A method and device for thermokinetic dynamic analysis of a continuous flow reaction system

Abstract

The present invention discloses a method and device for thermokinetic dynamic analysis of a continuous flow reaction system. By changing the flow conditions during the experiment in real time, the present invention rapidly measures the curve of the sample temperature varying with the experimental time, and then converts it into the curve of the sample temperature change during the reaction process. Using the mechanism parameters of the reaction process in the continuous flow system, a sample temperature distribution model for the reaction process is established, and reaction kinetic parameters such as activation energy and pre-exponential factor are optimized by means of non-linear fitting of the model output value and the sample temperature change curve. Compared with the traditional continuous flow kinetic analysis method, the present invention can rapidly generate high-density sample temperature data and take into account the influence of thermodynamic parameters on the reaction process. The present invention can achieve the dynamic analysis of the chemical reaction kinetics under continuous flow processes and improve the experimental efficiency and data analysis accuracy.

Description

Technical Field

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

[0002] Kinetic parameters such as the activation energy and pre-exponential factor in the chemical reaction process are key data for chemical synthesis, process development, and reactor design. Analyzing the reaction process by combining calorimetry and other thermal analysis techniques is one of the main means to obtain reaction kinetic data. The batch reaction calorimeter is a common laboratory-level chemical reaction thermal analysis instrument, but it has disadvantages such as a large volume, a large amount of chemical reagents used, a long control time required to reach the target temperature, and can only simulate batch or semi-batch process conditions. Therefore, continuous flow reactors with relatively small volumes and higher safety performance have received more and more attention and applications in the field of chemical reaction thermokinetics analysis.

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

[0004] In addition, there is also a method of non-linearly fitting kinetic parameters using the sample temperature data measured at a steady state under a fixed flow rate. However, this method is limited by the sensor layout, with fewer temperature points that can be collected in a single experiment, and the non-linear optimization process is prone to underfitting. Moreover, the time required for the system to reach a steady state is relatively long, reducing the experimental efficiency. Summary of the Invention

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

[0006] In the first aspect of the present invention, a method for dynamic analysis of the thermokinetics of a continuous flow reaction system is provided, including the following steps:

[0007] Step 1. Under the condition of a constant jacket temperature, set a fixed flow rate, start the injection of reactants, and allow the reactants to 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 segments according to the position distribution of the temperature sensors, and by adjusting the injection flow rate, make the measurement results of adjacent temperature sensors tend to be consistent when the system is in a steady state.

[0009] Gradually change the injection flow rate, record the temperature curves of each temperature sensor during the flow rate change process, and convert the abscissa of the temperature curve from the experimental time to the sample residence time to obtain the temperature change curves of the samples in each section of the pipeline;

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

[0011] Step 4. Establish a sample temperature distribution model during 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 non-linear fitting, and then calculate the reaction rate constant.

[0012] In the second aspect of the present invention, there is provided a device for thermokinetic 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] An injection system, including a constant flow pump, for controlling the injection flow rate of the reactants;

[0015] Temperature sensors, distributed at different positions of the pipeline, for measuring the sample temperature in real time;

[0016] A data processing unit, for converting the measurement data of the temperature sensor into a sample residence time curve and performing non-linear fitting based on the sample temperature distribution model during the reaction process to optimize the reaction kinetic parameters.

[0017] Advantages of the present invention:

[0018] 1. From the perspective of the experimental method, the experimental process of the present invention maintains a constant temperature condition, which is easier to control compared to traditional isothermal experiments, has relatively low requirements for the heat transfer efficiency of the reactor, and can be applied to highly exothermic and intense reactions; by controlling the sample flow rate in real time, temperature data during the reaction process can be generated quickly, greatly improving the experimental efficiency and data acquisition density.

[0019] 2. From the perspective of data analysis, the method of non-linearly fitting kinetic data with the sample temperature distribution model during the reaction process introduces thermodynamic parameters such as reaction enthalpy and heat transfer coefficient, taking into account the influence of the reaction process temperature on the reaction rate; a large amount of measured data is used in the fitting process, which can avoid falling into the situation of under-fitting during the parameter optimization process and improve the accuracy of the optimized results of the kinetic parameters.

[0020] 3. The kinetic parameter results obtained through the present invention can be applied to production scale-up, risk assessment, process optimization, etc. 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 process, and also overcomes the problem of fewer data points in the steady-state analysis method and the easy occurrence of underfitting in the parameter optimization process, improving the experimental efficiency and the accuracy of the parameter calculation results. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0023] Figure 2 is a flow chart of the non-linear optimization of kinetic parameters. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] In order to better understand the technical solution of the present invention, the present invention will be described in detail below in conjunction with the 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 herein. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosed content of the present invention more thorough and comprehensive.

