Method, device and equipment for preparing nano calcium carbonate based on model predictive control

Abstract

The invention provides a method, a device and equipment for preparing nano calcium carbonate based on model predictive control. The method comprises the following steps: acquiring power plant waste gas discharged by a combustion power plant; the method comprises the following steps: carrying out compression treatment, pretreatment and atomization fusion treatment on power plant waste gas to obtain a gas-liquid two-phase flow system; determining a state equation based on the current operation parameters of the gas-liquid two-phase flow system; predicting states in a preset number of sampling steps in the future based on the state equation to obtain a prediction result; performing parameter optimization based on the prediction result and a preset optimization target to obtain to-be-selected regulation and control parameters; atomization fusion processing is carried out through the to-be-selected regulation and control parameters, and dynamic monitoring parameters are obtained; and adjusting the state equation based on the dynamic monitoring parameters until the target nano calcium carbonate is generated. In the mode, the actual working condition change is accurately reflected, and the process parameters are updated in real time through the actual working condition change, so that the particle size distribution of the nano calcium carbonate product is more uniform, and the conversion rate fluctuation is further reduced.

Description

Technical Field

[0001] This invention relates to the field of nano-calcium carbonate preparation technology, and in particular to a method, apparatus and equipment for preparing nano-calcium carbonate based on model predictive control. Background Technology

[0002] Nano-calcium carbonate refers to calcium carbonate products with a particle size ranging from 0 to 100 nm. Currently, the main production methods for nano-calcium carbonate include mechanical methods, metathesis methods, precipitation methods, carbonation methods, and microemulsion methods. Among these, carbonation is the mainstream process, which is further divided into three main process routes: intermittent bubbling carbonation, multi-stage spray carbonation, and high-gravity carbonation.

[0003] Currently, the three methods for preparing nano-calcium carbonate mentioned above still have drawbacks. For example, the intermittent bubbling carbonation method produces products with uneven particle distribution; while the multi-stage spray carbonation method and the high gravity carbonation method produce more uniform products compared to intermittent bubbling carbonation, they require large equipment footprints and consume more energy. Furthermore, these methods cannot provide various operating conditions to ensure the equipment operates at its optimal state. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a method, apparatus and equipment for preparing nano-calcium carbonate based on model predictive control, which accurately reflects the changes in actual working conditions, updates the process parameters in real time through changes in actual working conditions, so that the particle size distribution of nano-calcium carbonate products is more uniform, thereby reducing the fluctuation of conversion rate.

[0005] In a first aspect, embodiments of the present invention provide a method for preparing nano-calcium carbonate based on model predictive control. The method includes: acquiring power plant exhaust gas emitted from a combustion power plant; compressing, pretreating, and atomizing the exhaust gas to obtain a gas-liquid two-phase flow system; determining a state equation based on the current operating parameters of the gas-liquid two-phase flow system; predicting the state within a preset number of sampling steps based on the state equation to obtain prediction results; optimizing parameters based on the prediction results and a pre-set optimization target to obtain candidate control parameters; performing atomization fusion treatment using the candidate control parameters and obtaining dynamic monitoring parameters; adjusting the state equation based on the dynamic monitoring parameters to continue predicting the state within a preset number of sampling steps until the target nano-calcium carbonate is generated.

[0006] In a preferred embodiment of the present invention, the above-mentioned compression, pretreatment, and atomization fusion treatment of power plant exhaust gas to obtain a gas-liquid two-phase flow system includes: compressing and collecting the power plant exhaust gas into a carbon dioxide tank using an air compressor; connecting the outlet pipe of the carbon dioxide tank to a water washing bottle to remove sulfur dioxide and dust from the power plant exhaust gas; connecting the outlet pipe of the water washing bottle to a carbon dioxide pressure swing adsorber to concentrate the carbon dioxide gas in the power plant exhaust gas; connecting the outlet pipe of the carbon dioxide pressure swing adsorber to an air compressor to compress the carbon dioxide gas; connecting the outlet pipe of the air compressor to a pressure stabilizing tank, and connecting the outlet pipe of the pressure stabilizing tank to the bottom of a spray reaction tank to deliver carbon dioxide gas; the top of the spray reaction tank is provided with multiple spray heads, a pressure reducing valve, and a liquid inlet pipe; atomizing the Ca(OH) solution obtained by filtration through the spray heads to obtain droplets; and mixing the droplets with the carbon dioxide gas delivered from the outlet pipe of the pressure stabilizing tank to obtain a gas-liquid two-phase flow system.

[0007] In a preferred embodiment of the present invention, the above-mentioned prediction of the state within a predetermined number of sampling steps based on the state equation to obtain the prediction result includes: predicting the state within the next H sampling steps based on the state equation using the following formula: x(k+j+1)=A·x(k+j)+B·Δu(k+j), j=0,1,…,H; where j represents the number of sampling steps, and H represents the total sampling step length; Δu(k)=u(k)-u ref , representing the adjustment amount relative to the reference operating condition, u ref The steady-state condition is represented by A and B, which represent matrices A and B, respectively; y(k+j)=C·x(k+j) represents the prediction result.

[0008] In a preferred embodiment of the present invention, the above-mentioned parameter optimization based on the prediction results and the pre-set optimization target to obtain the candidate control parameters includes: constructing a target convergence function based on the prediction results using the following formula: Where J represents the target approach function, H represents the total sampling step size, y(k+j) represents the prediction result, and P target Let λ represent the optimization objective, λ represent the weighting factor used to balance the state deviation and the magnitude of the control quantity change; Δu(k+j) represent the control quantity; under input constraints, solve for the optimal control increment; the constraint condition is: u_min≤u≤u_max; based on the optimal control increment and the current operating parameters, obtain the candidate control parameters.

[0009] In a preferred embodiment of the present invention, the above-mentioned determination of the state equation based on the current operating parameters of the gas-liquid two-phase flow system includes: inputting the current operating parameters of the gas-liquid two-phase flow system into a pre-established dynamic mathematical model to determine the correspondence between the current operating parameters and the product parameters; performing parameter coupling correlation based on the correspondence, and outputting the state equation.

[0010] In a preferred embodiment of the present invention, the above-mentioned product parameters include: average droplet diameter, average particle size of CaCO3 product, and final product particle size. Determining the correspondence between the current operating parameters and the product parameters includes: determining the correspondence between the current operating parameters and the average droplet diameter based on the following formula: Where d represents the average droplet diameter, σ represents the liquid surface tension, and ρ g Let represent gas density, U represent the characteristic relative velocity between the gas and liquid phases, α1 represent the scaling coefficient, and β1 represent the scaling exponent; the correspondence between the current operating parameters and the average particle size of the CaCO3 product is determined based on the following formula: Where p represents the average particle size of the CaCO3 product; k r A represents the reaction rate constant. contact V represents the gas-liquid contact area, and C represents the reactor volume. CO and C eq These represent the initial and equilibrium concentrations of carbon dioxide gas in the reaction, respectively. The relationship between the current operating parameters and the final product particle size is determined based on the following formula: Where p represents the final product particle size, d represents the initial reactant droplet size, and Q... g Q represents the flow rate of carbon dioxide gas. l The flow rate of Ca(OH)2 liquid is represented by N, the stirring rate by p0, and the reference or initial value of the target particle size by d. * This indicates the droplet size under preset operating conditions. This indicates the gas-liquid flow ratio under preset operating conditions, N * α1 represents the stirring rate under preset operating conditions, α2 represents the sensitivity of the final product particle size to changes in the initial reactant droplet size, α3 represents the sensitivity of the final product particle size to changes in the gas-liquid flow ratio, and α4 represents the sensitivity of the final product particle size to changes in the stirring rate.

