Temperature control method and system in production process of carboxymethyl starch sodium

Abstract

The invention discloses a temperature control method and system in a carboxymethyl starch sodium production process, and relates to the technical field of chemical engineering process control, and the method comprises the following steps: dividing the carboxymethyl starch sodium production process, and determining a temperature control target; establishing a temperature-viscosity-time correlation model; key temperature control points are determined, and different temperature control areas in the reaction kettle are subjected to differential regulation and control by adopting a sectional control strategy; when the temperature gradient in the reaction kettle is detected to exceed a set temperature gradient allowable range, cooperatively adjusting the flow and the flow direction of a heating medium in the jacket of the reaction kettle by combining the heat transfer relation of the adjacent temperature control areas; and evaluating the reaction progress by combining a temperature-viscosity-time correlation model, and controlling to switch to the next reaction stage through temperature step conversion. According to the method, the temperature-viscosity-time correlation model is constructed, so that the viscosity abrupt change point is accurately predicted, the limitation of a traditional single-variable linear model is overcome, and the temperature control precision and the process stability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of chemical engineering process control, and particularly to a temperature control method and system during the production process of sodium carboxymethyl starch. Background Art

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

[0003] During the existing production process of sodium carboxymethyl starch, temperature control mainly relies on traditional PID control or manual experience adjustment, and there are the following technical limitations: the reaction temperature fluctuates greatly, resulting in uneven carboxyl substitution degree and unstable product quality; it is impossible to flexibly adjust the temperature control strategy according to the requirements of different product specifications; there is a lack of temperature coordinated control for the entire production process, and the temperature control of each process is independent; the energy utilization efficiency is low, and the heat recovery and utilization are insufficient. Summary of the Invention

[0004] The present invention provides a temperature control method and system during the production process of sodium carboxymethyl starch, which is used to solve the technical problem of precise temperature control in each reaction stage during the production of sodium carboxymethyl starch.

[0005] In view of this, the first aspect of the present invention provides a temperature control method during the production process of sodium carboxymethyl starch, including:

[0006] Dividing the production process of sodium carboxymethyl starch into an alkalization stage, an etherification stage, and a neutralization stage in sequence, and determining the temperature control targets for each reaction stage;

[0007] Setting a temperature sensor array in the reaction kettle to monitor the temperature distribution in the reaction kettle, and simultaneously monitoring the viscosity of the reaction system to establish a temperature-viscosity-time correlation model;

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

[0009] When it is detected that the temperature gradient in the reaction kettle exceeds the allowable range of the set temperature gradient, in combination with the heat transfer relationship between adjacent temperature control regions, coordinately adjusting the flow rate and flow direction of the heat medium in the reaction kettle jacket;

[0010] Evaluating the reaction progress in combination with the temperature-viscosity-time correlation model, and when it is detected that the reaction progress reaches the predetermined completion degree, switching to the next reaction stage through temperature step conversion control.

[0011] Optionally, determining the temperature control objectives for each reaction stage includes:

[0012] Analyze the conformational change characteristics of the carboxymethyl starch sodium molecular chain, characterize the temperature response characteristics of the hydroxyl activity on the starch molecule, and determine the temperature-sensitive intervals for each reaction stage;

[0013] Based on the temperature-sensitive intervals, measure the exothermic characteristics of each reaction stage, analyze the heat change law, and determine the temperature process parameters for each reaction stage;

[0014] Establish a temperature conversion mechanism according to the thermodynamic characteristics of each reaction stage, and determine the temperature change strategy between stages;

[0015] Establish the mapping relationship between the temperature of each reaction stage and the reaction index, and form a segmented temperature control system.

[0016] Optionally, establishing the temperature-viscosity-time correlation model includes:

[0017] Set up a temperature sensor array along the radial, axial, and tangential directions inside the reactor to form a three-dimensional temperature monitoring network and obtain temperature distribution data;

[0018] Install an on-line viscosity measuring device at the bottom and side walls of the reactor to monitor the viscosity change of the reaction system at different temperature distribution positions and obtain viscosity data;

[0019] Match the temperature data and viscosity data in time series, establish the corresponding relationship between temperature and viscosity, and introduce the reaction time dimension to construct a temperature-viscosity-time correlation model;

[0020] Based on the temperature-viscosity-time correlation model, calculate the correlation gradient between viscosity and temperature during the reaction process, and determine the temperature-sensitive points inside the reactor according to the correlation gradient and temperature distribution characteristics as the key temperature control points.

[0021] Optionally, adopting a segmented control strategy to differentially regulate different temperature control regions inside the reactor includes:

[0022] According to the key temperature control points, divide the space inside the reactor into multiple temperature control regions, and set independent temperature control parameters for each temperature control region;

[0023] Calculate the heat exchange rate and influence factors, and obtain the temperature gradient transfer coefficient and heat exchange time-delay parameters;

[0024] Input the temperature gradient transfer coefficient and heat exchange time-delay parameters into the zone PID controller, calculate the control quantity of the heat medium flow rate for each temperature control region, and adjust the valve opening of each section of the jacket to form a differential flow distribution;

[0025] Calculate the heat change rate of each temperature control area according to the heat medium flow control amount, generate a differential temperature control instruction, and drive the operation of the jacket segmented heating system;

[0026] Collect the temperature response data of each temperature control area, compare the corresponding relationship between the temperature change and the heat medium flow control amount, and adjust the temperature gradient transfer coefficient.