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

[0026] A reaction system, including a reaction pipeline 9 and a jacket, for maintaining a constant reaction temperature;

[0027] An injection system, including a constant flow pump 2, for controlling the injection flow rate of the reactants;

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

[0029] A data processing unit, for converting the measurement data of the temperature sensor into a sample residence time curve, and performing non-linear fitting based on the sample temperature distribution model during the reaction process to optimize the reaction kinetic parameters.

[0030] The data processing unit includes:

[0031] A data conversion module, for converting the experimental time into the sample residence time;

[0032] A model fitting module, for performing non-linear fitting based on the sample temperature distribution model to optimize the activation energy and the pre-exponential factor;

[0033] A parameter calculation module, for calculating the reaction rate constant according to the Arrhenius formula.

[0034] During the experiment, two samples 1 were respectively transported by two constant flow pumps 2 in the sampling system 13, degassed by the vacuum degasser 3, and then flowed into the preheating system 14. After being preheated to the target temperature through the preheating pipeline 4, the samples flowed into the reaction system 15. The preheating system and the reaction system were connected by a three-way ball valve 5. When conducting kinetic experiments, the three-way ball valve was switched to the Ⅰ path. The two sample solutions were further preheated through the preheating pipeline in the reaction system. A No. 1 temperature sensor was installed at the end of one of the pipelines to detect the sample preheating temperature. The samples then flowed into the micro mixer 7, were mixed evenly, and then entered the reaction pipeline 9 through the three-way connector 8 for reaction. The reaction product flowed into the product collection bottle 10 for storage. The No. 2 - 7 temperature sensors were successively installed in the reaction pipeline to monitor the reaction process temperature. When the ball valve was switched to the Ⅱ path, the heat transfer coefficient calibration experiment could be carried out. The preheating system and the reaction system were connected to a constant temperature circulating water bath and an adiabatic jacket to keep the experimental environment temperature constant. During the experiment, functions such as monitoring the temperature measurement results of sensors, controlling the sampling flow rate, monitoring the pressure of the sampling pump, and controlling the temperature of the constant temperature bath were realized through the central control 11 and the upper computer 12.

[0035] In one embodiment, the total length of the reaction pipeline is 5m, and the total volume is , the No. 2 - 7 sensors are distributed at 0.1m, 0.5m, 1m, 2m, 3m, and 5m of the reaction pipeline. The No. 1 - 7 sensors divide the whole pipeline into 6 segments, and the volumes of each segment of the pipeline are respectively denoted as , , , , , . The residence times of the samples in these 6 segments of the pipeline are respectively denoted as , , , , , .

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

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

[0038] Specifically: During the experiment, keep the temperature of the jacket of the reaction system constant, set a fixed volume flow rate F 0 , start the constant flow pump for sampling, and make the two samples react; when the system reaches a steady state, record the average value of the temperature measurement results of the No. 1 - 7 sensors within 300s, which are respectively denoted 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 pipeline is , then the whole reaction process can be regarded as starting at time 0 and ending at time 1 ~T 7 can be regarded as the sample temperature at times 0, , , , , , .

[0040] Fitting these 7 data points with the sample temperature distribution model during the reaction process can optimize kinetic parameters such as the activation energy and pre-exponential factor in the model. However, the number of data points obtained from the above experiment is small, and underfitting is likely to occur during 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 flow rate of the injection pump so that when the system reaches a steady state, the measurement results of the second temperature sensor are basically the same as those of the first temperature sensor in Step 1. Gradually reduce the flow rate of the injection pump until it reaches the flow rate condition in Step 1. Record the temperature measurement curve of the second sensor during the process of reducing the flow rate. Convert the abscissa 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 period when the sample flows from the first sensor to the second sensor.

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

[0043] (1)

[0044] where represents the injection flow rate, t represents the experimental time, represents the pipeline volume, is the residence time of the sample in the pipeline under the maximum flow rate condition, and represents the flow rate adjustment rate.

[0045] Adjust the sample flow rate in real time according to the above formula, and then the process curve of the sample temperature change with the experimental time in this section of the pipeline 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 residence time of the sample; 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 measured by the No. 2 sensor within 300 s has an error less than 0.08 °C from T 1 , and at this time, the residence time of the sample in the pipeline between the No. 1 sensor and the No. 2 sensor is .

[0050] Reduce the flow rate of the injection pump according to formula (3) until it is reduced to , so that starts from the initial value and increases to at a certain rate ;

[0051] (3)

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

[0053] As shown in formula (4), the volume V 1 of the pipeline between the No. 1 and No. 2 sensors can be expressed as the integral of the available flow rate:

[0054] (4)

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

[0056] Solving the definite integral gives the relationship between and as shown in formula (5):

[0057] (5)

[0058] As shown in formula (6), can be expressed as the difference between and :

[0059] (6)

[0060] The temperature curve T measured by Sensor 2 during the experiment can be transformed through Equation (6) 1 ( ) into T 1 ( ). T 1 ( ) can be expressed as the temperature curve of the sample during the reaction process when the sample flows from Sensor 1 to Sensor 2 under the condition that the flow rate is .