[0011] In a preferred embodiment of the present invention, parameter coupling and correlation are performed based on the correspondence relationship to output the state equation, including: obtaining the state vector and the input vector by performing parameter coupling and correlation based on the following formula and formula correspondence relationship: z(t) represents the state vector, d(t) represents the droplet size, p(t) represents the product particle size, u(t) represents the input vector, and Q... g (t) represents the carbon dioxide gas flow rate, Q l (t) represents the liquid flow rate of Ca(OH)2, U spray (t) represents the adjustable parameters of the spray device, and N(t) represents the stirring rate; the state equation is obtained based on the state vector and input vector using the following formula: x(k+1)=A·x(k)+B·Δu(k); where, Δu(k)=u(k)-u ref, representing the adjustment amount relative to the reference operating condition, u ref Represents the steady-state condition; A represents matrix A, B represents matrix B, and matrix A ∈ R. 2×2 It is used to describe the dynamic coupling relationship between state variables; matrix B∈R 2×4 This indicates the regulatory effect of the input on the state variable.

[0012] Secondly, embodiments of the present invention also provide a nano-calcium carbonate preparation device based on model predictive control, comprising: a power plant exhaust gas acquisition module for acquiring power plant exhaust gas emitted from a combustion power plant; a power plant exhaust gas treatment module for compressing, pretreating, and atomizing the power plant exhaust gas to obtain a gas-liquid two-phase flow system; a state equation determination module for determining the state equation based on the current operating parameters of the gas-liquid two-phase flow system; a state prediction module for predicting the state within a preset number of sampling steps based on the state equation to obtain prediction results; a parameter optimization module for optimizing parameters based on the prediction results and a pre-set optimization target to obtain candidate control parameters; an atomization fusion treatment module for performing atomization fusion treatment using the candidate control parameters and obtaining dynamic monitoring parameters; and a target nano-calcium carbonate generation module for adjusting the state equation based on the dynamic monitoring parameters to continue predicting the state within a preset number of sampling steps until the target nano-calcium carbonate is generated.

[0013] Thirdly, embodiments of the present invention also provide an electronic device, including a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement the model predictive control-based method for preparing nano-calcium carbonate described in the first aspect.

[0014] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer-executable instructions. When the computer-executable instructions are invoked and executed by a processor, the computer-executable instructions cause the processor to implement the model predictive control-based method for preparing nano-calcium carbonate described in the first aspect.

[0015] The embodiments of the present invention bring the following beneficial effects:

[0016] This invention provides a method, apparatus, and equipment for preparing nano-calcium carbonate based on model predictive control. The method involves acquiring waste gas from a combustion power plant, compressing, pretreating, and atomizing the waste gas to obtain a gas-liquid two-phase flow system. A state equation is determined based on the current operating parameters of the gas-liquid two-phase flow system. The state equation is then used to predict the state within a preset number of sampling steps, yielding prediction results. Parameter optimization is performed based on the prediction results and pre-set optimization targets to obtain candidate control parameters. Atomization and fusion processing is then performed using these candidate control parameters to obtain dynamic monitoring parameters. The state equation is adjusted based on these dynamic monitoring parameters to continue predicting the state within a preset number of sampling steps until the target nano-calcium carbonate is generated. This method accurately reflects changes in actual operating conditions, updating process parameters in real time based on these changes, resulting in a more uniform particle size distribution in the nano-calcium carbonate product and thus reducing conversion rate fluctuations.

[0017] Other features and advantages of this disclosure will be set forth in the following description, or some features and advantages may be inferred from the description or determined without doubt, or may be learned by practicing the techniques described above.

[0018] To make the above-mentioned objects, features and advantages of this disclosure more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0019] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 A flowchart illustrating a model-predictive control-based method for preparing nano-calcium carbonate is provided in this embodiment of the invention.

[0021] Figure 2 A flowchart of another model-predictive control-based method for preparing nano-calcium carbonate provided in an embodiment of the present invention;

[0022] Figure 3 A process apparatus flow chart provided for an embodiment of the present invention;

[0023] Figure 4 A flowchart illustrating another method for preparing nano-calcium carbonate based on model predictive control, provided in an embodiment of the present invention;

[0024] Figure 5A schematic diagram of a model-predictive control-based nano-calcium carbonate preparation device provided in an embodiment of the present invention;

[0025] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] Nano-calcium carbonate refers to calcium carbonate products with a particle size ranging from 0 to 100 nm. Currently, the main production methods for nano-calcium carbonate include mechanical methods, metathesis methods, precipitation methods, carbonation methods, and microemulsion methods. Among these, carbonation is the mainstream process, which is further divided into three main process routes: intermittent bubbling carbonation, multi-stage spray carbonation, and high-gravity carbonation.

[0028] Currently, the three methods for preparing nano-calcium carbonate mentioned above still have drawbacks. For example, the intermittent bubbling carbonation method produces products with uneven particle distribution; while the multi-stage spray carbonation method and the high gravity carbonation method produce more uniform products compared to intermittent bubbling carbonation, they require large equipment footprints and consume more energy. Furthermore, these methods cannot provide various operating conditions to ensure the equipment operates at its optimal state.

[0029] Specifically, the main production methods for nano-calcium carbonate currently include mechanical methods, metathesis methods, precipitation methods, carbonation methods, and microemulsion methods. Among these, carbonation is the mainstream process, which is further divided into three main process routes: intermittent bubbling carbonation, multi-stage spray carbonation, and high-gravity carbonation.

[0030] Intermittent bubbling carbonation is a common method both domestically and internationally. 5-8°Be' lime slurry is cooled to below 25°C, pumped into a carbonation tower, and kept at a certain liquid level. Kiln gas is introduced from the bottom of the tower to bubble and carry out the carbonation reaction. By controlling process conditions such as reaction temperature, concentration, gas-liquid ratio, and additives, nano-calcium carbonate can be prepared intermittently.

[0031] The multi-stage spray carbonization method involves spraying Ca(OH)2 emulsion downwards from the top of a tower through a pressure nozzle, while simultaneously introducing CO2 gas upwards from the bottom of the tower. This ensures that the sprayed Ca(OH)2 and CO2 come into full contact and react. The resulting Ca(OH)2 slurry is then sprayed into another carbonization tower from the top, where CO2 gas is also introduced from the bottom, again ensuring that the sprayed Ca(OH)2 and CO2 come into full contact and react. This process is repeated in a cycle.