[0027] Optionally, the coordinated regulation of the flow rate and flow direction of the heat medium in the reactor jacket includes:

[0028] Analyze the abnormal temperature gradient situation, determine the temperature control area to be regulated and the regulation priority;

[0029] Measure the temperature difference data between adjacent temperature control areas, calculate the temperature conduction coefficient and convective heat transfer coefficient between adjacent temperature control areas, and establish a heat transfer parameter table under emergency conditions;

[0030] Real-time monitor the viscosity data of the reaction system, calculate the influence of viscosity change on the heat transfer efficiency and determine the correction coefficient, and correct the heat transfer parameter table;

[0031] Based on the heat transfer parameter table, analyze the heat transfer coupling strength between adjacent temperature control areas, and determine the main heat transfer direction and key transfer path;

[0032] According to the heat coupling analysis result, calculate the adjustment control amount of the heat medium flow rate and flow direction, and implement coordinated regulation until the temperature gradient is adjusted within the allowable range.

[0033] Optionally, the switching to the next reaction stage through temperature step conversion control includes:

[0034] Use the temperature-viscosity change rate as the reaction progress index, and calculate the temperature-viscosity change rate matrix of the current reaction stage based on the temperature-viscosity-time correlation model;

[0035] Construct a reaction progress determination model, combine the temperature-viscosity change rate matrix and the stage viscosity critical value, and calculate the completion degree of the current reaction stage in real time. When the reaction completion degree reaches the predetermined completion degree, trigger a stage conversion signal;

[0036] According to the temperature conversion mechanism between the alkalization stage, etherification stage and neutralization stage, plan the temperature step conversion control curve, and determine the temperature change rate and residence time of each stage conversion process;

[0037] Preset the temperature control parameters of the next reaction stage for each temperature control area, and make differential settings according to the area characteristics.

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

[0039] A stage division module, which is used to sequentially divide the production process of sodium carboxymethyl starch into an alkalization stage, an etherification stage, and a neutralization stage, and determine the temperature control targets for each reaction stage;

[0040] An association model construction module, which is used to set up a temperature sensor array in the reaction kettle to monitor the temperature distribution in the reaction kettle, and at the same time monitor the viscosity of the reaction system, and establish a temperature-viscosity-time association model;

[0041] A differential regulation module, which is used to determine the key temperature control points based on the temperature-viscosity-time association model, and adopt a segmented control strategy to differentially regulate different temperature control regions in the reaction kettle;

[0042] A coordinated regulation module, which is used to, when it is detected that the temperature gradient in the reaction kettle exceeds the allowable range of the set temperature gradient, combine the heat transfer relationship between adjacent temperature control regions, and coordinately regulate the flow rate and flow direction of the heat medium in the jacket of the reaction kettle;

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

[0044] The present invention has the following beneficial effects: accurately determining the temperature-sensitive interval at the molecular level, improving the effect of hydroxyl activation and carboxymethylation reactions, and being more scientific and accurate than the traditional empirical constant temperature method. Constructing a temperature-viscosity-time association model to accurately predict the viscosity mutation point, overcoming the limitations of the traditional single-variable linear model, and improving the temperature control accuracy and process stability. Adopting a segmented control strategy 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. Establishing a heat transfer model considering the influence of viscosity to solve the complex heat transfer problem caused by viscosity changes, enabling the temperature control system to cope with the thermal response delay effect. Determining the reaction stage conversion timing and temperature control curve based on multiple factors to ensure a smooth transition between stages and improve production stability and product quality consistency. Description of the Drawings

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0046] Figure 1 It is a flowchart of a temperature control method in the production process of sodium carboxymethyl starch.

[0047] Figure 2It is a flow chart for constructing and processing the temperature-viscosity-time correlation model of a temperature control method in the production process of sodium carboxymethyl starch.

[0048] Figure 3 It is a differential regulation flow chart of a temperature control method in the production process of sodium carboxymethyl starch. Detailed implementation manners

[0049] To make the objectives, features, and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the embodiments described below are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0050] Example 1, referring to Figures 1 to 3 , which is the first embodiment of the present invention. This embodiment provides a temperature control method in the production process of sodium carboxymethyl starch. The flow chart of this method is as Figure 1 shown, and this method includes:

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

[0052] In a specific embodiment of the present invention, step S1 specifically includes:

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

[0054] It should be noted that the synthesis process of sodium carboxymethyl starch follows a specific chemical reaction path, which determines that the production process must be carried out in a strict order. As the initial step, the alkalization stage mainly activates the hydroxyl groups on the starch molecules through an alkaline environment, converting them into a more reactive alkoxide form, laying the foundation for the subsequent etherification reaction. The etherification stage is the core link in the synthesis of sodium carboxymethyl starch. In this stage, the activated starch molecules react with a carboxymethylating reagent (such as monochloroacetic acid or its sodium salt) through an etherification reaction, successfully introducing carboxymethyl groups into the starch molecular structure and constructing the molecular skeleton of carboxymethyl starch. The neutralization stage is the last link in the production process, and its main task is to adjust the pH value of the reaction system to an appropriate range to stabilize the carboxymethyl structure and finally produce qualified sodium carboxymethyl starch products. This division not only conforms to the reaction mechanism of sodium carboxymethyl starch synthesis but also meets the requirements of process characteristics, laying the foundation for subsequent temperature control.

[0055] S1.2: Analyze the conformational change characteristics of the sodium carboxymethyl starch molecular chain, characterize the temperature-responsive characteristics of the hydroxyl activity on the starch molecule, and determine the temperature-sensitive intervals at each reaction stage.

