A real-time optimization method for continuous catalytic reforming reaction process with optimization trajectory

Abstract

The present invention discloses a real-time optimization method for a continuous catalytic reforming reaction process with an optimized trajectory. This method considers multiple constraints during the optimization process, uses iterative optimization to obtain all reactor inlet temperatures, combines the results to obtain the optimized trajectory, and executes the optimized trajectory in a control system. This method avoids excessive changes in the control system execution and achieves stable production during the RTO process. This is of great value for improving the economic benefits of the continuous catalytic reforming reaction process and stabilizing production operations.

Description

Technical Field

[0001] The present invention relates to the field of real-time optimization in the production process of a refining enterprise, and specifically to a method for performing real-time optimization on a continuous catalytic reforming reaction process to improve production efficiency. Background Art

[0002] Continuous catalytic reforming is generally optimized in real time (RTO) by adjusting the reactor inlet temperature. A typical continuous catalytic reforming process has four reactors. Generally, the continuous reforming process will be reasonably divided and lumped according to the reaction mechanism to establish a model. This model describes the relationship between the molar flow rates of raw materials and product components under the input parameters such as the reactor inlet temperature. RTO is often calculated based on this model. In order to ensure stable production during the RTO process, many constraints need to be considered. It should be pointed out that the conventional RTO solution often gives the final target and is directly handed over to the control system for execution. However, in the process of the control system executing the transition from the current state to the new target, the final target may be too far away from the current state, which may easily lead to excessive adjustment of the control parameters, resulting in large fluctuations in product quality, thereby affecting safe production and economic benefits.

[0003] If the actual execution of the control system can be taken into account during optimization, process and device-related constraints can be added to the RTO process, and a reasonable optimization trajectory can be given so that the control system can be gradually adjusted into place while satisfying the constraints, it will help the continuous catalytic reforming reaction to proceed safely and smoothly, and improve the economic benefits of the enterprise. Summary of the Invention

[0004] To address the above issues, the present invention discloses a real-time optimization method for a continuous catalytic reforming reaction process with an optimization trajectory. This method obtains the current raw material price and the price of each lumped component in the product to form an objective function. It considers the reaction heater temperature control adjustment range, the range of the reactor inlet and outlet temperature difference, the range of the difference between reactor inlet temperatures, the normal range of reactor inlet temperature, the range of the catalyst carbon deposition rate at the end reactor outlet, and the product property requirements as optimization constraints. All reactor inlet temperatures are selected as optimization decision variables, and a particle swarm algorithm is used for iterative optimization. Ultimately, the optimized trajectory of all reactor inlet temperatures is obtained, and the optimized trajectory is then sent to the control system for execution. The method includes the following steps:

[0005] 1) Obtain the aggregate price p, fuel price q and energy cost EC to form the objective function:

[0006]

[0007] Where T in represents the inlet temperature of the continuous reforming reactor, n represents the number of continuous reforming reactors, M krepresents the molar mass of the kth lumped product, g / mol, Y k represents the kth lumped molar flow rate in the product, kmol / h, X k represents the kth lumped molar flow rate in the feedstock, a represents the number of lumps divided by the continuous catalytic reforming model, β represents the net calorific value of the fuel, EC represents the energy required to heat all reactors to the target temperature, and η is the efficiency of converting fuel into energy;

[0008] 2) Obtain the continuous catalytic reforming process and device constraints to form the following constraints:

[0009] 2-1) Obtain the lower limit C1 of the reaction heater temperature adjustment range L and upper limit C1 U The following constraints are formed:

[0010] C1 L ≤T int -T int-1 ≤C1 U ,t=1,2,...,m

[0011] Where T int -T int-1 represents the difference in reactor inlet temperature between the tth and t-1th optimizations, and m represents the number of optimization iterations;

[0012] 2-2) Obtain the lower limit C2 of the reactor inlet and outlet temperature difference L and upper limit C2 U The following constraints are formed:

[0013] C2 L ≤ΔT t -ΔT0≤C2 U

[0014] Where ΔT0=T in 0-T out 0 represents the difference between the reactor inlet and outlet temperatures before optimization, ΔT t =T int -T outt represents the difference between the inlet and outlet temperatures of the optimized reactor for the tth time;

[0015] 2-3) Obtain the lower limit C3 of the difference between the reactor inlet temperatures L and upper limit C3 U The following constraints are formed:

[0016] C3 L ≤|T ini -T inj |≤C3 U ,i≠j,i=1,2,...,n,j=1,2,...,n

[0017] 2-4) Obtain the lower limit of normal change of reactor inlet temperature C4 L and upper limit C4 U The following constraints are formed:

