System and method for preparing tetrahydrofuran by using BDO tar

Abstract

The invention relates to the technical field of chemical reaction systems, in particular to a system and method for preparing tetrahydrofuran from BDO tar. In the invention, the mass transfer path is gradually optimized by combining the fitness function through parameterization search on the gas-liquid contact path by the differential evolution algorithm, so that a more efficient mass transfer parameter combination is generated, the mass transfer efficiency in the separation tower is remarkably improved, and the problems of non-uniform gas-liquid flow caused by improper path design are effectively reduced; stable and high-quality material input conditions are provided for subsequent reaction, energy consumption is reduced, global optimization is performed on multi-stage condition combination of temperature, catalyst concentration and reaction time by adopting a simulated annealing algorithm, limitation of local optimum is overcome through a step-by-step cooling search strategy, and the stability of the system is improved. The optimal parameter combination of the key reaction stage is accurately determined, the generation of intermediate products and by-products is reduced, and a scientific adjustment basis is provided for the multi-stage reaction process.

Description

Technical Field

[0001] The present invention relates to the technical field of chemical reaction systems, and in particular to a system and method for preparing tetrahydrofuran by utilizing BDO tar. Background Art

[0002] The field of chemical reaction system technology aims to achieve efficient, safe and economical material transformation and synthesis through scientific design and optimization of chemical reaction processes, enhance target product selectivity through catalyst design and process optimization, maximize resource and energy utilization and reduce consumption, and use separation and purification technology to ensure product quality.

[0003] The purpose of the system for preparing tetrahydrofuran from BDO tar is to prepare high-value-added tetrahydrofuran from low-value-added industrial by-products through chemical conversion and purification processes, achieve efficient resource utilization and waste reduction, aiming to achieve dual optimization of economic and environmental benefits, and improve the yield and purity of tetrahydrofuran through technical means.

[0004] Existing technologies for modeling and optimization of complex chemical reactions are mainly based on static analysis and linear approximation methods, which are difficult to accurately reveal the dynamic correlation between multiple variables. The coupling effect between parameters is easily ignored in the setting of multi-stage reaction conditions, which limits the further improvement of reaction rate and product selectivity. Independent operation processes are used in the separation and purification stages, and there is a lack of multi-step collaborative optimization design, resulting in low impurity removal efficiency and difficulty in achieving a higher level of product purity. At the same time, the energy consumption is large, which restricts the overall efficiency of resource utilization. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a system and method for preparing tetrahydrofuran using BDO tar.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme: A system for preparing tetrahydrofuran using BDO tar comprises: Separation tower parameter design module: Based on the gas-liquid flow characteristic parameters in the separation tower, the number of plates, reflux ratio and solvent feed rate are adjusted, and the gas-liquid flow in the separation tower is simulated. By adjusting the operating parameters in the separation tower, the adjustment results of the separation tower operating parameters are generated; Reaction process screening module: Based on the components of the reaction substances and the reaction time parameters, the reaction temperature, catalyst concentration and reaction time are adjusted, and the conditions of different reaction processes are evaluated at the same time. By calculating the reaction rate and the change in component concentration, a relationship diagram between the reaction yield and the process conditions is generated; Mass transfer optimization module: Based on the adjustment results of the separation tower operating parameters, the differential evolution algorithm is used to optimize the gas-liquid mass transfer path, adjust the solvent feed rate, the number of plates and the reflux ratio, and select the suitable mass transfer efficiency conditions by simulating the gas-liquid flow and material mass transfer process to generate the optimized mass transfer path parameters; Reaction process modeling module: Based on the relationship diagram between the reaction yield and the process conditions, a reaction kinetic model is established to analyze the effects of temperature, catalyst concentration, and reaction time on the reaction rate and the intermediate concentration. By establishing a reaction kinetic equation and a component change curve, reaction kinetic model parameters are generated; Multi-stage reaction condition optimization module: Based on the optimized mass transfer path parameters and the reaction kinetic model parameters, a simulated annealing algorithm is used to optimize the temperature, catalyst concentration and reaction time of each reaction stage, and a reaction condition combination is selected. By calculating the reaction product distribution and energy consumption, an optimized reaction condition set is generated; Tetrahydrofuran synthesis module: Based on the optimized reaction condition set, a multi-stage reaction process is implemented to adjust the temperature, catalyst concentration and reaction time of each reaction stage, and tetrahydrofuran product is generated by controlling the reaction conditions and performing separation and purification steps.

[0007] As a further solution of the present invention, the separation tower parameter design module includes a plate number adjustment submodule, a reflux ratio adjustment submodule and a material mass transfer effect evaluation submodule, wherein: Plate number adjustment submodule: based on the gas-liquid flow characteristics in the separation tower, adjust the plate number of the separation tower, simulate the gas-liquid flow in the tower, calculate the gas-liquid contact effect under different plate numbers, evaluate the influence of the plate number on the mass transfer effect, optimize the plate number through the simulation results, and obtain the plate number optimization result; Reflux ratio adjustment submodule: based on the gas-liquid flow characteristics in the separation tower, adjust the reflux ratio of the separation tower, set different reflux ratios, perform gas-liquid flow simulation, evaluate the effect of the reflux ratio on the gas-liquid contact effect in the separation tower, analyze the optimization effect of different reflux ratios on the mass transfer efficiency, and obtain the reflux ratio optimization result; Submodule for evaluating the effect of material transfer: Based on the simulation of the gas-liquid flow in the separation tower, combined with the adjustment results of the number of plates and the reflux ratio, the material transfer effect under different operating conditions is evaluated, the mass transfer efficiency under each operating condition is calculated, and a relationship diagram between the material transfer efficiency and the operating parameters is generated to obtain the adjustment results of the operating parameters of the separation tower; The adjustment results of the separation tower operating parameters are specifically the number of plates, reflux ratio, solvent feed rate and gas-liquid contact area.

