Mechanical vapor recompression control system and optimization method

Abstract

The present application relates to a kind of mechanical vapor recompression control system and optimization method.The conventional mechanical vapor recompression control system is often set system parameters to reduce system power consumption as target, but this can cause heat exchange area to be too large, and further cause system initial investment to be high, and the problems such as large floor area;And only consider heat exchange area, again can make system power consumption be too large, system operating cost is improved and does not meet the requirement of energy saving.The present application is by constructing steam compression re-evaporation system model, obtains the best evaporation temperature and compression temperature rise under current working condition and the total power consumption and total heat exchange area under this condition, according to the analysis of feed water working condition and system operation, set the best operating condition.The present application is to steam compression re-evaporation system parameter real-time operation input-output data analysis and calculation, involves mathematical modeling by the calculation of physical property parameters, gives the evaporation temperature and compression temperature rise suitable for system, guarantees the best economic operating cost of system.

Description

Technical Field

[0001] This invention belongs to the field of seawater, wastewater, and sewage treatment technology, and relates to a mechanical steam recompression control system and optimization method. Background Technology

[0002] Currently, my country's energy structure is characterized by "abundant coal, scarce oil, and limited gas." Coal and water are the two major resource elements for developing new coal chemical industries. However, my country's coal and water resources are generally distributed in reverse order, with most coal bases located in the water-scarce northwest region. Coal-fired power plants consume large amounts of water, resulting in significant wastewater discharge. Direct discharge of this wastewater would cause irreversible environmental damage. Therefore, it is necessary to treat the wastewater from coal-fired power plants before discharge. However, the main component of coal-fired power plant wastewater is Cl... - , Na + Ca 2+ For wastewater that is difficult to treat, traditional treatment methods mainly include biological, physical, and physicochemical methods. Evaporation and concentration technology is also a commonly used method in wastewater treatment. Figure 1 This is a typical wastewater treatment process. Wastewater undergoes sedimentation and filtration to remove impurities, and then reverse osmosis and evaporation crystallization technologies to obtain fresh water and concentrated brine, thus realizing the resource utilization of wastewater.

[0003] Evaporation concentration technology can not only produce fresh water, but also recover salt after certain crystallization processes, thus possessing economic value. However, traditional evaporation concentration technology involves large equipment, complex systems, high steam consumption, and low conversion efficiency, failing to achieve energy-saving and economical effects. This hinders its application and development in wastewater treatment. Mechanical vapor recompression (MVR) technology, on the other hand, uses a compressor to pressurize the secondary steam generated by evaporation, raising its temperature, and then re-enters the evaporator as a heat source to release energy, creating a continuous cycle. Its classic structural principle is as follows: Figure 2The energy of the secondary steam can be fully utilized in the normal operation of the whole system except for the steam input when starting the system and the power consumption of the compressor. Due to the re-compression technology of the MVR, a large amount of steam and condensate water is saved in the whole process, and the cost of waste water treatment is greatly saved. However, in order to realize more energy saving of the whole system, the process of the MVR technology needs to be considered and optimized, so that the energy efficiency and economic indicators of the system can be optimized. According to the steady-state mathematical simulation results of the system, under the premise that the evaporation capacity, the feed quantity and the feed temperature are constant, only reducing the system power consumption as the optimization target will cause the heat exchange area to be too large, and then cause the initial investment of the system to be high, the land occupation to be large and the like; only reducing the heat exchange area as the optimization target will cause the system power consumption to be too large, the operation cost of the system to be increased and the energy saving requirement to be not met. Therefore, the two operating parameters of the compression temperature rise and the evaporation temperature have a relatively optimal combination value, so that the total power consumption and the total heat exchange area of the system are relatively small.

[0004] On the basis of analyzing the MVR system process in the water treatment process, the application establishes a strict mathematical model of the MVR system, can calculate the evaporation temperature and the compression temperature rise suitable for the current working condition according to the input and output data of the real-time operation of the system, so that the total power consumption and the total heat exchange area of the system are the optimal combination value, and the application provides protection for the factory to select a suitable evaporator. SUMMARY

[0005] The application provides a mechanical vapor recompression control system and an optimization method.

