Biomass gasification process control method and system

Abstract

The invention provides a biomass gasification process control method and system.The method comprises the steps that historical process parameters in the gasification process are obtained, and a historical database is constructed after the historical process parameters are preprocessed; establishing a control system simulation model according to the historical process data; actual measurement process parameters at all simulation moments are collected and input into a control system simulation model, the simulation model responds to the actual measurement process parameters, parameter optimization is conducted on the simulation system according to the historical process parameters, and optimal matching control parameters are obtained; and controlling the biomass gasification process according to the optimal matching control parameter. According to the technical scheme, accurate control over the biomass gasification process can be achieved, and the problems that in an existing control mode of the biomass gasification process, reaction lags behind, and the control precision is not ideal are solved.

Description

Technical Field

[0001] This document relates to the technical field of biomass gasification, and particularly to a method and system for controlling the biomass gasification process. Background Art

[0002] Biomass gasification technology is one of the main technologies for the high-value utilization of biomass energy. Its energy conversion method is efficient and clean, with broad application prospects and development potential. During the biomass gasification process, the control of the gasification device will directly affect the efficiency of the gasification process, the quality of the products, and the stability of the process flow.

[0003] The control of the equipment state during the biomass gasification process involves multiple process parameters. Manual adjustment of the equipment state overly relies on the experience and skills of the operators. In large-scale gasification production, manual adjustment cannot meet the requirements for rapid and precise adjustment of the process, and it is prone to increasing safety risks. The existing intelligent control methods for gasification process devices are affected by various complex factors and are usually designed based on simplified models or empirical formulas. These models often cannot precisely control the equipment, resulting in deviations in the control of the process device. In addition, the existing control systems mostly rely on sensors to monitor and transmit process parameter signals, and some devices are prone to reaction lag problems, thus affecting the operating efficiency of the system. Summary of the Invention

[0004] According to an embodiment of the present invention, a method and system for controlling the biomass gasification process are provided, aiming to solve the above problems.

[0005] According to an embodiment of the present invention, a method for controlling the biomass gasification process is provided, including:

[0006] S1. Obtain the historical process parameters during the gasification process, preprocess the historical process parameters, and construct a historical database;

[0007] S2. Establish a control system simulation model according to the historical process data;

[0008] S3. Collect the measured process parameters at each simulation moment and input them into the control system simulation model. The simulation model responds to the measured process parameters, optimizes the parameters of the simulation system according to the historical process parameters, and obtains the best matching control parameters;

[0009] S4. Control the biomass gasification process according to the best matching control parameters.

[0010] According to an embodiment of the present invention, a control system for the biomass gasification process is provided, including:

[0011] A historical database module, which is used to obtain historical process parameters during the gasification process, preprocess the historical process parameters, and construct a historical database;

[0012] A simulation model module, which is used to establish a control system simulation model according to the historical process data;

[0013] A parameter acquisition module, which is used to collect measured process parameters at each simulation moment and input them into the control system simulation model. The simulation model responds to the measured process parameters, optimizes the parameters of the simulation system according to the historical process parameters, and obtains the best matching control parameters;

[0014] A control module, which is used to control the biomass gasification process according to the best matching control parameters.

[0015] The start-stop control method for the gasifier blower during the biomass gasification process provided in this embodiment is based on continuous accumulation and iteration of historical data, optimizes the simulation model, fully considers the equipment start-stop permission conditions in the actual process, improves the accuracy of the control system, effectively solves the control lag problem in the process control, can adjust the equipment and devices in time, reduces the process risk, and achieves a better control effect. Brief Description of the Drawings

[0016] In order to more clearly illustrate the technical solutions in one or more embodiments of this specification or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in this specification. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0017] Figure 1 It is a flowchart of a biomass gasification process control method according to an embodiment of the present invention;

[0018] Figure 2 It is a schematic diagram of a specific implementation of a biomass gasification process control method according to an embodiment of the present invention;

[0019] Figure 3 It is a schematic diagram of a biomass gasification process control system according to an embodiment of the present invention. Detailed Description of the Embodiment

[0020] To enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the following will clearly and completely describe the technical solutions in one or more embodiments of this specification in conjunction with the accompanying drawings in one or more embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this document.