[0008] As a further solution of the present invention, the reaction process screening module includes a reaction temperature adjustment submodule, a catalyst concentration adjustment submodule and a reaction rate calculation submodule, wherein: Reaction temperature adjustment submodule: based on the composition of the reaction material and the reaction time, adjust the reaction temperature, set different temperature conditions, simulate the effect of temperature on the reaction rate, evaluate the change of reaction rate at different temperatures, and obtain the reaction temperature optimization result; Catalyst concentration adjustment submodule: based on the components of the reactants and the reaction time, adjust the catalyst concentration, set different catalyst concentration conditions, simulate the effect of catalyst concentration changes on the reaction rate, evaluate the change of reaction rate under different concentration conditions, and obtain the catalyst concentration optimization result; Reaction rate calculation submodule: based on the reaction temperature and catalyst concentration optimization results, combined with the reaction material components and reaction time, calculate the reaction rate and component concentration changes under different reaction process conditions, generate a relationship diagram between the reaction yield and the process conditions, and obtain a relationship diagram between the reaction yield and the process conditions; The relationship diagram between the reaction yield and the process conditions includes the reaction temperature, the amount of catalyst, the tetrahydrofuran yield and the time dependence.

[0009] As a further solution of the present invention, the mass transfer optimization module includes a gas-liquid mass transfer path optimization submodule, an operation parameter adjustment submodule and a mass transfer efficiency evaluation submodule, wherein: Gas-liquid mass transfer path optimization submodule: based on the adjustment results of the separation tower operating parameters, the gas-liquid flow path in the separation tower is optimized, by simulating the gas-liquid flow under different operating conditions, calculating the influence of different paths on the mass transfer efficiency of the material, determining the mass transfer efficiency of each path, selecting an appropriate gas-liquid contact path, and generating optimized mass transfer path parameters; Operation parameter adjustment submodule: Based on the optimized mass transfer path parameters, the differential evolution algorithm is used to adjust the solvent feed rate, the number of plates and the reflux ratio, and the changes in the mass transfer effect are calculated by simulating the gas-liquid flow state under different operating conditions, and the influence of each operating parameter on the mass transfer of substances is analyzed. The operating conditions are adjusted to improve the mass transfer efficiency, and the operation parameter optimization results are generated; Mass transfer efficiency evaluation submodule: based on the optimization results of the operating parameters, evaluate the mass transfer efficiency under different operating conditions, simulate the gas-liquid flow and material mass transfer process, calculate the mass transfer efficiency under different conditions, generate a relationship diagram between the mass transfer efficiency and the operating conditions, and obtain the optimized mass transfer path results; The optimized mass transfer path parameters are specifically the solvent feed rate, the number of plates, the reflux ratio and the gas-liquid exchange rate.

[0010] As a further solution of the present invention, the differential evolution algorithm, formula: ; in: is the gas-liquid interface mass transfer coefficient, is the gas flow rate, is the solvent feed rate adjustment factor, is the gas-liquid contact area, is the plate number correction factor, is the concentration of the target component in the liquid phase, is the concentration of the target component in the gas phase, is the reflux ratio influence coefficient.

[0011] As a further solution of the present invention, the reaction process modeling module includes a kinetic model building submodule, a reaction condition analysis submodule and a reaction kinetic parameter calculation submodule, wherein: Kinetic model establishment submodule: based on the relationship diagram between the reaction yield and the process conditions, a reaction kinetic model is established, different reaction temperatures, catalyst concentrations and reaction times are set, and the reaction rate and concentration changes are simulated by calculation to derive the reaction kinetic equation and generate the reaction kinetic equation; Reaction condition analysis submodule: Based on the reaction kinetics equation, analyze the effects of different reaction temperatures, catalyst concentrations and reaction times on the reaction rate and intermediate concentration, simulate the changes in the reaction under different process conditions, obtain the effects of each reaction condition on the substance concentration and yield, and generate the reaction condition optimization results; Reaction kinetics parameter calculation submodule: based on the reaction condition optimization results, the reaction rate, component concentration and intermediate changes under different reaction conditions are calculated, and the reaction kinetics model parameters are obtained by establishing a reaction kinetics model and a component change curve; The reaction kinetics model parameters specifically include the reaction rate constant, the temperature influence coefficient and the influence of the catalyst concentration on the reaction rate.

[0012] As a further solution of the present invention, the multi-stage reaction condition optimization module includes a reaction condition selection submodule, a reaction product distribution calculation submodule and an optimization condition set generation submodule, wherein: Reaction condition selection submodule: based on the optimized mass transfer path parameters and reaction kinetic model parameters, select the operating temperature, catalyst concentration and reaction time of each reaction stage, select the combination of reaction conditions, and evaluate the reaction rate, material conversion rate and energy consumption under each combination to generate phased optimized reaction conditions; Reaction product distribution calculation submodule: Based on the staged optimization reaction conditions, a simulated annealing algorithm is used to calculate the product distribution at different reaction stages, simulate the conversion of the reaction substances at each stage, gradually estimate the product generation ratio and the generation of by-products, analyze the material distribution and energy consumption under different reaction conditions, and generate a reaction product distribution map; Optimization condition set generation submodule: based on the reaction product distribution map, select an appropriate reaction condition combination in combination with the energy consumption of each stage, and perform final optimization according to the reaction rate and product selectivity of each stage to generate an optimized reaction condition set; The optimized reaction condition set includes the optimized configuration of reaction stage temperature, catalyst concentration, reaction time and energy consumption.

[0013] As a further solution of the present invention, the simulated annealing algorithm is according to the formula: ; in: is the improved reaction rate constant, is the pre-factor, is the activation energy of the reaction, is the gas constant, is the temperature, is the concentration of the reactant, is the initial reactant concentration, is the effect index of concentration on reaction rate, is the pressure of the reactants, is the initial reactant pressure, is the effect index of pressure on reaction rate, is the reaction time, is the attenuation coefficient of the reaction time.

[0014] As a further embodiment of the present invention, the tetrahydrofuran synthesis module includes a reaction process implementation submodule, a reaction condition adjustment submodule and a separation and purification step submodule, wherein: Reaction process implementation submodule: Based on the optimized reaction condition set, a multi-stage reaction process is implemented, the temperature, catalyst concentration and reaction time of each reaction stage are adjusted in turn, and the reaction conditions are adjusted to execute each stage reaction, convert the reaction substances and generate intermediate products to obtain staged reaction products; Reaction condition adjustment submodule: Based on the staged reaction products, adjust the temperature, catalyst concentration and reaction time of each reaction stage, adjust the conditions according to the product distribution, optimize the reaction rate and control the generation of by-products, and obtain the optimized reaction conditions; Separation and purification step submodule: Based on the optimized reaction conditions, separation and purification steps are implemented to separate the reaction product and the by-product, and then impurities are removed through multiple purification operations to obtain the finished tetrahydrofuran; The tetrahydrofuran finished product specifically includes the purity of tetrahydrofuran, the yield of the target product and the residue removal rate.