[0018] C4 L ≤T int ≤C4 U

[0019] 2-5) Obtain the lower limit C5 of the carbon deposition rate CK of the catalyst at the outlet of the final reactor L and upper limit C5 U The following constraints are formed:

[0020] C5 L ≤CK≤C5 U

[0021] 2-6) Obtaining the properties of the continuous reforming reaction products requires the following constraints:

[0022]

[0023] Where Y l represents the total molar flow rate of alkanes and cycloalkanes with carbon 8 or above in the product, and o represents the upper limit of the constraint;

[0024] 3) Taking the inlet temperature of all reactors of continuous catalytic reforming as the decision variable, the iterative solution is performed under the above objective function and constraint conditions to obtain the optimization result of each step, where the tth optimization result is T′ int ={T′ in1 ,T′ in2 ,...,T′ inn};

[0025] 4) Combine the iterative results of each step to obtain the optimized trajectory;

[0026] 5) Send the optimized trajectory to the lower-level control system for execution.

[0027] Beneficial effects:

[0028] The present invention discloses a real-time optimization method for a continuous catalytic reforming reaction process with an optimized trajectory. This method considers numerous process and device constraints during the optimization process, obtains the optimized trajectory through iterative calculation, and then executes the optimized trajectory on a control system. This method avoids excessive changes in the control system execution and achieves stable production during the RTO process. This method is of great value for improving the economic benefits of the continuous catalytic reforming reaction process and stabilizing production operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1This is a flow chart of a continuous catalytic reforming reaction process optimization method of the present invention;

[0030] Figure 2 Optimization trajectory diagram of the inlet temperature of the four reactors in the embodiment;

[0031] Figure 3 This is a graph showing changes in the objective function value during the optimization process of the embodiment;

[0032] Figure 4 This is a graph showing the change in the reactor inlet and outlet temperature difference during the optimization process of the embodiment;

[0033] Figure 5 This is a graph showing the difference in reactor inlet temperatures during the optimization process of the embodiment;

[0034] Figure 6 This is a graph showing the change in carbon deposition rate of the catalyst at the final outlet during the optimization process of the embodiment;

[0035] Figure 7 This is a diagram showing changes in product properties during the optimization process of the embodiment. DETAILED DESCRIPTION

[0036] The present invention will be further described below in conjunction with the accompanying drawings and specific examples, and the implementation effect of this method in optimizing the continuous catalytic reforming reaction process will be described by a specific operation flow. This embodiment is implemented under the premise of the technical solution of the present invention, but the scope of protection of the present invention is not limited to the following examples.

[0037] The present invention takes the continuous catalytic reforming unit of a certain oil refinery as an example. The unit is a typical continuous catalytic reforming process with n=4 reactors. The raw material for continuous catalytic reforming is naphtha, and the products are mainly light aromatics and by-product hydrogen, etc. The oil refinery requires that the total molar flow rate of alkanes and cycloalkanes with carbon 8 and above in the product should not exceed 3%. The constraints for achieving stable production requirements are shown in Table 1. A total of 576 samples from June 11 to June 13, 2020 were selected for analysis and modeling, and the data at 13:20 on June 12, 2020 were used as the basis for optimization. The role of this method in the RTO of the continuous catalytic reforming process is introduced.

[0038] Table 1 Stable production constraints

[0039] name Lower limit Upper limit Heater temperature adjustment range (℃) -0.5 0.5 Reactor inlet and outlet temperature difference (℃) -3.0 3.0 Difference between reactor inlet temperatures (℃) 0 2.0 Reactor inlet temperature (℃) 510.0 530.0 Carbon deposition rate of catalyst at the final outlet (%) 0 3.6％ Total molar flow rate of alkanes and cycloalkanes with carbon content of 8 or more (%) 0 3.0％

[0040] The implementation process of this method is as follows Figure 1 As shown, the implementation steps are as follows:

[0041] 1) Obtain the aggregate price p as shown in Table 2, the fuel price q = 3.2 yuan / kg and the energy cost EC, and construct the objective function:

[0042]

[0043] Where T in represents the inlet temperature of the continuous reforming reactor, n represents the number of continuous reforming reactors, M k represents the molar mass of the kth lumped product, g / mol, Y k represents the kth lumped molar flow rate in the product, kmol / h, X k represents the molar flow rate of the kth lumped mass in the feedstock, a represents the number of lumped masses divided by the continuous catalytic reforming model, β represents the net calorific value of the fuel, EC represents the energy required to heat all reactors to the target temperature, and η = 60% is the efficiency of fuel conversion to energy;