[0056] Specifically, differential scanning calorimetry is used to measure the enthalpy change of the starch molecule under different temperature conditions to determine the critical temperature point at which the starch molecular chain undergoes conformational transformation; Fourier transform infrared spectroscopy is used to monitor the characteristic vibration frequencies of the hydroxyl groups at different temperatures to obtain the correlation information between the hydroxyl groups and temperature; nuclear magnetic resonance spectroscopy is used to analyze the changes in the chemical shifts of hydrogen atoms in the starch molecule at different temperatures to evaluate the reaction activity of the hydroxyl groups; based on the measured data, the temperature-sensitive intervals at each reaction stage are clearly defined; small-scale experiments are used to verify the reaction effects in each temperature-sensitive interval to ensure the scientificity and rationality of the division of the temperature-sensitive intervals. Compared with the traditional empirical constant-temperature method, the present invention determines the temperature-sensitive intervals from the microscopic perspective of molecular conformational changes, more accurately locks in the optimal temperature ranges at each reaction stage, and greatly improves the hydroxyl activation efficiency and the selectivity of the carboxymethylation reaction.

[0057] S1.3: Based on the determined temperature-sensitive intervals, measure the exothermic characteristics at each reaction stage, analyze the heat change law, and determine the temperature process parameters at each reaction stage.

[0058] Furthermore, an isothermal reaction calorimeter is used to measure the reaction heat value and exothermic rate in the alkalization stage, etherification stage, and neutralization stage; the reaction heat curves at each reaction stage are plotted to determine the peak points of heat release and their durations; considering the influence of the reaction system viscosity on heat transfer, the heat accumulation characteristics under different viscosity conditions are analyzed and determined; based on the above analysis, the upper limit value, lower limit value, and optimal control value of the temperature at each reaction stage are determined as the temperature process parameters; the heating, heat preservation, and cooling control strategies at each reaction stage are formulated to form a complete set of temperature process parameters.

[0059] S1.4: Establish a temperature conversion mechanism based on the thermodynamic characteristics at each reaction stage and determine the temperature change strategy between stages.

[0060] Furthermore, analyze the thermodynamic characteristics of each reaction stage, including entropy change, enthalpy change, and Gibbs free energy change; monitor the reaction system parameters at the end of the alkalization stage to determine the triggering conditions for the conversion to the etherification stage; design the temperature conversion curve from the alkalization stage to the etherification stage to ensure a smooth transition of the reaction system temperature; monitor the reaction progress at the end of the etherification stage to determine the optimal timing for the conversion to the neutralization stage; design the temperature control strategy from the etherification stage to the neutralization stage to avoid adverse effects of temperature fluctuations on product quality. Develop emergency control measures during the conversion process of each stage to ensure temperature safety during the stage conversion; establish a stage conversion criterion based on the viscosity change rate of the reaction system, identify the completion point of the alkalization stage by quantitatively monitoring the inflection point characteristics of the viscosity change rate, and determine the point where the etherification reaction proceeds sufficiently by the acceleration characteristics of the viscosity change rate, so as to determine the optimal conversion timing of the reaction stage, avoid the blind conversion relying only on time or temperature in the traditional process, improve the accuracy of the stage conversion, and provide a scientific decision-making basis for the temperature step conversion control. By establishing a scientific temperature conversion mechanism, achieve a smooth transition between each reaction stage and improve the stability of the production process.

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

[0062] Furthermore, in the alkalization stage, establish the mapping relationship between the reaction temperature and the alkalinity, and determine the optimal alkalization temperature control curve; in the etherification stage, establish the mapping relationship between the reaction temperature and the carboxymethyl substitution degree, and determine the optimal etherification temperature control curve; in the neutralization stage, establish the mapping relationship between the reaction temperature and the pH value change, and determine the optimal neutralization temperature control curve; integrate the mapping relationships between the temperature and the reaction index of each stage to establish a complete segmented temperature control system; fine-tune and optimize the segmented temperature control system in combination with the actual production conditions; construct a stage temperature control adaptive fine-tuning mechanism, which calculates the ratio of the viscosity change rate to the temperature change rate (viscosity-temperature ratio index) by real-time monitoring, automatically adjusts the temperature change rate when the index exceeds the set threshold, smooths the temperature control curve using the moving average method, and dynamically adjusts the heating / cooling power, enabling the temperature control to accurately predict and respond to the viscosity change, overcoming the defect of the lag adjustment of the traditional control system, and providing accurate temperature change rate and residence time parameters at each conversion point for the subsequent temperature step conversion control. By establishing the mapping relationship between the temperature and the key reaction index, form a segmented temperature control system for the entire process, which can perform differential temperature control according to the reaction characteristics of different stages, and is beneficial to improving the stability of the carboxymethyl substitution degree and production efficiency.

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

[0064] In a specific embodiment of the present invention, the flow chart for constructing and processing the temperature-viscosity-time correlation model is as Figure 2 shown, and specifically includes:

[0065] S2.1: Arrange a temperature sensor array along the radial, axial, and tangential directions inside the reaction kettle to form a three-dimensional temperature monitoring network and obtain temperature distribution data.

[0066] It should be noted that in the traditional production process of sodium carboxymethyl starch, the number of temperature monitoring points inside the reaction kettle is limited, making it difficult to comprehensively reflect the temperature distribution in the system. By reasonably arranging the three-dimensional temperature sensor array to form a temperature monitoring network, the present invention can capture the temperature field distribution characteristics during the reaction process and provide a data basis for precise temperature control. In a typical embodiment, according to the size of the reaction kettle, an appropriate number of temperature measurement points are evenly distributed along the radial, axial, and tangential directions to form a temperature monitoring network covering the main reaction area of the kettle body. The specific number and installation position of the sensors can be appropriately adjusted according to the specifications of the reaction kettle to obtain representative temperature distribution data.

[0067] S2.2: Install an on-line viscosity measurement device 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 obtain viscosity data.

[0068] It should be noted that during 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 invention adopts an appropriate on-line viscosity measurement method to monitor the viscosity change at key positions without disturbing the reaction. Specifically, viscosity measurement devices are installed at different height positions at 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, with a higher frequency adopted in the stage with faster viscosity change and a lower frequency adopted in the stage with slower change. By combining viscosity measurement with temperature monitoring, the temperature-viscosity correspondence relationship at specific positions can be established, providing an important reference for reaction process control.

