CN114595611A - Simulation method of catalytic oxidation reaction process of sulfur dioxide - Google Patents
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- RAHZWNYVWXNFOC-UHFFFAOYSA-N Sulphur dioxide Chemical compound O=S=O RAHZWNYVWXNFOC-UHFFFAOYSA-N 0.000 title claims abstract description 119
- 238000000034 method Methods 0.000 title claims abstract description 50
- 238000007254 oxidation reaction Methods 0.000 title claims abstract description 43
- 230000003197 catalytic effect Effects 0.000 title claims abstract description 42
- 230000008569 process Effects 0.000 title claims abstract description 22
- 238000004088 simulation Methods 0.000 title claims abstract description 18
- 239000003054 catalyst Substances 0.000 claims abstract description 76
- 238000009792 diffusion process Methods 0.000 claims abstract description 56
- 239000002245 particle Substances 0.000 claims abstract description 36
- 230000003647 oxidation Effects 0.000 claims abstract description 16
- 238000010801 machine learning Methods 0.000 claims abstract description 14
- 238000012546 transfer Methods 0.000 claims abstract description 14
- 238000009826 distribution Methods 0.000 claims abstract description 7
- 238000006243 chemical reaction Methods 0.000 claims description 46
- 239000007789 gas Substances 0.000 claims description 21
- AKEJUJNQAAGONA-UHFFFAOYSA-N sulfur trioxide Chemical compound O=S(=O)=O AKEJUJNQAAGONA-UHFFFAOYSA-N 0.000 claims description 21
- 239000000203 mixture Substances 0.000 claims description 16
- 238000004364 calculation method Methods 0.000 claims description 9
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 7
- 230000006870 function Effects 0.000 claims description 7
- 229910052760 oxygen Inorganic materials 0.000 claims description 7
- 239000001301 oxygen Substances 0.000 claims description 7
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 claims description 6
- 238000013178 mathematical model Methods 0.000 claims description 6
- 230000008859 change Effects 0.000 claims description 3
- 230000000694 effects Effects 0.000 claims description 3
- 238000012840 feeding operation Methods 0.000 claims description 3
- 229910052757 nitrogen Inorganic materials 0.000 claims description 3
- 239000012798 spherical particle Substances 0.000 abstract 1
- QAOWNCQODCNURD-UHFFFAOYSA-N Sulfuric acid Chemical compound OS(O)(=O)=O QAOWNCQODCNURD-UHFFFAOYSA-N 0.000 description 6
- 230000002159 abnormal effect Effects 0.000 description 5
- 238000006555 catalytic reaction Methods 0.000 description 3
- 238000013528 artificial neural network Methods 0.000 description 2
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- 239000011148 porous material Substances 0.000 description 2
- 238000012805 post-processing Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 229910052720 vanadium Inorganic materials 0.000 description 2
- LEONUFNNVUYDNQ-UHFFFAOYSA-N vanadium atom Chemical compound [V] LEONUFNNVUYDNQ-UHFFFAOYSA-N 0.000 description 2
- 150000001875 compounds Chemical class 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 230000002441 reversible effect Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
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Abstract
A simulation method for the catalytic oxidation reaction process of sulfur dioxide is characterized in that the traditional simulation method mainly simplifies the special-shaped catalyst particles into spherical particles without dead zones, and neglects the influence of the special shapes and the dead zones. Firstly, establishing a special-shaped catalyst reaction-mass transfer-heat transfer three-dimensional model, changing the operation conditions and solving the model to obtain a series of internal diffusion effective factors, and then programming and fitting the internal diffusion effective factors by using a machine learning method and applying the internal diffusion effective factors to a fixed bed reactor model to obtain the temperature and component distribution in a reactor, thereby achieving the aim of helping to accurately and reliably screen out the high-performance sulfur dioxide catalytic oxidation special-shaped catalyst.
