A two-layer data-driven model predictive control method for coal slurry concentration control system in coal gasification process

Abstract

This invention proposes a two-layer data-driven predictive control method for coal slurry concentration control in coal gasification processes. The method employs a two-layer control architecture: the upper layer dynamically updates model parameters based on historical operating data, solving an optimization problem involving coal / water flow constraints and concentration fluctuation limitations through an open-loop simulation model. It adjusts the flow setpoint in real-time to actively suppress concentration fluctuations caused by feed port blockage, maintaining dynamic consistency between the open-loop simulation model and the lower-layer control framework. The lower layer uses an incremental state space as the prediction model, implementing a model predictive control strategy with single-step input mapping. It utilizes a linear combination of historical data from a sliding window to construct a robust compensation mechanism to reduce the impact of parameter uncertainty, generating the optimal control sequence by solving a quadratic programming problem in real-time. This method significantly improves the system's ability to suppress time-delay characteristics and blockage disturbances, effectively solving the problem of large fluctuations in coal slurry concentration caused by feed port blockage in existing technologies.

Description

Technical Field

[0001] This invention relates to the field of coal gasification production process control technology, and in particular to a two-layer data-driven model predictive control method and system for coal slurry concentration control systems. Background Technology

[0002] Coal gasification is a crucial process for converting coal into syngas (primarily CO and H2). Coal slurry, as the carrier of the gasification feedstock, has a significant impact on process efficiency, energy consumption, and equipment operation. First, the coal needs to be crushed to a certain particle size to increase its specific surface area and promote subsequent gasification reactions. Then, pulverized coal is mixed with water to form a coal-water slurry. If the slurry concentration is too low, it will result in excessive moisture, increasing evaporation energy consumption in the gasifier; if the concentration is too high, it will cause increased viscosity, leading to pumping difficulties or pipeline blockage. The high-concentration coal slurry is transported by a high-pressure pump, atomized into fine droplets through nozzles, and then enters the gasifier where it undergoes partial oxidation under high temperature and pressure, generating CO, H2, and CO2, etc. Finally, it undergoes gas cooling, washing, and desulfurization treatment to produce the final products.

[0003] Coal slurry concentration is a major factor affecting the quality of syngas. In the process of coal slurry preparation, relevant research mainly focuses on two aspects: coal slurry concentration optimization and control of coal / water feeding devices.

[0004] Many factors influence the optimization of coal blending technology in coal-water slurry gasification units, making it difficult to achieve scientific and precise optimization based solely on personal experience. Commonly used coal slurry concentration optimization models comprehensively consider blending indicators, market prices, inventory costs, and the costs of implementing coal blending operations. The optimization objective is to minimize the total cost of coal use. Particle swarm optimization is used to solve the multi-objective coal blending optimization problem. Currently, significant progress has been made in optimizing coal slurry concentration during coal gasification. To avoid redundant research, this application only considers the control of the coal feeding / water feeding devices.

[0005] The coal / water feeding system control system mainly consists of a coal bunker / water storage tank, feeder / pump motor, baffles, conveyor belt / transmission pipeline, and nuclear scale / flowmeter. Changing the feeder / pump motor speed changes the coal / water flow rate. The coal feeding inlet is often clogged due to uneven coal quality, while the water feeding pipeline is often clogged due to scale or organic matter. This control system aims to ensure that the ratio of coal flow rate to water flow rate meets the coal slurry concentration requirements by adjusting the feeder / pump motor speed. Currently, the plant pre-determines the optimal coal / water flow rate based on production data and uses it as a fixed coal flow rate setpoint. Traditional control methods such as PID and fuzzy PID are used to control the feeder / pump motor to ensure the coal / water flow rate ratio meets the requirements. Under normal circumstances, this control scheme can maintain the coal slurry concentration at the setpoint. In the event of severe blockage at the feed inlet, on-site workers will use air cannons and chemical agents to promptly clear the feed inlet and pipeline. However, if the response is not timely, continuous blockage can cause the coal slurry concentration to deviate significantly from the target value, and over-reliance on manual intervention also limits the automation level of the coal slurry concentration control system. Although MPC control outperforms PID control when dealing with time-delay objects, the presence of parameter uncertainties in the system and model mismatch can actually lead to a decline in control performance. Summary of the Invention

[0006] This application provides a two-layer data-driven model predictive control method for coal slurry concentration control systems to solve the problem that the ratio of coal flow rate to water flow rate (i.e., coal slurry concentration) deviates from the set value due to blockage at the feed inlet.

