, thereby resulting in a more robust and shorter start-up process. The logics for permissives and interlocks as well as the cause-and-effect matrix for alarms and emergency shutdowns could be validated. Figure 6 Simplified diagram of two distillation column in series Each column is initialized to match the expected initial conditions. The steps or activities per the start-up procedure are then executed in the simulation in a timed sequence, and the dynamic response of each column is evaluated. Actual plant experience during start-up operations should be incorporated into the model. Typically, such analysis is iterative as the various steps are modified to improve the start-up process. Examples of simulation results in the form of trend plots for column flow rates, exchanger duties, and stream conditions are shown in Figure 7 and Figure 8. Other parameters that could be tracked include liquid hold-up in the column trays, overhead accumulator and column bottoms sump/ reboiler, as well as liquid/vapor flow rates in the column trays, product draw rates, and product specs. Siemens AG 2018, All rights reserved 8

9 Figure 7 - Trended Data for Column Flow Rates and Exchanger Duties Figure 8 - Trended Data for Column Stream Conditions Sensitivity analysis should be performed to identify operating constraints as well as to evaluate system response and stability with respect to changes in key operating parameters. Control, alarm, and emergency shutdown setpoints as well as operator intervention/ response time could also be validated. A modular approach may be appropriate to reduce modeling complexity based on individual systems or sections of the facilities. Integration of such modules within a simulation would then be performed upon validation of each module. Once the start-up model has been validated based on actual start-up operations, it could be integrated as part of the operational surveillance system discussed earlier. Siemens AG 2018, All rights reserved 9

10 With respect to shutdown operations, dynamic process simulation could also be used to validate the shutdown procedures, esp. for complex and interconnected systems. A related application is to evaluate the effects of process upsets on interconnected systems and validate methods to avoid a large-scale or full plant shutdown, such as by transition of such systems to a turndown mode of operation while the process upset is being resolved. This could in turn avoid unnecessary flaring associated with the shutdown. Pressure relief and flare load modelling Flare systems are designed to handle vapor and/or liquid relief loads due to individual as well as global overpressure scenarios. Flare headers, stacks and knockout drums are typically designed to handle the peak load from each contributing source in an overpressure scenario. As such, the calculation method of peak loads has significant impact on the sizing of the flare system, and thus on the costs and schedules of capital projects, plant operations and maintenance, and environmental effects of flaring. The example discussed in this section is based on a distillation column, which is typically a source of significant flare load. Using conventional steady state modeling to determine the relief requirement for a column system, the transitory behavior of the system fluid inventory and heat integration between the column and its associated equipment are often overlooked. Although the analysis and the calculations would be simplified using this method, the results in terms of relief rates are often too conservative, leading to over-designed relief systems and flare capacities. On the other hand, dynamic process simulation introduces a more rigorous approach in which the transient effects of the system inventory on the liquid and vapor flow rates within the column system and on the heat transfer with associated exchangers are accounted for in an integrated manner. By modeling the sensible heat and latent heat of the residing liquid dynamically, this approach usually results in reduced and more credible relief rates. When a column system begins to overpressure, the operating temperatures in the column trays would tend to rise over a period of time as the system operating pressure rises toward the relief pressure. The system temperature rise is limited by the inlet temperature of the reboiler heating medium. The rate at which the temperature rises is dependent on the available system inventory and the dynamic rate of heat transfer from the reboiler. The rate of vapor generation in the column would change as the fluid composition of the multi-component liquid inventory and its associated bubblepoint temperature change over time. For example, if the column feed and reflux flows are both lost, the residual liquid in the column would have increasing molecular weight over time as its lighter fraction boils off. As the column liquids become heavier, its bubblepoint temperature increases, thereby decreasing the reboiler duty due to reduced differential temperature between the hot and cold sides of the reboiler. Additionally, the time that the system takes to reach its peak pressure and the associated peak load to the flare could be modeled using dynamic process simulation. Depending on the mass and heat transfer limitations within the column system, full overpressure as implicitly assumed using steady-state approach may not necessarily be achieved, and the calculated relief load using the steady state approach may not be realistic or sustainable. To illustrate this further, consider the following example based on a Naphtha Splitter column with a pressure relief device set at 50 psig. In the event of a total power failure, the column feed pump, bottoms pump, reflux pump, and cooling water pump (for overhead condenser) are all assumed to be lost as these pumps are all motor-driven. Heat input from high-pressure steam is assumed to continue at the reboiler. Based on a steady state simulation, boil up of the inventory in the system is assumed, and the relief pressure is assumed to be 10% above the relief device set pressure. The relief rate thus obtained is 87,323 lb/hr. When analyzed under dynamic process simulation, the results indicate significantly lower peak relief rate. A constant flow rate of high pressure steam to the reboiler was assumed. In practice, the steam flow rate would be controlled based on a process temperature setpoint, but no credit for favorable control system response is taken to reduce the steam flow. Additional design parameters, such as column internals and initial liquid hold up basis for the trays, were specified to account for the available system volume and fluid inventory. A timed sequence of events was then initiated involving the stopping of the column feed and reflux flows into the column, the overhead condenser cooling, and the column bottoms flow. The dynamic modeling results revealed that the Naphtha Splitter would take about 9 minutes to reach its peak load of 48,655 lb/hr. Note that the duration to achieve peak load may provide some possible reaction time for plant personnel to initiate a mitigating action, such as manually shutting of the steam flow, but no credit was taken for such action. The time to reach peak load could Siemens AG 2018, All rights reserved 10

