Abstract:
A control system ( 207 ) for optimizing a chemical looping process of a power plant includes an optimizer ( 420 ), an income algorithm ( 230 ) and a cost algorithm ( 225 ) and a chemical looping process models. The process models are used to predict the process outputs from process input variables. Some of the process in puts and output variables are related to the income of the plant; and some others are related to the cost of the plant operations. The income algorithm ( 230 ) provides an income input to the optimizer ( 420 ) based on a plurality of input parameters ( 215 ) of the power plant. The cost algorithm ( 225 ) provides a cost input to the optimizer ( 420 ) based on a plurality of output parameters ( 220 ) of the power plant. The optimizer ( 420 ) determines an optimized operating parameter solution based on at least one of the income input and the cost input, and supplies the optimized operating parameter solution to the power plant.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present invention claims the benefit of U.S. Provisional Patent Application Ser. No. 61/033,202, entitled “CONTROL AND OPTIMIZATION SYSTEM”, U.S. Provisional Patent Application 61/033,210, entitled “FUZZY LOGIC CONTROL AND OPTIMIZATION SYSTEM”, and U.S. Provisional Patent Application Ser. No. 61/033,185, entitled “INTEGRATED CONTROLS DESIGN OPTIMIZATION”, all of which are incorporated herein by reference in their entirety. 
    
    
     STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT 
     
         
         The U.S. Government has rights in this invention pursuant to Contract No. DE-FC26-07NT43095 awarded by the U.S. Department of Energy. 
       
    
    
    
     TECHNICAL FIELD 
     The present disclosure relates generally to an optimization system and, more particularly, to a process design and control optimization system for a chemical looping plant. 
     BACKGROUND 
     Chemical looping (CL) is a recently developed process which can be utilized in electrical power generation plants which burn fuels such as coal, biomass, and other opportunity fuels. The CL process can be implemented in power plants, and provides promising improvements in terms of reduced plant size, reduced emissions, and increased plant operational efficiency, among other benefits. 
     A typical CL system utilizes a high temperature process, whereby solids such as calcium- or metal-based compounds, for example, are “looped” between a first reactor, called an oxidizer, and a second reactor, called a reducer. In the oxidizer, oxygen from air injected into the oxidizer is captured by the solids in an oxidation reaction. The captured oxygen is then carried by the oxidized solids to the reducer to be used for combustion and/or gasification of a fuel such as coal, for example. After a reduction reaction in the reducer, the solids, no longer having the captured oxygen, are returned to the oxidizer to be oxidized again, and the cycle repeats. 
     Depending on a ratio of the fuel to the air, different gases are produced in the oxidation and reduction reactions. As a result, the ratio of fuel to air can be controlled such that the CL system may be utilized in different ways, such as: as a hybrid combustion-gasification process which produces hydrogen for gas turbines, fuel cells and/or other hydrogen-based applications; as a hybrid combustion-gasification process which produces a synthesis gas (syngas) containing varying amounts of hydrogen and carbon dioxide for gas turbines and/or fuel cells; or as a combustion process for a combustion-based steam power plant. 
     The CL process is more complicated than processes of traditional plants such as conventional circulating fluidized bed (CFB) plants, for example. As a result, traditional plant controls applied to the CL process necessarily result in separate control loops for each CL loop. However, using separate control loops for each CL loop is inefficient and does not optimize performance of the CL process, since accurate control depends on coordinated control of multiple parameters in each loop, and parameters which crossover between loops. 
     In addition, the CL process has multi-phase flows and chemical reactions which are characterized by process nonlinearities and time delays due to mass transport and chemical reaction rates. As a result, traditional power plant design without considering control optimization systems in early stages of process design are further inadequate for integrated optimization of process performance and system operability. 
     Further, many of the variables in the CL process are nonlinear and/or have complex relationships with other variables, e.g., inter-loop interaction of variables. As a result, models which effectively simulate these multi-interdependent variable relationships have thus far been inaccurate, inefficient, and difficult and/or time consuming to work with. 
