Plant control systems and methods

Systems and methods provided herein. In one embodiment, a system includes an advisory system including a loss computation engine configured to derive a total system loss for an industrial plant based on a first sensor positioned in a first industrial plant component and on a first physical model of the first industrial plant component. The advisory system further includes a cost model configured to use a cost function to derive a cost based on the total system loss, and a control strategy system configured to derive an advisory report, a control correction factor, or a combination thereof, based on the cost, wherein a control system is configured to apply the control correction factor to control a process in the industrial plant.

BACKGROUND OF THE INVENTION

The present disclosure relates to operations of an industrial plant, and more particularly to systems and methods for improving the operations of the industrial plant.

An industrial plant, such as a power generation plant, includes a plurality of interrelated equipment and processes. For example, power generation plants may include turbine systems and processes for operating and maintaining the turbine systems. During plant operations, the equipment and processes may encounter losses, such as power production system losses related to real world usage of machinery, machinery age, and so on, potentially affecting overall plant effectiveness. It would be beneficial to address such issues to improve plant performance.

BRIEF DESCRIPTION OF THE INVENTION

In a first embodiment, a system includes an advisory system including a loss computation engine configured to derive a total system loss for an industrial plant based on a first sensor positioned in a first industrial plant component and on a first physical model of the first industrial plant component. The advisory system further includes a cost model configured to use a cost function to derive a cost based on the total system loss, and a control strategy system configured to derive an advisory report, a control correction factor, or a combination thereof, based on the cost, wherein a control system is configured to apply the control correction factor to control a process in the industrial plant.

In a second embodiment, a method includes creating a first component loss model configured to derive a first loss for a first component of an industrial plant, and building a second cost model configured to derive a second cost for operations of the second component by using the second loss. The method further includes combining the first and second cost models into a total cost model configured to derive a total cost based on a commercial input, and receiving a first data related to operations of the first component, and a second data related to operations of the second component. The method additionally includes computing the total loss using the first and second data as inputs to the total loss model.

In a third embodiment, a system includes a memory comprising a on-transitory, computer-readable medium comprising code configured to build a first cost model configured to derive a first cost for operations of the first component by using the first loss, and building a second cost model configured to derive a second cost for operations of the second component by using the second loss. The code further comprises code configured to combining the first and second cost models into a total cost model configured to derive a total cost based on a commercial input, and deriving the total cost.

DETAILED DESCRIPTION OF THE INVENTION

Embodiments of the present disclosure may apply to a variety of industrial plants, including but not limited to power plants, chemical plants, manufacturing plants, oil refineries, and the like. Industrial plants may include a variety of equipment and processes useful in providing a variety of operations and services. For example, power plant equipment or machinery may provide operations suitable for producing power. Likewise, chemical processing machinery may provide operations useful in the manufacturing and/or processing of chemicals. Similarly, manufacturing machinery may provide operations suitable for making or otherwise reshaping physical items.

Industrial plants, such as power plants, may include many different components and processes. For example, the plants may include turbine systems, (e.g., gas turbines, steam turbines), electrical generators, heat recovery steam generation (HRSG) systems, gas treatment systems (e.g., acid gas removal systems), feedwater systems, condensers, cooling systems, mixers, boilers, furnaces, gasifiers, and so forth. The aforementioned systems may experience losses. That is, the systems may not be performing to a rated or baseline performance level. For example, the systems may not be producing a desired level of performance level due to aging, degradation, unplanned maintenance events, and other issues. The losses may include pressure losses, thermal losses, alignment losses, (e.g., due to misalignment between rotating and stationary components) vibration losses, leakage, wear and tear losses, undesired fuel quality losses, losses due to undesired operational parameters, losses due to planned or unplanned maintenance events, planned or unplanned events (e.g., offlining for inspection, weather) and so on. The losses may also be due to degradation, and measured by a comparison between a sensor reading and an International Organization for Standardization (ISO) rated value. The losses may occur during steady state operations or during transients (e.g., startup, shutdown, testing, trips). Losses for individual systems and components may be observed and predicted, and a total loss (observed or predicted) for the plant (or any system) may then be tabulated.

Accordingly, the techniques disclosed herein may address the impact of losses on plant performance by quantifying component and total losses and by using loss measures to provide control actions, advisory actions, and scheduling actions (e.g., scheduling of outage and/or maintenance actions) to improve plant performance. In one embodiment, the control actions may include automatic outage actions and/or online corrective actions, as described in more detail below. Alternatively or additionally, advisories may be issued to aid the plant operators in determining potential benefits and/or costs of performing certain plant operations, outage actions, and/or maintenance actions. Further, in power generation plants, the techniques disclosed herein may provide for the simulation and/or results of utilizing “reserve power” at both steady state and transient conditions. Additionally, economic inputs and a cost function may be used, suitable for deriving an economic cost of plant operations. The economic cost may then be used to more efficiently operate the plant as well as to provide visibility of true cost of operations.