[0032] The hypergravity carbonation method refers to the process where a Ca(OH)₂ emulsion passes through a high-speed rotating packed bed in a hypergravity reactor, achieving centrifugal velocities 2-3 orders of magnitude greater than gravitational acceleration. Under these conditions, the Ca(OH)₂ emulsion is broken down into extremely small droplets, filaments, and very thin liquid films by the packing material, generating a large and rapid phase interface. This results in interphase mass transfer volume and rate that are 1-3 orders of magnitude greater than in a column reactor, thus significantly enhancing the carbonation reaction rate. Nano-sized calcium carbonate can be produced under this hypergravity environment.

[0033] Based on this, the present invention provides a model-predictive control-based method, apparatus, and equipment for preparing nano-calcium carbonate. This method involves acquiring waste gas from a combustion power plant, compressing, pretreating, and atomizing the waste gas to obtain a gas-liquid two-phase flow system. A state equation is determined based on the current operating parameters of the gas-liquid two-phase flow system. The state equation is then used to predict the state within a preset number of sampling steps, yielding prediction results. Parameter optimization is performed based on the prediction results and pre-set optimization targets to obtain candidate control parameters. Atomization and fusion processing is then performed using these candidate control parameters to obtain dynamic monitoring parameters. The state equation is adjusted based on these dynamic monitoring parameters to continue predicting the state within a preset number of sampling steps until the target nano-calcium carbonate is generated. This method accurately reflects changes in actual operating conditions, updating process parameters in real time based on these changes, resulting in a more uniform particle size distribution in the nano-calcium carbonate product and thus reducing conversion rate fluctuations.

[0034] To facilitate understanding of this embodiment, a detailed description of a model-predictive control-based method for preparing nano-calcium carbonate disclosed in this embodiment of the invention will be provided first.

[0035] Example 1

[0036] This invention provides a method for preparing nano-calcium carbonate based on model predictive control. Figure 1 This is a flowchart illustrating a model-predictive control-based method for preparing nano-calcium carbonate, as provided in an embodiment of the present invention. Figure 1 As shown, the model-predictive control-based method for preparing nano-calcium carbonate may include the following steps:

[0037] Step S101: Obtain the power plant exhaust gas emitted by the combustion power plant.

[0038] Power plant exhaust gases may contain carbon dioxide, sulfur dioxide, dust, etc.

[0039] Step S102 involves compressing, pretreating, and atomizing the power plant exhaust gas to obtain a gas-liquid two-phase flow system.

[0040] Among these methods, power plant exhaust gas can be collected by compression; pretreatment can be gas washing and pressure swing adsorption. Gas washing can remove impurities from carbon dioxide gas, while pressure swing adsorption can enrich and release carbon dioxide concentration; atomization fusion treatment can be performed by atomizing Ca(OH) solution to obtain droplets, and then fusing the droplets with carbon dioxide gas to obtain a gas-liquid two-phase flow system.

[0041] Step S103: Determine the state equation based on the current operating parameters of the gas-liquid two-phase flow system.

[0042] This allows for real-time monitoring of current operating parameters, enabling adjustments to the state equation and timely updates to potential control parameters.

[0043] Step S104: Based on the state equation, predict the state within a preset number of sampling steps in the future to obtain the prediction result.

[0044] Step S105: Optimize the parameters based on the prediction results and the pre-set optimization objectives to obtain the candidate control parameters.

[0045] Among these features, the state equation can be adjusted in real time to predict and optimize the state, thereby reducing conversion rate fluctuations and improving the efficiency of generating the target nano-calcium carbonate.

[0046] Step S106: Perform atomization fusion processing using the candidate control parameters and obtain dynamic monitoring parameters.

[0047] Step S107: Adjust the state equation based on dynamic monitoring parameters to continue predicting the state within a preset number of sampling steps until the target nano-calcium carbonate is generated.

[0048] By continuously adjusting the equation of state and then continuously optimizing the selected control parameters, the selected control parameters can be made closer to the target process parameters, thereby generating the target nano-calcium carbonate.

[0049] The model-predictive control-based method for preparing nano-calcium carbonate provided in this invention involves acquiring waste gas from a combustion power plant, compressing, pretreating, and atomizing the waste gas to obtain a gas-liquid two-phase flow system. A state equation is determined based on the current operating parameters of the gas-liquid two-phase flow system. The state equation is then used to predict the state within a preset number of sampling steps, yielding prediction results. Parameter optimization is performed based on the prediction results and pre-set optimization targets to obtain candidate control parameters. Atomization and fusion processing is then performed using these candidate control parameters to obtain dynamic monitoring parameters. The state equation is adjusted based on these dynamic monitoring parameters to continue predicting the state within a preset number of sampling steps until the target nano-calcium carbonate is generated. This method accurately reflects changes in actual operating conditions, updating process parameters in real time based on these changes, resulting in a more uniform particle size distribution in the nano-calcium carbonate product and thus reducing conversion rate fluctuations.

[0050] Example 2

[0051] This invention also provides another method for preparing nano-calcium carbonate based on model predictive control; this method is implemented on the basis of the method in the above embodiments; the method focuses on describing the specific implementation of compressing, pretreating and atomizing the power plant exhaust gas to obtain a gas-liquid two-phase flow system.

[0052] Figure 2 A flowchart of another model-predictive control-based method for preparing nano-calcium carbonate provided in this embodiment of the invention. Figure 3 A process apparatus flow chart is provided for an embodiment of the present invention.

[0053] like Figure 2 As shown, the process of compressing, pretreating, and atomizing power plant exhaust gas to obtain a gas-liquid two-phase flow system can include the following steps:

[0054] Step S201: The power plant exhaust gas is compressed and collected into a carbon dioxide tank using an air compressor.

[0055] like Figure 3 As shown, the power plant exhaust gas discharged from the flue gas collection pipeline is compressed and collected into a carbon dioxide tank (CO2 tank) by an air compressor.

[0056] In step S202, the outlet pipe of the carbon dioxide tank is connected to a water washing bottle to remove sulfur dioxide and dust from the power plant exhaust gas.

[0057] like Figure 3 As shown, the outlet pipe of the carbon dioxide tank is connected to a water washing bottle to remove sulfur dioxide and dust from the power plant exhaust gas.

[0058] In step S203, the outlet pipe of the water washing bottle is connected to a carbon dioxide pressure swing adsorber to concentrate the carbon dioxide gas in the power plant exhaust gas.

[0059] like Figure 3 As shown, the outlet pipe of the water washing bottle is connected to a carbon dioxide pressure swing adsorber to concentrate the carbon dioxide gas in the power plant exhaust gas.

[0060] In step S204, the outlet pipe of the carbon dioxide pressure swing adsorber is connected to an air compressor to compress the carbon dioxide gas.

[0061] like Figure 3 As shown, the outlet pipe of the carbon dioxide pressure swing adsorber is connected to an air compressor to compress carbon dioxide gas.

