Cooperative scheduling method for hydrogen network and process for preparing methanol from carbon dioxide
By constructing a response surface model of the hydrogen network and carbon dioxide methanol production process, the problem of inaccurate monitoring data in the chemical production environment is solved, and the optimization and efficiency improvement of the production process are achieved.
Patent Information
- Application Number
- CN202510477824.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-16
- Publication Date
- 2025-07-25
AI Technical Summary
The complexity and diversity of the chemical production environment lead to insufficient accuracy of monitoring data of hydrogen network and carbon dioxide methanol production process, affecting production scheduling decisions.
The response surface model of the hydrogen network and carbon dioxide methanol production process is constructed, the equipment parameters are fused through polynomial equations, the gas concentration and flow rate of the hydrogen network and CO2 methanol production device are optimized, and the response surface model is used for optimization scheduling.
It improves the accuracy of monitoring data, provides scientific data support and analysis basis, optimizes the production process, and improves production efficiency.
Smart Images
Figure CN120375950A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of process system integration, and particularly to a collaborative scheduling method for a hydrogen network and a carbon dioxide to methanol process. Background Art
[0002] Methanol is an extremely important bulk basic chemical raw material with wide applications, covering multiple fields such as solvents, fuels, and chemical raw materials. Inevitably, a large amount of CO2 emissions are generated during the refining process of methanol. For the CO2 generated during the methanol refining process, the CO2 captured by the refinery can react with the by-product hydrogen to be prepared into methanol again. This way of recycling can effectively reduce carbon emissions and improve economic benefits, which is of great significance for realizing green and low-carbon development.
[0003] The collaborative scheduling method for a hydrogen network and a carbon dioxide to methanol process mainly relies on a recommended flow dynamic monitoring system, and its implementation includes: First, according to the actual situation in the chemical production process, determine the key parameters such as the flow rate, pressure, temperature, and concentration of hydrogen and CO2 that need to be monitored; Second, according to the monitoring objectives and requirements, select high-precision sensors, detection instruments, and data acquisition and transmission systems; Then, transmit the real-time data collected by the sensors to the data processing center by wired or wireless means; Finally, perform statistical analysis, trend prediction, and anomaly detection on the collected data, etc., to timely discover problems in the production process and take corresponding measures for adjustment and optimization.
[0004] Due to the complexity and diversity of the chemical production environment, other chemical substances and preparation devices involved in methanol preparation will generate interference factors, affecting the accuracy of monitoring data and being unfavorable for the decision-making of production scheduling. Summary of the Invention
[0005] Based on this, it is necessary to provide a collaborative scheduling method for a hydrogen network and a carbon dioxide to methanol process in view of the above technical problems.
[0006] An embodiment of the present invention provides a collaborative scheduling method for a hydrogen network and a carbon dioxide to methanol process, which is applied to a device for producing methanol through a hydrogen network and carbon dioxide CO2. The device includes: a hydrogen network and a methanol production device connected to the hydrogen network. The methanol production device includes a reactor, two flash tanks, and a distillation column; wherein, a mixed gas composed of CO2 and hydrogen from the hydrogen network enters the reactor, and the outlet of the reactor is successively connected to flash tank 1, flash tank 2, and the distillation column, and the produced methanol is output through the outlet of the distillation column; The collaborative scheduling method for a hydrogen network and a carbon dioxide to methanol process includes: Obtain the gas concentration and gas flow rate of hydrogen sources and hydrogen sinks in the hydrogen network, as well as the parameter information of the reactor, two flash tanks, and the distillation column in the methanol production device; Based on the mechanism model of methanol production from CO2 hydrogenation, the parameter information of the reactor, flash tank, and distillation column in the methanol production device is parameter - fused to obtain a sample set consisting of the input parameter groups and output parameter groups of the reactor, two flash tanks, and distillation column; Taking the individual output parameters of the reactor, two flash tanks, and distillation column in the output parameter group of the sample set as output variables, and taking the input parameters of the corresponding equipment in the input parameter group of the sample set as input variables, a response surface model corresponding to the output parameters is constructed through polynomial equations; Taking all the input parameters in the input parameter group of the sample set as decision variables, taking the optimal annual total benefit as the objective function, and substituting the gas concentrations and gas flow rates of the hydrogen source and hydrogen sink as constraint conditions into the response surface models of each equipment, the device for producing methanol from CO2 through the hydrogen network is optimized to obtain the optimization results; the optimization results include the optimal value of the objective function, the input parameters corresponding to the optimal value of the objective function, and the optimal matching of the gas concentrations and gas flow rates of the hydrogen source and hydrogen sink in the hydrogen network; and the gas concentrations and gas flow rates of the hydrogen network and CO2 are scheduled through the optimization results.