[0006] A mechanical vapor recompression control system includes a sensor module, a data acquisition module, a data conversion module, a human-machine interface module, a central processing module, an analog calculation module, a parameter decision module, a display module, and a setting module. The sensor module includes a temperature sensor, a flow sensor, and a salinity sensor. The data acquisition module collects data from the sensor modules, including the feed water temperature, salinity, and flow rate; the temperature of the preheated feed water; the temperature, salinity, and flow rate of the circulating liquid; the temperature of the discharged condensate; the temperature, salinity, and flow rate of the concentrate; the temperature and flow rate of the secondary steam; and the temperature of the superheated steam. The data conversion module converts the collected analog quantities into corresponding digital quantities. The human-machine interface module sets the physical properties of the vapor compression re-evaporation system, the data acquisition cycle, and the required data acquisition parameters. The central processing module stores the mathematical model and thermophysical parameters of the vapor compression and re-evaporation system. It also receives and stores data from the data acquisition module and the processed data from the simulation and parameter decision modules, sending the results to the display module. The simulation module, based on the received data, calls the system program to calculate the optimal compression temperature rise and evaporation temperature, as well as the total power consumption and heat exchange area, and sends the results to the display and parameter decision modules. The parameter decision module compares and analyzes historical data to determine if the compression temperature rise and evaporation temperature are optimal, and sends the results to the display module. The display module shows the calculation results from the simulation and parameter decision modules. The setting module allows users to manually set the heat exchange area and corresponding compression temperature rise and evaporation temperature of the spray-type serpentine tube heat exchanger.

[0007] Based on the above system, there is a mechanical vapor recompression optimization control method, which specifically includes the following steps:

[0008] Step A1: Manually set the physical property parameters of the vapor compression and re-evaporation system through the human-computer interaction module, with a data acquisition cycle of t. c The limits of the data that need to be collected;

[0009] Step A2: Use the data acquisition module to collect the feed water temperature T at the current time point. i Flow rate M f Concentration X f The temperature T of the feed water after preheating by two heat exchangers f The temperature T of the concentrated brine at the evaporator outlet b Flow rate M c Concentration X b The temperature T of the condensate after passing through heat exchanger 1 do The temperature T of the concentrated brine produced after passing through heat exchanger 2 bo Flow rate M b Concentration X b And the temperature T of the secondary steam v Flow rate Md and the temperature T of the superheated steam s Let the variable t1 equal the current time, and then transmit the above parameters to the central processing module through the data conversion module;

[0010] Step A3: The central processing module checks the obtained feed water parameters, and if they meet the requirements, it enters step A4, otherwise it enters step A2;

[0011] Step A4: The central processing module calls the simulation calculation module according to the internally stored steam compression re-evaporation system model. The simulation calculation module calculates the optimization problem of the central processing module to obtain the optimal evaporation temperature and compression temperature rise under the current working condition, as well as the total power consumption and total heat exchange area under this condition, and sends them to the central processing module;

[0012] The steam compression re-evaporation system model is established through the following sub-steps:

[0013] Sub-step B1.1: Assume that the salt concentration in the steam is 0; Assume that the energy loss of the evaporator, heat exchanger, pipeline and pump is not considered; Assume that all the condensate water in the condensation process is liquid, i.e. the mass percentage concentration X d of the condensed steam is 0; Establish the steam compression re-evaporation system model:

[0014] The system material balance equation is: M f =M d +M b , M f X f =M b X b +M d X d ;

[0015] Wherein, M f represents the mass flow rate of the feed, M d represents the mass flow rate of the condensed steam water, M b represents the mass flow rate of the concentrated salt water, X f represents the mass percentage concentration of the feed water, X b represents the mass percentage concentration of the concentrated salt water, and X d represents the mass percentage concentration of the condensed steam;

[0016] In the preheater: Q HEX1 =M d C pd (T d -T do )=M f1 C pf (T i1 -T i );Q HEX2 =Mb C pb (T b -T bo ) = M f2 C pf (T i2 -T i );

[0017] Among them, Q HEX1 Q HEX2 C represents the heat generated in the two heat exchangers. pd C pf C pb M represents the specific heat capacity of condensate, feed water, and concentrated brine, respectively. f1 M represents the feed mass flow rate after passing through heat exchanger 1. f2 T represents the feed mass flow rate after passing through heat exchanger 2. i T represents the temperature of the feed water. b T represents the temperature of the concentrated brine at the evaporator outlet, which is the boiling point temperature of the feed water to the evaporator. d T represents the temperature of the condensate at the evaporator outlet. bo T represents the temperature of the concentrated brine produced after passing through heat exchanger 2. do T represents the temperature of the condensate after passing through heat exchanger 1. i1 This indicates the temperature (T) of the feed water after heat exchange in heat exchanger 1. i2 This indicates the temperature of the feed water after heat exchanger 2.

