Multi-parameter soft measurement method for pressurized entrained-flow bed gasifier

Abstract

The invention provides a pressurized entrained-flow gasifier multi-parameter soft measurement method, which belongs to the technical field of gasifiers and comprises the following steps: acquiring and establishing a gasifier parameter data set, and determining a target parameter set and a to-be-estimated parameter set based on the established gasifier parameter data set; constructing a gasifier operation model group, and calculating to obtain values of target parameters in the target parameter set; constructing a target function, calculating the value of the target function by using the value of the target parameter, and iteratively updating the value of the parameter in the to-be-estimated parameter set by using an optimization algorithm until an optimized estimated value of the to-be-estimated parameter, which enables the value of the target function to be smaller than a set value, is obtained; and calculating values of other unknown parameters according to the obtained optimized estimation value of the to-be-estimated parameter so as to obtain values of all parameters in the unknown parameter set. According to the method, a plurality of unknown parameters of gasification operation can be accurately evaluated and measured, and the performance of the gasification furnace can be evaluated, so that the operation improvement and transformation of the gasification furnace can be effectively guided.

Description

Technical Field

[0001] The invention relates to the technical field of gasifiers, and in particular to a multi-parameter soft measurement method for a pressurized fluidized flow gasifier. Background Art

[0002] A pressurized fluidized flow gasifier is a high-temperature and high-pressure gasification and combustion equipment, such as the Texaco gasifier. In the gasifier, the gasified raw materials, such as coal, petroleum coke, biomass, organic waste, etc., enter the reaction chamber of the gasifier in the form of fine particles, and are mixed with the oxidant, that is, oxygen or a mixture of oxygen and water vapor, CO2, etc., to undergo a violent combustion and gasification reaction under high temperature and pressure, releasing a large amount of heat to generate a synthesis gas with CO, H2, CO2, H2O and N2 as the main components and CH4, H2S, etc. as a small amount of components, which also contains unreacted gasification raw material residues and high-temperature ash. The high-temperature synthesis gas at the outlet of the reaction chamber has a temperature of up to 1300-1500℃, and needs to be further cooled in a quenching chamber or a heat exchange chamber. After the temperature drops to 200-600℃, it can be discharged from the gasifier and enter the subsequent equipment.

[0003] With the in-depth implementation of the energy conservation and carbon reduction strategy in the field of energy and chemical industry, modern factories have put forward higher standards of refinement and intelligence requirements for the control level of gasification equipment. The temperature, flow rate and specific gas composition of the synthesis gas at the outlet of the gasifier reaction chamber are important indicators that directly reflect the results and effects of the gasification reaction. However, the extreme high temperature and high pressure environment inside the gasifier and the presence of molten ash make it difficult to accurately measure some operating parameters. In fact, it is currently difficult to directly measure these key parameters, which restricts the upgrading and transformation of processes and equipment. The lack of key gasification parameters, inaccurate or untimely measurement is not conducive to the comprehensive grasp of the gasification process information, and also leads to deviations in the performance evaluation of the device, which is not conducive to the formulation of efficient and optimized control strategies.

[0004] In addition, the fuel conversion rate of the gasifier (usually measured by carbon conversion rate) also faces the problem of real-time and accurate measurement. On the one hand, since the syngas contains a high amount of water after water washing, its flow measurement is prone to errors, which in turn makes the carbon conversion rate calculated based on the flow rate have a large error; on the other hand, the ash production and its residual carbon content are not monitored in real time in industrial plants. This limitation directly affects the real-time calculation and evaluation of the carbon conversion rate.

[0005] In addition, for gasifiers with a water-quenching process or gasifiers with water cooling at the outlet of the gasification chamber, the reaction of H2O and the synthesis gas will occur during the heat exchange process between the high-temperature synthesis gas and water, causing changes in the composition of the synthesis gas. However, the amount of water reacting in this process cannot be measured, which affects the accurate modeling of the gasification process.

[0006] In response to the problem that some key gasification parameters or operating parameters are important for the evaluation of the gasification process but cannot be directly measured, with the advancement of technology, the industry has tried to use the measurement points and their measurement data already installed on the gasification device to indirectly estimate or calculate those parameters that have not been directly measured, and this method is called "soft measurement technology." For example, CN102175345A discloses a soft measurement method for the furnace temperature of a multi-nozzle opposed water-coal slurry gasifier, which collects data of several measurable process state auxiliary variables, trains the soft measurement model based on the BP artificial neural network, and then uses the trained soft measurement model to realize the soft measurement of the furnace temperature; CN102184452A discloses a soft measurement method for the component gases of "Texaco synthesis gas", which also uses auxiliary variables to obtain the CO, H2 and CO2 contents at the top outlet of the "carbon washing tower" through three independent BP neural network models corresponding to CO, H2 and CO2; CN105930666A discloses an online soft measurement method for the furnace temperature of an entrained flow gasifier, and CN109726493A discloses an online soft measurement method for the gas component concentration in the furnace of a residual oil gasifier, both of which use a mechanism model of the reaction process in the gasifier and an online solution method to obtain the soft measurement values ​​of the furnace temperature of the gasifier or the gas component concentration in the furnace.

