Claims:1. A system (100) to estimate fluid catalytic cracker (FCC) yield, the system (100) comprising:
a computing unit (200) comprising a processor (202) operatively coupled to a memory (204) storing instructions executable by the processor (202), the computing unit (200) configured with an artificial neural network (ANN) (102), and a kinetic model (104), wherein the computing unit (200) is configured to:
receive a first set of data packets pertaining to a set of feedstock attributes associated with one or more feedstocks;
determine, using the ANN (102). composition of the one or more feedstocks based on the set of feedstock attributes;
receive a second set of data packets pertaining to a set of physiochemical attributes associated with one or more known catalysts;
estimate, using the kinetic model (104), kinetic parameters associated with the one or more known catalysts for the determined composition;
regress, the estimated kinetic parameters of the known catalysts with the set of physiochemical attributes associated with the one or more known catalysts to estimate regression coefficients for a set of FCC reactions and establish model equations between the estimated kinetic parameters and the physiochemical attributes; and
determine kinetic parameters for an unknown catalyst using on the estimated regression coefficients and the model equations, and correspondingly estimate the FCC yield for the unknown catalyst.

2. The system (100) as claimed in claim 1, wherein the computing unit (200) is configured to study the effect of composition of the feedstocks on the FCC yield, which facilitates the computing unit (200) to determine and select an optimum feedstock for optimum FCC yield.

3. The system (100) as claimed in claim 1, wherein the set of feedstock attributes comprises density, specific gravity, Conradson carbon residue (CRR), ASTM distillation temperature, distillation curve (TBP, D86, D2887), total sulfur content by weight, and total nitrogen content by weight.

4. The system (100) as claimed in claim 1, wherein the set of physiochemical attributes corresponds to parameter ratios based upon any or a combination of Zeolite specific surface area (Z), matrix specific surface area (M), total Bronsted acidity (strong + weak) (B), total Lewis acidity (strong + weak) (L), unit cell size (UCS) of the zeolite, average pore dimension of the catalyst, and total pore volume of the catalyst.

5. The system (100) as claimed in claim 1, wherein the set of feedstock attributes associated with the one or more feedstocks are calculated using high-resolution mass spectrometry, and wherein the calculated set of feedstock attributes are stored as the first set of data packets in a database associated with the computing unit (200).

6. The system (100) as claimed in claim 1, wherein the one or more feedstocks comprises the composition of paraffin, naphthene, and aromatic contents, and wherein the kinetic parameters comprise frequency factor, and apparent activation energies associated with the set of FCC reactions.

7. The system (100) as claimed in claim 1, wherein the kinetic model (104) is a lumped kinetic model (104), and the kinetic parameters estimated through the lumped kinetic model (104) are correlated with the set of physiochemical attributes using a non-linear regression algorithm.

8. A method (600) for estimation of fluid catalytic cracker (FCC) yield, the method comprising the steps of:
receiving (602), by a computing unit, a first set of data packets pertaining to a set of feedstock attributes associated with one or more feedstocks;
determining (604), by an artificial neural network (ANN) configured with the computing unit. composition of the one or more feedstocks based on the set of feedstock attributes;
receiving (606), by the computing unit, a second set of data packets pertaining to a set of physiochemical attributes associated with one or more known catalysts;
estimating (608), by a kinetic model configured with the computing unit, kinetic parameters associated with the one or more known catalysts for the determined composition;
regressing (610), by the computing unit, the estimated kinetic parameters of the known catalysts with the set of physiochemical attributes associated with one or more known catalysts to estimate regression coefficients for a set of FCC reactions and establish model equations between the estimated kinetic parameters and the physiochemical attributes; and
determining (612), by the computing unit, kinetic parameters for an unknown catalyst using the estimated regression coefficients and the model equations, and correspondingly estimating the FCC yield for the unknown catalyst.

9. The method (600) as claimed in claim 8, wherein the set of feedstock attributes associated with the one or more feedstocks are calculated using high-resolution mass spectrometry, and the method (600) comprises the step of storing the calculated set of feedstock attributes as the first set of data packets in a database associated with the computing unit.

10. The method (600) as claimed in claim 8, wherein the method (600) comprises the step of studying the effect of the composition of the feedstocks on the FCC yield, which facilitates the computing unit to determine and select an optimum feedstock for optimum FCC yield.
, Description:TECHNICAL FIELD
[0001] The present disclosure relates to the field of fluid catalytic cracking (FCC) systems. More particularly, the present disclosure relates to a system and method for estimating fluid catalytic cracker (FCC) product yield and profitability based on feedstock and catalytic characteristics.

