Method of predicting and optimizing fuel production

Claim 2

A method according to claim 1, wherein the incoming material is a petroleum feedstock, the product is diesel fuel, the process is admixing cetane improver, and the characteristic of the product is cetane number.

Claim 3

A method according to claim 2, wherein the measured property is selected from hydrocarbon type, carbon content, hydrogen content, sulfur content, nitrogen content, API gravity, distillation range, cetane number, aniline point, heat content, kinematic viscosity, cloud point, pour point, and/or flash point.

Claim 4

A method according to claim 2, wherein the measured property includes at least API gravity, T50 and at least two additional properties selected from T10, T90, aniline point, viscosity, cloud point, and aromatics content.

Claim 5

A method according to claim 4, wherein either (a) the at least two additional properties are T90, aniline point, viscosity, and cloud point, (b) the at least two additional properties are T90, cloud point, and aromatics content, (c) the at least two additional properties are T10, T90, and cloud point, (d) the at least two additional properties are viscosity and aniline point or (e) the at least two additional properties are T10 and T90.

Claim 6

A method according to claim 5, wherein the at least two additional properties are T90, aniline point, viscosity and cloud point and the predictive equation is a set of non-linear equations comprising: CN0 = -0.07307 x T10 + 0.3080 x ANPT - 1.152 x VISC40 + 0.4957 x D976 + 0.1836 x CLOUD + 31.57 CN1000 = -0.09374 x T10 + 0.2738 x ANPT - 0.9512 x VISC40 + 0.5890 x D976 + 0.2069 x CLOUD + 40.16 CN2500 = -0.09681 x T10 + 0.2521 x ANPT - 0.9890 x VISC40 + 0.6700 x D976 + 0.1943 x CLOUD + 42.38 CN5000 = -0.09009 x T10 + 0.3084 x ANPT - 1.183 x VISC40 + 0.6461 x D976 + 0.1695 x CLOUD + 41.30 CN7500 = -0.07602 x T10 + 0.3624 x ANPT - 1.307 x VISC40 + 0.6495 x D976 + 0.1371 x CLOUD + 35.77 CN10000 = -0.06971 x T10 + 0.4190 x ANPT - 1.257 x VISC40 + 0.6033 x D976 + 0.1086 x CLOUD + 33.75 wherein CN is cetane number of the product at the noted concentration of cetane improver of from 0 to 10,000 ppmv, and intermediate values are interpolated between points, T10 is the temperature at which 10% of the product boils off, ANPT is the aniline point of the product, VISC40 is the viscosity of the product at 40°C according to ASTM D445, D976 is the cetane index of the product according to ASTM D976, and CLOUD is the cloud point of the product.

Claim 7

A method according to claim 5, wherein the at least two additional properties are T90, cloud point and aromatics content and the predictive equation is a set of non-linear equations comprising: CN0 = 0.1700 x T50 - 0.09022 x T90 - 238.4 x SPGR - 0.1199 x FIAAROM + 0.2187 x CLOUD + 237.8 CN1000 = 0.1802 x T50 - 0.1062 x T90 - 256.1 x SPGR - 0.1123 x FIAAROM + 0.2419 x CLOUD + 259.8 CN2500 = 0.1861 x T50 - 0.1073 x T90 - 264.7 x SPGR - 0.1213 x FIAAROM + 0.2310 x CLOUD + 269.9 CN5000 = 0.1928 x T50 - 0.1037 x T90 - 267.9 x SPGR - 0.1489 x FIAAROM + 0.2113 x CLOUD + 273.4 CN7500 = 0.2079 x T50 - 0.09233 x T90 - 283.8 x SPGR - 0.1700 x FIAAROM + 0.1829 x CLOUD + 282.1 CN10000 = 0.2163 x T50 - 0.08637 x T90 - 285.5 x SPGR - 0.1931 x FIAAROM + 0.1574 x CLOUD + 281.7 wherein CN is cetane number of the product at the noted concentration of cetane improver of from 0 to 10,000 ppmv, and intermediate values are interpolated between points, T50 is the temperature at which 50% of the product boils off, T90 is the temperature at which 90% of the product boils off, SPGR is the specific (API) gravity of the product, FIAAROM is the aromatics content of the product according to ASTM D1319, and CLOUD is the cloud point of the product.

Claim 8

A method according to claim 5, wherein the at least two additional properties are T10, T90 and cloud point and the predictive equation is a set of nonlinear equations comprising: CN0 = 0.05286 x T10 + 0.1329 x T50 - 0.07308 x T90 - 319.2 x SPGR + 0.1984 x CLOUD + 295.1 CN1000 = 0.05529 x T10 + 0.1391 x T50 - 0.08732 x T90 - 332.5 x SPGR + 0.2220 x CLOUD + 313.6 CN2500 = 0.06523 x T10 + 0.1357 x T50 - 0.08412 x T90 - 348.0 x SPGR + 0.2085 x CLOUD + 328.0 CN5000 = 0.09178 x T10 + 0.1182 x T50 - 0.06947 x T90 - 371.4 x SPGR + 0.1817 x CLOUD + 344.9 CN7500 = 0.1203 x T10 + 0.1059 x T50 - 0.04557 x T90 - 403.8 x SPGR + 0.1464 x CLOUD + 363.9 CN10000 = 0.1377 x T10 + 0.09925 x T50 - 0.03274 x T90 - 422.0 x SPGR + 0.1158 x CLOUD + 374.5 wherein CN is cetane number of the product at the noted concentration of cetane improver of from 0 to 10,000 ppmv, and intermediate values are interpolated between points, T10 is the temperature at which 10% of the product boils off, T50 is the temperature at which 50% of the product boils off, T90 is the temperature at which 90% of the product boils off, SPGR is the specific gravity of the product, and CLOUD is the cloud point of the product.

