A method for controlling the temperature of desorbed gas from a zeolite wheel

Abstract

The present application relates to the field of thermal control, and specifically discloses a kind of zeolite runner desorption gas temperature control method, comprising the following steps: S1, based on the dynamic mathematical model equation of zeolite runner CO system component of zeolite runner CO system is established, including the dynamic mechanism model of zeolite runner front desorption gas temperature and the dynamic mechanism model of CO furnace temperature T c Etc., connect the dynamic mathematical model of each component established;S2, determine the input parameter of system under design condition.The zeolite runner desorption gas temperature control method, through the desorption gas temperature control method designed by model predictive control algorithm with disturbance suppression, in zeolite runner CO system, it can be according to the disturbance situation of desorption gas import, change incinerator outlet flue gas temperature in advance, reduce the influence of disturbance on desorption gas outlet temperature, solve the problem that the outlet temperature of heat exchanger desorption gas cannot be stabilized at the desired value in the prior art.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of thermal control, and particularly relates to a zeolite wheel desorption gas temperature control method. BACKGROUND

[0002] Volatile organic compounds (VOCs) are common pollutants discharged by petrochemical, light industry, plastic, printing, and coating industries. Organic waste gas often contains hydrocarbon compounds, oxygen-containing organic compounds, nitrogen-, sulfur-, halogen-, and phosphorus-containing organic compounds, etc. If these waste gases are not treated and directly discharged into the atmosphere, they will cause serious environmental pollution.

[0003] In recent years, zeolite wheel adsorption concentration technology has been widely applied in the treatment of low-concentration and large-volume industrial organic waste gas. In the desorption process of the zeolite wheel, the temperature of the desorption gas determines the zeolite adsorption and desorption efficiency. If the temperature is too high, the zeolite is not easy to be cooled, which affects the adsorption effect of the adsorption zone; if the temperature is too low, the organic matter is not easy to be desorbed, and the desorption efficiency of the wheel is reduced. Therefore, stable and effective control of the desorption gas temperature plays a crucial role in the economy and stability of the entire system. At present, it is generally believed that the optimal desorption temperature is 200℃.

[0004] In order to obtain 200℃ desorption gas, reduce energy consumption, and save costs, a heat exchanger is arranged in front of the zeolite wheel system. The heat exchanger recovers the heat of the flue gas at the outlet of the catalytic incinerator, heats the desorption gas to about 200℃, and then sends the desorption gas to the desorption zone of the zeolite wheel. However, due to fluctuations in the flow rate and initial temperature of the desorption gas during operation, as well as fluctuations in the concentration of the waste gas and the temperature of the flue gas after catalytic incineration, the temperature of the desorption gas after heat exchange also fluctuates. The outlet temperature of the desorption gas of the heat exchanger cannot be stabilized at 200℃. SUMMARY

[0005] The purpose of the present application is to solve the problem that the outlet temperature of the desorption gas of the heat exchanger cannot be stabilized at the desired value in the prior art by providing a zeolite wheel desorption gas temperature control method.

[0006] To achieve the above-mentioned purpose, the present application provides a zeolite wheel desorption gas temperature control method, which comprises the following steps:

[0007] S1, a dynamic mathematical model equation of a zeolite wheel CO system component is established based on the zeolite wheel CO system, including a dynamic mechanism model of the desorption gas temperature before the zeolite wheel and a dynamic mechanism model of the CO furnace temperature T c , etc. The established dynamic mathematical models of each component are connected;

[0008] S2, the input parameters of the system under the design condition are determined, including: the power of the electric heater uw VOC exhaust gas concentration V o Desorption air volume G o Exhaust gas inlet temperature T o etc.