     Optimization systems which have been developed thus far are focused on optimizing conventional combustion power plants. Furthermore, these optimization systems have been focused on solving very specific, localized optimization problems rather than global optimization of plant operations. Furthermore, the associated statistical analysis for conventional combustion power plants is based upon an assumption of linear relationships between variables. As a result, the associated statistical analysis for conventional combustion power plants is cumbersome and inaccurate when used to analyze the complex, inter-related, nonlinear dynamics of variables in the CL process. 
     In the next generation power plants based on a CL system, steam-water side control requirements will remain essentially the same as in current conventional plants (e.g., feedwater and steam flows, steam pressures, steam temperatures, drum levels). However, it is expected that improved controls which utilize both steam-water side variables and combustion/gasification CL variables will be required to better handle inherent process variable interactions in the CL process. In addition, conventional power plant simulators are limited to steam/water side process dynamics and only very simple combustion or furnace process dynamics are modeled; dynamic models of complex atmosphere control systems such as in the CL process are not available at this time. 
     Process and equipment integration and optimization of the CL system is also needed. More specifically, CL integrated processes are currently not controlled at economically optimum operating conditions. This is especially true during load changes and when other plant disturbances occur. Complex relationships between the many variables and processes described above affect performance of the CL process, and further complicate efforts to optimally and efficiently control the CL process. 
     Accordingly, it is desired to develop an integrated process design and control optimization system and, more specifically, an integrated process design and control optimization system for a CL power plant, which overcomes the shortfalls described above. 
     SUMMARY 
     According to the aspects illustrated herein, there is provided a control system for optimizing a chemical looping process of a power plant includes an optimizer, an income algorithm and a cost algorithm. The income algorithm provides an income input to the optimizer based on a plurality of input parameters of the power plant. The cost algorithm provides a cost input to the optimizer based on a plurality of output parameters of the power plant. The optimizer determines an optimized operating parameter solution based on at least one of the income input and the cost input, and supplies the optimized operating parameter solution to the power plant. 
     According to the other aspects illustrated herein, a system for optimizing a power plant includes a chemical loop having an input for receiving an input parameter and an output for outputting an output parameter. The system further includes a nonlinear controller which receives the output parameter, optimizes the input parameter based on the received output parameter, and outputs the optimized input parameter to the input of the chemical loop. 
     According to yet other aspects illustrated herein, a system for optimizing a power plant includes a chemical loop having an input for receiving an input parameter, an output for outputting an output parameter, and a nonlinear model predictive controls controller operably connected to the chemical loop. The nonlinear model predictive controls controller includes a model part, a simulator part operably connected to the model part, and an optimizer part operably connected to the model part. The nonlinear model predictive controls controller receives the output parameter, optimizes the input parameter based on the received output parameter, and outputs the optimized input parameter to the input of the chemical loop. 
     The above described and other features are exemplified by the following figures and detailed description. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Referring now to the figures, which are exemplary embodiments, and wherein the like elements are numbered alike: 
         FIG. 1  is a block diagram of a calcium oxide-based two loop chemical looping (CL) system. The dual loop process designs is applicable to calcium based chemical looping as well. 
         FIG. 2  is a block diagram of a CL combustion-based steam power plant; 
         FIG. 3  is a block diagram of an integrated optimization system for a CO 2 -ready CL system; 
         FIG. 4  is a block diagram which illustrates an integrated CL process performance design and controls design optimization; and 
         FIG. 5  is a block diagram of a model predictive controls (MPC) controller for a CL process. 
     
    
    
     DETAILED DESCRIPTION 
     Disclosed herein is an integrated process design and control optimization system for a chemical looping (CL) system of a CL plant, similar to that described in greater detail in U.S. Pat. No. 7,083,658, which is incorporated herein by reference. Referring to  FIG. 1 , a CL system  5  includes a first loop  10 , e.g., a reducer  10 , and a second loop  20 , e.g., an oxidizer  20 . Air  30  is supplied to the oxidizer  20 , and calcium (Ca)  40  is oxidized therein to produce a calcium oxide (CaO)  50 . In the CL process of the CL system  5 , the CaO  50  is supplied to the reducer  10 , and acts as a carrier to deliver oxygen to fuel  60  (such as coal  60 , for example) supplied to the reducer  10 . As a result, the oxygen delivered to the reducer  10  interacts with the coal  60  in the reducer  10 . Reduced calcium oxide  40  is then returned to the oxidizer  20  to again be oxidized into CaO  50 , and the CL process repeats. 
