Evaporator control method and control system thereof

Abstract

The invention discloses an evaporator control method and a control system thereof, and relates to the technical field of evaporation process control. According to the method, a material characteristic coefficient, a thermal efficiency coefficient and a heat transfer performance coefficient are obtained, and the thermal working condition adaptation degree is obtained by combining the pressure of an evaporation chamber and the vapor phase temperature; based on the current feeding flow, the adaptation degree and the heat transfer performance coefficient, the optimized feeding flow is obtained through a flow optimization model. According to the method, through multi-model collaborative analysis, dynamic optimization adjustment of the feeding flow of the evaporator is achieved, the operation stability, the thermal working condition adaptability and the overall heat transfer efficiency of the evaporation process are effectively improved, and the self-adaptive control capacity of an evaporation system is remarkably improved.

Description

Technical Field

[0001] This invention belongs to the field of evaporation process control technology, and particularly relates to an evaporator control method and its control system. Background Technology

[0002] Evaporators are widely used in concentration and separation processes in industries such as chemical, pharmaceutical, and food, and are a key technology for achieving material evaporation and concentration.

[0003] Evaporators vaporize the solvent in materials through heating, thereby concentrating the solute or recovering the solvent. Their operational stability and efficiency directly affect product quality and energy consumption levels.

[0004] Currently, most existing evaporator control methods employ single-parameter adjustment or empirical control strategies, typically focusing on feed flow rate, evaporation temperature, or liquid level as primary control targets. They lack comprehensive consideration of multi-dimensional factors such as material characteristics, thermodynamic conditions, and heat transfer performance. Traditional control methods struggle to adapt to complex operating conditions such as fluctuations in material concentration, changes in heat sources, and differences in equipment status, leading to problems like operating condition mismatch, decreased thermal efficiency, and product quality fluctuations during evaporator operation. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an evaporator control method and its control system, which solves the aforementioned problems.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an evaporator control method, comprising: Based on feed concentration, product flow rate, and product concentration, material characteristic coefficients are obtained through a material characteristic model. The thermal efficiency coefficient is obtained through a thermal efficiency model based on the live steam flow rate, live steam pressure, and cooling water temperature rise. Based on the liquid level in the evaporator chamber, temperature difference, and compressor power, the heat transfer performance coefficient is obtained through a heat transfer performance model. Based on the material property coefficient and the thermodynamic efficiency coefficient, combined with the evaporation chamber pressure and vapor phase temperature, the thermodynamic condition fit degree is obtained through the thermodynamic condition fit degree model. Based on the current feed flow rate, and combined with the thermal condition adaptability and heat transfer performance coefficient, the optimized feed flow rate is obtained through the flow optimization model.

[0007] Based on the above technical solutions, the present invention also provides the following optional technical solutions: Further technical solution: The optimized feed flow rate is obtained as follows: Based on the thermodynamic condition adaptability and heat transfer performance coefficient, the comprehensive deviation coefficient is obtained through a comprehensive deviation model, which is expressed as follows: ; in, This represents the overall deviation coefficient. Indicates the degree of thermal compatibility. This indicates the threshold for thermal compatibility. Indicates the heat transfer performance coefficient. Indicates the threshold value of the heat transfer performance coefficient. Represents the weight coefficient and ; Based on the comprehensive deviation coefficient and the current feed flow rate, the optimized feed flow rate is obtained through a flow rate optimization model, which is expressed as follows: ; in, This indicates the optimized feed flow rate. Indicates the current feed flow rate. This represents the overall deviation coefficient. This indicates the adjustment step size.

[0008] Further technical solution: The method for obtaining the thermal condition adaptability is as follows: Based on material property coefficients and the temperature of pure water The saturation pressure is determined by obtaining the theoretical pressure through a pressure correction model, which is expressed as follows: ; in, Indicates theoretical pressure, Represents the material characteristic coefficient. Indicates the temperature of pure water The saturation pressure below, Indicates the concentration effect coefficient and ; Pressure deviation is obtained based on evaporator chamber pressure and theoretical pressure. , Indicates the pressure in the evaporation chamber. Indicates theoretical pressure; Based on pressure deviation and thermodynamic efficiency coefficient, the thermodynamic condition fit degree is obtained through a thermodynamic condition fit degree model, which is expressed as follows: ; in, Indicates the degree of thermal compatibility. Indicates pressure deviation. Indicates the allowable pressure deviation. Indicates the thermal efficiency coefficient. The coefficient representing the thermal efficiency loss penalty is... The higher the value, the better the operating conditions.

[0009] A further technical solution: The heat transfer performance coefficient is obtained as follows: The liquid level, temperature difference, and compressor power in the evaporation chamber are processed dimensionlessly to obtain the liquid level deviation index, temperature difference deviation index, and compressor power deviation index in the evaporation chamber. Based on the evaporator liquid level deviation index, temperature difference deviation index, and compressor power deviation index, the heat transfer performance coefficient is obtained through a heat transfer performance model, which is expressed as follows: ; in, Indicates the heat transfer performance coefficient. This indicates the deviation index of the liquid level in the evaporation chamber. This indicates a deviation from the temperature difference index. This indicates the compressor power deviation index. Represents the weight coefficient and The The higher the value, the better the heat transfer performance.

[0010] A further technical solution: The method for obtaining the thermal efficiency coefficient is as follows: The live steam flow rate, live steam pressure, and cooling water temperature rise are dimensionlessly processed to obtain the live steam flow rate deviation, live steam pressure deviation, and cooling water temperature rise deviation. Based on the deviations in live steam flow rate, live steam pressure, and cooling water temperature rise, the thermal efficiency coefficient is obtained through a thermal efficiency model, which is expressed as follows: ; in, Indicates the thermal efficiency coefficient. This indicates the deviation in live steam flow rate. Indicates the deviation in live steam pressure. Indicates the deviation in cooling water temperature rise. Represents the weight coefficient and The The higher the value, the better the thermal efficiency.

[0011] A further technical solution: The method for obtaining the material characteristic coefficient is as follows: The feed concentration, product flow rate, and product concentration are dimensionlessly processed to obtain the feed concentration index, product flow rate index, and product concentration index. Based on the feed concentration index, product flow rate index, and product concentration index, material characteristic coefficients are obtained through a material characteristic model, which is expressed as follows: ; in, Represents the material characteristic coefficient. This indicates the feed concentration index. Indicates the product traffic index. Indicates the product concentration index. Indicates the allowable deviation value and The Furthermore, the higher the value, the better the material properties.