[0018] In the evaporator: Q out =M d λ vp +M f ·C pf (T b -T f );Q in =M d λ d +M d ·C pv (T s -T d );Q in =Q out ;

[0019] T vp =T b -BPE;BPE=f·Δ0′; Δ0′=69.8×C b 3 -8.15×C b 2 +5.19×C b ;

[0020] T vp = T v ; ΔT = T d - T b ; Q e = U e · A e · LTMD

[0021]

[0022] wherein Q out , Q in represent the heat generated in the tube and outside the tube in the evaporator, respectively, T f represent the temperature of the feed water after passing through two heat exchangers, respectively, Cp v represents the specific heat capacity of the steam above the demister, λ vp , λ d represent the latent heat below the demister and the latent heat of the tube steam, respectively, T s represents the temperature of the superheated steam, T vp represents the temperature below the demister, BPE represents the boiling point elevation, f represents a correction factor, T v represents the temperature above the demister, Q e represents the heat transfer of the evaporator, U e represents the total heat transfer coefficient; Δ0' represents the boiling point elevation of the solution at normal pressure, r represents the latent heat of vaporization of water at a specified pressure, C b represents the concentration of brine, and ΔT represents the difference between the temperature of the condensed steam at the outlet of the evaporator and the boiling point temperature of the feed water.

[0023] In the compressor:

[0024] H s = 2499.15 + 1.955 × T s - 1.927 × 10 -3 × T s 2 ; W fre = (H s - H v ) · 0.000277 · ε / η com .

[0025] wherein H v represents the specific enthalpy of saturated steam, H s represents the specific enthalpy of superheated steam, ε represents the compression ratio, η com represents the efficiency of the compressor, and W fre represents the power of the variable frequency compressor.

[0026] Sub-step B1.2: Establishing the optimization objective function of the vapor compression re-evaporation system, taking the total heat exchange area and the total power consumption of the system as the optimization objectives, and taking the evaporation temperature T e , the compression temperature rise ΔT bs as the optimization variables, the first objective function g1(x) and the second objective function g2(x) are as follows:

[0027] The optimization model expression is as follows:

[0028] Wherein, x is the optimization variable vector, that is, the evaporation temperature and the compression temperature rise, g1(x) is the first objective function, g2(x) is the second objective function, S eva+pre represents the total heat exchange area of the evaporator and the heat exchanger, W com represents the total power consumption of the compressor, the constraint conditions are the system material balance equation and the energy balance equation in the evaporator in sub-step B1.1, and the superscripts L and U represent the upper and lower limits of the variable;

[0029] Sub-step B2.1: Solving the model in sub-step B1.2 by using the genetic algorithm to obtain the optimal scheme:

[0030] The specific solving steps are as follows:

[0031] Sub-step C1: Substituting the initial value of the system model, setting the model information and the algorithm parameters.

[0032] Sub-step C2: Randomly generating an initial population, and recording the iteration number gen=1.

[0033] Sub-step C3: Solving the system total energy consumption objective function value and the heat exchange area objective function value corresponding to each individual in the population.

[0034] Sub-step C4: Performing fast non-dominated sorting and crowdedness calculation on the initial population.

[0035] Sub-step C5: Judging whether the first generation of sub-population has been generated, if yes, setting the evolution generation number gen=2, otherwise, performing non-dominated sorting and selection, Gaussian crossover and mutation on the initial population to generate the first generation of sub-population and set the evolution generation number gen=2.

[0036] Sub-step C6: Merging the parent population and the child population.

[0037] Sub-step C7: Judging whether the new parent population has been generated, if not, calculating the objective function of the individual in the new population, and performing fast non-dominated sorting, crowdedness calculation, elite strategy and other operations to generate the new parent population; otherwise, entering sub-step C8.

[0038] Sub-step C8: Generating the child population by performing selection, crossover and mutation operations on the generated parent population.

[0039] Sub-step C9: judging whether the set maximum iteration number is reached, if not, evolution generation gen = gen + 1 and returning to sub-step C6; otherwise, the algorithm ends and the optimal control variable T corresponding to the maximum fitness is output e and ΔT bs , and calculating the total heat exchange area S eva+pre and total power consumption W com of the system corresponding to the case

[0040] Step A5: the parameter decision module compares the data G1 obtained in this period, including the evaporation temperature, the compression temperature rise, the total power consumption and the total heat exchange area of the system, with the data G m obtained in each period before, including the evaporation temperature, the compression temperature rise, the total power consumption and the total heat exchange area of the system (wherein m = 1, 2, …, n, n is the current period number), judges whether the current data is the optimal solution under the working condition, and then transmits the optimal result data after comparison to the central processing module, so as to improve the fault tolerance of the system

[0041] Step A6: the central processing module transmits the received data to the display module, and the display module displays the current time t1, the evaporation temperature T e , the compression temperature rise ΔT bs , the total power consumption W com and the total heat exchange area S eva+pre of the system; the heat exchange area of the heat exchanger and the parameter values of the compression temperature rise and the evaporation temperature of the system are set according to the values of the display module; the current time is recorded as t2, if t2-t1 < t c , the data collection is continued; otherwise, step A2 is turned to start a new round of data collection.