BACKGROUND
[0002] Catalytic cracking is a secondary conversion process in an oil refinery, which is used to convert heavy hydrocarbon streams into more valuable, lighter fractions such as gasoline and liquefied petroleum gas (LPG). Fine particulate solid material akin to dust (also referred to as catalyst) is fluidized pneumatically using superheated steam and reacted with hydrocarbon vapor. In this state, the catalyst flows similar to a fluid within the refinery unit in a cyclic fashion. Feedstock (feed) such as gas oil is then contacted with a hot catalyst and the heavy feedstock cracks into lighter products with a higher volume to mass ratio. These products with high specific volume are more easily marketable, especially gasoline, LPG, and C2/C3 olefins. However, heavier products such as Light cycle oil (LCO), heavy cycle oil (HCO) and clarified oil (CLO) have limited utility in the market and are either recycled or used as diesel blend stock or fuel oil cutterstock.
[0003] Coke is formed as a byproduct during the process due to complex series and parallel cracking reactions on the surface of the catalyst. This blocks the pores of the catalyst thereby limiting the surface area on which the cracking can occur. Heavily coked catalyst is a black-grayish powder having an insufficient activity to crack heavy hydrocarbon feed. Coke-laden catalyst is reactivated in the regenerator by combustion with hot air. The coke burns off into flue gasses which escape the top of the regenerator. Along with gasoline and LPG, some lighter gasses called dry gas or fuel gas are also formed during cracking. These gasses are products of the over-cracking of gasoline and LPG hydrocarbons. For better yield and profitability, refiners attempt to limit the excess yield of heavier products as well as byproducts, which may be achieved by highly selective catalysts as well as optimum operational conditions.
[0004] Modern Fluid Catalytic Cracker (FCC) units process a wide variety of feedstocks like light gas oils, bio oils, oxygenates, crude oil, shale oil, tight oils. They run on a wide range of operating severity to maximize the production of either gasoline, middle distillate (LCO) or light olefins to meet ever-changing market demands. Numerous catalyst manufacturers exist, which provide diverse formulations to maximize the desired products, enhance selectivity towards coke as well as process heavily contaminated feedstock. This further complicates the work of the refiner.
[0005] The yield pattern of any FCC plant is heavily predicated on its feedstock and the catalyst being used. Minor tweaks are generally carried out by altering the operational parameters of the FCC. In order to achieve a certain yield objective, the refiner carefully selects a suitable catalyst that may work well with the existing feedstock and plant setup. This sec=lection of a suitable catalyst requires plant test runs, lab scale/pilot scale catalyst testing, and modeling for many catalyst samples, which is time-consuming and may take several weeks. During such evaluations the market trend may be bound to reverse, thereby changing the objective yield. This limits the refiner’s receptiveness to the market demand and affects profitability.
[0006] Computational methods and systems for projecting laboratory-scale yields to commercial plants exist, which reduce the testing duration by a few weeks and eliminate the requirement of pilot scale testing. Still plant test runs and laboratory activity testing is required. The activity and selectivity showcased by any catalyst may be inferred from its physical and chemical makeup and at the same time taking into consideration the feedstock being processed and the operation condition of the plant.
[0007] Every FCC plant is unique and varies considerably from each other. Plants having the same licensor can vary since they are customized according to predetermined capacity and duty. Therefore, a single model that may work well for a particular FCC cannot be applied to another.
[0008] This has led to a requirement for FCC product yield estimation technique that does not require test runs and lab activities to be performed every time for different catalysts and makes the catalyst selection easier for any industrial FCC unit with different feed properties. To achieve this, the existing art involves various methods and correlation models that predict FCC product yields from catalyst physiochemical properties, however, they are valid only for a fixed feedstock. Thus, the existing art do not account for the varying feedstocks with differing properties.
[0009] Therefore, there is a need in the art to obviate the problems associated with fluid catalytic crackers and existing FCC yield estimations systems and provide a simple, cost-effective, and efficient system and method for estimation of fluid catalytic cracker (FCC) product yield and profitability based on feedstock and catalytic characteristics.

