Multi-raw-material synchronous intelligent dropwise adding system for water reducing agent production

Abstract

The invention discloses a multi-raw-material synchronous intelligent dropwise adding system for water reducing agent production, and relates to the technical field of water reducing agent production equipment.The system is characterized in that sensors are arranged through a partition heat collection module to obtain the temperature change rate, and a two-factor coupling module generates reaction state evaluation according to the temperature and the dropwise adding rate; the dynamic regulation and control module adjusts the raw material dripping rate, the waste heat recovery module preheats the raw materials through reaction heat release, and the closed-loop optimization module compares data deviation correction parameters; through cooperation of partition heat collection, two-factor coupling, dynamic regulation and control, waste heat recovery and closed-loop optimization modules, synchronous intelligent dropwise adding of multiple raw materials in water reducing agent production is achieved, the temperature dynamic state of the reaction kettle is accurately sensed, the regional state and the dropwise adding rate are associated, the rate is intelligently adjusted, waste heat is recovered to preheat the raw materials, parameters are continuously corrected, the process is optimized, and the production efficiency is improved. Controllability and product quality are improved, and large-scale high-quality production is supported.

Description

Technical Field

[0001] This invention relates to the field of water-reducing agent production equipment technology, and in particular to a multi-raw material synchronous intelligent dripping system for water-reducing agent production. Background Technology

[0002] As an indispensable key admixture in concrete engineering, water-reducing agents directly affect the fluidity, strength, and durability of concrete, playing an irreplaceable role in construction, infrastructure, and other fields. The synthesis process of water-reducing agents involves the synergistic reaction of multiple raw materials. The compatibility between the raw material dripping rate and the temperature change of the reaction system is the core factor determining the product performance. With the industry's continuous improvement in the performance requirements of water-reducing agents, the refined and intelligent control of the production process has become an inevitable trend. There are natural differences in the intensity of the reaction and the distribution of exothermic reactions in different areas of the reactor. The synchronicity of raw material dripping and the timeliness of control are crucial to the stability of the reaction process and the uniformity of the product. At the same time, the dual demands for energy conservation and improved production efficiency have driven the industry to urgently need integrated solutions that combine temperature monitoring, dynamic control, and waste heat utilization to meet the actual demands of large-scale, high-quality production.

[0003] Traditional raw material dripping systems in water-reducing agent production have many limitations, making it difficult to adapt to the actual needs of complex reaction processes. Temperature monitoring often uses single-point or limited-point acquisition methods, which cannot comprehensively capture the dynamic changes and distribution differences in temperature in different areas of the reactor. This leads to a one-sided judgment of the reaction state and makes it difficult to reflect the synergy of reactions and the distribution characteristics of exothermic intensity in different areas. The control of raw material dripping rate mostly relies on manual experience or fixed program settings, lacking dynamic linkage with the real-time reaction state. It cannot be accurately adjusted according to real-time changes in the reaction process, which easily leads to problems such as reaction imbalance and insufficient raw material matching, thus affecting the stability of product quality. In addition, traditional systems lack an effective waste heat recovery mechanism, and the heat generated during the reaction process is not rationally utilized, increasing production energy consumption. At the same time, the lack of a continuous parameter correction mechanism makes the system control accuracy prone to decline after long-term operation, making it difficult to adapt to complex production scenarios with multiple raw materials and multiple operating conditions. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a multi-raw material synchronous intelligent dripping system for water-reducing agent production. This system acquires temperature data of each area of ​​the reactor through a zoned heat acquisition module, analyzes the data through a two-factor coupling module to generate a reaction state evaluation result, adjusts the raw material dripping rate accordingly through a dynamic control module, preheats the raw materials using the exothermic reaction heat recovery module, and corrects the system parameters through a closed-loop optimization module, thereby achieving precise, efficient, and intelligent water-reducing agent production.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a multi-raw material synchronous intelligent dripping system for water-reducing agent production, the system comprising: Zoned heat acquisition module: Temperature sensors are installed in the upper, middle and lower parts of the reactor jacket to collect temperature data in each zone and preprocess the collected temperature data to obtain the real-time change rate of reaction temperature in each zone. The dual-factor coupling module receives real-time temperature change rate data and real-time dropping acceleration rate data of each raw material, processes the data through a partition-raw material dual-factor coupling algorithm, and generates reaction state assessment results. Dynamic control module: Receives the output reaction state evaluation results, uses the deviation feedback dynamic control algorithm to calculate the evaluation results, generates control commands for the acceleration rate of each raw material droplet, and executes the control operation; Waste heat recovery module: Receives reaction exothermic data from the output reaction state assessment results, adjusts the heat transfer process according to the reaction exothermic data, completes the preheating treatment of the raw materials to be added, and outputs the temperature data of the raw material preheating process to the closed-loop optimization module. Closed-loop optimization module: Receives actual temperature change rate data, reaction state assessment result calculation data, actual control data and preheating temperature data, compares the deviation between actual data and calculated data, generates coefficient correction instructions and outputs them to the two-factor coupling module and dynamic control module respectively, to complete the dynamic correction of system parameters.

