Temperature control method and system for sodium carboxymethyl starch production process

Abstract

The application discloses a temperature control method and system in sodium carboxymethyl starch production process, and relates to the technical field of chemical engineering process control, which comprises the following steps: dividing the sodium carboxymethyl starch production process, determining the temperature control target; establishing a temperature-viscosity-time correlation model; determining the key temperature control point, and adopting a segmented control strategy to differentially regulate and control different temperature control regions in the reaction kettle; when it is detected that the temperature gradient in the reaction kettle exceeds the set temperature gradient tolerance range, the flow and flow direction of the reaction kettle jacket heat medium are cooperatively adjusted in combination with the heat transfer relationship of adjacent temperature control regions; the reaction progress is evaluated in combination with the temperature-viscosity-time correlation model, and the next reaction stage is switched to through temperature ladder conversion control switching. By constructing the temperature-viscosity-time correlation model, the application realizes accurate prediction of the viscosity mutation point, overcomes the limitation of the traditional single variable linear model, and improves the temperature control precision and process stability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of chemical engineering process control, and particularly relates to a temperature control method and system in a sodium carboxymethyl starch production process. BACKGROUND

[0002] As an important starch derivative, sodium carboxymethyl starch is widely used in medicine, food, papermaking, textile, oil drilling and other industries, and the market demand continues to grow. With the increasing demand for product quality consistency in downstream industries, accurate temperature control in the production process of sodium carboxymethyl starch has become a key link to ensure product quality and improve production efficiency.

[0003] In the existing sodium carboxymethyl starch production process, temperature control mainly relies on traditional PID control or manual experience adjustment, which has the following technical limitations: large reaction temperature fluctuation, resulting in uneven carboxyl substitution degree and unstable product quality; unable to flexibly adjust the temperature control strategy according to different product specifications; lack of temperature coordination control for the whole production process, with independent temperature control for each process; low energy utilization efficiency and insufficient heat recovery. SUMMARY

[0004] The present application provides a temperature control method and system in a sodium carboxymethyl starch production process, which solves the technical problem of accurate temperature control in each reaction stage of sodium carboxymethyl starch production.

[0005] Therefore, the present application provides a temperature control method in a sodium carboxymethyl starch production process, which comprises:

[0006] The sodium carboxymethyl starch production process is divided into an alkalization stage, an etherification stage and a neutralization stage in sequence, and the temperature control targets of each reaction stage are determined;

[0007] A temperature sensor array is arranged in the reaction kettle to monitor the temperature distribution in the reaction kettle and simultaneously monitor the viscosity of the reaction system, and a temperature-viscosity-time correlation model is established;

[0008] Based on the temperature-viscosity-time correlation model, key temperature control points are determined, and a segmented control strategy is used to differentially regulate different temperature control regions in the reaction kettle;

[0009] When the temperature gradient in the reaction kettle is detected to be outside the set temperature gradient tolerance range, the flow rate and flow direction of the reaction kettle jacket heat medium are adjusted in coordination based on the heat transfer relationship of adjacent temperature control regions;

[0010] The reaction progress is evaluated in combination with the temperature-viscosity-time correlation model, and when the reaction progress is detected to reach a predetermined completion degree, the temperature ladder conversion control is switched to the next reaction stage.

[0011] Optionally, determining the temperature control target of each reaction stage comprises:

[0012] Analyzing the conformational change characteristics of sodium carboxymethyl starch molecular chain, characterizing the temperature response characteristics of hydroxyl activity on starch molecules, and determining the temperature sensitive interval of each reaction stage;

[0013] Based on the temperature sensitive interval, the exothermic characteristics of each reaction stage are determined, the heat change law is analyzed, and the temperature process parameters of each reaction stage are determined;

[0014] According to the thermodynamic characteristics of each reaction stage, a temperature conversion mechanism is established, and the temperature change strategy between stages is determined;

[0015] The mapping relationship between temperature and reaction index of each reaction stage is established, and a segmented temperature control system is formed.

[0016] Optionally, establishing a temperature-viscosity-time correlation model comprises:

[0017] A temperature sensor array is arranged along the radial, axial and tangential directions in the reaction kettle to form a three-dimensional temperature monitoring network, and temperature distribution data is obtained;

[0018] An online viscosity measuring device is installed at the bottom and side wall of the reaction kettle to monitor the viscosity change of the reaction system at different temperature distribution positions, and viscosity data is obtained;

[0019] The temperature data and viscosity data are time-matched to establish the corresponding relationship between temperature and viscosity, and the reaction time dimension is introduced to construct a temperature-viscosity-time correlation model;

[0020] Based on the temperature-viscosity-time correlation model, the correlation gradient of viscosity and temperature in the reaction process is calculated, and the temperature sensitive point in the reaction kettle is determined as the key temperature control point according to the correlation gradient and temperature distribution characteristics.

[0021] Optionally, the differential regulation and control of different temperature control regions in the reaction kettle by using a segmented control strategy comprises:

[0022] According to the key temperature control point, the space in the reaction kettle is divided into multiple temperature control regions, and independent temperature control parameters are set for each temperature control region;

[0023] The heat exchange rate and influence factor are calculated to obtain the temperature gradient transfer coefficient and heat exchange time lag parameter;

[0024] The temperature gradient transfer coefficient and heat exchange time lag parameter are input into the partition PID controller to calculate the heat medium flow control amount of each temperature control region, adjust the opening degree of the jacket section valve, and form differential flow distribution;

[0025] According to the heat medium flow control amount, the heat change rate of each temperature control area is calculated, the differentiated temperature regulation instruction is generated, and the jacket segmented heating system is driven to operate;

[0026] The temperature response data of each temperature control area is collected, the corresponding relationship between the temperature change and the heat medium flow control amount is compared, and the temperature gradient transmission coefficient is adjusted.

[0027] Optionally, the flow and flow direction of the reaction kettle jacket heat medium are cooperatively adjusted, including:

[0028] The temperature gradient abnormality is analyzed, and the temperature control area that needs to be controlled and the control priority are determined;

[0029] The temperature difference data between adjacent temperature control areas is measured, the temperature conduction coefficient and the convection heat transfer coefficient between adjacent temperature control areas are calculated, and the heat transfer parameter table under emergency state is established;

[0030] The viscosity data of the reaction system is monitored in real time, the influence of viscosity change on heat exchange efficiency is calculated, and the correction coefficient is determined, and the heat transfer parameter table is corrected;

[0031] Based on the heat transfer parameter table, the heat exchange coupling strength between adjacent temperature control areas is analyzed, and the heat transfer dominant direction and key transmission path are determined;

[0032] According to the heat coupling analysis result, the adjustment control amount of the heat medium flow and flow direction is calculated, and the cooperative adjustment is implemented until the temperature gradient is adjusted to the allowable range.