OBJECTS OF THE PRESENT DISCLOSURE
[00010] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.
[00011] It is an object of the present disclosure to overcome the drawbacks, shortcomings, and problems associated with fluid catalytic crackers (FCC).
[00012] It is an object of the present disclosure to predict the FCC plant yields using any FCC catalyst and feed.
[00013] It is an object of the present disclosure to provide a comprehensive, user-friendly, and time-saving solution for predicting the cracking behavior of feedstock in FCC.
[00014] It is an object of the present disclosure to correlate the physical and chemical properties of any FCC catalyst to the cracking reaction kinetics to estimate yields that shall be obtained at the commercial level.
[00015] It is an object of the present disclosure to select the best feed and catalyst to maximize the conversion and product yields for the overall profitability of the FCC process.
[00016] It is an object of the present disclosure to provide a simple, cost-effective, and efficient system and method for estimation of fluid catalytic cracker (FCC) product yield and profitability based on feedstock and catalytic characteristics, which helps select the best feedstock and catalyst for optimum FCC yield.

SUMMARY
[00017] The present disclosure relates to a system and method for estimating fluid catalytic cracker (FCC) product yield and profitability based on feedstock and catalytic characteristics.
[00018] According to an aspect of the present disclosure, the present disclosure pertains to a system to estimate fluid catalytic cracker (FCC) yield. The system may comprise a computing unit comprising a processor operatively coupled to a memory storing instructions executable by the processor. The computing unit may be configured with an artificial neural network (ANN), and a kinetic model and configured to receive a first set of data packets pertaining to a set of feedstock attributes associated with one or more feedstocks, and determine, using the ANN. composition of the one or more feedstocks based on the set of feedstock attributes. The computing unit may receive a second set of data packets pertaining to a set of physiochemical attributes associated with one or more known catalysts and may estimate, using the kinetic model, kinetic parameters associated with the one or more known catalysts for the determined composition. Further, the computing unit may regress, the estimated kinetic parameters of the known catalysts with the set of physiochemical attributes associated with one or more known catalysts to estimate regression coefficients for a set of FCC reactions and establish model equations between the estimated kinetic parameters and the physiochemical attributes. Accordingly, the computing unit may determine kinetic parameters for an unknown catalyst using on the estimated regression coefficients and the model equations, and correspondingly estimate the FCC yield for the unknown catalyst
[00019] In an aspect, the computing unit may be configured to study the effect of the composition of the feedstocks on the FCC yield, which may facilitate the computing unit to determine and select an optimum feedstock for optimum FCC yield.
[00020] In an aspect, the set of feedstock attributes may comprise density, specific gravity, Conradson carbon residue (CRR), ASTM distillation temperature, distillation curve (TBP, D86, D2887), total sulfur content by weight, and total nitrogen content by weight.
[00021] In an aspect, the set of physiochemical attributes may correspond to parameter ratios based upon any or a combination of Zeolite specific surface area (Z), matrix specific surface area (M), total Bronsted acidity (strong + weak) (B), total Lewis acidity (strong + weak) (L), unit cell size (UCS) of the zeolite, average pore dimension of the catalyst, and total pore volume of the catalyst.
[00022] In an aspect, the set of feedstock attributes associated with the one or more feedstocks may be calculated using high-resolution mass spectrometry. Further, the calculated set of feedstock attributes may be stored as the first set of data packets in a database associated with the computing unit.
[00023] In an aspect, the one or more feedstocks may comprise the composition of paraffin, naphthene, and aromatic contents. The kinetic parameters may comprise frequency factor, and apparent activation energies associated with the set of FCC reactions.
[00024] In an aspect, the kinetic model may be a lumped kinetic model, and the kinetic parameters that may be estimated through the lumped kinetic model are correlated with the set of physiochemical attributes using a non-linear regression algorithm.
[00025] According to another aspect of the present disclosure, the present disclosure pertains to a method for estimation of fluid catalytic cracker (FCC) yield. The method may comprise the steps of receiving, by a computing unit, a first set of data packets pertaining to a set of feedstock attributes associated with one or more feedstocks, followed by step of determining, by an artificial neural network (ANN) configured with the computing unit. composition of the one or more feedstocks based on the set of feedstock attributes. The method may further comprise a step of receiving, by the computing unit, a second set of data packets pertaining to a set of physiochemical attributes associated with one or more known catalysts, followed another step of estimating, by a kinetic model configured with the computing unit, kinetic parameters associated with the one or more known catalysts for the determined composition. The method may further comprise a step of regressing, by the computing unit, the estimated kinetic parameters of the known catalysts with the set of physiochemical attributes associated with one or more known catalysts to estimate regression coefficients for a set of FCC reactions and establish model equations between the estimated kinetic parameters and the physiochemical attributes. Further, the method may further comprise a step of determining, by the computing unit, kinetic parameters for an unknown catalyst using the estimated regression coefficients and the model equations, and correspondingly estimating the FCC yield for the unknown catalyst
[00026] In an aspect, the set of feedstock attributes associated with the one or more feedstocks may be calculated using high-resolution mass spectrometry, and the method may comprise the step of storing the calculated set of feedstock attributes as the first set of data packets in a database associated with the computing unit.
[00027] In an aspect, the method may comprise the step of studying the effect of the composition of the feedstocks on the FCC yield, which may facilitate the computing unit to determine and select an optimum feedstock for optimum FCC yield.
[00028] Various objects, features, aspects and advantages of the present disclosure will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like features.
[00029] Within the scope of this application it is expressly envisaged that the various aspects, embodiments, examples and alternatives set out in the preceding paragraphs, in the claims and/or in the following description and drawings, and in particular the individual features thereof, may be taken independently or in any combination. Features described in connection with one embodiment are applicable to all embodiments, unless such features are incompatible.

