# US 10,481,919 B2 · Automatic optimization of continuous processes

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- Number: US 10,481,919 B2
- Document ID: US10481919B2
- Status: Granted
- Kind: B2
- Application number: 14/665,292
- Filed: 2015-03-23
- Published: 2016-09-29
- Granted: 2019-11-19
- Earliest claimed: 2015-03-23
- Assignees: TIBCO Software Inc.
- Applicants: Tibco Software Inc.
- Inventors: Thomas Hill; Pawel Lewicki
- Web page: https://patentgrep.com/patent/US10481919B2

## CPC

- G06F 9/44505: {Configuring for program initiating, e.g. using registry, configuration files}
- G05B 19/41865: {characterised by job scheduling, process planning, material flow}
- G06F 9/44552: {Conflict resolution, i.e. enabling coexistence of conflicting executables}
- G06Q 10/06: Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- Y02P 90/02: Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G)
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- Y02P: CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS

## Abstract

A system, method, and computer-readable medium are disclosed performing an optimization operation. The optimization operation optimizes continuous processes by identifying process states associated with specific ranges for a limited subset of control parameter inputs. In certain embodiments, the optimization operation states comprise clear, stable, and robust process states. Such an optimization operation provides a simpler and cost effective means to optimize continuous processes. Additionally, such an optimization operation is applicable more rapidly to a wider range of real-world operational issues as they occur regularly in continuous process scenarios.

## Claims

### Claim 1

A computer-implementable method for performing an optimization operation on continuous processes generated from a plurality of system sensors, comprising:
transforming a continuous stream of data comprising process states associated with specific ranges of a subset of control parameter inputs, wherein transforming comprises:
dividing the control parameter inputs into outputs, controllable inputs, and uncontrollable inputs; and
identifying constraints for the outputs, controllable inputs, and uncontrollable inputs;
performing a clustering operation on select uncontrollable inputs, wherein the clustering operation includes dividing the ranges into a plurality of reliable repeatable process states;
identifying reliably repeatable optimal states from the reliable repeatable process states, the identifying the -reliably repeatable optimal states comprising defining a key performance indicator as a continuous process output by applying a recursive-partitioning operation to identify input parameter settings that are associated with a desirable process state, the recursive-partitioning operation comprising identifying surrogate parameters and alternative recursive-partitioning models to obtain a pool of combinations of possible parameter settings associated with an optimized process performance;
characterizing relationships among the control parameter inputs for the reliably repeatable optimal states and for non-optimal states; and
determining operational states for the control parameter inputs based on the characterized relationships;
wherein the operational states describe controllable parameters and settings to control the system.

### Claim 2 (depends on 1)

The method of claim 1, wherein:
the subset of control parameter inputs comprises optimization operation states, the optimization operation states comprising clear, stable, and robust process states.

### Claim 3 (depends on 1)

The method of claim 1, further comprising:
extracting the data comprising the process states.

### Claim 4 (depends on 3)

The method of claim 3, further comprising:
excluding data with data errors from the data comprising the process states; and
identifying an aggregation interval for the extracting.

### Claim 5 (depends on 1)

The method of claim 1, further comprising dividing the control parameter inputs into independent variables and dependent variables, wherein the independent variables comprise controllable inputs.

### Claim 6 (depends on 1)

The method of claim 1, wherein the reliably repeatable optimal states are identified using scatterplots.

### Claim 7 (depends on 6)

The method of claim 6, wherein the scatterplots identify multiple observed optimal states and models in data identified through clustering and recursive-partitioning modeling.

### Claim 8 (depends on 7)

The method of claim 7, further comprising performing an optimal model verification operation, wherein profiles of all inputs are executed over the optimal states and models.

### Claim 9 (depends on 7)

The method of claim 7, further comprising the step of translation of multiple models into performance curves for control systems.

### Claim 10 (depends on 1)

The method of claim 1, further comprising discarding an optimal state associated with an uncontrollable input from the uncontrolled inputs.

### Claim 11 (depends on 1)

The method of claim 1, further comprising:
determining final optimized parameter settings by relating results of application of the recursive partitioning operation back to historical data.