[0033] Optionally, the temperature ladder conversion control is switched to the next reaction stage, including:

[0034] The temperature-viscosity change rate is used as a reaction progress index, and a temperature-viscosity change rate matrix of the current reaction stage is calculated based on a temperature-viscosity-time correlation model;

[0035] A reaction progress determination model is constructed, the temperature-viscosity change rate matrix and the stage viscosity critical value are combined, the completion degree of the current reaction stage is calculated in real time, and when the reaction completion degree reaches the predetermined completion degree, a stage conversion signal is triggered;

[0036] According to the temperature conversion mechanism among the alkalization stage, the etherification stage and the neutralization stage, a temperature ladder conversion control curve is planned, and the temperature change rate and residence time of the conversion process of each stage are determined;

[0037] The next reaction stage temperature control parameters of each temperature control area are preset, and are differentially set according to the area characteristics.

[0038] The second aspect of the present application provides a temperature control system in a sodium carboxymethyl starch production process, including:

[0039] A stage division module is configured to divide the sodium carboxymethyl starch production process into an alkalization stage, an etherification stage and a neutralization stage in sequence, and determine the temperature control target of each reaction stage.

[0040] A correlation model construction module is configured to set a temperature sensor array in the reaction kettle to monitor the temperature distribution in the reaction kettle, monitor the viscosity of the reaction system, and establish a temperature-viscosity-time correlation model.

[0041] A differential regulation module is configured to determine the key temperature control point based on the temperature-viscosity-time correlation model, and adopt a segmented control strategy to differentially regulate different temperature control regions in the reaction kettle.

[0042] A collaborative regulation module is configured to, when detecting that the temperature gradient in the reaction kettle exceeds the set temperature gradient tolerance range, collaboratively regulate the flow and flow direction of the reaction kettle jacket heat medium in combination with the heat transfer relationship of adjacent temperature control regions.

[0043] A control switching module is configured to evaluate the reaction progress in combination with the temperature-viscosity-time correlation model, and switch to the next reaction stage through temperature step conversion control when detecting that the reaction progress reaches a predetermined completion degree.

[0044] The present application has the following beneficial effects: accurately determining the temperature sensitive interval from the molecular level, improving the hydroxyl activation and carboxymethylation reaction effect, and being more scientific and accurate than the traditional empirical temperature setting method. The temperature-viscosity-time correlation model is constructed to accurately predict the viscosity mutation point, overcome the limitations of the traditional single variable linear model, and improve the temperature control precision and process stability. The segmented control strategy is adopted to differentially regulate different regions of the reaction kettle, solving the temperature control problem of large industrial reaction kettles and providing a precise temperature control environment for high viscosity reaction systems. The heat transfer model considering the influence of viscosity is established to solve the complex problem of heat transfer caused by viscosity change, so that the temperature control system can cope with the heat response delay effect. The reaction stage switching time and temperature control curve are determined based on multiple factors to ensure smooth transition of the stage, improve the production stability and product quality consistency. BRIEF DESCRIPTION OF DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0046] Fig. 1 It is a flow chart of a temperature control method in a sodium carboxymethyl starch production process.

[0047] Fig. 2A temperature-viscosity-time correlation model is constructed for a temperature control method in a carboxymethyl starch sodium production process, and a flowchart is processed.

[0048] Fig. 3 A differentiated regulation flowchart is provided for a temperature control method in a carboxymethyl starch sodium production process. DETAILED DESCRIPTION

[0049] To make the invention purposes, features, and advantages of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the embodiments described below are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0050] Embodiment 1, Reference Figs. 1-3 In a first embodiment of the present application, a temperature control method in a carboxymethyl starch sodium production process is provided, and a flowchart of the method is shown as Fig. 1 The method comprises the following steps:

[0051] S1: Dividing the carboxymethyl starch sodium production process into an alkalization stage, an etherification stage, and a neutralization stage in sequence, and determining the temperature control targets of each reaction stage.

[0052] In a specific embodiment of the present application, step S1 specifically comprises:

[0053] S1.1: According to the reaction mechanism and process characteristics of carboxymethyl starch sodium synthesis, the carboxymethyl starch sodium production process is divided into an alkalization stage, an etherification stage, and a neutralization stage in sequence.

[0054] It should be noted that the synthesis process of carboxymethyl starch sodium follows a specific chemical reaction path, which determines that the production process must be carried out in strict order. As the initial step, the alkalization stage plays a key role in activating the hydroxyl groups on the starch molecules in an alkaline environment, converting them into alcoholate form with higher reactivity, and laying a foundation for the subsequent etherification reaction. The etherification stage is the core link of carboxymethyl starch sodium synthesis, in which the activated starch molecules react with carboxymethylation reagents (such as monochloroacetic acid or its sodium salt) to successfully introduce carboxymethyl groups into the starch molecule structure, building the molecular skeleton of carboxymethyl starch. The neutralization stage is the last link of the production process, which mainly adjusts the pH value of the reaction system to the appropriate range to stabilize the carboxymethyl structure, and finally produces qualified carboxymethyl starch sodium product. This division not only conforms to the reaction mechanism of carboxymethyl starch sodium synthesis, but also meets the requirements of process characteristics, laying a foundation for subsequent temperature control.

[0055] S1.2: Analyze the conformational change characteristics of sodium carboxymethyl starch molecular chains, characterize the temperature response characteristics of the hydroxyl activity on the starch molecules, and determine the temperature sensitive interval of each reaction stage.