BRIEF DESCRIPTION OF DRAWINGS
[00030] The accompanying drawings are included to provide a further understanding of the present disclosure, and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure. The diagrams are for illustration only, which thus is not a limitation of the present disclosure.
[00031] FIG. 1 illustrates an exemplary block diagram of the proposed system, in accordance with an embodiment of the present disclosure.
[00032] FIG. 2 illustrates an exemplary architecture of a computing unit of the proposed system, in accordance with an embodiment of the present disclosure
[00033] FIG. 3 illustrates an artificial neural network of the proposed system, in accordance with an embodiment of the present disclosure.
[00034] FIG. 4 illustrates an exemplary reaction mechanism between ten defined lumps in the kinetic model of the proposed system, in accordance with an embodiment of the present disclosure.
[00035] FIG. 5 illustrates an exemplary flow diagram for estimating the kinetics of the reactions as per the reaction mechanism of FIG. 4.
[00036] FIG. 6 illustrates an exemplary flow diagram of the proposed method, in accordance with an embodiment of the present disclosure

DETAILED DESCRIPTION
[00037] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.
[00038] The present disclosure relates to the field of fluid catalytic cracking (FCC) systems. More particularly, it elaborates upon a system and method for estimating fluid catalytic cracker (FCC) product yield and profitability based on feedstock and catalytic characteristics.
[00039] According to an aspect, the present disclosure elaborates upon a system and method for estimating fluid catalytic cracker (FCC) product yield. The system and method can involve a computing unit being configured with an artificial neural network (ANN) and a kinetic model. The ANN can determine the composition of one or more feedstocks based on a set of feedstock attributes associated with the feedstocks. The feedstock attributes can be calculated using high-resolution mass spectrometry. Further, the kinetic model can estimate the kinetic parameters associated with one or more known catalysts. The computing unit can then regress, the estimated kinetic parameters of the known catalysts with a set of physiochemical attributes associated with the known catalysts to estimate regression coefficients for a set of FCC reactions and establish model equations between the estimated kinetic parameters and the physiochemical attributes.
[00040] Accordingly, the computing unit can determine kinetic parameters for an unknown catalyst using on the estimated regression coefficients and the model equations, and can correspondingly estimate the FCC yield for the unknown catalyst. The kinetic model is used to study the effect of feed composition on the yields to maximize the conversion and product yields for the overall profitability of the FCC process
[00041] Referring to FIGs 1 and 2, the proposed system 100 can include a set of testing devices or apparatuses configured to perform high-resolution mass-spectrometry on one or more feedstocks (also referred to as feed, herein) to calculate a set of feedstock attributes/properties associated with the feedstocks. In an embodiment, the feedstock attributes can include density, specific gravity, Conradson carbon residue (CRR), ASTM distillation temperature, distillation curve (TBP, D86, D2887), total sulfur content by weight, and total nitrogen content by weight, but not limited to the likes. The system 100 can include a computing unit 200 in communication with the testing devices and apparatuses. The computing unit 200 can be configured with an artificial neural network (ANN) 102 and a kinetic model 104. The calculated set of feedstock attributes can be stored as a first set of data packets in a database 210 associated with the computing unit 200.
[00042] The ANN 102 can receive the first set of data packets pertaining to the set of feedstock attributes from the database 210, and the computing unit 200 can determine, using the ANN 102, composition of one or more feedstocks based on the set of feedstock attributes. In an exemplary embodiment, the paraffin, naphthene, and aromatic contents of the feed can be deduced from the ANN 102 using the feedstock attributes as input.