[0062] In step S205, the air compressor's outlet pipe is connected to the pressure stabilizing tank, and the pressure stabilizing tank's outlet pipe is connected to the bottom of the spray reaction tank to deliver carbon dioxide gas.

[0063] The top of the spray reaction tank is equipped with multiple spray heads, a pressure reducing valve, and an inlet pipe.

[0064] like Figure 3 As shown, the air compressor's outlet pipe is connected to a pressure stabilizing tank, and the pressure stabilizing tank's outlet pipe is connected to the bottom of the spray reaction tank to deliver carbon dioxide gas.

[0065] Step S206: The Ca(OH) solution obtained by filtration is atomized through a spray head to obtain droplets.

[0066] like Figure 3 As shown, the Ca(OH) solution obtained by filtration is atomized through a spray head to obtain droplets.

[0067] In this process, Ca(OH)2 is first thoroughly stirred and dissolved in an aging tank to form a saturated solution. The saturated solution is then pumped into a Ca(OH)2 solution tank and then injected into the spray reaction tank through an inlet pipe using a delivery pump.

[0068] Step S207: The droplets and carbon dioxide gas delivered from the outlet pipe of the pressure stabilizing tank are mixed to obtain a gas-liquid two-phase flow system.

[0069] like Figure 3 As shown, the liquid droplets and carbon dioxide gas delivered from the outlet pipe of the pressure stabilizing tank are mixed to obtain a gas-liquid two-phase flow system.

[0070] The process involves installing a discharge pipe at the bottom of the spray reaction tank to store the resulting suspension in a storage tank. This suspension is then placed into a filtration device to separate the nano-calcium carbonate and Ca(OH)₂ solution into solid and liquid components. The Ca(OH)₂ solution is refluxed back to a Ca(OH)₂ solution tank, while the nano-calcium carbonate is dried in an oven.

[0071] In practical applications, coal-fired power plants emit waste gas (mainly CO2). This waste gas is compressed and collected into several CO2 cylinders using an air compressor. The outlet pipes of the CO2 cylinders are connected to a water washing bottle to remove small amounts of SO2, dust, and other impurities from the gas. The outlet pipes of the water washing bottle are then connected to a CO2 pressure swing adsorber (PSA), with the pressure set between 0.2-1 MPa. At this pressure, CO2 is adsorbed into the dehydrated silica gel, activated carbon, and zeolite mixture in the PSA. Afterward, the pressure is reduced to about 5-10 kPa, and the adsorbed CO2 is released. This process can concentrate 10%-20% CO2 to about 40%.

[0072] The pressure swing adsorber (PSA) outlet pipe is connected to an air compressor to compress the CO2 gas. The air compressor outlet pipe is connected to a pressure stabilizing tank, and the pressure stabilizing tank outlet pipe is connected to the inlet pipe at the bottom of the spray reactor. The inlet pipe has a diameter of 10-20 mm. Plastic pipes are installed at 2 cm intervals. Four pores are arranged on the same transverse cross section.

[0073] The top of the spray reaction tank is equipped with several spray nozzles, a pressure reducing valve (which releases pressure when it reaches 0.6 MPa), and an inlet pipe. Ca(OH)₂ is first thoroughly dissolved in an aging tank by stirring to form a saturated solution. This saturated solution is then pumped into a Ca(OH)₂ solution tank and subsequently injected into the spray reaction tank via the inlet pipe using a delivery pump. Through the spray reaction, the Ca(OH)₂ solution is atomized into fine droplets within the reactor, the particle size of which can be controlled by the nozzle parameters. The resulting fine droplets are thoroughly mixed with CO gas introduced from the bottom inlet pipe, achieving a large-area gas-liquid interface contact and promoting rapid alkalization to generate CaCO₃. In this spray reaction tank, the gas and liquid phases are continuously mixed, broken up, and recombined under the action of a stirrer (or through flow field design), resulting in a significant reaction linkage effect. The reaction mainly occurs at the interface between the atomized droplets and the gas, where the generated primary CaCO₃ crystals gradually grow into finished particles.

[0074] A discharge pipe is installed at the bottom of the spray reaction tank to store the suspension generated by the reaction in a storage tank. The suspension is then placed into a vacuum filter to separate the nano-calcium carbonate and Ca(OH)2 solution generated by the reaction into solid and liquid components.

[0075] The suspension after the reaction is vacuum filtered using a vacuum pump to accelerate the separation of nano-calcium carbonate from the liquid, reduce residual moisture, and improve drying efficiency. The Ca(OH)₂ solution is refluxed back to the Ca(OH)₂ solution tank, and the nano-calcium carbonate is placed in an oven at 60°C until it is completely dried.

[0076] Example 3

[0077] This invention also provides another method for preparing nano-calcium carbonate based on model predictive control; this method is implemented based on the method in the above embodiments.

[0078] Figure 4 A flowchart illustrating another method for preparing nano-calcium carbonate based on model predictive control, as provided in this embodiment of the invention, is shown below. Figure 4 As shown, the model-predicted and controlled method for preparing nano-calcium carbonate may include the following steps:

[0079] Step S301: Obtain the power plant exhaust gas emitted by the combustion power plant.

[0080] Step S302 involves compressing, pretreating, and atomizing the power plant exhaust gas to obtain a gas-liquid two-phase flow system.

[0081] Step S302 has been specifically described in Example 2 and will not be repeated here.

[0082] Step S303: Input the current operating parameters of the gas-liquid two-phase flow system into the pre-established dynamic mathematical model to determine the correspondence between the current operating parameters and the product parameters.

[0083] Specifically, the product parameters include: average droplet diameter, average particle size of CaCO3 product, and final product particle size. Determining the correspondence between current operating parameters and product parameters includes: determining the correspondence between current operating parameters and average droplet diameter based on the following formula: Where d represents the average droplet diameter, σ represents the liquid surface tension, and ρ g Let α represent the gas density, U represent the characteristic relative velocity between the gas and liquid phases, α1 represent the scaling coefficient, and β1 represent the scaling exponent.

[0084] The characteristic relative velocity between the gas and liquid phases can be correlated with the CO2 flow rate and the stirring rate.

[0085] The scaling coefficient primarily determines the absolute size benchmark of the atomized droplet, with a value range of 0.5-1.0, and is an empirical parameter obtained through experimental fitting.

[0086] The scaling exponent characterizes the sensitivity of droplet size to the relative balance between surface tension (stabilizing effect) and airflow impact force (breaking effect), and depends on the power-law dependence of droplet size on factors such as relative velocity. The value ranges from 5 to 30, and is an empirical parameter obtained through experimental fitting.

[0087] It should be noted that in the spray reaction stage, the liquid is atomized into fine droplets by the spray device and thoroughly mixed with CO2 gas. The droplet size directly affects the reaction contact area and the reaction rate; therefore, a droplet breakup model can be established.