[0007] One aspect of this application provides a two-layer data-driven model predictive control method for a coal slurry concentration control system, the method comprising the following steps:

[0008] Acquire real-time coal / water flow rate data and feeder / pump motor speed data from the coal slurry concentration control system. The coal / water flow rate data includes delays caused by transport via conveyor belt / transmission pipeline.

[0009] An augmented state-space model of the coal slurry concentration control system is constructed with the feeder / pump motor speed as input and the coal / water flow rate as output. This model describes the time-delay relationship between the dynamic characteristics of the coal / water flow rate and the feeder / pump motor speed. The model parameters include static gain, inertia time coefficient, and fixed time delay.

[0010] Record historical coal / water flow output values ​​and corresponding feeder / pump motor speeds to identify and update system parameters. Parameter identification can employ least squares methods with forgetting factors, Moore-Penrose inverse methods, etc.

[0011] Define the length of the sliding window, store historical data of augmented state variables and system control variables, and express the augmented state variables at the current time, the state variables at the next time, and the control variables as the sum of a linear combination of historical data within the sliding window and the residual term. The combination coefficients and the residual term are optimization variables.

[0012] A two-layer control framework is designed. The upper-layer optimization module updates model parameters based on historical data, solves an optimization problem including constraints on maximum coal / water flow rate and coal slurry concentration fluctuation, and dynamically adjusts the coal / water flow rate setpoints. The lower-layer control module adopts Input Mapping Model Predictive Control (IASS-IMMPC) based on incremental augmented state space, using the control sequence, combination coefficients, and residual terms as optimization variables, and generates the optimal feeder / pump motor speed sequence by solving a quadratic programming problem.

[0013] Apply the first speed optimization to the system, update the augmented state variables, shift the sliding window, and continue the above process.

[0014] In some alternative embodiments, the augmented state quantity χ i (t) includes the current output coal / water flow rate change, the historical output coal / water flow rate change, and the historical control increment.

[0015] In some optional embodiments, parameter identification is implemented using Moore-Penrose generalized inverse, using historical coal / water flow data of a specified length and the corresponding feeder / pump motor speed.

[0016] In some optional embodiments, the MPC with input mapping of the coal / water feeding device i is implemented through the following steps: defining the historical state within the sliding window. Input increment vector and the corresponding residual term δχ i (t),δu i (t) Constructing linear combination relations and The combination coefficients and residual terms are the variables to be optimized.

[0017] In some optional embodiments, the objective function of the quadratic programming problem is: Where the Hessian matrix M i For a positive definite matrix, optimize the variable Γ. i Including residual term δu i (t), combination coefficient ξ i and the rotational speed sequence U after the next moment i (t+1).

[0018] In some optional embodiments, the upper coal / water flow rate setpoint optimization strategy includes:

[0019] Based on historical data, Moore-Penrose generalized inverse update of system parameters is used to determine the maximum allowable flow output value of the system.

[0020] The current coal / water flow rate trial value is used as the open-loop simulation structure setting value, where the simulation structure is consistent with the lower-level IASS-IMMPC, and is used to calculate the predicted coal / water flow rate under the current trial value.

[0021] During the iterative solution process, a trial value update step size or update range is set until the predicted coal slurry concentration of the coal slurry concentration control system (i.e., the ratio of the coal flow output value of the coal feeding device to the water flow output value of the water feeding device) is within the set fluctuation range and minimizes the difference from the optimal set value. If the predicted values ​​do not meet the constraints, the current flow rate is set to the minimum value between the previous flow rate set value and the current maximum flow rate output.

[0022] In some optional embodiments, the set coal slurry concentration fluctuation range decays exponentially with respect to time. By adjusting the range size and decay rate, the feasibility of the upper-level optimization problem can be ensured. The optimal setting value is the plant's empirical value for coal / water flow rate.