[00043] FIG. 3 depicts the input/output and the structure of the ANN (ANN model) 102. The ANN 102 relates the properties of hydrocarbon feedstock such as specific gravity, ASTM distillation temperatures, Conradson carbon residue (CCR), total sulfur, and total nitrogen to feed compositions in terms of hydrocarbon types (Paraffins, Naphthenes, Aromatics). The ANN 102 can be developed based on the database 210 comprising of a multitude of feed samples which can be tested in the laboratory by using high-resolution mass-spectrometry to deduce concentrations of heavy paraffins, heavy naphthenes, and heavy aromatics. These feed samples can also be analyzed in terms of routinely measured properties such as specific gravity, ASTM distillation temperatures, CCR, total sulfur, and total nitrogen by using the different ASTM test methods known in the art. A feed-forward back propagation network with a different number of neurons in hidden layers can be studied using Levenberg Marquardt (LM) training algorithm.
[00044] As there is a huge diversity of chemical species participating in the FCC cracking reactions, the chemical species are segregated into ten groups called ‘lumps’ based upon similar chemical properties, structure, and molecular weight. These lumps are treated as reactants and products in the proposed reaction scheme used in this method as depicted in FIG. 4. In the present invention, reactant kinetic lumps can be defined by virtue of the molecular composition of the hydrocarbon feedstock (paraffinic, naphthenic or aromatic) and its volatility (heavy +343°C or light -343°C). The product lumps are defined as coke (C), gasoline (G), LPG, and dry gas (DG). Further, to determine the molecular composition of hydrocarbon feedstock, numerous empirical models exist and can be used, which can correlate feed properties such as carbon residue, density (Conradson, Ramsbottom or Micro), distillation temperatures ASTM D 2887), sulfur and nitrogen content to predict the weight percentage of paraffin, naphthenes, and aromatics.
[00045] The computing unit 200 can receive a second set of data packets pertaining to a set of physiochemical attributes associated with one or more known catalysts. The kinetic model 104 can then estimate the kinetic parameters associated with one or more known catalysts for the determined composition. In an implementation, subsequent to physical characterization of the known catalysts, the catalysts can be hydrothermally deactivated under identical conditions in a laboratory scale reactor. The activity and selectivity of the deactivated catalysts can be tested by cracking a fixed feedstock at a laboratory scale. Based upon the cracking data, a detailed feed invariant kinetic model can be used by the kinetic model 104 to estimate kinetic parameters for all the FCC catalysts. In an exemplary embodiment, the kinetic model 104 can calculate twenty-five kinetic parameters one for each reaction in the proposed mechanism. Several sets of experimental data obtained from laboratory scale activity experiments were regressed using an evolutionary optimization technique, genetic algorithm, to evaluate the rate constants. The algorithm is shown in FIG. 5. The product yields obtained by integrating the model equations using the present values of the rate constants were found to be in close agreement with experimental data.
[00046] The computing unit 200 can regress the estimated kinetic parameters of the known catalysts with the set of physiochemical attributes associated with the one or more known catalysts to estimate regression coefficients for a set of FCC reactions and establish model equations between the estimated kinetic parameters and the physiochemical attributes. In an exemplary embodiment, the set of physiochemical attributes corresponds to parameter ratios based upon any or a combination of Zeolite specific surface area (Z), matrix specific surface area (M), total Bronsted acidity (strong + weak) (B), total Lewis acidity (strong + weak) (L), unit cell size (UCS) of the zeolite, average pore dimension of the catalyst, and total pore volume of the catalyst, but not limited to the likes.