To derive losses, empirical data may be analyzed from a variety of sensors, including pressure sensors, flow sensors, temperature sensors, clearance (e.g., difference between a rotary and stationary component) sensors, vibration sensors, speed sensors, emission sensors (e.g., carbon oxides, nitrogen oxides, sulfur oxides, and the like), combustion dynamics sensors, and the like. In addition to sensor data, the data may also include current plant data, historical plant data, technical and commercial data, environmental data, degradation data, optimization criteria, and so forth. Commercial data, for example, may be used to calculate plant parameters related to current quantity of desired power production based on market conditions. Further, federal regulations, code, and/or standards (e.g., industry standards) may be used to derive operational parameters such as emission levels, testing intervals, reporting methods, and so forth. Current plant data generally refers to data generated by the plant during plant operations that are subject to change over time. For example, data generated through the use of plant instrumentation such as sensor instrumentation may be current plant data. Historic plant data generally refers to stable data, including data determined during plant construction, plant configuration data, or records of past plant behavior and/or trends.

By using the systems and methods described herein, the abovementioned data may be combined and manipulated to build a plurality of loss-based models, including component-based loss-based models and total loss (e.g., plant loss) models, which may be used to derive information useful in improving plant operations. The loss-based models may include physics based models, statistical models, and heuristic models (e.g., artificial intelligence models) useful in predicting loss. For example, the physics-based models, statistical models, heuristic models, or a combination thereof, may predict an expected loss based on age of the component, type of component, maintenance of the component, and so on.

Turning now to the figures,FIG. 1is a block diagram of an exemplary power plant10(e.g., integrated gasification combined cycle [IGCC]) configured to provide operational control and advisory actions. As illustrated, the power plant10is powered by a fuel source12, such as a solid feed, which is used to generate a syngas. The fuel source12may include coal, petroleum coke, biomass, wood-based materials, agricultural wastes, tars, coke oven gas and asphalt, or other carbon containing items. The solid fuel of the fuel source12may be passed to a feedstock preparation unit14. The feedstock preparation unit14may, for example, resize or reshape the fuel source12by chopping, crushing, milling, shredding, pulverizing, briquetting, or pelletizing the fuel source12to generate feedstock. Additionally, water, or other suitable liquids, may be added to the fuel source12in the feedstock preparation unit14to create slurry feedstock. In other embodiments, no liquid is added to the fuel source12, thus yielding dry feedstock.

The feedstock may be passed to a gasifier16from the feedstock preparation unit14. The gasifier16may convert the feedstock into a combination of carbon monoxide, carbon dioxide, water, and hydrogen, e.g., syngas. This conversion may be accomplished by subjecting the feedstock to a controlled amount of steam and oxygen at elevated pressures. The gasification process may include the feedstock undergoing a pyrolysis process, whereby the feedstock is heated, generating a solid, e.g., char, and residue gases, e.g., carbon monoxide, and hydrogen.

A combustion process may then occur in the gasifier16. The combustion may include introducing oxygen to the char and residue gases. The char and residue gases may react with the oxygen to form carbon dioxide and carbon monoxide, which provides heat for the subsequent gasification reactions. Next, steam may be introduced into the gasifier16during a gasification step. The char may react with the carbon dioxide and steam to produce carbon monoxide and hydrogen at temperatures ranging from approximately 800° C. to 1100° C. In essence, the gasifier16utilizes steam and oxygen to allow some of the feedstock to be “burned” to produce carbon monoxide and energy, which drives a second reaction that converts further feedstock to hydrogen and additional carbon dioxide. In this way, a resultant gas is manufactured by the gasifier16. This resultant gas may be termed raw syngas. The gasifier16may also generate waste, such as slag18, which may be a wet ash material. This slag18may be removed from the gasifier16and disposed of, for example, as road base or as another building material.

The raw syngas from the gasifier16may then be cleaned in a gas treatment system20. For example, the gas treatment system20may separate sulfur22and salts24from the cooled raw (e.g., non-scrubbed) syngas. Subsequently, the gas from the gas treatment system20may include clean (e.g., scrubbed) syngas. In certain embodiments, a gas processor26may be utilized to remove residual gas components28from the clean (e.g., scrubbed) syngas, such as ammonia, methanol, or any residual chemicals. In addition, in certain embodiments, a carbon capture system30may remove and process the carbonaceous gas (e.g., carbon dioxide that is approximately 80-100 percent pure by volume) contained in the syngas. The scrubbed syngas may be then transmitted to a combustor32, e.g., a combustion chamber, of a gas turbine system34as combustible fuel.