[0007] The above technical scheme can only perform soft measurement on a specific parameter or a certain aspect of parameters. For example, CN102175345A and CN105930666A only measure the furnace temperature of the chemical furnace, and CN102184452A and CN109726493A only measure the gas content. Moreover, CN102184452A obtains the contents of CO, H2 and CO2 respectively by constructing three independent BP neural network models. In fact, it is still a measurement of a single specific parameter. Summary of the invention In view of the above problems, the present invention provides a multi-parameter soft measurement method for a pressurized entrained flow gasifier, which can obtain multiple gasification parameters of the gasifier that cannot be directly measured, thereby improving the efficiency of obtaining the gasification parameters.

[0008] The present invention provides a multi-parameter soft measurement method for a pressurized entrained flow gasifier, comprising: Acquire and establish a gasifier parameter data set, wherein the gasifier parameter data set includes a gasification raw material parameter set, a gasifier operation parameter set, and an unknown parameter set including a plurality of unknown parameters to be measured, wherein the gasifier operation parameter includes an input parameter subset and an output parameter subset; Determining a target parameter set and a parameter set to be estimated based on the established gasifier parameter data set, and setting estimated values ​​and value ranges of parameters in the parameter set to be estimated; Constructing a gasifier operation model group, and inputting the measured values ​​of the parameters in the gasification raw material parameter set, the input parameter subset, and the output parameter subset that are not selected into the target parameter set and the estimated values ​​of the parameters in the parameter set to be estimated into the constructed gasifier operation model group to calculate the values ​​of the target parameters in the target parameter set; Constructing an objective function, calculating the value of the objective function using the obtained values ​​of the objective parameters in the objective parameter set, and iteratively updating the values ​​of the parameters in the parameter set to be estimated using an optimization algorithm, until an optimized estimated value of the parameter to be estimated is obtained that makes the value of the objective function less than a set value; According to the obtained optimized estimated values ​​of the parameters to be estimated, a solution is performed based on the constructed gasifier operation model group to calculate the values ​​of other unknown parameters, thereby obtaining the values ​​of all parameters in the unknown parameter set.

[0009] As a further improvement of the present invention, the objective function is constructed based on the sum of the residuals of the measured values ​​and calculated values ​​of the target parameters in the target parameter set and the products of their respective weights, and the objective function is expressed as: , In the formula, e m is the residual between the measured and calculated values ​​of the target parameter; w m is the weight; m is the sequence number of the target parameter, M is the number of target parameters.

[0010] As a further improvement of the present invention, the residual between the measured value and the calculated value of the target parameter e m It is expressed as: , In the formula, o m is the measured value of the target parameter, ô m It is the calculated value of the target parameter obtained by solving the gasifier operation model group.

[0011] As a further improvement of the present invention, the constructing of the objective function, using the obtained value of the objective parameter in the objective parameter set to calculate the value of the objective function and using the optimization algorithm to iteratively update the value of the parameter in the parameter set to be estimated until the optimized estimated value of the parameter to be estimated that makes the value of the objective function less than the set value is obtained, includes inputting the measured value and calculated value of the objective parameter in the objective parameter set and its weight into the objective function respectively. F obj Calculate the objective function F objThe value of is determined to determine whether the value is less than the set value. F obj,set If the requirement is met, the estimated value is directly used as the optimized estimated value of the parameter set to be estimated to proceed to the next step. If it is not met, the optimization algorithm is called to update the estimated value of the parameter set to be estimated, and the value of the target parameter in the target parameter set is returned to calculate, and the objective function is calculated using the obtained value of the target parameter in the target parameter set. F obj The value is determined again to see whether the value is less than the set value. F obj,set The requirement is to iterate until the objective function F obj The value is less than the set value F obj,set The requirement is met, and the estimated values ​​of the parameter set to be estimated are no longer updated, the parameter estimation process is completed, and the estimated values ​​at this time are used as the optimized estimated values ​​of the parameter set to be estimated.

[0012] As a further improvement of the present invention, the gasifier operation model group is expressed as:

[0013] In the formula, n Indicates the serial number of the gasifier operation model, N represents the number of models, A represents the gasification raw material parameter set, X is represented as a subset of the input parameters, Y is represented as the output parameter subset, Z Represented as the unknown parameter set.

[0014] As a further improvement of the present invention, the target parameter set is determined based on the output parameter subset, and the parameter set to be estimated is determined based on the unknown parameter set.

[0015] As a further improvement of the present invention, the target parameter set and the parameter set to be estimated are determined based on the established gasifier parameter data set, and the estimated values ​​and value ranges of the parameters in the parameter set to be estimated are set, including taking at least three components containing CH4 in the composition of the washing tower outlet synthesis gas of the output parameter subset as target parameters in the target parameter set.

[0016] As a further improvement of the present invention, the target parameter set and the parameter set to be estimated are determined based on the established gasifier parameter data set, and the estimated values ​​and value ranges of the parameters in the parameter set to be estimated are set, including taking the five components of CO, H2, CO2, CH4, and N2 in the synthesis gas composition at the outlet of the washing tower of the output parameter subset as the target parameters in the target parameter set.

[0017] As a further improvement of the present invention, the target parameter set and the parameter set to be estimated are determined based on the established gasifier parameter data set, and the estimated values ​​and value ranges of the parameters in the parameter set to be estimated are set, including taking the four components H2, CO2, CH4, N2 or CO, CO2, CH4, N2 or CO, H2, CH4, N2 in the synthesis gas composition at the outlet of the washing tower of the output parameter subset as the target parameters in the target parameter set.