[0056] Specifically, the enthalpy change of starch molecules under different temperature conditions is determined by differential scanning calorimetry to determine the critical temperature point of conformational transition of starch molecular chains; the characteristic vibration frequency of hydroxyl groups at different temperatures is monitored by Fourier transform infrared spectroscopy to obtain the correlation information between hydroxyl groups and temperature; the change of hydrogen atom chemical shift in starch molecules at different temperatures is analyzed by nuclear magnetic resonance spectroscopy to evaluate the reactivity of hydroxyl groups; based on the determination data, the temperature sensitive interval of each reaction stage is clearly defined; the reaction effect of each temperature sensitive interval is verified by small scale experiment to ensure the scientificity and rationality of the division of temperature sensitive interval. Compared with the traditional empirical constant temperature method, the temperature sensitive interval is determined from the microscopic view of molecular conformational change, which more accurately locks the optimal temperature range of each reaction stage and greatly improves the activation efficiency of hydroxyl groups and the selectivity of carboxymethylation reaction.

[0057] S1.3: Based on the determined temperature sensitive interval, the exothermic characteristics of each reaction stage are determined, the heat change rule is analyzed, and the temperature process parameters of each reaction stage are determined.

[0058] Further, the reaction heat value and exothermic rate of the alkalization stage, etherification stage and neutralization stage are determined by isothermal reaction calorimeter; the reaction heat curve of each reaction stage is drawn to determine the peak point and its duration of heat release; the heat accumulation characteristics under different viscosity conditions are analyzed and determined in combination with the influence of reaction system viscosity on heat transfer; based on the above analysis, the upper limit value, lower limit value and optimal control value of each reaction stage are determined as the temperature process parameters; the temperature control strategy of each reaction stage is formulated, and a complete set of temperature process parameters is formed.

[0059] S1.4: Establish temperature conversion mechanism according to the thermodynamic characteristics of each reaction stage, and determine the temperature change strategy between stages.

[0060] Further, the thermodynamic properties of each reaction stage are analyzed, including entropy change, enthalpy change, and Gibbs free energy change; the reaction system parameters at the end of the alkalization stage are monitored to determine the trigger conditions for switching to the etherification stage; the temperature transition curve from the alkalization stage to the etherification stage is designed to ensure smooth transition of the reaction system temperature; the reaction progress at the end of the etherification stage is monitored to determine the optimal timing for switching to the neutralization stage; the temperature control strategy from the etherification stage to the neutralization stage is designed to avoid adverse effects of temperature fluctuations on product quality. Emergency control measures are developed during the transition process of each stage to ensure temperature safety during the transition process; a stage transition criterion based on the viscosity change rate of the reaction system is established to identify the completion point of the alkalization stage by quantitatively monitoring the inflection point characteristics of the viscosity change rate, and to determine the fully conducted etherification reaction point by the accelerated characteristics of the viscosity change rate, thereby determining the optimal transition timing of the reaction stage, avoiding the blind transition of traditional processes relying solely on time or temperature, and improving the accuracy of stage transition, providing a scientific decision basis for temperature step transition control. By establishing a scientific temperature transition mechanism, smooth transition between stages is achieved, and the stability of the production process is improved.

[0061] S1.5: Establish the mapping relationship between temperature and reaction indicators in each reaction stage to form a segmented temperature control system.

[0062] Further, in the alkalization stage, a mapping relationship between reaction temperature and alkalization degree is established to determine the optimal alkalization temperature control curve; in the etherification stage, a mapping relationship between reaction temperature and carboxymethyl substitution degree is established to determine the optimal etherification temperature control curve; in the neutralization stage, a mapping relationship between reaction temperature and pH change is established to determine the optimal neutralization temperature control curve; the temperature and reaction indicator mapping relationships of each stage are integrated to establish a complete segmented temperature control system; the segmented temperature control system is fine-tuned and optimized in combination with actual production conditions; a stage temperature control adaptive fine-tuning mechanism is constructed, which automatically adjusts the temperature change rate when the viscosity-temperature ratio index exceeds the set threshold value, smooths the temperature control curve using the moving average method, and dynamically adjusts the heating / cooling power, so that the temperature control can accurately predict and respond to viscosity changes, overcoming the defects of lagging adjustment in traditional control systems, and providing accurate temperature change rate and residence time parameters for subsequent temperature step transition control. By establishing the mapping relationship between temperature and key reaction indicators, a segmented temperature control system is formed for the entire process, which can perform differentiated temperature control according to the reaction characteristics of different stages, and is beneficial to improving the stability and production efficiency of carboxymethyl substitution degree.

[0063] S2: Set up a temperature sensor array in the reaction kettle to monitor the temperature distribution in the reaction kettle, and monitor the viscosity of the reaction system to establish a temperature-viscosity-time correlation model.

[0064] In a specific embodiment of the present application, the temperature-viscosity-time correlation model construction and processing flow chart is as shown in Fig. 2 Specifically, it includes:

[0065] S2.1: A three-dimensional temperature monitoring network is formed by arranging temperature sensor arrays in the radial, axial and tangential directions in the reaction kettle, and temperature distribution data is obtained.

[0066] It should be noted that in the traditional production process of sodium carboxymethyl starch, the number of temperature monitoring points in the reaction kettle is limited, and it is difficult to fully reflect the temperature distribution in the system. The present application can capture the temperature field distribution characteristics in the reaction process by reasonably arranging the three-dimensional temperature sensor array to form a temperature monitoring network, and provide data basis for precise temperature control. In a typical embodiment, a certain number of temperature measuring points are uniformly distributed in the radial, axial and tangential directions according to the size of the reaction kettle, forming a temperature monitoring network covering the main reaction area of the kettle. The specific number and installation position of the sensors can be adjusted according to the specifications of the reaction kettle to obtain representative temperature distribution data.

[0067] S2.2: Online viscosity measurement devices are installed at the bottom and side wall of the reaction kettle to monitor the viscosity changes of the reaction system at different temperature distribution positions, and viscosity data is obtained.

[0068] It should be noted that in the reaction process of sodium carboxymethyl starch, the viscosity of the system is an important parameter reflecting the reaction progress and product characteristics. The present application uses appropriate online viscosity measurement methods to monitor the viscosity changes at key positions without interfering with the reaction. Specifically, viscosity measurement devices are installed at different heights of the bottom and side wall of the reaction kettle to form a viscosity monitoring network, and the data acquisition frequency is adjusted according to the characteristics of the reaction stage. Higher frequency is used in the stage with faster viscosity change, and lower frequency is used in the stage with slower viscosity change. By combining viscosity measurement with temperature monitoring, the temperature-viscosity correspondence relationship at a specific position can be established, providing an important reference for reaction process control.

[0069] S2.3: Time sequence matching of temperature data and viscosity data is performed to establish the basic correspondence relationship between temperature and viscosity, and the reaction time dimension is introduced to construct the temperature-viscosity-time correlation model.

