Patent Abstract:
A system for optimizing a power plant includes a chemical loop having an input for receiving an input parameter ( 270 ) and an output for outputting an output parameter ( 280 ), a control system operably connected to the chemical loop and having a multiple controller part ( 230 ) comprising a model-free controller. The control system receives the output parameter ( 280 ), optimizes the input parameter ( 270 ) based on the received output parameter ( 280 ), and outputs an optimized input parameter ( 270 ) to the input of the chemical loop to control a process of the chemical loop in an optimized manner.

Full Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    The present invention claims the benefit of co-pending U.S. Provisional Patent Application Ser. No. 61/033,202, entitled “CONTROL AND OPTIMIZATION SYSTEM”, co-pending U.S. Provisional Patent Application 61/033,210, entitled “FUZZY LOGIC CONTROL AND OPTIMIZATION SYSTEM”, and co-pending 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 
       [0002]    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 
       [0003]    The present disclosure relates generally to a control and optimization system and, more particularly, to a control and optimization system for a chemical looping process. 
       BACKGROUND 
       [0004]    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 existing or new power plants, and provides promising improvements in terms of reduced plant size, reduced emissions, and increased plant operational efficiency, among other benefits. 
         [0005]    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. 
         [0006]    The CL process is more complicated than processes of traditional plants such as conventional circulating fluidized bed (CFB) plants, for example. In particular, control of circulating solids in the CL process requires multi-loop interactive flow and inventory controls which are not required in traditional plants. 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 between individual loops. Thus, interactions between variables for each loop of the CL process have to be taken into account to optimize overall CL process performance. 
         [0007]    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 is further inadequate for integrated optimization of process performance and system operability. 
         [0008]    Optimization tools which have been developed thus far are focused on optimizing conventional combustion power plants. As a result, these optimization tools have been focused on solving very specific, localized optimization problems rather than global optimization of complex plant operations. Additionally, statistical analysis methods associated with optimization of conventional combustion power plants is based upon an assumption of linear relationships between variables. As a result, these statistical analysis methods are cumbersome and inaccurate when used to analyze the complex, inter-related, nonlinear dynamics of variables in the CL process. 
         [0009]    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. Neural network (NN) modeling has been used for conventional power plant simulators, but implementing this approach for a CL-based power plant has thus far required a prohibitive amount of time and effort to collect the required amount of statistically significant test data to develop a validated NN model for the more complex process dynamics associated with the CL plant. 
         [0010]    Accordingly, it is desired to develop a control and optimization system and, more particularly, an integrated control and optimization system for a chemical looping process, which overcomes the shortfalls described above. 
       SUMMARY 
       [0011]    According to the aspects illustrated herein, there is provided a system for optimizing a power plant which includes a chemical loop having an input for receiving an input parameter and an output for outputting an output parameter, a control system operably connected to the chemical loop and having a multiple controller part comprising a model-free controller. The control system receives the output parameter, optimizes the input parameter based on the received output parameter, and outputs an optimized input parameter to the input of the chemical loop to control a process of the chemical loop in an optimized manner. 
         [0012]    According to the other aspects illustrated herein, a system for optimizing a power plant includes a first chemical loop having a first input for receiving a first input parameter and a first output for outputting a first output parameter. The first chemical loop includes a first reactor having at least a first inlet and a first outlet, a first separator operably connected to the first outlet of the first reactor, and a first seal pot control valve disposed between the first separator and the first inlet of the first reactor. The system further includes a control system operably connected to the first chemical loop and having a multiple controller part including a model-free controller. The control system receives the first output parameter, optimizes the first input parameter based on the received first output parameter, and outputs an optimized first input parameter to the first input of the first chemical loop to control a process of the first chemical loop in an optimized manner 
         [0013]    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 and an output for outputting an output parameter. The chemical loop includes a reactor having at least an inlet and an outlet, a separator operably connected to the reactor outlet, and a seal pot control valve disposed between the separator and the reactor inlet. The system further includes a control system having a multiple controller part including at least one of a model-based controller, a proportional-integral-derivative controller, a fuzzy controller, and a neural network adaptive controller, and a control set algorithm operably connected to the multiple controller part. The multiple controller part further includes an optimizer operably connected to the control set algorithm, a security watchdog model connected to the control set algorithm, and a chemical looping process simulator operably connected to the chemical loop. The control system receives the output parameter from the output of the chemical loop, optimizes the input parameter based on the received output parameter, and outputs an optimized input parameter to the input of the chemical loop to control a process of the chemical loop in an optimized manner. 