[0015] A method for preparing tetrahydrofuran using BDO tar, wherein the method for preparing tetrahydrofuran using BDO tar is performed based on the above-mentioned system for preparing tetrahydrofuran using BDO tar, and comprises the following steps: Step 1: Based on the chemical composition information of BDO tar, the solvent feed rate, the number of plates and the reflux ratio are adjusted to simulate the gas-liquid flow characteristics in the separation tower, calculate the amount of material exchange between gas and liquid and the absorption efficiency during the mass transfer process, and generate the adjustment results of the separation tower operating parameters; Step 2: Based on the adjustment result of the separation tower operating parameters, the reaction temperature, catalyst concentration and reaction time are adjusted, the chemical reaction rate of the reactants and the changes in the concentration of the generated components are analyzed, the reactant concentration consumption and the product generation under different conditions are calculated, and the reaction rate and component concentration change data are generated; Step 3: Based on the reaction rate and component concentration change data, a differential evolution algorithm is used to calculate the mass transfer path and mass transfer rate of the gas-liquid interface by adjusting the number of tower plates, solvent feed rate and reflux ratio, and the gas-liquid interface efficiency under multiple groups of operating conditions is compared to generate adaptive mass transfer path parameters; Step 4: Based on the adapted mass transfer path parameters, analyze the effects of temperature, catalyst concentration and reaction time on the reaction process, establish a kinetic change curve of substance concentration and reaction rate, calculate the concentration change and generation rate of key intermediates in the reaction process, and generate reaction kinetic model parameters; Step 5: Based on the reaction kinetic model parameters, a simulated annealing algorithm is used to optimize the combination of temperature, catalyst concentration and reaction time in the multi-stage reaction stage, adjust the separation operation parameters to obtain the target product with a purity that meets the standard, and perform the material separation and purification steps in the reaction stage to generate a tetrahydrofuran product.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are: 1. In the present invention, the differential evolution algorithm is used to perform parameterized search on the gas-liquid contact path, and the mass transfer path is gradually optimized in combination with the fitness function, thereby generating a more efficient mass transfer parameter combination, significantly improving the mass transfer efficiency in the separation tower, and effectively reducing the uneven gas-liquid flow caused by improper path design, providing stable and high-quality input material conditions for subsequent reactions, while reducing energy consumption; 2. In the present invention, a simulated annealing algorithm is used to globally optimize the multi-stage condition combination of temperature, catalyst concentration and reaction time. The limitation of local optimum is overcome by the gradual cooling search strategy, and the optimal parameter combination of the key reaction stage is accurately determined, which reduces the generation of intermediate products and by-products and provides a scientific adjustment basis for the multi-stage reaction process. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a system flow chart of the present invention; Figure 2 Schematic diagram of the system framework of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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 intended to limit the present invention.

[0019] See also Figure 1 The present invention provides a technical solution: a system for preparing tetrahydrofuran using BDO tar comprises: Separation tower parameter design module: Based on the gas-liquid flow characteristic parameters in the separation tower, the number of plates, reflux ratio and solvent feed rate are adjusted, and the gas-liquid flow in the separation tower is simulated. By adjusting the operating parameters in the separation tower, the adjustment results of the separation tower operating parameters are generated; Reaction process screening module: Based on the components of the reaction substances and the reaction time parameters, the reaction temperature, catalyst concentration and reaction time are adjusted, and the conditions of different reaction processes are evaluated at the same time. By calculating the reaction rate and the change in component concentration, a relationship diagram between the reaction yield and the process conditions is generated; Mass transfer optimization module: Based on the adjustment results of the separation tower operating parameters, the differential evolution algorithm is used to optimize the gas-liquid mass transfer path, adjust the solvent feed rate, the number of plates and the reflux ratio, and select the suitable mass transfer efficiency conditions by simulating the gas-liquid flow and material mass transfer process to generate the optimized mass transfer path parameters; Reaction process modeling module: Based on the relationship diagram between reaction yield and process conditions, a reaction kinetics model is established to analyze the effects of temperature, catalyst concentration, and reaction time on reaction rate and intermediate concentration. By establishing a reaction kinetics equation and component change curve, reaction kinetics model parameters are generated. Multi-stage reaction condition optimization module: Based on the optimization of mass transfer path parameters and reaction kinetic model parameters, the simulated annealing algorithm is used to optimize the temperature, catalyst concentration and reaction time of each reaction stage, and the reaction condition combination is selected. By calculating the reaction product distribution and energy consumption, the optimized reaction condition set is generated; Tetrahydrofuran synthesis module: Based on the optimized reaction condition set, a multi-stage reaction process is implemented to adjust the temperature, catalyst concentration and reaction time of each reaction stage. The finished tetrahydrofuran is generated by controlling the reaction conditions and performing separation and purification steps.