[0070] Specifically, first, the temperature data and viscosity data in the historical production data are wavelet transform filtered and preprocessed to eliminate high-frequency noise and random fluctuations; the data set is divided into a training set (80%) and a validation set (20%) for model construction and calibration; the temperature data is converted into a temperature spatial distribution feature tensor through a tensor decomposition method (such as Tucker decomposition) that preserves spatial topological relationships, and the viscosity data at different positions is converted into a viscosity distribution feature vector. A temperature-viscosity-time correlation model is constructed, which specifically includes: first, based on the time window method, the continuous time series data is segmented into multiple sample points, each sample point containing temperature distribution features, viscosity distribution features and corresponding time stamps; second, the reaction time is introduced as an independent feature dimension into the model, by converting the absolute time into a percentage representation of the reaction progress, a normalized reaction progress time axis is established, solving the problem of different batch reaction total time differences; third, a deep neural network containing an encoder-decoder structure is designed, the encoder compresses the temperature distribution features and the reaction time features into a low-dimensional latent representation, and the decoder predicts the viscosity distribution based on the latent representation; finally, the Arrhenius equation constraint of the carboxymethylation reaction is introduced as a regularization term to ensure that the model prediction conforms to the physical relationship between temperature and reaction rate.

[0071] The historical production data includes temperature distribution data, viscosity data and corresponding time sequence information in the past carboxymethyl starch sodium production process.

[0072] Preferably, through the temperature-viscosity-time correlation model, the viscosity mutation point can be accurately predicted to provide a decision basis for temperature control strategy optimization. The model overcomes the limitations of traditional single variable linear models through the encoder-decoder structure of the deep neural network and the Arrhenius equation physical constraint, realizes the multi-dimensional nonlinear dynamic mapping of temperature distribution features, reaction time and viscosity distribution, and realizes the real-time prediction of viscosity evolution through the rolling prediction function, which helps to improve the temperature control accuracy and process stability of the carboxymethyl starch sodium production process.

[0073] S2.4: Use the validation set data to evaluate the prediction accuracy of the temperature-viscosity-time correlation model, and optimize the model parameters and structure based on the evaluation results.

[0074] Further, based on the optimized temperature-viscosity-time correlation model, the viscosity distribution is rolling predicted by iteratively inputting the temperature distribution features and normalized reaction time at the current time, to generate the viscosity evolution sequence within the prediction window.

[0075] S2.5: Based on the temperature-viscosity-time correlation model, calculate the correlation gradient of viscosity and temperature in the reaction process, and determine the temperature sensitive point in the reaction kettle as the key temperature control point according to the correlation gradient and temperature distribution features.

[0076] Specifically, a multi-dimensional sensitivity analysis framework is constructed by using a temperature-viscosity-time correlation model, a partial derivative matrix of temperature to viscosity and a partial derivative matrix of time to viscosity are calculated, and a multi-dimensional sensitivity analysis field is constructed; in the multi-dimensional sensitivity analysis field, partial derivative local extreme points and viscosity mutation early warning points are identified as the first type of temperature sensitive points in the reaction kettle; based on temperature distribution data, the spatial gradient distribution of the temperature field is calculated, and the heat transfer limited area is determined as the second type of temperature sensitive point by combining heat conduction theory analysis; combined with viscosity data and temperature distribution data, the flow characteristics in the reaction system are analyzed, and the area with obvious difference in heat transfer efficiency is identified as the third type of temperature sensitive point; for the above three types of temperature sensitive points, a hierarchical weight evaluation system is established, and the final key temperature control point distribution is determined by a spatial clustering algorithm.

[0077] Preferably, by determining the key temperature control point, the present application realizes precise temperature control based on multi-dimensional sensitivity analysis. Compared with the traditional fixed position monitoring method, this innovative point makes the selection of temperature sensitive points more scientific, effectively identifies the most sensitive area to temperature change in the reaction process, and avoids the problem of control lag or inaccuracy caused by improper selection of temperature monitoring points in the traditional method.

[0078] S3: determining the key temperature control point based on the temperature-viscosity-time correlation model, and adopting a segmented control strategy to differentially regulate different temperature control regions in the reaction kettle.

[0079] In one specific embodiment of the present application, the differential regulation flowchart is as shown in Fig. 3 , and specifically includes:

[0080] S3.1: according to the key temperature control point, dividing the space in the reaction kettle into multiple temperature control regions, setting independent temperature control parameters for each temperature control region, and determining the reaction kettle jacket section corresponding to each temperature control region.

[0081] Specifically, based on the three types of temperature sensitive points determined in S2.4, a temperature control point density distribution map is constructed using a spatial point cloud analysis technique to identify the aggregation area of temperature control points; a region growing algorithm is applied, taking the temperature control point aggregation area as the seed point, combining the geometric structure characteristics of the reaction kettle, and dividing the initial temperature control area with similar temperature variation characteristics to form the temperature control area boundary contour; the initial temperature control area is analyzed for thermodynamic characteristics to evaluate the temperature field uniformity and thermal response consistency within each area, and the initial temperature control area is merged or subdivided according to the similarity of the thermal response characteristics to optimize the temperature control area division structure; the thermal conduction relationship between each temperature control area and the jacket section is analyzed in combination with the jacket structure design and heat medium flow characteristics of the reaction kettle to establish an optimal correspondence matrix between the temperature control area and the jacket section, ensuring that each temperature control area can achieve effective temperature regulation through the corresponding jacket section; according to the temperature sensitivity characteristics and viscosity variation characteristics of each temperature control area, a regional temperature control parameter configuration scheme is developed in combination with the segmented temperature control system established in S1.5, including target temperature set value, temperature fluctuation tolerance range, temperature rise and fall rate limit, and PID control parameter group, to achieve precise temperature management of the temperature control area.

[0082] Preferably, the present application breaks through the limitation of traditional temperature control of reaction kettle as a whole, and can provide more precise temperature control environment for high viscosity reaction system for large industrial reaction kettle center-wall temperature difference, uneven temperature rise and fall, etc.

[0083] S3.2: Establish a heat transfer model between the temperature control areas, calculate the heat exchange rate and influence factor, and obtain the temperature gradient transfer coefficient and heat exchange time lag parameter.

