Use modeling to fine-tune cracking operations

Transcription

1 Originally appeared in: September 2008, pgs Used with permission. Use modeling to fine-tune cracking operations Spanish refiner uses next-generation simulation tools to cost-effectively select new catalysts J. M. LLANES and M. MIRANDA, CEPSA, La Rábida, Spain and S. MULLICK, AspenTech, Houston, Texas R igorous nonlinear reactor kinetic models have traditionally been used to support refinery planning, especially in defining linear programming (LP) sub-models and operating constraints for reactors. Applications of kinetic models in engineering, operations support and optimization are less common due to the intense efforts required to produce accurate results for each operating scenario. Today, embedding kinetic models into a process simulator can enable a variety of applications that are easily developed and deployed. Next-generation advanced refinery simulators can provide distinct advantages. Case history. CEPSA s La Rábida refinery in Spain has used non-linear kinetic fluid catalytic cracking (FCC) model in combination with other advanced simulation tools for several operational studies (Fig. 1). The business objective of these applications is to improve FCC unit performance in various areas and to include: New catalyst selection Unit monitoring, troubleshooting and optimization FIG. 1 CEPSA La Rábida refinery is a five million-tpy facility that has been undergoing recent revamps/expansions to meet the growing demand for kerosine and diesel in the European market. LP planning model updates Examining new feed scenarios. The model and the benefits achieved are discussed in the following case study. Modeling the FCC unit. The FCC model comprises a reac- tor-regenerator section block where all the data related to the reaction section is introduced. The FCC reactor-regenerator model seamlessly integrates with other segments of the simulation model where downstream separation units are constructed with standard simulator unit operations such as distillation columns, heat exchangers, drums, pumps, compressors and more (Fig. 2). The process of developing this model may be divided into several steps: The first step is to configure the reactor-regenerator section to the appropriate unit configuration and geometry, and to select a catalyst from the catalyst database. This database covers a large number of commercial catalysts with information about the basic characteristics of the catalysts and their yield information. Reaction kinetics are automatically calibrated to actual operating performance. This step requires, as a minimum, a full set of test run data with sufficient quality as determined by material balance. The data are input into a calibration utility, which automatically tunes the model. Once calibration is done, the reaction section block can now predict the reactor effluent using a component list selected by the user. The total effluent stream contains information on product yields, light-ends composition and distribution of refinery properties such as sulfur, RON and aromatics. The fractionation section and gas plant may optionally use a simplified main fractionator and gas separation, or a rigorous stage-by-stage fractionation for all distillation columns and detailed heat exchanger models for key units (Fig. 3). Several spreadsheets automate data entry and reporting for a convenient summary of the FCC unit s predicted performance. Most of the data for advanced simulation model and the FCC reactor-regenerator model are automatically input through MS Excel. In addition, process historian and laboratory data can be transferred to Excel using their standard applications. After reviewing and conditioning this data, this information can be automatically passed on to the simulator.

2 FIG. 2 Simulation model flowsheet with crude blend selector, crude distillation unit, FCC unit including reactor-regenerator section and downstream separation FCC reactor-regenerator model calibration. Once a week, a sufficient set of lab data is available for a calibration case. However, it is not always possible to run such a case because feed quality may have changed, or other parameters such as reactor outlet temperature (ROT) may have been changed to adjust unit performance to achieve a particular target as defined by the planning department. Over time, a library of different calibration cases for different operating conditions is developed. For example, calibrations are now available for high or low feed sulfur content, high severity (555 C ROT) or low severity (535 C ROT) and for various feedrates. Calibration cases are selected based on the degree of adjustment required between the simulation and plant data. These cases are then classified by the calibration factors generated, and become available for use in predicting future operating performance. When applying to predictive cases, we can average the calibration factors to fit the specific operating scenario being modeled. Table 1 shows two different calibration cases comparing simulation data vs. actual plant data. The first calibration case is valid for generating calibration factors while the other case is not. This is determined by the error in yield predictions of key products, while differences for non-core products are less important. For example, propylene prediction is critical for CEPSA. The error in propylene yield prediction in the second data set is more than 2%, and, therefore, this data set is deemed unacceptable for calibration use. Such differences can normally be explained by changes in feed quality, daily operations, lab data and/or sampling errors and more. Applications and benefits. The daily activities of the FCC unit in the La Rábida refinery demonstrate how measurable benefits can be achieved using a well-tuned model and good data sets. Fig. 4 shows the data flow and hierarchy of applications using advanced simulation software/modeling for an integration FCC reactor and separation model. Unit performance monitoring. Once a good tuned model is available from the calibration step, the model can be used daily to simulate plant performance. Using only a few plant measurements, it is possible to simulate the previous day s operation very quickly. This provides simulation data that can be compared to actual plant data. Naturally, yields are checked first. In addition, with a plantwide simulation, engineers can check temperatures, pressures, heat exchanger duties and even product properties. This enables operations staff to perform a quick check on plant performance and ensure that optimal operating performance targets are being met. When significant differences are observed, model parameters are examined to explain the source for these differences. It is important to manage the accuracy of the model and eliminate uncertainty. On some occasions, it is necessary to recalibrate plant instruments (e.g., TI and TC) since the model analysis indicated an error in the instruments, while the simulation seemed to be otherwise trustworthy. One recent case relates to the temperature controller in the debutanizer bottoms. Using the plant-measured temperature as input in the lights end simulation, the butane content in the light naphtha product was less than the actual content reported by the lab. The instruments maintenance staff proceeded to recalibrate the thermocouple and found a 3 C error. Once this was fixed, the plant and model results matched very well. Similarly, some product properties are simulated well. However, when simulation data does not match lab data, it is a good practice to re-sample the product and reconfirm the values. The demands of daily plant operations often make it difficult to run the simulation model every day. However, running the model twice a week is adequate to track unit performance, compare it to operational targets and to identify areas of improvement. Another important contribution of the simulation model is its ability to identify and validate plant constraints. Simulation studies can help identify and analyze plant limitations with more detail and accuracy, and determine if the constraint is real or only an operational problem. FCC unit optimization. The combined FCC reactor-regenerator and separation train model can also be optimized using a built-in optimizer in an advanced simulation model. The optimizer maximizes or minimizes an objective function with respect to several independent variables that may have bounds placed on them, along with several constraints.