[0069] S2.3: Perform time series matching on the temperature data and viscosity data, establish the basic correspondence relationship between temperature and viscosity, and introduce the reaction time dimension to construct a temperature-viscosity-time correlation model.

[0070] Specifically, first, wavelet transform filtering preprocessing is performed on the temperature data and viscosity data in the historical production data to eliminate the influence of high-frequency noise and random fluctuations. The dataset is divided into a training set (accounting for 80%) and a validation set (accounting for 20%) for model construction and calibration. The temperature data is converted into a temperature spatial distribution feature tensor through a tensor decomposition method that preserves the spatial topological relationship (such as Tucker decomposition), and at the same time, the viscosity data at different positions is converted into a viscosity distribution feature vector. A temperature-viscosity-time correlation model is constructed, specifically including: First, based on the time window method, continuous time series data is segmented into multiple sample points, and each sample point includes a temperature distribution feature, a viscosity distribution feature, and the corresponding timestamp. 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 process, a normalized reaction process time axis is established to solve the problem of differences in the total reaction duration of different batches. Third, a deep neural network with an encoder-decoder structure is designed. The encoder jointly compresses the temperature distribution feature and the reaction time feature into a low-dimensional latent representation, and the decoder predicts the viscosity distribution based on this 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] Among them, the historical production data includes the temperature distribution data, viscosity data, and the corresponding time series information in the previous production process of sodium carboxymethyl starch.

[0072] Preferably, through the temperature-viscosity-time correlation model, the viscosity mutation point can be accurately predicted, providing a decision basis for optimizing the temperature control strategy. Through the encoder-decoder structure of the deep neural network and the physical constraint of the Arrhenius equation, this model overcomes the limitations of traditional single-variable linear models, realizes the multi-dimensional non-linear 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 in the production process of sodium carboxymethyl starch.

[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] Furthermore, based on the optimized temperature-viscosity-time correlation model, by iteratively inputting the temperature distribution feature and the normalized reaction time at the current moment, the rolling prediction of the viscosity distribution is performed 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 between viscosity and temperature during the reaction process, and determine the temperature-sensitive points in the reaction kettle according to the correlation gradient and the temperature distribution feature as the key temperature control points.

[0076] Specifically, a multi-dimensional sensitivity analysis framework is constructed using the temperature-viscosity-time correlation model. The partial derivative matrix of temperature with respect to viscosity and the partial derivative matrix of time with respect to viscosity are calculated to construct a multi-dimensional sensitivity analysis field. In the multi-dimensional sensitivity analysis field, the local extreme points of the partial derivatives and the viscosity mutation warning points are identified as the first type of temperature-sensitive points inside the reactor. Based on the temperature distribution data, the spatial gradient distribution of the temperature field is calculated, and the heat transfer restricted area is determined by combining the heat conduction theory analysis as the second type of temperature-sensitive points. Combining the viscosity data and the temperature distribution data, the flow characteristics inside the reaction system are analyzed, and the areas with obvious differences in heat transfer efficiency are identified as the third type of temperature-sensitive points. For the above three types of temperature-sensitive points, a hierarchical weight evaluation system is established, and the final distribution of key temperature control points is determined through a spatial clustering algorithm.

[0077] Preferably, by determining the key temperature control points, the present invention realizes precise temperature control based on multi-dimensional sensitivity analysis. Compared with the traditional fixed-position monitoring method, this innovation makes the selection of temperature-sensitive points more scientifically based, effectively identifies the areas most sensitive to temperature changes during the reaction process, and avoids the problems of control lag or inaccuracy caused by improper selection of temperature monitoring points in the traditional method.

[0078] S3: Based on the temperature-viscosity-time correlation model, determine the key temperature control points, and adopt a segmented control strategy to differentially regulate different temperature control regions inside the reactor.

[0079] In a specific embodiment of the present invention, the differential regulation flow chart is as Figure 3 shown, specifically including:

[0080] S3.1: According to the key temperature control points, divide the space inside the reactor into multiple temperature control regions, set independent temperature control parameters for each temperature control region, and determine the corresponding reactor jacket sections for each temperature control region.

[0081] Specifically, based on the three types of temperature-sensitive points determined in S2.4, a density distribution map of temperature control points is constructed using spatial point cloud analysis technology to identify the aggregation areas of temperature control points. The region growing algorithm is applied, with the aggregation areas of temperature control points as seed points, and combined with the geometric structure characteristics of the reactor, the initial temperature control regions with similar temperature change characteristics are divided to form the boundary contours of the temperature control regions. The thermodynamic characteristics of the initial temperature control regions are analyzed to evaluate the temperature field uniformity and thermal response consistency within each region. According to the similarity of thermal response characteristics, the initial temperature control regions are merged or subdivided to optimize the temperature control region division structure. Combining the jacket structure design of the reactor and the flow characteristics of the heat medium, the heat conduction relationship between each temperature control region and the jacket section is analyzed, and the optimal correspondence matrix between the temperature control regions and the jacket sections is established to ensure that each temperature control region can achieve effective temperature regulation through the corresponding jacket section. For the temperature sensitivity characteristics and viscosity change characteristics of each temperature control region, combined with the segmented temperature control system established in S1.5, a regional temperature control parameter configuration plan is formulated, including the target temperature set value, the allowable range of temperature fluctuation, the heating and cooling rate limit, and the PID control parameter group, to achieve precise temperature management of the temperature control regions.

[0082] Preferably, the present invention breaks through the limitation of the traditional overall temperature control of the reactor, and can provide a more precise temperature control environment for high-viscosity reaction systems for problems such as large temperature difference between the center and the wall and uneven heating and cooling in large industrial reactors.