The power plant10may further include an air separation unit (ASU)36. The ASU36may operate to separate air into component gases by, for example, distillation techniques. The ASU36may separate oxygen from the air supplied to it from an ASU compressor38, and the ASU36may transfer the separated oxygen to the gasifier16. Additionally, the ASU36may transmit separated nitrogen to a diluent gaseous nitrogen (DGAN) compressor40.

The DGAN compressor40may compress the nitrogen received from the ASU36at least to pressure levels equal to those in the combustor32of the gas turbine system34, for proper injection into the combustor chamber. Thus, once the DGAN compressor40has adequately compressed the nitrogen to a proper level, the DGAN compressor40may transmit the compressed nitrogen to the combustor32of the gas turbine system34. The nitrogen may be used as a diluent to facilitate control of emissions, for example.

The gas turbine system34may include a turbine42, a drive shaft44and a compressor46, as well as the combustor32. The combustor32may receive fuel, such as syngas, which may be injected under pressure from fuel nozzles. This fuel may be mixed with compressed air as well as compressed nitrogen from the DGAN compressor40, and combusted within combustor32. This combustion may create hot pressurized combustion gases. The combustor32may direct the combustion gases towards an inlet of the turbine42. As the combustion gases from the combustor32pass through the turbine42, the combustion gases force turbine blades in the turbine42to rotate the drive shaft44along an axis of the gas turbine system34. As illustrated, drive shaft44is connected to various components of the gas turbine system34, including the compressor46. The drive shaft44may connect the turbine42to the compressor46to form a rotor. The compressor46includes blades coupled to the drive shaft44. Thus, rotation of turbine blades in the turbine42causes the drive shaft44connecting the turbine42to the compressor46to rotate blades within the compressor46. This rotation of blades in the compressor46causes the compressor46to compress air received via an air intake in the compressor46. The compressed air may then be fed to the combustor32and mixed with fuel and compressed nitrogen to allow for higher efficiency combustion. The drive shaft44may also be connected to a first load48, which may be a stationary load, such as an electrical generator for producing electrical power, for example, in a power plant. Indeed, the first load48may be any suitable device that is powered by the rotational output of the gas turbine system34.

The power plant10also may include a steam turbine engine50and a heat recovery steam generation (HRSG) system52. The steam turbine engine50may drive a second load54. The second load54may also be an electrical generator for generating electrical power. However, both the first and second loads48,54may be other types of loads capable of being driven by the gas turbine system34and steam turbine engine50, respectively.

Additionally, heated exhaust gas from the gas turbine system34may be transported into the HRSG52and used to heat water and produce steam used to power the steam turbine engine50. Exhaust from, for example, a low-pressure section of the steam turbine engine50may be directed into a condenser56. The condenser56may utilize a cooling tower58to exchange heated water for cooled water. The cooling tower58acts to provide cool water to the condenser56to aid in condensing the steam transmitted to the condenser56from the steam turbine engine50. Condensate from the condenser56may, in turn, be directed into the HRSG52. Again, exhaust from the gas turbine system34may also be directed into the HRSG52to heat the water from the condenser56and produce steam.

The illustrated industrial plant10ofFIG. 1includes a variety of different systems and components that perform different tasks, such as the fuel source12, the gasifier16, the gas treatment system20, the carbon capture system30, gas turbine system34, the heat recovery steam generator52, the steam turbine engine50, and so forth. These components may be coupled to a plurality of instrumentation60(e.g., sensors, controllers, actuators, etc.) that exchange signals or data regarding conditions, attributes, and actions of the respective components. The instrumentation60may be configured to monitor a plurality of parameters related to the operation and performance of the components. For example, one of the plurality of instrumentation60may be coupled to the gas turbine system34. The instrumentation60may measure, for example, environmental conditions, such as ambient temperature and ambient pressure, as well as a plurality of engine parameters related to the operation and performance of the gas turbine system34, such as exhaust gas temperature, rotor speed, engine temperature, engine pressure, gas temperature, engine fuel flow, vibration, clearance between rotating and stationary components, compressor discharge pressure, exhaust emissions/pollutants, and turbine exhaust pressure. Further, the instrumentation60may also measure actuator information such as valve position, and a geometry position of variable geometry components (e.g., air inlet). The instrumentation60may also act as a controller, controlling certain aspects of the respective component according to received control signals. Measurements taken by the instrumentation60may be transmitted via a control network62and received by a plant control system64. Likewise, data, such as control signals, from the plant control system64may be transmitted to the instrumentation60.