[0007] Optionally, the parameter information of the reactor, two flash tanks, and distillation column in the methanol production device includes: the raw gas parameters of the mixed gas, the parameters of the reactor, and the operating parameters of the flash tank and distillation column; The raw gas parameters of the mixed gas include: the total flow rate, temperature, pressure, component information, and component concentration of the raw gas; The parameters of the reactor include: the concentration and flow rate of hydrogen at the reactor inlet; the reaction rate constant, activation energy, exponent of the adsorption expression, concentration constant, and concentration exponent of the CO2 hydrogenation to methanol mechanism model, and the forward and reverse concentration exponents and coefficient of the driving force constant of the power expression; the reaction temperature, pressure, number of tubes, tube length, diameter, and catalyst loading amount of the reactor; The operating parameters of the flash tank include: the temperature and pressure of flash tank 1, and the temperature and pressure of flash tank 2; The operating parameters of the distillation column include: the temperature, pressure, number of trays, feed location, reboil ratio, and reflux ratio of the distillation column.
[0008] Optionally, taking the individual output parameters of the reactor, two flash tanks, and distillation column in the output parameter group of the sample set as output variables, and taking the input parameters of the corresponding equipment in the input parameter group of the sample set as input variables, specifically includes: The input variables of the reactor include the hydrogen concentration and flow rate at the reactor inlet, the temperature and pressure of the reactor, and the output variables of the reactor include the flow rates of each component of the product; The input variables of flash tank 1 include the temperature, pressure, and inlet flow rates of each component of flash tank 1, and the output variables of flash tank 1 include the flow rates of each component of the gas - phase and liquid - phase products; The input variables of the flash tank 2 include the temperature, pressure of the flash tank 2, and the flow rates of each component at the inlet. The output variables of the flash tank 2 include the flow rates of each component in the liquid-phase product. The input variable of the distillation column is the flow rate of each component at the inlet. The output variables of the distillation column include the flow rate and concentration of methanol in the product.
[0009] Optionally, the polynomial equation is: ; where x n is the input variable, y is the output variable, f is the coefficient of the order term, is the second-order term, n is the formal parameter, n = 1, 2, 3,....., N , x is the independent variable.
[0010] Optionally, taking the optimal annual total benefit as the objective function, specifically including: The annual total benefit includes the change in utility hydrogen ∆ C (H2), the change in product output value ∆ C ( E ), and the change in the manufacturing cost of the flash tank ∆ C ( P ). The calculation formula is: ; By maximizing the change in product output value ∆ C ( E ), minimizing the sum of the change in utility hydrogen ∆ C (H2) and the change in the manufacturing cost of the flash tank ∆ C ( P ), the optimal annual total benefit is achieved.
[0011] Optionally, the hydrogen at the inlet of the reactor is a hydrogen trap, and the hydrogen released from the gas phase of the flash tank 2 is a hydrogen source.
[0012] The above-mentioned method for coordinated scheduling of a hydrogen network and a carbon dioxide to methanol process provided by the embodiments of the present invention has the following beneficial effects compared with the prior art: The present invention incorporates the parameters of each device in the hydrogen network and the CO2 - to - methanol plant into the model construction process. Taking the individual output parameters of each device in the output parameter group of the sample set as output variables and the input parameters of the corresponding device in the input parameter group of the sample set as input variables, a response surface model corresponding to the output parameters is constructed through polynomial equations. This can flexibly capture the non - linear relationship between the input parameters and the output parameters, enabling the constructed response surface model to comprehensively capture and reflect the influence of interference factors on the output performance of the device, ensuring more accurate simulation of the actual production environment when predicting the device output, improving the accuracy of monitoring data, providing solid data support and accurate analysis basis for scientific decision - making in the production scheduling of the hydrogen network and the CO2 - to - methanol process, optimizing the production process, and enhancing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS
[0013] Figure 1 FIG. is a schematic flow chart of a collaborative scheduling method for a hydrogen network and a carbon dioxide - to - methanol process provided in an embodiment; Figure 2 FIG. is a diagram of a CO2 hydrogenation - to - methanol plant for a collaborative scheduling method for a hydrogen network and a carbon dioxide - to - methanol process provided in an embodiment; Figure 3 FIG. is a schematic diagram of a reactor response surface model for a collaborative scheduling method for a hydrogen network and a carbon dioxide - to - methanol process provided in an embodiment; Figure 4 FIG. is a schematic diagram of a flash tank 1 response surface model for a collaborative scheduling method for a hydrogen network and a carbon dioxide - to - methanol process provided in an embodiment; Figure 5 FIG. is a schematic diagram of a flash tank 2 response surface model for a collaborative scheduling method for a hydrogen network and a carbon dioxide - to - methanol process provided in an embodiment; Figure 6 FIG. is a schematic diagram of a distillation column response surface model for a collaborative scheduling method for a hydrogen network and a carbon dioxide - to - methanol process provided in an embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0014] In order to make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.
