Five-effect evaporation tank control method based on big data model

Abstract

The invention belongs to the technical field of sugar industry, and particularly relates to a five-effect evaporation tank control method based on a big data model, and the method comprises the following steps: S1, an evaporation joint control system sends an execution action instruction; s2, the PLC collects feedback data of the intelligent equipment and submits the feedback data to a model training task; s3, establishing a big data model; s4, performing model evaluation, and if the predicted value is close to the actual value, starting the model; if the difference is large, returning to S2; s5, adjusting scales of the valve and the pump are calculated according to the model, and an action execution instruction is issued; and S6, the PLC receives the instruction, and opening / frequency adjustment is achieved. The problems that in the prior art, when operation of feeding, discharging, steam feeding, ammonia gas discharging, vacuumizing and the like of the five-effect evaporation tank of the evaporation section of a refining workshop is controlled based on an application system, independent control over each effect tank is limited, joint control over the whole process cannot be achieved, and mutual influence between the effect tanks is likely to be caused can be solved, and good market application prospects are achieved.

Description

Technical Field

[0001] The invention belongs to the technical field of sugar industry, and particularly relates to a five-effect evaporator control method based on a big data model. Background Art

[0002] The sugar industry is the basic industry of the food industry, and is also the raw material industry for a variety of products such as papermaking, chemicals, fermentation, medicine, building materials, furniture, etc. It occupies an important position in the national economy. In the traditional sugar-making process, although the five-effect evaporation process has been widely used, most of the operations of the five-effect evaporation in the production process still rely on manual labor, which has many shortcomings. For example, the energy utilization rate often fails to reach the ideal state, and there is a certain amount of energy waste in the evaporation process; at the same time, there is room for further improvement in evaporation efficiency, and the overall process needs to be improved in terms of refinement and optimization.

[0003] At present, in the production process of sugar factories, there are usually two ways to control the feeding, discharging, steam injection, ammonia emission, vacuum extraction and other operations of the five-effect evaporator in the evaporation section of the refining workshop: one is to rely on manual operation control. Due to the uneven experience of the operators, it is difficult to grasp the operation scale; the other is based on application system control, but it is limited to the individual control of each effect tank, and it is impossible to achieve joint control of the whole process, which is easy to cause mutual influence between the effect tanks, and it is impossible to achieve the optimal evaporation efficiency, energy saving, and the effect of maintaining stable production.

[0004] In order to solve the above problems, the present invention proposes a five-effect evaporator control method based on a big data model. The correlation between the control parameters of the five effect tanks under various working conditions is found through big data, and a complete control model is formed to achieve the optimal balance point of evaporation efficiency, energy consumption and production safety.

[0005] The information disclosed in this background technology section is only intended to enhance the understanding of the overall background of the invention and should not be regarded as an acknowledgment or any form of suggestion that the information constitutes the prior art already known to a person skilled in the art. Summary of the invention

[0006] The purpose of the present invention is to provide a five-effect evaporator control method based on a big data model to solve the problem that the prior art is limited to the individual control of each effect tank when controlling the five-effect evaporator feeding, discharging, steam introduction, ammonia discharge, vacuuming and other operations of the evaporation section of the refining workshop based on an application system, and cannot achieve joint control of the entire process, which easily causes the mutual influence between the effect tanks.

[0007] In order to achieve the above object, the present invention provides the following technical solutions:

[0008] A five-effect evaporator control method based on a big data model comprises the following steps:

[0009] S1. Send an execution action instruction to the smart device on the client provided by the evaporation joint control system;

[0010] S2, the evaporation joint control system collects feedback data from all intelligent devices once a second through the PLC controller and submits the data to the model training task;

[0011] S3, the evaporation joint control system uses the clear juice flow rate, the liquid level of each effect evaporation tank, the steam drum pressure, the steam drum temperature, the steam chamber pressure, the steam chamber temperature, the steam flow rate and the syrup hammer as parameters to establish a big data model;

[0012] S4, conduct model evaluation, input the real-time values ​​of each intelligent device in the evaporation joint control system into the big data model, output the predicted clear juice pump frequency, steam valve opening, juice outlet valve opening of each effect evaporation tank, ammonia valve opening, and final vacuum regulating valve opening, and judge whether the predicted value is consistent with the actual value; if the two are close, enable the model and switch the system to automatic control; if the difference is large, return to S2;

[0013] S5. Calculate the adjustment scales of each valve and pump based on the big data model, and issue execution instructions;

[0014] S6, the PLC controller receives the instruction to adjust the frequency of the clear juice pump, the opening of the steam valve, the opening of the juice outlet valve of each effect evaporator, the opening of the ammonia valve and the opening of the final effect vacuum regulating valve.

