Feed properties

The following are the process variables in a visbreaking process feed properties, temperature, pressure, residence time (flow rate), and steam injection (Negin and Van Tine, 2004 Quignard and Kressmann, 2011). 

The variations in feed quality will impact the level of conversion obtained at a specific severity. The main properties that affect visbreaking convCTsion are as follows  

Asphaltene content. High content of asphaltenic molecules in the feed will reduce conversion. Asphaltenes pass through the furnace relatively unaffected at moderate severities. Under severe conditions, the asphaltenes present in the feed tend to be broken down, which in turn leads to asphaltenes precipitation, resulting in an unstable visbroken residue unsuitable for preparing fuel oil. When this happens, the process lines and the furnace heater tubes are fouled, provoking incomplete combustion of the fuel oil and an early shutdown of the units (shorter run length). 

Modeling of Processes and Reactors for Upgrading of Heavy Petroleum 

Sodium content. High amount of sodium can increase the rate of coking in the heater tubes. 

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It has been shown that coke yield as a fraction of feed does give a linear relationship with second-order conversion (13) indicating a positive coke yield at 2ero conversion. This coke yield at 2ero conversion is the additive coke contribution to the total coke yield and is related to feed properties, particularly Conradson carbon content. The amount of this additive coke is significantly less than the Conradson carbon value of the feed (14), probably in the range of 50% of the Conradson carbon. 

Instead of conversion, some producers prefer to use other identifications of severity, including coil outlet temperature, propylene to methane ratio, propylene to ethylene ratio, or cracking severity index (33). Of course, all these definitions are somewhat dependent on feed properties, and most also depend on the operating conditions. 

Cake properties Slurry feed properties Equipment properties... 

Small variations in feed properties can have a pronounced effect on maximum pressure P, and press performance. RoU presses are scaled on the basis of constant maximum pressure. The required roll loading increases approximately with the square root of increasing roll diameter or gap width. 

Some typical parameters for design are shown in Table 9-4. The actual optimum to use for any given process will vary depending on actual feed properties, product specifications, etc. 

FCC feed characterization is one of the most important activities in monitoring cat cracking operation. Understanding feed properties and knowing their impact on unit performance are essential. Troubleshooting, catalyst selection, unit optimization, and subsequent process evaluation all depend on the feedstock. 

The simple API gravity test provides valuable information about the quality of a feed. But the shift in API usually signals changes in other feed properties, such as carbon residue and aniline point. Addi tional tests are needed to fully characterize the feed. 

Characterizing FCC feed provides quantitative and qualitative csti mates of the FCC unit s performance. Process modeling uses the feed properties to predict FCC yields and product qualities. The process model should be used in daily unit monitoring, catalyst evaluations, optimization, and process studies. 

Determine KjjQp and properties Watson using the following FCC feed Feed Properties ... 

Using the feed property data in Example 2-1. determine MW, C, Cj,, and Cp using the n-d-M method. 

Changes in Feed Properties Changes in Catalyst Conditions Changes in Operating Conditions Changes in Mechanical Conditions ... 

Plot properties of the fresh and equilibrium catalysts ensure that the catalyst vendor is meeting the agreed quality control specifications. Verify that the catalyst vendor has the latest data on feed properties, unit condition, and target products. Verify the fresh makeup rate. Check for recent temperature excursions in the regenerator or afterburning problems. 

Keep in mind that all the feed properties, or forcing functions, are given Eq PC, o>... 

The number of neurons in the hidden layer was therefore increased systematically. It was found that a network of one hidden layer consisting of twenty neurons, as shown in Figure 2.6, performed well for both the training and testing data set. More details about the performance of this network will be given later. The network architecture depicted in Figure 2.6 consists of an input layer, a hidden layer, and an output layer. Each neuron in the input layer corresponds to a particular feed property. The neurons... 

As can be seen from the tables, the ANN model consistently gives better predictions. Themainreasonis that the simulator required a lot of input information which had to be estimated while the neural network model required only four feed properties. 

Several different reactor types were used for catalyst evaluation, including a DCR pilot riser [3] an ACE fixed fluidized bed (FFB) reactor [7], a Riser simulator [4,9], and a specially designed extended residence time circulating pilot unit. The reaction conditions of each of the reactors will be reported in the sections dealing with the specific reactor type. Different grades of Brazilian Campos Basin derived VGOs were used in the experiments. Feed properties are presented in Table 2.1. 

In an exploratory experiment, 13 different powder materials were tested in a FFB ACE unit. Most of the results were unremarkable except for three catalysts a low Z/M commercial maximum distillate catalyst (the same LZM catalyst used in the pilot riser experiment), a spray dried low surface area silica (inert) and the minimum aromatics breakthrough (MAB) catalyst. The inert material was included in the study to represent thermal cracking. The catalysts were steam deactivated in the fixed bed steamer prior to testing. Catalysts and the VGO-B feed properties are displayed in Tables 2.3 and 2.1, respectively. LCO aromatics were measured with 2D GC. Figures 2.7 through 2.9 illustrate the main results. 

A feasibility study on the application of H-NMR petroleum product characterization to predict physicochemical properties of feeds and catalyst-feed interactions has been performed. The technique satisfactorily estimates many feed properties as well as catalyst-feed interactions to forecast products yield. There are, however, limitations that have to be understood when using the H-NMR method. The technique, in general, is not capable either to estimate the level of certain contaminants such as nitrogen, sulfur, nickel, and vanadium when evaluating feed properties or the effect of these contaminants on products yields while testing catalyst-feed interactions. 

Feeds. Properties of two hydrofined test feeds are given in Table I. The California gas oil blend was used in tests simulating a hydrocracking unit producing both naphthas and jet fuel, the Mid-Continent blend in tests representing a unit producing naphtha as the major product. 

The yields of products are determined by the feed properties, the temperature of the fluid bed, and the residence time in the bed. The use of a fluidized bed reduces the residence time of the vapor-phase products in comparison to delayed coking, which in turn reduces cracking reactions. The yield of coke is thereby reduced, and the yield of gas oil and olefins increased. An increase of 5°C (9°F) in the operating temperature of the fluid-bed reactor typically increases gas yield by 1% w/w and naphtha by about 1% w/w. 

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