[0044] Table 2: Lump sum prices

[0045]

[0046] 2) Obtain the continuous catalytic reforming process and device constraints to form the following constraints:

[0047] 2-1) Get the lower limit C1 of the heater temperature adjustment range L =-0.5 and upper limit C1 U = 0.5 constitutes the following constraint:

[0048] C1 L ≤T int -T int-1 ≤C1 U ,t=1,2,...,m

[0049] Where T int -T int-1 represents the difference in reactor inlet temperature between the tth and t-1th optimizations, and m represents the number of optimization iterations;

[0050] 2-2) Obtain the lower limit C2 of the reactor inlet and outlet temperature difference L = -3.0 and upper limit C2 U =3.0 constitutes the following constraint:

[0051] C2 L ≤ΔT t -ΔT0≤C2 U

[0052] Where ΔT0=T in 0-T out 0 represents the difference between the reactor inlet and outlet temperatures before optimization, ΔT t =T int -T outt represents the difference between the inlet and outlet temperatures of the optimized reactor for the tth time;

[0053] 2-3) Obtain the lower limit C3 of the difference between the reactor inlet temperatures L =0 and upper limit C3 U =2.0 constitutes the following constraint:

[0054] C3 L ≤|T ini -T inj |≤C3 U ,i≠j,i=1,2,3,4,j=1,2,3,4

[0055] 2-4) Obtain the lower limit of normal change of reactor inlet temperature C4 L =510.0 and upper limit C4 U =530.0 constitutes the following constraint:

[0056] C4 L ≤T int ≤C4 U

[0057] 2-5) Obtain the lower limit C5 of the carbon deposition rate CK of the catalyst at the outlet of the final reactor L =0 and upper limit C5 U =3.6 constitutes the following constraint:

[0058] C5 L ≤CK≤C5 U

[0059] 2-6) Obtaining the properties of the continuous reforming reaction products requires the following constraints:

[0060]

[0061] Where Y l represents the total molar flow rate of the product, including the total alkanes and cycloalkanes with carbon content of 8 or more in the product, and o = 3.0% represents the upper limit of the constraint;

[0062] 3) Under the constraints of continuous catalytic reforming process and equipment, the inlet temperatures of the four continuous catalytic reforming reactors are used as decision variables and the objective function in step (1) is used as the target. The particle swarm algorithm is used to perform iterative optimization calculations to obtain the optimization results of each step, that is, the inlet temperatures of the four reactors. The particle swarm algorithm uses the following formula to calculate the new position:

[0063]

[0064] Where V i,(t+1) represents the temperature velocity at the inlet of the i-th reactor in the t+1-th optimization of the particle swarm calculation. In this example, θ=10, which indicates the threshold at which the optimization falls into the local optimum. T i,gbestis the optimal value of the inlet temperature of the i-th reactor, ζ=1.5, u,v obey the normal distribution, σ v =1, Γ is the standard Gamma function;

[0065] 4) Combine the optimization results of each step (directly connect the optimization results of each step) to obtain the optimization trajectory. The final inlet temperature optimization trajectory is as follows: Figure 2 As shown, the objective function value changes as Figure 3 As shown, the reactor inlet and outlet temperature difference changes as Figure 4 As shown, the difference between the reactor inlet temperatures changes as Figure 5 As shown, the carbon deposition rate of the catalyst at the end of the reactor outlet changes as follows Figure 6 As shown, the product properties change as Figure 7 shown.

[0066] 5) Send the optimized trajectory to the control system below for execution.

[0067] Depend on Figure 2 It can be seen that the inlet temperatures of the four reactors change steadily, and each adjustment satisfies the temperature adjustment range constraint of the reaction heater, and Figure 4 、 Figure 5 、 Figure 6 and Figure 7 It can be seen that all constraints are strictly satisfied during the reactor inlet temperature adjustment process. Figure 3 It can be seen that the production efficiency has been improved. Figure 2 The optimization trajectory of the inlet temperature of the intermediate reactor is handed over to the control system for execution, which can achieve stable production, meet product quality requirements and improve production efficiency.