[0088] The relationship between the current operating parameters and the average particle size of the CaCO3 product is determined based on the following formula: Where p represents the average particle size of the CaCO3 product; k r A represents the reaction rate constant. contact V represents the gas-liquid contact area, and C represents the reactor volume. CO and C eq These represent the initial and equilibrium concentrations of carbon dioxide gas in the gas, respectively.

[0089] It should be noted that the droplet size d directly affects the gas-liquid contact area, and the gas-liquid contact area A contact The reaction rate between the gas-liquid contact area A and the formation of nano-calcium carbonate, which is proportional to 1 / d², can be expressed by a first-order kinetic equation, i.e.

[0090] The correspondence between current operating parameters and final product particle size is determined based on the following formula: Where p represents the final product particle size, d represents the initial reactant droplet size, and Q... g Q represents the flow rate of carbon dioxide gas. l The flow rate of Ca(OH)2 liquid is represented by N, the stirring rate by p0, and the reference or initial value of the target particle size by d. * This indicates the droplet size under preset operating conditions. This indicates the gas-liquid flow ratio under preset operating conditions, N * α1 represents the stirring rate under preset operating conditions, α2 represents the sensitivity of the final product particle size to changes in the initial reactant droplet size, α3 represents the sensitivity of the final product particle size to changes in the gas-liquid flow ratio, and α4 represents the sensitivity of the final product particle size to changes in the stirring rate.

[0091] The preset operating condition is the standard operating condition, that is, the operating condition under normal atmospheric pressure.

[0092] The value of α2 ranges from +0.5 to +2.0 and can be obtained through experimental fitting.

[0093] The value of α3 ranges from -10 to -50 nm and can be obtained through experimental fitting; the value of α4 ranges from -0.01 to -0.04 nm / RPM and can be obtained through experimental fitting.

[0094] Step S304: Perform parameter coupling association based on the correspondence relationship and output the state equation.

[0095] Specifically, based on the correspondence, parameter coupling and association are performed to output state equations, including:

[0096] The state vector and input vector are obtained by coupling parameters based on the correspondence of the following formulas: z(t) represents the state vector, d(t) represents the droplet size, p(t) represents the product particle size, u(t) represents the input vector, and Q... g (t) represents the carbon dioxide gas flow rate, Q l (t) represents the liquid flow rate of Ca(OH)2, U spray (t) represents the adjustable parameters of the spray device, and N(t) represents the stirring rate.

[0097] The state vector is a core set of variables used to describe the dynamic behavior of the system, including the droplet size d(t) and the product particle size p(t).

[0098] Among them, the adjustable parameters of the spray device, such as the spray pressure or the nozzle orifice diameter, directly affect the droplet size.

[0099] The input vector can contain adjustable process parameters that directly affect droplet size and reaction rate.

[0100] The state equation is obtained based on the state vector and input vector using the following formula: x(k+1)=A·x(k)+B·Δu(k); where, Δu(k)=u(k)-u ref , representing the adjustment amount relative to the reference operating condition, u ref Represents the steady-state condition; A represents matrix A, B represents matrix B, and matrix A ∈ R. 2×2 It is used to describe the dynamic coupling relationship between state variables; matrix B∈R 2×4 This indicates the regulatory effect of the input on the state variable.

[0101] The state equation is based on the linearization assumption, discretizing the continuous-time dynamic model (sampling period T). s )get.

[0102] In matrix A, A11 represents the natural decay coefficient of droplet size d(t) over time (e.g., evaporation); A21 represents the transfer coefficient (hysteresis effect) of droplet size d(t) to product particle size p(t); A12 represents the influence coefficient of droplet size d(k) on product particle size p(k+1) at the next time step; and A22 represents the correlation coefficient of current product particle size p(k) to product particle size p(k+1) at the next time step.

[0103] Among them, A11 to A22 take values ​​between 0 and 1.

[0104] Matrix B quantifies the impact of adjusting CO2 flow rate, Ca(OH)2 flow rate, spray parameters, and stirring speed on the size of the generated droplets and particles in the next time step. Matrix B is a 2×4 matrix, where each column corresponds to the influence of a control input variable on the state variable, as detailed below:

[0105] B11, B12: Gas flow rate ΔQ g It affects d(k+1) and p(k+1).

[0106] B21, B22: Liquid flow rate ΔQ l It affects d(k+1) and p(k+1).

[0107] B31, B32: Spray parameter ΔU spray It affects d(k+1) and p(k+1).

[0108] B41, B42: The stirring rate N affects d(k+1) and p(k+1).

[0109] The output equation is: y(k)=C·x(k); where y(k) represents the product particle size p(k), and the corresponding matrix C=

[01] . The matrix C determines the transformation of the system state x(k) into the measurement output y(k). Essentially, it selects which states or combinations of states to observe or control.

[0110] Since the output y(k) is defined as the product granularity value (k) and x(k) is [d(k), p(k)], T Matrix C is simply the second element of the state vector. Therefore, C =

[01] . It depends on the selected value (k) for controlling / monitoring particle size.

[0111] Step S305: Based on the state equation, predict the state within a preset number of sampling steps in the future to obtain the prediction result.

[0112] Based on process parameters such as gas-liquid flow rate, spray state, and stirring rate, a model predictive control (MPC) framework is adopted to achieve the optimal product particle size. The current state x(k) = [d(k), p(k)] can be obtained using online sensors. T The system uses various input parameters (gas-liquid flow rate, spray state, stirring rate, etc.) and multivariate coupling relationships (gas-liquid flow rate, spray state, stirring rate, etc.) to achieve dynamic process control and predict the state within the next H sampling steps.

[0113] Specifically, the state prediction within the next H sampling steps is performed based on the state equation using the following formula: x(k+j+1)=A·x(k+j)+B·Δu(k+j), j=0,1,…,H; where j represents the number of sampling steps and H represents the total sampling step length; Δu(k)=u(k)-u ref , representing the adjustment amount relative to the reference operating condition, u ref The steady-state condition is represented by A and B, which represent matrices A and B, respectively; y(k+j)=C·x(k+j) represents the prediction result.

[0114] Step S306: Optimize the parameters based on the prediction results and the pre-set optimization objectives to obtain the candidate control parameters.

[0115] Among them, the target product particle size P target For reference, the difference between the actual output particle size and the target product particle size is used to construct a function J, which makes the system output (product particle size) as close as possible to and maintain the optimization target P in the future. target Avoid excessively drastic or frequent changes in the control inputs (flow rate, spray parameters, stirring rate, etc.).

[0116] Specifically, state optimization based on the prediction results yields candidate control parameters, which may include constructing a target convergence function based on the prediction results using the following formula: Where J represents the target approach function, H represents the total sampling step size, y(k+j) represents the prediction result, and P target Let λ represent the optimization objective, λ represent the weighting factor used to balance the state deviation and the magnitude of the control quantity change; Δu(k+j) represent the control quantity; under input constraints, solve for the optimal control increment; the constraint condition is: u_min≤u≤u_max; based on the optimal control increment and the current operating parameters, obtain the candidate control parameters.