[0018] As a further improvement of the present invention, the unknown parameters of the unknown parameter set include at least two of the following: gasification chamber temperature, carbon conversion rate, gasification chamber outlet syngas composition, gasification chamber outlet syngas volume, cooling water reaction volume, and washing tower outlet syngas flow rate.

[0019] As a further improvement of the present invention, the optimization algorithm is a particle swarm algorithm.

[0020] The present invention provides a multi-parameter soft measurement method for a pressurized fluidized bed gasifier, which obtains and establishes a gasifier parameter data set, wherein the gasifier parameter data set includes a gasification raw material parameter set, a gasifier operation parameter set and an unknown parameter set, wherein the gasifier operation parameter includes an input parameter subset and an output parameter subset; based on the established gasifier parameter data set, a target parameter set and a parameter set to be estimated are determined, and estimated values ​​and value ranges of the parameters in the parameter set to be estimated are set; a gasifier operation model group is constructed, and the measured values ​​of the parameters in the gasification raw material parameter set, the input parameter subset and the output parameter subset that are not selected in the target parameter set and the estimated values ​​of the parameters in the parameter set to be estimated are input into the constructed gasifier operation model group to calculate the target parameter set. The objective function is constructed, and the value of the objective parameter in the target parameter set is calculated using the obtained value of the target parameter in the target parameter set, and the value of the parameter in the parameter set to be estimated is iteratively updated using an optimization algorithm, until an optimized estimated value of the parameter to be estimated is obtained that makes the value of the objective function less than a set value; according to the obtained optimized estimated value of the parameter to be estimated, a solution is performed based on the constructed gasifier operation model group, and the values ​​of other unknown parameters are calculated, so as to obtain the values ​​of all parameters in the unknown parameter set; the values ​​of multiple unknown parameters are obtained using known measurement parameters and the gasifier operation model group, the operation information of the gasifier is fully understood, and inaccurate measurement values ​​can be corrected according to the measurement results, so that the calculated value of the gasifier performance is more accurate, which is beneficial to the optimized operation and design of the gasifier. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic flow chart of a multi-parameter soft measurement method for a pressurized fluidized flow gasifier according to an embodiment of the present invention.

[0022] Figure 2It is a specific schematic diagram of the process of the multi-parameter soft measurement method of the pressurized fluidized flow gasifier according to an embodiment of the present invention.

[0023] Figure 3 It is a schematic diagram of the differences in unknown parameter estimation results when different target parameters are selected in the multi-parameter soft measurement method for a pressurized fluidized flow gasifier according to an embodiment of the present invention.

[0024] Figure 4 It is a schematic diagram of parameter estimation results of an objective function in the form of relative square error in a multi-parameter soft measurement method for a pressurized entrained flow gasifier according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The following is combined with specific embodiments and appendix Figure 1-4 The invention is described in detail so that those skilled in the art can more fully understand the purpose, features and effects of the invention.

[0026] Unless otherwise specified, all technical and scientific terms used in the present invention have the same meanings as commonly understood by those skilled in the art to which the present invention belongs. When the definition of a term in the present invention conflicts with the meaning commonly understood by those skilled in the art to which the present invention belongs, the definition described in the present invention shall prevail.

[0027] The temperature, flow rate and specific gas composition of the synthesis gas at the outlet of the gasifier reaction chamber are important indicators that directly reflect the results and effects of the gasification reaction. However, due to the interference of the high temperature environment and slag, it is still difficult for current industrial devices to directly measure these key parameters in practical applications. The present invention provides a multi-parameter soft measurement method for a pressurized entrained flow gasifier to solve the problem of being unable to measure some key gasification parameters of the gasifier.

[0028] The present invention provides a multi-parameter soft measurement method for a pressurized entrained flow gasifier, referring to Figure 1 , Figure 2 ,include: S100, obtain and establish gasifier parameter data set The gasifier parameter data set established by the present invention includes a gasification raw material parameter set, a gasifier operation parameter set and an unknown parameter set, and the gasification raw material parameter set, the gasifier operation parameter set and the unknown parameter set are established respectively. The parameters in the gasification raw material parameter set and the parameters in the gasifier operation parameter set can be obtained by measurement.

[0029] The gasification raw material may be coal, biomass, or other gasification raw materials, and the gasification raw material parameters include but are not limited to industrial analysis composition, elemental analysis composition, calorific value, ash melting characteristics, and ash viscosity-temperature characteristic parameters of the gasification raw material. The gasification raw material parameter set is expressed as:

[0030] In the formula, a i Indicates the specific gasification raw material parameters, i Indicates the serial number of the parameter. I Indicates the number of parameters.

[0031] The gasifier operation parameters include an input parameter subset and an output parameter subset.

[0032] Specifically, the input parameters in the input parameter subset refer to the operating conditions of the gasifier, including but not limited to gasification raw material flow, gasification agent (such as oxygen, water vapor) flow, conveying medium flow, protective medium flow, gasification chamber water wall cooling water flow, inlet temperature or density, and gasifier pressure. The input parameter subset is expressed as:

[0033] In the formula, x j Indicates the specific input parameters. j Indicates the serial number of the parameter. J Indicates the number of parameters.

