Thermodynamic dynamic analysis method and device for continuous flow reaction system

Abstract

The invention discloses a thermal dynamic analysis method and device for a continuous flow reaction system. By changing the flow condition in the experiment in real time, the change curve of the sample temperature along with the experiment time is rapidly measured, and then the change curve is converted into the sample temperature change curve in the reaction process. A sample temperature distribution model in the reaction process is established by utilizing mechanism parameters in the reaction process of a continuous flow system, and reaction kinetic parameters such as activation energy and pre-exponential factors are optimized in a mode of nonlinear fitting of a model output value and a sample temperature change curve. Compared with a traditional continuous flow kinetic analysis method, the method has the advantages that high-density sample temperature data can be quickly generated, and the influence of thermodynamic parameters on the reaction process is considered. According to the invention, the dynamic analysis of the chemical reaction kinetics under the continuous flow process can be realized, and the experiment efficiency and the data analysis accuracy are improved.

Description

Technical Field

[0001] The invention belongs to the field of fine chemical industry and reaction kinetics thermal analysis, and relates to a thermodynamic dynamic analysis method and device for a continuous flow reaction system. Background Art

[0002] Kinetic parameters such as activation energy and pre-exponential factor of chemical reaction processes are key data for chemical synthesis, process development and reactor design. Combining thermal analysis techniques such as calorimetry to analyze the reaction process is one of the main means of obtaining reaction kinetic data. A kettle reaction calorimeter is a common laboratory-level chemical reaction thermal analysis instrument, but it has the disadvantages of large size, large amount of chemical reagents used, and long control time required to reach the target temperature. It can only simulate intermittent or semi-intermittent process conditions. Therefore, continuous flow reactors with relatively small size and higher safety performance have received more and more attention and application in the field of chemical reaction thermodynamic analysis.

[0003] For thermodynamic analysis under continuous flow conditions, the traditional method for calculating kinetic parameters under isothermal conditions is usually applied to microchannel reactors, which has high requirements on the heat transfer efficiency of the reactor. It is difficult to control the sample temperature to be constant in a tubular reactor. For highly exothermic reactions, the effect of the reaction process temperature on the reaction rate constant cannot be ignored.

[0004] In addition, there is also a method of using the sample temperature data measured at a fixed flow rate to nonlinearly fit the kinetic parameters. However, this method is limited by the sensor layout, and a single experiment can collect fewer temperature points. The nonlinear optimal process is prone to underfitting, and the system takes a long time to reach a steady state, which reduces the experimental efficiency. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a method and device for thermodynamic dynamic analysis of a continuous flow reaction system.

[0006] A first aspect of the present invention provides a method for thermodynamic dynamic analysis of a continuous flow reaction system, comprising the following steps:

[0007] Step 1. Under the condition of constant jacket temperature, set a fixed flow rate, start the reactant injection, and make the reactants react in the continuous flow reaction system. When the system reaches a steady state, record the temperature measurement results of each temperature sensor;

[0008] Step 2. Divide the reaction pipeline into multiple sections according to the position distribution of the temperature sensors, and adjust the injection flow rate so that the measurement results of adjacent temperature sensors tend to be consistent when the system is in a steady state;

[0009] The injection flow rate is gradually changed, and the temperature curve of each temperature sensor is recorded during the flow change process. The abscissa of the temperature curve is converted from the experimental time to the sample residence time to obtain the temperature change curve of the sample in each section of the pipeline;

[0010] Step 3. Splice the temperature change curves in each section of the pipeline to form a temperature change curve of the sample during the entire reaction process;

[0011] Step 4. Establish a sample temperature distribution model for the reaction process, take the minimum sum of squares of the difference between the model output value and the measured sample temperature change curve as the objective function, optimize the reaction kinetic parameters through nonlinear fitting, and then calculate the reaction rate constant.

[0012] A second aspect of the present invention provides a device for thermodynamic dynamic analysis of a continuous flow reaction system, comprising:

[0013] A reaction system, including a reaction pipeline and a jacket, for maintaining a constant reaction temperature;

[0014] The sample injection system includes a constant flow pump for controlling the injection flow rate of the reactants;

[0015] Temperature sensors, located at different locations in the pipeline, are used to measure the sample temperature in real time;

[0016] The data processing unit is used to convert the measurement data of the temperature sensor into a sample residence time curve, and perform nonlinear fitting based on the sample temperature distribution model during the reaction process to optimize the reaction kinetic parameters.