     Nitrogen gas (N 2 )  70 , extracted from the air  30  during oxidation, as well as heat (not shown) resulting from the oxidation, exit the oxidizer  20 . Likewise, a gas  80  produced during reduction in the reducer  10  exits the reducer  10 . The gas  80  includes, for example, a synthesis gas (syngas), hydrogen gas (H 2 ), and/or carbon dioxide gas (CO 2 ). Composition of the gas  80 , e.g., proportions of the syngas, the H 2 , and/or the CO 2  therein, varies based upon a ratio of the coal  60  to the air  30 . 
     Exemplary embodiments are not limited to two loops, as described above with reference to  FIG. 1 , but instead may include either a single loop or more than two loops. For example, in an alternative exemplary embodiment, the CL system  5  includes a third loop (not shown), such as a calciner loop, for example, which allows H 2  generation from reformed syngas  80 . 
     The calcium-based CL system  5  may also include a thermal loop which generates steam to drive a turbine, for example. Specifically, referring to  FIG. 2 , a thermal loop  90  includes a steam turbine  95  which drives a power generator  100  using steam  105  generated by boiling feedwater  110  with heat produced during oxidation in the oxidizer  20 . 
     The air  30  is supplied to the oxidizer  20 , as described above with reference to  FIG. 1 , while waste  115 , such as ash and/or excess calcium sulfate (CaSO 4 ), are removed from the oxidizer  20  for disposal in an external facility (not shown). The coal  60 , as well as calcium carbonate (CaCO 3 )  120  and recirculated steam  125 , are supplied to the reducer  10  for a reduction reaction therein. 
     In operation, the reduction reaction occurs between carbon and sulfur in the coal  60 , the CaCO 3    120 , and CaSO 4    127 . The reduction reaction produces calcium sulfide (CaS)  128 , which is separated by a separator  130  and is thereafter supplied to the oxidizer  20  through a seal pot control valve (SPCV)  135 . A portion of the CaS  128 , based upon CL plant load, for example, is recirculated to the reducer  10  by the SPCV  135 , as shown in  FIG. 2 . In addition, the separator separates the gas  80 , e.g., CO 2 , from the CaS  128 . 
     The CaS  128  is oxidized in an oxidation reaction in the oxidizer  20 , thereby producing the CaSO 4    127  which is separated from the N 2    70  by a separator  130  and is supplied back to the reducer  10  via a SPCV  135 . A portion of the CaSO 4    127  is recirculated back to the oxidizer  20  by the SPCV  135  based upon CL plant load, for example. The oxidation reaction also produces heat which boils the feedwater  110  into the steam  105  supplied to the steam turbine  95 . 
     While a calcium oxide based CL system has been described, the present invention is also applicable to a metal oxide based CL system similar to that described in U.S. patent application Ser. No. 10/542,749, which is incorporated herein by reference. 
     An exemplary embodiment of an integrated process design and control optimization system for a CL plant will now be described in further detail with reference to  FIGS. 3 through 5 . It will be noted that the integrated process design and control optimization system is not limited to the CL plant configurations described herein. For example, in alternative exemplary embodiments, the integrated process design and control optimization system may be used with any and all CL-based systems, including but not limited to: single, dual, and multiple, e.g., two or more, loop CL systems, whether calcium- or metal oxide-based; with or without steam activation loops; with/without calcinations loop; CL-based next generation CL-based plant with CO 2  capture for utilization or sequestration; and CL-based CO 2 -ready power plants, but is not limited thereto. 