         [0014]    The above described and other features are exemplified by the following figures and detailed description. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0015]    Referring now to the figures, which are exemplary embodiments, and wherein the like elements are numbered alike: 
           [0016]      FIG. 1  is a block diagram of a metal oxide-based two loop chemical looping (CL) system; 
           [0017]      FIG. 2  is a block diagram of a CL combustion-based steam power plant; 
           [0018]      FIG. 3  is a block diagram of a control and optimization system for a CL process or a CL-based plant; 
           [0019]      FIG. 4  is block diagram which illustrates implementation of a control and optimization system in a in a single loop of a calcium CL process with multiple loops; and 
           [0020]      FIG. 5  is block diagram which illustrates implementation of a control and optimization system in a in a dual loop of a calcium CL process with multiple loops. 
       
    
    
     DETAILED DESCRIPTION 
       [0021]    Disclosed herein is a control and optimization system for a chemical looping (CL) system of a CL-based power 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 . 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 metal oxide, i.e., the metal  40 , is then returned to the oxidizer  20  to again be oxidized into CaO  50 , and the cycle described above repeats. 
         [0022]    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 . 
         [0023]    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 . 
         [0024]    Yet another alternative exemplary embodiment utilizes a calcium-based CL system  5  which includes 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 . 
         [0025]    The air  30  is supplied to the oxidizer  20 , as described above with reference to  FIG. 1 , while waste  115 , such as ash  115  and/or excess calcium sulfate (CaSO 4 )  115 , 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. 
         [0026]    In operation, the reduction reaction occurs between carbon and sulfur in the coal  60 , the CaCO 3    120 , and CaSO 4    127 , and produces calcium sulfide (CaS)  128 , which is separated by a separator  130 , such as a cyclone 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    80 , from the CaS  128 . 
         [0027]    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 . 
         [0028]    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. 
         [0029]    An exemplary embodiment of a control and optimization system for a CL process of a CL-based plant will now be described in further detail with reference to  FIGS. 3 and 4 . It will be noted that the control and 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 tool 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; CL-based plant with CO 2  capture for utilization or sequestration; and CL-based CO 2 -ready power plants, but is not limited thereto. 
         [0030]    As described above, the CL process involves multi-phase flows and chemical reactions which are characterized by process nonlinearities and time delays due to, among other things, mass transport rates and chemical reaction rates. As a result, nonlinear control and optimization techniques are required for 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., data driven models, such as neural networks (NN), nonlinear autoregressive network with exogenous inputs (NARX), nonlinear auto regressive moving average with exogenous inputs (NARMAX), Wiener-Hammerstein models, and wavelet network 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, as will be described in further detail below. 
         [0031]    Referring to  FIG. 3 , a control and optimization system  200  for a CL-based power plant  205  is shown. In an exemplary embodiment, the control and optimization system  200  is an MPC system  200 , but alternative exemplary embodiments are not limited thereto. 
         [0032]    The control system  205  according to an exemplary embodiment includes an optimizer  210 , a control algorithm set  220 , a multiple controller part  230 , a multiple controller output adapter  240 , a multiple controller input adapter  244 , and a power plant output adapter  247 . The multiple controller part  230  provides regulatory control of the CL system  5 , and includes individual controllers, e.g., control modules, such as a proportional-integral-derivative (PID) controller  250 , a fuzzy controller  255 , an adaptive controller  260 , and a model-based controller  265 . The adaptive controller  260  includes self-tuning adaptive controls, neuro-adaptive controls, a neural network (NN) and/or a wavelet network. The CL-based power plant  205  has an input parameter  270  and an output parameter  280 . In addition, a CL process simulator  290  is provided, as shown in  FIG. 3 . 
         [0033]    The control and optimization system  200  according to an exemplary embodiment further includes a security “watchdog” module  295  which monitors the control and optimization system  200  to maintain system security against software and/or hardware faults, as well as external attacks (e.g., hackers). More specifically, the control algorithm set  220  communicates with the security watchdog module  295  and determines, based upon inputs from security watchdog module  295  and the optimizer  210 , whether to switch controllers, for example, as described below in further detail. 
         [0034]    In an exemplary embodiment, the input parameter  270  includes, but is not limited to, fuel flow, sorbent flow, air flow, water flow, limestone flow, solids circulation flow, a plant start-up control logic algorithm, a plant shut-down control logic algorithm, and a ratio of at least two of fuel flow, air flow, limestone flow and steam flow. Likewise, the output parameter  280  includes power generation rate, CO 2  flow, CO 2  utilization, CO 2  capture, CO 2  storage, CO 2  sequestration, load demand, solids transport inventory, reactor temperature, loop temperature, bed temperature, pressure, differential pressure, reactor pressure, reactor differential pressure, H 2  flow, N 2  flow, and syngas flow, for example, but alternative exemplary embodiments are not limited thereto. 