[0034] The output parameters in the output parameter subset refer to the measurable operating results of the gasifier, including but not limited to the composition and flow rate of the syngas at the outlet of the scrubber. The output parameter subset is represented as:

[0035] In the formula, y k Indicates the specific output parameters. k Indicates the serial number of the parameter. K Indicates the number of parameters.

[0036] The unknown parameters in the unknown parameter set refer to parameters that are of great significance to the operation process of the gasifier but are not directly measured and parameters that are inaccurately measured, that is, the parameters to be soft-measured in the present invention, including but not limited to the temperature, flow rate and gas composition of the synthesis gas at the outlet of the gasifier, the carbon conversion rate of the gasified raw material, and the amount of cooling medium reacting with the high-temperature synthesis gas, such as the temperature, carbon conversion rate, composition of the synthesis gas at the outlet of the gasifier, the flow rate of the synthesis gas at the outlet of the gasifier, the reaction amount of cooling water, and the flow rate of the synthesis gas at the outlet of the scrubber. The specific composition of the synthesis gas at the outlet of the gasifier includes CO, H2, CO2, H2O, N2, CH4, H2S, HCN, and NH3. According to the emphasis on the gas components in the synthesis gas, in an optional embodiment, the composition of the synthesis gas at the outlet of the gasifier includes CO, H2, CO2, H2O, N2, CH4, and H2S, that is, in this embodiment, the focus is on soft measurement of the parameters of CO, H2, CO2, H2O, N2, CH4, and H2S in the composition of the synthesis gas at the outlet of the gasifier. The unknown parameter set is expressed as:

[0037] In the formula, z l Represents specific unknown parameters, l Indicates the serial number of the parameter. L Indicates the number of parameters.

[0038] S200: Determine a target parameter set and a parameter set to be estimated based on the established gasifier parameter data set, and set estimated values ​​and value ranges of the parameters in the parameter set to be estimated. In the present invention, the parameters in the target parameter set are from the gasification raw material parameter set A and the input parameter subset X and the output parameter subset Y Specifically, from the gasification raw material parameter set A and the input parameter subset X and the output parameter subset Y Select M The target parameter set is composed of parameters. The target parameter set is expressed as:

[0039] In the formula, o m Describe specific target parameters, m Indicates the serial number of the parameter. M Indicates the number of parameters.

[0040] Preferably, the target parameter set O obj The parameters are selected from the output parameter subset YWhen the target parameter set O obj The parameters are selected from the output parameter subset Y Sometimes, there is , .

[0041] In an optional embodiment, the target parameter in the target parameter set is the composition of the syngas at the syngas sampling and detection location (such as the outlet of the scrubber), including five gas parameters of CO, H2, CO2, N2 and CH4. In another optional embodiment, the target parameter in the target parameter set is the composition of the syngas at the outlet of the scrubber, including four gas parameters of CO, H2, CO2 and CH4.

[0042] The output parameter subset Y is selected from the target parameter set O obj The remaining parameters constitute the other output parameter sets Y ot .

[0043] The parameters in the parameter set to be estimated come from the gasification raw material parameter set A and the input parameter subset X and the output parameter subset Y and the unknown parameter set Z Specifically, from the gasification raw material parameter set A and the input parameter subset X and the output parameter subset Y and the unknown parameter set Z Select P The parameters to be estimated constitute the parameter set. The parameter set to be estimated is expressed as:

[0044] In the formula, s p represents the specific parameters to be estimated, p Indicates the serial number of the parameter. P Indicates the number of parameters.

[0045] Preferably, the parameter set to be estimated S es The parameters are selected from the unknown parameter set Z When the parameter set to be estimated S es The parameters are selected from the unknown parameter set Z Sometimes, there is , .

[0046] The unknown parameter set ZExcept for the parameter set to be estimated S es The remaining parameters constitute the other unknown parameter set Zot .

[0047] S300, constructing a gasifier operation model group, and inputting the measured values ​​of the parameters in the gasification raw material parameter set, the input parameter subset, and the output parameter subset that are not selected into the target parameter set and the estimated values ​​of the parameters in the parameter set to be estimated into the constructed gasifier operation model group to calculate the values ​​of the target parameters in the target parameter set In the present invention, a mathematical model of the main processes in the process from the gasifier inlet to the output parameter detection point (such as the synthesis gas parameter detection point at the washing tower outlet) is constructed, including the gasification process, water quenching process, heat exchange process, and water bath washing process.

[0048] The gasifier operation model group is expressed as:

[0049] In the formula, n Indicates the serial number of the gasifier operation model, N Indicates the number of models.

[0050] The gasifier operation model group of the present invention includes but is not limited to the energy conservation equation, mass conservation equation, element conservation equation, chemical reaction kinetic equation, chemical reaction thermodynamic equilibrium equation, and phase equilibrium equation of the above-mentioned main processes. The element conservation equation includes the element conservation equations of C, H, O, and N, and further includes the element conservation equations of C, H, O, N, S, and Cl.

[0051] The thermodynamic equilibrium equations of chemical reactions include the water-gas shift reaction equilibrium equation, the methane reaction equilibrium equation, the sulfide reaction equilibrium equation, the nitride reaction equilibrium equation, etc. The heat transfer process also includes the radiation heat transfer and convection heat transfer equations. The water quenching process also includes the droplet evaporation, heat transfer and surface reaction equations. The water bath washing process also includes the gas-liquid phase equilibrium equation and the material transfer equation.