[0021] Method Embodiment

[0022] According to an embodiment of the present invention, a method for controlling a biomass gasification process is provided. The figure is a flowchart of a method for controlling a biomass gasification process according to an embodiment of the present invention. According to Figure 1 , a method for controlling a biomass gasification process according to an embodiment of the present invention specifically includes:

[0023] S1. Obtain historical process parameters during the gasification process, preprocess the historical process parameters, and construct a historical database;

[0024] The step of obtaining historical process parameters during the gasification process includes:

[0025] Set the no-fault signal of the blower frequency conversion device and the signal of the gas booster fan in the operating position, adjust the valve openings of the blower outlet valve and the inlet valve, and detect the fan bearing temperature. Adjust the opening of the blower inlet valve and control the start and stop of the gasifier blower;

[0026] Real-time monitor the operating state of the gasifier blower;

[0027] Real-time monitor the fan bearing temperature through the fan bearing temperature sensor;

[0028] Monitor the operating state of the blower through the frequency conversion monitoring device or the automatic alarm of the frequency conversion device of the blower.

[0029] S2. Establish a control system simulation model according to the historical process data; specifically including:

[0030] Construct a matching correlation formula for each process parameter according to the historical database, propose an empirical algorithm based on actual data, input a test process parameter signal into the matching correlation formula, optimize the response of the correlation formula to the most matching data, and integrate the most matching correlation formula as the simulation model.

[0031] S3. Collect the measured process parameters at each simulation moment and input them into the control system simulation model. The simulation model responds to the measured process parameters, optimizes the parameters of the simulation system according to the historical process parameters, and obtains the best matching control parameters;

[0032] S4. Control the biomass gasification process according to the optimal matching control parameters. The S4 specifically includes:

[0033] Judge whether the accuracy of the simulation model optimized according to the optimal matching control parameters reaches the established accuracy requirement. If the accuracy of the optimized simulation model reaches the established accuracy requirement, use the simulation model as the prediction model for the subsequent control moment. If the accuracy of the optimized simulation model does not reach the established accuracy requirement, supplement the optimized process parameters.

[0034] The method further includes:

[0035] Supplement the optimized process parameters to the historical database, optimize the empirical algorithm according to the optimized process parameters, and continuously correct and improve the empirical algorithm through parameter accumulation until the empirical algorithm is optimized to the preset accuracy standard.

[0036] Figure 2 It is a schematic diagram of a specific implementation of a biomass gasification process control method according to an embodiment of the present invention. According to Figure 2 As shown, a specific implementation of a biomass gasification process control method according to an embodiment of the present invention includes:

[0037] Step S1: Collect historical data from the historical gasification process, screen out abnormal data, construct a historical database, and propose an empirical algorithm based on historical data;

[0038] In this embodiment, the historical data includes: the opening degrees of each valve in the process flow, equipment start and stop, the temperature of the blower bearing, etc.;

[0039] Specifically, in the historical gasification process, historical data can be generated and used to construct a historical database by performing the following operations:

[0040] Step S11: Set the no-fault signal of the blower frequency conversion device and the signal that the gas booster fan is in the running position, adjust the opening degrees of the outlet valve and inlet valve of the blower, detect the temperature of the blower bearing, adjust the opening degree of the inlet valve, and control the start of the gasifier blower;

[0041] Step S12: Real-time monitor the operating state of the gasifier blower;

[0042] Step S13: Real-time monitor the temperature of the blower bearing through a blower bearing temperature sensor;

[0043] Step S14: Monitor the operating state of the frequency conversion device through the monitoring device of the frequency conversion device of the gasifier blower or the automatic alarm of the frequency conversion device;

[0044] By performing the above steps S11 - S14, multiple historical data can be obtained. Based on the summary and analysis of the historical data, an empirical algorithm based on the historical database is constructed.

[0045] Step S2: When implementing the gasification process control once, at each simulation moment: collect the measured data and screen the abnormal data, input the process parameters into the simulation model, and let the simulation system respond to the process parameters; combine the historical data to inversely deduce and optimize the corresponding parameters of the simulation system to obtain the operating state of the blower at the corresponding sampling moment.

[0046] In step S2, the control quantity signal for starting and stopping the gasification furnace blower is specifically obtained by performing the following operations:

[0047] Step S21: Input the process parameters into the simulation model, and let the empirical algorithm respond to the operating state of the gasification furnace blower;

[0048] Step S22: Combine the adjacent or matching historical data in the historical database to inversely deduce and optimize the response process parameters obtained in step S21 to obtain the operating state control parameters of the blower at the corresponding sampling moment.

[0049] The simulation model is established according to the process parameter historical database of the controlled gasification system: construct the matching correlation formula for each process parameter based on the historical database, propose an empirical algorithm based on the actual data; input the test process parameter signal into the formula, optimize the correlation formula to respond to the most matching data; integrate the most matching correlation formula as the simulation model, and propose a prediction algorithm for the working condition system control.