[0042] The application sets the best operating condition by analyzing the working condition of the feed water and the system operation. The traditional design often sets the system parameters to reduce the system power consumption, but this will cause the heat exchange area to be too large, and further cause the system initial investment to be high, the land occupation to be large and other problems; and only considering the heat exchange area will make the system power consumption too large, the system operation cost is increased and does not meet the energy saving requirement. The application analyzes and calculates the input and output data of the real-time operation of the steam compression re-evaporation system parameters, involves the calculation of the property parameters under the modeling combined with mathematics, gives the evaporation temperature and the compression temperature rise suitable for the system, and guarantees the best economic operation cost of the system. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 is a typical process flow chart of wastewater treatment;

[0044] Figure 2 is a schematic diagram of the overall structure of the system of the application;

[0045] Figure 3The operation flow chart of the method of the present application;

[0046] Figure 4 The principle diagram of the steam compression re-evaporation system of the present application;

[0047] Figure 5 The flow chart related to the algorithm of the present application. DETAILED DESCRIPTION

[0048] The present application is further analyzed below in combination with the drawings and specific examples.

[0049] As shown in the figure, a mechanical steam re-compression control system comprises a sensor module, a data acquisition module, a data conversion module, a man-machine interaction module, a central processing module, an analog calculation module, a parameter decision module, a display module and a setting module. Figure 2

[0050] The sensor module comprises a temperature sensor, a flow sensor and a salt content sensor; the data acquisition module is used to acquire the temperature, salt content and flow of the feed water, the temperature of the preheated feed water, the temperature, salt content and flow of the circulating liquid, the temperature of the condensed water, the temperature, salt content and flow of the concentrated liquid, the temperature and flow of the secondary steam and the temperature of the superheated steam.

[0051] The data conversion module is used to convert the collected analog quantity into corresponding digital quantity.

[0052] The man-machine interaction module is used to transmit the physical property parameters of the steam compression re-evaporation system, the data acquisition cycle and the limit value of the data to be acquired, which are set by human being, to the central processing module.

[0053] The central processing module is used to store the mathematical model and the thermal physical property parameters of the steam compression re-evaporation system, receive and store the data of the data acquisition module and the data processed by the analog calculation module and the parameter decision module, and deliver the results to the display module.

[0054] The analog calculation module calculates the compression temperature rise and evaporation temperature suitable for the system, the total power consumption and the heat exchange area according to the received data, and delivers the results to the display module and the parameter decision module.

[0055] The parameter decision module compares and analyzes whether the historical data is the best compression temperature rise and evaporation temperature, and delivers the results to the display module.

[0056] The display module is used to display the calculation results of the analog calculation module and the parameter decision module.

[0057] The setting module is used to set the heat exchange area of the spray type coil heat exchanger and the corresponding compression temperature rise and evaporation temperature by human being. ​

[0058] As Figure 3 shown, based on the above control system, there is a mechanical vapor recompression optimization control method, which specifically includes the following steps:

[0059] Step A1: The operator or engineer sets the property parameters of the vapor compression re-evaporation system through the human-computer interaction module: compressor efficiency η com = 75%, set the data acquisition period t c = 2h, the limit value of the data to be collected, including the temperature, flow rate, and concentration of the feed water, the temperature of the preheated feed water, the temperature, flow rate, and concentration of the circulating liquid, the temperature of the discharged condensed water, the flow rate, concentration, and temperature of the concentrated liquid, and the temperature, flow rate, and superheated steam temperature of the secondary steam;

[0060] Step A2: Use the data acquisition module to collect the feed water temperature T i , flow rate M f , and concentration X f at the current time point, the temperature T f of the feed water after preheating through two heat exchangers, the temperature T b , flow rate M c , and concentration X b of the concentrated brine at the outlet of the evaporator, the temperature T do of the condensed water after passing through heat exchanger 1, the temperature T bo , flow rate M b , and concentration X b of the concentrated brine after passing through heat exchanger 2, and the temperature T v , flow rate M d , and superheated steam temperature T s of the secondary steam, let the variable t1 equal the current time, and then transmit the above parameters to the central processing module through the data conversion module;

[0061] Step A3: The central processing module checks the obtained feed water parameters, and if they meet the requirements, it enters step A4, otherwise it enters step A2;

[0062] Step A4: The central processing module calls the simulation calculation module according to the internally stored vapor compression re-evaporation system model, the simulation calculation module calculates the optimization problem of the central processing module to obtain the optimal evaporation temperature and compression temperature rise under the current working condition, as well as the total power consumption and total heat exchange area under this condition, and sends them to the central processing module;

[0063] As Figure 4 shown, the vapor compression re-evaporation system model is established through the following sub-steps:

[0064] Sub-step B1.1: Assume the salt concentration in the steam is 0; assume energy losses from the evaporator, heat exchanger, pipes, and pump are negligible; assume the condensate is entirely liquid, i.e., the mass percentage concentration of the condensate steam is X. d Set the value to 0; establish a model for the vapor compression and re-evaporation system:

[0065] The system material balance equation is: M f =M d +M b M f X f =M b X b +M d X d ;

[0066] Among them, M f M represents the feed mass flow rate. d M represents the mass flow rate of condensed steam water. b X represents the mass flow rate of concentrated brine. f X represents the mass percentage concentration of the feed water. b X represents the mass percentage concentration of concentrated saline solution. d This indicates the mass percentage concentration of condensed steam.