[0117] Step S307: Perform atomization fusion processing using the candidate control parameters and obtain dynamic monitoring parameters.

[0118] Step S308: Adjust the state equation based on dynamic monitoring parameters to continue predicting the state within a preset number of sampling steps until the target nano-calcium carbonate is generated.

[0119] In this process, the state equation is adjusted in real time, thereby optimizing the selected control parameters in real time. The optimized control parameters are then atomized and fused, and monitored in real time to adjust the state equation in real time, thus forming a closed-loop control. This reduces the fluctuation of the conversion rate and makes the particle size distribution of the nano-calcium carbonate product more uniform.

[0120] For ease of understanding, the following are several simulated operating conditions:

[0121] Operating Condition 1: Baseline (Conventional Control): CO gas flow rate (Q) g Fixed at 25m 3 / hr, calcium hydroxide solution flow rate (Q) l Fixed at 2.0m 3 / hr, forming a fixed gas-liquid flow ratio (12.5). Spray pressure (P spray The pressure was set at a relatively low fixed value of 0.3 MPa, which resulted in a relatively large estimated initial droplet size (d0), approximately 150 μm. The reaction temperature (T...) rxn The temperature was also maintained at a constant 35°C, and no crystal form control agent was added under these conditions (H = none).

[0122] Operating Condition 2: MPC Control (Target 80nm): Demonstrates how the proposed system uses MPC to achieve a target average particle size of 80nm.

[0123] Operating Condition 3: MPC Control (Target 40nm): Displays the system's ability to produce finer particles by setting a 40nm target.

[0124] Operating Condition 4: MPC Control (Disturbance Response): Starting from Operating Condition 3 (target 40nm), a disturbance of a sudden drop in inlet CO concentration (e.g., -15%) is introduced. Demonstrate how MPC adjusts other parameters to maintain the target particle size.

[0125] Condition 5: MPC Control + Crystal Form Control Agent (Target 30nm): Simulates Condition 3 conditions, but adds crystal form control agent 'A' to achieve a smaller target particle size of 30nm. This utilizes the model's... The control agent effect captured by the fmorph(H) term.

[0126] Table 1 shows the simulation results for the above working conditions.

[0127] Table 1: Simulation Results

[0128]

[0129]

[0130] Notes to Table 1:

[0131] [1] Spray pressure is an example controllable parameter that affects the initial droplet size (d0). MPC may directly control the pressure or nozzle settings.

[0132] [2]d0 is estimated based on spray pressure / conditions; a smaller d0 is generally advantageous for obtaining a smaller final particle size p.

[0133] [3]σ pThe standard deviation or similar metric represents the particle size distribution. A lower value indicates higher uniformity.

[0134] Interpretation of the above results:

[0135] 1. Baseline vs. MPC: Condition 1 (baseline) showed larger, more uneven particles and lower conversion rate than conditions (2 and 3) controlled by MPC, demonstrating the limitations of the fixed parameter method.

[0136] 2. MPC Target Achieved: Conditions 2 and 3 demonstrate that MPC successfully adjusted the input parameters (Q). g Q l P spray T rxn This improved the average particle size (p) to be close to the desired target (80 nm and 40 nm, respectively), while also improving the uniformity (σ). p And conversion rate (XCO2). Producing smaller particles (condition 3 vs condition 2) usually requires more stringent conditions (higher gas flow rate, higher spray pressure).

[0137] 3. Disturbance Suppression: Operating condition 4 demonstrates the robustness of the MPC. When the inlet CO concentration decreases (disturbance), the controller detects the deviation (possibly through intermediate measurements or predicted output drift) and increases Q. g and Q l Compensation was performed to restore the particle size (p) to the target of close to 40 nm, although the conversion efficiency decreased slightly temporarily.

[0138] 4. Effect of Crystal Form Control Agent: Condition 5 shows that the addition of the crystal form control agent (H = Type A) enables the system to achieve a smaller target particle size (30nm) and exhibits excellent uniformity (σ). p =6nm) and high conversion / yield, which is likely due to the influence of the MPC model. The nucleation / growth kinetics captured by the project are realized.

[0139] In this embodiment of the invention, sensor data is monitored in real time, and the state equation is updated in real time to accurately reflect changes in actual operating conditions. Then, the optimal control input is solved by MPC to achieve fine control of the gas-liquid ratio, spray parameters, and stirring rate. This closed-loop control not only enables the formation of an optimal gas-liquid interface in the reactor, significantly improving mass transfer and reaction rates, but also ensures that the product particle size and quality indicators remain stable within the predetermined target range.

[0140] Example 4

[0141] Corresponding to the above method embodiments, this invention provides a nano-calcium carbonate preparation device based on model predictive control. Figure 5This is a schematic diagram of a model predictive control-based nano-calcium carbonate preparation device provided in an embodiment of the present invention, as shown below. Figure 5 As shown, the model-predictive control-based nano-calcium carbonate preparation device may include:

[0142] The power plant exhaust gas acquisition module 401 is used to acquire the power plant exhaust gas emitted by the combustion power plant.

[0143] The power plant exhaust gas treatment module 402 is used to compress, pretreat, and atomize the power plant exhaust gas to obtain a gas-liquid two-phase flow system.

[0144] The state equation determination module 403 is used to determine the state equation based on the current operating parameters of the gas-liquid two-phase flow system.

[0145] The state prediction module 404 is used to predict the state within a preset number of sampling steps based on the state equation, and obtain the prediction result.

[0146] The parameter optimization module 405 is used to optimize parameters based on the prediction results and the pre-set optimization objectives to obtain the candidate control parameters.

[0147] The atomization fusion processing module 406 is used to perform atomization fusion processing through candidate control parameters and obtain dynamic monitoring parameters.

[0148] The target nano-calcium carbonate generation module 407 is used to adjust the state equation based on dynamic monitoring parameters so as to continue to predict the state within a preset number of sampling steps until the target nano-calcium carbonate is generated.

[0149] The nano-calcium carbonate preparation device based on model predictive control provided in this invention can obtain power plant exhaust gas from a combustion power plant, compress, pretreat, and atomize the exhaust gas to obtain a gas-liquid two-phase flow system. A state equation is determined based on the current operating parameters of the gas-liquid two-phase flow system. The state equation is then used to predict the state within a preset number of sampling steps, yielding prediction results. Parameter optimization is performed based on the prediction results and pre-set optimization targets to obtain candidate control parameters. Atomization fusion processing is then performed using these candidate control parameters to obtain dynamic monitoring parameters. The state equation is adjusted based on these dynamic monitoring parameters to continue predicting the state within a preset number of sampling steps until the target nano-calcium carbonate is generated. This method accurately reflects changes in actual operating conditions, updating process parameters in real time based on these changes, resulting in a more uniform particle size distribution of the nano-calcium carbonate product and thus reducing conversion rate fluctuations.