[0052] The present invention does not improve the equations involved in the gasifier operation process, that is, the gasifier operation model group of the present invention is constructed based on the existing equations, and the gasifier operation model group of the present invention can be obtained through logical reasoning based on the existing equations.

[0053] The gasification raw material parameter set A, the input parameter subset X, and the output parameter subset Y that are not selected into the target parameter set O obj The measured values ​​of the parameters and the parameter set to be estimated S esThe estimated values ​​of the parameters are input into the constructed gasifier operation model group to calculate the target parameter set O obj The value of the target parameter in is used as the calculated value of the target parameter.

[0054] When the target parameter set O obj The target parameters are all selected from the output parameter subset Y When the gasification raw material parameter set A and the input parameter subset X All parameters in and the output parameter subset described Y The target parameter set is not selected O obj The measured values ​​of the parameters in the gasifier operation model set were input into the constructed gasifier operation model set.

[0055] When the parameter to be estimated S es All the parameters in are selected from the unknown parameter set Z , then the unknown parameter set will be selected Z The estimated values ​​of all parameters in are input into the constructed gasifier operation model group.

[0056] S400, constructing an objective function, calculating the value of the objective function using the obtained value of the objective parameter in the objective parameter set, and iteratively updating the value of the parameter in the parameter set to be estimated using an optimization algorithm, until an optimized estimated value of the parameter to be estimated is obtained that makes the value of the objective function less than a set value The objective function is composed of the objective parameter set O obj The objective function is composed of the residual of the measured value and the calculated value of the target parameter and the product of their respective weights. The objective function is expressed as:

[0057] In the formula, e m is the residual between the measured value and the calculated value of the target parameter, which can be in the form of absolute difference, square difference, or relative square difference; w m is the weight, the value can be set; m is the sequence number of the target parameter, M is the number of target parameters.

[0058] In an optional embodiment, the relative square difference in logarithmic form is selected according to the numerical distribution characteristics of the composition content of the synthesis gas. e m It is expressed as:

[0059] In the formula, o m is the measured value of the target parameter, ô m It is the calculated value of the target parameter obtained by solving the gasifier operation model group.

[0060] In another optional embodiment, the relative square difference may also be directly used. e m It is expressed as:

[0061] In the formula, o m is the measured value of the target parameter, ô m It is the calculated value of the target parameter obtained by solving the gasifier operation model group.

[0062] Set the target parameter O obj The measured and calculated values ​​of the target parameters and their weights are input into the objective function F obj Calculate the objective function F obj The value of is determined to determine whether the value is less than the set value. F obj,set If the requirement is met, the estimated value is directly used as the parameter set to be estimated. S es If the optimized estimated value is not satisfied, the optimization algorithm is called to update the parameter set to be estimated. S es The estimated value of the target parameter set is returned to S300 to calculate O obj The target parameter set is obtained by O obj The objective function is calculated by taking the value of the objective parameter F obj The value is determined again to see whether the value is less than the set value. F obj,set The requirement is to iterate until the objective function F obj The value satisfies the setting value F obj,set The requirement is to no longer update the set of parameters to be estimated. S es The estimated value of , completes the parameter estimation process, and uses the estimated value at this time as the parameter set to be estimated S es The optimized estimate of .

[0063] The optimization algorithm in the present invention can be a particle swarm algorithm, a genetic algorithm, or a gradient descent method. Preferably, the optimization algorithm is a particle swarm algorithm.

[0064] S500, according to the obtained optimized estimated value of the parameter to be estimated, solving based on the constructed gasifier operation model group, calculating the value of other unknown parameters, so as to obtain the value of all parameters in the unknown parameter set When the parameter set to be estimated S es The parameters are all selected from the unknown parameter set Z When the parameter set to be estimated is obtained S es After the values ​​of the parameters are obtained, the other unknown parameter sets are calculated based on the constructed gasifier operation model set. Z ot The values ​​of other unknown parameters in. S es Parameters in and other unknown parameter sets Z ot The set of parameters in is the entire unknown parameter set Z , thereby obtaining the unknown parameter set Z The values ​​of all parameters in .

[0065] The present invention provides a soft measurement method for unknown parameters of a pressurized fluidized flow gasifier, which is different from the prior art. Without increasing the hardware cost, the values ​​of multiple unknown parameters can be obtained through known measurement parameters and a gasifier operation model group, and the gasifier operation information can be fully grasped. Inaccurate measurement values ​​can be corrected according to the measurement results, so that the calculated value of the gasifier performance is more accurate, which is beneficial to the optimized operation and design of the gasifier.

[0066] Embodiment 1 As a specific embodiment of the present invention, this embodiment provides a multi-parameter soft measurement method for a pressurized fluidized flow gasifier, taking a pulverized coal pressurized fluidized flow gasifier of a quenching process as an example to obtain values ​​of multiple unknown parameters. The gasification raw material in this embodiment is coal.

[0067] The gasification raw material parameter set A (coal quality parameter set), the gasifier operation parameter set (input parameter subset X and output parameter subsets Y ) and the unknown parameter set Z As shown in Table 1-4.

[0068] Table 1 Coal quality parameter set

[0069] Table 2 Input parameter subset

[0070] Table 3 Output parameter subset

[0071] Table 4 Unknown parameter set

[0072] The gasifier operation model group is constructed. Since there are many equations involved, the present invention only shows them as examples.