         [0035]    The control and optimization system  200  according to an exemplary embodiment uses model free advanced controls such as fuzzy controls and/or NN adaptive controls, for example. Adding the model free advanced controls provides an additional advantage of allowing efficient optimization of multiple performance objectives of complex processes such as the CL process of the CL-based power plant  205 , for example. In addition, the model free advanced controls can also serve as fault tolerant controls which enhance overall reliability and availability of the CL-based power plant  205 . 
         [0036]    In an exemplary embodiment, the CL-based power plant  205  is a calcium-based three loop CL system  205 , as described above in greater detail with reference to  FIG. 2 , but alternate exemplary embodiments are not limited thereto. For example, the CL-based power plant  205  may be one of 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); CL-based plants with CO 2  capture for utilization or sequestration; and CL-based CO 2 -ready power plants, but alternative exemplary embodiments are not limited thereto. 
         [0037]    Still referring to  FIG. 3 , the optimizer  210  in an exemplary embodiment is a system/plant optimizer  210 . More specifically, the system/plant optimizer  210  is a high level supervisor engine which computes an optimal plant operating setting (or settings), e.g., the input parameter  270 , to meet overall operating performance objectives. The system/plant optimizer  210  may be based on an existing CL-based power plant  205  (or system/subsystem thereof) model or, alternatively, the system/plant optimizer  210  may be a rule based decision-making engine, such as an engine based on fuzzy logic rules and/or deterministic logic rules. 
         [0038]    The system/plant optimizer  210  communicates with the control algorithm set  220 . The control algorithm set  220  includes modules (not shown) each having associated control laws, and more specifically, fault tolerant control laws, as well as other control and information system firmware (not shown). The control set algorithm may also include a fuzzy controller, an adaptive controller and a MPC controller to oversee the regulatory controls of the multiple controller part  230 . The MPC controller may include PID based regulatory controls, self-tuning adaptive controls, neuro-adaptive controls, NN controls, and/or wavelet network controls. MPC controllers can be used in a parallel or cascade. In a cascade MPC configuration, one MPC can supervise one or more MPC controllers or other type of controllers as well. 
         [0039]    In an exemplary embodiment, the multiple controller part  230  includes the PID controller  250 , the fuzzy controller  255 , the NN adaptive controller  260 , and the model-based controller  265  of the multiple controller part  230 , but alternative exemplary embodiments are not limited thereto. Based upon a value of the output parameter  280 , as well as interactions of the optimizer  210  and the security watchdog module  295 , and using the control laws of the control algorithm set  220 , the multiple controller part  230  selects one of the PID controller  250 , the fuzzy controller  255 , the NN adaptive controller  260 , and the model-based controller  265  of the multiple controller part  230  to adjust a value of the input parameter  270 , thereby effectively controlling the CL-based power plant  205  in an optimal manner. More specifically, the multiple controller part  230  chooses a controller based upon an operating state/condition of the CL-based power plant  205 , as well as the control laws of the control set algorithm  210 , an input from the system/plant optimizer  210 , and an input from the security watchdog module  295 . 
         [0040]    As a result of the multiple controller part  23  having a plurality of controllers to select from, the control and optimization system  200  according to an exemplary embodiment is tolerant to both disturbances and/or noise therein, and is able to continue operating despite a failure of a given controller, e.g., one of the PID controller  250 , the fuzzy controller  255 , the adaptive controller  260 , and/or the model-based controller  265  of the multiple controller part  230 . 
         [0041]    Each of the individual controllers of the multiple control part will now be described in further detail. The PID controller  250  is a standard conventional controller, e.g., a controller with proportional, integral, and (optionally) derivative terms. The PID controller  250  is a simple model-free, general purpose automatic controller which is used to control simple processes of the CL-based power plant  205 , for example. Thus, the PID controller  250  may be used for steady state, time-invariant, substantially linear, processes, for example, where complex controllers are not required. 
         [0042]    The fuzzy controller  255  is an advanced model-free controller based on fuzzy logic design. More specifically, the fuzzy controller  255  uses fuzzy math algorithms, which take advantage of relational functionalities between complex process dynamics, rather than a conventional plant model. As a result, control decisions for the CL-based power plant  205  are thereby generated based on the fuzzy math algorithms and the control set algorithm  220 . Thus, in an exemplary embodiment, the fuzzy controller  255  is utilized, for example, when using the PID controller  250  would be less efficient under current operating conditions of the CL-based power plant  205  such as during plant transients or load changes, for example. 
