Transfer line exchanger

Patent metadata

Field	Value
Publication number	US-11828405-B2
Application number	US-202017030432-A
Country	US
Kind code	B2
Filing date	Sep 24, 2020
Priority date	Dec 20, 2012
Publication date	Nov 28, 2023
Grant date	Nov 28, 2023

Abstract

The present invention provides a transfer line exchanger which is optimized for one or more objective functions of interest such as pressure drop, erosion rate, fouling, coke deposition and operating costs. The transfer line exchanger is designed by computer modeling a transfer line exchanger in which the cross section of flow path is substantially circular and modeling the operation of the transfer line under industrial conditions to validate the model design and its operation. Then iteratively the model design is deformed and the operation of the deformed part is modeled and compared to values obtained with other deformed models until the value of the objective function is optimized (e.g. at an extreme) or the change in the objective function is approaching zero.

First claim

What is claimed is:

1. A method to optimize one or more of the operating characteristics selected from the group consisting of pressure drop, erosion rate, fouling rate, and cost (capital, operating, or both) of a fixed industrial flow path defined by a continuous metal envelope, wherein the metal is chosen from steel having a high nickel and chrome content, steel having a high cobalt and high nickel content, and austenitic high temperature alloys (HTAs), comprising:
a) building a computational model comprising not less than 5,000, computational cells of the portion of flow channel from 5% of the length of the flow channel downstream of the inlet to from 5% of the length of the flow channel upstream of the outlet of the initial design of said industrial flow path;
b) using computer software that solves the fundamental laws of fluid and energy dynamics for each cell, simulating and summing the results of the operation of the model design from step a) under the industrial pressure, temperature, and flow rate conditions of operation to verify one or more objective functions of interest;
c) iteratively
1) deforming said computational model comprising not less than 5,000 computational cells so that the resulting ARQ is from 1.02 to 1.15, where ARQ is defined as the ratio of aspect ratio(AR) to isoperimetric quotient (Q) of a section of the flow path perpendicular to the direction of flow;
2) applying the same computer software as in step b), that solves the fundamental laws of fluid and energy dynamics for each cell, simulating and summing the results of the operation of the deformed model from step c1) under the industrial pressure, temperature, and flow rate conditions of operation used in step b) to predict one or more objective functions of interest for the operation of the deformed model;
3) storing the predicted results from step c2);
4) using some or all of the stored results from step c3) with an optimization algorithm to estimate a deformation that will improve the objective function;
5) repeating steps 1), 2), 3), and 4) until one or both of the following conditions are met:
i) the objective function of interest goes through a beneficial local extrema; or
ii) the rate of change of all of the functions of interest starts to approach 0.

2. The method according to claim 1 , wherein the computational model has from 10,000 to 100,000 computational cells.

3. The method according to claim 2 , wherein the flow path does not change by more than 7% over a 5% length of the flow path.

4. The method according to claim 3 , wherein the ARQ at one or more sections over the flow path is from 1.02 and 1.12.

5. The method according to claim 4 , wherein the ARQ over 80% of the length of the flow path does not change by more than 5% over a 5% length of the flow path.

6. The method according to claim 5 , wherein the calculated total pressure drop across the flow path is decreased by not less than 10% compared to the calculated pressure drop for a flow path having an ARQ along its length from 1.00 to 1.02.

7. The method according to claim 6 , wherein normalized calculated erosion rate of the flow path is decreased by not less than 10% compared to the normalized erosion rate for a flow path having an ARQ along its length from 1.00 to 1.02.

8. The method according to claim 7 , wherein the flow path has an increasing cross sectional area in the direction of flow such that the angle between the transverse normal vector and the flow path walls range from 0° to 85°.

9. The method according to claim 7 , wherein the flow path has a smooth curve in its longitudinal direction and a radius of curvature on the internal surface of the curve from unbound to half the vertical of the section radius.

10. The method according to claim 7 , wherein the flow path is defined by a continuous metal envelope comprising from 20 to 50 weight % of chromium, 25 to 50 weight % of Ni, from 1.0 to 2.5 weight % of Mn, less than 1.0 weight % of niobium, less than 1.5 weight % of silicon, less than 3 weight % of titanium, and all other trace metals and carbon in an amount less than 0.75 weight %, and from 0 to 6 weight % of aluminum.

11. The method according to claim 7 , wherein the flow path is defined by a continuous metal envelope comprising from 40 to 65 weight % of Co; from 15 to 20 weight % of Cr; from 20 to 13 weight % of Ni; less than 4 weight % of Fe and the balance of one or more trace elements and up to 20 weight % of W the sum of the components adding up to 100 weight %.

12. The method according to claim 7 , wherein the flow path is defined by a continuous metal envelope comprising from about 55 to 65 weight % of Ni; from about 20 to 10 weight % of Cr; from about 20 to 10 weight % of Co; and from about 5 to 9 weight % of Fe, and the balance of one or more trace elements.

Assignees

• Nova Chem Int Sa

Inventors

• Clavelle Eric
• Yajure Edgar
• Benum Leslie Wilfred
• Foy Edward Christopher
• Eisenhawer David

Classifications

• G06F2111/08

  Probabilistic or stochastic CAD · CPC title

• G06F2113/14

  Pipes · CPC title

• G06F30/28

  using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD] · CPC title

• G06F2111/06

  Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA] · CPC title

• F16L9/006 Primary

  specially profiled (F16L9/003 takes precedence) · CPC title