[0009] S3, design system control matrix, select desorption gas temperature as controlled variable y1, select electric heater power as control variable u1; select VOC exhaust gas concentration V o , desorption air volume G o , exhaust gas inlet temperature T o as disturbance variable v1, v2, v3, and the transfer function between the input variable, disturbance variable and output variable obtained by identifying the dynamic model equation established in S1 is written in the form of state space as follows:

[0010]

[0011] In the formula, x(k) is the internal state variable of the system; u(k) is the input variable of the system: electric heater power, y(k) is the output variable of the system: desorption gas temperature; d(k) is the measurable disturbance variable: exhaust gas concentration, desorption air volume and exhaust gas inlet temperature; A, B u , B d , C are the corresponding coefficient matrices;

[0012] S4, change the model into incremental form:

[0013]

[0014] After subtracting formula 4-1 from formula 3-1, the following formula can be obtained:

[0015]

[0016] In the formula, Δx(k+1) = x(k+1)-x(k); Δu(k) = u(k)-x(k-1); Δd(k) = d(k)-d(k-1);

[0017] S5, take the current time k as the starting point of sampling, design the prediction time domain as p and the control time domain as m, if the output of the system at future p (p≥m) time points is needed to be predicted, the system state at future p time points under the action of u(k), u(k+1), … u(k+m-1) can be predicted by the model, then the predicted output of the system at future p steps can be represented by the following formula:

[0018] Y p = S x Δx(k) + S d Δd(k) + S u ΔU(k) (5-3)

[0019] In the formula,

[0020]

[0021]

[0022] S6, the system is optimized, and the optimization performance index can be expressed as the following formula:

[0023]

[0024]

[0025] In the formula, W(k) is the vector representation of the output expectation value;Q, R are output and control weighting matrices respectively, T e is the highest flue gas temperature, T l is the minimum CO furnace temperature;The predicted model Y p of the output variable is substituted into the above formula, and the minimum value of the optimization performance index J(k) is solved under the condition of the constraint equation, and the control amount Δu(k+1) of the next moment is output;

[0026] S7: the output control amount u1 of the predictive control system is executed to the actuator, and S3-S6 are repeated in each sampling period to make the outlet temperature of the heat exchanger desorption gas stable at about 200 DEG C.

[0027] Beneficial effects: the existing zeolite runner CO system has many components, and the desorption gas temperature is affected by many factors. The present application adopts a model predictive control algorithm with disturbance suppression, and designs a desorption gas temperature control method. When the desorption gas temperature is low, an additional electric heater is not needed before the zeolite runner;When the desorption gas temperature is too high, the bypass does not need to be opened, which causes heat waste;At the same time, the catalytic temperature of the incinerator is taken as a constraint condition to avoid the influence of the large power adjustment amount of the electric heater on the normal work of the catalyst, improve the operation stability and economy of the system;By adjusting the power of the electric heater before the catalytic incinerator, the outlet flue gas temperature of the incinerator is adjusted, so that the desorption gas temperature is controlled at the expected value, which can effectively solve the system constraint problem, avoid the low temperature in the catalytic incinerator, and avoid the high exhaust gas temperature. Compared with the traditional temperature control strategy, the present application can more stably and effectively control the desorption gas temperature, reduce the system energy consumption, avoid adding process equipment, and reduce the equipment land use, etc. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 It is the structure schematic diagram of the zeolite runner CO system of the embodiment of the present application. DETAILED DESCRIPTION

[0029] The present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments. These embodiments are implemented based on the technical solutions of the present invention, and it should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention.

[0030] The zeolite rotor CO system mainly consists of components such as a catalytic incinerator, an electric heater, a rotor, and a heat exchanger. The system structure is as follows: Figure 1 As shown. A method for controlling the temperature of desorbed gas in a zeolite rotor, based on a zeolite rotor CO system, includes the following steps:

[0031] S1. Establish dynamic mathematical model equations for the components of the zeolite rotor CO system, including the dynamic mechanism model of the desorbed gas temperature before the zeolite rotor and the CO furnace temperature T. c The dynamic mechanism model, etc., will connect the established dynamic mathematical models of each component.