[0084] Further, based on the spatial position relationship and boundary characteristics of each temperature control area, a regional heat transfer network topology structure is constructed to identify the main heat transfer channel and key heat exchange interface; the computational fluid dynamics principle is applied, combined with the rheological characteristics of the reaction system, to establish a heat convection transfer differential equation considering the influence of viscosity variation, and the regional heat exchange flux is solved by the finite element method; through real-time temperature monitoring data, the temperature gradient change rate between adjacent temperature control areas is calculated, the relationship function between heat transfer rate and temperature gradient is fitted, and the temperature gradient transfer coefficient is obtained; the time sequence response characteristics of temperature variation between the temperature control areas are analyzed, the propagation delay of temperature variation signal between the areas is measured, and the heat exchange time lag parameter is determined.

[0085] Preferably, by establishing a heat transfer model considering the influence of viscosity, the problem of heat transfer complexity caused by viscosity variation in the production process of sodium carboxymethyl starch is successfully solved, so that the temperature control system can predictively cope with the thermal response delay effect caused by viscosity variation of the reaction system.

[0086] S3.3: Input the temperature gradient transfer coefficient and heat exchange time lag parameter into the partitioned PID controller, calculate the heat medium flow control amount of each temperature control area, adjust the valve opening degree of each section of the jacket, and form differential flow distribution.

[0087] Further, a partitioned PID control system based on regional characteristics is constructed, an independent PID control unit is configured for each temperature control area, and a PID parameter setting method modified by a temperature gradient transfer coefficient is used for each control unit. Considering the obvious thermal response lag phenomenon in the sodium carboxymethyl starch reaction process, a heat exchange time lag parameter is introduced as a feedforward compensation term to establish an improved PID control algorithm of Smith predictor structure, thereby improving the adaptability of the system to heat transfer hysteresis. Based on the real-time viscosity change trend and temperature response characteristics, the proportional, integral and differential parameters of the PID controller are dynamically adjusted to realize adaptive control of the reaction system under different viscosity conditions. The output of the PID controller of each temperature control area is converted into a jacket heat medium flow control instruction, and the optimal flow distribution ratio and regulating valve opening degree set value of each jacket section are calculated through a flow distribution optimization algorithm. A hierarchical flow control mechanism is designed to meet the temperature control requirements of the main temperature control area under the premise of coordinating the heat medium flow distribution between each jacket section through cascade adjustment, forming a differential but overall coordinated heat medium flow distribution strategy between regions.

[0088] Preferably, the technical solution effectively overcomes the nonlinearity and hysteresis problems of temperature control in the sodium carboxymethyl starch reaction process through the partitioned PID control system combined with the heat exchange time lag parameter, and improves the heat medium utilization efficiency.

[0089] S3.4: According to the heat medium flow control amount, the heat change rate of each temperature control area is calculated, differential temperature regulation instructions are generated, and the jacket segmented heating system is driven to operate.

[0090] Further, based on the heat medium flow control amount and the heat medium inlet and outlet temperature difference data, the heat transfer rate of each temperature control area is calculated in real time. According to the thermal response characteristics and reaction progress requirements of each temperature control area, differential temperature regulation curves are set to convert temperature regulation requirements into combined control instructions of jacket heat medium temperature, flow and flow direction. A collaborative control strategy for the jacket segmented heating system is established to optimize the execution order of the control instructions according to the temperature control priority and heat demand urgency of each temperature control area, and to realize balanced regulation of multi-area temperature. A jacket heat medium flow path optimization algorithm is designed to dynamically adjust the series-parallel flow mode of the heat medium between each jacket section based on the temperature attenuation characteristics of the heat medium in the jacket section, thereby maximizing the heat medium utilization efficiency.

[0091] The jacket sectional heating system comprises a plurality of reaction kettle jacket sections corresponding to the temperature control regions, independent flow regulating devices for each section, temperature monitoring points, and inter-regional heat medium flow direction switching devices. Through independent regulation and control of the heat medium flow and flow direction of different jacket sections, precise temperature control of the corresponding temperature control regions is achieved.

[0092] S3.5: Collect temperature response data of each temperature control region, compare the corresponding relationship between temperature change and heat medium flow control amount, adjust the temperature gradient transfer coefficient, and realize balanced control of temperature distribution between temperature control regions.

[0093] Further, through the temperature sensor array, temperature data of each temperature control region is collected at regular intervals, and the temperature response rate and steady-state deviation are calculated. The corresponding record of heat medium flow control amount and temperature change rate is established, and the actual influence degree of flow adjustment on temperature change is analyzed. According to the actual operation data, the temperature gradient transfer coefficient is updated by a small amount (adjustment amplitude is not more than ±5%) according to the preset period (usually 4-8 hours), so that it can more accurately reflect the heat transfer characteristics under the current process state. Through real-time analysis of the temperature response relationship of adjacent temperature control regions, the heat transfer bottleneck point is identified, and the temperature gradient transfer coefficient of the boundary region is adjusted by a small amount.

[0094] The boundary region refers to the junction between adjacent temperature control regions, which is determined when the region growing algorithm is used to form the temperature control region boundary profile in step S3.1. The present application particularly focuses on the temperature gradient control of these boundary regions, because the heat transfer between adjacent temperature control regions mainly occurs in these boundary regions, which are the key channels for heat transfer and may become the "bottleneck point" of heat transfer. By adjusting the temperature gradient transfer coefficient of these boundary regions, more effective balanced control of temperature distribution between temperature control regions can be achieved.

[0095] S4: When it is detected that the temperature gradient in the reaction kettle exceeds the set temperature gradient tolerance range, the flow and flow direction of the reaction kettle jacket heat medium are adjusted in combination with the heat transfer relationship of adjacent temperature control regions.

[0096] In one specific embodiment of the present application, step S4 specifically comprises:

[0097] S4.1: Analyze the temperature gradient abnormality, and determine the temperature control region that needs to be regulated and the control priority according to the temperature gradient exceeding degree and distribution position.