[0012] Further technical solutions: The methods for obtaining the evaporator liquid level deviation index, temperature difference deviation index, and compressor power deviation index are as follows: The absolute differences between the evaporator liquid level, temperature difference, and compressor power and the corresponding reference values ​​are compared with the corresponding allowable deviations from the optimal values ​​to obtain the evaporator liquid level deviation index, temperature difference deviation index, and compressor power deviation index.

[0013] Further technical solution: The method for obtaining the deviation of live steam flow rate, live steam pressure, and cooling water temperature rise is as follows: The differences between the live steam flow rate, live steam pressure, and cooling water temperature rise and the corresponding reference values ​​are compared with the corresponding reference values ​​to obtain the live steam flow rate deviation, live steam pressure deviation, and cooling water temperature rise deviation.

[0014] Further technical solution: The method for obtaining the feed concentration index, product flow rate index, and product concentration index is as follows: The feed concentration, product flow rate, and product concentration are compared with the corresponding reference values ​​to obtain the feed concentration index, product flow rate index, and product concentration index.

[0015] An evaporator control system employs the aforementioned evaporator control method.

[0016] This invention provides an evaporator control method and its control system, which have the following advantages compared with the prior art: 1. This invention achieves multi-dimensional quantitative evaluation of the evaporator's operating status by constructing material property models, thermodynamic efficiency models, and heat transfer performance models, providing comprehensive and accurate operating condition information for subsequent control decisions; 2. This invention integrates material characteristics and thermodynamic efficiency through a thermodynamic condition adaptability model, and combines evaporation chamber pressure and vapor phase temperature to accurately determine the degree of matching between the current operating conditions and the ideal operating conditions, thereby improving the ability to identify and diagnose operating conditions. 3. This invention optimizes and adjusts the feed flow rate based on the comprehensive deviation coefficient formed by the thermodynamic condition adaptability and heat transfer performance coefficient. It uses the tanh function to achieve smooth and stable flow rate adjustment, avoids drastic fluctuations during the control process, and significantly enhances the system's adaptive control capability and operational stability under complex conditions. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0019] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0020] Please see Figure 1 An evaporator control method provided in one embodiment of the present invention includes: Based on the feed concentration, product flow rate (discharge flow rate), and product concentration (discharge concentration), the material characteristic coefficients are obtained through the material characteristic model; The thermal efficiency coefficient is obtained through a thermal efficiency model based on the live steam flow rate, live steam pressure, and cooling water temperature rise. Based on the liquid level in the evaporation chamber, the temperature difference (the steam temperature in the heating chamber minus the liquid temperature in the evaporation chamber), and the compressor power, the heat transfer performance coefficient is obtained through a heat transfer performance model. Based on the material property coefficient and the thermodynamic efficiency coefficient, combined with the evaporation chamber pressure and vapor phase temperature (secondary steam temperature), the thermodynamic condition fit is obtained through the thermodynamic condition fit model. Based on the current feed flow rate, and combined with the thermal condition adaptability and heat transfer performance coefficient, the optimized feed flow rate is obtained through the flow optimization model.

[0021] The following example will provide a more detailed explanation of the above technical solution: In a chemical production scenario, an evaporator is used to concentrate a high-viscosity solution, and a control method is deployed to optimize its operation. First, the system continuously monitors the feed concentration, product flow rate, discharge flow rate, and product concentration. For example, concentration sensors on the feed line, flow meters on the discharge line, and concentration analyzers collect data in real time. This data is input into a material property model, which calculates a material property coefficient based on these parameters. For instance, this coefficient changes accordingly when the feed concentration fluctuates or the product flow rate or concentration deviates from target values, reflecting the impact of material properties on the evaporation process.

[0022] Meanwhile, to assess thermal efficiency, the system monitors live steam flow rate, live steam pressure, and cooling water temperature rise. For example, flow meters and pressure sensors on the steam pipeline, as well as temperature sensors at the cooling water inlet and outlet, continuously provide data. This data is input into the thermal efficiency model to calculate the thermal efficiency coefficient. For instance, if the live steam flow rate or pressure is unstable, or if the cooling water temperature rise is abnormal, this coefficient will decrease, indicating a loss in thermal efficiency.

[0023] In addition, to assess heat transfer efficiency, the system monitors the liquid level in the evaporator chamber, the steam temperature in the temperature difference heating chamber minus the feed liquid temperature in the evaporator chamber, and the compressor power. For example, level sensors and temperature sensors in the evaporator chamber, as well as the power sensor on the compressor, collect these parameters in real time. This data is input into the heat transfer performance model to calculate the heat transfer performance coefficient. For example, if the liquid level in the evaporator chamber is too high or too low, the temperature difference deviates from the optimal value, or the compressor power is abnormal, this coefficient will decrease, indicating that the heat transfer efficiency is affected.

[0024] Subsequently, the material characteristic coefficients and thermodynamic efficiency coefficients obtained above, combined with real-time data on evaporator chamber pressure, vapor phase temperature, and secondary steam temperature, are input into a thermodynamic condition fit model. This model comprehensively evaluates these factors and calculates a thermodynamic condition fit. For example, if the material characteristics are good, the thermodynamic efficiency is high, and the evaporator chamber pressure and vapor phase temperature are stable and close to the ideal operating state, the thermodynamic condition fit will be high, indicating that the evaporator is currently operating in a relatively ideal state. Conversely, if any one or more factors deviate from the ideal operating state, the fit will decrease.

[0025] Finally, based on the current feed flow rate of the evaporator, and combined with the thermodynamic fit and heat transfer coefficient calculated above, the flow optimization model is used to obtain the optimized feed flow rate. For example, if the thermodynamic fit is high and the heat transfer coefficient is good, the flow optimization model may suggest appropriately increasing the feed flow rate to improve production efficiency. Conversely, if the thermodynamic fit or heat transfer coefficient is low, indicating a problem with evaporator operation, the flow optimization model may suggest reducing the feed flow rate to avoid equipment overload, product quality degradation, or increased energy consumption. In this way, the evaporator feed flow rate is dynamically adjusted to adapt to constantly changing material properties, thermodynamic conditions, and equipment status, thereby achieving overall optimization of the evaporation process.