The plant control system64may be a control station or computer which includes a distributed control system (DCS)66, a manufacturing execution system (MES68), a human machine interface (HMI) system70, and/or a supervisor control and data acquisition (SCADA) system72. The plant control system64may also employ several types of control systems, such as a multivariable control system, an H infinite control system, an H2 control system, a linear quadratic regulator, a linear quadratic Gaussian control system, and so forth. The HMI system70may include a display and interface system, which may enable an operator to interact with the plant control system64and other plant components. For example, the display and interface system may include screens suitable for entering information and displaying a variety of data. In certain embodiments, the display and interface system may enable remote access to the various components of the plant10, such as intranet, and internet or web access.

Additionally, in one embodiment, the control system64may be coupled to an advisory system76. In another embodiment, the advisory system76may be included in the control system64as a subsystem of the control system64. Further, the control system64may include a triple modular redundant (TMR) controller configured to provide redundant control operations by using three processing cores. The advisory system76may receive data from the control system64to perform certain calculations to produce control or maintenance related advisories. The advisory system76may provide recommended control actions and/or optimal maintenance schedules based on a plurality of data, including current plant data, historic plant data, environmental factors and forecasts, market factors and forecasts, and so forth. Such data and the manipulating of such data to obtain the advisories will be discussed in further detail below. The advisory system76may output recommended manual control actions to operators and/or automatically implement control parameters by sending a signal via a channel74to the control system64, which may then send appropriate control signals to the instrumentation60. The manual control actions may be communicated to the operator, for example, through alerts, alarms, and messages. This communication may include textual information and multimedia (e.g., images, videos, 3D views, audio) descriptive of the recommended control action. The advisory system66may be implemented as a single computing device at the plant site or remotely, or the advisory system76may be a plurality of computing devices accessible from different locations.

FIG. 2is a schematic representation of an embodiment of the advisory system76. The advisory system76may include a processor78, a memory80, a display82, a graphical user interface84, one or more I/O ports86, and a networking device88. The processor78may be configured to execute non-transient machine readable code (e.g., computer instructions) to carry out calculations, decisions, data processing, and communications associated with operation of the advisory system76. Such code may be included in the memory80. The memory80may include volatile memory, non-volatile memory, random access memory (RAM), read only memory (ROM), and so forth. The memory80may also store a plurality of data, including historic plant data, business and environmental data, physics data, and so forth. The advisory system76may further include several modules, including a data receiving module90, a model building module92, a calculation module94, and an advisory generation module96.

The data receiving module90may receive current plant data from the instrumentation60as well as any stored data from the memory, such as historic plant data and technical and commercial data, environmental data, degradation data, optimization criteria, regulatory data, and so forth. In certain embodiments, the data receiving module90may be configured to download certain data, such as commercial data (e.g., energy market data, energy futures data, fuel market data, fuel futures data), environmental data (e.g., “green” credits data, cap emissions data), regulatory data (e.g., allowed emissions data, fines, regulatory compliance cost) degradation data (e.g., aging, wear and tear, maintenance logs, inspection logs), and optimization criteria, from a network or database. Such data may be subject to frequent updates.

As previously mentioned, the advisory system76may use several types of data to generate loss-based control advisories. The current plant data, which may be received from the instrumentation60, may include measurements and derivations based on the instrumentation60. For example, the data may include temperature measurements, pressure measurements, flow measurements, clearance measurements (e.g., measuring distances between a rotating component and a stationary component), vibration measurements, position measurements, chemical measurements, power production measurements, exhaust emissions measurements, stress or strain measurements, leakage measurements, speed measurements, fuel utilization measurements, and so forth. The plant equipment data may include data related to individual equipment. For example, the data may include operating conditions of the equipment of plant10(e.g., speed, temperature, pressure, vibration, flow, fuel consumption, power production, clearance), maintenance history (e.g., maintenance logs), performance history (e.g., power production logs), and the like.

The business or market data may include data associated with economic and business conditions that may impact the plant10. For example, the data may include market data for the demand and supply of electrical power, manufactured goods, fuel, raw materials (e.g., metals, chemicals), and/or processed materials (e.g., processed chemicals, refined oil). Further, the data may include data related to futures market, e.g., sales of future power output, future commodities, future raw material, and the like. Additionally, the data may include supply and demand data in regulatory markets, such as cap and trade markets (i.e., emissions markets). Further, the data may include business data related to tax credits for emission controls, tax credits for the use of certain technologies (e.g., carbon capture technologies, carbon sequestration technologies), regulatory costs related to the emissions of certain chemicals (e.g., sulfur emissions, CO2emissions), and so forth. Environmental data may include data such as weather prediction information, which also impact maintenance scheduling and the like. Power grid data may also be used, including current use, offlining of other plants10, maintenance schedules for other plants10, and so on.