[0015] In one embodiment, a device for producing methanol from a hydrogen network and CO2 is provided, such as Figure 2As shown in the figure, it includes a hydrogen network and a methanol production device connected to the hydrogen network. The methanol production device includes a reactor, two flash tanks, and a distillation column. Among them, a mixed gas composed of CO2 and hydrogen generated by the hydrogen network enters the reactor. The outlet of the reactor is sequentially connected to Flash Tank 1, Flash Tank 2, and the distillation column, and the produced methanol is output through the outlet of the distillation column.
[0016] Before the mixed gas enters the reactor, it also needs to pass through a heat exchanger. The output gas of the reactor enters Flash Tank 1 through the heat exchanger. The output gas of Flash Tank 1 enters Flash Tank 2. The output gas of Flash Tank 2 enters the distillation column, and methanol, light components, and water are obtained through the distillation column. In addition, a part of the output gas of Flash Tank 1 and Flash Tank 2 will flow back to the heat exchanger.
[0017] In one embodiment, a coordinated scheduling method for a hydrogen network and a carbon dioxide to methanol process is provided, which is applied to a device for producing methanol from a hydrogen network and carbon dioxide CO2. As Figure 1 shown, the method includes:
[0018] (1) Obtain the gas concentration and gas flow rate of hydrogen sources and hydrogen sinks in the hydrogen network, and the parameter information of the reactor, two flash tanks, and distillation column in the methanol production device.
[0019] (2) Based on the CO2 hydrogenation to methanol mechanism model, fuse the parameter information of the reactor, flash tanks, and distillation column in the methanol production device to obtain a sample set composed of the input parameter group and output parameter group of the reactor, two flash tanks, and distillation column.
[0020] (3) Take the individual output parameters of the reactor, two flash tanks, and distillation column in the output parameter group of the sample set as output variables, and take the input parameters of the corresponding equipment in the input parameter group of the sample set as input variables, and construct a response surface model corresponding to the output parameters through a polynomial equation.
[0021] The input variables of the reactor include the hydrogen concentration and flow rate at the reactor inlet, the temperature and pressure of the reactor, and the output variables of the reactor include the flow rates of the various components of the product. The input variables of Flash Tank 1 include the temperature, pressure of Flash Tank 1, and the flow rates of the various components at the inlet, and the output variables of Flash Tank 1 include the flow rates of the various components of the gas-phase and liquid-phase products. The input variables of Flash Tank 2 include the temperature, pressure of Flash Tank 2, and the flow rates of the various components at the inlet, and the output variables of Flash Tank 2 include the flow rates of the various components of the liquid-phase product. The input variable of the distillation column is the flow rate of the various components at the inlet, and the output variables of the distillation column include the flow rate and concentration of methanol in the product.
[0022] Specifically, as Figure 3As shown, the input variables of the reactor are the reactor temperature Rec_T and pressure Rec_P, and the output variables are the flow rates of components H2, N2, CO, CO2, CH4, H2O, and CH3OH (Rec_out_H2, Rec_out_N2, Rec_out_CO, Rec_out_CO2, Rec_out_CH4, Rec_out_H2O, Rec_out_CH3OH).
[0023] As Figure 4 shown, the input variables of flash tank 1 are the temperature Flash1_T, pressure Flash1_P of flash tank 1, and the flow rates of each component at the inlet. The output variables are the flow rates of each component in the gas and liquid phases (Flash1_G_out_H2, Flash1_G_out_N2, Flash1_G_out_CO, Flash1_G_out_CO2, Flash1_G_out_CH4, Flash1_G_out_H2O, Flash1_G_out_CH3OH, Flash1_L_out_H2, Flash1_L_out_N2, Flash1_L_out_CO, Flash1_L_out_CO2, Flash1_L_out_CH4, Flash1_L_out_H2O, Flash1_L_out_CH3OH).