[0026] Based on the above examples, this method demonstrates its technological contribution by comprehensively considering multiple key parameters during evaporator operation. Traditional evaporator control methods, as described in the background section, often rely solely on a single parameter such as feed flow rate or liquid level for adjustment, or employ empirical control strategies. This approach struggles to achieve accurate control when faced with complex operating conditions such as fluctuations in material concentration, changes in heat sources, or differences in equipment status, easily leading to operating condition mismatch, decreased thermal efficiency, and fluctuations in product quality.

[0027] This method quantifies and evaluates the core influencing factors in the evaporation process by introducing material property coefficients, thermodynamic efficiency coefficients, and heat transfer performance coefficients. For example, in the example above, the material property model can capture the impact of changes in feed concentration, product flow rate, and product concentration on the material evaporation behavior, which is more comprehensive than the traditional method that only focuses on the single control of feed flow rate. The thermodynamic efficiency model and the heat transfer performance model provide in-depth analysis of the evaporator's operating status from the perspectives of energy utilization and heat transfer efficiency, respectively, which goes beyond the simple monitoring of evaporation temperature or liquid level in traditional methods.

[0028] Preferably, the material characteristic coefficient is obtained in the following way: The feed concentration, product flow rate (discharge flow rate), and product concentration (discharge concentration) are compared with the corresponding reference values ​​to obtain the feed concentration index, product flow rate index, and product concentration index. Based on the feed concentration index, product flow rate index, and product concentration index, material characteristic coefficients are obtained through a material characteristic model, which is expressed as follows: ; in, Represents the material characteristic coefficient. This indicates the feed concentration index. Indicates the product traffic index. Indicates the product concentration index. Indicates the allowable deviation value and The Furthermore, the higher the value, the better the material properties.

[0029] Specifically, feed concentration, product flow rate (discharge flow rate), and product concentration (discharge concentration) are key parameters characterizing the material state and processing effect inside the evaporator. Feed concentration reflects the compositional characteristics of the raw material entering the evaporator, product flow rate (discharge flow rate) indicates the rate at which the evaporator processes the material, and product concentration (discharge concentration) reflects the evaporator's concentration effect on the material. Real-time monitoring of these parameters is crucial for evaluating the evaporator's operating conditions. The corresponding reference values ​​can be understood as parameter values ​​under preset, ideal, or optimal operating conditions; for example, they could be the optimal operating point obtained through historical data analysis or target values ​​set based on production objectives. Ratio processing compares actual measured values ​​with these reference values ​​to quantify the deviation of the current material state from the ideal state. This processing method eliminates the influence of different parameter dimensions, making the deviations between different parameters comparable. The feed concentration index, product flow rate index, and product concentration index are dimensionless parameters obtained after ratio processing; they intuitively reflect the deviations of the current feed concentration, product flow rate, and product concentration relative to their reference values. For example, when the exponent is close to 1, it indicates that the current parameter is close to the reference value; when the exponent deviates from 1, it indicates that there is a deviation. The material property model is a mathematical function whose function is to combine these deviation exponents and quantify them into a single material property coefficient. This model employs an exponential decay mechanism, effectively mapping the deviations of multiple input parameters to a coefficient between 0 and 1. The larger the value, the closer the material properties are to the ideal state, meaning the better the material properties. Among these, This indicates the allowable deviation value, defining the sensitivity of the material property coefficient to the decrease when each index deviates from the reference value. For example, The larger the value, the higher the tolerance for deviation from the parameter, and the slower the material property coefficient decreases; conversely, The smaller the value, the lower the tolerance for deviation from the parameter, and the faster the material property coefficient decreases.

[0030] The proposed solution obtains quantified feed concentration, product flow rate, and product concentration indices by comparing key parameters such as feed concentration, product flow rate, and product concentration during evaporator operation with preset reference values. These indices are then input into a carefully designed material property model, which uses an exponential decay function to comprehensively calculate these deviation indices into a material property coefficient. This calculation method can accurately reflect the degree of agreement between the current material state and the ideal state, in which... The closer the value is to 1, the closer the material properties are to ideal operating conditions. Through this quantification method, the evaporator control system can obtain an accurate and real-time assessment of material properties, providing a reliable input for subsequent thermodynamic condition adaptation calculations. This allows the entire evaporator control method to more accurately determine the system's operating status and make optimization adjustments accordingly.

[0031] The following is a specific example to illustrate this, assuming the evaporator is operating under ideal conditions and the feed concentration reference value is... Product traffic reference value The product concentration reference value is At a certain moment, the actual measured feed concentration was... Product traffic is The product concentration is First, calculate the feed concentration index. = / Product traffic index = / Product concentration index = / Then, these indices are substituted into the material property model: For example, if set = 0.1、 = 0.05、 = 0.08, when = 1.05、 = 0.98、 When = 1.02, the calculated result is This value will reflect the degree to which the current material properties deviate from the ideal state. The value will then be used in the thermodynamic condition fit model as an important basis for evaluating the overall operating condition of the evaporator.

[0032] Through the above technical solution, this application provides a method for accurately quantifying the material properties of an evaporator. By comparing key material parameters with reference values ​​and combining this with an exponential decay model, an intuitive and physically meaningful material property coefficient can be obtained. This coefficient accurately reflects the degree of deviation between the current material state and the ideal state, thus providing a more reliable and refined input for subsequent thermodynamic condition adaptation calculations. This helps improve the overall accuracy and response speed of the evaporator control system, enabling the evaporator to operate more stably and efficiently, reducing process deviations caused by fluctuations in material properties, and thereby optimizing the production process.

[0033] Preferably, the thermal efficiency coefficient is obtained as follows: The differences between the live steam flow rate, live steam pressure, and cooling water temperature rise and the corresponding reference values ​​are compared with the corresponding reference values ​​to obtain the live steam flow rate deviation, live steam pressure deviation, and cooling water temperature rise deviation. Based on the deviations in live steam flow rate, live steam pressure, and cooling water temperature rise, the thermal efficiency coefficient is obtained through a thermal efficiency model, which is expressed as follows: ; in, Indicates the thermal efficiency coefficient. This indicates the deviation in live steam flow rate. Indicates the deviation in live steam pressure. Indicates the deviation in cooling water temperature rise. Represents the weight coefficient and The The higher the value, the better the thermal efficiency.