Claims

1. A real-time optimization method for a continuous catalytic reforming reaction process with an optimized trajectory, characterized in that With the goal of maximizing enterprise production profits, and taking into account the constraints of the continuous catalytic reforming process and equipment, an iterative optimization calculation is performed based on the continuous catalytic reforming mechanism model. The optimization results are combined into a trajectory and handed over to the lower-level control system for execution. The following steps are included: 1) Obtain the aggregate price p, fuel price q and energy cost EC to form the objective function: Where T in represents the inlet temperature of the continuous catalytic reforming reactor, n represents the number of continuous catalytic reforming reactors, M k represents the molar mass of the kth lumped product, g / mol; Y k represents the kth lumped molar flow rate in the product, kmol / h; X k represents the kth lumped molar flow rate in the feedstock, a represents the number of lumps divided by the continuous catalytic reforming model, β represents the net calorific value of the fuel, EC represents the energy required to heat all reactors to the target temperature, and η is the efficiency of converting fuel into energy; 2) Obtain the continuous catalytic reforming process and device constraints to form the following constraints: C L ≤g≤C U Where g represents the continuous catalytic reforming process or device parameters, C L Represents the lower limit of parameter constraint, C U Indicates the upper limit of parameter constraint; 3) Taking the inlet temperature of all reactors of continuous catalytic reforming as the decision variable, the iterative solution is performed under the above objective function and constraint conditions to obtain the optimization result of each step, where the tth optimization result is T′ int ={T′ in1 ,T′ in2 ,...,T′ inn }; 4) Combine the iterative results of each step to obtain the optimized trajectory; 5) Send the optimized trajectory to the lower-level control system for execution.

2. The method for real-time optimization of a continuous catalytic reforming reaction process with an optimized trajectory according to claim 1, characterized in that Step 2) includes the lower limit C1 of the reaction heater temperature adjustment range L and upper limit C1 U constraint: C1 L ≤T int -T int-1 ≤C1 U ,t=1,2,...,m Where T int -T int-1 represents the difference in reactor inlet temperature between the t-th and t-1-th optimizations, and m represents the number of optimization iterations.

3. The method for real-time optimization of a continuous catalytic reforming reaction process with an optimized trajectory according to claim 1, characterized in that Step 2) includes the lower limit C2 of the reactor inlet and outlet temperature difference L and upper limit C2 U constraint: C2 L ≤ΔT t -ΔT0≤C2 U Where ΔT0=T in 0-T out 0 represents the difference between the reactor inlet and outlet temperatures before optimization, ΔT t =T int -T outt Represents the difference between the inlet and outlet temperatures of the optimized reactor for the tth time.

4. The method for real-time optimization of a continuous catalytic reforming reaction process with an optimized trajectory according to claim 1, characterized in that Step 2) includes the lower limit C3 of the difference between the reactor inlet temperatures L and upper limit C3 U constraint: C3 L ≤|T ini -T inj |≤C3 U ,i≠j,i=1,2,...,n,j=1,2,...,n Where |T ini -T inj | represents the absolute value of the difference between the inlet temperatures of the i-th and j-th reactors.

5. The method for real-time optimization of a continuous catalytic reforming reaction process with an optimized trajectory according to claim 1, characterized in that Step 2) includes the lower limit of normal change of reactor inlet temperature C4 L and upper limit C4 U constraint: C4 L ≤T int ≤C4 U Where T int Represents the t-th optimized reactor inlet temperature value.

6. The method for real-time optimization of a continuous catalytic reforming reaction process with an optimized trajectory according to claim 1, characterized in that Step 2) includes the lower limit C5 of the carbon deposition rate CK of the catalyst at the end of the reaction L and upper limit C5 U constraint: C5 L ≤CK≤C5 U , 7. The method for real-time optimization of a continuous catalytic reforming reaction process with an optimized trajectory according to claim 1, characterized in that Step 2) includes the requirements and constraints for the properties of the continuous reforming reaction products: Where Y l Indicates the total molar flow rate of alkanes and cycloalkanes with carbon 8 or above in the product, M l represents the total molar mass of alkanes and cycloalkanes with carbon numbers of 8 or more in the product, and o represents the upper limit of the constraint.

8. The method for real-time optimization of a continuous catalytic reforming reaction process with an optimized trajectory according to claim 1, characterized in that In step 3), under the constraints of the continuous catalytic reforming process and device, the inlet temperature of all continuous catalytic reforming reactors is used as the decision variable, and the objective function in step 1) is used as the target. The particle swarm algorithm is used to perform iterative optimization calculations to obtain the optimization results of each step.

9. The method for real-time optimization of a continuous catalytic reforming reaction process with an optimized trajectory according to claim 1, characterized in that The particle swarm algorithm calculates the new position using the following formula: Where V i,(t+1) represents the temperature velocity of the inlet of the i-th reactor in the t+1-th optimization of the particle swarm calculation, θ represents the threshold value of the optimization falling into the local optimum, T i,gbest is the optimal value of the inlet temperature of the i-th reactor, ζ=1.5, u,v obey the normal distribution, σ v =1, Γ is the standard Gamma function,

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