[0083] S3.2: Establish a heat transfer model between temperature control regions, calculate the heat exchange rate and influencing factors, and obtain the temperature gradient transfer coefficient and heat exchange time lag parameters.

[0084] Furthermore, based on the spatial position relationship and boundary characteristics of each temperature control region, a topological structure of the inter-region heat transfer network is constructed to identify the main heat transfer channels and key heat exchange interfaces. The principle of computational fluid dynamics is applied, combined with the rheological characteristics of the reaction system, to establish a differential equation for convective heat transfer considering the influence of viscosity change, and the heat exchange flux between regions is solved by the finite element method. Through real-time temperature monitoring data, the temperature gradient change rate between adjacent temperature control regions is calculated, and the relationship function between the heat transfer rate and the temperature gradient is fitted to obtain the temperature gradient transfer coefficient. Analyze the time-series response characteristics of temperature changes between temperature control regions, measure the propagation delay of temperature change signals between regions, and determine the heat exchange time lag parameters.

[0085] Preferably, by establishing a heat transfer model considering viscosity effects, the problem of heat transfer complexity caused by viscosity changes during the production process of sodium carboxymethyl starch is successfully solved, enabling the temperature control system to predictably respond to the thermal response delay effect caused by viscosity changes in the reaction system.

[0086] S3.3: Input the temperature gradient transfer coefficient and the heat exchange time-delay parameter into the zone PID controller, calculate the control quantity of the heat medium flow rate for each temperature control zone, and adjust the valve opening of each section of the jacket to form a differential flow distribution.

[0087] Furthermore, construct a zone-type PID control system based on regional characteristics, configure an independent PID control unit for each temperature control zone, and each control unit adopts a PID parameter tuning method corrected by the temperature gradient transfer coefficient; considering the obvious heat response lag phenomenon in the reaction process of sodium carboxymethyl starch, introduce the heat exchange time-delay parameter as a feedforward compensation term, establish an improved PID control algorithm with a Smith predictor structure, and improve the adaptability of the system to the lag of heat transfer; based on the real-time viscosity change trend and temperature response characteristics, dynamically adjust the proportional, integral, and derivative parameters of the PID controller to achieve adaptive control of the reaction system under different viscosity conditions; convert the output of the PID controller in each temperature control zone into a control command for the heat medium flow rate of the jacket, and calculate the optimal flow distribution ratio and the set value of the regulating valve opening for each section of the jacket through a flow distribution optimization algorithm; design a hierarchical flow control mechanism, and on the premise of meeting the temperature control requirements of the main temperature control zone, coordinate the heat medium flow distribution between each section of the jacket through a cascade adjustment method to form a heat medium flow distribution strategy with regional differences but overall coordination.

[0088] Preferably, the technical solution effectively overcomes the problems of nonlinearity and lag in temperature control during the reaction process of sodium carboxymethyl starch through a zone-type PID control system that integrates the heat exchange time-delay parameter, and improves the utilization efficiency of the heat medium.

[0089] S3.4: According to the control quantity of the heat medium flow rate, measure the heat change rate of each temperature control zone, generate a differential temperature control command, and drive the operation of the jacket sectional heating system.

[0090] Furthermore, based on the heat medium flow control quantity and the heat medium inlet and outlet temperature difference data, calculate the heat transfer rate of each temperature control zone in real time; set differential temperature control curves according to the heat response characteristics and reaction progress requirements of each temperature control zone, and convert the temperature control requirements into a combined control command for the temperature, flow rate, and flow direction of the jacket heat medium; establish a cooperative control strategy for the jacket sectional heating system, optimize the execution order of the control commands according to the temperature control priority and heat demand urgency of each temperature control zone, and achieve balanced control of the multi-zone temperature; design an optimization algorithm for the flow path of the jacket heat medium, and dynamically adjust the series-parallel flow mode of the heat medium between each section of the jacket based on the temperature decay characteristics of the heat medium in the jacket section to maximize the utilization efficiency of the heat medium.

[0091] Among them, the jacket segmented heating system includes multiple reactor jacket sections corresponding to each temperature control area, independent flow regulating devices for each section, temperature monitoring points, and an inter-region heat medium flow direction switching device. By independently regulating the heat medium flow rate and flow direction of different jacket sections, precise temperature control of the corresponding temperature control area is achieved.

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

[0093] Furthermore, the temperature data of each temperature control area is collected regularly through a temperature sensor array, and the temperature response rate and steady-state deviation are calculated; a corresponding record of the heat medium flow control amount and the temperature change rate is established, and the actual influence degree of the flow adjustment on the temperature change is analyzed; according to the actual operation data, the temperature gradient transfer coefficient is slightly updated according to a preset period (usually 4 - 8 hours) (the adjustment range does not exceed ±5%), so that it can more accurately reflect the heat transfer characteristics under the current process state; by analyzing the temperature response relationship between adjacent temperature control areas in real time, the heat transfer bottleneck points are identified, and the temperature gradient transfer coefficient of the boundary area is slightly optimized and adjusted.

[0094] Among them, the boundary area refers to the junction between adjacent temperature control areas, and these areas are determined when forming the boundary contour of the temperature control area through the region growing algorithm in step S3.1. The present invention particularly focuses on the temperature gradient control of these boundary areas because the heat transfer between adjacent temperature control areas mainly occurs in these boundary areas. They are the key channels for heat transfer and may also become the "bottleneck points" for heat transfer. By adjusting the temperature gradient transfer coefficient of these boundary areas, the balanced control of the temperature distribution between the temperature control areas can be more effectively achieved.

[0095] S4: When it is detected that the temperature gradient in the reactor exceeds the allowable range of the set temperature gradient, in combination with the heat transfer relationship between adjacent temperature control areas, the flow rate and flow direction of the heat medium in the reactor jacket are jointly adjusted.