[0024] As Figure 5 shown, the input variables of flash tank 2 are the temperature Flash2_T, pressure Flash2_P of flash tank 2, and the flow rates of each component at the inlet. The output variables are the flow rates of each component in the liquid phase (Flash2_L_out_H2, Flash2_L_out_N2, Flash2_L_out_CO, Flash2_L_out_CO2, Flash2_L_out_CH4, Flash2_L_out_H2O, Flash2_L_out_CH3OH).
[0025] As Figure 6 shown, the input variables of the distillation column are the flow rates of each component at the inlet of the distillation column (Dis_in_H2, Dis_in_N2, Dis_in_CO, Dis_in_CO2, Dis_in_CH4, Dis_in_H2O, Dis_in_CH3OH), and the output variables are the flow rate and concentration of methanol in the product (F_Dis_out_CH3OH, Y_Dis_out_CH3OH).
[0026] (5) All input parameters in the input parameter group of the sample set are used as decision variables, the annual total benefit optimization is used as the objective function, and the gas concentration and gas flow rate of the hydrogen source and hydrogen sink are used as constraint conditions and substituted into the response surface model of each device to optimize the device for hydrogen production from the hydrogen network and CO2 to methanol, and the optimization results are obtained. The optimization results include the optimal value of the objective function, the input parameters corresponding to the optimal value of the objective function, and the optimal matching of the gas concentration and gas flow rate of the hydrogen source and hydrogen sink in the hydrogen network; and the gas concentration and gas flow rate of the hydrogen network and CO2 are scheduled through the optimization results.
[0027] Among them, the polynomial equation is: ; Among them, x n is the input variable, y is the output variable, f is the coefficient of the order term, is the second-order term, n is the formal parameter, n = 1, 2, 3,....., N , x is the independent variable.
[0028] The annual total benefit includes the change in utility hydrogen ∆ C (H2), the change in product output value ∆ C ( E ), the change in the manufacturing cost of the flash tank ∆ C ( P ), and the calculation formula is: ; By maximizing the change in product output value ∆ C ( E ), minimizing the sum of the change in utility hydrogen ∆ C (H2) and the change in the manufacturing cost of the flash tank ∆ C ( P ), the annual total benefit is optimized.
[0029] The specific implementation of a collaborative scheduling method for a hydrogen network and a carbon dioxide to methanol process is as follows: (1) Identify the hydrogen source and hydrogen sink of the hydrogen network in the refinery and extract data; (2) Obtain the parameter information of the CO2 hydrogenation to methanol device and construct a mechanism model of the CO2 hydrogenation to methanol device based on the parameter information; In one example, the main equipment of the CO2 hydrogenation to methanol device includes a reactor, a flash tank 1, a flash tank 2, and a distillation column, and the auxiliary equipment includes a compressor, a heat exchanger, and a mixer / splitter.
[0030] Among them, the parameter information includes the raw gas parameters of CO2, including the total flow rate, temperature, pressure, component information and component concentration of the raw gas. The concentration and flow rate of hydrogen at the reactor inlet. The reaction kinetic parameters of reactions (1), (2) and (3), including the reaction rate constant, activation energy, exponents of the adsorption expression, concentration constant and concentration exponent, forward and reverse concentration exponents of the kinetic expression and the coefficients of the driving force constant. The reaction temperature and pressure of the reactor, the number of tubes, tube length, diameter, and catalyst loading. The temperature and pressure of flash tank 1; the temperature and pressure of flash tank 2; the temperature, pressure, number of trays, feed location, reboil ratio and reflux ratio of the distillation column.
[0031] The mechanism model of the CO2 hydrogenation to methanol device in this example can be built using, but not limited to, Aspen Plus, Aspen HYSYS software or Matlab, Python, C++, C, VB languages.
[0032] (1) (2) (3) (3)Generate multiple sets of input and output parameter groups for the internal reactors, flash tanks, and distillation columns of the device according to the mechanism model of the CO2 hydrogenation to methanol device; Among them, the input parameters of the reactor include the hydrogen concentration and flow rate at the reactor inlet, the temperature and pressure of the reactor, and the output parameters include the flow rates of each component of the product. The input parameters of flash tank 1 include the temperature, pressure of flash tank 1 and the flow rates of each component at the inlet, and the output parameters include the flow rates of each component of the gas-phase and liquid-phase products. The input parameters of flash tank 2 include the temperature, pressure of flash tank 2 and the flow rates of each component at the inlet, and the output parameters include the flow rates of each component of the liquid-phase product. The input parameter of the distillation column is the flow rate of each component at the inlet, and the output parameters include the flow rate and concentration of methanol in the product.