[0034] Specifically, the difference between the live steam flow rate, live steam pressure, and cooling water temperature rise and the corresponding reference values ​​is compared with the corresponding reference values ​​to obtain the live steam flow rate deviation. , live steam pressure deviation and cooling water temperature rise deviation This method aims to quantify the deviation of actual evaporator operating parameters from ideal or optimal operating conditions. By calculating relative deviations, the deviations of different parameters can be standardized, making them comparable and providing a unified input for subsequent thermal efficiency evaluation. This process uses sensors to collect instantaneous values ​​of live steam flow, live steam pressure, and cooling water temperature rise in real time and compares them with preset reference values. Reference values ​​can be based on the average value of historical operating data, the setpoint under optimal operating conditions, or the target value determined according to current process requirements. The ratio can be calculated as (actual value - reference value) / reference value. Alternatively, dynamic reference values ​​determined based on expert experience or machine learning models can be used. For example, the reference value can be dynamically adjusted based on factors such as current production load and product type; in this case, the ratio could be (actual value / reference value) - 1.

[0035] Based on live steam flow deviation , live steam pressure deviation and cooling water temperature rise deviation The thermal efficiency coefficient is obtained through a thermal efficiency model. This thermal efficiency model is used to integrate multiple independent deviation indices into a single thermal efficiency coefficient. Its function is to provide a quantitative indicator to reflect the efficiency and stability of the evaporator thermal system. This model uses squared terms and weighting coefficients. To punish deviations, ensuring that the greater the deviation, the smaller the thermal efficiency coefficient, and The range is limited to (0, 1], with larger values ​​indicating better thermal efficiency. This model can be implemented programmatically in the evaporator control system, for example, using a programmable logic controller (PLC), a distributed control system (DCS), or an industrial computer for real-time calculation. Weighting coefficients The model can be calibrated and adjusted through historical data analysis, expert experience, or optimization algorithms (such as genetic algorithms and particle swarm optimization) to reflect the relative importance of different deviations on thermal efficiency. Furthermore, the model can be embedded in model-based predictive control strategies, receiving deviation data in real time and outputting the thermal efficiency coefficient to guide evaporator operation adjustments. Model parameters can be determined through regression analysis combining the evaporator's thermodynamic characteristics and actual operating data.

[0036] The solution proposed in this application precisely quantifies the deviations of three key thermal input parameters—live steam flow rate, live steam pressure, and cooling water temperature rise—and standardizes them into live steam flow rate deviation. , live steam pressure deviation and cooling water temperature rise deviation This standardization process makes the deviations of different physical quantities comparable. Subsequently, these standardized deviations are input into a thermodynamic efficiency model, which calculates a thermodynamic efficiency coefficient between 0 and 1 by weighted summation of the squares of each deviation and using this sum as part of the denominator. This nonlinear penalty mechanism ensures that any significant deviation from the parameter will affect the thermal efficiency coefficient. The significant decrease in efficiency accurately reflects the actual operating condition of the thermal system. This is achieved by providing a robust and quantitative thermal efficiency coefficient. The calculation method described in this application directly improves the adaptability to the aforementioned thermal conditions. The accuracy of the assessment. A more accurate one. This value allows the thermodynamic condition fit model to more accurately assess the evaporator's operating status under current thermodynamic conditions. When the thermodynamic efficiency coefficient... When deviating from the ideal value, the thermal condition fit is This results in more reasonable penalties, thus more accurately reflecting the overall operating condition of the evaporator. This precise assessment forms the basis for the subsequent flow optimization model to obtain the optimized feed flow rate, ensuring that the evaporator operates at its best thermal efficiency and avoiding feed flow rate adjustment errors caused by inaccurate thermal efficiency assessments.

[0037] The following example illustrates this. Assume that at a certain moment, the reference value for the live steam flow rate of the evaporator is 1000 kg / h, and the actual measured value is 1050 kg / h; the reference value for the live steam pressure is 0.5 MPa, and the actual measured value is 0.48 MPa; the reference value for the cooling water temperature rise is 10℃, and the actual measured value is 12℃. First, calculate the deviations: Live Steam Flow Rate Deviation = (1050-1000) / 1000=0.05; Live steam pressure deviation = (0.48-0.5) / 0.5 =-0.04; Cooling water temperature rise deviation = (12-10) / 10 = 0.2. Next, assume the weighting coefficient... = 5, = 8, =3. Substituting these deviation values ​​into the thermal efficiency model, then... =0.873. This is the calculated thermal efficiency coefficient. (0.873) will be used as input for subsequent thermodynamic condition adaptation models, thereby more accurately evaluating the overall operating conditions of the evaporator.

[0038] Through the aforementioned technical solution, the evaporator control method can accurately quantify the impact of live steam flow rate, live steam pressure, and cooling water temperature rise on the evaporator's thermal efficiency. By standardizing the deviations of these key parameters and integrating them into a unified thermal efficiency coefficient, this application provides an objective and accurate indicator to reflect the operating efficiency of the evaporator's thermal system. This allows the evaporator to more sensitively capture changes in thermal input conditions during operation and convert them into a quantifiable thermal efficiency coefficient, thus providing a solid data foundation for subsequent thermal condition suitability assessment. Ultimately, this accurate thermal efficiency assessment helps the evaporator control system make more informed decisions, optimize feed flow, ensure stable operation of the evaporation process at optimal thermal efficiency, and avoid production efficiency decline or energy waste caused by thermal efficiency fluctuations.

[0039] Preferably, the heat transfer performance coefficient is obtained in the following way: The absolute differences between the evaporation chamber liquid level, temperature difference (heating chamber steam temperature minus evaporation chamber liquid temperature), and compressor power and the corresponding reference values ​​are compared with the corresponding allowable deviation from the optimal value to obtain the evaporation chamber liquid level deviation index, temperature difference deviation index, and compressor power deviation index. Based on the evaporator liquid level deviation index, temperature difference deviation index, and compressor power deviation index, the heat transfer performance coefficient is obtained through a heat transfer performance model, which is expressed as follows: ; in, Indicates the heat transfer performance coefficient. This indicates the deviation index of the liquid level in the evaporation chamber. This indicates a deviation from the temperature difference index. This indicates the compressor power deviation index. Represents the weight coefficient and The The higher the value, the better the heat transfer performance.