[0020] See also Figure 2 The separation tower parameter design module includes a plate number adjustment submodule, a reflux ratio adjustment submodule and a material mass transfer effect evaluation submodule, among which: Plate number adjustment submodule: Based on the gas-liquid flow characteristics in the separation tower, adjust the number of separation tower plates, simulate the gas-liquid flow in the tower, calculate the gas-liquid contact effect under different plate numbers, evaluate the influence of the plate number on the mass transfer effect, optimize the plate number through simulation results, and obtain the plate number optimization result; Reflux ratio adjustment submodule: Based on the gas-liquid flow characteristics in the separation tower, adjust the reflux ratio of the separation tower, set different reflux ratios, perform gas-liquid flow simulation, evaluate the impact of the reflux ratio on the gas-liquid contact effect in the separation tower, analyze the optimization effect of different reflux ratios on the mass transfer efficiency, and obtain the reflux ratio optimization results; Submodule for evaluating the effect of material transfer: Based on the simulation of gas-liquid flow in the separation tower, combined with the adjustment results of the number of plates and the reflux ratio, the material transfer effect under different operating conditions is evaluated, the mass transfer efficiency under each operating condition is calculated, and the relationship diagram between the material transfer efficiency and the operating parameters is generated to obtain the adjustment results of the separation tower operating parameters; Plate number adjustment submodule: Based on the gas-liquid flow characteristic parameters in the separation tower, a numerical simulation method is used, specifically a computational fluid dynamics method. The model uses the Reynolds time-averaged equation combined with a turbulence model to simulate the gas-liquid flow, simulates and calculates the separation tower, and adjusts the number of plates in the separation tower. The plate number ranges from 20 to 60, with an interval of 5. The simulation results of different plate numbers are statistically analyzed, and the gas-liquid contact area and the gas-liquid separation effect are calculated through the flow field distribution. The effect of the plate number on the mass transfer efficiency is evaluated in combination with the statistical analysis results. The Monte Carlo method is used to generate multiple groups of sample values ​​within a given plate number interval. The plate number optimization result is obtained by optimizing the simulation results corresponding to the sample values. Reflux ratio adjustment submodule: Based on the gas-liquid flow characteristic parameters in the separation tower, a numerical simulation method, specifically a steady-state simulation method, is used to model and simulate different reflux ratios. The reflux ratio ranges from 0.2 to 0.8 with an interval of 0.1. After adjusting the reflux ratio parameters, the gas-liquid flow field is simulated and calculated. The flow distribution and contact time of the gas-liquid two phases are calculated by the dynamic coupling solution. Combined with the calculated values ​​of the gas-liquid contact area and the separation efficiency, the particle swarm optimization algorithm is used to optimize the separation efficiency within the reflux ratio range. The particle swarm size is set to 50, the number of iterations is set to 100, the initial value of the inertia weight is set to 0.9 and gradually decays to 0.4, and finally the reflux ratio optimization result is obtained; Submodule for evaluating the effect of material transfer: Based on the simulation of gas-liquid flow in the tower, the numerical analysis method is used, specifically direct numerical simulation, to calculate the material transfer efficiency of the operating conditions after the number of tower plates and the reflux ratio are adjusted. The calculation of the mass transfer efficiency is completed by solving the second-order partial differential equation. The equation is in the form of a convection-diffusion equation, which is discretized using the finite volume method. The time step is 0.01 seconds, and the spatial discrete grid size is 1 mm. The gas-liquid two-phase concentration distribution is generated, and the efficiency is calculated by combining the material diffusion coefficient and the interface migration rate. By generating a relationship diagram between the material transfer efficiency and the number of tower plates and the reflux ratio, the adjustment results of the separation tower operating parameters are obtained; The adjustment results of the separation tower operating parameters are specifically the number of plates, reflux ratio, solvent feed rate and gas-liquid contact area.

[0021] See also Figure 2 The reaction process screening module includes a reaction temperature adjustment submodule, a catalyst concentration adjustment submodule and a reaction rate calculation submodule, wherein: Reaction temperature adjustment submodule: Based on the components of the reaction substances and the reaction time, adjust the reaction temperature, set different temperature conditions, simulate the effect of temperature on the reaction rate, evaluate the change of reaction rate at different temperatures, and obtain the reaction temperature optimization results; Catalyst concentration adjustment submodule: Based on the components of the reactants and the reaction time, adjust the catalyst concentration, set different catalyst concentration conditions, simulate the effect of catalyst concentration changes on the reaction rate, evaluate the change of reaction rate under different concentration conditions, and obtain the catalyst concentration optimization result; Reaction rate calculation submodule: Based on the optimization results of reaction temperature and catalyst concentration, combined with the reaction material components and reaction time, calculate the reaction rate and component concentration changes under different reaction process conditions, generate a relationship diagram between reaction yield and process conditions, and obtain a relationship diagram between reaction yield and process conditions; Reaction temperature adjustment submodule: Based on the reaction material composition and reaction time, the relationship between temperature and reaction rate is calculated using numerical calculation methods. The temperature condition range is set to 273 to 373 Kelvin, with an interval of 10 Kelvin. The reaction rate constant under different temperature conditions is calculated, and the influence of temperature on the reaction rate is simulated and analyzed. The simulation results are converged using an iterative algorithm. During the simulation process, the input parameters include temperature value, reaction time and reactant concentration. The output is the reaction rate value under the corresponding conditions. A temperature and reaction rate relationship data table is generated, and the reaction temperature optimization result is obtained by combining statistical analysis; Catalyst concentration adjustment submodule: Based on the reaction material components and reaction time, the catalytic reaction kinetic model is used to calculate the relationship between catalyst concentration and reaction rate. The catalyst concentration range is set to 0.1 to 1 mol per liter, with an interval of 0.1 mol per liter. The input parameters include catalyst concentration, reaction time and initial reactant concentration. The output is the reaction rate value under the corresponding concentration conditions. The catalyst concentration and reaction rate relationship curve is generated by normalizing the simulation results. The gradient descent algorithm is used to optimize and analyze the simulation results. Combined with the optimized parameter values, the catalyst concentration optimization results are obtained; Reaction rate calculation submodule: Based on the optimization results of reaction temperature and catalyst concentration, combined with the reaction material components and reaction time, the numerical simulation method is used to calculate the reaction rate under different reaction process conditions. The input parameters include temperature, catalyst concentration, initial reactant concentration and reaction time range. The time range is set to 0 to 100 seconds, and the time interval is 1 second. The simulation output is the component concentration change data corresponding to the time point. The numerical integration method is used to accumulate the reaction yield and generate a data table of the relationship between component concentration and time. Combined with the optimized temperature and catalyst concentration results, a reaction yield and process condition relationship diagram is generated to obtain the reaction yield and process condition relationship diagram; The relationship diagram between reaction yield and process conditions includes reaction temperature, catalyst dosage, tetrahydrofuran yield and time dependence.