### Claim 12

A system for performing an optimization operation on continuous processes generated from a plurality of system sensors, the system comprising:
a processor;
a data bus coupled to the processor; and
a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus, the computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor and configured for:
transforming a continuous stream of data comprising process states associated with specific ranges of a subset of control parameter inputs, wherein transforming comprises:
dividing the control parameter inputs into outputs, controllable inputs, and uncontrollable inputs; and
identifying constraints for the outputs, controllable inputs, and uncontrollable inputs;
performing a clustering operation on select uncontrollable inputs, wherein the clustering operation includes dividing the ranges into a plurality of reliable repeatable process states;
identifying reliably repeatable optimal states from the reliable repeatable process states, the identifying the -reliably repeatable optimal states comprising defining a key performance indicator as a continuous process output by applying a recursive-partitioning operation to identify input parameter settings that are associated with a desirable process state, the recursive-partitioning operation comprising identifying surrogate parameters and alternative recursive-partitioning models to obtain a pool of combinations of possible parameter settings associated with an optimized process performance;
characterizing relationships among the control parameter inputs for the reliably repeatable optimal states and for non-optimal states; and
determining operational states for the control parameter inputs based on the characterized relationships;
wherein the operational states describe controllable parameters and settings to control the system.

### Claim 13 (depends on 12)

The system of claim 12, wherein:
the subset of control parameter inputs comprises optimization operation states, the optimization operation states comprising clear, stable, and robust process states.

### Claim 14 (depends on 12)

The system of claim 12, wherein the computer program code further comprises instructions executable by the processor for:
extracting the data comprising the process states.

### Claim 15 (depends on 12)

The system of claim 12, wherein the computer program code further comprises instructions executable by the processor for:
excluding data with data errors from the data comprising the process states; and
identifying an aggregation interval for the extracting.

### Claim 16 (depends on 12)

The system of claim 12, wherein the computer program code further comprises instructions executable by the processor for:
determining final optimized parameter settings by relating results of application of the recursive partitioning operation back to historical data.

### Claim 17

A non-transitory, computer-readable storage medium embodying computer program code, the computer program code comprising computer executable instructions configured for:
transforming a continuous stream of data comprising process states associated with specific ranges of a subset of control parameter inputs, wherein transforming comprises:
dividing the control parameter inputs into outputs, controllable inputs, and uncontrollable inputs; and
identifying constraints for the outputs, controllable inputs, and uncontrollable inputs;
performing a clustering operation on select uncontrollable inputs, wherein the clustering operation includes dividing the ranges into a plurality of reliable repeatable process states;
identifying reliably repeatable optimal states from the reliable repeatable process states, the identifying the -reliably repeatable optimal states comprising defining a key performance indicator as a continuous process output by applying a recursive-partitioning operation to identify input parameter settings that are associated with a desirable process state, the recursive-partitioning operation comprising identifying surrogate parameters and alternative recursive-partitioning models to obtain a pool of combinations of possible parameter settings associated with an optimized process performance;
characterizing relationships among the control parameter inputs for the reliably repeatable optimal states and for non-optimal states; and
determining operational states for the control parameter inputs based on the characterized relationships;
wherein the operational states describe controllable parameters and settings to control the system.

### Claim 18 (depends on 17)

The non-transitory, computer-readable storage medium of claim 17, wherein:
the subset of control parameter inputs comprises optimization operation states, the optimization operation states comprising clear, stable, and robust process states.

### Claim 19 (depends on 17)

The non-transitory, computer-readable storage medium of claim 17, wherein the computer program code further comprises computer executable instructions for: extracting the data comprising the process states.

### Claim 20 (depends on 17)

The non-transitory, computer-readable storage medium of claim 17, wherein the computer program code further comprises computer executable instructions for:
excluding data with data errors from the data comprising the process states; and
identifying an aggregation interval for the extracting.

### Claim 21 (depends on 17)

The non-transitory, computer-readable storage medium of claim 17, wherein the computer program code further comprises computer executable instructions for:
determining final optimized parameter settings by relating results of application of the recursive partitioning operation back to historical data.