[0067] In the preheater: Q HEX1 =M d C pd (T d -T do ) = M f1 C pf (T i1 -T i );Q HEX2 =M b C pb (T b -T bo ) = M f2 C pf (T i2 -T i );

[0068] Among them, Q HEX1 Q HEX2 C represents the heat generated in the two heat exchangers. pd C pf C pb M represents the specific heat capacity of condensate, feed water, and concentrated brine, respectively. f1 M represents the feed mass flow rate after passing through heat exchanger 1. f2 T represents the feed mass flow rate after passing through heat exchanger 2. i T represents the temperature of the feed water. bT represents the temperature of the concentrated brine at the evaporator outlet, which is the boiling point temperature of the feed water to the evaporator. d T represents the temperature of the condensate at the evaporator outlet. bo T represents the temperature of the concentrated brine produced after passing through heat exchanger 2. do T represents the temperature of the condensate after passing through heat exchanger 1. i1 This indicates the temperature (T) of the feed water after heat exchange in heat exchanger 1. i2 This indicates the temperature of the feed water after heat exchanger 2.

[0069] In the evaporator: Q out =M d λ vp +M f ·C pf (T b -T f );Q in =M d λ d +M d ·C pv (T s -t d );Q in =Q out ;

[0070] T vp =T b -BPE;BPE=f·Δ0′; Δ0′=69.8×C b 3 -8.15×C b 2 +5.19×C b ;

[0071] T vp =T v ; ΔT = T d -T b Q e =U e ·A e ·LTMD;

[0072]

[0073] Among them, Q out Q in T represents the heat generated inside and outside the tubes of the evaporator, respectively. f Cp represents the temperature of the feed water after preheating through two heat exchangers. v λ represents the specific heat capacity of the steam above the demister; vp , λ dH s denotes the latent heat of the steam below the demister and the latent heat of the steam inside the tube, T s denotes the temperature of the superheated steam, T vp denotes the temperature below the demister, BPE denotes the boiling point elevation, and f denotes the correction factor, T v denotes the temperature above the demister, Q e denotes the heat transfer of the evaporator, U e denotes the total heat transfer coefficient; Δ0' denotes the boiling point elevation of the solution at normal pressure, r denotes the latent heat of vaporization of water at the specified pressure, and C b denotes the concentration of brine, and ΔT denotes the difference between the condensing steam temperature at the outlet of the evaporator and the boiling point temperature of the feed water.

[0074] In the compressor:

[0075] H s = 2499.15 + 1.955 x T s - 1.927 x 10 -3 - 1.927 x 10 s 2 ; W fre = (H s - H v ) x 0.000277 x ε / η com .

[0076] where H v denotes the specific enthalpy of the saturated steam, H s denotes the specific enthalpy of the superheated steam, ε denotes the compression ratio, η com denotes the efficiency of the compressor, and W fre denotes the power of the variable frequency compressor.

[0077] Sub-step B1.2: Establish an optimization objective function for the steam compression re-evaporation system, with the minimum total heat exchange area of the system and the minimum total power consumption of the system as the optimization objectives, and with the evaporation temperature T e and the compression temperature rise ΔT bs as the optimization variables for optimization control:

[0078] The optimization model expression is:

[0079] where x is the optimization variable, that is, the evaporation temperature and the compression temperature rise, g1(x) is the first objective function, g2(x) is the second objective function, S eva+pre denotes the total heat exchange area of the evaporator and the heat exchanger, W com denotes the total power consumption of the compressor, and the constraint condition is the system material balance equation and the energy balance equation in the evaporator in sub-step B1.1, and the superscripts L and U denote the upper and lower limits of the variable.

[0080] Sub-step B2.1: Solve the model of sub-step B1.2 using genetic algorithm to obtain the optimal scheme. For a solution, if it meets the constraint condition, it is called a feasible solution, and if it does not meet the constraint condition, it is called an infeasible solution. For a feasible solution, the dominance relationship in the solution set of the steam compression re-evaporation system model is defined according to the degree of approaching the target. If feasible solution a is superior to or not worse than feasible solution b in terms of system energy consumption and heat exchange area target, then solution a dominates solution b. In the genetic algorithm, all solutions in the population that are not dominated by any other solution constitute the non-dominated solution, which is called the non-dominated solution set.