[0022] See also Figure 2 The mass transfer optimization module includes a gas-liquid mass transfer path optimization submodule, an operation parameter adjustment submodule and a mass transfer efficiency evaluation submodule, wherein: Gas-liquid mass transfer path optimization submodule: Based on the adjustment results of the separation tower operating parameters, the gas-liquid flow path in the separation tower is optimized. By simulating the gas-liquid flow under different operating conditions, the influence of different paths on the mass transfer efficiency of the material is calculated, the mass transfer efficiency of each path is determined, the appropriate gas-liquid contact path is selected, and the optimized mass transfer path parameters are generated; Operation parameter adjustment submodule: Based on the optimization of mass transfer path parameters, the differential evolution algorithm is used to adjust the solvent feed rate, the number of plates and the reflux ratio. By simulating the gas-liquid flow state under different operating conditions, the changes in mass transfer effect are calculated, and the influence of each operating parameter on material mass transfer is analyzed. The operating conditions are adjusted to improve the mass transfer efficiency and generate the operation parameter optimization results; Mass transfer efficiency evaluation submodule: Based on the optimization results of operating parameters, evaluate the mass transfer efficiency under different operating conditions, simulate the gas-liquid flow and material mass transfer process, calculate the mass transfer efficiency under different conditions, generate the relationship diagram between mass transfer efficiency and operating conditions, and obtain the optimized mass transfer path results; Gas-liquid mass transfer path optimization submodule: Based on the adjustment results of the separation tower operating parameters, the computational fluid dynamics method is used to optimize the gas-liquid flow path in the tower. The input parameters include operating pressure, temperature, number of plates, reflux ratio and solvent feed rate. The turbulence model is combined with the multiphase flow model to simulate the gas-liquid flow process, and the gas-liquid contact area and mass transfer rate of different paths are calculated. The Lagrangian particle tracking method is used to refine the gas-liquid flow path. During the simulation process, the number of particles is set to 5000 and the time step is 0.01 second. The gas-liquid contact path data table is generated through path analysis. Combined with the mass transfer rate calculation results of each path, the path with the largest mass transfer rate is selected to generate the optimized mass transfer path parameters; Operation parameter adjustment submodule: Based on the optimization of mass transfer path parameters, a multi-objective optimization algorithm is used to adjust the solvent feed rate, the number of plates and the reflux ratio. The input parameters include a feed rate range of 1 to 10 kg per hour with an interval of 1 kg per hour, a number of plates ranging from 20 to 50 with an interval of 5, and a reflux ratio range of 0.2 to 0.8 with an interval of 0.1. By simulating the gas-liquid flow state under different operating conditions, the control volume method is used to discretize the flow field under the operating conditions. The simulation output includes gas-liquid flow distribution, gas-liquid contact time and mass transfer efficiency. A genetic algorithm is used for multi-objective optimization, with the population size set to 100, the number of iterations to 50, the crossover probability to 0.8, and the mutation probability to 0.2. The optimization calculation is combined to generate the operation parameter optimization results. Mass transfer efficiency evaluation submodule: Based on the optimization results of operating parameters, the direct numerical simulation method is used to evaluate the mass transfer efficiency under different operating conditions. The input parameters include the optimized operating conditions, gas-liquid physical properties and reaction time. The fluid distribution grid division method is used in the simulation process. The grid unit size is set to 5 mm and the time step is 0.005 seconds. The coupling process of gas-liquid flow and material mass transfer is calculated. The output results include the mass transfer efficiency values ​​corresponding to the time points, and the relationship curve between mass transfer efficiency and operating conditions is generated. Combined with the simulation results, the optimized mass transfer path results are generated; Among them, the optimized mass transfer path parameters are specifically the solvent feed rate, the number of plates, the reflux ratio and the gas-liquid exchange rate.

[0023] Differential evolution algorithm, formula: ; in: is the gas-liquid interface mass transfer coefficient, is the gas flow rate, is the solvent feed rate adjustment factor, is the gas-liquid contact area, is the plate number correction factor, is the concentration of the target component in the liquid phase, is the concentration of the target component in the gas phase, is the influence coefficient of reflux ratio; Execution process: First, initialize the operating parameter population and randomly generate the gas flow rate , Solvent feed rate adjustment factor , Plate number correction factor and reflux ratio influence coefficient The initial value of the gas-liquid flow state is then used to calculate the mass transfer effect under each set of operating parameters, evaluate the fitness value of each individual, and perform differential mutation operations at the same time. New candidate solutions are generated through mutation operations, and improved populations are further generated through crossover operations. For each candidate solution, the gas flow rate is used and solvent feed rate adjustment factor Calculate the corrected flow rate by adjusting to optimize the effect of solvent feed rate on gas flow and then use the plate number correction factor To calculate the gas-liquid contact area of ​​the tower plate structure The influence of reflux ratio is then calculated. , which is used to correct the nonlinear effect of the reflux ratio on the mass transfer coefficient. Finally, the corrected parameters are substituted into the calculation to obtain the new gas-liquid interface mass transfer coefficient , evaluate the optimized operating parameter combination, and obtain the optimal operating parameter configuration through multiple iterations.

[0024] See also Figure 2 The reaction process modeling module includes a kinetic model building submodule, a reaction condition analysis submodule and a reaction kinetic parameter calculation submodule, among which: Kinetic model building submodule: Based on the relationship diagram between reaction yield and process conditions, a reaction kinetic model is built, and different reaction temperatures, catalyst concentrations and reaction times are set. The reaction rate and concentration changes are simulated by calculation to derive the reaction kinetic equation and generate the reaction kinetic equation; Reaction condition analysis submodule: Based on the reaction kinetics equation, analyze the effects of different reaction temperatures, catalyst concentrations and reaction times on reaction rates and intermediate concentrations, simulate the changes in reactions under different process conditions, obtain the effects of various reaction conditions on substance concentrations and yields, and generate reaction condition optimization results; Reaction kinetics parameter calculation submodule: Based on the reaction condition optimization results, the reaction rate, component concentration and intermediate changes under different reaction conditions are calculated, and the reaction kinetics model parameters are obtained by establishing the reaction kinetics model and component change curve; Kinetic model building submodule: Based on the relationship diagram between reaction yield and process conditions, the numerical fitting method is used to model the reaction rate and concentration changes under different reaction conditions. The reaction temperature range is set to 273 to 373 Kelvin, with an interval of 10 Kelvin, the catalyst concentration range is set to 0.1 to 1 mol per liter, with an interval of 0.1 mol per liter, and the reaction time range is set to 0 to 100 seconds, with a time interval of 1 second. The finite difference method is used to calculate the value of the concentration changing with time, and the least squares method is used to fit the relationship between the reaction rate and concentration to generate the reaction kinetic equation parameters. The reaction kinetic equation is generated based on the fitting results; Reaction condition analysis submodule: Based on the reaction kinetics equation, the sensitivity analysis method is used to analyze the effects of different reaction temperatures, catalyst concentrations and reaction times on the reaction rate and intermediate concentration changes. The input parameters include the optimized reaction temperature and catalyst concentration range. The initial value of the concentration distribution is set to 1 mol per liter. The simulation time range is 0 to 100 seconds, and the time step is 1 second. The Euler method is used to discretize and solve the equation. The simulation results include the reaction rate and concentration distribution curves under each condition. The differential analysis method is used to calculate the change rate of each condition on the concentration and yield, and the reaction condition optimization results are generated in combination with the analysis results; Reaction kinetics parameter calculation submodule: Based on the reaction condition optimization results, the direct numerical simulation method is used to calculate the kinetic parameters under different reaction conditions. The input parameters include the reaction rate constant, the initial substance concentration and the catalyst concentration. The simulation time range is 0 to 100 seconds, and the time step is 1 second. The implicit difference method is used to numerically integrate the kinetic model to generate a data table of component concentration and intermediate concentration changes corresponding to the time step. The nonlinear regression method is used to fit the kinetic model parameters, and the reaction kinetic model parameters are generated by combining the fitting data; The reaction kinetic model parameters are specifically the reaction rate constant, the temperature influence coefficient and the effect of catalyst concentration on the reaction rate.

