Economics-based coordination of advanced process control and real-time optimization

• US 9,268,317 B2
• Filed: 11/30/2010
• Issued: 02/23/2016
• Est. Priority Date: 12/02/2009
• Status: Active Grant

• Current Assignee: Shell USA Incorporated (Shell Plc)
• Original Assignee: Shell Oil Company (Shell Plc)
• Inventors: Carrette, Pierre Christian Marie, Linn, Richard Eugene, Venkat, Aswin Nataraj
• Primary Examiner(s): Barnes-Bullock, Crystal J
• Application Number:
  US13/512,900
• Publication Number:
  US 20120239167A1
• Time in Patent Office: 1,911 Days
• Field of Search: 700/28, 700/29, 700 32- 34, 700/36, 705/7.11, 705/7.12, 705/7.29, 705/7.31, 705 735- 737, 703/2
• US Class Current: 1/1
• CPC Class Codes: G05B 13/042 in which a parameter or coefficient is automatically adjusted to optimise the performance
• CPC Class Codes: G05B 19/41865 characterised by job scheduling, process planning, material flow
• CPC Class Codes: G05B 2219/32015 Optimize, process management, optimize production line
• CPC Class Codes: G05B 2219/32019 Dynamic reconfiguration to maintain optimal design, fabrication, assembly
• CPC Class Codes: G05B 2219/32135 APC advanced process control applications
• CPC Class Codes: G05B 2219/32291 Task sequence optimization
• CPC Class Codes: Y02P 90/02 Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Abstract

A system and method for coordinating advanced process control and real-time optimization of a manufacturing process are provided. The system and method receive process data and economic data corresponding to the manufacturing process to be controlled and optimized. Based on the process data, the economic data and a nonlinear steady-state model of the process, an economic objective function is calculated by a real-time optimization module. A reduced-order non-linear approximation of the economic objective function is thereafter calculated by the real-time optimization module and transmitted to an advanced process control module. The advanced process control module utilizes the reduced-order non-linear approximation of the economic objective function to control the manufacturing process towards the constrained economic optimum.

Claims

1. A method for control and optimization of a process, comprising:

receiving process data and economic data corresponding to the process to be controlled;

calculating an economic objective function based on the process data, the economic data and a nonlinear steady-state model of the process;

deriving a non-linear approximation of the economic objective function in terms of a set of process control variables associated with the process;

transmitting the non-linear approximation to an advanced process control module; and

controlling the process according to the non-linear approximation and within a constraint to drive the process toward a constrained economic optimum by the advanced process control module;

wherein the calculation of the economic objective function and the derivation of the non-linear approximation of the economic objective function are performed at a frequency lower than controlling the process according to the non-linear approximation by the advanced process control module.

2. The method of claim 1, wherein controlling the process according to the non-linear approximation comprises:

utilizing the non-linear approximation in the advanced process control module to calculate a steady state target for a process control variable, wherein the steady state target is calculated relative to the economic objective within the constraint.

3. The method of claim 2, wherein controlling the process according to the non-linear approximation further comprises:

utilizing the non-linear approximation in the advanced process control module to compute a path to dynamically reach the steady state target.

4. The method of claim 1, wherein controlling the process according to the non-linear approximation further comprises:

utilizing the non-linear approximation in the advanced process control module to calculate a dynamic path relative to the economic objective within the constraint.

5. The method of claim 1, wherein the calculation of the economic objective function, the derivation of the non-linear approximation of the economic objective function, and the transmission of the non-linear approximation are initiated when the process is in a steady state.

6. The method of claim 1, wherein control of the process according to the non-linear approximation and within the constraint to drive the process toward the economic objective by the advanced process control module is performed independent of the process being in a steady state.

7. The method of claim 1, wherein the economic objective function is calculated by a real-time optimization module.

8. A method for control and optimization of a process, comprising:

receiving process data and economic data corresponding to the process to be controlled;

calculating an economic objective function based on the process data, the economic data and a nonlinear steady-state model of the process;

deriving a non-linear approximation of the economic objective function in terms of a set of process control variables associated with the process;

transmitting the non-linear approximation to an advanced process control module; and

controlling the process according to the non-linear approximation and within a constraint to drive the process toward a constrained economic optimum by the advanced process control module;

wherein the non-linear approximation is comprised of a Hessian matrix and a gradient vector which represent an economic surface in terms of the set of process control variables of the process.

9. A method for control and optimization of a process, comprising:

receiving process data and economic data corresponding to the process to be controlled;

calculating an economic objective function based on the process data, the economic data and a nonlinear steady-state model of the process;

deriving a non-linear approximation of the economic objective function in terms of a set of process control variables associated with the process;

transmitting the non-linear approximation to an advanced process control module; and

controlling the process according to the non-linear approximation and within a constraint to drive the process toward a constrained economic optimum by the advanced process control module;

wherein the non-linear approximation of the economic objective function is a local quadratic approximation of the economic objective function.

10. A system for coordinating advanced process control and real-time optimization of a process, comprising:

a real-time optimization (RTO) module for calculating an economic objective function based on process data, economic data and a nonlinear steady-state model of the process and deriving a non-linear approximation of the economic objective function in terms of a set of process control variables of the process; and

an advanced process control (APC) module for controlling the process according to the non-linear approximation and within a constraint to drive the process toward the economic objective;

wherein the RTO module operates at a lower frequency than the APC module.

11. The system of claim 10, wherein controlling the process according to the non-linear approximation by the APC module comprises:

utilizing the non-linear approximation to calculate a steady state target for one of the process control variables, wherein the steady state target is calculated relative to the economic objective within the constraint.

12. The system of claim 11, wherein controlling the process according to the non-linear approximation by the APC module further comprises:

utilizing the non-linear approximation to compute a path to dynamically reach the steady state target.

13. The system of claim 10, wherein controlling the process according to the non-linear approximation by the APC module further comprises:

re-calculating the steady state target for the process control variables based on the non-linear approximation in response to changes in the process or constraint changes to constraints on the process;

re-computing the path based on the recalculated steady state target with the non-linear approximation.

14. The system of claim 10, wherein the RTO module operates when the process is in a steady state.

15. An article of manufacture comprising a machine readable medium having stored instructions that, when executed by a computer system:

receive process data and economic data corresponding to the process to be controlled;

calculate an economic objective function by a real-time optimization module based on the process data, the economic data and a nonlinear steady-state model of the process;

derive a non-linear approximation of the economic objective function by the real-time optimization module in terms of a set of process control variables associated with the process;

transmit the non-linear approximation to an advanced process control module; and

control the process according to the non-linear approximation and within a constraint to drive the process toward the economic objective by the advanced process control module;

wherein the non-linear approximation of the economic objective function is a local quadratic approximation of the economic objective function.

16. The article of manufacture of claim 15, wherein the machine readable medium has instructions that causes the computer system to:

utilize the non-linear approximation in the advanced process control module to calculate a steady state target for a process control variable, wherein the steady state target is calculated relative to the economic objective within the constraint.

17. The article of manufacture of claim 16, wherein the machine readable medium has instructions that causes the computer system to:

utilize the non-linear approximation in the advanced process control module to compute a path to dynamically reach the steady state target.

18. The article of manufacture of claim 17, wherein the machine readable medium has instructions that causes the computer system to:

re-calculate the steady state target for the process control variable based on the non-linear approximation in response to changes in the process or a constraint change to the process;

re-compute the path based on the recalculated steady state target with the non-linear approximation.