[0032] Among them, the lumped parameter method can be used to establish a dynamic mathematical model of heat exchange components (heat exchangers, rotor cooling zones, etc.), which mainly consists of energy balance equations and momentum equations:

[0033] Q b =KA(T) j -T2)

[0034]

[0035]

[0036]

[0037] In the formula, Q1 and Q b These represent the heat transfer on the hot side and the heat transfer through the metal wall of the heat exchanger, respectively; K is the heat transfer film coefficient; A is the heat transfer area; D1 and A2 are the inlet and outlet flow rates of the cold-side working fluid, respectively; T1 and T2 are the inlet and outlet temperatures of the cold-side working fluid, respectively; T j V0 is the heat exchanger wall temperature; V0 is the equivalent volume of the cold-side working fluid channel; c j M is the specific heat capacity of the metal; j Let p1 be the mass of the metal, in kg; p2 be the inlet and outlet pressures of the working fluid, respectively. The mathematical model for the working fluid on the hot side is consistent with that on the cold side.

[0038] The mathematical model of a CO incinerator can be simplified to an energy balance equation:

[0039]

[0040] In the formula, M g h is the total mass of exhaust gas inside the furnace. in h out These are the enthalpy of imported and exported exhaust gases, respectively; Cin , G out are the inlet and outlet exhaust gas flow rate respectively; s r is a time constant used to modify the inertial time of the furnace during model tuning; η B is the combustion efficiency, which can be considered as inversely proportional to the concentration of combustible components in the exhaust gas.

[0041] The mathematical model of the electric heater can be simplified as an energy balance equation:

[0042]

[0043] In the equation, M f is the total mass of the exhaust gas in the control volume of the electric heater; h f1 , h f2 are the inlet and outlet exhaust gas enthalpy respectively; η w is the electric heater efficiency; u w is the electric heater power.

[0044] The pipe model considers that there is no heat exchange such as heat dissipation, and only a pressure-flow channel needs to be established, with the resistance being lumped at the inlet. The pressure and density in the pipe are equal to the pressure and density at the outlet. The dynamic equation of the model is shown below:

[0045]

[0046]

[0047] In the equation, ξ is the resistance coefficient of the pipe, which can be treated as a constant in engineering if the working condition does not change much; and V is the volume of the pipe.

[0048] S2, determine the input parameters of the system under the design condition, including: the electric heater power u w , the VOC exhaust gas concentration V o , the desorption air volume G o , the exhaust gas inlet temperature T o , etc.

[0049] S3, design the system control matrix, select the desorption gas temperature as the controlled variable y1, and select the electric heater power as the control variable u1; select the VOC exhaust gas concentration V o , the desorption air volume G o , and the exhaust gas inlet temperature T o as the disturbance variables v1, v2, and v3. The transfer function between the input variables, the disturbance variables, and the output variables is obtained by identifying the dynamic model equation established in S1, and is written in the form of state space as follows:

[0050]

[0051] Wherein, x(k) is the internal state variable of the system and can be measured in real time; u(k) is the input variable of the system; y(k) is the output variable of the system; d(k) is the measurable disturbance variable; A, B u , B d , C are the corresponding coefficient matrices.

[0052] Based on the above established dynamic mathematical model of each component, the transfer function between the input and output is identified. The principle of the system identification method adopted is as follows:

[0053] Suppose that the model transfer function to be identified can be decomposed as:

[0054] When the input is a unit step function, the Laplace inverse transform is performed on the above formula to obtain the unit step response of the system in the time domain as:

[0055]

[0056] That is,

[0057]

[0058] Let w1=kw2(k>1) to obtain

[0059]

[0060] Take the logarithm with base e on both sides of the above formula to obtain

[0061]

[0062] When t→∞, then the above formula is simplified as

[0063]

[0064] The form of the formula satisfies the linear equation

[0065] y * (t)=at+b

[0066] Wherein y * (t)=ln[1-y(t)], a=-w2

[0067] The fitting of the straight line is realized by the least square algorithm to obtain the values of a and b, that is, the values of w1 and w2, and then the transfer function of the model can be obtained. For a higher order transfer function, the identification principle is similar to the above.