[0017] Beneficial effects of the present invention:

[0018] 1. From the perspective of experimental methods, the invention maintains constant temperature conditions during the experiment, which is easier to control than traditional isothermal experiments, has relatively low requirements on the heat transfer efficiency of the reactor, and can be applied to highly exothermic and violent reactions; by controlling the sample flow in real time, the temperature data of the reaction process can be quickly generated, which greatly improves the experimental efficiency and data acquisition density.

[0019] 2. From the perspective of data analysis, the method of nonlinear fitting of kinetic data by the temperature distribution model of the reaction process introduces thermodynamic parameters such as reaction enthalpy and heat transfer coefficient, and takes into account the influence of reaction process temperature on reaction rate. A large amount of measured data is used in the fitting process, which can avoid under-fitting during parameter optimization and improve the accuracy of kinetic parameter optimization results.

[0020] 3. The kinetic parameter results of the reaction obtained by the present invention can be applied to production scale-up, risk assessment, process optimization and other links of chemical reactions under continuous flow conditions.

[0021] In summary, the present invention does not need to design an isothermal control method under continuous flow conditions, introduces the influence of the reaction process temperature on the reaction progress, and overcomes the problem of fewer data points in the steady-state analysis method and the problem of underfitting in the parameter optimization process, thereby improving the experimental efficiency and the accuracy of the parameter calculation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of the structure of the continuous flow reaction experimental platform;

[0023] Figure 2 It is the flow chart of nonlinear optimization of kinetic parameters. DETAILED DESCRIPTION

[0024] In order to better understand the technical solution of the present invention, the present invention is described in detail below with reference to the accompanying drawings and specific implementation cases. It should be noted that the present invention can be implemented in many different forms and is not limited to the embodiments described in this document. On the contrary, the purpose of providing these embodiments is to provide a more thorough and comprehensive understanding of the contents disclosed in the present invention.

[0025] like Figure 1 As shown, the embodiment of the present application provides a device for thermodynamic dynamic analysis of a continuous flow reaction system, comprising:

[0026] A reaction system, including a reaction pipeline 9 and a jacket, is used to maintain a constant reaction temperature;

[0027] The injection system includes a constant flow pump 2 for controlling the injection flow rate of the reactants;

[0028] Temperature sensors 6, distributed at different locations of the pipeline, for measuring the sample temperature in real time;

[0029] The data processing unit is used to convert the measurement data of the temperature sensor into a sample residence time curve, and perform nonlinear fitting based on the sample temperature distribution model during the reaction process to optimize the reaction kinetic parameters.

[0030] The data processing unit comprises:

[0031] A data conversion module, used to convert the experimental time into the sample residence time;

[0032] Model fitting module, used to perform nonlinear fitting based on the sample temperature distribution model to optimize activation energy and pre-exponential factor;

[0033] Parameter calculation module, used to calculate the reaction rate constant according to the Arrhenius formula.

[0034] During the experiment, the two samples 1 are respectively transported by the two constant flow pumps 2 in the injection system 13, and flow into the preheating system 14 after being degassed by the vacuum degasser 3. The samples are preheated to the target temperature through the preheating pipeline 4 and then flow into the reaction system 15. The preheating system and the reaction system are connected by a three-way ball valve 5. When conducting a kinetic experiment, the three-way ball valve is switched to the I passage, and the two sample solutions are further preheated through the preheating pipeline in the reaction system. A temperature sensor No. 1 is installed at the end of one of the pipelines to detect the sample preheating temperature. The sample then flows into the micro mixer 7 and is mixed evenly, and then enters the reaction pipeline 9 through the three-way connector 8 for reaction. The reaction product flows into the product collection bottle 10 for storage. The temperature sensors No. 2 to No. 7 are installed in the reaction pipeline in sequence to monitor the temperature of the reaction process. The ball valve is switched to the II passage, and the heat transfer coefficient calibration experiment can be carried out. The preheating system and the reaction system are connected with a constant temperature circulating water bath and an insulating jacket to keep the experimental environment temperature constant. During the experiment, the central control 11 and the host computer 12 are used to realize functions such as sensor temperature measurement result monitoring, injection flow control, injection pump pressure monitoring, and constant temperature bath temperature control.