[0081] As shown in Figure 5 , the specific solving steps are as follows:

[0082] Sub-step C1: Substitute the initial value of the system model, set the model information and algorithm parameters.

[0083] Sub-step C2: Randomly generate an initial population, and record the iteration number gen = 1.

[0084] Sub-step C3: Solve the system total energy consumption target function value and heat exchange area target function value corresponding to each individual in the population.

[0085] Sub-step C4: Perform fast non-dominated sorting and crowding calculation on the initial population.

[0086] Sub-step C5: Determine whether the first generation of sub-population has been generated. If it has been generated, set the evolution generation number gen = 2, otherwise, perform non-dominated sorting and selection, Gaussian crossover and mutation on the initial population to generate the first generation of sub-population and set the evolution generation number gen = 2.

[0087] Sub-step C6: Combine the parent generation and the child generation.

[0088] Sub-step C7: Determine whether a new parent population has been generated. If not, calculate the target function of the individuals in the new population and perform fast non-dominated sorting, crowding calculation, elite strategy and other operations to generate a new parent population; otherwise, go to sub-step C8.

[0089] Sub-step C8: Perform selection, crossover and mutation operations on the generated parent population to generate a child population.

[0090] Sub-step C9: Determine whether the maximum number of iterations has been reached. If not, set the evolution generation number gen = gen + 1 and return to sub-step C6; otherwise, the algorithm ends and outputs the optimal control variables T e and ΔT bs under the condition of maximum fitness, and calculates the total heat exchange area S eva+pre and the total power consumption W com of the system under the corresponding condition.

[0091] Step A5: the parameter decision module compares the data G1 obtained in this cycle, including the evaporation temperature, the compression temperature rise, the total power consumption of the system and the total heat exchange area, with the data G (m=1, 2, …, n, n is the current cycle number) obtained in previous cycles, analyzes and judges whether the current data is the optimal solution under the working condition, and then transmits the optimal result data after comparison to the central processing module, thereby improving the fault tolerance of the system. m

[0092] Step A6: the central processing module transmits the received data to the display module, and the display module displays the current time t1, the evaporation temperature T e , the compression temperature rise ΔT bs , the total power consumption W com of the system and the total heat exchange area S eva+pre ; an operator or an engineer sets the heat exchange area of the heat exchanger and the parameter values of the compression temperature rise and the evaporation temperature of the system according to the values of the display module.

[0093] The current time is recorded as t2, if t2-t1<t c , the waiting continues; otherwise, step A2 is turned to start a new round of data acquisition. t c The setting of the parameters will affect the time range of data acquisition. If in a stable environment of the feed, the influence of t c parameters on the result is not large; if in the case of unstable feed, different values of t c will obtain the optimal solution in the current time period.

[0094] The optimization results under the current working condition are shown in the following table:

[0095]

[0096] * Change rate = (after optimization-before optimization) / before optimization × 100%

[0097] From the data in the table, it can be seen that the evaporation temperature and the compression temperature rise after optimization are higher than the original set values selected according to experience, thereby changing the inlet and outlet steam pressure, reducing the compression ratio of the compressor, and reducing the energy consumption of the system. It can be seen that the mechanical vapor recompression control system and the optimization method proposed in the application have actual energy-saving effect.

[0098] The above content is a further detailed description of the application in combination with a specific preferred embodiment, and cannot be regarded as limiting the specific implementation of the application to these descriptions. For those skilled in the art to which the application belongs, certain simple deductions or substitutions can be made without departing from the concept of the application, and all of them should be regarded as falling within the protection scope of the application.​