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

[0062] Specifically: Generalize the measurement method in Step 2 to the other 5 sections of the pipeline, measure the temperature curve of the sample during the reaction process when the sample flows through each section of the pipeline, and the experimental process is as follows:

[0063] Increase the flow rate of the injection pump to F n (n = 2, 3, 4, 5, 6). When the system reaches a steady state, make the average value measured by the (n + 1)-th sensor within 300 s have an error less than 0.08 °C from T n . According to Equation (7), reduce the flow rate of the injection pump until it is reduced to . Record the temperature curve T n measured by the (n + 1)-th sensor during the process when the sample flow rate decreases from F 0 to F n ( ), indicating the moment when the sample flows through the (n + 1)-th sensor;

[0064] (7)

[0065] The temperature curve T n ( ) measured by the (n + 1)-th sensor during the experiment can be transformed through Equation (8) into T n ( ).

[0066] (8)

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

[0068] Step 4: Establish a sample temperature distribution model for the reaction process. Using the sum of the squares of the differences between the model output value and the measured sample temperature in Step 3 as the objective function, perform nonlinear fitting, and iteratively optimize the reaction kinetic data such as the activation energy and pre-exponential factor. Then calculate the reaction rate constant according to the Arrhenius formula

[0069] Specifically: The reaction pipeline is finely divided into multiple micro-elements. The heat balance equation within each micro-element is as shown in formula (9):

[0070] (9)

[0071] In the formula, represents the reaction rate; represents the reaction enthalpy; represents the volume of the micro-element; represents the specific heat capacity of the sample; represents the mass of the sample within the micro-element; represents the rate of change of the sample temperature within the micro-element; represents the total heat transfer coefficient between the reactants and the heat-conducting medium within the pipeline; represents the heat exchange area between the sample and the heat-conducting medium outside the pipe; represents the average temperature of the sample within the micro-element; represents the temperature of the heat-conducting medium within the jacket

[0072] As shown in formula (10), transform the heat balance equation into a sample temperature distribution model for the reaction process:

[0073] (10)

[0074] In the formula, represents the sample density, represents the pre-exponential factor, represents the activation energy, represents 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 pipeline

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

[0076] (11)

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

[0078] The non-linear optimization process of kinetic parameters is as Figure 2 shown. In the reaction process temperature model (Equation (10)), can be measured by designing a flow reaction calorimetry experiment. Given the sample density, sample specific heat capacity, initial concentration of the reactant, pre-exponential factor, and initial values of activation energy A0 and E0, the model can predict the temperature value at any i-th second within j seconds of the reaction process , and take the sum of the squared errors between it and the experimental temperature value at the i-th second obtained by the experimental methods in Steps One, Two, and Three as the objective function (Equation (12)), and use the optimization method of minimizing the squared difference for non-linear fitting to iteratively optimize and fit the kinetic parameters A and E.

[0079] (12)

[0080] After obtaining the optimal parameters A and E, substitute them into Equation (10) to obtain the sample temperature distribution during the reaction process. According to the Arrhenius equation (Equation (13)), the reaction rate constant k can be calculated.

[0081] (13)

[0082] The method of the present invention does not require controlling the sample temperature to be constant during the reaction process. By adjusting the fluid flow rate in real time, high-density sample temperature data during the reaction process can be quickly generated. It is a highly efficient experimental analysis method, and 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 one embodiment of the present invention and is not used to limit the present invention. For those skilled in the art, the present invention can be variously modified and changed according to actual situations. Any modifications, equivalent replacements, and improvements made within the principle of the present invention should be included within 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 during 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; The injection flow rate is adjusted according to the following formula: Where F(t) represents the injection flow rate, t represents the experimental time, V r represents the volume of the pipeline, τ0 represents the residence time of the sample in the pipeline under the maximum flow condition, and ɑ represents the flow regulation rate; The sample temperature distribution model during the reaction is: in, Indicates the temperature change rate of the sample within the microelement segment, T r represents the average temperature of the sample in the microelement segment, ρ represents the sample density, c p represents the specific heat capacity of the sample, A represents the pre-exponential factor, E represents the activation energy, R represents the molar gas constant, c1 represents the concentration of the first reactant, c2 represents the concentration of the second reactant, ΔH represents the reaction enthalpy, U 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, T j Indicates the temperature of the heat transfer medium in the jacket.

2. The thermodynamic dynamic analysis method of a continuous flow reaction system according to claim 1, characterized in that: The conversion relationship between experimental time and sample residence time is: Where τ represents the sample residence time, t f Indicates the temperature sensor sampling time.

3. 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.

4. 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.

5. A device for thermodynamic dynamic analysis of a continuous flow reaction system, using the thermodynamic dynamic analysis method according to any one of claims 1 to 4, 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.

6. The device according to claim 5, characterized in that The reaction system also includes a preheating pipeline and a micro mixer, which are used to preheat and mix the reactants.

7. The device according to claim 5, 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.

8. The device according to claim 5, 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.

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