[0150] In some embodiments, the power plant exhaust gas treatment module is further configured to compress and collect the power plant exhaust gas into a carbon dioxide tank using an air compressor; the outlet pipe of the carbon dioxide tank is connected to a water washing bottle to remove sulfur dioxide and dust from the power plant exhaust gas; the outlet pipe of the water washing bottle is connected to a carbon dioxide pressure swing adsorber to concentrate the carbon dioxide gas in the power plant exhaust gas; the outlet pipe of the carbon dioxide pressure swing adsorber is connected to an air compressor to compress the carbon dioxide gas; the outlet pipe of the air compressor is connected to a pressure stabilizing tank, and the outlet pipe of the pressure stabilizing tank is connected to the bottom of a spray reaction tank to deliver carbon dioxide gas; the top of the spray reaction tank is equipped with multiple spray heads, a pressure reducing valve, and a liquid inlet pipe; the Ca(OH) solution obtained by filtration is atomized through the spray heads to obtain droplets; the droplets and the carbon dioxide gas delivered from the outlet pipe of the pressure stabilizing tank are mixed to obtain a gas-liquid two-phase flow system.

[0151] In some embodiments, the state prediction module is further configured to predict the state within the next H sampling steps based on the state equation using the following formula: x(k+j+1)=A·x(k+j)+B·Δu(k+j), j=0,1,…,H; where j represents the number of sampling steps and H represents the total sampling step length; Δu(k)=u(k)-u ref , representing the adjustment amount relative to the reference operating condition, u ref The steady-state condition is represented by A and B, which represent matrices A and B, respectively; y(k+j)=C·x(k+j) represents the prediction result.

[0152] In some embodiments, the state prediction module is further configured to construct a target convergence function based on the prediction results using the following formula: Where J represents the target approach function, H represents the total sampling step size, y(k+j) represents the prediction result, and P target Let λ represent the optimization objective, λ represent the weighting factor used to balance the state deviation and the magnitude of the control quantity change; Δu(k+j) represent the control quantity; under input constraints, solve for the optimal control increment; the constraint condition is: u_min≤u≤u_max; based on the optimal control increment and the current operating parameters, obtain the candidate control parameters.

[0153] In some embodiments, the state equation determination module is further configured to input the current operating parameters of the gas-liquid two-phase flow system into a pre-established dynamic mathematical model, determine the correspondence between the current operating parameters and the product parameters, perform parameter coupling correlation based on the correspondence, and output the state equation.

[0154] In some embodiments, the product parameters include: average droplet diameter, average particle size of CaCO3 product, and final product particle size. The equation of state determination module is further configured to determine the correspondence between the current operating parameters and the average droplet diameter based on the following formula: Where d represents the average droplet diameter, σ represents the liquid surface tension, and ρ g Let represent gas density, U represent the characteristic relative velocity between the gas and liquid phases, α1 represent the scaling coefficient, and β1 represent the scaling exponent; the correspondence between the current operating parameters and the average particle size of the CaCO3 product is determined based on the following formula: Where p represents the average particle size of the CaCO3 product; k r A represents the reaction rate constant. contact V represents the gas-liquid contact area, and C represents the reactor volume. CO and C eq These represent the initial and equilibrium concentrations of carbon dioxide gas in the reaction, respectively. The relationship between the current operating parameters and the final product particle size is determined based on the following formula: Where p represents the final product particle size, d represents the initial reactant droplet size, and Q... g Q represents the flow rate of carbon dioxide gas. l The flow rate of Ca(OH)2 liquid is represented by N, the stirring rate by p0, and the reference or initial value of the target particle size by d. * This indicates the droplet size under preset operating conditions. This indicates the gas-liquid flow ratio under preset operating conditions, N * α1 represents the stirring rate under preset operating conditions, α2 represents the sensitivity of the final product particle size to changes in the initial reactant droplet size, α3 represents the sensitivity of the final product particle size to changes in the gas-liquid flow ratio, and α4 represents the sensitivity of the final product particle size to changes in the stirring rate.

[0155] In some embodiments, the state equation determination module is further configured to obtain the state vector and the input vector by performing parameter coupling association based on the following formula correspondence: z(t) represents the state vector, d(t) represents the droplet size, p(t) represents the product particle size, u(t) represents the input vector, and Q... g (t) represents the carbon dioxide gas flow rate, Q l (t) represents the liquid flow rate of Ca(OH)2, U spray (t) represents the adjustable parameters of the spray device, and N(t) represents the stirring rate; the state equation is obtained based on the state vector and input vector using the following formula: x(k+1)=A·x(k)+B·Δu(k); where, Δu(k)=u(k)-u ref , representing the adjustment amount relative to the reference operating condition, u ref Represents the steady-state condition; A represents matrix A, B represents matrix B, and matrix A ∈ R. 2×2 It is used to describe the dynamic coupling relationship between state variables; matrix B∈R 2×4 This indicates the regulatory effect of the input on the state variable.

[0156] The device provided in this embodiment of the invention has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0157] Example 5

[0158] This invention also provides an electronic device for running the above-described model-predictive control-based method for preparing nano-calcium carbonate; see also Figure 6 The diagram shows the structure of an electronic device, which includes a memory 500 and a processor 501. The memory 500 is used to store one or more computer instructions, which are executed by the processor 501 to realize the above-mentioned model predictive control-based method for preparing nano-calcium carbonate.

[0159] Furthermore, Figure 6 The electronic device shown also includes a bus 502 and a communication interface 503. The processor 501, the communication interface 503 and the memory 500 are connected via the bus 502.

[0160] The memory 500 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 503 (which can be wired or wireless), such as the Internet, wide area network, local area network, or metropolitan area network. The bus 502 may be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0161] Processor 501 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 501 or by instructions in software form. Processor 501 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a readily available storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 500, and processor 501 reads information from memory 500 and, in conjunction with its hardware, completes the steps of the method described in the foregoing embodiments.

[0162] This invention also provides a computer-readable storage medium storing computer-executable instructions. When these computer-executable instructions are called and executed by a processor, they cause the processor to implement the above-described model predictive control-based method for preparing nano-calcium carbonate. For specific implementation details, please refer to the method embodiments, which will not be repeated here.

[0163] The computer program product for the preparation method of nano-calcium carbonate based on model predictive control provided in this embodiment of the invention includes a computer-readable storage medium storing non-volatile program code executable by a processor. The instructions included in the program code can be used to execute the methods described in the preceding method embodiments. For specific implementation, please refer to the method embodiments, which will not be repeated here.

[0164] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0165] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0166] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0167] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0168] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0169] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for preparing nano-calcium carbonate based on model predictive control, characterized in that, The method includes: Obtain the exhaust gas emitted by the combustion power plant; The power plant exhaust gas is compressed, pretreated, and atomized to obtain a gas-liquid two-phase flow system. The equation of state is determined based on the current operating parameters of the gas-liquid two-phase flow system. Based on the state equation, the state within a predetermined number of sampling steps is predicted to obtain the prediction result; Based on the prediction results and the pre-set optimization objectives, the parameters are optimized to obtain the candidate control parameters; Atomization fusion processing is performed using the selected control parameters, and dynamic monitoring parameters are obtained. The state equation is adjusted based on the dynamic monitoring parameters to continue predicting the state within a preset number of sampling steps until the target nano-calcium carbonate is generated.