[0068] S4, change the model into an incremental form:

[0069]

[0070] Subtracting equation 4-1 from equation 3-1 gives:

[0071]

[0072] where Δx(k+1) = x(k+1) - x(k); Δu(k) = u(k) - x(k-1); Δd(k) = d(k) - d(k-1);

[0073] S5, taking the current time k as the sampling starting point, designing the prediction time domain as p and the control time domain as m, if the outputs of the system at future p (p≥m) time points under the action of u(k), u(k+1),... u(k+m-1) are needed to be predicted, the system states at future p time points under the action of u(k), u(k+1),... u(k+m-1) can be predicted by the model:

[0074]

[0075] where Δx(k+1|k) represents the state observation of the k+1 time point at the k time point, and further conversion can obtain the expression of the output variable:

[0076]

[0077]

[0078] The following vectors are defined:

[0079]

[0080] Then the prediction output of the system at future p steps can be represented by the following formula:

[0081] Y p = S x Δx(k) + S d Δd(k) + S u ΔU(k) (5-3) where,

[0082]

[0083]

[0084] For the optimization problem, two aspects are considered: one is to make the state of the controlled object under the control action at future P time points as soon as possible to follow the expected value, and the other is to reduce the control amplitude as much as possible and suppress the violent change of the control action. The optimization performance index can be expressed as the following formula:

[0085]

[0086]

[0087] wherein W(k) is a vector representation of the output expectation value; Q, R are output and control weighting matrices, respectively, T e is the highest flue gas temperature, T l is the minimum CO furnace temperature. The predicted model Y p is substituted into the above equation, and the minimum value of the optimization performance index J(k) is solved under the condition of the constraint equation, and the optimal control amount Δu(k+1) of the next time is output.

[0088] S7: The control amount u1 of the prediction control system is output to the actuator, and S3-S6 are repeated in each subsequent sampling period, so that the outlet temperature of the heat exchanger desorption gas is stabilized at about 200°C.

[0089] The above only describes the preferred embodiments of the present application, and it should be noted that other non-specifically described parts belong to the prior art or common knowledge for ordinary skilled in the art. Without departing from the principles of the present application, several improvements and refinements can also be made, which should be considered as the protection scope of the present application.

Claims

1. A method for controlling the temperature of desorbed gas in a zeolite rotor, characterized in that, Includes the following steps: S1. Establish dynamic mathematical model equations for the components of the zeolite rotor CO system, including a dynamic mechanism model of the desorbed gas temperature before the zeolite rotor and the CO furnace temperature. The dynamic mechanism model connects the established dynamic mathematical models of each component; S2. Determine the system input parameters under design conditions, including: electric heater power. VOC exhaust gas concentration Desorption air volume exhaust gas inlet temperature ; S3. Design the system control matrix and select the desorbed gas temperature as the controlled variable. The power of the electric heater is selected as the control variable. Select VOC exhaust gas concentration Desorption air volume exhaust gas inlet temperature As a disturbance The dynamic model equations established by S1 are identified to obtain the transfer functions between the input, disturbance, and output quantities, which are then written in the following state-space form: （3-1）； In the formula, These are internal state variables of the system. The system's input variable is the electric heater power. The system's output variable is the temperature of the desorbed gas. Measurable disturbance variables: exhaust gas concentration, desorption air volume, and exhaust gas inlet temperature; This is the corresponding coefficient matrix; S4. Change the model to incremental form: （4-1） Subtracting formula 3-1 from formula 4-1, we get: （4-2） In the formula, ; ; ; S5. Taking the current time k as the sampling starting point, design the prediction time domain as follows: Control time domain is If it is necessary to predict the output of the system at p future time points, the model predicts the output at the p future time points. If the system state at p future moments is determined by the action of the system, where p ≥ m, then the predicted output of the system at p future steps is expressed by the following formula; （5-3）； In the formula, , ； ； S6. Optimize the system. The optimized performance index is expressed as follows: （6-1）； ； In the formula, The vector representation of the expected output value; These are the output and control weighting matrices, respectively. The highest exhaust temperature, Minimum CO furnace temperature; Predictive models of output variables Substituting into the above equation, and under the constraint equations, the optimal performance index can be solved. The minimum value is used to output the control quantity for the next time step. ; S7: Output control quantity of predictive control system The actuator repeats S3-S6 in each subsequent sampling cycle to stabilize the outlet temperature of the desorbed gas from the heat exchanger at around 200℃.