[0073] 1. Conservation of Elements C, H, O, N

[0074]

[0075]

[0076]

[0077] 2. Energy Conservation Equation

[0078] 3. Chemical reaction equilibrium equation: Transformation reaction:

[0079]

[0080] in, is the shift reaction equilibrium constant, is the vaporization chamber outlet temperature The function of

[0081] Methane reaction:

[0082]

[0083] in, is the methane reaction equilibrium constant, is the gasification chamber outlet temperature and vaporization chamber pressure The function of

[0084] 4. Energy conservation equation for water cooling process

[0085] Reaction of cooling water and synthesis gas in water cooling process

[0086] In this embodiment, the output parameter subset is selected Y The CO ( y 1), H2 ( y2 )、CO2( y 3) CH4 y 4) N2 ( y 5) A total of 5 parameters are used as target parameters to form the target parameter set O obj , the unknown parameter set Z The carbon conversion rate in z 2) and cooling water reaction volume ( z 10 ) two parameters as the parameters to be estimated constitute the parameter set to be estimated S es .

[0087] The objective function is:

[0088] In the formula, o m is the measured value of the target parameter, ô m It is the calculated value of the target parameter obtained by solving the gasifier operation model group.

[0089] The optimization algorithm adopts multi-parameter particle swarm algorithm.

[0090] In order to verify the effectiveness of the method, based on a set of theoretical calculation parameters, random errors of 3% and 2% are added to the theoretical values ​​of coal quantity and syngas composition at the outlet of the scrubber, respectively, as simulated measurement values. The "true value" is the value of the theoretical model. Because the soft-measured parameters cannot be directly measured in actual industrial devices, they will be compared with the calculated values ​​when evaluating the accuracy of the soft-measured values. The comparison between the soft-measured values ​​and the "true values" using the above technical solution is shown in Table 4. It can be seen that the soft-measured errors of most parameters are less than 2%, the smallest being CO, which is only 0.01%, and only CH4 is higher, about 5%, indicating that the multi-parameter soft-measurement method of the present invention has a high accuracy.

[0091] This embodiment also compares the influence of the selection of target parameters in the target parameter set on parameter estimation. Specifically, for the case where CO, H2, CO2, CH4, and N2 are used as target parameters and CO, H2, CO2, and N2 are used as target parameters, 10 soft measurement calculations are performed respectively. The results are as follows: Figure 3 shown.

[0092] Figure 3 a is the soft measurement result of the gasification chamber outlet temperature under four target parameters, Figure 3 b is the soft measurement result of the gasification chamber outlet temperature under five target parameters. Figure 3 It can be seen that when CH4 is missing as the target parameter, the error range of the soft measurement value of the gasifier outlet temperature is ±15°C; when CH4 is added as the target parameter, the error range of the soft measurement value of the gasifier outlet temperature is reduced to within ±5°C.

[0093] In another optional embodiment, four components containing CH4 are used as target parameters in the target parameter set, and the gasifier outlet temperature is soft-measured. The results show that when CH4 is contained, the error range is relatively small. Specifically, using H2, CO2, CH4, and N2 as target parameters, or CO, CO2, CH4, and N2 as target parameters, or CO, H2, CH4, and N2 as target parameters, the error range of the soft measurement value of the gasifier outlet temperature is within ±5°C, indicating that when the target parameter contains the CH4 component, the soft measurement result will be more accurate.

[0094] Furthermore, in order to verify the importance of CH4 as a target parameter in the target parameter set, three components containing CH4 were selected as target parameters for the soft measurement of the outlet temperature of the vaporizer, and the results also showed that the relative error was small. Specifically, after deducting the content of N2 from the total gas composition and normalizing the remaining components, three components containing CH4 were selected as target parameters, including CO, H2 and CH4, H2, CO2 and CH4, or CO, CO2 and CH4. The results all showed that the error range of the soft measurement value of the outlet temperature of the vaporizer was within ±5°C.

[0095] According to the above scheme, in the present invention, in the output parameter subset Y When selecting the target parameter from the five parameters of the composition of the syngas at the outlet of the scrubber, the parameters can be determined in the following order: 1. Select the output parameter subset Y The five components of CO, H2, CO2, CH4 and N2 in the composition of the syngas at the outlet of the scrubber are used as the target parameters in the target parameter set; 2. Select the output parameter subset Y The four components containing CH4 in the composition of the scrubber outlet synthesis gas are used as the target parameters in the target parameter set, such as H2, CO2, CH4, N2, CO, CO2, CH4, N2 or CO, H2, CH4, N2; 3. Select the output parameter subset YThe three components containing CH4 in the composition of the synthesis gas at the outlet of the scrubber are used as the target parameters in the target parameter set, such as CO, H2 and CH4, H2, CO2 and CH4, or CO, CO2 and CH4.

[0096] In another optional embodiment, the output parameter subset is selected Y The CO ( y 1), H2 ( y2 )、CO2( y 3) CH4 y 4) N2 ( y 5) A total of 5 parameters are used as target parameters to form the target parameter set O obj , the unknown parameter set Z The carbon conversion rate in z 2) and cooling water reaction volume ( z 10 ) Two parameters are used as the parameters to be estimated to form the parameter set to be estimated S es， The objective function is constructed based on the relative square error. Specifically, the objective function F obj for:

[0097] In the formula, o m is the measured value of the target parameter, ô m is the calculated value of the target parameter obtained by solving the gasifier operation model group. The soft measurement result of parameter estimation using the target function is as follows: Figure 4 As shown. Figure 4 It can be seen that the soft measurement result of the gasification chamber outlet temperature has a large fluctuation, and the effect is not as good as the objective function constructed based on the relative square difference in logarithmic form. Therefore, preferably, the objective function is constructed based on the relative square difference in logarithmic form.