[0098] Specifically, the temperature data of each temperature control region in the reaction kettle is collected in real time by a three-dimensional temperature monitoring network, the temperature gradient value between adjacent temperature control regions is calculated, and compared with the set temperature gradient tolerance range; the deviation coefficient is calculated according to the amplitude of the temperature gradient exceeding the tolerance range, and the abnormal level is divided based on the deviation coefficient, and the high, medium and low three intervention thresholds are set; the spatial distribution characteristics of the temperature gradient anomaly are analyzed, the temperature anomaly distribution heat map in the reaction kettle is constructed by a thermal imaging algorithm, the core area and diffusion trend of the temperature gradient anomaly are identified; based on the temperature sensitivity characteristics and viscosity change rate of the current reaction stage, the potential influence degree of the temperature anomaly of each temperature control region on the reaction process is evaluated, and an influence score matrix is generated; combined with the deviation coefficient, the abnormal distribution heat map and the influence score matrix, a multi-factor weighted algorithm is used to calculate the regulation urgency index of each temperature control region, and the regulation priority sequence is determined according to the regulation urgency index value, and the region with the highest regulation urgency index value is set as the primary regulation target.

[0099] Among them, the temperature gradient tolerance range is determined based on the temperature sensitive point characteristics, the segmented temperature regulation system requirements and the equipment condition restrictions, and an adaptive adjustment mechanism is set for different reaction stages.

[0100] S4.2: According to the divided temperature control region and its corresponding reaction kettle jacket section, the working state of each section control device is confirmed.

[0101] It should be noted that the working state of each section control device is confirmed to ensure the accuracy and reliability of subsequent coordinated regulation, to avoid temperature regulation failure due to equipment failure or abnormal response, and to provide basic data support for accurate calculation of heat medium flow adjustment amount.

[0102] S4.3: For temperature gradient abnormality, the temperature difference data between adjacent temperature control regions is re-measured, the temperature conduction coefficient and the convective heat transfer coefficient between adjacent temperature control regions are calculated, and the heat transfer parameter table under emergency state is established.

[0103] Further, for abnormal conditions beyond the temperature gradient tolerance range, the temperature gradient value calculated by S4.1 is used to determine the abnormal strength and direction of heat transfer; according to the heat flow and temperature difference between adjacent temperature control regions, Fourier's law of heat conduction is applied to recalculate the temperature conduction coefficient between adjacent temperature control regions; by analyzing the interface characteristics of adjacent temperature control regions, the interface thermal resistance value is determined to evaluate the influence of the interface on heat transfer under abnormal conditions; combined with the flow state and thermal physical parameters of the reaction system, the temperature change rate caused by fluid flow is measured using the fixed-point temperature measurement method to calculate the convective heat transfer coefficient between adjacent temperature control regions; the temperature conduction coefficient, convective heat transfer coefficient, and interface thermal resistance value are integrated to construct a heat transfer parameter table under emergency conditions. This parameter table is suitable for emergency regulation under abnormal temperature gradient conditions and complements the fine-tuning mechanism under normal operating conditions in S3.5, together ensuring the stability of system temperature control.

[0104] S4.4: Real-time monitoring of reaction system viscosity data, calculation of the influence of viscosity change on heat transfer efficiency and determination of correction coefficient, correction of heat transfer parameter table.

[0105] Further, the viscosity data of the reaction system is obtained by an online viscosity measuring device, the collected viscosity data is correlated with the reaction temperature data for analysis, and a viscosity-temperature response curve is established; the influence of reaction progress on viscosity is also considered to ensure the accuracy of the viscosity-temperature relationship; based on fluid dynamics theory, a correction relationship between viscosity and Nusselt number is constructed to quantify the influence of viscosity change on convective heat transfer efficiency; according to the flow characteristics and viscosity sensitivity of different temperature control regions, the viscosity correction coefficients of each temperature control region are calculated; the viscosity correction coefficients are applied to the heat transfer parameter table to correct the convective heat transfer coefficient, forming a viscosity-corrected heat transfer parameter table.

[0106] S4.5: Based on the heat transfer parameter table, analyze the heat transfer coupling strength between adjacent temperature control regions, determine the dominant direction of heat transfer and the key transfer path.

[0107] Specifically, the heat flow size and direction between each adjacent temperature control region are calculated using the heat transfer parameter table to quantify the strength of the inter-regional thermal coupling; by analyzing the direction and gradient of the heat flow vector, the dominant direction of heat transfer in the reactor is identified; according to the heat flow size and transfer efficiency, the main heat transfer channels formed between multiple temperature control regions are determined, and the key nodes and bottleneck positions in these channels are identified; the influence weight of each temperature control region in the overall heat transfer process is analyzed to determine the key temperature control regions that have a decisive effect on system temperature uniformity; combined with the temperature gradient abnormality analysis results in S4.1, the heat transfer paths that need to be focused on are determined to provide directional guidance for subsequent coordinated regulation.

[0108] S4.6: According to the results of the thermal coupling analysis, calculate the adjustment control amount of the heat medium flow rate and flow direction, implement coordinated adjustment, and adjust the temperature gradient to the allowable range.

[0109] Specifically, based on the heat transfer dominant direction and key transfer path determined in S4.5, a multi-region coordinated adjustment strategy is developed; a hierarchical control structure is adopted to decompose the temperature gradient control target into the heat medium flow rate and flow direction adjustment targets of each relevant temperature control region; through a predictive control algorithm, the optimal adjustment amount of the heat medium flow rate and flow direction of each jacket section is calculated to realize accurate control of the temperature gradient; during the adjustment process, the temperature gradient change trend is continuously monitored, and the control parameters are dynamically adjusted until the temperature gradient returns to the allowable range. The execution of this step ensures effective correction of the temperature gradient anomaly, realizes balanced optimization of the temperature field through multi-region coordinated adjustment, avoids the "trade-off" problem that may be caused by single-region adjustment, and provides a strong guarantee for the stability of the reaction process and the product quality.

[0110] S5: Combine the temperature-viscosity-time correlation model to evaluate the reaction progress, and when the reaction progress reaches the predetermined completion degree, switch to the next reaction stage through temperature step conversion control.

[0111] In one specific embodiment of the present application, step S5 specifically includes:

[0112] S5.1: Use the temperature-viscosity change rate as the reaction progress indicator, and calculate the temperature-viscosity change rate matrix of the current reaction stage based on the temperature-viscosity-time correlation model.

[0113] Wherein, the temperature-viscosity change rate is calculated by the partial derivative of viscosity with respect to temperature. Based on the temperature-viscosity-time correlation model, the derivative value of viscosity with respect to temperature at each monitoring point is calculated; the derivative values of each monitoring point are arranged according to the spatial position to form the temperature-viscosity change rate matrix.