Claims

1. A mechanical vapor recompression optimized control method, characterized by: Specifically comprising the following steps: Step A1: Manually set the property parameters of the vapor compression re-evaporation system through the human-computer interaction module, data acquisition period t c , limit value of data required to be collected; Step A2: Collecting the temperature T of the feed water at the current time point by using the data collection module i , flow M f , concentration X f , the temperature T of the feed water after being preheated by two heat exchangers respectively f , the temperature T of the concentrated brine at the outlet of the evaporator b , flow M c , concentration X b , the temperature T of the condensed water after passing through the heat exchanger 1 do , the temperature T of the concentrated brine after passing through the heat exchanger 2 bo , flow M b , concentration X b , and the temperature T of the secondary steam v , flow M d , and the temperature T of the superheated steam s , let the variable t1 equal to the current time, and then transmit the above parameters to the central processing module by using the data conversion module; Step A3: the central processing module checks the obtained feed water parameters, and if the requirements are met, enters step A4, otherwise enters step A2; Step A4: the central processing module calls the simulation calculation module according to the internally stored steam compression re-evaporation system model, the simulation calculation module obtains the optimal evaporation temperature and compression temperature rise under the current working condition and the system total power consumption and total heat exchange area under this condition by calculating the optimization problem of the central processing module, and sends them to the central processing module; The steam compression re-evaporation system model is established through the following sub-steps: Sub-step B1.1 : Assume the salt concentration in the steam is 0; assume the energy loss of the evaporator, heat exchanger, pipe and pump is not counted; assume that all the condensate water in the condensation process is liquid, i.e. the mass percentage concentration X of the condensed steam is 0; establish the model of the steam compression re-evaporation system: d Sub-step B1.1 : Assume the salt concentration in the steam is 0; assume the energy loss of the evaporator, heat exchanger, pipe and pump is not counted; assume that all the condensate water in the condensation process is liquid, i.e. the mass percentage concentration X of the condensed steam is 0; establish the model of the steam compression re-evaporation system: The system material balance equation is: M f = M d + M b , M f X f = M b X b + M d X d ; where M f represents the mass flow rate of the feed water, M d represents the mass flow rate of the condensed steam water, M b represents the mass flow rate of the concentrated brine, X f represents the mass percentage concentration of the feed water, X b represents the mass percentage concentration of the concentrated brine, X d represents the mass percentage concentration of the condensed steam; Q HEX1 = M d C pd (T d -T do ) = M f1 C pf (T i1 -T i ); Q HEX2 = M b C pb (T b -T bo ) = M f2 C pf (T i2 -T i ); where Q HEX1 , Q HEX2 represent the heat generated in the two heat exchangers, respectively, C pd , C pf , C pb represent the specific heat capacity of the condensate water, feed water and concentrated brine, respectively, M f1 represents the mass flow rate of the feed water through the heat exchanger 1, M f2 represents the mass flow rate of the feed water through the heat exchanger 2, T i represents the temperature of the feed water, T b represents the temperature of the concentrated brine at the outlet of the evaporator, i.e. the boiling point temperature of the feed water of the evaporator, T d represents the temperature of the condensed steam at the outlet of the evaporator, T bo represents the temperature of the concentrated brine after passing through the heat exchanger 2, T do represents the temperature of the condensed water after passing through the heat exchanger 1, T i1 represents the temperature of the feed water after heat exchange through the heat exchanger 1, T i2 represents the temperature of the feed water after heat exchange through the heat exchanger 2; In the evaporator: Q out = M d λ vp + M f · C pf (T b - T f ) ; Q in = M d λ d + M d · C pv (T s - T d ) ; Q in = Q out ; T vp = T b - BPE; BPE = f Δ0'; Δ0' = 69.8 x C b 3 - 8.15 x C b 2 + 5.19 x C b ; T vp = T v ; ΔT = T d - T b ; Q e = U e · A e · LTMD; where Q out , Q in represent the heat generated inside and outside the tubes of the evaporator, respectively, T f represents the temperature of the feed water after preheating through the two heat exchangers, respectively, Cp v represents the specific heat capacity of the steam above the demister; λ vp , λ d represent the latent heat below the demister and the latent heat of the steam inside the tubes, respectively, T s represents the temperature of the superheated steam, T vp represents the temperature below the demister, BPE represents the boiling point elevation, f represents a correction factor, T v represents the temperature above the demister, Q e represents the heat transfer of the evaporator, U e represents the overall heat transfer coefficient; Δ0' represents the boiling point elevation of the solution at atmospheric pressure, r represents the latent heat of vaporization of water at the specified pressure, C b represents the concentration of the brine, ΔT represents the difference between the temperature of the condensed steam at the outlet of the evaporator and the boiling point temperature of the feed water; In the compressor: H s = 2499.15 + 1.955 x T s - 1.927 x 10 -3 - 1.927 x 10 s 2 ; W fre = (H s - H v ). 0.000277. ε / η com ; where H v represents the specific enthalpy of saturated vapor, H s represents the specific enthalpy of superheated vapor, ε represents the compression ratio, η com represents the efficiency of the compressor, W fre represents the power of the variable frequency compressor; Sub-step B1.2: Establishing the optimization objective function of the vapor compression re-evaporation system, taking the minimum of the total heat exchange area of the system and the total power consumption of the system as the optimization objective, and taking the evaporation temperature T e , the compression temperature rise ΔT bs as the optimization control variable to perform optimization control: The optimized model expression is: where x is the vector of optimization variables, i.e. the evaporating temperature and the compressor temperature rise, g1(x) is the first objective function, g2(x) is the second objective function, S eva+pre represents the total heat exchange area of the evaporator and the heat exchanger, W com represents the total power consumption of the compressor, the constraints are the system material balance equation and the energy balance equation in the evaporator in sub-step B1.1, the superscripts L and U represent the upper and lower limits of the variable; Sub-step B2.1: the genetic algorithm is used to solve the sub-step B1.2 model to obtain the optimal scheme: The specific solving steps are as follows: Sub-step C1: the initial value of the system model is substituted, and the model information and algorithm parameters are set; Sub-step C2: an initial population is randomly generated, and the iteration number gen is recorded as 1; Sub-step C3: the system total energy consumption target function value and the heat exchange area target function value corresponding to each individual in the population are solved; Sub-step C4: the initial population is quickly non-dominated sorted and crowdedness is calculated; Sub-step C5: it is judged whether the first generation of sub-population has been generated, if yes, the evolution generation gen is set as 2, otherwise, the initial population is non-dominated sorted and selected, Gaussian crossover and mutation are performed to generate the first generation of sub-population so that the evolution generation gen is set as 2; Sub-step C6: the parent generation and the child generation are combined; Sub-step C7: it is judged whether the new parent population has been generated, if not, the target function of the individual in the new population is calculated, and the fast non-dominated sorting, crowdedness calculation and elite strategy operation are performed to generate the new parent population; otherwise, sub-step C8 is entered; Sub-step C8: the selection, crossover and mutation operations are performed on the generated parent population to generate the child population; Sub-step C9: judging whether the set maximum iteration number is reached, if not, evolution generation gen = gen + 1 and returning to sub-step C6; otherwise, the algorithm ends and outputs the optimal control variable T corresponding to the maximum fitness e and ΔT bs , and calculates the total heat exchange area S eva+pre and total power consumption W com of the system corresponding to the case; Step A5: The parameter decision module compares the data G1 obtained in this cycle, including the evaporation temperature, the compression temperature rise, the total power consumption of the system and the total heat exchange area, with the data Gm (m = 1, 2, …, n, n is the current cycle number) obtained in the previous cycles, including the evaporation temperature, the compression temperature rise, the total power consumption of the system and the total heat exchange area, to analyze and determine whether the current data is the optimal solution under the working condition, and then transmits the optimal result data after comparison to the central processing module to improve the fault tolerance of the system. m , including the evaporation temperature, the compression temperature rise, the total power consumption of the system and the total heat exchange area (wherein m = 1, 2, …, n, n is the current cycle number), to analyze and determine whether the current data is the optimal solution under the working condition, and then transmits the optimal result data after comparison to the central processing module to improve the fault tolerance of the system. Step A6: the central processing module transmits the received data to the display module, which displays the current time t1, the evaporation temperature T e , the compression temperature rise ΔT bs , the total power consumption of the system W com , and the total heat exchange area S eva+pre ; according to the numerical values of the display module, the heat exchange area of the heat exchanger and the system compression temperature rise and evaporation temperature parameter values are set artificially; the current time is recorded as t2, if t2-t1<t c , continue to wait; otherwise, go to step A2 and start a new round of data collection.