[0098] Embodiment 2 As another specific embodiment of the present invention, the present invention provides a multi-parameter soft measurement method for a pressurized fluidized flow gasifier, taking a pulverized coal pressurized fluidized flow gasifier of a quenching process as an example to obtain the values ​​of unknown parameters. The gasification raw material in this embodiment is coal.

[0099] The gasification raw material parameter set in this embodiment A (Coal quality parameter set) is the same as in Example 1, the gasifier operation parameter set (input parameter subset X and a subset of output parameters Y ) are shown in Table 5-6.

[0100] Table 5 Input parameter subset

[0101] Table 6 Output parameter subset

[0102] The parameters in the gasifier operation parameter set are as follows: the coal quantity entering the furnace is verified by the discharge quantity record, and the deviation is within 3%; the composition content of the syngas at the outlet of the scrubber is verified by the manual analysis value in the laboratory, and the deviation is within 2%, and the accuracy is within the acceptable range; however, the syngas flow rate at the outlet of the scrubber (calculated according to the crude syngas flow rate, temperature, and pressure detection values ​​at the outlet of the scrubber) is verified by the product output, and the deviation is about 5-10%. It is preliminarily judged that there may be a large measurement error. Therefore, the parameter of the syngas flow rate at the outlet of the scrubber is not used in the soft measurement process, and it is used as an unknown parameter to form the unknown parameter set. The soft measurement results of the syngas flow rate at the outlet of the scrubber are shown in Table 7.

[0103] Table 7 Unknown parameter set

[0104] The CO ( y 1), H2 ( y2 )、CO2( y 3) CH4 y 4) N2 ( y 5) A total of 5 parameters are used as target parameters to form the target parameter set O obj The objective function is constructed based on the relative square difference in logarithmic form, a multi-parameter particle swarm optimization algorithm is adopted, and soft measurement is performed based on the gasifier operation model group to obtain the soft measurement value of the unknown parameter of the synthesis gas flow rate at the outlet of the washing tower.

[0105] Furthermore, the gasifier performance parameters were calculated using the original measurement value and soft measurement value of the synthesis gas flow at the outlet of the scrubber, and the results are shown in Table 8.

[0106] Table 8 Comparison of gasification performance

[0107] It can be seen from Table 8 that the original measured value of the syngas flow rate at the outlet of the scrubber is seriously low, resulting in oxygen consumption and coal consumption significantly higher than the typical performance of pulverized coal gasification of this type of coal, which is obviously unreasonable and will cause great misleading to the overall economic performance evaluation of the plant. After soft measurement correction, the oxygen consumption and coal consumption values ​​are in line with the general law of gasification of this type of coal and can be used for the overall economic performance evaluation of the plant.

[0108] Embodiment 3 As another specific embodiment of the present invention, the present invention provides a multi-parameter soft measurement method for a pressurized fluidized flow gasifier, taking a pulverized coal pressurized fluidized flow gasifier of a certain quenching process as an example to obtain values ​​of multiple unknown parameters. The gasification raw material in this embodiment is coal.

[0109] The gasification raw material parameter set in this embodiment A (Coal quality parameter set) is the same as in Example 1, the gasifier operation parameter set (input parameter subset X and a subset of output parameters Y ) are shown in Table 9-10.

[0110] Table 9 Input parameter subset

[0111] Table 10 Output parameter subset

[0112] In this embodiment, two different working conditions are selected when determining the gasifier operation parameter set. The coal amount of the two working conditions is similar, but the oxygen amount of working condition 2 is significantly reduced. However, from the value of the output parameter, the composition of the syngas does not change significantly. By adopting the soft measurement method of the present invention, the soft measurement values ​​of the gasifier outlet parameters are obtained, as shown in Table 11.

[0113] Table 11 Soft measurement results

[0114] It can be seen from Table 11 that in the second working condition, the carbon conversion rate is reduced and the outlet temperature of the gasification chamber is reduced. It can be judged that the decrease in oxygen content in the second working condition causes the reduction in gasification performance. According to the results of soft measurement, the second working condition can be optimized and adjusted to appropriately increase the oxygen content.

[0115] The multi-parameter soft measurement method for a pressurized fluidized flow gasifier of the present invention can effectively solve the problems that key parameters such as the syngas, temperature, flow rate at the outlet of the gasification chamber of the pressurized fluidized flow gasifier cannot be directly measured, the carbon conversion rate of the gasifier cannot be accurately measured in real time, and the syngas flow measurement deviation is large, thereby effectively guiding the operation improvement and transformation of the gasifier.

[0116] The above description is only a preferred embodiment of the present invention and does not constitute any other form of limitation to the present invention. Any modification or equivalent change made based on the technical essence of the present invention still falls within the scope of protection required by the present invention.