[0006] Furthermore, in the partitioned heat acquisition module, when the temperature sensor is installed in the upper part of the reactor jacket, it is used to collect real-time temperature data, instantaneous temperature fluctuation data, and temperature change trend data of the surface area of ​​the reaction system inside the reactor; when the temperature sensor is installed in the middle part of the reactor jacket, it is used to collect real-time temperature data of the main reaction area inside the reactor, temperature difference data of different points in the main area, and temperature change gradient data of the main reaction area; when the temperature sensor is installed in the lower part of the reactor jacket, it is used to collect real-time temperature data of the bottom reaction area inside the reactor, temperature data of the bottom raw material mixing area, and temperature difference data between the bottom reaction area temperature and the raw material addition temperature.

[0007] Furthermore, in the dual-factor coupling module, the mathematical expression of the partition-raw material dual-factor coupling algorithm is: in, For the first The real-time rate of change of actual reaction temperature in each region, in °C / min; For the first The weighting coefficient of each region reflects the degree of contribution of that region to the overall response; This represents the total number of reactants involved in the reaction. For the first The characteristic coefficient of a raw material reflects its sensitivity to temperature changes; For the first The real-time dripping acceleration rate of the raw materials; For the first Basic correction items for each region; real-time reception of temperature change rates for each region from the zoned thermal acquisition module. Real-time drip rate of each raw material from the metering system By adjusting the dropping rates of different raw materials according to their characteristic coefficients Weighted summation, combined with the weight coefficients of the corresponding regions. With basic correction items The theoretical temperature change rate matching the current multi-raw material dropping state was calculated, and this calculation result was compared with the measured temperature. Perform comparative analysis.

[0008] Furthermore, in the dual-factor coupling module, the reaction state evaluation results specifically include: information on the severity grading of reactions in each region, information on the matching degree between the acceleration rate of each raw material droplet and the corresponding region's reaction exothermic effect, information on the overall reaction process stage determination, information on the synergy parameters of reactions in different regions, and information on the distribution of reaction exothermic intensity. The information on the severity grading of reactions in each region is determined based on the numerical range of the real-time change rate of the reaction temperature in each zone. The information on the matching degree between the acceleration rate of each raw material droplet and the corresponding region's reaction exothermic effect is obtained based on the calculation results of the zone-raw material dual-factor coupling algorithm. The information on the overall reaction process stage determination is combined with a comprehensive assessment of the reaction state in each region. The synergy parameters of reactions in different regions reflect the consistency of the reaction rhythm in each region. The information on the distribution of reaction exothermic intensity characterizes the differences in the exothermic distribution in different regions within the reactor.

[0009] Furthermore, in the dynamic control module, the mathematical expression of the deviation feedback dynamic control algorithm is: in, For the first The drip rate was accelerated after the raw material was regulated; For the first The current drip rate of the raw material; This is the feedback adjustment coefficient, used to adjust the control sensitivity, with a value range of 0.1-1.0; For the first The real-time rate of change of actual reaction temperature in each region, in °C / min; For the first The dynamic control module receives the reaction state assessment results for each region from the two-factor coupling module, which is preset with real-time rate of change of reaction temperature in each region. Data, retrieve preset areas The data is substituted into the above mathematical expression for calculation. Based on the calculation results, the acceleration control instructions for each raw material droplet are generated and the control operation is executed.

[0010] Furthermore, in the dynamic control module, the generated acceleration rate control instructions for each raw material droplet are specifically as follows: based on the reaction state assessment results of each region... With preset The deviation relationship is used to determine the corresponding raw material control direction. Greater than When a command is generated to reduce the rate of acceleration of the raw material droplets, Less than When the raw material droplet acceleration rate is increased, an instruction is generated. and When the deviation is within the preset range of -0.1℃ / min to 0.1℃ / min, a raw material droplet acceleration rate maintenance command is generated; according to the raw material characteristic coefficient The magnitude of the values ​​determines the priority of regulation. For raw materials with higher values, the control command should be executed first; the range of each control should be clearly defined, and the range of each control should not exceed the current dropping rate of the raw material. 20%; Set the execution sequence of the control commands, first execute the control operation of high-priority raw materials, and collect the corresponding area again after an interval of 30 seconds. The data is then used to determine whether to generate a secondary control command.