[0025] See also Figure 2 The multi-stage reaction condition optimization module includes a reaction condition selection submodule, a reaction product distribution calculation submodule and an optimization condition set generation submodule, wherein: Reaction condition selection submodule: Based on the optimized mass transfer path parameters and reaction kinetic model parameters, the operating temperature, catalyst concentration and reaction time of each reaction stage are selected, and a combination of reaction conditions is selected. The reaction rate, material conversion rate and energy consumption under each combination are evaluated to generate phased optimized reaction conditions. Reaction product distribution calculation submodule: Based on the phased optimization of reaction conditions, the simulated annealing algorithm is used to calculate the product distribution at different reaction stages, simulate the conversion of reaction substances at each stage, gradually estimate the product generation ratio and by-product generation, analyze the material distribution and energy consumption under different reaction conditions, and generate a reaction product distribution map; Optimization condition set generation submodule: Based on the reaction product distribution map, the appropriate reaction condition combination is selected in combination with the energy consumption of each stage, and the final optimization is performed according to the reaction rate and product selectivity of each stage to generate an optimized reaction condition set; Reaction condition selection submodule: Based on the optimization of mass transfer path parameters and reaction kinetic model parameters, a combined optimization algorithm is used to select the operating temperature, catalyst concentration and reaction time for each reaction stage. The input parameters include a temperature range of 273 to 373 Kelvin with an interval of 10 Kelvin, a catalyst concentration range of 0.1 to 1 mol per liter with an interval of 0.1 mol per liter, and a reaction time range of 10 to 100 seconds with an interval of 10 seconds. During the simulation, the Latin hypercube sampling method is used to generate a combination of reaction conditions. The reaction kinetic model is used to calculate the reaction rate, material conversion rate and energy consumption under each combination condition. The generated reaction conditions are optimized in combination with a multi-objective optimization algorithm. The objective function weights are set to 0.5 for reaction rate, 0.3 for material conversion rate, and 0.2 for energy consumption, to generate phased optimized reaction conditions. Reaction product distribution calculation submodule: Based on the phased optimization of reaction conditions, the direct numerical simulation method is used to calculate the product distribution of different reaction stages. The input parameters include the optimized reaction conditions and the initial substance concentration. The implicit difference method is used to discretize the substance conversion during the simulation process. The time step is set to 1 second, and the reaction stage is divided into 3 to 5 sub-stages. The product generation ratio and the amount of by-product generation are calculated according to the reaction conditions of each sub-stage. The simulation results include the substance concentration changes and energy consumption corresponding to the time point. The material distribution curve is generated by the normalization method, and the reaction product distribution map is generated in combination with the simulation data; Optimization condition set generation submodule: Based on the reaction product distribution map and combined with the energy consumption of each stage, the analytic hierarchy process is used to optimize the reaction condition combination. The input parameters include the energy consumption, reaction rate and product selectivity of each stage. The optimization index is scored and calculated using the weight matrix. The energy consumption weight is set to 0.4, the reaction rate weight is set to 0.3, and the product selectivity weight is set to 0.3. The score value of each condition combination is calculated by the analytic hierarchy process. The final reaction condition combination is selected based on the optimal score value to generate the optimized reaction condition set. Among them, the optimized reaction condition set includes the optimized configuration of reaction stage temperature, catalyst concentration, reaction time and energy consumption.

[0026] Simulated annealing algorithm, according to the formula: ; in: is the improved reaction rate constant, is the pre-factor, is the activation energy of the reaction, is the gas constant, is the temperature, is the concentration of the reactant, is the initial reactant concentration, is the effect index of concentration on reaction rate, is the pressure of the reactants, is the initial reactant pressure, is the effect index of pressure on reaction rate, is the reaction time, is the attenuation coefficient of the reaction time; Implementation process: First determine the initial conditions of the reaction system, including the initial concentration , initial pressure and time , and set the temperature required for the reaction , and determine the pre-factor ,activation energy , concentration index , Pressure Index and the time decay coefficient The specific value of Substitute the exponential term Calculate the effect of temperature on the reaction rate by calculating the concentration With initial concentration Ratio of The power represents the effect of concentration change on the reaction rate, and then the pressure and initial pressure Ratio Substitute into the formula, through its The power describes the effect of pressure change, and finally the time Substitute the time decay factor Calculate the dynamic effect of time on the reaction rate and obtain the reaction rate constant .