2. The method for controlling the temperature of desorbed gas in a zeolite rotor according to claim 1, characterized in that, A dynamic mathematical model of the heat exchange component is established using the lumped parameter method. The model mainly consists of energy balance equations and momentum equations. ； ； ； ； In the formula, These are the heat transfer on the hot side and the heat transfer through the metal wall of the heat exchanger, respectively. The heat transfer coefficient; For heat exchange area; These are the inlet and outlet flow rates of the cold-side working fluid, respectively. These are the temperatures of the working fluid on the cold side; This refers to the heat exchanger wall temperature. This is the equivalent volume of the cold-side working fluid channel; Specific heat capacity of the metal; Metal quality; These represent the inlet and outlet pressures of the working fluid, respectively; the mathematical model of the working fluid on the hot side is consistent with that on the cold side.

3. The method for controlling the temperature of desorbed gas in a zeolite rotor according to claim 2, characterized in that, The mathematical model of the CO incinerator in the system is simplified to an energy balance equation: ； In the formula, The total mass of the exhaust gas inside the furnace; These are the enthalpy of imported and exported exhaust gases, respectively. These are the inlet and outlet exhaust gas flow rates, respectively. This is a constant time coefficient used to correct the inertial time of the furnace during model debugging; Combustion efficiency is inversely proportional to the concentration of combustible components in the exhaust gas.

4. The method for controlling the temperature of desorbed gas in a zeolite rotor according to claim 3, characterized in that, The mathematical model of the electric heater in the system is simplified to an energy balance equation: ; In the formula, The total mass of waste gas inside the body is controlled by the electric heater; These are the enthalpy of imported and exported exhaust gases, respectively. For the efficiency of the electric heater; This refers to the power of the electric heater.

5. The method for controlling the temperature of desorbed gas in a zeolite rotor according to claim 4, characterized in that, The pipeline model in the system does not have heat exchange such as heat dissipation by default. Only a pressure-flow channel needs to be established. The resistance is concentrated at the inlet. The pressure and density inside the pipeline are numerically equal to the pressure and density at the outlet. The dynamic equations of the model are shown below: ； ； In the formula, This is the resistance coefficient of the pipeline. In engineering, if the operating conditions do not change significantly, it can be treated as a constant. This refers to the pipe volume.

6. The method for controlling the temperature of desorbed gas in a zeolite rotor according to claim 5, characterized in that, Based on the dynamic mathematical models of each component established above, the transfer function between the input and output is identified. The principle of the system identification method used is as follows: Suppose the transfer function of the model to be identified is decomposed as follows: When the input is a unit step function, performing an inverse Laplace transform on the above equation yields the system's unit step response in the time domain: Right now ; make (k>1) ； Taking the logarithm to the base e of both sides of the above equation, we get... ； When t→ hour, Then the above equation simplifies to ; The form of this expression satisfies the equation of a straight line. ； in ; The least squares algorithm is used to fit the line, resulting in... The value can be obtained. The value of can then be used to obtain the transfer function of the model.

7. The method for controlling the temperature of desorbed gas in a zeolite rotor according to claim 1, characterized in that, In step S6, system optimization should consider two aspects: first, to ensure that the state of the controlled object at time P under control can catch up with the expected value as soon as possible; and second, to minimize the control amplitude and suppress drastic changes in control action.

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