Feedstock properties

Contaminants such as the sulfur and nitrogen contained in the effluent gas are directly dependent on the feedstock properties. 

Changes in Operating Conditions Changes in Catalyst Properties Changes in feedstock Properties Changes in Mechanical Conditions of the Equipment... 

Trend the feedstock properties look for changes in the K factor, 1,050°F+ (565°C+), aniline point, refractive index, and °API gravity. The feed endpoint may have been increased to fill the unit. The conversion penalty may be a small price to pay for the increased capacity, but the penalty can be minimized. Verify that the refinery LP reflects current data on yields and product quality. 

The yields for the various products were given as a function of the properties of the feedstocks and the volume percent conversion. The feedstock properties are the feed sulfur content expressed as a weight percent, the feed API and Watson characterization factor, K. The API is related to specific gravity by the following equation ... 

Strategies for upgrading petroleum emphasize the difference in feedstock properties that, in turn, influences the choice of methods or combinations thereof for conversion to various products (Scheutze and Hofmann, 1984). Naturally, similar principles are applied to heavy oils and residua and the availability of processes that can be employed to convert heavy feedstocks to usable products has increased significantly in recent years (Chapter 8) (Cantrell, 1987 Speight, 1999). 

In summary, protection of the catalyst and the reactor is essential if shutdowns are to be minimized, and this is particularly the case when the heavier feedstocks are used in the hydrodesulfurization process. Some measure of protection from the asphaltic constituents can be achieved by removal of these materials by means of a deasphalting step (Chapter 7). The overall relative merits of any particular total processing scheme should be assessed in terms of feedstock properties, product yields, process variables, and last but not least, process economics. 

There is, however, one aspect of feedstock properties that has not yet been discussed fully and that is feedstock composition. This particular aspect of the nature of the feedstock is, in fact, related to the previous section where the influence of various feedstock types on the hydrodesulfurization process was noted, but it is especially relevant when residua and heavy oils from various sources are to be desulfurized. 

Thus, solvent deasphalting allows for the removal of sulfur compounds as well as nitrogen compounds and metallic constituents by balancing yield with the desired feedstock properties (Table 7-18 Figure 7-22) (Flynn et al., 1961). 

The low energy solvent deasphalting process selectively extracts the more paraffinic components from vacuum residua while rejecting the condensed ring aromatics. As expected, deasphalted oil yields vary as a function of solvent type and quantity, and feedstock properties (Chapter 7). 

Analysis determine the properties of a feedstock prior to refining, inspection (q.v.) of feedstock properties. 

Models have been developed to predict cat cracker yields based on operating parameters and feedstock properties (34) These have aided in application and evaluation of metals passivation benefits. Miller and Pawloski (35) reported the use of mathematical models to calculate the benefits of vanadium passivation, and Teran (27) reported the need for FCCU hydrogen modeling and metals tracking to optimize passivation benefits. 

The total combination of data obtained permitted optimization of process variables relative to reactor geometry employed, and optimization of carbon black quality produced. The array of samples tested enabled us to prepare a mathematical relationship of these principal variables to commercial feedstock properties. 

The changes in feedstock properties (heavier and more metal loaded, the so-called Resid ) due to the whitening of the barrel (lower demand for fuel and bunker oils and increased demand for cleaner products)... 

When the new catalyst charge does not meet these expectations, significant financial losses are incurred. Technical efforts are initiated to investigate the causes of nonconformance. Feedstock properties, start-up procedures, plant operations, and catalyst properties usually are being considered. 

Through a series of round robin tests conducted by participating laboratories, ASTM Committee D-32 on Catalysts has characterized a variety of catalyst materials using standard test methods. Materials include fluid cracking catalysts, zeolites, silicas, aluminas, supported metals, and a gas oil feedstock. Properties characterized include surface area, crush strength, catalytic microactivity, particle size, unit cell dimensions and metal content. These materials are available from the National Institute of Standards and Technology as reference materials. 

Ibs/ft hr (0.10 kg/m s) in minimum as liquid mass velocity utilized in a pilot plant test and a correction method involving mainly effects of feedstock properties on desulfurization activity are proposed to get better agreement in evaluating aging performance and cycle length of a catalyst system with multiple catalysts between pilot and commercial units. 

Catalyst lives were compared with reactor metal distribution, obtained from micro, pilot and commercial units. It was found that a micro reactor with low liquid mass velocity caused metal pass-through to down stream catalysts, resulting in a shorter life of catalyst system than that in the commercial operation. Therefore in this paper, effects of liquid mass velocity and feedstock properties on catalyst aging performance of the catalyst system are discussed. 

Assuming that the correction with the feedstock properties includes all the differences between the pilot and commercial unit, and that each commercial unit has the optimum correction equations with the feedstock properties, pilot test results obtained at a liquid mass velocity of above 70 Ibs/ft hr and commercial results were corrected with the feedstock properties to get the optimum correlation between two. 

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