3 TABLE 1. FCC reactor-regenerator calibration case examples Date: 23-Nov-2007 Yields Simulation data, H2S 0.28 Calibration case: OK Real data, % Fuel gas C3= C C4s LCN HCN LCO CFB Coke Total 5.92 Date: 05-Mar-2007 Yields Simulation data, H2S 0.43 Calibration case: Not valid Real data, % FIG Fuel gas C3= C C4s LCN HCN LCO CFB Coke Total 5.09 Selecting the optimization variables and constraints, and their equally important upper and lower limits, is the key to successfully applying the optimization process to maximize margins. A good knowledge of the FCC process in general and a deep understanding of your specific unit is essential in properly defining the optimization variables, constraints and their limits. To realistically make any refining unit more profitable in the face of ever-increasing fluctuations and volatility in the petroleum markets today, it is imperative to have the necessary and correct information mainly feed and product prices. Applying the correct economics (since not all of the products from an FCC unit are final products) to the optimizer ensures that the objective function (net profit of the unit or profit per feed barrel) is driven in the right direction by the optimizer, and that the optimized variables are realistic and accurate. Once the optimizer completes its calculations, the simulation provides optimized values for independent variables and predicted values for dependent variables across the unit such as pressures, temperatures, flowrates, compositions and more. All of these details are useful to guide control-room operators, especially when the unit s operation is being adjusted in a noticeable way. Case history. To illustrate the significance of this tool, the following examples of FCC unit optimization at the CEPSA La Rábida refinery are useful: Aerial view of the CEPSA La Rábida one million-tpy FCC unit. For any given feed quality, varying ROT will give different yield profiles. A 2 C 3 C variation in ROT can mean more than a 2% variation in total profit. In the case of CEPSA La Rábida, this translates to approximately 6,000/day in early 2008 economics. Therefore, selecting the right ROT is important. Similarly, optimizing feed rate is another example. A 5-m3/h variation in feedrate can result in a 3% 5% variation in margin. In the case of CEPSA La Rábida, this translates to 8,000/day in early 2008 economics. The optimizer is also used to resolve constraints; for example: Using extra oxygen to resolve air to regenerator constraint can be profitable depending on the price of oxygen, amount needed and extra revenue generated. On some occasions, based on the optimizer results, plant engineers have adjusted the feed quality after consulting with the planning department. New catalyst evaluations. In the FCC world, new cata- lysts are developed continuously. Typically, every 3 5 years, catalyst suppliers come out with new products. CEPSA explores the catalyst market every 2 3 years, looking for interesting catalysts or additives to improve FCC yields and profits. Once a group of interesting catalysts is identified, they are tested in CEPSA s Research Center in Madrid (Fig. 5). Several steps are involved when working with a catalyst supplier before any new catalyst is adopted for industrial use: The first step is a study in the Research Center. Standard tests are performed to obtain data on catalyst behavior. The main study is the microactivity test (MAT). It is a simple way to estimate yields a catalyst may produce with standard feeds and conditions. This preliminary study supplies enough data to choose the three or four suitable catalysts for further testing in the next phase against identified target conditions. These catalysts are tested in an FCC pilot plant at the Research Center. This test provides the necessary data for calculating catalysts factors for use in simulation models. The final step is simulation and economic assessment. Using the

