Predicting hydrotreater performance while co-processing vegetable oil
Catalyst performance prediction model based on test data assesses the impact of co-processing renewable feedstocks for optimal hydrotreater operations.
Eelko Brevoord, Catalyst Intelligence
Tiago Vilela, Avantium
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Many refineries rely on pilot plant test data for selecting catalysts, as this provides direct insight into their performance, facilitating selection of the best catalyst for increasing margins. Against this backdrop, processing renewable feedstocks can be a complex process due to the wide range and quality of feedstocks available. Therefore, Avantium developed a 16-reactor unit that can effectively test the impact of co-processing vegetable oil on the overall performance of catalysts. The selection of the right catalyst is crucial, given the diversity of feedstocks.
In parallel, Catalyst Intelligence developed a catalyst performance prediction model (HydroScope) that translates pilot plant test data into commercial performance, enabling assessment of the impact of catalyst quality on overall profitability. Avantium partnered with Catalyst Intelligence to use its performance prediction model to optimise hydrotreating unit performance employing pilot plant test data and to enhance its catalyst testing services.
The pilot plant test allows for the calculation of hydrodesulphurisation (HDS) activity, product selectivity, and inhibition factors. The HydroScope model allows for assessing the impact of co-processing renewable feedstocks on cycle length, hydrogen consumption, and product yields. It is therefore recommended that test results be further simulated in this hydroprocessing model. By doing so, an assessment of the most optimal unit conditions and their impact on cycle length can be made.
While processing soybean oil, a portion of the oil is converted through the decarbonylation route, resulting in the formation of carbon monoxide (CO), which inhibits the HDS reaction. The CO can be removed by purging recycle gas, but this comes at the cost of valuable hydrogen. The impact of purging on cycle length can be calculated for each catalyst system, resulting in the selection of the most economical solution.
High-throughput catalyst testing
Accurate catalyst evaluation is important in catalyst selection, and increased product yields, energy efficiency, and overall product quality. High-throughput catalyst testing and small-scale reactors offer several advantages compared to larger reactor systems.¹ Using reactors of smaller scale to evaluate catalysts with renewable feedstocks presents a clear advantage; smaller volumes reduce the amount of feed required, avoiding the typical issues associated with obtaining large quantities such as handling, shipping, and storage (also for longer-term availability of reference feed material). Overall, small-scale parallel reactor systems like the unit used for this test² are more cost-effective than large-scale reactors.
Avantium performed a test with 0-100% soybean oil, achieving close to 100% mass balance without having pressure drop issues (see Figure 1). The results were published,² the main conclusion being that the Flowrence 16-microreactor test unit proved reliable and consistent for predicting start-of-run performance, simulating the impact of co-processing renewable feedstocks on cycle length, hydrogen consumption and product yields, and predicting the impact in the commercial unit.
Vegetable oils decompose according to various reaction routes, such as hydrodeoxygenation, decarboxylation, and decarbonylation, producing water, CO₂ and CO, respectively:
Hydrodeoxygenation: C₃H₅(C17H33COO)₃ + 15 H₂ à C₃H₈ + 6 H₂O + 3 C18H38
Decarboxylation: C₃H₅(C17H33COO)3 + 6 H₂ à C₃H₈ + 3 CO₂ + 3 C17H36
Decarbonylation: C₃H₅(C17H33COO)3 + 9 H₂ à C3H8 + 3 CO + 3 H₂O + 3 C17H36
Using the product yields from the test, the importance of each reaction route can be calculated, allowing the refiner to make a good assessment of the yields originating from fossil feed and vegetable oil.
H₂S partial pressure affects the degree of hydrodeoxygenation (HDO) vs decarboxylation.³ CO is known to inhibit desulphurisation activity.4 A significantly higher temperature is usually required to achieve the same HDS performance, reducing cycle length. CO does not dissolve in oil products and is mostly removed by purging some recycle gas. Increasing the gas purging can lower the CO partial pressure, but this inevitably comes with a cost. Evaluating the effects of purge gas rates and cycle duration can assist the refiner in optimising the unit with minimal expenses.
Catalyst suppliers have developed catalysts with reduced susceptibility to inhibition. Several catalyst characteristics, such as metal composition, affect the sensitivity to CO and the reaction mechanisms. According to Bezergianni,5 the HDS effectiveness of the NiMo catalyst remains unaffected by the addition of waste cooking oil, while the CoMo catalyst is significantly impacted. This can pose a problem for low-pressure units that require the most active and stable performance of CoMo catalysts. Ni is said to exclusively promote the decarboxylation of fatty acids, while Mo promotes HDO.⁶ HDO is preferred over decarboxylation as the yields are higher and less CO is formed, thereby reducing the need for purging gas. Catalyst suppliers have developed various catalyst systems to provide the best catalyst performance.
Refiners should conduct tests on different catalyst systems to determine the impact on unit operation and profitability. However, this approach is not straightforward as the impact of conditions on CO formation and purge gas rates must be assessed, which varies for each catalyst system due to its sensitivity to CO being dependent on catalyst quality. The HydroScope prediction model translates pilot plant test data into commercial performance for all hydrotreating applications, including optimising units to process vegetable oil. Proper assessment of the optimal catalyst system requires quantifying CO formation and its effect on catalyst inhibition and gas purge.
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