The advisory system76may also employ physics related data, which take into account failure mode analysis and risk data, operational models, and physical properties and algorithms. Failure mode analysis and risk data may include data useful in deriving certain risks associated with plant operations. For example, the failure mode analysis and risk data may include physics-based models, such as low cycle fatigue (LCF) life prediction models, computational fluid dynamics (CFD) models, finite element analysis (FEA) models, solid models (e.g., parametric and non-parametric modeling), and/or 3-dimension to 2-dimension FEA mapping models that may be used to predict the risk of equipment malfunction or the need for equipment maintenance. In conjunction with the failure mode analysis, the operational modes and physical properties may be used to simulate the operation of the power plant10and power plant10components. Accordingly, power output, fuel utilization, syngas production, HRGS52energy recovery, turbine34engine speed, and so on, may be simulated.

The failure mode analysis and risk data, and operational models may also include statistical models, such as regression analysis models, data mining models (e.g., clustering models, classification models, association models), and the like. For example, clustering techniques may discover groups or structures in the data that are in some way “similar.” Classification techniques may classify data points as members of certain groups, for example, components having a higher probability of encountering an unplanned maintenance event. Regression analysis may be used to find functions capable of modeling future trends within a certain error range. Association techniques may be used to find relationship between variables. For example, using associative rule learning techniques may lead to associating certain cold start procedures with increased blade wear in a turbine system. It should be noted that the data types described above are examples of data that may be used by the advisory system76in determining optimal control actions and schedules. Embodiments of the advisory system76may utilize only a subset of the mentioned data types or include other data described herein.

The model building module92may be configured to use data received by the data receiving module90to build desired models. The models may include loss-based models suitable for computing estimated losses that each equipment of the plant10may experience. Each of the loss-based models generally models an amount of loss (e.g., power loss, heat loss, pressure loss, leakage, energy recovery loss) for a specific component or system of the plant10. All equipment in the plant10may be thus modeled. One or more total loss models may then be built based on the individual loss models. For example, in one embodiment, the individual loss models may be combined through combinatorics, summation, transfer functions, and the like, to result in system level loss models, which may then be combined to result in a plant level total loss model.

One or more cost functions may also be modeled by the model building module92. Each cost function may compute a cost of operating any of the equipment of the plant10. For example, the cost (accounting, economic and/or engineering cost) for each loss-based model may be calculated. A plant level cost model may then be created as function of total system loss and critical parameters, such as cost, power, heat rates, emissions, expected demand, expected supply, regulatory costs, and so on. An empirical relation between component system loss and total system loss may also be developed, that may be a component of a plant level transfer function relating the cost function to the total system loss, fuel cost, power cost, revenue, and so on. A control algorithm may then be used to derive a more optimal control point, such as a point where the cost function is minimized, a revenue is maximized, a return on investment is maximized, or a combination thereof. The controller may then generate correction factors for control set points.

In one embodiment, the calculation module94uses the abovementioned models, inputs, and at least a subset of the abovementioned data to determine a more optimal control parameter. If the plant is already in an acceptable state, the calculation module may return a value indicative of such. Otherwise, the calculation module94may return an output corresponding to a difference between a current control parameter and a more optimal control parameter. The advisory generation module96may be configured to translate the output from the calculation model94into an operational command that may be performed by the operator or implemented automatically. As such, the advisory generation module96may output an advisory to the operator via a display. The advisory generation module96may also produce an improved operational and/or maintenance schedule, which is provided to the operator. For example, a maintenance schedule may include actions such as inspecting plant equipment, replacing certain components, performing equipment tests, and so forth. Likewise, an operational schedule may include time-based actions useful in, for example, starting up or shutting down plant operations and/or equipment. For example, a turbine startup may include a schedule of operational actions based on delivering fuel, igniting the fuel, and controlling the delivery of fuel and air so as to reach a certain turbine speed. The outputs of the advisory generation module96may be presented to the operator on the display82. The operator may then implement certain recommended control actions via the GUI84. The outputs may also be transmitted to other control stations or devices via the networking device88, which may include a wireless router, a modem, an Ethernet card, a gateway, or the like. The outputs may also be communicated via the communication channel74.