[0040] Specifically, the absolute differences between the evaporator liquid level, temperature difference (heating chamber steam temperature minus evaporator liquid temperature), and compressor power and their corresponding reference values ​​are compared with their respective allowable deviations from the optimal values. This aims to quantify the degree of deviation of the evaporator operating parameters (liquid level, temperature difference, and power) from the ideal or optimal operating state. By calculating the absolute differences and comparing them with the allowable deviation values, physical quantities of different dimensions and ranges can be standardized into dimensionless deviation indices, facilitating subsequent unified processing and evaluation. This process can determine the "allowable deviation from the optimal value" for each parameter using a pre-set lookup table or empirical formula, for example, based on evaporator design specifications or historical operating data statistics; alternatively, it can be achieved through online learning or adaptive algorithms, dynamically adjusting or optimizing these "allowable deviations from the optimal value" based on the evaporator's operating data under different conditions. The evaporator liquid level deviation index, temperature difference deviation index, and compressor power deviation index are obtained. These indices are quantified and standardized deviations of the parameters, directly reflecting the difference between the current operating state and the ideal state, and are direct inputs for subsequent calculations of the heat transfer performance coefficient. The aforementioned ratio processing can be performed using a data processing module that receives raw measurement data and reference values ​​and outputs the calculated deviation index. Alternatively, it can be implemented using an algorithm block within a programmable logic controller (PLC) or distributed control system (DCS) that periodically calculates and updates these indices. The core step is obtaining the heat transfer performance coefficient through a heat transfer performance model based on the evaporator liquid level deviation index, temperature difference deviation index, and compressor power deviation index. This step integrates multiple independent deviation indices and maps them through a mathematical model to a single coefficient that comprehensively reflects the evaporator's heat transfer performance. The magnitude of this coefficient directly indicates the quality of the heat transfer performance. The heat transfer performance model can be a pre-established nonlinear function, such as the one given in this application. Form, where weighting coefficients The heat transfer performance coefficient can be set by fitting historical data or expert experience; alternatively, a machine learning model, such as a neural network or support vector machine, can be used to learn and predict the coefficient by training on a large amount of historical operating data. The heat transfer performance model is expressed as: This model provides a specific mathematical form for converting the deviation exponent into a heat transfer performance coefficient. The use of functions makes the output The values ​​are constrained within a specific range (between 0 and 1), and the sensitivity to deviations is non-linear, enabling a better simulation of actual physical processes. Weighting coefficients The importance of different deviation indices can be adjusted. This mathematical model can be implemented directly in the software module of the control system, with input... and preset Calculations yielded Alternatively, the mathematical operation can be performed efficiently using a dedicated computing unit or digital signal processor (DSP) to meet the needs of real-time control.

[0041] This application's solution effectively quantifies the deviation of key operating parameters such as evaporator liquid level, temperature difference, and compressor power into dimensionless deviation indices by comparing and ratioing real-time measurements with corresponding reference values. This quantification eliminates dimensional differences between different physical quantities, allowing them to be uniformly input into the heat transfer performance model. These deviation indices are then mapped to a heat transfer performance coefficient between 0 and 1 through a nonlinear heat transfer performance model. The weighting coefficients in this model allow for adjustments to the sensitivity of different parameters based on actual operating conditions or experience. When these deviation indices are small, the heat transfer performance coefficient is close to 1, indicating good heat transfer performance; when the deviation indices increase, the heat transfer performance coefficient decreases, reflecting a decline in heat transfer performance. In this way, this application can comprehensively and quantitatively reflect the real-time status of multiple key operating parameters as the evaporator's heat transfer performance coefficient, providing accurate and reliable input for subsequent thermodynamic condition adaptation and flow optimization models. This enables the entire evaporator control method to more accurately assess the system state and make optimization adjustments.

[0042] The following example illustrates this. In an evaporator control system, to obtain the heat transfer performance coefficient, the liquid level in the evaporator chamber, the temperature difference, and the compressor power are first processed. Assuming the reference value for the liquid level in the evaporator chamber is 1.5 meters, the allowable deviation from the optimal value is 0.2 meters, and the current measured liquid level is 1.8 meters, then the absolute difference in liquid level is |1.8 - 1.5| = 0.3 meters. The evaporator chamber liquid level deviation index is then calculated. = 0.3 / 0.2 = 1.5. Assuming the reference temperature difference is 10℃, the allowable deviation from the optimal value is 2℃, and the current measured temperature difference is 7℃, then the absolute temperature difference is |7-10| = 3℃. The temperature difference deviation index is calculated from this. =3 / 2 = 1.5. Assuming the compressor power reference value is 100kW, the allowable deviation from the optimal value is 10kW, and the current measured power is 115kW, then the absolute power difference is |115-100|=15kW. The compressor power deviation index can be calculated from this. =15 / 10 =1.5. Assuming weighting coefficients... = 0.5, = 0.8, = 0.3. Substituting these deviation indices and weighting coefficients into the heat transfer performance model, then... = 0.0163. This heat transfer coefficient of 0.0163 is very close to 0, indicating that the current heat transfer performance of the evaporator is very poor and needs to be adjusted immediately.

[0043] By employing the aforementioned technical solution, real-time measurements of key operating parameters such as evaporator liquid level, temperature difference, and compressor power are compared with corresponding reference values ​​and ratios are performed. This application effectively quantifies the deviation of these parameters into dimensionless deviation indices. This quantification eliminates dimensional differences between different physical quantities, allowing them to be uniformly input into the heat transfer performance model. Furthermore, through a nonlinear heat transfer performance model, these deviation indices are comprehensively mapped into a single heat transfer performance coefficient. This coefficient can intuitively and accurately reflect the current heat transfer efficiency and operating status of the evaporator, avoiding the ambiguity and inaccuracy of directly using raw parameters for judgment. Therefore, this application provides a more accurate and reliable basis for evaluating the heat transfer performance of the evaporator control system, making subsequent flow optimization decisions more scientific and reasonable, helping to maintain the evaporator operating under optimal or near-optimal heat transfer conditions, and improving overall production efficiency and energy utilization.

[0044] Preferably, the method for obtaining the thermal condition adaptability is as follows: Based on material property coefficients and the temperature of pure water The saturation pressure is determined by obtaining the theoretical pressure through a pressure correction model, which is expressed as follows: ; in, Indicates theoretical pressure, Represents the material characteristic coefficient. Indicates the temperature of pure water The saturation pressure below, Indicates the concentration effect coefficient and (Used to quantify the degree to which material concentration reduces the vapor pressure of the solution); Pressure deviation is obtained based on evaporator chamber pressure and theoretical pressure. , Indicates the pressure in the evaporation chamber. Indicates theoretical pressure; Based on pressure deviation and thermodynamic efficiency coefficient, the thermodynamic condition fit degree is obtained through a thermodynamic condition fit degree model, which is expressed as follows: ; in, Indicates the degree of thermal compatibility. Indicates pressure deviation. Indicates the allowable pressure deviation. Indicates the thermal efficiency coefficient. This represents the thermal efficiency loss penalty coefficient (used to control the overall operating condition adaptability when thermal efficiency deviates from the ideal state). (the severity of the punishment), the The higher the value, the better the operating conditions.