PHYSIOCHEMICAL ATTRIBUTES OF CATALYSTS
Zeolite to Matrix specific surface area (Z/M)
[00047] An FCC catalyst generally has a complex porous structure that effectively occupies a huge surface area per unit mass. The Zeolitic portion of the catalyst contributes to its activity and selectivity and the matrix provides mechanical resistance to crushing, attrition, and some contaminant metal tolerance. The matrix also contributes to the overall catalyst’s activity. Although the zeolite and matrix are part of the same catalyst crystal and made using the same primary constituents, they are texturally different. The zeolitic portion of the catalyst is characterized by a fixed crystalline structure and pore size of the order of 5 microns. The matrix however has an amorphous structure and pore size ranging from 20-50 microns. The pores can be divided into meso-pores and micro-pores depending on their size. These pore sizes act likes sieves as to which hydrocarbons can be allowed access to the active sites within the catalyst structure. The ratio of surface areas of the micro-pores (zeolitic surface area) (Z) and macro-pores (matrix surface area) (M) is called the Zeolite to Matrix specific surface area ratio (Z/M). This ratio is instrumental in defining the expected selectivity the catalyst can demonstrate. Matrix surface area contributes to the cracking of heavy hydrocarbons and bottom cracking whereas zeolite surface area contributes to cracking gasoline range molecules into lighter olefins. Z/M ratio hereafter refers to the Zeolite to Matrix specific surface area ratio.
Total Bronsted to Lewis acidity ratio (B/L)
[00048] A typical zeolite consists of silicon and aluminum atoms that are tetrahedrally joined by four oxygen atoms. Silicon is in a +4 oxidation state; therefore, a tetrahedron containing silicon is neutral in charge. In contrast, aluminum is in a +3 oxidation state. This indicates that each tetrahedron containing aluminum has a net charge of -1, which must be balanced by a positive ion. This results in an active site having acidic nature i.e. proton donating or electron abstracting action. The cracking of hydrocarbons is an acid-catalyzed reaction. The catalyst can have either strong or weak Bronsted sites or strong or weak Lewis sites. A Bronsted-type active site is capable of donating a proton. A Lewis-type active site can accept a pair of electrons. During cracking, Bronsted sites generate carbonium ions by donating protons to initiate the cracking. Both Bronsted and Lewis sites generate carbenium ions that propagate the cracking. The net acidity of the catalyst is subject to the method of preparation, dehydration temperature, and silica to alumina ratio. The strength and nature of active site acidity is calculated by desorption experiments using an organic basic medium. The ratio of total Bronsted acidity to the total Lewis acidity of a catalyst is hereafter referred to as the B/L ratio.
Zeolite’s unit cell size (UCS)
[00049] The unit cell size (UCS) is the distance between the repeating cells in the zeolite structure. It is an indirect measure of silica to alumina ratio or total potential acidity per unit cell. Other key properties used in the present matter are the pore dimensions like pore size and pore volume.
[00050] In an embodiment, the computing unit 200 can then determine the kinetic parameters for any unknown catalyst using the estimated regression coefficients and the model equations. The determined kinetic parameters can correspondingly enable the computing unit 200 to estimate the FCC yield for the unknown catalyst. Further, the computing unit 200 can be configured to and/or can allow a user to study the effect of the composition of the feedstocks on the FCC yield, which can facilitate the computing unit 200 or user to determine and select an optimum feedstock for optimum FCC yield
[00051] The parameter ratios obtained from the catalyst’s physiochemical properties can be correlated with these kinetic parameters. The kinetic parameters estimated through the detailed ten lump kinetic model 104 can be correlated with the catalyst’s physiochemical properties using non-linear regression algorithms. The kinetic parameters such as frequency factors and apparent activation energies can be correlated with all the catalyst properties and corresponding coefficients have been estimated. In order to make the catalyst properties incorporated lump kinetic model 104 applicable to different reaction temperatures, catalyst properties can be directly correlated with frequency factors and apparent activation energies rather than with rate constants at a fixed reaction temperature. The inputs for regression can be the catalyst properties, whereas the outputs can be frequency factors and activation energies for all the reactions. Accordingly, the coefficient for activation energy and frequency factors can be estimated. The summation of these diverse and multiple approaches as shown in FIG. 1 lead to a reasonably accurate tool for predicting the crackability of any feedstock for the FCC.
[00052] FIG. 6 illustrates an exemplary flow diagram of the proposed method 600. As illustrated, method 600 can include step 602 of receiving, by a computing unit, a first set of data packets pertaining to a set of feedstock attributes associated with one or more feedstocks, followed by step 604 of determining, by an artificial neural network (ANN) configured with the computing unit. composition of the one or more feedstocks based on the set of feedstock attributes received at step 602. Method 600 can further include step 606 of receiving, by the computing unit, a second set of data packets pertaining to a set of physiochemical attributes associated with one or more known catalysts. Further, method 600 can include step 608 of estimating, by a kinetic model configured with the computing unit, kinetic parameters associated with the one or more known catalysts for the determined composition using the set of physiochemical attributes received at step 606. Method 600 can further include step 610 of regressing, by the computing unit, the kinetic parameters of the known catalysts estimated at step 608 with the set of physiochemical attributes associated with one or more known catalysts to estimate regression coefficients for a set of FCC reactions and establish model equations between the estimated kinetic parameters and the physiochemical attributes. Furthermore, method 600 can include step 612 of determining, by the computing unit, kinetic parameters for an unknown catalyst using the regression coefficients and the model equations estimated and established at step 610, and correspondingly estimating the FCC yield for the unknown catalyst.
[00053] In an embodiment, the method can include a step of studying the effect of the composition of the feedstocks on the FCC yield, which can facilitate the computing unit and/or a user to determine and select an optimum feedstock for optimum FCC yield.
[00054] Thus, the present disclosure provides a simple, cost-effective, and efficient system and method for estimation of fluid catalytic cracker (FCC) product yield and profitability based on feedstock and catalytic characteristics, which helps select the best feedstock and catalyst for optimum FCC yield.
Reference Examples and Experimental Data
[00055] Refinery feed was taken for the multiple lab reactions with different catalysts. The activity of different catalysts with the same feed has been measured in the lab scale FCC reactor. The feed properties were measured in the lab and hydrocarbon type analysis of feed were calculated from an in-house developed Artificial Neural Network (ANN) Model reported in the below Table 1.
TABLE 1
Feed Name Y-P Model