     The CL process involves multi-phase flows and chemical reactions characterized by process nonlinearities and time delays due to mass transport rates and chemical reaction rates. Thus, as will be described in greater detail below, nonlinear optimization and control techniques are beneficial for controlling the CL process. Specifically, an exemplary embodiment includes nonlinear dynamic chemical looping modeling and simulation derived from first principle equations (mass, momentum, and energy balances, for example). The modeling and simulation includes any combination of ordinary differential equations (ODEs), algebraic equations (AEs), and partial differential equations (PDEs). In addition, empirical modeling methods, e.g., neural networks (NN) such as nonlinear autoregressive network with exogenous inputs (NARX), nonlinear auto regressive moving average with exogenous inputs (NARMAX), wavelet network models, and Wiener-Hammerstein models, for example, are used in a hybrid dynamic model structure which combines simplified first-principle models with data-driven models. Further, multivariable model predictive controls (MPC) using both linearized models and nonlinear models provide solutions to dynamic optimization of the CL process. In addition to providing optimized modeling, simulation and control, the multivariable MPC according to an exemplary embodiment is robust to disturbances and model inaccuracy, thereby providing stabilized control of the CL process. MPC can be used as a supervisory controller overseeing the regulatory controls using, for example, PID controllers, fuzzy controllers, or any type of adaptive controllers (self-tuning regulators, neuro-adaptive controllers, wavelet network model reference adaptive controllers). MPC can also be used as direct controllers to regulate and optimize the CL process with multiple interactive loops. 
     Referring to  FIG. 3 , an optimization system  200  for a CL-based CO 2 -ready power plant  205  includes a system  207  such as a control system  207 , e.g., a plant control system  207 , having an optimizer  210 . In an exemplary embodiment, the system optimizer  210  is a multivariable optimizer  210  which performs a total economics-based optimization of the power plant  205 . More specifically, the multivariable optimizer  210  focuses on thermo-economic performance, emissions reduction and/or control, and life extension criteria for equipment associated with the power plant  205 . 
     To perform the total economics-based optimization of the power plant  205 , the multivariable optimizer  210  receives input parameters  215  and output parameters  220  of the power plant  205  through a cost algorithm  225  and an income algorithm  230 , respectively, as shown in  FIG. 3 . In an exemplary embodiment, the input parameters  215  include, but are not limited to, fuel flow, sorbent flow, air flow, water flow, limestone flow, and solids circulation rate. The output parameters  220  include power generation rate, CO 2  utilization, CO 2  capture, and CO 2  storage, for example, but are not limited thereto. 
     The multivariable optimizer  210  receives outputs from the cost algorithm  225  and the income algorithm  230  to determine an optimized operating parameter solution for the power plant  205 , based on predetermined operating constraints  235  and environmental constraints  240 , for example. Specifically, in an exemplary embodiment, the cost algorithm  225  sums a set of products of predetermined individual cost factors C i  and individual inputs X i  of the input parameters  215 , while the income algorithm  230  sums a set of products of predetermined individual income factors P i  and individual outputs Y i  of the output parameters  220 . The individual cost factors C i  include, for example, but are not limited to auxiliary power cost, limestone cost, and fuel cost. The individual income factors P i  include, for example, emissions credit and life extension credit, but are not limited thereto. 
     The multivariable optimizer  210  applies the optimized operating parameter solution to the power plant  205  using a distributed control system  245  and an advance process control (APC) system  250  as shown in  FIG. 3 . As a result, the power plant  205  is operated at an optimal total-economics-based operating point. 
     In an exemplary embodiment, the APC system  250  includes components (not shown) such as a filter, a flash dryer absorber (FDA), a spray dryer absorber (SDA), an electrostatic precipitator (ESP), and/or a flue gas desulfurization (FGD) system, for example, but is not limited thereto. 
     An exemplary embodiment may further include a soft sensor module  255 , as shown in  FIG. 3 . The soft sensor module  255  includes a soft sensor (not shown), e.g. a virtual sensor which uses software to process signals obtained from the output parameters  220  (or other parameters of the power plant  205 ). Soft sensors are able to combine and process measured parameters to provide additional parameters, without directly measuring the additional parameters. The soft sensor according to an exemplary embodiment is based on fusion of existing sensors; alternatively, the soft sensor may be based on models developed for simulation and control, for example, but alternative exemplary embodiments are not limited thereto. 
     In addition, the optimization system  200  according to alternative exemplary embodiments is not limited to utilization with the CL-based-CO 2  ready power plant  205  as shown in  FIG. 3 . For example, the optimization system  200  may be used with any CL-based power plant, such as single or multiple loop CL systems (whether calcium- or metal oxide-based) and CL-based plants with CO 2  capture for utilization or sequestration, but alternative exemplary embodiments are not limited thereto. 