Description
Technical Field
The invention relates to the field of simulation of catalysts and reactors, in particular to a simulation method of a catalytic oxidation reaction process of sulfur dioxide.
Background
Sulfuric acid plays an important role in national economy and is a basic raw material for various industrial productions. From 1949 to 2020, the yield of sulfuric acid in China shows a trend of rapid growth. One key reaction for the production of sulfuric acid is the introduction of gaseous SO over a catalyst2By oxidation to SO3. The existing sulfur dioxide catalytic oxidation catalysts on the market are many, and the shapes of the catalysts are different, such as cylindrical, annular and star-shaped annular.
Aiming at the research of the catalytic oxidation reaction process of sulfur dioxide, the current reactor simulation considers more the condition that no dead zone exists in catalyst particles, and does not consider the influence of dead zone caused by the diffusion in the catalyst. And for the sulfur dioxide catalytic oxidation special-shaped catalyst, a large dead zone exists in the particles, and effective factors diffused in the catalyst can be seriously influenced. For a single-particle catalyst model, the traditional solution method of the internal diffusion effective factor is to calculate the abnormal shape according to a sphere, a cylinder or a slice with the same specific external surface area as the particle, or simplify the abnormal shape into a one-dimensional and two-dimensional model, and solve by an orthogonal configuration method. For a fixed bed reactor model, the traditional solution method is to simplify the particle phase into a spherical shape without dead zones and ignore the influence of abnormal shapes and dead zones, so that a method for applying an effective diffusion factor in machine learning programming fitting to a mathematical model of the fixed bed reactor is provided to solve the problem.
Disclosure of Invention
The invention aims to overcome the defects that when the sulfur dioxide catalytic oxidation reaction process is researched in the prior art, the particle phase is generally simplified into a spherical shape without dead zones, the influence of abnormal shapes and dead zones is ignored, and the condition of catalytic reaction in an amplification-stage reactor cannot be accurately predicted.
In order to achieve the purpose, the invention adopts the following technical scheme:
a simulation method of a catalytic oxidation reaction process of sulfur dioxide is characterized by comprising the following steps:
step 1: determining the structure, the size and the operation data of the sulfur dioxide catalytic oxidation special-shaped catalyst, wherein the structure, the size and the operation data comprise the hollow diameter, the inner edge diameter, the outer edge diameter, the height, the particle density and the operation pressure of the catalyst;
step 2: aiming at the catalytic oxidation reaction of sulfur dioxide, making a basic assumption on a single-particle model;
and step 3: establishing a three-dimensional reaction-mass transfer-heat transfer single particle mathematical model according to the structure of the sulfur dioxide catalytic oxidation special-shaped catalyst;
and 4, step 4: carrying out parameter initialization setting on the temperature, the initial composition of sulfur dioxide, the conversion rate, the average radius of catalyst micropores, the porosity and the ratio of the curve factor, and solving and calculating;
and 5: carrying out global calculation on the solved result to obtain effective factors of internal diffusion under various conditions;
step 6: fitting the effective factors of the internal diffusion under various conditions by using a machine learning method; a
And 7: determining the size of the sulfur dioxide catalytic oxidation fixed bed reactor and feeding operation conditions, establishing a mass and energy balance equation, substituting the fitted effective internal diffusion factor into the fixed bed reactor model, solving a differential equation, and outputting the temperature and component distribution in the reactor.
In step 1, the catalyst data ranges are as follows: the hollow diameter is 3-8 mm, the inner edge diameter is 7-12 mm, the outer edge diameter is 12-20 mm, the height is 12-20 mm, and the particle density is 1200-2500 kg/m3The operating pressure is 1-1.4 atm.
In step 2, the basic assumption is that the catalyst particles are in a homogeneous porous structure and have isotropy inside, the external mass transfer and heat transfer resistance is ignored, the gas mixture follows an ideal gas law, and the system is in a stable state.