KR-HT VGO 90%
MCB 10%
Density, @ 15 0C gm/ml. 0.892
Specific Gravity 0.89287
Sim Distillation ASTM D2887
IBP 162.0
5% v 330.5
10% v 376.5
20% v 288.5
30% v 405.5
40% v 419.5
50% v 434.0
60% v 449.5
70% v 469.0
80% v 492.5
90% v 525.5
95% v 551.5
99% v 607.5
Sulfur, %wt 0.1473
CCR, %wt 0.1289
ANN Model Output
Paraffins , wt % 15.5
Naphthenes , wt % 20.5
Aromatics, wt % 64.0

[00056] The physiochemical attributes/properties of 29 different catalysts are measured in the lab. The FCC yield data were collected from the FCC ACE unit for the given feed and different catalysts at different operating conditions. The kinetic parameters for all the catalysts are estimated by using a 10 –lump kinetic model in FIG. 4 by using an optimization method with the algorithm presented in FIG. 5. Further, all the calculated kinetic parameters for different catalysts were regressed with the catalyst properties for estimating the regression coefficient for all the reactions. Also, the model equations between kinetic parameters and catalyst properties were established. The estimated regression coefficients and the model equations are used to find out the kinetic parameters with the unknown catalyst as shown in below Table 2.
TABLE 2
Catalyst 1 Catalyst 2 Catalyst 3
Frequency factor (g/m3)-1.(hr)-1 Activation energy ( KJ/Mol) Frequency factor (g/m3)-1.(hr)-1 Activation energy ( KJ/Mol) Frequency factor (g/m3)-1.(hr)-1 Activation energy ( KJ/Mol)
Ph ? Ah 65386 145 54576 109 27402 69
Ph ? Pl 276840 97 137908 77 231773 72
Ph ? G 236382 97 620998 108 941364 82
Ph ? DG 254246 236 671725 113 655351 88
Ph ? Coke 3694524 131 4645825 128 4907587 103
Nh?Ah 743073 95 131115 144 408558 111
Nh?Nl 59421 62 34598 94 14162 44
Nh? G 93405 91 68110 84 108966 80
Nh? LPG 36062 171 32609 89 37742 59
Ah ? Al 266855 208 158986 89 76309 66
Ah ? G 944413 44 795492 100 582463 142
Ah ? LPG 63836 220 32909 88 18242 144
Ah ? Coke 100400 134 84014 101 90255 149
Pl ? G 53597 240 51493 123 42297 96
Pl ? DG 895860 158 607639 115 377653 131
Pl ? Coke 27177 70 125636 114 149887 99
Nl ? G 536294 101 122373 112 279330 50
Nl ? LPG 6316649 130 4170415 104 4304696 90
Nl ? DG 1296324 119 306387 106 342594 110
Al ? G 1168111 101 480973 95 530094 79
Al ? LPG 8453 252 31701 116 10421 150
Al ? Coke 2675975 71 876066 135 1240245 114
G ?LPG 741229 104 383743 105 119367 81
G ? Coke 1160759 180 236702 114 647255 102
LPG ? Coke 478699 209 197326 135 174326 136
0.52 2.19 3.7
2.11 0.65 1.37

[00057] These parameters are used to calculate the yields for the particular catalyst. and the yields are as below Table 3 and 4.
TABLE 3: Yield Comparison between Experimental Data and Simulation for Catalyst 1
Exp Sim Exp Sim Exp Sim