[0015] Preferably, the intelligent devices connected to the PLC include: a clear juice flow meter, an evaporator liquid level meter, an evaporator drum pressure sensor, an evaporator drum temperature sensor, an evaporator steam chamber pressure sensor, an evaporator steam chamber temperature sensor, a steam flow meter and a syrup hammer meter.

[0016] Preferably, each effect evaporation tank comprises a first-effect evaporation tank, a second-effect evaporation tank, a third-effect evaporation tank, a fourth-effect evaporation tank and a fifth-effect evaporation tank connected in sequence; the steam inlet of the first-effect evaporation tank is connected to a steam source, the juice inlet thereof is connected to a clear juice pump, and the juice outlet thereof is connected to the juice inlet of the second-effect evaporation tank through a pipeline; the steam inlet of the second-effect evaporation tank is closely connected to the steam outlet of the first-effect evaporation tank through a pipeline, and the juice outlet thereof is connected to the juice inlet of the third-effect evaporation tank through a pipeline; the steam inlet of the third-effect evaporation tank is connected to the steam outlet of the second-effect evaporation tank, and the juice outlet thereof is connected to the juice inlet of the fourth-effect evaporation tank through a pipeline; the steam inlet of the fourth-effect evaporation tank is connected to the steam outlet of the third-effect evaporation tank, and the juice outlet thereof is connected to the juice inlet of the fifth-effect evaporation tank through a pipeline; the steam inlet of the fifth-effect evaporation tank is connected to the steam outlet of the fourth-effect evaporation tank, and the juice outlet thereof is connected to the juice inlet of the fifth-effect evaporation tank through a pipeline; the steam generated by the fifth-effect evaporation tank is discharged through a vacuum condenser to form condensed water.

[0017] The establishment of a big data model includes the following steps:

[0018] 1. Data Preparation

[0019] 1. Data collection: The evaporation joint control system collects the liquid level, steam drum pressure, steam drum temperature, steam chamber pressure, steam chamber temperature, juice outlet valve opening, clear juice flow rate, and clear juice pump frequency of each effect evaporator once every second through the PLC controller.

[0020] 2. Data cleaning: Identify outliers using the standard deviation method, calculate the average and standard deviation of the data collected in the last 60 times, and then calculate the deviation between the data collected at that time and the average. If the deviation exceeds three standard deviations, the data is considered an outlier and deleted.

[0021] 2. Determine the network structure

[0022] 1. Determine the number of input layer nodes.

[0023] Since there are 32 parameters such as juice flow, the number of input layer nodes is 32.

[0024] 2. Choose an appropriate number of hidden layers and the number of nodes in each hidden layer.

[0025] This usually needs to be determined through experimentation and experience. You can start with a smaller hidden layer, gradually increase the number of hidden layers and nodes, and observe the performance changes of the model.

[0026] 3. Determine the number of nodes in the output layer.

[0027] It is determined according to the specific prediction target. For example, if a single numerical indicator is predicted, the number of output layer nodes can be one.

[0028] 3. Initialize network weights and biases

[0029] Randomly initialize the weights and biases in the network. Use random values ​​between 0 and 1 for initialization to avoid the impact of too large or too small initial values ​​on model training.

[0030] 4. Forward Propagation

[0031] 1. Input a data sample into the network, starting from the input layer, and calculate the output of each neuron in turn.

[0032] 2. For neurons in the input layer, their output is equal to the input data. For neurons in the hidden layer and output layer, their output is calculated by the activation function tanh on the weighted sum of the input plus the bias.

[0033] 5. Calculation Error

[0034] 1. Calculate the mean absolute error based on the network output and the actual target value.

[0035] 2. Error back propagation, starting from the output layer, calculates the error contribution of each neuron in turn and propagates the error back to the previous layer.