[0033] Specifically, the correspondence between the input parameters and the output parameters is based on the equipment. One output parameter corresponds to a set of input parameters of the equipment. Taking the hydrogen flow rate at the reactor inlet, reactor temperature, reactor pressure, flash tank 1 temperature, flash tank 1 pressure, flash tank 2 temperature and flash tank 2 pressure as decision variables, multiple sets of decision variables are selected, and multiple sets of input and output parameter groups are generated according to the built mechanism model.
[0034] (4)Construct the response surface models of the reactors, flash tanks, and distillation columns; Among them, after obtaining multiple sets of input and output parameter groups, taking the single output parameter of the equipment as the output variable and all the input parameters of the equipment as the input variables, the response surface model of the equipment is fitted and established.
[0035] Specifically, taking the H2 flow rate at the reactor outlet among the reactor output parameters as an example, under the conditions of unchanged feed gas and unchanged inlet hydrogen concentration, the H2 flow rate at the reactor outlet is related to the temperature, pressure of the reactor, and the flow rate of hydrogen.
[0036] Taking a single output parameter of the reactor, flash drum, or distillation column as the output variable, and the input parameters of the corresponding equipment as the input variables, a response surface model of this output parameter is established using a polynomial equation.
[0037] (5) Optimize the refinery hydrogen network using the response surface model; Among them, the hydrogen network data is obtained from step (1). Specifically, taking the hydrogen flow rate at the reactor inlet, reactor temperature, reactor pressure, flash drum 1 temperature, flash drum 1 pressure, flash drum 2 temperature, flash drum 2 pressure, and the flow rate between hydrogen sources and hydrogen sinks in the hydrogen network as decision variables, determine the adjustable range of the decision variables according to the actual process, and taking the annual total benefit as the objective function, including hydrogen cost, equipment cost, and product output value, substitute the response surface model into the constraint conditions (gas concentration and gas flow rate of hydrogen sources and hydrogen sinks in the hydrogen network) to optimize the refinery hydrogen network, obtain the optimization result, and the optimization result includes the optimal value of the objective function and the corresponding decision variable values at this time, as well as the optimal matching of the hydrogen source and hydrogen sink streams in the hydrogen network.
[0038] (6) Substitute the optimal decision variable values into the mechanism model to obtain the accurate output results of the reactor, flash drum, and distillation column, and optimize the hydrogen network based on this to obtain the accurate optimal value of the objective function.
[0039] Among them, under the conditions of known hydrogen flow rate at the reactor inlet, reactor temperature, reactor pressure, flash drum 1 temperature, flash drum 1 pressure, flash drum 2 temperature, and flash drum 2 pressure, taking the flow rate between hydrogen sources and hydrogen sinks in the hydrogen network as the decision variable, optimize the hydrogen network.
[0040] (7) Determine whether to converge. If not converged, add the optimal decision variable values and the accurate optimal value of the objective function in (6) to the sample set in (4); if converged, stop the iteration.
[0041] (8) Repeat steps (4) to (7) until the process converges.
[0042] The above-described embodiments only represent several implementation manners of the present invention, and their descriptions are relatively specific and detailed, but should not be construed as limiting the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention.