FIG. 3is a block diagram representation of an embodiment of the advisory system76, by which data102may be used to produce automated correction factors104and operator advisories106. The data, as discussed, may include current plant data, historical plant data, physics data, business and market data, environmental data, and so forth. The data may also include empirical data, such as temperatures, flows, valve positions, fuel type, rotor speed, clearance, pressures, requested power output, and so on. In some circumstances, the data102may be provided to an estimator system108. The estimator system108may provide an estimate of certain measurements that may be more difficult to measure directly. For example, the HRSG52may include a steam receiving inlet having a diameter in excess of 1, 5, 10, 15 feet, which may result in flow measurements through the receiving inlet being more difficult to measure. Accordingly, the estimator system108may provide for an estimated measurements of HRSG flows rather than direct measurements. In other embodiments, the estimator system108may not be used and direct measurements may be provided.

One or more physics based models110may also be provided, such as physics based models derived by using the model building module92described in more detail with respect toFIG. 2. Indeed, all of models created by using the model building module92may be used. In the depicted embodiment, the data inputs102, estimated outputs derived by the estimator system108, and the physics based models110may be used by a loss computation engine112to compute losses for all of the components of the plant10, such as the gas turbine34, steam turbine50, loads48,54, HRSG52, condenser56, cooling tower58, ASU36, air compressor38, DGAN compressor40, gasifier16, syngas cleaning system20, gas processor26, carbon capture system30, or a combination thereof. Additionally, the loss computation engine112may aggregate the component losses into a total system loss function. The total system loss function may generally represent losses for all components of the plant10. That is, deviations from a rated pressure, power production, energy recovery, fuel use, energy transfer, temperature, pressure, and/or flow may be derived by the loss computation engine112and combined to produce the total loss for the plant10.

Economic inputs114and the total loss and/or component loss may then be used by a cost model or function116to derive a cost of plant operations, including transient as well as steady-state operations. In one embodiment, the plant level cost model may generally determine what it costs the plant to achieve a desired output, such as the amount of power produced. The plant level cost model116is generally a function of certain desired outputs (e.g., power generated), operating parameters for achieving the desired output (e.g., equipment pressure, heat rate, temperature, flow rate, operating speed, emissions, cost and amount of fuel), and the losses derived by the loss computation engine112. As such, the plant level cost model116may be able to determine the cost (e.g., maintenance costs, fuel costs, risks, operational costs) of producing the desired output. By incorporating real-world losses, the plant level cost model and associated submodels (e.g., physics-based models, statistical models) may improve plant10operations and maintenance.

The cost model116may include one or more objective cost functions, one or more utility cost functions, or a combination thereof, that may be mathematically optimized by finding f(xo)≦f(x) (e.g., minima) for all x in A, where A is a set of constraints. Solvers, including but not limited to linear programming (LP) solvers, second order cone programming (SOCP) solvers, semidefinite programming (SDP) solvers, conic programming solvers, non-linear programming solvers, constraint satisfaction solvers, and/or heuristic solvers, may be used to solve the cost model116. In another embodiment, the cost function116may be mathematically optimized using the solvers described herein, by finding f(xo)≦f(x) (e.g., maxima) for all x in A.

A control strategy system118may then be used to derive the advisories106and the correction factors104based on a desired control strategy. For example, in one embodiment, the control strategy may include minimizing overall costs of plant operations. Accordingly, the correction factors104and advisories106may be derived by the control strategy system118so that the cost function116returns a more minimal cost point. In another embodiment, such as when a neighboring plant10is out of commission due to inclement weather or other unplanned events, the control strategy system118may provide a control strategy that maximizes plant10output but may issue advisories on cost overruns and/or new maintenance schedules. The control strategy system118may employ an artificial intelligence (AI) and/or machine learning system to make advisory decisions based upon the output of the cost function116and other data. The other data may include business/market data, environmental data, regulation data, and so forth. Such data, though not based on physical conditions of the plant, may be worth considering as they may impact control decisions. For example, certain regulations may award credits or benefits to the plant or parent company if the plant emissions are below a certain threshold. However, in order to produce a target power output, the plant may emit greater emissions, and potentially lose the emissions credit. Thus, the control strategy system118, via the AI or machine learning system may calculate the gains and losses of either scenario and make a more advantageous control decision. Additionally, the AI or machine learning system may also use historical data to facilitate its decision making. This may include updating or adjusting certain decision thresholds according to past decisions, results, and trends, as well as gaining more refined input-output causal relationships. For example, the control strategy system118may apply these methods in determining an more optimal plant10outage schedule that maximizes returns and minimizes loss.