         [0043]    Similarly, the NN adaptive controller  260  is a type of adaptive controller which uses a neural network, rather than a conventional plant model, as a control engine. More specifically, the NN adaptive controller  260  includes a group of nodes, or processing elements, interconnected to form a network. A mathematical algorithm is then used to determine interactions between the nodes as a signal, e.g., a control signal for the CL-based power plant  205 , travels from an input node or nodes, “through” the network and on to an output node or nodes. The algorithm may, over time, alter a preference for interactions between the nodes, making the NN an adaptive model. Thus, the NN models complex relationships between inputs and outputs in an adaptive manner. In addition, the NN adaptive controller  260  is multivariate and nonlinear, thereby capable of analyzing multiple-variable processes wherein relationships between variables are complex and nonlinear. Therefore, in an exemplary embodiment the NN adaptive controller  255  is utilized when using the PID controller  250  and/or the fuzzy controller  255  would be less efficient under current operating conditions of the CL-based power plant  205 , for example. 
         [0044]    The model-based controller  265  is a conventional controller which uses a model (or models), unlike the PID controller  250 , the fuzzy controller  255 , and the NN adaptive controller  260 , which are all model-free, as described above. In an exemplary embodiment, the model-based controller  265  is used, for example, when the PID controller  250 , the fuzzy controller  255 , and the NN adaptive controller  260  are not available, or when the CL-process simulator  290 , described below, is used for simulation of the CL-based power plant  205  during personnel training, for example. 
         [0045]    Still referring to  FIG. 3 , the multiple controller input adapter  244  according to an exemplary embodiment is a switch which selects one or more of the PID controller  250 , the fuzzy controller  255 , the NN adaptive controller  260 , and the model-based controller  265  of the multiple controller part  230 . More specifically, the multiple controller input adapter  244  selects an appropriate controller/controllers based upon the multiple controller part  230 , which chooses a controller or controllers according to an operating state/condition of the CL-based power plant  205 , as well as the control laws of the control set algorithm  210 , the input from the system/plant optimizer  210 , and the input from the security watchdog module  295 , as described above. In an alternative exemplary embodiment, the multiple controller input adapter  244  may also perform other logic and/or mathematic operations over the controller signals such as averaging selected control signals from two or more parallel multiple controllers, for example, but is not limited thereto. 
         [0046]    In an exemplary embodiment, the multiple controller output adapter  240  is a switch which closes associated control loops (not shown) based upon which controller or controllers (of the PID controller  250 , the fuzzy controller  255 , the NN adaptive controller  260 , and/or the model-based controller  265 ) are selected by the multiple controller input adapter  244 , as described above. In an alternative exemplary embodiment, however, the multiple controller output adapter  240  may be, for example, a weighted average function module  240  (not shown) which optimizes the control signals selected by the multiple controller input adapter  244  before the control signals are sent to actuators (not shown) or cascade controllers (not shown), for example, of the CL-based power plant  205 . 
         [0047]    The power plant output adapter  247  is an operator, and more specifically, may be a switch  247 , a weighted average engine  247 , a signal conditioner  247 , or a fault detector  247 . Thus, the power plant output adapter  247  conditions the output parameters  280  such that the output parameters  280  are in an appropriate form to be used according a configuration of the control and optimization system  200 , e.g., based upon which controller or controllers (of the PID controller  250 , the fuzzy controller  255 , the NN adaptive controller  260 , and/or the model-based controller  265 ) are selected by the multiple controller input adapter  244 , as described above. 
         [0048]    The CL process simulator  290  provides a simulator output  297  which is used, for example, in controls design and testing, training of personnel, and fault simulations to support fault diagnosis of the control system  205 . The training may be performed offline, e.g., standalone, or online, e.g. tied to a distributed control system (DCS) (not shown) of the CL-based power plant. Further, the CL process simulator  290  itself may be offline or online. 
         [0049]    Referring now to  FIG. 4 , implementation of the control and optimization system  200  ( FIG. 3 ) with a single loop  300  of the CL-based power plant  205  will be described in further detail. In an exemplary embodiment, the single loop  300  is an oxidizer loop  300 , such as in a calcium-based three loop CL system described in greater detail with reference to  FIGS. 2 and 5  (with complementary components reference with a prime ′), but alternative exemplary embodiments are not limited thereto. For example, the control and optimization system  200  may be implemented with one of 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); CL-based plants with CO 2  capture for utilization or sequestration; and CL-based CO 2 -ready power plants, as described in U.S. Pat. No. 7,083,658 and U.S. patent application Ser. No. 10/542,749, but alternative exemplary embodiments are not limited thereto. In addition, the control and optimization system  200  may be a single control and optimization system  200 , or multiple control and optimization systems  200  may be utilized in the abovementioned variations of CL-based systems. 