In step 3, the sulfur dioxide is catalytically oxidized into a multi-component diffusion model, and the effective diffusion coefficient in the model is a function of concentration distribution and temperature distribution. The effective diffusion coefficient consists of a molecular diffusion coefficient and a Knudsen diffusion coefficient, and the molecular diffusion coefficient of the component i in the multi-component gas mixture can adopt a Stefan-Maxwell equation; the effective heat conductivity coefficient of the catalyst particles in the heat transfer equation is mainly influenced by the framework structure of the catalyst and the heat conductivity of gas in holes, and the reaction enthalpy change is a function of temperature; the reaction kinetics equation uses a land Chongqing reaction kinetics model.
In step 4, the parameters are initialized as follows: the temperature is 380-620 ℃, and the temperature difference is 10 ℃; the initial composition of sulfur dioxide is 9.00-11.00%, and the difference of mole fraction intervals is 0.50%; the conversion rate is 0-80.00%, and the interval difference of the conversion rate is 10.00%; the average radius of the catalyst micropores is 188-557 nm; the ratio of the porosity to the kink factor is 0.10-0.20, and the interval difference between the porosity and the kink factor is 0.02; and solving and calculating by adopting a direct iteration method.
In step 5, the global calculation is a volume division of the reaction rate in the whole catalyst particle, wherein the effective factor of internal diffusion is expressed by the following expression:
wherein η represents a catalyst internal diffusion efficiency factor, V represents a catalyst particle volume, r (V) represents a reaction rate in the catalyst particle,
indicating the reaction rate on the outer surface of the catalyst.
And 6, fitting the internal diffusion effective factors under the condition of multiple inputs and single output by adopting a machine learning method.
In step 7, the fixed bed reactor size and feed operating conditions are in industrial and laboratory scale conditions, and the mass and energy balance equations comprise:
and (3) mass balance:
energy balance:
in the formula, XiIndicating the conversion of a gas phase component i, wherein i comprises sulfur dioxide, oxygen, sulfur trioxide, nitrogen, L indicates the height of the fixed bed reactor, pbDenotes the bulk density of the fixed bed reactor, A denotes the cross-sectional area of the fixed bed reactor, riDenotes the rate of reaction of the outer surface of component i, FiRepresents the molar flow of component i, T represents the reactor temperature, Δ H represents the reaction heat effect, CpiRepresents the specific heat capacity of the component i, and eta represents the effective factor of the catalyst internal diffusion
Compared with the prior art, the technical scheme of the invention has the following beneficial effects:
1. on the basis of research on the catalytic oxidation reaction process of sulfur dioxide, strategies are further changed, compared with the traditional method that the particle phase is simplified into a spherical shape without dead zones and influence of irregular shapes and dead zones is ignored, the method adopts finite element simulation to obtain the internal diffusion effective factors of the actual irregular catalyst, the internal diffusion effective factors are programmed and fitted by using a machine learning method and are applied to a fixed bed reactor model, and the simulation result of the model is matched with corresponding single tube experimental data. Because the machine learning includes the effective factors of internal diffusion under the operation conditions of various catalysts, the method can provide guidance for the reaction conditions of different operation conditions in industrial production, and can reflect the catalytic reaction conditions in the reactor more truly, thereby achieving the purpose of helping to accurately and reliably screen out the high-performance sulfur dioxide catalytic oxidation special-shaped catalyst.
2. The simulation speed of the fixed bed reactor model obtained by programming and fitting the internal diffusion effective factors by using a machine learning method is higher, and the simulation speed is more suitable for the actual situation.
Drawings
FIG. 1 is a flow chart of a simulation method of the present invention;
FIG. 2 is a schematic diagram of a sulfur dioxide catalytic oxidation shaped catalyst of a certain enterprise;
FIG. 3 is a schematic view of a one-tenth shaped catalyst;
FIG. 4 is a schematic diagram of meshing;
FIG. 5 is a plot of a fitted regression;
figure 6 is a graph of temperature and conversion as a function of reactor height.