11 vary from system to system. Using dynamic simulation to model such scenarios allows plant personnel to prioritize response strategies. For this example, the relief device would not achieve full lift but only simmer to relieve the expected peak load from the system. Therefore, the dynamic simulation approach not only provided an estimate of the duration to overpressure, but also resulted in a significantly lower peak load to the flare, i.e. 45% less than relief rate estimated using steady state approach for this example. The drop in relief rate obtained from the dynamic simulation approach can be attributed to the decrease in the log mean temperature difference (LMTD) between the reboiler heating medium and the inventory in the system over time as the molecular weight of the residual system inventory and the operating temperature in the column increase. Additionally, the impact of sensible heat and latent heat for the residual liquid are dynamically modeled. The main results using the two approaches are summarized in Table 2. Characteristic Units Steady state approach Dynamic approach Relief rate lb/hr 87,323 48,655 Time to overpressure seconds Peak pressure in the system psig Reboiler duty MMBtu/hr 14 7 PSV % opening % Table 2 - Naphtha Splitter Relief Loads based on Steady State and Dynamic Process Simulations The results of the dynamic simulation are further illustrated in Figure 9 and Figure 10 to show how the system would respond over a time line. From Figure 9, it can be seen that from the point of initiation of the overpressure scenario (t = 0 seconds) the pressure inside the system begins to rise as shown by the red curve. At around t = 400 seconds, it begins to approach its peak pressure. At about 514 seconds, the peak pressure in the system is achieved. This peak pressure corresponds also to the peak in the relief device percent opening curve. In Figure 10, the reboiler duty (red curve) decreases over a period of time to where it eventually pinches out. The peak relief rate is obtained at the point when the reboiler duty drops to almost half its normal operating duty. It should be noted that these curves are specific to the application or scenario. If reflux flow were to continue, the average liquid composition in the column would tend to be lighter, and the reboiler duty and relief rate curves could be significantly different from those shown in the figure. Other examples of using dynamic process simulations for estimating relief loads are provided in Ref. 2. Combining the dynamic peak relief loads from individual sources, substantial reduction in total flare load could be realized as shown in Table 3. If a flare system were to be designed based on the total steady state relief load, the flare system would end up being designed for 300,000 lb/hr. This simplified and conservative approach may result in overestimation of the total flare loads, which in turn could lead to excessive back pressure concerns on the participating relief devices as well as undersized flare equipment concerns. However, if the flare system were designed for the total dynamic simulation peak relief loads, the flare system could be designed for 225,000 lb/hr, which would be significantly lower than the steady state estimates. Another important point is the duration that each system would take to achieve its peak pressure or peak load. Using the steady state approach, the maximum loads from all systems participating in that scenario are considered to relieve simultaneously. Siemens AG 2018, All rights reserved 11

12 Figure 9 Naphtha Splitter Pressure Dynamics during Total Power Failure Figure 10 - Naphtha Splitter Relief Load and Reboiler Duty during Total Power Failure Table 3 Comparison of Total Flare Loads (Steady State versus Dynamic Simulations) Relief device Steady state Dynamic simulation peak relief rate (lb/hr) peak relief rate (lb/hr) PSV-1 50,000 35,000 PSV-2 55,000 40,000 PSV-3 60,000 45,000 PSV-4 65,000 50,000 PSV-5 70,000 55,000 Total 300, ,000 Siemens AG 2018, All rights reserved 12