The AI and/or machine learning systems used to carry out the abovementioned functions may include a k-nearest neighbor system (k-NN) implementing a k-NN algorithm, in which objects or situations are classified based on the closest known example, or nearest neighbor. The AI and/or machine learning system may also include an expert system, in which the expert system emulates the decision making of a human expert. The expert system may include a knowledge database, which may be expressed as a series of “if . . . then . . . ” statements and the like. The expert system may also employ an inference engine to facilitate reasoning and decision making. The inference engine may include propositional logic, temporal logic, modal logic, fuzzy logic, and the like. In addition to the discussed KNN algorithm and expert system, control strategy system118may also use other AI and/or machine learning systems, such as a genetic algorithm, a state vector machine, fuzzy logic, neural networks, and so forth.

The results of the control strategy system118may include correction factors104that may be communicated to an industrial controller120. Accordingly, the controller120may output more optimum firing temperatures, exhaust temperatures, fuel flows, pressures, speeds, valve positions, and so forth. Likewise, the result may be used to generate operator advisories106, which may be presented to the operator in the form of an alert, alarm, or message. The operator advisories106may include instructions to perform a certain control action. The operator advisories106may also include a maintenance planner, which shows the operator the best times to make certain control or maintenance actions, some of which may involve taking the plant or certain components of the plant offline. As discussed, business and environmental data may be used to build such a schedule. For example, the techniques described herein may take into consideration expected demand, cost, profitability, weather conditions, as well as the criticality of the maintenance or outage. Additionally, historical plant data may also be used to build the schedule. Additionally, in certain embodiments, the advisories106may include an advisory display or interface provided through the GUI84, with which the operator may interact to visualize and to directly implement the suggested recommendations, as described in more detail below with respect toFIG. 4.

FIG. 4illustrates an embodiment of an interface122that may be displayed on the GUI84. In this particular embodiment, the interface includes a data window124. The data window124may present a plurality rows of plant operations126and rows of losses128,130,132,134,136, and138associated with the operations126. The losses128may include calculated optimal losses associated with, for example, start operations (e.g., plant10startup, turbine34startup, gasifier16startup, HRSG52startup). The losses130may include actual losses found during start operations. The losses132may include calculated optimal losses associated with, for example, steady state or normal operations. The losses134may include actual losses found during steady state or normal operations. The losses136may include calculated optimal losses associated with, for example, shutdown operations. The losses138may include actual losses found during shutdown operations. By providing for a visual (graphic and/or numeric) representation of losses128,130,132,134,136, and138associated with the operations126, the screen122may provide a more intuitive and readable visualization of plant10losses.

More detailed graphical views, such as charts190and192may also be provided. For example, the user may select one or more of the operations126and the charts190and192may then be dynamically displayed to present detailed graphical views associated with the selected operations126. For example, chart190may present detailed graphical data of start up losses associated with the selected operations126, while chart192may present detailed graphical data of steady state losses associated with the selected operations126. Status information146for the operations126may also be presented, along with a performance bar146detailing performance metrics for the operations126.

A button148or other suitable mechanism may be provided to store or log the data presented by the interface122. Likewise, a button150or other suitable mechanism may be provided to send notifications (e.g., email, instant message, cell phone call, short message service [SMS] notification) of the data presented by the interface122. Set point corrections152may also be presented, detailing certain corrections advised by the control strategy system118. Accordingly, a button154or other suitable mechanism may be provided to automatically enter the set point corrections152, for example, into the controller120to be acted upon by the controller120.

FIG. 5illustrates a process160that may be employed by the plant10, and in certain embodiments, by the controller120and/or the advisory system76. The process160may be implemented as non-transient machine readable code (e.g., computer instructions) to carry out calculations, decisions, data processing, and communications for the process160and executable by the controller120and/or the advisory system76. The process160may receive (block164) data162from various sources. The data162may include current plant data, which may come from the instrumentation60(FIG. 1), historical plant data, business and environmental data, optimization criteria, physics data, and so forth. Some data may be received from the memory80and/or be downloaded from a network or database located on another machine. After the appropriate data is received (block164), the data162may be used to derive (block166) component level loss models. As mentioned above, physics-based models, statistical models, and/or heuristic models may be used to model a component's loss based on inputs such as component age, inspection data, maintenance records, fleet historical records, and so on.

The component loss models may be combined to build (block168) one or more total loss models, such as plant total loss models. Accordingly, the total loss of transient and/or steady state operations for the plant10may be derived by the techniques described herein. The process160may then build (block170) component cost models or functions, incorporating, for example, commercial data (e.g., energy market data, energy futures data, fuel market data, fuel futures data), environmental data (e.g., “green” credits data, cap emissions data), and/or regulatory data (e.g., allowed emissions data, fines, regulatory compliance cost). One or more plant level cost models may also be built (block172). For example, the component cost models may be aggregated into a plant level model that may more accurately derive a cost for transient and steady state operations of the plant10.