     Referring now to  FIG. 4 , an optimization process, including performance design and controls design, of an integrated CL plant system according to an exemplary embodiment will be described in further detail. In an exemplary embodiment, the optimization process shown in  FIG. 4  is included in the system optimizer  210  ( FIG. 3 ), but implementation of the optimization process shown in  FIG. 4  is not limited thereto; instead, the optimization process shown in  FIG. 4  may be implemented in any optimizer and, in particular, in any optimizer associated with various CL-based power plants, as described in greater detail above. 
     In an optimization process  300 , e.g., an optimization process  300  for an integrated CL plant system, the optimization process  300  includes performance design optimization and controls system design optimization functions. Specifically, process performance design specifications  305  and control system design specifications  310  are both optimized according to process performance design standards  315  and control system design standards  320 , respectively. In an exemplary embodiment, the process performance design specifications  305  include predetermined properties of fuel, properties of sorbent, desired plant capacity, heat rate for a given power generation rate, CO 2  quality and quantity, H 2  quality, H 2  generation efficiency, for example, but are not limited thereto. The control system design specifications  310  include, for example, control system type, response speeds, and tolerance/error margins of operational parameters, but are not limited thereto. 
     A process performance design module  325  supplies the process performance design specifications  305  and the process performance design standards  315  to a process performance simulation analyzer  330  and a control system design module  335 . The process performance simulation analyzer  330  analyzes the process performance design specifications  305  and the process performance design standards  315  based on an output from a process performance simulator  340 . At the same time, a dynamics and control simulation analyzer  345  analyzes an output from the control system design module  335  based on an output from a dynamic simulator  350 . In an exemplary embodiment, the dynamic simulator  350  is a reduced order modeling (ROM) dynamic simulator  350 . Outputs from the process performance simulation analyzer  330  and the dynamics and control simulation analyzer  345  are supplied to a process performance and control evaluator  355  which determines whether the outputs from the process performance simulation analyzer  330  and the dynamics and control simulation analyzer  345  are individually optimized. If the outputs from the process performance simulation analyzer  330  and the dynamics and control simulation analyzer  345  are not individually optimized, the non-optimized outputs from the process performance simulation analyzer  330  and the dynamics and control simulation analyzer  345  are supplied back to the process performance design module  325  for additional analysis, e.g., a subsequent iteration of the abovementioned analysis. If the outputs from the process performance simulation analyzer  330  and the dynamics and control simulation analyzer  345  are optimized, the outputs from the process performance simulation analyzer  330  and the dynamics and control simulation analyzer  345  are combined and sent to an overall system optimizer  360 , such as a genetic algorithm (GA) optimizer  360 , for example, to output optimized plant performance and operating parameters  365 . 
     As shown in  FIG. 4  and described in greater detail above, the optimization process  300  according to an exemplary embodiment uses parallel process performance and control system design analyses. By using both the process performance simulator  340  and the dynamic simulator  350 , the optimized plant performance and operating parameters  365  are predicted, evaluated and thereby effectively optimized. The process performance simulator  340  includes, e.g., thermodynamic, thermo-economic, and emission predictions using theoretical and empirical models such as process models and/or regression models in design standards and NN models based on operational databases, for example, but is not limited thereto. The dynamic simulator  350  includes first principle models, or alternatively, combined first principle and data driven empirical models, and/or wavelet network models, as well as control logic simulation modules, for example. For life extending controls (not shown), material models are included, thereby allowing material damage prediction and life extending control simulations (not shown). Similarly, environmental economic models (not shown) may be included in alternative exemplary embodiments, thereby providing analysis of and optimization of emissions such as SO 2 , NO X , particulates, and CO 2 , for example. 
     Since determination of the optimized plant performance and operating parameters  365  may involve multiple iterations to choose among a number of design scenarios, additional optimizers (not shown) may be included such that both process performance and control system design are pre-optimized prior to optimization by the optimizer  360 . 