Cracking Temperature, °C 545 545 535 535 525 525
Catalyst-to-Oil, wt/wt 6 6 8 8 4 4
Ph 6.49 6.28 8.11
Nh 0 0 0
Ah 0 0 0
Bottom (343+ deg C) 6.22 6.49 5.14 6.28 9.17 8.11
Pl 1.48 1.39 1.50
Nl 17.77 17.57 21.6
Al 0 0 0
LCO (192-343) 18.99 19.25 18.64 18.96 24.53 23.10
Gasoline (C 5- 192) 45.10 44.70 44.87 44.51 44.57 46.72
LPG 22.98 23.68 23.19 24.16 17.85 17.63
Dry Gas 2.06 2.09 2.18 2.09 1.17 1.59
Coke 4.66 3.80 5.98 4.00 2.72 2.85

TABLE 4: Yield Comparison between Experimental Data and Simulation for Catalyst 2
EXP SIM EXP SIM EXP SIM
CRACKING TEMPERATURE, °C 545 545 535 535 525 525
CATALYST-TO-OIL, WT/WT 6 6 8 8 4 4
PH 0.01 0.01 0.1
NH 6.84 5.75 11.3
AH 1.86 1.44 5.02
BOTTOM( 343+ DEG C) 8.70 8.71 7.53 7.20 17.02 16.42
PL 12.39 12.35 13.24
NL 0.55 0.5 0.8
AL 5.64 5.13 8.06
LCO( 192-343 ) 18.67 18.58 17.51 17.98 22.71 22.10
GASOLINE ( C 5- 192) 42.69 44.54 46.41 45.08 41.13 41.29
LPG 23.74 22.31 21.82 23.67 15.75 16.07
DRY GAS 2.07 1.98 1.95 2.07 0.98 1.32
COKE 4.13 3.88 4.78 4.00 2.41 2.79
TOTAL 100.00 100.00 100.00 100.00 100.00 99.99

[00058] In an aspect, referring to FIG. 2, the computing unit 200 may comprise one or more processor(s) 202. The one or more processor(s) 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that manipulate data based on operational instructions. Among other capabilities, one or more processor(s) 202 are configured to fetch and execute computer-readable instructions stored in a memory 204 of the computing unit 200. The memory 204 may store one or more computer-readable instructions or routines, which may be fetched and executed to create or share the data units over a network service. The memory 204 may comprise any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as EPROM, flash memory, and the like.
[00059] Computing unit 200 may also comprise an interface(s) 206. The interface(s) 206 may comprise a variety of interfaces, for example, interfaces for data input and output devices, referred to as I/O devices, storage devices, and the like. The interface(s) 206 may facilitate communication of s computing unit. The interface(s) 206 may also provide a communication pathway for one or more components of the computing unit 200. Examples of such components include, but are not limited to, processing engine(s) 208 and database 210.
[00060] The processing engine(s) 208 may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) 208. In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) 208 may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing engine(s) 208 may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine(s) 208. In such examples, computing unit 200 may comprise the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to computing unit and the processing resource. In other examples, the processing engine(s) 208 may be implemented by electronic circuitry.
[00061] The data 210 may comprise data that is either stored or generated as a result of functionalities implemented by any of the components of the processing engine(s) 208 or computing unit. In an exemplary embodiment, the processing engine(s) 208 may include the ANN 102, the kninetic model 104, and other unit(s) 212. Other unit(s) can supplement the functionalities of the processing engine 208 or the computing unit 200.
[00062] While some embodiments of the present disclosure have been illustrated and described, those are completely exemplary in nature. The disclosure is not limited to the embodiments as elaborated herein only and it would be apparent to those skilled in the art that numerous modifications besides those already described are possible without departing from the inventive concepts herein. All such modifications, changes, variations, substitutions, and equivalents are completely within the scope of the present disclosure. MANISH

ADVANTAGES OF THE PRESENT INVENTION
[00063] The present invention overcomes the drawbacks, shortcomings, and problems associated with fluid catalytic crackers.
[00064] The present invention predicts the FCC plant yields using any FCC catalyst and feed.
[00065] The present invention provides a comprehensive, user-friendly, and time-saving solution for predicting the cracking behavior of feedstock in FCC.
[00066] The present invention correlates the physical and chemical properties of any FCC catalyst to the cracking reaction kinetics to estimate yields that shall be obtained at the commercial level.
[00067] The present invention selects the best feed and catalyst to maximize the conversion and product yields for the overall profitability of the FCC process.
[00068] The present invention provides a simple, cost-effective, and efficient system and method for estimation of fluid catalytic cracker (FCC) product yield and profitability based on feedstock and catalytic characteristics, which helps select the best feedstock and catalyst for optimum FCC yield.