13 As shown in Figure 11, taking account the time dependency of the individual peak loads per the dynamic process simulation could result in a significant reduction in total flare load. In this example, three load sources are considered, each with its own time-dependent load curve. By combining the three load curves, a total dynamic load curve could be derived. Figure 11 Time Dependency of Total Flare Load Estimates (Steady State versus Dynamic Simulations) The resulting peak load from the total dynamic load curve is significantly lower than the total flare load from the steady state approach. It could also be lower than the total peak load obtained by adding the individual dynamic peak load from each source as the three loads are not expected to peak at the same point in time. Sensitivity of the timing of the individual peak loads based on relevant parameters such as process conditions and process control responses should be evaluated. Addition of safety margin to the estimated total dynamic flare load may be warranted to account for any modeling uncertainties. Therefore, the dynamic simulation approach can provide for a more efficient flare system design capacity than a steady state approach could. Significant cost savings in terms of capital, operating, and maintenance costs may be realized. Routine flaring would likely also be decreased due to reduced purge, sweep, and/or pilot gas requirements for a smaller flare system. Caveats on Dynamic Process Simulations As with all other modeling techniques, the quality of the modeling results is highly dependent on the quality of the inputs. A well calibrated model could serve as a powerful tool for the applications noted in this paper, whereas a poorly developed one serves no useful purpose. A significant amount of effort is required upfront to incorporate all necessary inputs to model the physical system, associated fluid properties, process control behavior, and operating procedures accurately. Validation against actual operational data is beneficial in terms of both steady-state and transient operating modes. It is important to recognize the limitations of the model in terms of the simplifying assumptions, the applicable ranges of input parameters, the dependency on the initial conditions and boundary specifications, as well as sensitivity to variances in operating conditions and process control responses. The baseline for dynamic model should match steady-state model (based on initial conditions). Effects of interconnected equipment, such as heat integration, should be considered. Sensitivity analysis is typically performed to ensure confidence in the model predictions of the system behavior and process control response over the expected range and various modes of operations. Periodic calibration and tuning may be warranted to ensure that significant process or operational changes in the field are reflected accurately in the model. Siemens AG 2018, All rights reserved 13

14 With respect to model calibration and online surveillance, the dependency on the sampling frequency and accuracy of available process instrumentation readings should be recognized. For example, fast transients may be hard to model. In addition, limitations due to numerical convergence and computational time may justify simplification of complex systems. API Standard 521 (Ref. 3) allows the use of dynamic simulations for pressure relief and flare load modeling, but consideration must be taken to ensure that the worst-case requirement is being adequately captured. No credit for favorable control response should be taken to reduce load requirement. Conclusions Specific opportunities to apply dynamic process simulation toward reduction of both routine and non-routine flaring are presented in this paper. Application of dynamic process simulation for operational surveillance, optimization of start-up and shutdown procedures, and pressure relief and flare load modeling are discussed. Proper application of this tool could have significant benefits in terms of equipment availability, plant performance, capital and operating expenditures, and emission reduction. Unplanned shutdowns could be minimized by better understanding of process dynamics and optimization of operating procedures and control setpoints. Improved equipment reliability and operational efficiency could be realized through enhanced operational surveillance and by extension preventive maintenance activities. Capital and operating expenditures could be minimized by optimizing flare design and capacity. Reduced frequency and duration of process upsets/trips that result in flaring as well as reduced routine flaring due to optimized flare sizing would both contribute toward emissions reduction. References 1. US Code of Federal Regulations, Title 40, Part 60, Subpart Ja, Standards of Performance for Petroleum Refineries for Which Construction, Reconstruction, or Modification Commenced After May 14, Calculating column relief loads, Chittibabu H. et al, API Standard 521, Pressure-relieving and Depressuring Systems, 6 th edition, 2014 Siemens AG 2018, All rights reserved 14

15 Siemens Energy, Inc. Process Safety Consulting 4615 Southwest Freeway, Suite 900 Houston, TX USA All rights reserved. All trademarks used are owned by Siemens or their respective owners. Siemens AG 2018 The information contained in this paper represents the current view of the authors at the time of publication. Process safety management is complex and this document cannot embody all possible scenarios or solutions related to compliance. This document contains examples for illustration and is for informational purposes only. Siemens makes no warranties, express or implied, in this paper. Siemens AG 2018, All rights reserved 15