[0114] S5.2: Construct a reaction progress judgment model, combine the temperature-viscosity change rate matrix and the critical value of the stage viscosity, and calculate the completion degree of the current reaction stage in real time. When the reaction completion degree reaches the predetermined completion degree, a stage conversion signal is triggered.

[0115] It should be noted that the stage viscosity critical value is obtained by analyzing the viscosity characteristics of the conversion points in each stage of the historical production data. In addition, the reaction progress calculation method is: the difference between the current viscosity and the stage viscosity critical value is normalized to obtain the basic completion degree index; combined with the eigenvalue analysis of the temperature-viscosity change rate matrix, the stability index of the viscosity change rate is calculated; comprehensive two indicators, through the weighted sum to get the completion degree score of the current reaction stage. When the completion degree score exceeds the predetermined completion degree (usually 90%-95%), the system determines that the current stage reaction has been fully carried out, triggering the stage conversion signal.

[0116] wherein the determination of the predetermined completion degree is based on the following scientific basis: by analyzing the correlation between the quality of the historical batch products and the stage completion degree, the minimum completion degree requirement that can guarantee the stability of the product quality is determined; considering the importance difference of different reaction stages, the threshold values of the alkalization stage and the etherification stage are set to 92%-95%, and the threshold value of the neutralization stage is set to 90%-93%; combined with the different requirements of product specifications, the threshold value of high viscosity product is set at the upper limit of the interval, and the threshold value of low viscosity product is set at the lower limit of the interval; through temperature-viscosity change rate trend analysis, when the change rate is stable in a low fluctuation state (such as fluctuation amplitude <2%) for 3 consecutive sampling periods, the threshold value can be appropriately reduced by 1-2 percentage points to optimize production efficiency. This scientific threshold system ensures the accuracy of the reaction stage switching and the consistency of the product quality.

[0117] S5.3: According to the temperature conversion mechanism between the alkalization stage, the etherification stage and the neutralization stage, the temperature step conversion control curve is planned, and the temperature change rate and residence time of each stage conversion process are determined.

[0118] Specifically, for the conversion from the alkalization stage to the etherification stage: according to the calculation results of the entropy change and enthalpy change of the alkalization stage reaction system, the maximum allowed temperature change rate is determined; combined with the activation energy characteristics of the etherification reaction, a three-stage temperature rising curve is designed, including an initial slow heating section, an intermediate rapid heating section and a final constant temperature stabilization section; after the etherification reaction temperature reaches the set value, the residence time is set to ensure that the reaction is fully carried out. For the conversion from the etherification stage to the neutralization stage: based on the Gibbs free energy change analysis of the etherification reaction, the cooling rate is determined; a smooth cooling curve is designed to avoid temperature sudden drop caused by product quality fluctuation; after the neutralization temperature is stable, sufficient residence time is set to ensure that the pH value adjustment is sufficient and the product performance is stable.

[0119] S5.4: Set the temperature gradient buffer zone to control the temperature change rate during the stage conversion process.

[0120] It should be noted that the setting of the temperature gradient buffer zone is to prevent local overheating or overcooling caused by rapid temperature change during the stage conversion process, and to avoid uneven product quality and reaction safety risk.

[0121] S5.5: preset the next reaction stage temperature control parameters of each temperature control area, and make differential settings according to the area characteristics.

[0122] Among them, for the top area of the reactor, since the heat loss of this area is large, the preset temperature value is 1-2℃ higher than the standard set value, and the integral time in the PID control parameter is shorter, so as to quickly respond to temperature fluctuations; for the bottom area of the reactor, considering the heat accumulation effect of this area, the preset temperature value is 0.5-1℃ lower than the standard set value, and the differential time in the PID control parameter is longer, so as to prevent temperature overshoot; for the center area of the reactor, as a standard temperature control area, the target temperature specified by the process is directly used, and the PID parameter setting balances stability and response speed. In addition, the differential setting mainly considers the differences in heat conduction characteristics, fluid flow state and reaction sensitivity of each area, and through the spatial differential temperature control strategy, the overall reaction system is uniformly controlled, and the reaction conversion rate and product quality stability are improved.

[0123] Further, the embodiment also provides a temperature control system in a carboxymethyl starch sodium production process, comprising: a stage division module, used for dividing the carboxymethyl starch sodium production process into an alkalization stage, an etherification stage and a neutralization stage in sequence, and determining the temperature control targets of each reaction stage; a correlation model construction module, used for setting a temperature sensor array in the reactor to monitor the temperature distribution in the reactor and simultaneously monitor the viscosity of the reaction system, and establishing a temperature-viscosity-time correlation model; a differential regulation module, used for determining key temperature control points based on the temperature-viscosity-time correlation model, and using a segmented control strategy to differentially regulate different temperature control areas in the reactor; a cooperative adjustment module, used for, when detecting that the temperature gradient in the reactor exceeds the set temperature gradient tolerance range, cooperatively adjusting the flow and flow direction of the reactor jacket heat medium in combination with the heat transfer relationship of adjacent temperature control areas; and a control switching module, used for evaluating the reaction progress in combination with the temperature-viscosity-time correlation model, and when detecting that the reaction progress reaches a predetermined completion degree, switching to the next reaction stage through temperature step conversion control.

[0124] By the technical scheme, the application has the following beneficial effects: the temperature sensitive range is accurately determined from the molecular level, the effect of hydroxyl activation and carboxymethylation reaction is improved, and the method is more scientific and accurate than the traditional empirical temperature determination method. A temperature- viscosity- time correlation model is constructed to accurately predict the viscosity mutation point, overcome the limitations of the traditional single variable linear model, and improve the temperature control precision and process stability. A segmented control strategy is adopted to differentially regulate different regions of the reaction kettle, solve the temperature control problem of large industrial reaction kettles, and provide a precise temperature control environment for high-viscosity reaction systems. A heat transfer model considering the influence of viscosity is established to solve the complex problem of heat transfer caused by viscosity change, so that the temperature control system can cope with the thermal response delay effect. Based on multiple factors, the reaction stage conversion timing and temperature control curve are determined to ensure smooth transition between stages and improve production stability and product quality consistency.

[0125] The above examples are only used to illustrate the technical solutions of the application, and not to limit it; although the application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing examples, or make equivalent substitutions for part of the technical features; and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the application.