[0036] 6. Weight Update

[0037] 1. Based on the error contribution obtained by error back propagation, the weights and biases in the network are updated using the gradient descent method.

[0038] 2. Repeat the steps of forward propagation, error calculation and weight update, and train each data sample until the predetermined number of training times is reached or the error meets the requirements.

[0039] 7. Model Evaluation and Adjustment

[0040] 1. Use a test set other than the training set to evaluate the trained model and calculate the model error and other performance indicators on the test set.

[0041] 2. If the model performance is not ideal, you can adjust the network structure, optimize the algorithm parameters, increase the amount of training data, etc., retrain and evaluate until satisfactory model performance is obtained.

[0042] Through the above steps, the feedforward neural network algorithm can be used to train the model of the evaporation joint control system with parameters such as clear juice flow as input, so as to achieve the prediction of the opening of the juice outlet valve, the frequency of the clear juice pump and the optimal control of the evaporation tank liquid level.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] (1) The five-effect evaporator control method based on the big data model of the present invention can realize unmanned control of the evaporation process, effectively control the liquid level of the evaporator, and prevent the occurrence of dry pot, burnt pot and sugar leakage.

[0045] (2) The five-effect evaporator control method based on the big data model of the present invention can realize refined and accurate control, optimally control steam distribution and syrup flow, achieve energy-saving effect, and can maximize production stability, which is beneficial to improving the economic benefits of the sugar factory. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic diagram of the connection of each effect evaporation tank of the present invention;

[0047] Figure 2 It is a schematic diagram of the control flow of the method of the present invention. DETAILED DESCRIPTION

[0048] The following is a clear and complete description of the technical solution of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0049] In the description of the present invention, it should be noted that the orientations or positional relationships indicated by terms such as "center", "up", "down", "left", "right", "vertical", "horizontal", "inside" and "outside" are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the invention.

[0050] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0051] The five-effect evaporator control method based on the big data model of the present invention is realized by combining a set of evaporation joint control system with application software, including five-effect evaporator, clear juice pump, steam valve, evaporator juice valve, ammonia valve, final effect vacuum regulating valve and other actuators, clear juice flow meter, evaporator level meter, evaporator drum pressure sensor, evaporator drum temperature sensor, evaporator steam chamber pressure sensor, evaporator steam chamber temperature sensor, syrup hammer meter and steam flow meter. The valves of each actuator can selectively adopt Honeywell V8BFW16-100, the temperature sensor adopts Meiyi SUP-WZFK-F, the pressure sensor adopts Meiyi SUP-PX400G, the hammer meter adopts Fives MONOTRAC IIID, the level meter adopts Meiyi SUP-2051LT-D, and the flow meter adopts Meiyi LUGB-SUP-DN100-C.

[0052] See attached Figure 1, the first-effect evaporator is used as the starting link, and its steam inlet is directly connected to a reliable steam source to ensure sufficient steam supply, while its condensate outlet is connected to an external condensate collection or treatment device to achieve effective discharge of condensate. The steam inlet of the second-effect evaporator is closely connected to the steam outlet of the first-effect evaporator through a pipeline to achieve orderly transfer of steam. The steam inlet of the three-effect evaporator is connected to the steam outlet of the second-effect evaporator, the steam inlet of the four-effect evaporator is connected to the steam outlet of the three-effect evaporator, the steam inlet of the five-effect evaporator is connected to the steam outlet of the four-effect evaporator, and the steam outlet of the five-effect evaporator is connected to the vacuum condenser to form condensate discharge. The entire design forms a complete steam transmission chain.

[0053] On the other hand, the juice inlet of the first-effect evaporator is connected to the clear juice pump, and the juice outlet is connected to the juice inlet of the second-effect evaporator through a pipeline; the juice outlet of the second-effect evaporator is connected to the juice inlet of the third-effect evaporator through a pipeline; the juice outlet of the third-effect evaporator is connected to the juice inlet of the fourth-effect evaporator through a pipeline; the juice outlet of the fourth-effect evaporator is connected to the juice inlet of the fifth-effect evaporator through a pipeline, and the discharge port of the fifth-effect evaporator is connected to the syrup box, and finally goes to the sugar boiling section.