[0023] One aspect of this application provides a two-layer data-driven model predictive control system for a coal slurry concentration control system, used to implement the above-mentioned two-layer data-driven model predictive control method for a coal slurry concentration control system. The system includes:

[0024] The data acquisition module acquires and stores coal / water flow rate and corresponding feeder speed data in real time;

[0025] The model building module generates an augmented state-space model and uses the Moore-Penrose generalized inverse to update parameters online.

[0026] The upper-level optimization module dynamically adjusts the coal / water flow rate setpoints;

[0027] The lower-level control module combines single-step input mapping, incremental augmented state variables, and MPC to further reduce the impact of system parameter uncertainties and ensure consistency with the upper-level open-loop simulation results as much as possible.

[0028] The execution module sends the optimized setpoints and control commands to the feeder. The optimized control quantity calculated by the lower-level control module is the setpoint speed of the feeder / pump motor, which can be quickly tracked to the setpoint by incremental PID control of the feeder / pump motor speed.

[0029] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure.

[0030] The two-layer data-driven model predictive control method and system for coal slurry concentration control systems disclosed in this application have the following beneficial effects:

[0031] (1) A single-step input mapping MPC control strategy based on incremental augmented state space was designed, which accelerated the system settling time and improved the multi-step prediction accuracy under model uncertainty.

[0032] (2) A double-layer frame was introduced, which can maintain a relatively stable coal slurry concentration in the presence of both continuous slow blockage and sudden major blockage. Attached Figure Description

[0033] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings.

[0034] Figure 1 This is a flowchart of a two-layer data-driven model predictive control method for a coal slurry concentration control system in a coal gasification process according to an embodiment of this application;

[0035] Figure 2 This is a schematic diagram of a coal slurry concentration control system according to an embodiment of this application. A complete system can consist of one or more coal feeding devices and one water feeding device. As shown in the figure, the coal feeding device consists of a coal bunker, baffle, disc feeder, variable speed motor, conveyor belt, and nuclear weighing device. The variable speed motor is the control mechanism, and the nuclear weighing device is the sensing mechanism. The water feeding device consists of a water storage tank, water pump motor, transmission pipeline, and flow meter. The water pump motor is the control mechanism, and the flow meter is the sensing mechanism.

[0036] Figure 3 This is a schematic diagram of a double-layer structure according to an embodiment of this application, taking two coal feeding devices and one water feeding device as an example;

[0037] Figure 4 This is an overall algorithm flowchart of an embodiment of this application;

[0038] Figure 5 and Figure 6 This is a simulation result diagram of an embodiment of this application, wherein... Figure 5 This diagram illustrates the flow rate ratios of various devices when a significant blockage occurs at the feed inlet / feeding pipe. Figure 6 This is a diagram illustrating the flow rate ratios of each device when the feed inlet / feeding pipe is continuously blocked. Detailed Implementation

[0039] This application provides a two-layer data-driven model predictive control method for coal slurry concentration control in coal gasification processes. By constructing an incremental augmented state-space model and combining a single-step input mapping method with MPC, it can achieve accurate tracking of the coal / water flow rate setpoint even when system parameters are uncertain. Furthermore, the method employs a two-layer structure, using updated system parameters to solve the upper-level optimization problem and recalculate the coal / water flow rate setpoint, thus maintaining stable coal slurry concentration even in the presence of blockages. Simulation results demonstrate the effectiveness of this application.

[0040] like Figure 1 As shown, the specific implementation steps of the two-layer data-driven model predictive control method for a coal slurry concentration control system in a coal gasification process provided in this application are as follows:

[0041] P100. Acquiring real-time coal / water flow rate data and feeder / pump motor speed data for the coal slurry concentration control system refers to the coal / water flow rate (unit: t / h) and feeder / pump motor speed (unit: RPM) data collected sequentially over time during the coal blending process. The flow rate ratio of each device directly reflects the coal slurry concentration. In practical applications, the corresponding data can be obtained through nuclear scales, flow meter sensors, and photoelectric encoders.