4 data provided by the pilot-plant tests, we calculate catalyst factors, define targets and scenarios (economic and operations parameters), and study each catalyst s yields by simulation. These simulation tests use the standard calibration factors and standard conditions, and are used to study yields and operational constraints. The simulation results taking into account operational targets, economics, and constraints indicate the best catalyst for the plant. If the new catalyst can provide a measurable advantage in profits, with no big constraints and no significant deviation from desired targets, all in comparison with the current catalyst, then the decision to switch to the new catalyst is made. The decision to change the FCC unit catalyst is 95% based on this simulation study. Finally, an industrial test is conducted. This test is the ultimate proof and validation of the tests and simulation studies performed. The catalyst is tested in the real plant. When enough catalyst has been replaced and data is available for a calibration case, a test run is done to calibrate the model for the new catalyst. The calibrated model is used to predict results for real situations and to evaluate the true profitability of the new catalyst. This rigorous model-based approach has proven to be very reliable. In a recent catalyst selection and replacement at the CEPSA La Rábida FCC unit, the simulation pre-study projected a net profit of 16,000/day. Plant data after switching to the new catalyst showed an actual benefit of 18,000/day. LP planning model updates. At the La Rábida Refinery, the FCCU feed quality changes quite often. One of the consequences of this variation is the change in product yields. This poses a challenge to the planning department as they then have to adjust the total refinery operation to meet production targets. Planners must know the yields for every feed type the unit processes in terms of quantities and properties, both are critical for blending operations. For this objective, the LP model requires sufficient and good quality data for accurate planning and scheduling. Simulations using the advanced simulation model of the FCC unit provide standard yields and properties for every type of feed, thereby helping to accurately update the refinery LP (Fig. 4). The process involves starting with feed data from the lab. Simulations are done for different operating modes (e.g., high severity, low severity) to generate yields and properties for every feed type that can be potentially processed in the unit. These data points are used to seamlessly integrate the FCC production with the refinery s global production, especially in gasoline and diesel blending. Benefit vs. time dedicated. Sufficient time must be dedicated to develop the model at the start of the project. The golden rule is: The more time dedicated in the beginning to ensure that appropriate processes, unit setup and feedstock selections are in place, the less time needed later to address issues that will inevitably develop. It is also important and necessary to devote enough time to keep the simulation model current. New calibration cases have to be prepared for new operations or to improve current calibration factors. You must also adapt the simulations to the unit s real operating situations. For example, heat losses in the regenerator are not the same after a turnaround compared to just before the turnaround. Finally, the model must be used regularly to maintain relevance, tuning and user skills. Two prediction cases per week and one optimization case every two to three weeks are enough when operating under normal conditions. FIG. 4 FIG. 5 Rigorous FCC reactor model and product fractionation LP planning model for improved crude selection Plant data Excel/Simulation workbook Unit operations based reconciliation and calibration Update LP FCC model Operations decision support Plant optimization for performance improvements Data flow and hierarchy of applications using the integrated FCC reactor and separation model. Catalyst analysis at CEPSA s Research Center in Madrid. Future benefits. The benefits of a rigorous FCC simulation model are substantial. Advanced simulation modeling can be used in monitoring performance, in confirming unit mass balances, in checking suspect instrumentation, and in evaluating how well and how close to optimum the process is performing. Using a built-in optimizer, engineers can assess the benefits of better managing any variable, such as feed or reactor outlet temperature. In addition, it enables a clear identification and understanding of constraints and how to resolve them. These simulations also help avoid test runs that can be expensive and often not totally definitive. Additionally, they are an excellent approach for economic assessment when exploring catalyst changes or for undertaking new projects. Using simulations for FCC units can provide a great tool to understand situations, predict performance and to study all sorts of technical and business cases. HP ACKNOWLEDGEMENTS The authors wish to thank Clive Beautyman of AspenTech for his help with building the FCC model and optimizer, consulting and training. The authors send thanks to the CEPSA Research Centre in Madrid, especially to the FCC group, for its great work in providing catalyst data. Our appreciation and thanks go to the FCC crew at the La Rábida Refinery for their help and patience with the unit test runs that generated simulation data for the models.

5 BIBLIOGRAPHY 1 Morales, A. M., and M. J. Guerra, Process Simulation for Planning, Hydrocarbon Engineering, November Miranda, M., Improving FCC Unit Performance Using Aspen RefSYS Simulations, AspenTech User Conference, Berlin, April J. M. Llanes has a BS degree in chemical engineering from Seville University and an MS degree in industrial safety. He joined CEPSA La Rábida Refinery nine years ago. Mr. Llanes has worked as an FCC process engineer for five years and is currently the FCC operations supervisor. Modesto Miranda holds a technical industrial engineering degree from Seville University and an MS degree in industrial safety. He joined the CEPSA La Rábida Refinery in 1990, and has worked in several plants and processes. Assigned to the FCC plant since its construction in 1992, he has served in many roles in operations and engineering. Mr. Miranda is now the process engineer for the FCCU, with direct responsibilities in FCC unit operations. Sanjeev Mullick is the director of product marketing for AspenTech s process engineering business. He has over 20 years of experience in engineering, process automation, consulting, sales and marketing in the oil & gas, refining and petrochemical industries. Mr. Mullick holds an MS degree in chemical engineering from the University of Michigan. Article copyright 2008 by Gulf Publishing Company. All rights reserved. Printed in U.S.A. Not to be distributed in electronic or printed form, or posted on a Website, without express written permission of copyright holder.