[0035] In one embodiment, the total length of the reaction pipeline is 5m and the total volume is , sensors 2 to 7 are distributed at 0.1, 0.5, 1, 2, 3, and 5 m of the reaction pipeline. Sensors 1 to 7 divide the entire pipeline into 6 sections, and the volume of each section is recorded as , , , , , The residence time of the sample in these six sections of the pipeline is recorded as , , , , , .

[0036] According to the above device configuration, a thermodynamic dynamic analysis method of a continuous flow reaction system provided in an embodiment of the present application includes the following steps:

[0037] Step 1: Keep the jacket temperature of the reaction system constant, set the fixed flow condition, start the constant flow pump to inject samples, make the two samples react, and when the system reaches a steady state, record the temperature measurement results of each temperature sensor.

[0038] Specifically: During the experiment, keep the jacket temperature of the reaction system constant and set a fixed volume flow rate F 0 , start the constant flow pump to inject samples, so that the two samples react; when the system reaches a steady state, record the average value of the temperature measurement results of sensors 1 to 7 within 300s, which are recorded as T 1 , T 2 , T3 、T 4 、T 5 、T 6 、T 7 .

[0039] In the experiment, the residence time of the sample in the reaction tube is , then the entire reaction process can be considered to start at time 0. The moment ends. Therefore, T 1 ~T 7 The reaction can be considered to proceed to 0. , , , , , The sample temperature at the time.

[0040] Fitting these 7 data points with the sample temperature distribution model during the reaction process can optimize the kinetic parameters such as activation energy and pre-exponential factor in the model. However, the data points obtained in the above experiment are few, and underfitting is prone to occur in the parameter optimization process. Therefore, subsequent steps are still needed to enrich the sample temperature data during the reaction process.

[0041] Step 2: Adjust the injection pump flow rate so that when the system reaches a steady state, the measurement results of the second temperature sensor are basically consistent with those of the first temperature sensor in step 1. Gradually reduce the injection pump flow rate until it reaches the flow condition of step 1. Record the temperature measurement curve of the second sensor during the flow reduction process. Convert the horizontal axis of the temperature curve from the experimental time to the residence time of the sample in the reaction pipeline, and then obtain the sample temperature curve during the reaction process when the sample flows from the first sensor to the second sensor.

[0042] Furthermore, after the system reaches a steady state in step 2, the flow rate of the injection pump is reduced according to formula (1):

[0043] (1)

[0044] In the formula, represents the injection flow rate, t represents the experimental time, Indicates the volume of the pipeline. is the residence time of the sample in the pipeline under the maximum flow condition, Indicates the flow regulation rate.

[0045] By adjusting the sample flow rate in real time according to the above formula, the process curve of the sample temperature in this section of the pipeline changing with the experimental time can be measured.

[0046] Furthermore, the conversion relationship between the experimental time and the residence time in step 2 is shown in formula (2):

[0047] (2)

[0048] In the formula, is the sample residence time; is the sampling time of the temperature sensor.

[0049] Specifically: Increase the flow rate of the injection pump to F 1 When the system reaches a steady state, the average value of the measurement within 300s of sensor 2 is equal to T 1 The error is less than 0.08℃. At this time, the residence time of the sample in the pipeline between sensor 1 and sensor 2 is for .

[0050] According to formula (3), reduce the injection pump flow rate until it reaches , so that From the initial value Start at a certain rate Increase to ;

[0051] (3)

[0052] Record the sample flow rate from F 1 Down to F 0 During the process, the temperature curve T measured by sensor No. 2 1 ( ), Indicates the moment when the sample flows through sensor No. 2;

[0053] As shown in formula (4), the volume of the pipeline between sensors 1 and 2 is V 1 The integral of the available flow is expressed as:

[0054] (4)

[0055] In the formula, Indicates the moment when the sample flows through sensor No. 1;

[0056] Solving the definite integral gives , The relationship is shown in formula (5):

[0057] (5)

[0058] As shown in formula (6), It can be expressed as , Difference:

[0059] (6)

[0060] The temperature curve T measured by sensor No. 2 during the experiment can be obtained by formula (6): 1 ( ) is transformed into T 1 ( ). 1 ( ) can be expressed as Under the same conditions, the sample temperature curve during the reaction period when the sample flows from sensor 1 to sensor 2.