[0045] Based on material property coefficients and the temperature of pure water The saturation pressure is used to obtain the theoretical pressure through a pressure correction model. This represents the temperature of the solution in the evaporation chamber under the current material properties. The ideal vapor pressure at that time can be obtained by means of a pre-established database or lookup table, based on material property coefficients. and pure water at temperature Saturation pressure Combined with the concentration influence coefficient The theoretical pressure can be directly calculated. Alternatively, it can be acquired through real-time sensor data. and This data is then input into a pre-programmed controller or calculation module, which incorporates a pressure correction model to dynamically calculate the theoretical pressure. The pressure correction model is expressed as: This model can be implemented as a software algorithm in the central processing unit of the evaporator control system. The formula is encoded using a programming language, and the input parameters are received and the calculation results are output in real time. Alternatively, it can be implemented using a dedicated hardware computing unit, embedding the mathematical model in the hardware logic to achieve high-speed, low-latency theoretical pressure calculation.

[0046] Next, the pressure deviation is obtained based on the evaporator chamber pressure and the theoretical pressure. This pressure deviation It directly reflects the degree of deviation between the actual operating pressure of the evaporator and the ideal theoretical pressure. Evaporator pressure The pressure can be collected in real time by a pressure sensor installed in the evaporation chamber, and then the controller will compare the real-time pressure value with the calculated theoretical pressure. The pressure deviation is obtained by performing a subtraction operation. Alternatively, the evaporator chamber pressure and theoretical pressure data can be transmitted to a host computer or cloud platform via a data acquisition system, where data processing and calculations can be performed to obtain the pressure deviation.

[0047] Subsequently, based on the pressure deviation and thermodynamic efficiency coefficient, the thermodynamic condition fit is obtained through a thermodynamic condition fit model. This step aims to comprehensively consider pressure deviation and thermodynamic efficiency to fully evaluate the thermodynamic operating status of the evaporator. The control system can be configured with a dedicated fit calculation module, which receives the pressure deviation... and thermal efficiency coefficient As input, it is calculated according to a preset thermodynamic condition fit model. Alternatively, a machine learning model, such as a neural network, can be trained on historical operating data to predict or evaluate the current thermodynamic condition fit based on the input pressure deviation and thermodynamic efficiency coefficient. The thermodynamic condition fit model is expressed as: This model can be implemented in the firmware of the evaporator controller, utilizing a floating-point unit to efficiently perform exponentiation and square operations, ensuring the accuracy and speed of real-time calculations. Alternatively, it can be implemented as a function block in industrial control software, allowing operators to configure parameters through a graphical interface. and And monitor compatibility in real time. The changes.

[0048] This application's solution first calculates the theoretical pressure based on material property coefficients and pure water saturation pressure, providing a quantitative benchmark for the ideal operating state of the evaporator chamber. Then, the actual evaporator chamber pressure is compared with this theoretical pressure to obtain the pressure deviation, thus intuitively reflecting the degree of deviation between the actual and ideal operating conditions. Furthermore, this pressure deviation is combined with the thermodynamic efficiency coefficient and comprehensively evaluated using a thermodynamic condition fit model. This model cleverly utilizes an exponential function to penalize pressure deviation and thermodynamic efficiency loss, enabling the fit value to comprehensively and sensitively reflect the quality of the evaporator's thermodynamic conditions. This systematic evaluation method solves the problem of how to accurately and quantitatively evaluate the thermodynamic condition fit of the evaporator, providing precise and reliable input for subsequent comprehensive deviation calculations and feed flow optimization, thereby more effectively guiding the operation of the evaporator and ensuring it remains under efficient and stable thermodynamic conditions.

[0049] The following is a concrete example. In the control system of a multi-effect evaporator, to accurately assess its thermodynamic adaptability, the following approach can be taken: First, the feed concentration, product flow rate, and product concentration are measured in real time using online density meters and flow meters, and then input into the material characteristic model to calculate the material characteristic coefficients. Meanwhile, the temperature of the liquid in the evaporation chamber... The temperature is measured in real time by a temperature sensor installed in the evaporation chamber, and then the temperature of pure water is calculated by referring to a preset steam meter or using empirical formulas such as the Antoine equation. Saturation pressure Concentration effect coefficient It can be pre-calibrated based on the type of material to be processed and experimental data, for example, for a specific solution. It can be set to 0.85. The calculation module in the controller calculates according to the above parameters. Theoretical pressure was calculated Next, the pressure in the evaporation chamber is measured in real time by a pressure sensor installed at the top of the evaporation chamber. The controller will measure the evaporation chamber pressure in real time. Subtract the calculated theoretical pressure Thus, pressure deviation is obtained. Furthermore, the thermal efficiency coefficient is calculated by measuring the live steam flow rate, live steam pressure, and cooling water temperature rise, and inputting these measurements into the thermal efficiency model. Pressure tolerance The pressure can be set according to the evaporator's design parameters and process requirements, for example, to 0.05 MPa. Thermal efficiency loss penalty coefficient. The sensitivity requirement for thermal efficiency deviation can be set, for example, to 5. Finally, the controller, based on the above parameters, will... The thermal condition fit was calculated. This calculation result This will serve as an important input for the subsequent comprehensive deviation model, enabling accurate assessment and optimized control of the evaporator's operating status.

[0050] Through the above technical solution, this application can accurately quantify the thermodynamic condition adaptability of the evaporator. By introducing a pressure correction model and combining material characteristic coefficients and pure water saturation pressure, a reliable theoretical pressure benchmark can be provided for evaluating the actual operating state of the evaporation chamber. By calculating the deviation between the evaporation chamber pressure and the theoretical pressure, and combining it with the thermodynamic efficiency coefficient, a comprehensive and sensitive quantitative evaluation of the evaporator's thermodynamic condition adaptability is achieved using the thermodynamic condition adaptability model. This evaluation method not only considers the influence of material characteristics on steam pressure but also integrates thermodynamic efficiency, making the adaptability index more comprehensive and accurate. Therefore, it can provide more accurate and reliable input for the above-mentioned comprehensive deviation model, enabling the evaporator to operate more stably near the optimal thermodynamic condition, effectively improving evaporation efficiency and product quality, while significantly reducing energy consumption and avoiding low operating efficiency or product quality fluctuations caused by inaccurate thermodynamic condition evaluation.