Detailed Description
In order to make the technical problems, technical schemes and beneficial effects to be solved by the invention clearer and more obvious, the following example is described by combining a single-tube-level sulfur dioxide catalytic oxidation reactor experimental device and the used special-shaped catalyst.
The invention aims to carry out modeling calculation from two aspects of a single-particle catalyst and a fixed bed reactor, and a flow chart is shown in figure 1, wherein the simulation process of the single-particle catalyst model comprises the following steps: (1) determining related parameters of the special-shaped catalyst, establishing a geometric model aiming at the special-shaped catalyst, carrying out grid division, and checking the grid independence. (2) A mathematical model describing the catalytic oxidation reaction of sulfur dioxide within the catalyst was determined. (3) Setting initial calculation boundary conditions, solving parameters and variables, and performing iterative solution by parameter initialization setting. (4) And carrying out post-processing on the calculated result. The fixed bed reactor model simulation process comprises: (5) and programming and fitting the effective factors of the internal diffusion under various conditions by using a machine learning method to obtain a model. (6) A mathematical model describing the catalytic oxidation reaction of sulfur dioxide in the reactor is determined. (7) And applying the machine learning fitting model to the fixed bed reactor model to perform programming solution to obtain the catalytic condition in the reactor, and analyzing the result of numerical simulation.
Determining parameters, geometric modeling and mesh division
Referring to the actual size of the sulfur dioxide catalytic oxidation special-shaped catalyst of a certain enterprise, geometric modeling is carried out on the catalyst. To increase the computation speed, one tenth of the model shown in fig. 3 can be selected for computation by using symmetry. The meshing model is shown in fig. 4.
Secondly, establishing a catalyst internal reaction-mass transfer-heat transfer model
(1) Model assumptions
Catalyst particles are of a homogeneous porous structure and have isotropy inside;
neglecting external mass and heat transfer resistance;
the gas mixture follows the ideal gas law;
fourthly, the system is in a stable state.
(2) Diffusion-reaction equation
In the formula (I), the compound is shown in the specification,
representing the effective diffusion coefficient of sulfur dioxide, oxygen and sulfur trioxide components;
representing sulfur dioxide, oxygen, and sulfur trioxide component concentrations; rhocatRepresents the catalyst particle density;
indicating the rate of reaction of sulfur dioxide, oxygen, and sulfur trioxide.
The effective diffusion coefficient consists of a molecular diffusion coefficient and a Knudsen diffusion coefficient, and the molecular diffusion coefficient of the component i in the multi-component gas mixture can be expressed by a Stefan-Maxwell equation as follows:
in the formula, Deff,iRepresents the effective diffusion coefficient of component i; dM,iRepresents the molecular diffusion coefficient of component i; di,jThe binary diffusion coefficients of the components i and j are represented; dK,iRepresents the Knudsen diffusion coefficient of component i; component i represents SO2、O2、SO3(ii) a Component j represents SO2、O2、SO3、N2；εpRepresents the catalyst porosity; v represents the diffusion volume; tau ismRepresents a catalyst kink factor; y represents the component mole fraction; m represents the relative molecular mass; r isaRepresents the average pore diameter of the catalyst particles; n is a radical ofjRepresents the mass transfer flux of the j component; n is a radical ofiRepresents the mass transfer flux of the i component; t represents the temperature of the system; r isgRepresenting the ideal gas constant.