[0061] Step 3: According to the method described in step 2, record the temperature curves measured by all subsequent temperature sensors as they change with residence time, that is, the temperature change curves of the sample as it flows through each subsequent section of the pipeline, and splice the temperature curves of each section into the sample temperature change curve during the entire reaction process.

[0062] Specifically, the measurement method of step 2 is extended to the other 5 sections of pipelines, and the sample temperature curve of the reaction process is measured when the sample flows through each section of pipeline. The experimental process is as follows:

[0063] Increase the injection pump flow rate to F n (n=2,3,4,5,6), when the system reaches a steady state, the average value of the n+1 sensor within 300s is equal to T n The error is less than 0.08℃. According to formula (7), reduce the flow rate of the injection pump until it is reduced to , record the sample flow rate from F n Down to F 0 During the process, the temperature curve T measured by sensor n+1 n ( ), Indicates the moment when the sample flows through sensor n+1;

[0064] (7)

[0065] The temperature curve T measured by sensor n+1 during the experiment can be obtained by formula (8): n ( ) is transformed into T n ( ).

[0066] (8)

[0067] The sample temperature curve T 1 ( ), T 2 ( ) 、T 3 ( ) 、T 4 ( ) 、T5 ( ), T 6 ( ) to obtain the sample temperature change curve during the entire reaction process from the sample flowing from the pipeline inlet to the pipeline outlet .

[0068] Step 4: Establish a sample temperature distribution model for the reaction process, use the minimum sum of squares of the difference between the model output value and the sample temperature measured in step 3 as the objective function for nonlinear fitting, and perform rolling optimization on the activation energy, pre-exponential factor and other reaction kinetics data, and then calculate the reaction rate constant according to the Arrhenius formula.

[0069] Specifically, the reaction pipeline is divided into multiple micro-segments, and the heat balance equation in each micro-segment is shown in formula (9):

[0070] (9)

[0071] In the formula, represents the reaction rate; represents the reaction enthalpy; It represents the volume of microelement segment; represents the specific heat capacity of the sample; Indicates the sample mass within the microelement segment; Indicates the rate of change of sample temperature within the microelement segment; It represents the total heat transfer coefficient between the reactants and the heat transfer medium in the pipeline; Indicates the heat exchange area between the sample and the heat-conducting medium outside the tube; Indicates the average temperature of the sample in the microelement segment; Indicates the temperature of the heat transfer medium in the jacket.

[0072] As shown in formula (10), the heat balance equation is transformed into the sample temperature distribution model during the reaction process:

[0073] (10)

[0074] In the formula, represents the sample density, represents the pre-exponential factor, represents the activation energy, is the molar gas constant, represents the concentration of the first reactant, represents the concentration of the second reactant, and D represents the inner diameter of the reaction tube.

[0075] The concentration of the first reactant and the concentration of the second reactant can be calculated iteratively by formula (11):

[0076] (11)

[0077] In the formula, represents the rate of change of the concentration of the first reactant in the microelement segment, represents the reaction rate constant.

[0078] The nonlinear optimization process of kinetic parameters is as follows Figure 2 As shown in the reaction process temperature model (Equation (10)), It can be measured by designing a flow reaction calorimetry experiment. Given the sample density, sample specific heat capacity, initial concentration of reactants, pre-exponential factor and initial activation energy values ​​A0 and E0, the model can predict the temperature value at any i-th second within j seconds of the reaction process. , and compare it with the experimental temperature value of the i-th second obtained by the experimental method in steps one, two, and three The square sum of the errors is used as the objective function (Equation (12)), and the nonlinear fitting is performed using the optimization method of minimizing the square error, and the dynamic parameters A and E are fitted by rolling optimization.

[0079] (12)

[0080] After obtaining the optimal parameters A and E and inserting them into formula (10), the sample temperature distribution during the reaction process can be obtained, and the reaction rate constant k can be calculated according to the Arrhenius formula (formula (13)).

[0081] (13)

[0082] The method of the present invention does not need to control the sample temperature to be constant during the reaction process. High-density sample temperature data of the reaction process can be quickly generated by adjusting the fluid flow rate in real time. It is an efficient experimental analysis method. At the same time, it has good universality and can be applied to highly exothermic reactions such as acid-base neutralization.