[0042] P200. Using the feeder / pump motor speed increment as input and coal / water flow rate as output, an incremental augmented state-space model is constructed using the current change in coal / water flow rate and the historical change in control parameters as state variables. In this application, variable rather than absolute quantities are chosen to constitute the state variables in order to reduce the impact of parameter uncertainty and further improve the accuracy of the predicted output; the augmented state-space model is chosen to implicitly include the system's time delay factor in the state variables, facilitating model derivation and controller design.

[0043] For P300, the system parameters can be identified using methods such as least squares with a forgetting factor or Moore-Penrose generalized inverse. Since the control system parameters addressed in this application are simple, it is recommended to prioritize the Moore-Penrose generalized inverse.

[0044] P400 specifies the length of the sliding window and stores historical data of augmented state variables and system control variables. Based on the characteristics of a linear system, the augmented state variables at the current and next time steps, and the control variables at the next time step, are represented as a linear combination of the historical data from the sliding window and the sum of the residual terms. This reduces the influence range of uncertain parameters from the original state variables to the residuals of the state variables. The combination coefficients and residual terms are used as optimization variables for online optimization. This application uses a single-step input mapping method, representing only the next predicted output as the above linear combination form. Although the compensation capability gradually weakens in multi-step prediction, the prediction model combined with the incremental augmented state space still possesses high prediction accuracy. Considering computational complexity, this application does not adopt a multi-step input mapping method.

[0045] P500 presents a two-layer control problem, setting constraints on coal slurry concentration fluctuations and solving for the current maximum allowable coal / water flow output based on updated model parameters. The upper layer dynamically adjusts the coal flow setpoint by solving an optimization problem based on the maximum flow constraint and coal slurry concentration fluctuation constraint. The lower layer solves a single-step input mapping MPC optimization problem based on the optimized setpoint to obtain the optimized rotational speed sequence. In this application, the cost function of the upper-layer optimization is defined as... Where Y * To determine the optimal coal slurry flow rate, it can be set to the plant's empirical value for coal slurry flow rate, Y. r The coal slurry flow rate setpoint is used as an optimization variable. The coal / water flow rate setpoint Y for a single unit can be determined by the flow rate ratio of each unit (which represents the coal slurry concentration, where the ratio of coal flow rates of each coal feeding unit reflects the proportion of different coals in the coal slurry). i,r The upper-level optimization is subject to three constraints: 1) Y min ≤Y r ≤Y max , where Y min Y is the minimum coal slurry flow rate requirement determined based on plant experience. max The sum of the maximum output flow rates of each device; 2) This indicates that at the set value Y i,r Below, the predicted flow output of the i-th device in the k-th step in the future, the flow output prediction model is the aforementioned augmented state-space model, and the predicted control quantity is obtained by solving IASS-IMMPC; 3) Where p max Defined as p max (k)=θ k p0+(1-θ k )p ∞ , θ∈(0,1),p0,p ∞ >0. η ilLet p0 be the flow ratio between device i and device l. Taking two coal feeding devices and one water feeding device as an example, if the coal slurry concentration is set to 50%, and the proportions of the two types of coal are 1 / 3 and 1 / 6, then the flow ratio of the water feeding device and the coal feeding device is set to 3:2:1. In practical applications, the value of p0 should be large enough to ensure that the initial flow ratio meets the constraints.

[0046] P600: Apply the first control variable of the optimized control sequence to the system, update the augmented state variable, shift the sliding window, and update the window data information. The optimized value output by the lower-level MPC includes the control increment residual term, the augmented state residual term, the linear combination coefficients, and the optimized control increment sequence after the next time step. The control increment at the next time step is represented as the historical control data within the sliding window × the linear combination coefficients + the control increment residual term.

[0047] Combination Figure 3 and Figure 4 The algorithm flow of a two-layer data-driven model predictive control method for a coal slurry concentration control system in a coal gasification process is shown, including the following steps:

[0048] S1: Input time series data from the coal gasification and coal slurry production process to form a sliding window. Where χ i (tk) represents the augmented system state data of the i-th device at the k-th production time in the past. Δy i (tk) represents the system output increment at the k-th production moment in the past, Δu i (tkj) represents the control increment at the (k+j)th production moment in the past, where n L This represents the inherent time delay of the system. A positive definite symmetric weight matrix Q, R, R1 and coefficient r0 > 0 are set.