[0096] In a specific embodiment of the present invention, step S4 specifically includes:

[0097] S4.1: Analyze the abnormal temperature gradient situation, and determine the temperature control area to be regulated and the regulation priority according to the degree and distribution position of the temperature gradient exceeding.

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

[0099] Among them, the allowable range of temperature gradient is determined comprehensively based on the characteristics of temperature sensitive points, the requirements of the segmented temperature control system, and the limitations of equipment conditions, and an adaptive adjustment mechanism is set for different reaction stages.

[0100] S4.2: According to the divided temperature control areas and their corresponding reactor jacket sections, confirm the working status of the control devices in each section.

[0101] It should be noted that confirming the working status of the control devices in each section is to ensure the accuracy and reliability of subsequent coordinated regulation, avoid temperature regulation failure caused by equipment failure or abnormal response, and at the same time provide basic data support for accurately calculating the adjustment amount of the heat medium flow rate.

[0102] S4.3: For the abnormal temperature gradient situation, re-measure the temperature difference data between adjacent temperature control areas, calculate the temperature conduction coefficient and convective heat transfer coefficient between adjacent temperature control areas, and establish a heat transfer parameter table under emergency conditions.

[0103] Furthermore, for abnormal conditions beyond the allowable temperature gradient range, the abnormal intensity and direction of heat transfer are determined using the temperature gradient value calculated in S4.1; based on the heat flux and temperature difference between adjacent temperature control regions, the Fourier heat conduction law is applied to recalculate the temperature conduction coefficient between adjacent temperature control regions; by analyzing the interface characteristics of adjacent temperature control regions, the interfacial thermal resistance is measured to evaluate the influence degree of the interface on heat transfer under abnormal conditions; combined with the flow state and thermophysical parameters of the reaction system, the temperature change rate caused by fluid flow is measured using the fixed-point temperature measurement method, and the convective heat transfer coefficient between adjacent temperature control regions is calculated; the temperature conduction coefficient, convective heat transfer coefficient, and interfacial thermal resistance are integrated to construct a heat transfer parameter table for emergency situations. This parameter table is applicable to the emergency regulation of abnormal temperature gradient situations, complementing the fine-tuning mechanism under normal operating conditions in S3.5 to jointly ensure the stability of system temperature control.

[0104] S4.4: Monitor the viscosity data of the reaction system in real time, calculate the influence of viscosity change on the heat transfer efficiency, and determine the correction coefficient to correct the heat transfer parameter table.

[0105] Furthermore, the viscosity data of the reaction system is obtained through an on-line viscosity measurement device, and the collected viscosity data is correlated with the reaction temperature data to establish a viscosity-temperature response curve; at the same time, the influence of the reaction progress on the viscosity is considered to ensure the accuracy of the viscosity-temperature relationship; based on the fluid dynamics theory, a corrected relationship between viscosity and Nusselt number is constructed to quantify the influence degree of viscosity change on the convective heat transfer efficiency; according to the flow characteristics and viscosity sensitivity of different temperature control regions, the viscosity correction coefficient of each temperature control region is calculated; the viscosity correction coefficient is applied to the heat transfer parameter table to correct the convective heat transfer coefficient, forming a heat transfer parameter table corrected by viscosity.

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

[0107] Specifically, the heat flux magnitude and direction between each adjacent temperature control region are calculated using the heat transfer parameter table to quantify the strength of the thermal coupling relationship between regions; by analyzing the direction and gradient of the heat flux vector, the dominant direction of heat transfer in the reaction kettle is identified; according to the heat flux magnitude 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 marked; the influence weight of each temperature control region in the overall heat transfer process is analyzed to determine the key temperature control region that plays a decisive role in the system temperature uniformity; combined with the abnormal temperature gradient analysis results in S4.1, the heat transfer path that needs to be key-regulated is determined to provide directional guidance for subsequent coordinated regulation.

[0108] S4.6: According to the results of the thermal coupling analysis, calculate the adjustment and control quantities of the heat medium flow rate and direction, and implement collaborative adjustment until the temperature gradient is adjusted within the allowable range.

[0109] Specifically, based on the dominant direction of heat transfer and the key transfer path determined in S4.5, formulate a multi-region collaborative adjustment strategy; adopt a hierarchical control structure, decompose the temperature gradient control target into the adjustment targets of the heat medium flow rate and direction in each relevant temperature control region; through the predictive control algorithm, calculate the optimal adjustment quantities of the heat medium flow rate and direction in each jacket section to achieve precise control of the temperature gradient; during the adjustment process, continuously monitor the change trend of the temperature gradient and dynamically adjust the control parameters until the temperature gradient returns to the allowable range. The execution of this step ensures the effective correction of the temperature gradient anomaly, realizes the balanced optimization of the temperature field through multi-region collaborative adjustment, avoids the problem of "neglecting one while attending to another" 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: Evaluate the reaction progress in combination with the temperature-viscosity-time correlation model. When it is detected that the reaction progress reaches the predetermined completion degree, switch to the next reaction stage through temperature step conversion control.

[0111] In a specific embodiment of the present invention, step S5 specifically includes:

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

[0113] Among them, 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, calculate the derivative value of viscosity with respect to temperature at each monitoring point; arrange the derivative values of each monitoring point according to the spatial position to form a temperature-viscosity change rate matrix.

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

[0115] It should be noted that the phased viscosity critical value is obtained by analyzing the viscosity characteristics of the conversion points in historical production data. In addition, the reaction progress calculation method is as follows: normalize the difference between the current viscosity and the phased viscosity critical value to obtain the basic completion index; combine the eigenvalue analysis of the temperature-viscosity change rate matrix to calculate the stability index of the viscosity change rate; comprehensively consider the two indexes and obtain the completion score of the current reaction stage through weighted summation. When the completion score exceeds the predetermined completion degree (usually 90%-95%), the system determines that the reaction in the current stage has proceeded sufficiently and triggers the stage conversion signal.