(3) Equation of heat transfer
The effective thermal conductivity of the catalyst particles is mainly influenced by the catalyst framework structure and the thermal conductivity of the gas in the pores; the enthalpy of reaction changes as a function of temperature are expressed as follows:
λeff＝εpλm+(1-εp)λp
λi＝Ai+BiT+CiT2
λp＝0.15
ΔH＝22034.3+5.618T-10.4575×10-3T2+6.4212×10-6T3-1.648×10-9T4cal/mol
in the formula of lambdaeffRepresents the effective thermal conductivity; lambda [ alpha ]iRepresents the pure component gas thermal conductivity; lambda [ alpha ]mExpressing the heat conductivity coefficient of the mixed gas; lambda [ alpha ]pRepresents the thermal conductivity of the catalyst particles; a. thei、Bi、CiIndicating an empirical coefficient.
(4) Reaction kinetics model
The catalytic oxidation reaction of sulfur dioxide is a strong exothermic reversible reaction, and the reaction formula is as follows:
the intrinsic kinetic equation of the catalytic oxidation reaction of sulfur dioxide on the domestic S101 type vanadium catalyst measured by Luchongqing and the like (the intrinsic kinetic model of the oxidation process of sulfur dioxide on the vanadium catalyst) is as follows:
in the formula, k1、k2Representing a kinetic parameter;
respectively, the equilibrium partial pressures of sulfur dioxide, oxygen, and sulfur trioxide.
KpIs the equilibrium constant of the catalytic oxidation reaction of sulfur dioxide, expressed as follows:
lg Kp＝4812.3/T-2.8254lgT+2.284×10-3T-7.012×10-7T2+1.197×10-10T3+2.23
thirdly, setting boundary conditions and parameters
The parameters are set according to industrial conditions, and the concentration temperature of the outer surface of the catalyst is not changed. The parameter initialization settings are as follows: the temperature is 380-620 ℃, and the temperature difference is 10 ℃; the initial composition of sulfur dioxide is 9.00-11.00%, and the difference of mole fraction intervals is 0.50%; the conversion rate is 0-80.00%, and the interval difference of the conversion rate is 10.00%; the average radius of the catalyst micropores is 188-557 nm; the ratio of the porosity to the kink factor is 0.10-0.20, and the interval difference between the porosity and the kink factor is 0.02; and solving and calculating by adopting a direct iteration method.
Fourthly, post-processing the result obtained by calculation
And performing global calculation to perform volume division on the reaction rate in the whole catalyst particles, wherein the internal diffusion effective factor is expressed by the following expression:
wherein eta represents a catalyst internal diffusion efficiency factor and V represents catalyst particlesVolume, r (V), represents the reaction rate within the catalyst particles,
indicating the reaction rate on the outer surface of the catalyst.
Fifthly, fitting the internal diffusion effective factor by machine learning
The machine learning can adopt a neural network programming fitting method, five input layers are respectively reaction temperature, initial mole fraction of sulfur dioxide, conversion rate, average radius of catalyst micropores and ratio of porosity to a curve factor, the output is an internal diffusion effective factor, and a hidden layer is 11 layers. Wherein the proportion of 18866 samples in the training set is 70%, the proportion of 4043 samples in the testing set is 15%, the proportion of 4043 samples in the verification set is 15%, Levenberg-Marquardt is adopted in the training algorithm, mean square error is adopted in the error algorithm, a neural network fitting regression curve is shown in figure 5, and finally the model of the internal diffusion effective factor is obtained.
Sixthly, establishing a mathematical model of the catalytic oxidation reaction of the sulfur dioxide in the reactor
(1) Model assumptions
The temperature and concentration change in the radial direction of the reactor can be ignored;
secondly, adiabatic operation;
③ the gas has constant flow velocity in the catalyst bed;
the gas mixture follows the ideal gas law;
the system is in stable state.
(2) Equation of mass balance
(3) Equation of energy balance
The boundary conditions are as follows:
l is 0, Ci＝Ci0,T＝T0
In the formula, XiIndicating the conversion of a gas phase component i, wherein i comprises sulfur dioxide, oxygen, sulfur trioxide, nitrogen, L indicates the height of the fixed bed reactor, pbDenotes the bulk density of the fixed bed reactor, A denotes the cross-sectional area of the fixed bed reactor, riDenotes the rate of reaction of the outer surface of component i, FiRepresents the molar flow of component i, T represents the reactor temperature, Δ H represents the reaction heat effect, CpiRepresents the specific heat capacity of the component i, and η represents the catalyst internal diffusion effective factor.