[0050] Step S3: When implementing the current gasification process control, at each control moment: based on the state of the blower frequency conversion device, the outlet valve of the booster, the opening of the blower inlet valve, and the bearing temperature of the blower after response, obtain the control quantity signal for starting the gasification furnace blower; and based on the parameter signal, control the operating state of the gasification furnace blower.

[0051] Combine the data to inversely deduce and optimize the response parameters to obtain the most matching control parameters; supplement the optimized process parameter data to the historical database, and optimize the empirical algorithm according to the optimized data;

[0052] Step S4: Supplement the optimized process parameter data to the historical database, and optimize the empirical algorithm according to the optimized data.

[0053] Step S41: Compare the process parameters responded by the empirical model with the historical data;

[0054] Step S42: Determine whether the accuracy of the simulation model optimized according to the cumulative historical data reaches the established accuracy requirement:

[0055] Step S421: If the accuracy of the optimized simulation model has reached the established accuracy requirement, use the simulation model as the prediction model for the subsequent control moment to control the operating state of the gasifier blower at each moment.

[0056] Step S422: If the accuracy of the optimized simulation model does not reach the established accuracy requirement, supplement the optimized process parameters to the historical database, and optimize the empirical model according to the supplemented process parameters.

[0057] The start-stop control method for the gasifier blower in the biomass gasification process provided in this embodiment continuously accumulates and iterates based on historical data, optimizes the simulation model, fully considers the equipment start-stop permission conditions in the actual process, improves the accuracy of the control system, effectively solves the control lag problem in the process control, can adjust the equipment and devices in time, reduce the process risk, and achieve better control effects.

[0058] By adopting the embodiment of the present invention, the following beneficial effects are achieved:

[0059] The start-stop control method for the gasifier blower in the biomass gasification process provided in this embodiment continuously accumulates and iterates based on historical data, optimizes the simulation model, fully considers the equipment start-stop permission conditions in the actual process, improves the accuracy of the control system, effectively solves the control lag problem in the process control, can adjust the equipment and devices in time, reduce the process risk, and achieve better control effects.

[0060] System embodiment

[0061] According to the embodiment of the present invention, a biomass gasification process control system is provided. The figure is a schematic diagram of a biomass gasification process control system according to the embodiment of the present invention. According to Figure 3 , a biomass gasification process control system according to the embodiment of the present invention specifically includes:

[0062] A historical database module 20, configured to obtain historical process parameters in the gasification process, and construct a historical database after preprocessing the historical process parameters;

[0063] The construction process of the historical database module 30 includes:

[0064] Set the no-fault signal of the blower frequency conversion device and the signal that the gas booster fan is in the operating position, adjust the valve openings of the blower outlet valve and the inlet valve and detect the fan bearing temperature, adjust the opening of the blower inlet valve and control the start and stop of the gasifier blower;

[0065] Real-time monitor the operating state of the gasifier blower;

[0066] The temperature of the fan bearing is monitored in real time by a temperature sensor for the fan bearing;

[0067] The operating state of the blower is monitored by a frequency conversion monitoring device or an automatic alarm of the frequency conversion device for the blower.

[0068] A simulation model module 32, configured to establish a control system simulation model according to the historical process data; specifically, the simulation model module 32 is used for:

[0069] Construct a matching association formula for each process parameter according to the historical database, propose an empirical algorithm based on actual data, input a test process parameter signal into the matching association formula, optimize the response of the association formula to the most matching data, and integrate the most matching association formula as the simulation model.

[0070] A parameter acquisition module, configured to collect measured process parameters at each simulation moment and input them into the control system simulation model. The simulation model responds to the measured process parameters, optimizes the parameters of the simulation system according to the historical process parameters, and obtains the best matching control parameters;

[0071] A control module 36, configured to control the biomass gasification process according to the best matching control parameters. Specifically, the control module 36 is used for: determining whether the accuracy of the simulation model optimized according to the best matching control parameters reaches a predetermined accuracy requirement. If the accuracy of the optimized simulation model reaches the predetermined accuracy requirement, the simulation model is used as a prediction model for subsequent control moments. If the accuracy of the optimized simulation model does not reach the predetermined accuracy requirement, the optimized process parameters are supplemented.

[0072] The biomass gasification process control system further includes an optimization module, specifically including:

[0073] Supplement the optimized process parameters to the historical database, optimize the empirical algorithm according to the optimized process parameters, and continuously correct and improve the empirical algorithm through parameter accumulation until the empirical algorithm is optimized to a preset accuracy standard.