Claims

1. A multi-parameter soft measurement method for a pressurized entrained flow gasifier, characterized in that: The method comprises: Acquire and establish a gasifier parameter data set, wherein the gasifier parameter data set includes a gasification raw material parameter set, a gasifier operation parameter set, and an unknown parameter set including a plurality of unknown parameters to be measured, wherein the gasifier operation parameter includes an input parameter subset and an output parameter subset; Determining a target parameter set and a parameter set to be estimated based on the established gasifier parameter data set, and setting estimated values ​​and value ranges of parameters in the parameter set to be estimated; Constructing a gasifier operation model group, and inputting the measured values ​​of the parameters in the gasification raw material parameter set, the input parameter subset, and the output parameter subset that are not selected into the target parameter set and the estimated values ​​of the parameters in the parameter set to be estimated into the constructed gasifier operation model group to calculate the values ​​of the target parameters in the target parameter set; Constructing an objective function, calculating the value of the objective function using the obtained values ​​of the objective parameters in the objective parameter set, and iteratively updating the values ​​of the parameters in the parameter set to be estimated using an optimization algorithm, until an optimized estimated value of the parameter to be estimated is obtained that makes the value of the objective function less than a set value; According to the obtained optimized estimated values ​​of the parameters to be estimated, a solution is performed based on the constructed gasifier operation model group to calculate the values ​​of other unknown parameters, thereby obtaining the values ​​of all parameters in the unknown parameter set.

2. The multi-parameter soft measurement method for a pressurized entrained flow gasifier according to claim 1, characterized in that: The objective function is constructed based on the sum of the residuals of the measured values ​​and calculated values ​​of the target parameters in the target parameter set multiplied by their respective weights, and the objective function is expressed as: ， In the formula, e m is the residual between the measured and calculated values ​​of the target parameter; w m is the weight; m is the sequence number of the target parameter, M is the number of target parameters.

3. The multi-parameter soft measurement method for a pressurized entrained flow gasifier according to claim 2, characterized in that: The residual difference between the measured and calculated values ​​of the target parameter e m It is expressed as: ， In the formula, o m is the measured value of the target parameter, ô m It is the calculated value of the target parameter obtained by solving the gasifier operation model group.

4. The multi-parameter soft measurement method for a pressurized entrained flow gasifier according to claim 2, characterized in that: The constructing of the objective function, using the obtained value of the objective parameter in the objective parameter set to calculate the value of the objective function and using the optimization algorithm to iteratively update the value of the parameter in the parameter set to be estimated until the optimized estimated value of the parameter to be estimated that makes the value of the objective function less than the set value is obtained, includes inputting the measured value and calculated value of the objective parameter in the objective parameter set and its weight into the objective function respectively. F obj Calculate the objective function F obj The value of is determined to determine whether the value is less than the set value. F obj,set If the requirement is met, the estimated value is directly used as the optimized estimated value of the parameter set to be estimated to proceed to the next step. If it is not met, the optimization algorithm is called to update the estimated value of the parameter set to be estimated, and the value of the target parameter in the target parameter set is returned to calculate, and the objective function is calculated using the obtained value of the target parameter in the target parameter set. F obj The value is determined again to see whether the value is less than the set value. F obj,set The requirement is to iterate until the objective function F obj The value is less than the set value F obj,set The requirement is met, and the estimated values ​​of the parameter set to be estimated are no longer updated, the parameter estimation process is completed, and the estimated values ​​at this time are used as the optimized estimated values ​​of the parameter set to be estimated.

5. The multi-parameter soft measurement method for a pressurized entrained flow gasifier according to claim 1, characterized in that: The gasifier operation model group is expressed as: In the formula, n Indicates the serial number of the gasifier operation model, N represents the number of models, A represents the gasification raw material parameter set, X is represented as a subset of the input parameters, Y is represented as the output parameter subset, Z Represented as the unknown parameter set.

6. The multi-parameter soft measurement method for a pressurized entrained flow gasifier according to claim 1, characterized in that: The target parameter set is determined based on the output parameter subset, and the to-be-estimated parameter set is determined based on the unknown parameter set.

7. The multi-parameter soft measurement method for a pressurized entrained flow gasifier according to claim 6, characterized in that: The target parameter set and the parameter set to be estimated are determined based on the established gasifier parameter data set, and the estimated values ​​and value ranges of the parameters in the parameter set to be estimated are set, including taking at least three components containing CH4 in the composition of the washing tower outlet synthesis gas of the output parameter subset as the target parameters in the target parameter set.

8. The multi-parameter soft measurement method for a pressurized entrained flow gasifier according to claim 7, characterized in that: The target parameter set and the parameter set to be estimated are determined based on the established gasifier parameter data set, and the estimated values ​​and value ranges of the parameters in the parameter set to be estimated are set, including taking the five components of CO, H2, CO2, CH4, and N2 in the synthesis gas composition of the washing tower outlet of the output parameter subset as the target parameters in the target parameter set.

9. The multi-parameter soft measurement method for a pressurized entrained flow gasifier according to claim 1, characterized in that: The unknown parameters of the unknown parameter set include at least two of the following: gasification chamber temperature, carbon conversion rate, gasification chamber outlet synthesis gas composition, gasification chamber outlet synthesis gas volume, cooling water reaction volume, and scrubber outlet synthesis gas flow rate.

10. The multi-parameter soft measurement method for a pressurized entrained flow gasifier according to claim 1, characterized in that: The optimization algorithm is a particle swarm optimization algorithm.

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