Claims

1. A method for temperature control during the production of sodium carboxymethyl starch, characterized in that, include: The sodium carboxymethyl starch production process was divided into an alkalization stage, an etherification stage, and a neutralization stage, and the temperature control targets for each reaction stage were determined. A temperature sensor array is installed inside the reactor to monitor the temperature distribution inside the reactor, and the viscosity of the reaction system is monitored at the same time to establish a temperature-viscosity-time correlation model; Based on the temperature-viscosity-time correlation model, key temperature control points are determined, and a segmented control strategy is adopted to differentiate the control of different temperature control zones in the reactor. When the temperature gradient inside the reactor is detected to exceed the set temperature gradient allowable range, the flow rate and direction of the heat transfer medium in the reactor jacket are adjusted in conjunction with the heat transfer relationship between adjacent temperature control zones. The reaction progress is evaluated using the temperature-viscosity-time correlation model. When the reaction progress is detected to have reached the predetermined completion rate, the reaction is switched to the next reaction stage through temperature step transition control. The establishment of the temperature-viscosity-time correlation model includes: A temperature sensor array is set up in the reactor along the radial, axial and tangential directions to form a three-dimensional temperature monitoring network and acquire temperature distribution data. Online viscosity measuring devices are installed at the bottom and side walls of the reactor to monitor the viscosity changes of the reaction system at different temperature distribution locations and obtain viscosity data; Temperature and viscosity data are matched over time to establish the correspondence between temperature and viscosity, and the reaction time dimension is introduced to construct a temperature-viscosity-time correlation model. Based on the temperature-viscosity-time correlation model, the correlation gradient between viscosity and temperature during the reaction process is calculated. Based on the correlation gradient and temperature distribution characteristics, the temperature-sensitive points in the reactor are determined as key temperature control points. The segmented control strategy for differentiated regulation of different temperature control zones within the reactor includes: Based on the key temperature control points, the space inside the reactor is divided into multiple temperature control zones, and independent temperature control parameters are set for each temperature control zone. Calculate the heat exchange rate and influencing factors, and obtain the temperature gradient transfer coefficient and heat exchange time delay parameters; The temperature gradient transfer coefficient and heat exchange time delay parameter are input into the zone PID controller to calculate the heat medium flow control quantity of each temperature control zone, adjust the valve opening of each section of the jacket, and form a differentiated flow distribution. Based on the heat medium flow rate control, the rate of heat change in each temperature control zone is calculated, and differentiated temperature control commands are generated to drive the operation of the jacketed segmented heating system. Collect temperature response data for each temperature control zone, compare the correlation between temperature changes and heat medium flow control, and adjust the temperature gradient transfer coefficient.

2. The temperature control method in the production process of sodium carboxymethyl starch according to claim 1, characterized in that, The determination of temperature control targets for each reaction stage includes: The conformational change characteristics of sodium carboxymethyl starch molecular chains were analyzed, the temperature response characteristics of the activity of hydroxyl groups on starch molecules were characterized, and the temperature-sensitive range of each reaction stage was determined. Based on the temperature-sensitive range, the exothermic characteristics of each reaction stage are measured, the heat change law is analyzed, and the temperature process parameters of each reaction stage are determined. Establish a temperature conversion mechanism based on the thermodynamic characteristics of each reaction stage, and determine the temperature change strategy between stages; Establish a mapping relationship between temperature and reaction indicators at each reaction stage to form a segmented temperature control system.

3. The temperature control method in the production process of sodium carboxymethyl starch according to claim 1, characterized in that, The coordinated regulation of the flow rate and direction of the heat medium in the reactor jacket includes: Analyze abnormal temperature gradient situations to determine the temperature control area and control priority that need to be regulated; Measure the temperature difference data between adjacent temperature control zones, calculate the temperature conduction coefficient and convective heat transfer coefficient between adjacent temperature control zones, and establish a heat transfer parameter table under emergency conditions; Real-time monitoring of viscosity data of the reaction system, calculation of the impact of viscosity change on heat transfer efficiency and determination of correction coefficients, and correction of heat transfer parameter table; Based on the heat transfer parameter table, the heat exchange coupling strength between adjacent temperature control zones is analyzed to determine the dominant direction of heat transfer and key transfer paths. Based on the results of thermal coupling analysis, the adjustment and control quantities of the heat medium flow rate and flow direction are calculated, and coordinated adjustment is implemented until the temperature gradient is adjusted to within the allowable range.

4. The temperature control method in the production process of sodium carboxymethyl starch according to claim 1, characterized in that, The switching to the next reaction stage via temperature gradient control includes: The temperature-viscosity change rate was used as the reaction progress indicator, and the temperature-viscosity change rate matrix of the current reaction stage was calculated based on the temperature-viscosity-time correlation model. A reaction progress determination model is constructed, which combines the temperature-viscosity change rate matrix and the critical viscosity value of each stage to calculate the completion rate of the current reaction stage in real time. When the reaction completion rate reaches the predetermined completion rate, a stage transition signal is triggered. Based on the temperature transition mechanism between the alkalization, etherification and neutralization stages, a temperature step transition control curve is planned to determine the temperature change rate and residence time of each stage of the transition process. The temperature control parameters for the next reaction stage of each temperature control zone are preset and set differently according to the characteristics of each zone.

5. A temperature control system for the production process of sodium carboxymethyl starch, used to implement the temperature control method for the production process of sodium carboxymethyl starch according to claim 1, characterized in that, include: The stage division module is used to divide the sodium carboxymethyl starch production process into the alkalization stage, the etherification stage and the neutralization stage, and to determine the temperature control target for each reaction stage. The correlation model building module is used to set up a temperature sensor array inside the reactor to monitor the temperature distribution inside the reactor, and at the same time monitor the viscosity of the reaction system to establish a temperature-viscosity-time correlation model; The differential control module is used to determine key temperature control points based on the temperature-viscosity-time correlation model and to perform differential control on different temperature control zones in the reactor using a segmented control strategy. The coordinated adjustment module is used to coordinate the flow rate and direction of the heat transfer medium in the reactor jacket when the temperature gradient inside the reactor exceeds the set temperature gradient allowable range, taking into account the heat transfer relationship between adjacent temperature control zones. The control switching module is used to evaluate the reaction progress in conjunction with the temperature-viscosity-time correlation model. When the reaction progress is detected to have reached a predetermined completion level, the module switches to the next reaction stage through temperature step transition control.

Images