[0074] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A biomass gasification process control method, characterized in that include: S1. Acquire historical process parameters in the gasification process, and construct a historical database after preprocessing the historical process parameters; S2. Establishing a control system simulation model based on the historical process data; S3, collecting the measured process parameters at each simulation time and inputting them into the control system simulation model, wherein the simulation model responds to the measured process parameters, optimizes the parameters of the simulation system according to the historical process parameters, and obtains the best matching control parameters; S4. Controlling the biomass gasification process according to the optimal matching control parameters.

2. The method according to claim 1, characterized in that: The step of obtaining historical process parameters during the gasification process comprises: Set the blower frequency converter fault-free signal and the gas booster blower in operation position signal, adjust the valve opening of the blower outlet valve and inlet valve and detect the blower bearing temperature, adjust the blower inlet valve opening and control the start and stop of the gasifier blower; Real-time monitoring of the operating status of the gasifier blower; The fan bearing temperature sensor is used to monitor the fan bearing temperature in real time; The operating status of the blower is monitored by means of a frequency conversion monitoring device or an automatic alarm of the frequency conversion device.

3. The method according to claim 1, characterized in that The establishing of a control system simulation model according to the historical process data specifically includes: According to the historical database, a matching correlation formula of each process parameter is constructed, an empirical algorithm based on actual data is proposed, a test process parameter signal is input into the matching correlation formula, the correlation formula is optimized to respond to the most matching data, and the most matching correlation formula is integrated as the simulation model.

4. The method according to claim 1, characterized in that The S4 specifically includes: Determine whether the accuracy of the simulation model after optimization according to the best matching control parameters meets the established accuracy requirements. If the accuracy of the optimized simulation model has reached the established accuracy requirements, the simulation model will be used as a prediction model for subsequent control moments. If the accuracy of the optimized simulation model does not meet the established accuracy requirements, the optimized process parameters will be supplemented.

5. The method according to claim 3, characterized in that: The method further comprises: The optimized process parameters are added to the historical database, and the empirical algorithm is optimized according to the optimized process parameters. Through parameter accumulation, the empirical algorithm is continuously corrected and improved until the empirical algorithm is optimized to the preset accuracy standard.

6. A biomass gasification process control system, characterized in that: include: A historical database module is used to obtain historical process parameters in the gasification process and construct a historical database after preprocessing the historical process parameters; A simulation model module, used to establish a control system simulation model based on the historical process data; A parameter acquisition module, used to collect the measured process parameters at each simulation moment and input them into the control system simulation model, wherein the simulation model responds to the measured process parameters, optimizes the parameters of the simulation system according to the historical process parameters, and obtains the best matching control parameters; A control module is used to control the biomass gasification process according to the optimal matching control parameters.

7. The system according to claim 6, characterized in that The construction process of the historical database module includes: Set the blower frequency converter fault-free signal and the gas booster blower in operation position signal, adjust the valve opening of the blower outlet valve and inlet valve and detect the blower bearing temperature, adjust the blower inlet valve opening and control the start and stop of the gasifier blower; Real-time monitoring of the operating status of the gasifier blower; The fan bearing temperature sensor is used to monitor the fan bearing temperature in real time; The operating status of the blower is monitored by means of a frequency conversion monitoring device or an automatic alarm of the frequency conversion device.

8. The system according to claim 6, characterized in that The simulation model module is specifically used for: According to the historical database, a matching correlation formula of each process parameter is constructed, an empirical algorithm based on actual data is proposed, a test process parameter signal is input into the matching correlation formula, the correlation formula is optimized to respond to the most matching data, and the most matching correlation formula is integrated as the simulation model.

9. The system according to claim 6, characterized in that The control module is specifically used to determine whether the accuracy of the simulation model after optimization according to the best matching control parameters meets the established accuracy requirements. If the accuracy of the optimized simulation model has reached the established accuracy requirements, the simulation model will be used as a prediction model for subsequent control moments. If the accuracy of the optimized simulation model does not meet the established accuracy requirements, the optimized process parameters will be supplemented.

10. The system according to claim 6, characterized in that The biomass gasification process control system further includes an optimization module, specifically including: The optimized process parameters are added to the historical database, and the empirical algorithm is optimized according to the optimized process parameters. Through parameter accumulation, the empirical algorithm is continuously corrected and improved until the empirical algorithm is optimized to the preset accuracy standard.

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