(4) Reactor and operating conditions
The size of the fixed bed reactor used in the examples was 0.80m in height and 0.098m in cross-sectional area2The feeding operation conditions are that the inlet temperature of the reactor is 400 ℃, the inlet pressure is 1.08atm and SO2Initial composition mole fraction 10.20%, O2Initial composition mole fraction 10.20%, SO3Initial composition mole fraction 0, N2Initial composition mole fraction of 79.60%, inlet volume flow rate under standard condition of 148.90Nm3/h。
Seven, inner diffusion effective factor model coupling fixed bed reactor model
The effective factors of internal diffusion in the mass and energy balance equation are obtained by machine learning programming and loaded into a fixed bed reactor model, five inputs at different height positions of the reactor are different, and the obtained effective factors of internal diffusion in the output are also different, so that the calculation is accelerated. Finally, the experimental values and the calculated values of the fixed bed reactor were compared to obtain the temperature and conversion rate as a function of the reactor height as shown in FIG. 6. It can be seen that the temperature variation with reactor height substantially coincided with the experimental values, calculated final conversion of 63.05% and experimental value final conversion of 62.67%.
In conclusion, the invention can overcome the defects that the particle phase is simplified into a spherical shape without dead zones, the influence of abnormal shapes and dead zones is neglected, and the condition of catalytic reaction in an amplification-stage reactor cannot be accurately predicted when the sulfur dioxide catalytic oxidation reaction process is researched in the prior art, and provides the simulation method of the sulfur dioxide catalytic oxidation reaction process.
Claims (9)
1. A simulation method of a catalytic oxidation reaction process of sulfur dioxide is characterized by comprising the following steps:
1) determining the structure, the size and the operation pressure of the sulfur dioxide catalytic oxidation special-shaped catalyst;
2) aiming at the catalytic oxidation reaction of sulfur dioxide, making a basic assumption on a single-particle model;
3) establishing a three-dimensional reaction-mass transfer-heat transfer single particle mathematical model according to the structure of the sulfur dioxide catalytic oxidation special-shaped catalyst;
4) carrying out parameter initialization setting on the temperature, the initial composition of sulfur dioxide, the conversion rate, the average radius of micropores of the catalyst, the porosity and the ratio of the curve factor, and solving and calculating;
5) carrying out global calculation on the solved result to obtain effective factors of internal diffusion under various conditions;
6) fitting the effective factors of internal diffusion under various conditions by adopting a machine learning method;
7) determining the size of the sulfur dioxide catalytic oxidation fixed bed reactor and feeding operation conditions, establishing a mass and energy balance equation, substituting the fitted effective internal diffusion factor into the fixed bed reactor model, solving a differential equation, and outputting the temperature and component distribution in the reactor.
2. A method for simulating a catalytic oxidation reaction process for sulfur dioxide according to claim 1, comprising: the structure and the size of the sulfur dioxide catalytic oxidation special-shaped catalyst comprise the hollow diameter, the inner edge diameter, the outer edge diameter, the height and the particle density of the catalyst.
3. A method for simulating a catalytic oxidation reaction process for sulfur dioxide according to claim 2, comprising: the hollow diameter is 3-8 mm, the inner edge diameter is 7-12 mm,the diameter of the outer edge is 12-20 mm, the height is 12-20 mm, and the particle density is 1200-2500 kg/m3The operating pressure is 1-1.4 atm.
4. A method for simulating a catalytic oxidation reaction process for sulfur dioxide according to claim 1, comprising: in step 2), the basic assumption is that the catalyst particles are in a homogeneous porous structure and have isotropy inside, the external mass transfer and heat transfer resistance is ignored, the gas mixture follows an ideal gas law, and the system is in a stable state.