[0027] See also Figure 2 The tetrahydrofuran synthesis module includes a reaction process implementation submodule, a reaction condition adjustment submodule and a separation and purification step submodule, wherein: Reaction process implementation submodule: Based on the optimized reaction condition set, a multi-stage reaction process is implemented, and the temperature, catalyst concentration and reaction time of each reaction stage are adjusted in turn. By adjusting the reaction conditions, each stage reaction is executed, the reaction substances are converted and intermediate products are generated, and the staged reaction products are obtained; Reaction condition adjustment submodule: Based on the staged reaction products, adjust the temperature, catalyst concentration and reaction time of each reaction stage, adjust the conditions according to the product distribution, optimize the reaction rate and control the generation of by-products to obtain the optimized reaction conditions; Separation and purification step submodule: Based on the optimized reaction conditions, separation and purification steps are implemented to separate the reaction products and by-products, and then impurities are removed through multiple purification operations to obtain tetrahydrofuran finished product; Reaction process implementation submodule: Based on the optimized reaction condition set, a multi-stage reaction control algorithm is used for the multi-stage reaction process. The input parameters include the temperature range of each reaction stage is 273 to 373 Kelvin, the catalyst concentration range is 0.1 to 1 mol per liter, and the reaction time range is 10 to 100 seconds. The process controller is used to gradually adjust the reaction conditions. The real-time monitoring system is used to record the changes in the concentration of substances during the reaction. The control parameters include the heat source power, the catalyst injection rate and the reaction time control instructions. By executing the reaction in stages, the reaction substances are gradually converted and intermediate products are generated. The staged reaction product data table is output to generate staged reaction products; Reaction condition adjustment submodule: Based on the staged reaction products, the temperature, catalyst concentration and reaction time of each reaction stage are adjusted using a dynamic condition optimization algorithm. The input parameters include the product concentration distribution and generation rate output in the previous stage. The least squares method is used to fit the relationship between material distribution and time. The reaction conditions are adjusted in real time in combination with the control optimization algorithm. The adjustment range is set to be consistent with the optimized reaction condition set. The by-product generation amount is dynamically calculated, and the maximum tolerance value of the by-product generation amount is set through the feedback control system. The penalty factor is used to adjust the weight of the reaction conditions that exceed the tolerance value, and finally the optimized reaction conditions are generated; Separation and purification step submodule: Based on the optimized reaction conditions, a multi-step separation and purification process is used to separate and purify the reaction products and by-products. The input parameters include the solvent type of dichloromethane, the solvent ratio of 1:5, the distillation temperature range of 50 to 80 degrees Celsius, and the distillation pressure range of 1 to 5 kPa. The target product is extracted by batch distillation equipment, and the target product is purified by recrystallization technology. The solvent evaporation time is set to 4 hours, and the crystallization temperature range is 0 to 10 degrees Celsius. Impurities are removed by repeated purification, and the quality and purity parameters of the purified product are recorded to finally generate tetrahydrofuran finished product; The finished tetrahydrofuran product specifically includes the purity of tetrahydrofuran, the output of the target product and the residue removal rate.

[0028] A method for preparing tetrahydrofuran using BDO tar comprises the following steps: Step 1: Based on the chemical composition information of BDO tar, the solvent feed rate, the number of plates and the reflux ratio are adjusted to simulate the gas-liquid flow characteristics in the separation tower, calculate the amount of material exchange between gas and liquid and the absorption efficiency during the mass transfer process, and generate the adjustment results of the separation tower operating parameters; Step 2: Based on the adjustment results of the separation tower operating parameters, adjust the reaction temperature, catalyst concentration and reaction time, analyze the chemical reaction rate of the reactants and the changes in the concentration of the generated components, calculate the reactant concentration consumption and product generation under different conditions, and generate reaction rate and component concentration change data; Step 3: Based on the reaction rate and component concentration change data, the differential evolution algorithm is used to calculate the mass transfer path and mass transfer rate of the gas-liquid interface by adjusting the number of tower plates, solvent feed rate and reflux ratio, and the gas-liquid interface efficiency under multiple groups of operating conditions is compared to generate the adaptive mass transfer path parameters; Step 4: Based on the adapted mass transfer path parameters, analyze the effects of temperature, catalyst concentration and reaction time on the reaction process, establish a kinetic change curve of substance concentration and reaction rate, calculate the concentration change and generation rate of key intermediates in the reaction process, and generate reaction kinetic model parameters; Step 5: Based on the reaction kinetic model parameters, a simulated annealing algorithm is used to optimize the combination of temperature, catalyst concentration and reaction time in the multi-stage reaction stage, adjust the separation operation parameters to obtain the target product with a purity that meets the standard, and perform the material separation and purification steps in the reaction stage to generate tetrahydrofuran finished product.

[0029] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A system for preparing tetrahydrofuran using BDO tar, characterized in that: The system comprises: Separation tower parameter design module: Based on the gas-liquid flow characteristic parameters in the separation tower, the number of plates, reflux ratio and solvent feed rate are adjusted, and the gas-liquid flow in the separation tower is simulated. By adjusting the operating parameters in the separation tower, the adjustment results of the separation tower operating parameters are generated; Reaction process screening module: Based on the components of the reaction substances and the reaction time parameters, the reaction temperature, catalyst concentration and reaction time are adjusted, and the conditions of different reaction processes are evaluated at the same time. By calculating the reaction rate and the change in component concentration, a relationship diagram between the reaction yield and the process conditions is generated; Mass transfer optimization module: Based on the adjustment results of the separation tower operating parameters, the differential evolution algorithm is used to optimize the gas-liquid mass transfer path, adjust the solvent feed rate, the number of plates and the reflux ratio, and select the suitable mass transfer efficiency conditions by simulating the gas-liquid flow and material mass transfer process to generate the optimized mass transfer path parameters; Reaction process modeling module: Based on the relationship diagram between the reaction yield and the process conditions, a reaction kinetic model is established to analyze the effects of temperature, catalyst concentration, and reaction time on the reaction rate and the intermediate concentration. By establishing a reaction kinetic equation and a component change curve, reaction kinetic model parameters are generated; Multi-stage reaction condition optimization module: Based on the optimized mass transfer path parameters and the reaction kinetic model parameters, a simulated annealing algorithm is used to optimize the temperature, catalyst concentration and reaction time of each reaction stage, and a reaction condition combination is selected. By calculating the reaction product distribution and energy consumption, an optimized reaction condition set is generated; Tetrahydrofuran synthesis module: Based on the optimized reaction condition set, a multi-stage reaction process is implemented to adjust the temperature, catalyst concentration and reaction time of each reaction stage, and tetrahydrofuran product is generated by controlling the reaction conditions and performing separation and purification steps.

2. The system for preparing tetrahydrofuran using BDO tar according to claim 1, characterized in that: The adjustment results of the separation tower operating parameters are specifically the number of plates, reflux ratio, solvent feed rate and gas-liquid contact area.