[0054] The electronic intelligent devices mentioned above are all connected to the PLC controller. The PLC controller adopts the central control G3Smart and is connected to the evaporation joint control system. The evaporation joint control system collects the data fed back by each sensor and actuator in real time through the PLC controller.

[0055] In the modeling stage, the collected data is cleaned according to certain rules, and the clear juice flow rate, the liquid level of each effect evaporator, the steam drum pressure, the steam drum temperature, the steam chamber pressure, the steam chamber temperature, the sugar juice hammer, and the steam flow rate are used as parameters to establish a big data model and evaluate and improve it. The specific rules for cleaning the collected data are: cluster the data, identify the data that does not belong to any cluster, and discard these data.

[0056] The range of the clear juice flow rate is determined based on the daily squeeze volume. The evaporation joint control system uses the trained big data model to automatically adjust the clear juice pump frequency to achieve the set range of the clear juice flow rate. The clear juice flow rate, the syrup hammer of the clear juice tank, the syrup hammer of the final effect tank outlet, and the steam flow of each effect tank are used to calculate the hammer of the syrup at the outlet of each effect tank, and then calculate the evaporation amount that each effect tank needs to achieve. The calculation formula is:

[0057] Evaporation amount = evaporation efficiency * evaporation time, evaporation efficiency = coefficient * steam flow rate * (steam drum temperature - steam chamber temperature difference).

[0058] The steam drum temperature is controlled by adjusting the amount of steam entering, and the steam chamber temperature is controlled by adjusting the juice inlet valve and the juice outlet valve. The higher the steam drum temperature, the lower the liquid level and the lower the steam chamber temperature. When the temperature difference between the steam drum temperature and the steam chamber temperature is greater, the evaporation efficiency is higher, which makes it easier to control the hammer within the set range when the liquid level is within the safe range.

[0059] See attached Figure 2 The five-effect evaporator control method based on the big data model of the present invention comprises the following steps:

[0060] S1. After starting work, the operator issues execution instructions to the smart device on the client provided by the evaporation joint control system according to the on-site situation;

[0061] S2, the evaporation joint control system collects feedback data from all intelligent devices once a second through the PLC controller and submits the data to the model training task;

[0062] S3, evaporation joint control system uses clear juice flow, liquid level of each effect evaporator, steam drum pressure, steam drum temperature, steam chamber pressure, steam chamber temperature, steam flow and syrup hammer as parameters to establish a big data model and conduct evaluation and improvement;

[0063] S4, conduct model evaluation, input the real-time values ​​of each intelligent device in the evaporation joint control system into the big data model, output the predicted clear juice pump frequency, steam valve opening, juice outlet valve opening of each effect evaporation tank, ammonia valve opening, and final vacuum regulating valve opening, and compare whether the predicted value is consistent with the actual value; if the two are close, enable the model and switch the system to automatic control; if the difference is large, return to S2 until the model evaluation passes;

[0064] S5. Calculate the adjustment scales of each valve and pump based on the big data model, and issue execution instructions;

[0065] S6, the PLC controller receives the instruction to adjust the frequency of the clear juice pump, the opening of the steam valve, the opening of the juice outlet valve of each effect evaporator, the opening of the ammonia valve and the opening of the final effect vacuum regulating valve.

[0066] Among them, according to the set range of the clear juice flow rate, the evaporation joint control system automatically adjusts the clear juice pump frequency according to the big data model to achieve the set range of the clear juice flow rate.

[0067] The evaporation joint control system calculates the hammer degree of syrup at the outlet of each effect tank and the evaporation amount required for each effect tank through parameters such as clear juice flow rate, syrup hammer degree in the clear juice tank, syrup hammer degree at the outlet of the last effect tank, and steam flow rate of each effect tank. It then calculates the amount of steam required for an evaporation tank based on the evaporation amount, calculates the size of the steam valve opening based on the steam flow rate, and finally sends a command to the PLC controller to adjust the opening of the steam valve.