Accordingly, costs, such as plant level costs and component level costs, may be derived (block174) during operations. The operations may include steady state operations (e.g., normal operations), and/or transient operations (e.g., startup, shutdown, trip). Based on the derived costs, a control strategy may be applied (block176). For example, the control strategy may attempt to minimize costs, maximize profit, maximize return on investment, maximize production, and so forth. The correction factors104may then be issued, for example, to the controller120, to automate the control strategy. Additionally or alternatively, the advisories106may be provided, useful in advising courses of action so that plant operators may more efficiently operate the plant10.

By way of example only, an air inlet system pressure loss may reduce gas turbine34power and heat rate, and may be expressed as ΔP=f×(Lift/Drag)×(V×V/(2×g)), where V is air input (e.g., velocity) to a gas turbine duct and g is gravity. Similarly, pressure loss for a gas turbine exhaust system may be a function of reference temperature and air temperature as well as respective velocity squares. Feed-water system losses may be derived as described below.

For a feed-water system including a heater and a pump providing water or steam to intermediate pressure (IP) and high pressure (HP) sections of the HRSG52, HP/IP economizer hot exhaust gases may pass through feed-water output and may be delivered to HP/IP drums. The feed-water pump may have pumping losses and pressure losses, expressed as Pump efficiency=(P2−P1)/rho×(h2−h1) where P1 is first pressure, P2 is second pressure, rho is a density measure, and h1 is a first pressure head and h2 is a second pressure head. Accordingly, pressure loss=Pump efficiency×(rho×(h2−1h1)). Given that Terminal Temperature difference (TTD)=saturation temperature−feedwater outlet temperature, and Drain Cooler Approach (DCA) Temp diffeference=Drain outlet temp−FW inlet temp, and Temperature rise (TR)=FW outlet temp−FW inlet temp, then TTD may be an indicator of an amount of heat transfer and DCA may be an indicator of fluid leakage, crack in baffles, or other losses.

By measuring steam extraction temperature, inlet flows, outlet temperatures, pressures and flows, we can calculate the above parameters for the plant10or any system included in the plant10. Thus, a Total System Loss (e.g., for Feedwater system, Inlet system, and exhaust system only)=[k1×Pump efficicency+k2×TTD+k3×DCA+k4×TR+k5×ΔP inlet+k6×ΔP exh], where k1 . . . k6 are factors of importance that is dependent on time of day, season, transient or steady state, etc. One can then define thresholds for TTD, DCA, TR and Pump efficiency to quantify these losses. Accordingly, a plant level cost function may be developed, including feedwater, inlet and exhaust systems, where the plant level cost function ═C1×[Total System Loss]+C2×Fuel cost+C3×(Supply˜Demand)+C4×(Electricity Cost), where C1 . . . C4 are weighting factors that normalize the above cost function to be minimized.

Thus, an objective function (e.g., based on the cost function) may be set up for the controller120, such that {Objective function: Optimize (Plant level cost function)::constraints→plant/component design limits, TTD>threshold_TTD, TR<threshold_TR, DCA>threshold_DCA, ΔP>threshold_ΔP}. The controller120may then output flow and valve settings. These settings may in turn be mapped to exhaust temperature and exhaust flow rate values such that total system loss may remain approximately mathematically optimal for a desired plant10performance. The correction factors104may be used depending on a mode of operation. For example if the plant10is operating in a transient (e.g., start-up) mode, a power increase may be a primary objective, thus the controller may not honor the correction factors104but may instead issue advisories, including a running log of losses that may be affecting the plant's performance. If the plant10is in a steady state mode of operation, then the controller120may provide corrective action by using the correction factors104. The techniques described herein may be used at base load, e.g., manufacturer recommended percentage of maximum continued rating or produced power, peak load, e.g., maximum recommended continued rating or produced power, part load, e.g., a load less than base load, or a combination thereof. For example, based on derived losses, the techniques described herein may choose an improved operating point at part load that is derived to consume least life either in terms of first stage temperature (e.g., if the component is a multi-stage turbine34) and/or rate of degradation of components.

Technical effects of the invention include providing systems and methods that improve operations for an industrial plant and plant components. A total loss for plant operations, including steady state and transient operations may be derived, based on constructing a set of models (e.g., physics-based models, statistical models, heuristic models). One or more cost functions may then be derived, suitable for using the total loss as input and providing for a more accurate cost of plant operations. The cost function may then be used to find a more optimal operating strategy, and advisories and control actions may be issued based the operating strategy. Thus, the systems and methods enable plants to be controlled in a more efficient manner.