[0116] Among them, the determination of the predetermined completion degree is based on the following scientific basis: by analyzing the correlation between the quality of historical batch products and the stage completion degree, determine the minimum completion degree requirement that can ensure the quality stability of the products; considering the importance differences of different reaction stages, the thresholds for the alkalization stage and the etherification stage are set at 92%-95%, and the threshold for the neutralization stage is set at 90%-93%; combined with the different requirements of product specifications, the threshold for high-viscosity products is set at the upper limit of the interval, and the threshold for low-viscosity products is set at the lower limit of the interval; through the trend analysis of the temperature-viscosity change rate, when the change rate remains stable in a low-fluctuation state (such as the fluctuation amplitude < 2%) for 3 consecutive sampling periods, the threshold 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 product quality.

[0117] S5.3: According to the temperature conversion mechanism between the alkalization stage, the etherification stage and the neutralization stage, plan the temperature ladder conversion control curve, and determine the temperature change rate and residence time during the conversion process of each stage.

[0118] Specifically, for the conversion from the alkalization stage to the etherification stage: based on the calculation results of the entropy change and enthalpy change of the reaction system in the alkalization stage, determine the maximum allowable temperature change rate; combine the activation energy characteristics of the etherification reaction to design a three-stage temperature rise curve, including an initial slow heating section, an intermediate rapid heating section and a final constant temperature stable section; after the etherification reaction temperature reaches the set value, set the residence time to ensure that the reaction proceeds sufficiently. For the conversion from the etherification stage to the neutralization stage: based on the analysis of the Gibbs free energy change of the etherification reaction, determine the cooling rate; design a smooth cooling curve to avoid product quality fluctuations caused by sudden temperature drops; after the neutralization temperature is stable, set a sufficient residence time to ensure sufficient pH value adjustment and stable product performance.

[0119] S5.4: Set up a 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 aimed at preventing local overheating or overcooling phenomena caused by sudden temperature changes during the stage conversion process, avoiding uneven product quality and reaction safety risks.

[0121] S5.5: Preset the temperature control parameters for the next reaction stage of each temperature control region, and perform differential settings according to the region characteristics.

[0122] Among them, for the top region of the reactor, since the heat loss in this region is relatively large, the preset temperature value is 1-2 °C higher than the standard setting value, and the integral time in the PID control parameters is shorter to quickly respond to temperature fluctuations; for the bottom region of the reactor, considering the heat accumulation effect in this region, the preset temperature value is 0.5-1 °C lower than the standard setting value, and the derivative time in the PID control parameters is longer to prevent temperature overshoot; for the central region of the reactor, as the standard temperature control region, the target temperature specified by the process is directly adopted, and the PID parameter settings balance stability and response speed. In addition, the differential settings mainly consider the differences in heat conduction characteristics, fluid flow states, and reaction sensitivities of each region. 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] Furthermore, this embodiment also provides a temperature control system during the production process of sodium carboxymethyl starch, including: a stage division module for sequentially dividing the production process of sodium carboxymethyl starch into an alkalization stage, an etherification stage, and a neutralization stage, and determining the temperature control targets for each reaction stage; a correlation model construction module for setting up a temperature sensor array in the reactor to monitor the temperature distribution in the reactor, and simultaneously monitoring the viscosity of the reaction system to establish a temperature-viscosity-time correlation model; a differential regulation module for determining the key temperature control points based on the temperature-viscosity-time correlation model and performing differential regulation on different temperature control regions in the reactor using a segmented control strategy; a coordinated regulation module for, when detecting that the temperature gradient in the reactor exceeds the allowable range of the set temperature gradient, coordinating the flow rate and flow direction of the heating medium in the reactor jacket in combination with the heat transfer relationship between adjacent temperature control regions; a control switching module for evaluating the reaction progress in combination with the temperature-viscosity-time correlation model, and when detecting that the reaction progress reaches the predetermined completion degree, switching to the next reaction stage through temperature step conversion control.

[0124] Through the above technical solutions, the present invention has the following beneficial effects: accurately determining the temperature-sensitive range at the molecular level, improving the effects of hydroxyl activation and carboxymethylation reactions, and being more scientific and accurate than the traditional empirical temperature determination method. Constructing a temperature-viscosity-time correlation model to accurately predict the viscosity mutation point, overcoming the limitations of the traditional single-variable linear model, and improving the temperature control accuracy and process stability. Adopting a segmented control strategy 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. Establishing a heat transfer model considering the influence of viscosity to solve the complex heat transfer problem caused by viscosity changes, enabling the temperature control system to cope with the thermal response delay effect. Determining the reaction stage transition timing and temperature control curve based on multiple factors to ensure a smooth transition between stages and improve production stability and product quality consistency.

[0125] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than limiting it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A temperature control method in the production process of sodium carboxymethyl starch, characterized in that, Including: Dividing the production process of sodium carboxymethyl starch into an alkalization stage, an etherification stage, and a neutralization stage in sequence, and determining the temperature control targets for each reaction stage; Setting up a temperature sensor array in the reaction kettle to monitor the temperature distribution in the reaction kettle, and simultaneously monitoring the viscosity of the reaction system to establish a temperature-viscosity-time correlation model; Determining the key temperature control points 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; When it is detected that the temperature gradient in the reaction kettle exceeds the allowable range of the set temperature gradient, in combination with the heat transfer relationship between adjacent temperature control regions, coordinately adjusting the flow rate and flow direction of the heating medium in the jacket of the reaction kettle; Evaluating the reaction progress in combination with the temperature-viscosity-time correlation model, and when it is detected that the reaction progress reaches the predetermined completion degree, switching to the next reaction stage through temperature step conversion control.