[0068] Liquid level control of the first-effect evaporator: if the liquid level is lower than the set value, close the juice outlet valve, and after the liquid level returns to the normal range, open the juice outlet valve to rebalance the inlet and outlet to stabilize the liquid level; if the liquid level is higher than the set value and the opening of the juice outlet valve is less than 100, open the juice outlet valve, wait for the liquid level to recover, and then adjust the juice outlet valve to rebalance the liquid level; if the liquid level is normal, check the outlet syrup hammer degree. If the syrup hammer degree is lower than the set minimum value, close the frequency converter, reduce the feed amount, and close the discharge valve at the same time to rebalance the inlet and outlet to stabilize the liquid level; if the syrup hammer degree is higher than the set maximum value and the final effect hammer degree is too high, open the frequency converter, increase the feed amount, and open the discharge valve at the same time to rebalance the inlet and outlet to stabilize the liquid level. If the hammer degree is normal, no action is taken; if the hammer degree of the syrup is normal, check the hammer degree of the syrup at the outlet of the last effect. If the hammer degree of the last effect is lower than the set value, determine whether the hammer degree of the outlet of the evaporator of the current effect has reached the maximum value. If it has not reached the maximum value, take the maximum value of the hammer degree of the syrup configured for the evaporator of the current effect as the target, adjust the feed amount and discharge amount to increase the hammer degree of the syrup; if it has reached the maximum value, do not take any action; if the hammer degree of the last effect is higher than the set value, determine whether the hammer degree of the outlet of the evaporator of the current effect has reached the minimum value. If it has not reached the minimum value, take the minimum value of the hammer degree of the syrup configured for the evaporator of the current effect as the target, adjust the feed amount and discharge amount to reduce the hammer degree of the syrup; if it has reached the minimum value, do not take any action; if the hammer degree of the last effect is normal, do not take any action.

[0069] Liquid level control of the second-effect evaporator: if the liquid level is lower than the set value, close the juice outlet valve, wait for the liquid level to recover, then open the juice outlet valve again to stabilize the liquid level; if the liquid level is higher than the set value, open the juice outlet valve, wait for the liquid level to recover, then close the juice outlet valve again to stabilize the liquid level; if the liquid level is normal, check the outlet syrup hammer. If the outlet syrup hammer of the second-effect evaporator is higher than the set value, check the final effect hammer. If the final effect hammer is normal, do not operate; if the final effect hammer is too high, first close the juice outlet valve, slowly increase the liquid level, then open the juice outlet valve again to keep the liquid level stable. If the outlet syrup hammer is lower than the set value, first open the juice outlet valve, slowly lower the liquid level, and then close the juice outlet valve to keep the liquid level stable; if the syrup hammer is normal, check the outlet syrup hammer of the last effect (if the current effect evaporator is the last effect tank, skip the following logic). If the last effect hammer is lower than the set value, determine whether the outlet hammer of the current effect evaporator has reached the maximum value. If it has not reached the maximum value, adjust the discharge with the maximum value of the syrup hammer configured for the current effect evaporator as the target, so that the syrup hammer increases; if it has reached the maximum value, do not take action. If the last effect hammer is higher than the set value, determine whether the outlet hammer of the current effect evaporator has reached the minimum value. If it has not reached the minimum value, adjust the discharge with the minimum value of the syrup hammer configured for the current effect evaporator as the target, so that the syrup hammer decreases; if it has reached the minimum value, do not take action. If the last effect hammer is normal, do not take action.

[0070] Table 1 below measures the liquid level, drum pressure, drum temperature, chamber pressure, chamber temperature, steam flow, syrup hammer and other data in each evaporator tube when the clear juice flow rate is 412m³ / h and the steam inlet pressure is 0.176MPa.

[0071] Table 1

[0072] Evaporation tank Liquid level Steam drum pressure (MPa) Steam drum temperature (℃) Steam chamber pressure (MPa) Steam chamber temperature (℃) Steam flow rate (t / h) Syrup Brix (Bx) One-effect tank 56% 0.174 131.9 0.088 119.6 9.5 35 Two-effect tank 48% 0.054 117.2 0.02 1.6.6 8.1 46 Three-effect tank 33% 0.009 103.8 0.001 98.6 6.4 51 Four-effect tank 25% -0.01 96.1 -0.043 86.9 5.8 62 Five-effect can 10% -0.042 83.3 -0.069 68.7 2.4 67

[0073] Among them, the opening degree of the juice discharge valve of the first-effect tank is 23%, the opening degree of the juice discharge valve of the second-effect tank is 28%, the opening degree of the juice discharge valve of the third-effect tank is 30%, the opening degree of the juice discharge valve of the fourth-effect tank is 40%, and the opening degree of the juice discharge valve of the fifth-effect tank is 75%.