[0051] Preferably, the optimized feed flow rate is obtained as follows: Based on the thermodynamic condition adaptability and heat transfer performance coefficient, the comprehensive deviation coefficient is obtained through a comprehensive deviation model, which is expressed as follows: ; in, This represents the overall deviation coefficient. Indicates the degree of thermal compatibility. This indicates the threshold for thermal compatibility. Indicates the heat transfer performance coefficient. Indicates the threshold value of the heat transfer performance coefficient. Represents the weight coefficient and ; Based on the comprehensive deviation coefficient and the current feed flow rate, the optimized feed flow rate is obtained through a flow rate optimization model, which is expressed as follows: ; in, This indicates the optimized feed flow rate. Indicates the current feed flow rate. This represents the overall deviation coefficient. This indicates the adjustment step size.

[0052] Among them, the comprehensive deviation model is used to quantify the overall deviation between the current operating conditions of the evaporator and the ideal or target operating conditions. It does this by assessing the thermodynamic adaptability. thermal compatibility threshold The deviation between them, and the heat transfer performance coefficient With heat transfer performance coefficient threshold The overall deviation coefficient is calculated by weighted summation of the deviations between them. Its core function is to integrate deviations from multiple key performance indicators into a unified quantitative indicator, providing a basis for subsequent flow optimization decisions. Thermal condition adaptability threshold. and heat transfer performance coefficient threshold This represents the thermodynamic adaptability of the evaporator under ideal or desired operating conditions. and heat transfer performance coefficient The benchmark values ​​to be achieved can be set based on the evaporator's design parameters, historical operating data, process requirements, or expert experience. For example, they can be set to values ​​corresponding to optimal energy consumption or optimal product quality. Weighting coefficients. and Used to adjust the deviation of thermodynamic condition fit and heat transfer performance coefficient in the comprehensive deviation coefficient. The relative importance in the calculation, for example, if the thermal conditions have a more critical impact on evaporator performance, can be assigned... Larger values ​​can be achieved through offline optimization, online adaptive adjustment, or configuration based on expert knowledge. The traffic optimization model is used to adjust these weighting coefficients based on the overall deviation coefficient. For the current feed flow rate Adjustments are made to obtain the optimized feed flow rate. The model employs a hyperbolic tangent function. The nonlinear expression serves to provide a smooth and bounded adjustment mechanism when the comprehensive deviation coefficient... When the value is positive, it indicates that the current operating condition is better than the threshold, and the feed flow rate can be appropriately increased; when... A negative value indicates that the current operating condition is worse than the threshold, and the feed flow rate needs to be appropriately reduced. The hyperbolic tangent function ensures that the flow rate adjustment range is within a certain range, avoiding excessive or oscillating adjustments. Adjustment step size. It is a positive parameter used to control the sensitivity and magnitude of feed flow rate adjustment; a larger value indicates a higher sensitivity. Values ​​that lead to more aggressive flow adjustments, while smaller values... A lower value will make the adjustment smoother. The setting of this parameter needs to take into account the system's response speed requirements, stability, and impact on the process. For example, a suitable value can be determined through system identification, simulation testing, or empirical adjustment. value.

[0053] The solution in this application has obtained the thermal condition adaptability. and heat transfer performance coefficient Instead of simply adjusting the flow rate, the method first quantifies and integrates these two key performance indicators using a comprehensive deviation model. Specifically, this method integrates the thermal condition adaptability... Adaptability threshold with preset thermal operating conditions Comparison, while also considering the heat transfer performance coefficient Compared with the preset heat transfer performance coefficient threshold Compare and determine the weighting coefficients based on the preset weighting coefficients. and Calculate a comprehensive deviation coefficient. This comprehensive deviation coefficient It can comprehensively and quantitatively reflect the degree and direction of the evaporator's current operating conditions deviating from the ideal state. Subsequently, this comprehensive deviation coefficient... This is input into the flow optimization model. The flow optimization model utilizes the current feed flow rate. and adjusting step size Through a function containing hyperbolic tangent The nonlinear relationship was used to calculate the optimized feed flow rate. The hyperbolic tangent function serves to provide a smooth and bounded adjustment curve. When the overall deviation is small, the flow rate adjustment is also small, thus avoiding oversensitivity and system oscillation. When the overall deviation is large, the flow rate adjustment gradually increases, but eventually tends towards an upper limit to prevent excessive flow rate adjustment from causing system instability. In this way, the proposed solution organically combines the two core performance indicators of evaporator operation—thermodynamic condition fit and heat transfer coefficient—to form a unified overall deviation assessment. This assessment not only considers the degree of deviation of each indicator but also reflects their different importance to overall performance through weighting coefficients. Based on this overall deviation, the flow rate optimization model can adjust the feed flow rate in a controlled and smooth manner, enabling the evaporator to operate more accurately and stably at its optimal or near-optimal operating point. This effectively solves the problems of insufficient accuracy and poor stability that may result from adjusting flow rate based on a single indicator or simple superposition.

[0054] The following is a concrete example to illustrate this. Assume that during evaporator operation, the corresponding coefficients have been obtained through the material property model, thermodynamic efficiency model, and heat transfer performance model, and the current thermodynamic condition fit is further calculated. The coefficient of performance is 0.85. The preset thermal condition adaptability threshold is 0.7. The threshold value for the heat transfer performance coefficient is 0.9. The weighting coefficient is 0.8. Set to 0.6. Set to 0.4. Current feed flow rate. The speed is 1000 kg / h, adjust the step size. Set the value to 0.1. First, calculate the comprehensive deviation coefficient based on the comprehensive deviation model. = -0.07. Due to the comprehensive deviation coefficient A negative value indicates that the current evaporator operating condition is slightly below the ideal state. Next, the optimized feed flow rate is calculated based on the flow optimization model. =993.01 kg / h. Therefore, the optimized feed flow rate is... It was adjusted to approximately 993.01 kg / h. This example demonstrates how precise optimization of the feed flow rate can be achieved by quantifying the overall deviation and using a nonlinear function for smooth adjustment.