2. The temperature control method in the production process of sodium carboxymethyl starch according to claim 1, wherein, The determination of the temperature control targets for each reaction stage includes: Analyzing the conformational change characteristics of the sodium carboxymethyl starch molecular chain, characterizing the temperature response characteristics of the hydroxyl activity on the starch molecule, and determining the temperature-sensitive intervals for each reaction stage; Based on the temperature-sensitive intervals, measuring the exothermic characteristics of each reaction stage, analyzing the heat change law, and determining the temperature process parameters for each reaction stage; Establishing a temperature conversion mechanism according to the thermodynamic characteristics of each reaction stage, and determining the temperature change strategy between stages; Establishing the mapping relationship between the temperature of each reaction stage and the reaction index, and forming a segmented temperature regulation system.

3. The temperature control method in the production process of sodium carboxymethyl starch according to claim 1, characterized in that, The establishment of the temperature-viscosity-time correlation model includes: Setting up a temperature sensor array along the radial, axial, and tangential directions in the reaction kettle to form a three-dimensional temperature monitoring network and obtain temperature distribution data; Installing an on-line viscosity measuring device at the bottom and side walls of the reaction kettle to monitor the viscosity change of the reaction system at different temperature distribution positions and obtain viscosity data; Performing time series matching on the temperature data and the viscosity data, establishing the corresponding relationship between temperature and viscosity, and introducing the reaction time dimension to construct a temperature-viscosity-time correlation model; Based on the temperature-viscosity-time correlation model, calculating the correlation gradient between viscosity and temperature during the reaction process, and determining the temperature-sensitive points in the reaction kettle according to the correlation gradient and the temperature distribution characteristics as the key temperature control points.

4. The temperature control method in the production process of sodium carboxymethyl starch according to claim 1, characterized in that, The adoption of a segmented control strategy to differentially regulate different temperature control regions in the reaction kettle includes: According to the key temperature control points, dividing the space in the reaction kettle into multiple temperature control regions, and setting independent temperature control parameters for each temperature control region; Calculating the heat exchange rate and influence factors, and obtaining the temperature gradient transfer coefficient and the heat exchange time delay parameter; Inputting the temperature gradient transfer coefficient and the heat exchange time delay parameter into the partitioned PID controller, calculating the control quantity of the heating medium flow rate for each temperature control region, and adjusting the valve opening of each section of the jacket to form a differential flow distribution; According to the control quantity of the heating medium flow rate, calculating the heat change rate of each temperature control region, generating a differential temperature regulation instruction, and driving the operation of the jacket segmented heating system; Collecting the temperature response data of each temperature control region, comparing the corresponding relationship between the temperature change and the control quantity of the heating medium flow rate, and adjusting the temperature gradient transfer coefficient.

5. The temperature control method in the production process of sodium carboxymethyl starch according to claim 1, characterized in that, The coordinated adjustment of the flow rate and flow direction of the heating medium in the jacket of the reaction kettle includes: Analyze the abnormal temperature gradient situation to determine the temperature control areas that need to be regulated and the regulation priorities; Measure the temperature difference data between adjacent temperature control areas, calculate the temperature conduction coefficient and convective heat transfer coefficient between adjacent temperature control areas, and establish a heat transfer parameter table under emergency conditions; Real-time monitor the viscosity data of the reaction system, calculate the influence of viscosity change on the heat transfer efficiency and determine the correction coefficient, and correct the heat transfer parameter table; Based on the heat transfer parameter table, analyze the heat transfer coupling strength between adjacent temperature control areas to determine the dominant direction of heat transfer and the key transfer path; According to the results of the thermal coupling analysis, calculate the adjustment control quantities of the heat medium flow rate and direction, and implement coordinated adjustment until the temperature gradient is adjusted within the allowable range.

6. 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 through temperature step conversion control includes: Use the temperature-viscosity change rate as the reaction progress index, and calculate the temperature-viscosity change rate matrix of the current reaction stage based on the temperature-viscosity-time correlation model; Construct a reaction progress determination model, and combine the temperature-viscosity change rate matrix and the stage viscosity critical value to calculate the completion degree of the current reaction stage in real time. When the reaction completion degree reaches the predetermined completion degree, trigger a stage conversion signal; According to the temperature conversion mechanism between the alkalization stage, etherification stage and neutralization stage, plan the temperature step conversion control curve, and determine the temperature change rate and residence time during the conversion process of each stage; Preset the temperature control parameters of the next reaction stage for each temperature control area, and make differential settings according to the regional characteristics.

7. A temperature control system during the production process of sodium carboxymethyl starch, characterized in that, Include: A stage division module for sequentially dividing the sodium carboxymethyl starch production process into an alkalization stage, an etherification stage and a neutralization stage, and determining the temperature control objectives of each reaction stage; A correlation model construction module for setting up a temperature sensor array in the reaction kettle to monitor the temperature distribution in the reaction kettle, and at the same time monitoring the viscosity of the reaction system to establish a temperature-viscosity-time correlation model; A differential regulation module for determining the 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 reaction kettle; A coordinated adjustment module for, when it is detected that the temperature gradient in the reaction kettle exceeds the allowable range of the set temperature gradient, combining the heat transfer relationship between adjacent temperature control areas to coordinately adjust the flow rate and direction of the heat medium in the jacket of the reaction kettle; A control switching module for evaluating the reaction progress in combination with the temperature-viscosity-time correlation model, and when it is detected that the reaction progress reaches the predetermined completion degree, switching to the next reaction stage through temperature step conversion control.

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