         [0050]    Further, for purposes of discussion with reference to  FIG. 4 , the control and optimization system  200  optimizes and controls a single input parameter  270  and output parameter  280 . Specifically, the control and optimization system  200  optimizes and controls regulation of a differential pressure (described in further detail below). However, alternative exemplary embodiments are not limited to the single input parameter  270  and output parameter  280 . Instead, the control and optimization system  200  according to alternative exemplary embodiments may regulate multiple input parameters  270  and output parameters  280 . As discussed above, the input parameters  270  according to an exemplary embodiment include, but are not limited to: fuel flow, sorbent flow, air flow, water flow, limestone flow, solids circulation flow, a plant start-up control logic algorithm, a plant shut-down control logic algorithm, and a ratio of at least two of fuel flow, air flow, limestone flow and steam flow. In addition, the output parameters  280  include: power generation rate, CO 2  flow, CO 2  utilization, CO 2  capture, CO 2  storage, CO 2  sequestration, load demand, solids transport inventory, reactor temperature, loop temperature, bed temperature, pressure, differential pressure, reactor pressure, reactor differential pressure, H 2  flow, N 2  flow, and syngas flow, for example, but alternative exemplary embodiments are not limited thereto. 
         [0051]    Referring to  FIG. 4 , the control and optimization system  200  receives an output parameter  280 , such as a differential pressure difference  280 . Specifically, the differential pressure difference  280  is a difference between a riser differential pressure (D/P)  310  and a seal D/P (not shown). More specifically, the seal D/P is a sum of a dip leg D/P 320 and a return leg D/P 330. Thus, the control and optimization system  200  controls a difference between a riser section, e.g., a portion of the single loop  300  which corresponds to the oxidizer  20  (described in greater detail above with reference to  FIG. 2 ), and a seal section, e.g., a portion of the single loop which corresponds to the SPCV  135  (also described in greater detail above with reference to  FIG. 2 ). 
         [0052]    As described above in greater detail, the control and optimization system  200  according to an alternative exemplary embodiment may be utilized in a two-loop CL-based plant, to control, e.g., crossover flow from the SPCV  135  to the reducer  10 , as shown in  FIGS. 4 and 5 . In an exemplary embodiment utilized in a single-loop CL-based plant, however, the crossover flow to the reducer  10  (as shown in  FIG. 4 ) is not required. 
         [0053]    The single loop  300  receives air  30  from an air source  340 , and a solids source  350  supplies makeup solids (not shown) to the single loop  300  as required. In operation, solids (e.g., CaS  128  and CaSO 4    127 ) (not shown) which flow (e.g., “loop”) in the single loop  300  are oxidized in the oxidizer  20 , are separated from N 2    80  ( FIG. 2 ) by the cyclone  130 , and are the supplied to the SPCV  135 . A flow rate of SPCV air  360  from the air source  340  effectively controls a flow rate of the solids in and through the SPCV based upon a desired value of the differential pressure difference  280 . More specifically, the control and optimization signal optimally controls the flow rate of the SPCV air  360  by adjusting a position of a throttle valve  370  according to the input parameter  270 , as shown in  FIG. 4 . 
         [0054]    As a result, the control and optimization system  200  according to an exemplary embodiment effectively controls the flow rate of the solids of the single loop  300  during operation of the CL-based power plant  205  ( FIG. 3 ). Further, as described in greater detail above with reference to  FIG. 3 , the control and optimization system  200  optimally controls the flow rate of the solids during all operations, e.g., steady state and transient, of the CL-based power plant  205 . Thus, by utilizing the control and optimization system  200  to optimally control multiple variables of the CL process, such as a flow rate of the air  30  and/or the makeup solids, for example, an overall operational efficiency of the CL-based power plant  205  is substantially improved. 
         [0055]    In summary, a control and optimization tool for a CL process of a CL-based power plant according to an exemplary embodiment provides stabilization of solid transport loops in the CL process, substantially improved delivery of demanded solids to reactors (e.g., reducers and oxidizers) to meet balance of plant production requirements, thereby: effectively minimizing power consumption for solids transport; maximizing the CL-process and overall power plant productivity; maximizing system operability during load changes, start-up and shut-down, for example; and effectively improving power plant reliability and/or availability. 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. 
         [0056]    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.

Technology Classification (CPC): 6