[0074] The liquid level control logic of the three-effect, four-effect and five-effect evaporator is the same as that of the two-effect evaporator.

[0075] The five-effect evaporator control method based on the big data model of the present invention applies a deep learning neural network algorithm to the five-effect evaporation control in the sugar production process. By collecting data from various sensors in the evaporation process and inputting them into the data model, the scales of actuators such as pumps and valves can be accurately calculated, thereby achieving precise control of the evaporation process. The method is very practical and has good market application prospects.

[0076] The foregoing description of specific exemplary embodiments of the present invention is for the purpose of illustration and demonstration. These descriptions are not intended to limit the present invention to the precise form disclosed, and it is clear that many changes and variations can be made based on the above teachings. The purpose of selecting and describing the exemplary embodiments is to explain the specific principles of the present invention and its practical application, so that those skilled in the art can realize and utilize various different exemplary embodiments of the present invention and various different selections and changes. The scope of the present invention is intended to be limited by the claims and their equivalents.

Claims

1. A five-effect evaporator control method based on a big data model, characterized in that: The following steps are involved: S1. Send an execution action instruction to the smart device on the client provided by the evaporation joint control system; S2, the evaporation joint control system collects feedback data from all smart devices once a second through the PLC controller and submits the data to the model training task; S3, the evaporation joint control system uses the clear juice flow rate, the liquid level of each effect evaporation tank, the steam drum pressure, the steam drum temperature, the steam chamber pressure, the steam chamber temperature, the steam flow rate and the syrup hammer as parameters to establish a big data model; S4, conduct model evaluation, input the real-time values ​​of each intelligent device in the evaporation joint control system into the big data model, output the predicted clear juice pump frequency, steam valve opening, juice outlet valve opening of each effect evaporation tank, ammonia valve opening, and final vacuum regulating valve opening, and judge whether the predicted value is consistent with the actual value; if the two are close, enable the model and switch the system to automatic control; if the difference is large, return to S2; S5. Calculate the adjustment scales of each valve and pump based on the big data model, and issue execution instructions; S6, the PLC controller receives the instruction to adjust the frequency of the clear juice pump, the opening of the steam valve, the opening of the juice outlet valve of each effect evaporator, the opening of the ammonia valve and the opening of the final effect vacuum regulating valve.

2. The five-effect evaporator control method based on the big data model according to claim 1 is characterized in that: The intelligent devices connected to the PLC include: a clear juice flow meter, an evaporator liquid level meter, an evaporator drum pressure sensor, an evaporator drum temperature sensor, an evaporator steam chamber pressure sensor, an evaporator steam chamber temperature sensor, a steam flow meter and a syrup hammer meter.

3. The five-effect evaporator control method based on the big data model according to claim 1 is characterized in that: Each effect evaporation tank includes a first-effect evaporation tank, a second-effect evaporation tank, a third-effect evaporation tank, a fourth-effect evaporation tank and a fifth-effect evaporation tank connected in sequence; the steam inlet of the first-effect evaporation tank is connected to a steam source, the juice inlet thereof is connected to a clear juice pump, and the juice outlet is connected to the juice inlet of the second-effect evaporation tank through a pipeline; the steam inlet of the second-effect evaporation tank is closely connected to the steam outlet of the first-effect evaporation tank through a pipeline, and the juice outlet thereof is connected to the juice inlet of the third-effect evaporation tank through a pipeline; the steam inlet of the third-effect evaporation tank is connected to the steam outlet of the second-effect evaporation tank, and the juice outlet thereof is connected to the juice inlet of the fourth-effect evaporation tank through a pipeline; the steam inlet of the fourth-effect evaporation tank is connected to the steam outlet of the third-effect evaporation tank, and the juice outlet thereof is connected to the juice inlet of the fifth-effect evaporation tank through a pipeline; the steam inlet of the fifth-effect evaporation tank is connected to the steam outlet of the fourth-effect evaporation tank, and the juice outlet thereof is connected to the juice inlet of the fifth-effect evaporation tank through a pipeline; the steam generated by the fifth-effect evaporation tank is discharged through a vacuum condenser to form condensed water.

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