[0049] S2: Construct an augmented state-space model χ based on the current parameters. i (t+1)=A i χ i (t)+B i Δu i (t), setting the prediction time domain N c and control time domain N u Solving IASS-IMMPC yields the setpoints for the feeder / pump motor speeds. The matrices are defined as follows:

[0050]

[0051] S3: Based on the setpoint obtained in S2, the PWM duty cycle is adjusted through incremental PID control algorithm to control the motor speed and achieve setpoint tracking.

[0052] S4: Record h LThe length of the filtered system output data [y] i (t),…,y i (th L +1)] and input data [Δu i (tn L ),…,Δu i (th L -n L -1)], parameters of the Moore-Penrose generalized inverse update system are:

[0053]

[0054] S5: Solve for the current maximum allowable coal / water flow rate based on updated parameters. Where u i,max Let y be the upper limit of the motor speed of the i-th device. i,max The change is made at the plant's minimum coal slurry flow rate Y. min Optimal coal slurry flow rate Y * Traversal step size δy and coal slurry concentration fluctuation constraints The following optimization problem is solved using a traversal method:

[0055]

[0056] stY min ≤Y r ≤Y max

[0057]

[0058] Where Y is traversed in the kth iteration r =Y min +k×δy,u i To solve the IASS-IMMPC problem in step S2 under the current Y setting, if no traversal value satisfies the above condition, the coal / water flow rate is set to min{current maximum flow output, previous flow rate setting}; otherwise, the optimized Y is... r As the setting value for the lower-level IASS-IMMPC.

[0059] S6: Update the current state and input values ​​to the sliding window, and move the sliding window forward by 1 step. Repeat steps S2 to S5. If the current setpoint is less than the plant's minimum coal slurry flow rate, stop the solution and shut down for maintenance.

[0060] The IASS-IMMPC problem to be solved in step S2 is:

[0061]

[0062] stui,min ≤u i (t+k|t)≤u i,max k = 0, 1, ..., N u -1

[0063] u i (t+k+1|t)=u i (t+k|t)+△u i (t+k+1|t), k=0,1,...,N u -1

[0064] △u i (t|t)=△u i,h (t-1)ξ i +δu i (t)

[0065]

[0066] in, U i =[Δu i (t+1|t),…,Δu i (t+N c -1|t)] T u i,min and u i,max These are the lower and upper limits of the input for the i-th device, respectively. U is the upper bound for the input increment of the i-th device. i ξ i and δu i Let (t) be the variable to be optimized. The above problem can be solved by quadratic programming.

[0067] Figure 5 , Figure 6 The following is a simulation example of the proposed method. Figure 5 For sudden traffic jams, Figure 6 This is for the case of continuous, small-scale blockage. It can be seen that the proposed method has adjustment capabilities in both situations, maintaining a relatively stable flow ratio among the various devices, thereby ensuring a relatively stable concentration of the coal-water slurry.

[0068] The above description merely illustrates specific embodiments of this application and should not be construed as limiting the scope of protection of this application to these embodiments. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of this application, and all such modifications and substitutions should be considered within the scope of protection of this application.

Claims

1. A two-layer data-driven model predictive control method for a coal slurry concentration control system in a coal gasification process, characterized in that, The method includes the following steps: Acquire real-time coal / water flow rate data and feeder / pump motor speed data from the coal slurry concentration control system. The flow rate data includes delayed measurement values ​​due to transport via conveyor belt / transmission pipeline. An augmented state-space model of a coal blending system is constructed with the feeder / pump motor speed as input and the coal / water flow rate as output. This model describes the time-delay relationship between the dynamic characteristics of the coal / water flow rate and the feeder / pump motor speed. The model parameters include static gain, inertia time coefficient, and fixed time delay. Based on historical coal / water flow output values ​​and corresponding feeder / pump motor speeds, the system parameters are updated using the least squares method with a forgetting factor or the Moore-Penrose inverse method. Set up a sliding window to store historical data of augmented state variables and control variables. Represent the augmented state variable at the current time, the state variable at the next time, and the control variable as a linear combination of the historical data in the sliding window and the sum of the residual term. The combination coefficient and the residual term are the optimization variables. The design employs a two-layer control framework: an upper-layer optimization module that updates model parameters based on historical data, solves an optimization problem involving constraints on maximum coal / water flow rate and coal slurry concentration fluctuation, and dynamically adjusts the coal / water flow rate setpoints; and a lower-layer control module that uses an input mapping model predictive control (IASS-IMMPC) based on incremental augmented state space, taking the control sequence, combination coefficients, and residual terms as optimization variables, and generating the optimal feeder / pump motor speed sequence by solving a quadratic programming problem. The first speed optimization is applied to the system, the augmented state variables are updated and the sliding window is shifted, and the above process is repeated.