[0083] The above is only an embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention can be modified and varied according to actual conditions. Any modification, replacement and improvement made within the principle of the present invention should be included in the scope of the present invention.

Claims

1. A thermodynamic dynamic analysis method for a continuous flow reaction system, characterized in that: The following steps are involved: Step 1. Under the condition of constant jacket temperature, set a fixed flow rate, start the reactant injection, and make the reactants react in the continuous flow reaction system. When the system reaches a steady state, record the temperature measurement results of each temperature sensor; Step 2. Divide the reaction pipeline into multiple sections according to the position distribution of the temperature sensors, and adjust the injection flow rate so that the measurement results of adjacent temperature sensors tend to be consistent when the system is in a steady state; The injection flow rate is gradually changed, and the temperature curve of each temperature sensor is recorded during the flow change process. The abscissa of the temperature curve is converted from the experimental time to the sample residence time to obtain the temperature change curve of the sample in each section of the pipeline; Step 3. Splice the temperature change curves in each section of the pipeline to form a temperature change curve of the sample during the entire reaction process; Step 4. Establish a sample temperature distribution model for the reaction process, take the minimum sum of squares of the difference between the model output value and the measured sample temperature change curve as the objective function, optimize the reaction kinetic parameters through nonlinear fitting, and then calculate the reaction rate constant.

2. The thermodynamic dynamic analysis method of a continuous flow reaction system according to claim 1, characterized in that: In step 2, the injection flow rate is adjusted according to the following formula: ， in, Indicates the injection flow rate, Indicates the experimental time, Indicates the volume of the pipeline. It indicates the residence time of the sample in the pipeline under the maximum flow condition. Indicates the flow regulation rate.

3. The thermodynamic dynamic analysis method of a continuous flow reaction system according to claim 2, characterized in that: The conversion relationship between experimental time and sample residence time is: ， in, represents the sample residence time, Indicates the temperature sensor sampling time.

4. The thermodynamic dynamic analysis method of a continuous flow reaction system according to claim 1, characterized in that: The sample temperature distribution model during the reaction is: ， in, represents the temperature change rate of the sample within the microelement segment, represents the average temperature of the sample in the microelement segment, represents the sample density, represents the specific heat capacity of the sample, represents the pre-exponential factor, represents the activation energy, is the molar gas constant, represents the concentration of the first reactant, represents the concentration of the second reactant, represents the reaction enthalpy, It represents the total heat transfer coefficient between the reactants and the heat transfer medium in the pipeline, D represents the inner diameter of the reaction pipeline, Indicates the temperature of the heat transfer medium in the jacket.

5. The thermodynamic dynamic analysis method of a continuous flow reaction system according to claim 1, characterized in that: The nonlinear fitting optimization process adopts the least square method, takes the minimum sum of squares of the difference between the model output value and the measured sample temperature as the objective function, and optimizes the activation energy and the pre-exponential factor in a rolling manner.

6. The thermodynamic dynamic analysis method of a continuous flow reaction system according to claim 1, characterized in that: The method also includes calculating the reaction rate constant via the Arrhenius equation.

7. A device for thermodynamic dynamic analysis of a continuous flow reaction system, characterized in that: include: A reaction system, including a reaction pipeline and a jacket, for maintaining a constant reaction temperature; The sample injection system includes a constant flow pump for controlling the injection flow rate of the reactants; Temperature sensors, located at different locations in the pipeline, are used to measure the sample temperature in real time; The data processing unit is used to convert the measurement data of the temperature sensor into a sample residence time curve, and perform nonlinear fitting based on the sample temperature distribution model during the reaction process to optimize the reaction kinetic parameters.

8. The device according to claim 7, characterized in that The reaction system also includes a preheating pipeline and a micro mixer, which are used to preheat and mix the reactants.

9. The device according to claim 7, characterized in that The data processing unit comprises: A data conversion module, used to convert the experimental time into the sample residence time; Model fitting module, used to perform nonlinear fitting based on the sample temperature distribution model to optimize activation energy and pre-exponential factor; Parameter calculation module, used to calculate the reaction rate constant according to the Arrhenius formula.

10. The device according to claim 7, characterized in that The device also includes a constant temperature circulating water bath for maintaining a constant temperature of the jacket of the reaction system.

Images