Sootblowing optimization for improved boiler performance

• US 8,498,746 B2
• Filed: 09/07/2012
• Issued: 07/30/2013
• Est. Priority Date: 10/05/2007
• Status: Active Grant

• Current Assignee: GE Digital Holdings LLC (GE Vernova, Inc.)
• Original Assignee: NeuCo Incorporated (General Electric Company)
• Inventors: James, John Robert, McDermott, John, Piche, Stephen, Pickard, Fred, Parikh, Neel J.
• Primary Examiner(s): Gordon, Brian R
• Assistant Examiner(s): Sasaki, Shogo
• Application Number:
  US13/606,311
• Publication Number:
  US 20130018831A1
• Time in Patent Office: 326 Days
• Field of Search: 700/266, 700/274, 702/31, 702/32, 422/2, 422/62, 422/105, 435/3, 436/3, 436/55, 122/379, 122/390, 122/392, 153161-3181
• US Class Current: 700/274
• CPC Class Codes: F22B 37/48 Devices for removing water, salt, or sludge from boilers cleaning internal or external surfaces of water tubes F28G; Arrangements of cleaning apparatus in boilers cleaning external surfaces of tubes by soot blowers F23J; Combinations thereof with boilers
• CPC Class Codes: F22B 37/56 Boiler cleaning control devices, e.g. for ascertaining proper duration of boiler blow-down
• CPC Class Codes: F23J 3/023 cleaning the fireside of watertubes in boilers
• CPC Class Codes: Y10T 436/12 Condition responsive control

Abstract

A sootblowing control system that uses predictive models to bridge the gap between sootblower operation and boiler performance goals. The system uses predictive modeling and heuristics (rules) associated with different zones in a boiler to determine an optimal sequence of sootblower operations and achieve boiler performance targets. The system performs the sootblower optimization while observing any operational constraints placed on the sootblowers.

Claims

1. A computer-based soot cleaning optimization system for optimizing soot cleaning operations in a boiler of a power generating unit, wherein the boiler is divided into a plurality of zones, the system comprising:

a zone selection component for receiving current operating conditions of the power generating unit that include current operating conditions associated with soot cleaning devices and the boiler, and for selecting one of said plurality of zones of the boiler for a soot cleaning operation given said current operating conditions of the power generating unit, wherein said zone selection component includes an expert system comprised of;

an inference engine,

a first knowledge base comprising a plurality of propose rules, wherein each of said plurality of propose rules has associated therewith;

(a) one or more trigger conditions, (b) one or more enabling conditions indicative of whether soot cleaning can be currently initiated in a zone of the boiler, and (c) a proposed action having an associated rank, and

a second knowledge base comprising a plurality of apply rules;

said zone selection component programmed to select one of said plurality of zones of the boiler by executing the following steps;

accessing the expert system;

using the inference engine for evaluating the plurality of propose rules to generate a list of proposed actions for achieving boiler performance goals for operation of the boiler, wherein each proposed action identifies a zone for a soot cleaning operation,

wherein the proposed action associated with a propose rule is added to the generated list of proposed actions only when the following conditions are satisfied;

(a) the trigger conditions associated with the propose rule and (b) the enabling conditions associated with the propose rule, and

wherein satisfaction of the trigger conditions and enabling conditions are determined using the current operating conditions transmitted by the communications interfaces;

using the inference engine for evaluating the plurality of apply rules of the second knowledge base to select one proposed action from the generated list of one or more proposed actions determined by evaluating the plurality of propose rules, wherein said one proposed action is selected from the generated list of proposed actions according to the rank associated with each proposed action; and

a soot cleaning device selection component for selecting at least one soot cleaning device within the zone identified by the selected proposed action.

2. A computer-based soot cleaning optimization system according to claim 1, wherein said trigger conditions are associated with at least one of the following:

(1) boiler performance, or (2) a monetary effect of cleaning a zone yielding a predicted cost savings.

3. A computer-based soot cleaning optimization system according to claim 1, wherein at least one of said apply rules is based upon a monetary effect of a proposed action on the operation of said power generating unit.

4. A computer-based soot cleaning optimization system according to claim 1, wherein said computer-based soot cleaning optimization system uses said apply rules to dynamically adjust ranks associated with the proposed actions based on their expected impact on boiler performance.

5. A computer-based soot cleaning optimization system according to claim 1, wherein said apply rules are based on a neural network model.

6. A computer-based soot cleaning optimization system according to claim 5, wherein said neural network model determines effects on boiler performance resulting from cleaning a boiler zone.

7. A computer-based soot cleaning optimization system according to claim 6, wherein said computer-based optimization system adjusts ranks associated with the proposed actions in accordance with said effects on boiler performance, as determined by said neural network model.

8. A computer-based soot cleaning optimization system according to claim 1, wherein said rank associated with each proposed action is a fixed rank having an assigned fixed value.

9. A computer-based soot cleaning optimization system according to claim 1, wherein said rank associated with each proposed action is a monetary rank indicative of cost savings for operation of said power generating unit.

10. A computer-based soot cleaning optimization system according to claim 1, wherein said soot cleaning device selection component includes:

a scenario generator for generating one or more soot cleaning scenarios for activating one or more soot cleaning devices within the selected zone in accordance with current operating conditions; and

a scenario evaluator for determining which of said one or more soot cleaning scenarios results in best predicted future boiler performance.

11. A computer-based soot cleaning optimization system according to claim 10, wherein said scenario evaluator includes:

a neural network (NN) model for predicting boiler performance for each of said one or more soot cleaning scenarios; and

a cost function for determining a cost associated with each of the one or more soot cleaning scenarios.

12. A computer-based soot cleaning optimization system according to claim 10, wherein said soot cleaning device selection component is programmed to select said at least one soot cleaning device within the selected zone by:

using said scenario generator to generate said one or more soot cleaning scenarios, wherein for each scenario one or more soot cleaning devices are activated within the selected zone in accordance with said current operating conditions;

using the scenario evaluator to determine which of said one or more soot cleaning scenarios results in a best predicted future boiler performance; and

selecting one or more soot cleaning devices for activation according to the soot cleaning scenario resulting in the best predicted future boiler performance.

13. A computer-based soot cleaning optimization system according to claim 12, wherein said one or more soot cleaning scenarios are generated with consideration of one or more constraints on said soot cleaning devices.

14. A computer-based soot cleaning optimization system according to claim 13, wherein said one or more constraints include time limits since last activation of said soot cleaning devices.

15. A computer-based soot cleaning optimization system according to claim 10, wherein said scenario evaluator includes:

a neural network (NN) model for predicting boiler performance for each of said one or more soot cleaning scenarios; and

a cost function for determining a cost associated with each of the one or more soot cleaning scenarios, wherein said scenario evaluator determines which of said one or more soot cleaning scenarios results in the best predicted future boiler performance by;

inputting each of the one or more soot cleaning scenarios into the neural network (NN) model;

determining a predicted boiler performance for each of the one or more soot cleaning scenarios using the respective neural network model;

determining the cost associated with each of the one or more soot cleaning scenarios using the cost function; and

activating the one or more soot cleaning devices associated with the soot cleaning scenario that has the lowest cost in accordance with the cost function.