3. The system for preparing tetrahydrofuran using BDO tar according to claim 1, characterized in that: The relationship diagram between the reaction yield and the process conditions includes the reaction temperature, the amount of catalyst, the tetrahydrofuran yield and the time dependence.

4. The system for preparing tetrahydrofuran using BDO tar according to claim 1, characterized in that: The mass transfer optimization module includes a gas-liquid mass transfer path optimization submodule, an operation parameter adjustment submodule and a mass transfer efficiency evaluation submodule, wherein: Gas-liquid mass transfer path optimization submodule: based on the adjustment results of the separation tower operating parameters, the gas-liquid flow path in the separation tower is optimized, by simulating the gas-liquid flow under different operating conditions, calculating the influence of different paths on the mass transfer efficiency of the material, determining the mass transfer efficiency of each path, selecting an appropriate gas-liquid contact path, and generating optimized mass transfer path parameters; Operation parameter adjustment submodule: Based on the optimized mass transfer path parameters, the differential evolution algorithm is used to adjust the solvent feed rate, the number of plates and the reflux ratio, and the changes in the mass transfer effect are calculated by simulating the gas-liquid flow state under different operating conditions, and the influence of each operating parameter on the mass transfer of substances is analyzed. The operating conditions are adjusted to improve the mass transfer efficiency, and the operation parameter optimization results are generated; Mass transfer efficiency evaluation submodule: based on the optimization results of the operating parameters, evaluate the mass transfer efficiency under different operating conditions, simulate the gas-liquid flow and material mass transfer process, calculate the mass transfer efficiency under different conditions, generate a relationship diagram between the mass transfer efficiency and the operating conditions, and obtain the optimized mass transfer path results; The optimized mass transfer path parameters are specifically the solvent feed rate, the number of plates, the reflux ratio and the gas-liquid exchange rate.

5. A system for preparing tetrahydrofuran using BDO tar according to claim 4, characterized in that: The differential evolution algorithm, formula: ； in: is the gas-liquid interface mass transfer coefficient, is the gas flow rate, is the solvent feed rate adjustment factor, is the gas-liquid contact area, is the plate number correction factor, is the concentration of the target component in the liquid phase, is the concentration of the target component in the gas phase, is the reflux ratio influence coefficient.

6. The system for preparing tetrahydrofuran using BDO tar according to claim 1, characterized in that: The reaction kinetics model parameters specifically include the reaction rate constant, the temperature influence coefficient and the influence of the catalyst concentration on the reaction rate.

7. The system for preparing tetrahydrofuran using BDO tar according to claim 1, characterized in that: The multi-stage reaction condition optimization module includes a reaction condition selection submodule, a reaction product distribution calculation submodule and an optimization condition set generation submodule, wherein: Reaction condition selection submodule: based on the optimized mass transfer path parameters and reaction kinetic model parameters, select the operating temperature, catalyst concentration and reaction time of each reaction stage, select the combination of reaction conditions, and evaluate the reaction rate, material conversion rate and energy consumption under each combination to generate phased optimized reaction conditions; Reaction product distribution calculation submodule: Based on the staged optimization reaction conditions, a simulated annealing algorithm is used to calculate the product distribution at different reaction stages, simulate the conversion of the reaction substances at each stage, gradually estimate the product generation ratio and the generation of by-products, analyze the material distribution and energy consumption under different reaction conditions, and generate a reaction product distribution map; Optimization condition set generation submodule: based on the reaction product distribution map, select an appropriate reaction condition combination in combination with the energy consumption of each stage, and perform final optimization according to the reaction rate and product selectivity of each stage to generate an optimized reaction condition set; The optimized reaction condition set includes the optimized configuration of reaction stage temperature, catalyst concentration, reaction time and energy consumption.

8. The system for preparing tetrahydrofuran from BDO tar according to claim 7, characterized in that: The simulated annealing algorithm is based on the formula: ； in: is the improved reaction rate constant, is the pre-factor, is the activation energy of the reaction, is the gas constant, is the temperature, is the concentration of the reactant, is the initial reactant concentration, is the effect index of concentration on reaction rate, is the pressure of the reactants, is the initial reactant pressure, is the effect index of pressure on reaction rate, is the reaction time, is the attenuation coefficient of the reaction time.

9. The system for preparing tetrahydrofuran from BDO tar according to claim 1, characterized in that: The tetrahydrofuran finished product specifically includes the purity of tetrahydrofuran, the yield of the target product and the residue removal rate.

10. A method for preparing tetrahydrofuran using BDO tar, characterized in that: The system for preparing tetrahydrofuran by using BDO tar according to any one of claims 1 to 9 comprises the following steps: Step 1: Based on the chemical composition information of BDO tar, the solvent feed rate, the number of plates and the reflux ratio are adjusted to simulate the gas-liquid flow characteristics in the separation tower, calculate the amount of material exchange between gas and liquid and the absorption efficiency during the mass transfer process, and generate the adjustment results of the separation tower operating parameters; Step 2: Based on the adjustment result of the separation tower operating parameters, the reaction temperature, catalyst concentration and reaction time are adjusted, the chemical reaction rate of the reactants and the changes in the concentration of the generated components are analyzed, the reactant concentration consumption and the product generation under different conditions are calculated, and the reaction rate and component concentration change data are generated; Step 3: Based on the reaction rate and component concentration change data, a differential evolution algorithm is used to calculate the mass transfer path and mass transfer rate of the gas-liquid interface by adjusting the number of tower plates, solvent feed rate and reflux ratio, and the gas-liquid interface efficiency under multiple groups of operating conditions is compared to generate adaptive mass transfer path parameters; Step 4: Based on the adapted mass transfer path parameters, analyze the effects of temperature, catalyst concentration and reaction time on the reaction process, establish a kinetic change curve of substance concentration and reaction rate, calculate the concentration change and generation rate of key intermediates in the reaction process, and generate reaction kinetic model parameters; Step 5: Based on the reaction kinetic model parameters, a simulated annealing algorithm is used to optimize the combination of temperature, catalyst concentration and reaction time in the multi-stage reaction stage, adjust the separation operation parameters to obtain the target product with a purity that meets the standard, and perform the material separation and purification steps in the reaction stage to generate a tetrahydrofuran product.

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