2. The mechanical vapor recompression optimized control method of claim 1, wherein: Based on the following control system, the system comprises a sensor module, a data acquisition module, a data conversion module, a man-machine interaction module, a central processing module, a simulation calculation module, a parameter decision module, a display module and a setting module; the sensor module comprises a temperature sensor, a flow sensor and a salt content sensor; the data acquisition module is used to acquire the feed water temperature, the salt content and the flow rate obtained by the sensor module, and the temperature of the preheated feed water, the temperature, the salt content and the flow rate of the circulating liquid, the temperature of the discharged condensed water, the temperature, the salt content and the flow rate of the concentrated liquid and the temperature, the flow rate of the secondary steam and the temperature of the superheated steam; The data conversion module is used to convert the collected analog quantity into corresponding digital quantity; the man-machine interaction module is used to set the physical property parameters of the steam compression re-evaporation system, the data acquisition cycle and the limit value of the data to be acquired; the central processing module is used to store the mathematical model and the thermophysical property parameters of the steam compression re-evaporation system, receive and store the data of the data acquisition module and the data processed by the simulation calculation module and the parameter decision module, and deliver the results to the display module; the simulation calculation module calls the system program for calculation according to the received collected data, obtains the compression temperature rise and the evaporation temperature suitable for the system and the system total power consumption and the heat exchange area, and transmits the results to the display module and the parameter decision module; The parameter decision module compares the historical data with the optimal compression temperature rise and evaporation temperature, and transmits the result to the display module; the display module is used for displaying the calculation results of the simulation calculation module and the parameter decision module; the setting module is used for manually setting the heat exchange area of the spray type coil heat exchanger and the corresponding compression temperature rise and evaporation temperature.

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