Claims
1. A collaborative scheduling method for a hydrogen network and a carbon dioxide to methanol process, which is applied to a device for producing methanol from a hydrogen network and carbon dioxide CO2, and the device includes: A hydrogen network and a methanol production device connected to the hydrogen network. The methanol production device includes a reactor, two flash drums, and a distillation column. Among them, a mixed gas composed of CO2 and hydrogen generated by the hydrogen network enters the reactor. The outlet of the reactor is successively connected to flash drum 1, flash drum 2, and the distillation column, and the produced methanol is output through the outlet of the distillation column. It is characterized in that the collaborative scheduling method of the hydrogen network and the carbon dioxide to methanol process includes: Obtaining the gas concentration and gas flow rate of hydrogen sources and hydrogen sinks in the hydrogen network, and the parameter information of the reactor, two flash drums, and distillation column in the methanol production device; Based on the CO2 hydrogenation to methanol mechanism model, fusing the parameter information of the reactor, flash drums, and distillation column in the methanol production device to obtain a sample set composed of an input parameter group and an output parameter group of the reactor, two flash drums, and distillation column; Taking the individual output parameters of the reactor, two flash drums, and distillation column in the output parameter group of the sample set as output variables, and taking the input parameters of the corresponding equipment in the input parameter group of the sample set as input variables, and constructing a response surface model corresponding to the output parameters through a polynomial equation; Taking all the input parameters in the input parameter group of the sample set as decision variables, taking the optimal annual total benefit as the objective function, and substituting the gas concentration and gas flow rate of hydrogen sources and hydrogen sinks as constraint conditions into the response surface models of each equipment to optimize the device for producing methanol from hydrogen network and CO2, and obtaining the optimization result. The optimization result includes the optimal value of the objective function, the input parameters corresponding to the optimal value of the objective function, and the optimal matching of the gas concentration and gas flow rate of hydrogen sources and hydrogen sinks in the hydrogen network; and scheduling the gas concentration and gas flow rate of the hydrogen network and CO2 through the optimization result.
2. The co - scheduling method of a hydrogen network and carbon dioxide to methanol process according to claim 1, wherein, The parameter information of the reactor, two flash drums, and distillation column in the methanol production device includes: the raw gas parameters of the mixed gas, the parameters of the reactor, and the operating parameters of the flash drums and distillation column; The raw gas parameters of the mixed gas include: the total raw gas flow rate, temperature, pressure, component information, and component concentration; The parameters of the reactor include: the concentration and flow rate of hydrogen at the reactor inlet; the reaction rate constant, activation energy, exponent of the adsorption expression, concentration constant, and concentration exponent of the CO2 hydrogenation to methanol mechanism model; the positive and negative concentration exponents of the power expression and the coefficient of the driving force constant; the reaction temperature and pressure of the reactor, the number of tubes, tube length, diameter, and catalyst loading amount; The operating parameters of the flash drums include: the temperature and pressure of flash drum 1, and the temperature and pressure of flash drum 2; The operating parameters of the distillation column include: the distillation column temperature, pressure, number of trays, feed position, reboil ratio, and reflux ratio.
3. The co - scheduling method for a hydrogen network and carbon dioxide to methanol process according to claim 1, characterized in that, Taking the individual output parameters of the reactor, two flash drums, and distillation column in the output parameter group of the sample set as output variables, and taking the input parameters of the corresponding equipment in the input parameter group of the sample set as input variables specifically includes: The input variables of the reactor include the hydrogen concentration and flow rate at the reactor inlet, the temperature and pressure of the reactor, and the output variables of the reactor include the flow rates of each component of the product; The input variables of the flash tank 1 include the temperature, pressure of the flash tank 1, and the flow rates of each component at the inlet. The output variables of the flash tank 1 include the flow rates of each component in the gas-phase and liquid-phase products; The input variables of the flash tank 2 include the temperature, pressure of the flash tank 2, and the flow rates of each component at the inlet. The output variables of the flash tank 2 include the flow rates of each component in the liquid-phase product; The input variable of the distillation column is the flow rate of each component at the inlet. The output variables of the distillation column include the flow rate and concentration of methanol in the product.
4. The collaborative scheduling method of a hydrogen network and a carbon dioxide to methanol process according to claim 1, wherein The polynomial equation is: ； Among them, x n is the input variable, y is the output variable, f is the coefficient of the order term, is the second-order term, n is the formal parameter, n = 1, 2, 3,....., N , x is the independent variable.
5. The collaborative scheduling method for a hydrogen network and a carbon dioxide to methanol process according to claim 1, characterized in that, Taking the annual total benefit optimization as the objective function specifically includes: The annual total benefit includes the change in utility hydrogen ∆ C (H2), the change in product output value ∆ C ( E ), the change in the manufacturing cost of the flash tank ∆ C ( P ), and the calculation formula is: ； By maximizing the change in product output value ∆ C ( E ), minimizing the change in utility hydrogen ∆ C (H2) and the change in the manufacturing cost of the flash tank ∆ C ( P ), the sum of which is to achieve the optimal annual total benefit.
6. The co - scheduling method for a hydrogen network and a carbon dioxide to methanol process according to claim 1, wherein, The hydrogen at the inlet of the reactor is a hydrogen sink, and the hydrogen vented from the gas phase of the flash tank 2 is a hydrogen source.