[0055] Through the above technical solution, this application effectively integrates the two key indicators of thermodynamic condition adaptability and heat transfer performance coefficient during evaporator operation, forming a quantified comprehensive deviation coefficient. This integration avoids the limitations of judging by a single indicator, making the assessment of the overall operating status of the evaporator more comprehensive and accurate. Based on this comprehensive deviation coefficient, combined with the current feed flow rate, nonlinear adjustments are made through a flow optimization model, enabling precise, smooth, and stable optimized control of the feed flow rate. This not only improves the adaptability and robustness of the evaporator under complex operating conditions but also effectively avoids system oscillations or efficiency declines caused by excessive or improper adjustments, thereby ensuring that the evaporator always operates near its optimal, efficient, and stable operating conditions, improving the overall control accuracy and operating efficiency of the production process.

[0056] An evaporator control system employs the aforementioned evaporator control method.

[0057] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0058] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An evaporator control method, characterized in that, include: Based on feed concentration, product flow rate, and product concentration, material characteristic coefficients are obtained through a material characteristic model. The thermal efficiency coefficient is obtained through a thermal efficiency model based on the live steam flow rate, live steam pressure, and cooling water temperature rise. Based on the liquid level in the evaporator chamber, temperature difference, and compressor power, the heat transfer performance coefficient is obtained through a heat transfer performance model. Based on the material property coefficient and the thermodynamic efficiency coefficient, combined with the evaporation chamber pressure and vapor phase temperature, the thermodynamic condition fit degree is obtained through the thermodynamic condition fit degree model. Based on the current feed flow rate, and combined with the thermal condition adaptability and heat transfer performance coefficient, the optimized feed flow rate is obtained through the flow optimization model.

2. The evaporator control method according to claim 1, characterized in that, The optimized feed flow rate is obtained as follows: Based on the thermodynamic condition adaptability and heat transfer performance coefficient, the comprehensive deviation coefficient is obtained through a comprehensive deviation model, which is expressed as follows: ； in, This represents the overall deviation coefficient. Indicates the degree of thermal compatibility. This indicates the threshold for thermal compatibility. Indicates the heat transfer performance coefficient. Indicates the threshold value of the heat transfer performance coefficient. Represents the weight coefficient and ; Based on the comprehensive deviation coefficient and the current feed flow rate, the optimized feed flow rate is obtained through a flow rate optimization model, which is expressed as follows: ； in, This indicates the optimized feed flow rate. Indicates the current feed flow rate. This represents the overall deviation coefficient. This indicates the adjustment step size.

3. The evaporator control method according to claim 2, characterized in that, The method for obtaining the thermal condition adaptability is as follows: Based on material property coefficients and the temperature of pure water The saturation pressure is determined by obtaining the theoretical pressure through a pressure correction model, which is expressed as follows: ； in, Indicates theoretical pressure, Represents the material characteristic coefficient. Indicates the temperature of pure water The saturation pressure below, Indicates the concentration effect coefficient and ; Pressure deviation is obtained based on evaporator chamber pressure and theoretical pressure. , Indicates the pressure in the evaporation chamber. Indicates theoretical pressure; Based on pressure deviation and thermodynamic efficiency coefficient, the thermodynamic condition fit degree is obtained through a thermodynamic condition fit degree model, which is expressed as follows: ； in, Indicates the degree of thermal compatibility. Indicates pressure deviation. Indicates the allowable pressure deviation. Indicates the thermal efficiency coefficient. The coefficient representing the thermal efficiency loss penalty is... The higher the value, the better the operating conditions.

4. The evaporator control method according to claim 2, characterized in that, The heat transfer performance coefficient is obtained as follows: The liquid level, temperature difference, and compressor power in the evaporation chamber are processed dimensionlessly to obtain the liquid level deviation index, temperature difference deviation index, and compressor power deviation index in the evaporation chamber. Based on the evaporator liquid level deviation index, temperature difference deviation index, and compressor power deviation index, the heat transfer performance coefficient is obtained through a heat transfer performance model, which is expressed as follows: ； in, Indicates the heat transfer performance coefficient. This indicates the deviation index of the liquid level in the evaporation chamber. This indicates a deviation from the temperature difference index. This indicates the compressor power deviation index. Represents the weight coefficient and The The higher the value, the better the heat transfer performance.

5. The evaporator control method according to claim 3, characterized in that, The thermal efficiency coefficient is obtained as follows: The live steam flow rate, live steam pressure, and cooling water temperature rise are dimensionlessly processed to obtain the live steam flow rate deviation, live steam pressure deviation, and cooling water temperature rise deviation. Based on the deviations in live steam flow rate, live steam pressure, and cooling water temperature rise, the thermal efficiency coefficient is obtained through a thermal efficiency model, which is expressed as follows: ； in, Indicates the thermal efficiency coefficient. This indicates the deviation in live steam flow rate. Indicates the deviation in live steam pressure. Indicates the deviation in cooling water temperature rise. Represents the weight coefficient and The The higher the value, the better the thermal efficiency.

6. The evaporator control method according to claim 3, characterized in that, The material characteristic coefficients are obtained as follows: The feed concentration, product flow rate, and product concentration are dimensionlessly processed to obtain the feed concentration index, product flow rate index, and product concentration index. Based on the feed concentration index, product flow rate index, and product concentration index, material characteristic coefficients are obtained through a material characteristic model, which is expressed as follows: ； in, Represents the material characteristic coefficient. This indicates the feed concentration index. Indicates the product traffic index. Indicates the product concentration index. Indicates the allowable deviation value and The Furthermore, the higher the value, the better the material properties.

7. The evaporator control method according to claim 4, characterized in that, The methods for obtaining the evaporator liquid level deviation index, temperature difference deviation index, and compressor power deviation index are as follows: The absolute differences between the evaporator liquid level, temperature difference, and compressor power and the corresponding reference values ​​are compared with the corresponding allowable deviations from the optimal values ​​to obtain the evaporator liquid level deviation index, temperature difference deviation index, and compressor power deviation index.

8. The evaporator control method according to claim 5, characterized in that, The methods for obtaining the deviations in live steam flow rate, live steam pressure, and cooling water temperature rise are as follows: The differences between the live steam flow rate, live steam pressure, and cooling water temperature rise and the corresponding reference values ​​are compared with the corresponding reference values ​​to obtain the live steam flow rate deviation, live steam pressure deviation, and cooling water temperature rise deviation.

9. The evaporator control method according to claim 6, characterized in that, The methods for obtaining the feed concentration index, product flow rate index, and product concentration index are as follows: The feed concentration, product flow rate, and product concentration are compared with the corresponding reference values ​​to obtain the feed concentration index, product flow rate index, and product concentration index.

10. An evaporator control system, characterized in that, The evaporator control method according to any one of claims 1-9 is adopted.

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