     Referring now to  FIG. 5 , an MPC optimal controller according to an exemplary embodiment, and more specifically, an MPC optimal controller for a CL process, will now be described in further detail. In an exemplary embodiment, a controller  400  such as an MPC controller  400  is an advanced optimal control system which uses MPC to control a CL process  405 . As described above, the CL process  405  may be used in a single loop or a multiple loop CL system, as well as CL-based plants with CO 2  capture for utilization or sequestration and/or CL-based CO 2 -ready power plants, for example, but is not limited thereto. 
     As also described above, the CL process involves multi-phase flows and chemical reactions characterized by process nonlinearities and time delays due to mass transport rates and chemical reaction rates. Thus, conventional, e.g., linear, optimization and control are not sufficient for the CL process optimization. Hence, the MPC controller  400  according to an exemplary embodiment includes nonlinear dynamic chemical looping modeling and simulation derived from first principle equations such as mass, momentum, and energy balances, for example. Furthermore, empirical modeling methods such as nonlinear neural networks are used in a hybrid dynamic model structure which combines simplified first-principle models with data-driven models. In particular, the MPC controller  400  includes a model part  410  such as a model  410 , a simulator part  415  such as a simulator  415  and an optimizer part  420  such as an optimizer  420 . 
     In an exemplary embodiment, the MPC controller  400  leverages current plant control system components, e.g., existing proportional-integral-derivative (PID) controllers, to supplement and/or replace current plant control systems with model based predictive controls having optimization capabilities. More specifically, the model  410  of the MPC controller  400  according to an exemplary embodiment includes a nonlinear steady state model and one ore more linear or nonlinear dynamic model. In addition, the steady state model and/or the dynamic model may each use adaptive, fuzzy, and/or NN modeling techniques, and/or first principle modeling techniques to model the complex, nonlinear multi-phase flows and chemical reactions of the CL process  405 . 
     Further, in an exemplary embodiment, the model  410  may include a CL system model or, alternatively, CL subsystem and/or CL component models used as a basis for model-based state estimators, parameter estimators, and/or fault detectors. As a result, new soft sensors of the soft sensor control module  255  ( FIG. 3 ) can be derived therefrom and integrated with a control system for optimizing the CL process  405 . 
     In an exemplary embodiment, the simulator  415  is a dynamic simulator  415  which simulates the CL process  405  using advanced techniques. Specifically, the dynamic simulator  415  may be a ROM simulator, e.g., substantially the same as the dynamic simulator  350 , described above with reference to  FIG. 4 , for example, but alternative exemplary embodiments are not limited thereto. Likewise, the optimizer part  420  according to an exemplary embodiment is substantially the same as the optimizer  360  ( FIG. 4 ), but is not limited thereto. For example, in an alternative exemplary embodiment, the optimizer part  420  includes the system optimizer  210  and, more specifically, the multivariable optimizer  210  of the plant control system  207  ( FIG. 3 ). 
     In operation, the MPC controller  400  receives CL process output parameters  425  from the CL process  405 . The CL process output parameters  425  include, but are not limited to, load demand, power, and gas (e.g., H 2 , N 2 , CO 2  and/or syngas) flow rates. Using set points  430  and predetermined parameters  435 , the MPC controller  400  optimizes modeled plant parameters and provides an optimized CL process input control parameter  440  based thereon to the CL process  405 . In an exemplary embodiment, the optimized CL process input control parameter  440  is a solids transport inventory control variable, but alternative exemplary embodiments are not limited thereto. For example, the optimized CL process input control parameter  440  may be a reactor temperature control variable, a loop temperature control variable, a bed temperature control variable, a load ramping control variable, a plant start-up control logic algorithm, a reactor pressure variable, a reactor differential pressure variable, a plant shut-down control logic algorithm, and a fuel/air/limestone/steam ratio, but alternative exemplary embodiments are not limited to the foregoing list. 
     In summary, a process design and control optimization system according to an exemplary embodiment includes a multi-variable, non-linear control optimization system which provides integrated, dynamic and steady state performance and controls design optimization for a chemical looping plant. As a result, plant emissions are substantially reduced and/or effectively minimized while overall economic plant efficiency is substantially improved, resulting in lower overall operating costs. 
     While the invention has been described with reference to various exemplary embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the essential scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed as the best mode contemplated for carrying out this invention, but that the invention will include all embodiments falling within the scope of the appended claims.