2. The two-layer data-driven model predictive control method for coal slurry concentration control system in coal gasification process according to claim 1, characterized in that, The augmented state quantity It includes the current output coal / water flow rate change value, the historical output coal / water flow rate change value, and the historical control increment.

3. The two-layer data-driven model predictive control method for coal slurry concentration control system in coal gasification process according to claim 1, characterized in that, The parameters are identified using Moore-Penrose generalized inverse, and the data length is the user-defined historical coal / water flow rate and the corresponding feeder / pump motor speed data.

4. The two-layer data-driven model predictive control method for coal slurry concentration control system in coal gasification process according to claim 1, characterized in that, The MPC for the coal / water feeding device i, which includes input mapping, is implemented by the following steps: defining the historical state within the sliding window. Input increment vector and the corresponding residual terms Constructing linear combination relations and The combination coefficients and residual terms are the variables to be optimized.

5. The two-layer data-driven model predictive control method for the coal slurry concentration control system in the coal gasification process according to claim 4, characterized in that, The objective function of the quadratic programming problem is: The Hessian matrix For a positive definite matrix, optimize the variables. Including residuals Combination coefficients and the rotational speed sequence after the next moment .

6. The two-layer data-driven model predictive control method for coal slurry concentration control system in coal gasification process according to claim 1, characterized in that, The optimization strategy for the upper coal / water flow rate setpoint is as follows: Based on historical data, Moore-Penrose generalized inverse update system parameters are used to determine the current maximum flow output of the coal feeding unit / water feeding unit; The current flow rate trial value is used as the set value of the open-loop simulation structure, where the simulation structure is consistent with the lower-level IASS-IMMPC, and is used to calculate the predicted coal flow rate under the current trial value. In the traversal solution, the step size or update range of the trial value is set until the predicted value of the coal slurry concentration of the coal slurry concentration control system, that is, the ratio of the coal flow output value of the coal feeding device to the water flow output value of the water feeding device, is within the set fluctuation range and the difference from the optimal set value is minimized; if the predicted value does not meet the constraints, the current flow rate is set to the minimum value between the flow rate set value at the previous moment and the current maximum flow rate output.

7. The two-layer data-driven model predictive control method for coal slurry concentration control system in coal gasification process according to claim 6, characterized in that, The set coal slurry concentration fluctuation range decays exponentially with respect to time. By adjusting the range size and decay rate, the feasibility of the upper-level optimization problem can be ensured. The optimal setting value is the plant's empirical value for coal / water flow rate.

8. A two-layer data-driven model predictive control system for a coal slurry concentration control system in a coal gasification process, used to implement the method described in any one of claims 1 to 7, characterized in that, The system includes: The data acquisition module acquires and stores coal / water flow rate and corresponding feeder speed data in real time; The model building module generates an augmented state-space model and uses the Moore-Penrose generalized inverse to update parameters online. The upper-level optimization module dynamically adjusts the coal / water flow rate setpoints; The lower-level control module combines single-step input mapping, incremental augmented state variables, and MPC to further reduce the impact of system parameter uncertainties and ensure consistency with the upper-level open-loop simulation results as much as possible. The execution module sends optimized settings and control commands to the feeder; The optimized control quantity calculated by the lower-level control module is the set value of the feeder / water pump motor speed, which can be quickly tracked to the set value by incremental PID control of the feeder / water pump motor speed.

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