Claim 9

A method according to claim 5, wherein the at least two additional properties are viscosity and aniline point and the predictive equation is a set of non-linear equations comprising: CN0 = 0.2811 x ANPT - 1.030 x VISC40 + 0.6519 x D976 CN1000 = 0.2403 x ANPT - 0.9091 x VISC40 + 0.7946 x D976 CN2500 = 0.2171 x ANPT - 1.075 x VISC40 + 0.9147 x D976 CN5000 = 0.2632 x ANPT - 1.335 x VISC40 + 0.9365 x D976 CN7500 = 0.3048 x ANPT - 1.322 x VISC40 + 0.9346 x D976 CN10000 = 0.3534 x ANPT - 1.259 x VISC40 + 0.9055 x D976 wherein CN is cetane number of the product at the noted concentration of cetane improver of from 0 to 10,000 ppmv, and intermediate values are interpolated between points, ANPT is the aniline point of the product, VISC40 is the viscosity of the product at 40°C according to ASTM D445, and D976 is the cetane index of the product according to ASTM D976.

Claim 10

A method according to claim 5, wherein the at least two additional properties are T10 and T90 and the predictive equation is a set of non-linear equations comprising: CN0 = -0.5659 x T10 + 0.000458 x T50 x T90 - 0.000992 x (T90) 2 + 0.5261 x T10/SPGR + 0.4263 x T90/SPGR- 64.40 CN1000 = -0.4433 x T10 + 0.000483 x T50 x T90 - 0.001215 x (T90) 2 + 0.4254 x T10/SPGR + 0.5364 x T90/SPGR - 81.53 CN2500 = -0.4733 x T10 + 0.000468 x T50 x T90 - 0.001226 x (T90) 2 + 0.4588 x T10/SPGR + 0.5420 x T90/SPGR- 79.54 CN5000 = -0.5823 x T10 + 0.000399 x T50 x T90 - 0.001130 x (T90) 2 + 0.5736 x T10/SPGR + 0.5086 x T90/SPGR- 73.52 CN7500 = -0.6247 x T10 + 0.000359 x T50 x T90 - 0.001125 x (T90) 2 + 0.6320 x T10/SPGR + 0.5245 x T90/SPGR- 79.91 CN10000 = -0.7223 x T10 + 0.000346 x T50 x T90 - 0.001046 x (T90) 2 + 0.7272 x T10/SPGR + 0.4876 x T90/SPGR- 74.34 wherein: CN is cetane number of the product at the noted concentration of cetane improver of from 0 to 10,000 ppmv, and intermediate values are interpolated between points, T10 is the temperature at which 10% of the product boils off, T50 is the temperature at which 50% of the product boils off, T90 is the temperature at which 90% of the product boils off, and SPGR is the specific gravity of the product.

Claim 12

A method as claimed in claim 1, wherein the product is diesel fuel, and at least one characteristic of the product is pour point.

Claim 16

A method according to claim 15, comprising the further steps, repeated as necessary prior to step (ii), of changing the selected process and repeating step (i), to optimise the said characteristic.

Claim 17

A computer program comprising computer program code means which, when executed on a computer, instruct the computer to carry out the steps of a method according to any one of the preceding claims.

Claim 18

A computer readable medium having recorded thereon a computer program according to claim 17.

Claim 19

A process for the value optimization of a plant which processes at least one incoming material and produces at least one product, comprising (a) assessing at least one property of the or each incoming material; (b) assessing the cost of the or each incoming material; (c) inputting said property and cost of the or each incoming material into a database; (d) determining at least one characteristic of the or each product; (e) assessing the value of the or each product; (f) inputting said at least one characteristic and the value of the or each product into a database; and (g) calculating a value - optimized process for obtaining the or each product from the incoming material using a computing device accessing said database and calculating the cheapest way of producing the or each product, using a non-linear equation derived from regressive analysis of data from a history of properties of incoming materials, processes, and products.

Claim 20

A process according to claim 19, wherein the plant comprises a petroleum products blending plant.

Claim 21

A process according to claim 19 or 20, wherein there are a plurality of properties of the or each incoming material which are assessed.

Claim 22

A process according to claim 19, wherein there are a plurality of incoming materials and a plurality of products.

Claim 23

A process according to any one of claims 19 to 22, wherein step (a) includes gathering information in at least one category selected from hydrocarbon type, carbon content, hydrogen content, sulfur content, nitrogen content, API gravity, distillation range, cetane number, aniline point, heat content, kinematic viscosity, cloud point, pour point, and flash point.

Claim 24

A process for the optimization of diesel fuel production, comprising (a) providing at least one database of diesel fuel stocks, additives and products, having a set of known properties; (b) providing a non-linear formula for the prediction of diesel fuel properties based upon a regressive analysis of the known properties collected from a series of samples; (c) providing a computing device connected to said at least one database; (d) providing computing instructions incorporating said formula for the prediction of diesel fuel properties; and (e) calculating the diesel fuel properties utilizing said computing device.

Claim 25

A process according to claim 24, wherein the computing device is a computer and the instructions comprise computer software.

Claim 26

A process according to claim 24 or 25, wherein the database is a spreadsheet of the set of known properties.

Claim 27

A process according to any one of claims 24 to 26, wherein the database includes the price of the diesel fuel stocks, additives, and products and the cost of processing, and the software includes an optimizer, whereby the maximum profitability of the process may be calculated.

Claim 29

A computer program comprising computer program code means which, when executed on a computer, instruct the computer to carry out the steps of a process according to any one of claims 19 to 27.

Claim 30

A computer readable medium having recorded thereon a computer program according to claim 29.