5. A method for simulating a catalytic oxidation reaction process for sulfur dioxide according to claim 1, comprising: in the step 3), sulfur dioxide is catalytically oxidized into a multi-component diffusion model, and the effective diffusion coefficient in the model is a function of concentration distribution and temperature distribution; the effective diffusion coefficient consists of a molecular diffusion coefficient and a Knudsen diffusion coefficient, and the molecular diffusion coefficient of the component i in the multi-component gas mixture can adopt a Stefan-Maxwell equation; the effective heat conductivity coefficient of the catalyst particles in the heat transfer equation is mainly influenced by the framework structure of the catalyst and the heat conductivity of gas in holes, and the reaction enthalpy change is a function of temperature; the reaction kinetics equation uses a land Chongqing reaction kinetics model.
6. The method for simulating a catalytic oxidation reaction process of sulfur dioxide as claimed in claim 1, wherein in step 4), the initialization settings of the parameters are as follows: the temperature is 380-620 ℃, and the temperature difference is 10 ℃; the initial composition of sulfur dioxide is 9.00-11.00%, and the difference of mole fraction intervals is 0.50%; the conversion rate is 0-80.00%, and the interval difference of the conversion rate is 10.00%; the average radius of micropores of the catalyst is 188-557 nm; the ratio of the porosity to the kink factor is 0.10-0.20, and the interval difference between the porosity and the kink factor is 0.02; and solving and calculating by adopting a direct iteration method.
7. The method for simulating a catalytic oxidation reaction process of sulfur dioxide as claimed in claim 1, wherein in step 5), the global calculation is performed by performing a volume division on the reaction rate in the whole catalyst particles, wherein the internal diffusion effective factor is expressed by the following expression:
wherein η represents a catalyst internal diffusion efficiency factor, V represents a catalyst particle volume, r (V) represents a reaction rate in the catalyst particle,
indicating the reaction rate on the outer surface of the catalyst.
8. The method for simulating a catalytic oxidation reaction process of sulfur dioxide according to claim 1, wherein: and 6), fitting the internal diffusion effective factors under the condition of multiple inputs and single output by adopting a machine learning method.
9. The method for simulating a catalytic oxidation reaction process of sulfur dioxide as claimed in claim 1, wherein in step 7, the fixed bed reactor size and the feed operation conditions are industrial and laboratory scale conditions, and the mass and energy balance equation comprises:
and (3) mass balance:
energy balance:
in the formula, XiIndicating the conversion of a gas phase component i, wherein i comprises sulfur dioxide, oxygen, sulfur trioxide, nitrogen, L indicates the height of the fixed bed reactor, pbDenotes the bulk density of the fixed bed reactor, A denotes the cross-sectional area of the fixed bed reactor, riDenotes the rate of reaction of the outer surface of component i, FiRepresents the molar flow of component i, T represents the reactor temperature, Δ H represents the reaction heat effect, CpiRepresents the specific heat capacity of the component i, and η represents the catalyst internal diffusion effective factor.
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| CN118496195A (en) * | 2024-07-17 | 2024-08-16 | 格润科技(大连)有限责任公司 | Preparation process of vinyl sulfate based on oxidation of vinyl sulfite |
| CN118496195B (en) * | 2024-07-17 | 2024-10-25 | 格润科技(大连)有限责任公司 | Preparation process of vinyl sulfate based on oxidation of vinyl sulfite |
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| CN118496195A (en) * | 2024-07-17 | 2024-08-16 | 格润科技(大连)有限责任公司 | Preparation process of vinyl sulfate based on oxidation of vinyl sulfite |
| CN118496195B (en) * | 2024-07-17 | 2024-10-25 | 格润科技(大连)有限责任公司 | Preparation process of vinyl sulfate based on oxidation of vinyl sulfite |
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