2. The method according to claim 1, characterized in that, The process of compressing, pretreating, and atomizing the power plant exhaust gas to obtain a gas-liquid two-phase flow system includes: The power plant exhaust gas is compressed and collected into a carbon dioxide tank using an air compressor; The outlet pipe of the carbon dioxide tank is connected to a water washing bottle to remove sulfur dioxide and dust from the power plant exhaust gas. The outlet pipe of the water washing bottle is connected to a carbon dioxide pressure swing adsorber to concentrate the carbon dioxide gas in the power plant exhaust gas. The outlet pipe of the carbon dioxide pressure swing adsorber is connected to an air compressor to compress the carbon dioxide gas; The air compressor's outlet pipe is connected to a pressure stabilizing tank, and the pressure stabilizing tank's outlet pipe is connected to the bottom of the spray reaction tank to deliver the carbon dioxide gas; the top of the spray reaction tank is equipped with multiple spray heads, a pressure reducing valve, and a liquid inlet pipe; The Ca(OH) solution obtained by filtration is atomized through the spray head to obtain droplets; The liquid droplets and the carbon dioxide gas delivered from the outlet pipe of the pressure stabilizing tank are mixed to obtain the gas-liquid two-phase flow system.

3. The method according to claim 1, characterized in that, The prediction of the state within a predetermined number of sampling steps based on the state equation, to obtain the prediction result, includes: The state prediction for the next H sampling steps is performed based on the state equation using the following formula: x(k+j+1)=A·x(k+j)+B·Δu(k+j), j=0,1,…,H; where j represents the number of sampling steps and H represents the total sampling step length; Δu(k)=u(k)-u ref , representing the adjustment amount relative to the reference operating condition, u ref The steady-state condition is represented by A and B, which represent matrices A and B, respectively; y(k+j)=C·x(k+j) represents the prediction result.

4. The method according to claim 2, characterized in that, The parameter optimization based on the prediction results and the pre-set optimization objective yields candidate control parameters, including: The target approximation function is constructed based on the prediction results using the following formula: Where J represents the target approach function, H represents the total sampling step size, y(k+j) represents the prediction result, and P target Let λ represent the optimization objective, λ represent the weighting factor used to balance the state deviation and the magnitude of the control variable change; and Δu(k+j) represent the control variable. Under the input constraints, solve for the optimal control increment; the constraints are: u_min≤u≤u_max; Based on the optimal control increment and the current operating parameters, the candidate control parameters are obtained.

5. The method according to claim 1, characterized in that, The determination of the state equation based on the current operating parameters of the gas-liquid two-phase flow system includes: The current operating parameters of the gas-liquid two-phase flow system are input into a pre-established dynamic mathematical model to determine the correspondence between the current operating parameters and the product parameters; Based on the aforementioned correspondence, parameter coupling and association are performed to output the state equation.

6. The method according to claim 5, characterized in that, The product parameters include: average droplet diameter, average particle size of CaCO3 product, and final product particle size. Determining the correspondence between the current operating parameters and the product parameters includes: The correspondence between the current operating parameters and the average droplet diameter is determined based on the following formula: Where d represents the average droplet diameter, σ represents the liquid surface tension, and ρ g α1 represents the gas density, U represents the characteristic relative velocity between the gas and liquid phases, α1 represents the scaling coefficient, and β1 represents the scaling exponent. The correspondence between the current operating parameters and the average particle size of the CaCO3 product is determined based on the following formula: Where p represents the average particle size of the CaCO3 product; k r A represents the reaction rate constant. contact V represents the gas-liquid contact area, and C represents the reactor volume. CO and C eq These represent the initial and equilibrium concentrations of carbon dioxide gas in the gas, respectively. The correspondence between the current operating parameters and the final product particle size is determined based on the following formula: Where p represents the final product particle size, d represents the initial reactant droplet size, and Q g Q represents the flow rate of carbon dioxide gas. l The flow rate of Ca(OH)2 liquid is represented by N, the stirring rate by p0, and the reference or initial value of the target particle size by d. * This indicates the droplet size under preset operating conditions. N represents the gas-liquid flow ratio under preset operating conditions. * α1 represents the stirring rate under preset operating conditions, α2 represents the sensitivity of the final product particle size to changes in the initial reactant droplet size, α3 represents the sensitivity of the final product particle size to changes in the gas-liquid flow ratio, and α4 represents the sensitivity of the final product particle size to changes in the stirring rate.

7. The method according to claim 6, characterized in that, The parameter coupling association based on the correspondence, and the output of the state equation, includes: The state vector and input vector are obtained by coupling parameters based on the correspondence of the following formulas: z(t) represents the state vector, d(t) represents the droplet size, p(t) represents the product particle size, u(t) represents the input vector, and Q... g (t) represents the carbon dioxide gas flow rate, Q l (t) represents the liquid flow rate of Ca(OH)2, U spray (t) represents the adjustable parameters of the spray device, and N(t) represents the stirring rate; The state equation is obtained based on the state vector and the input vector using the following formula: x(k+1)=A·x(k)+B·Δu(k); where, Δu(k)=u(k)-u ref , representing the adjustment amount relative to the reference operating condition, u ref Represents the steady-state condition; A represents matrix A, B represents matrix B, and matrix A ∈ R. 2×2 It is used to describe the dynamic coupling relationship between state variables; matrix B∈R 2×4 This indicates the regulatory effect of the input on the state variable.

8. A nano-calcium carbonate preparation device based on model predictive control, characterized in that, The device includes: The power plant exhaust gas acquisition module is used to acquire the exhaust gas emitted by combustion power plants. The power plant exhaust gas treatment module is used to compress, pretreat, and atomize the power plant exhaust gas to obtain a gas-liquid two-phase flow system. The equation of state determination module is used to determine the equation of state based on the current operating parameters of the gas-liquid two-phase flow system. The state prediction module is used to predict the state within a preset number of sampling steps based on the state equation, and obtain the prediction result; The parameter optimization module is used to optimize the parameters based on the prediction results and the pre-set optimization objectives to obtain the candidate control parameters. The atomization fusion processing module is used to perform atomization fusion processing through the selected control parameters and obtain dynamic monitoring parameters; The target nano-calcium carbonate generation module is used to adjust the state equation based on the dynamic monitoring parameters, so as to continue to predict the state within a preset number of sampling steps until the target nano-calcium carbonate is generated.

9. An electronic device, characterized in that, The method includes a processor and a memory, the memory storing computer-executable instructions that can be executed by the processor, the processor executing the computer-executable instructions to implement the model predictive control-based method for preparing nano-calcium carbonate according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when invoked and executed by a processor, cause the processor to implement the model predictive control-based method for preparing nano-calcium carbonate as described in any one of claims 1 to 7.

Images