[0011] Furthermore, in the dynamic control module, the control operation is specifically as follows: before the control command is executed, the unobstructedness of each raw material dripping pipeline and the operating status of the corresponding metering pump are checked; after the check is passed, according to the priority order in the control command, the metering pump control program corresponding to the high-priority raw material is started, and the metering pump speed is adjusted according to the control range determined by the control command; during the control process, the actual output flow data of each metering pump is collected in real time and compared with the dripping acceleration rate required by the control command to ensure that the deviation between the actual dripping acceleration rate and the command requirement does not exceed ±5%; after a single round of control operation is completed, a data acquisition signal is triggered according to a preset time sequence of 30 seconds, combined with the real-time change rate data of the actual reaction temperature, and the real-time change rate of the actual reaction temperature and the actual operating parameters of the metering pump are fed back to the deviation feedback dynamic control algorithm of the dynamic control module for the determination of subsequent secondary control commands.

[0012] Furthermore, the heat transfer control process of the waste heat recovery module specifically includes: analyzing the reaction exothermic data, extracting information such as exothermic intensity and regional distribution, and classifying the recovery level according to exothermic intensity >5℃ / min as high-grade, 2-5℃ / min as medium-grade, and <2℃ / min as low-grade; adjusting the circulation rate of the heat exchange medium in the reactor jacket according to the level to collect heat; adjusting the pipeline valves to control the flow rate, and using electric heating for temperature compensation when the exothermic effect is insufficient; adjusting the heating power according to the deviation between the real-time temperature of the raw material to be preheated and the preset temperature, adjusting the heating power to 80-100% of the rated power when the deviation is greater than 2℃, adjusting the heating power to 40-60% of the rated power when the deviation is 1-2℃, and adjusting the heating power to 10-30% of the rated power when the deviation is less than 1℃; monitoring the temperature of the heat exchange medium and raw material, and the pipeline pressure in real time, and dynamically fine-tuning the parameters; switching to the heat preservation circulation mode after the raw material reaches the preset temperature of 33-37℃.

[0013] Furthermore, in the closed-loop optimization module, the specific process of comparing the deviation between actual data and calculated data and generating coefficient correction instructions is as follows: comparing the actual temperature change rate with the calculated temperature change rate, the actual control data with the calculated control data, and the preheating temperature data with the calculated preheating temperature data, setting deviation thresholds of ±0.2℃ / min, ±15%, and ±1℃ respectively; when any data deviation exceeds the corresponding threshold, a coefficient correction instruction is generated to perform gradient correction of ±3% to ±8% on the raw material matching coefficient of the dual-factor coupling module, and step correction of ±0.1 to ±0.3 on the feedback adjustment coefficient k of the dynamic control module; when the deviation does not exceed the threshold, the existing coefficient remains unchanged.

[0014] Compared with existing technologies, this multi-raw material synchronous intelligent dripping system for water-reducing agent production has the following advantages: I. This invention achieves refined perception and evaluation of the reaction process by dynamically collecting temperatures in different areas of the reactor through zoned thermal acquisition, combined with a zone-raw material dual-factor coupling algorithm to deeply correlate the reaction state and raw material droplet acceleration rate in different areas. The dynamic control module intelligently adjusts the raw material droplet acceleration rate based on the evaluation results using a deviation feedback mechanism. Through priority sorting and step-by-step execution strategies, the stability of the control operation is ensured, and reaction imbalance is avoided. The waste heat recovery module efficiently utilizes the exothermic reaction to preheat the raw materials, reducing energy consumption while optimizing the reactivity of the raw materials. The closed-loop optimization module continuously corrects system parameters, constantly reducing the deviation between actual data and expectations, forming a complete intelligent control closed loop. This improves the controllability and reaction synergy of the water-reducing agent production process, ensuring the uniformity and stability of product quality.

[0015] II. This invention analyzes the exothermic reaction data through a waste heat recovery module, regulates the heat transfer process according to different recovery levels, preheats the raw materials to be added, and adjusts the heating power according to temperature deviation to achieve efficient heat recovery and rational utilization, reducing energy consumption. The modules work together, and the zone-raw material dual-factor coupling algorithm fully considers the influence of different regional characteristics and raw material properties. The reaction status evaluation results comprehensively reflect the reaction process and the coordination of each region. Pre-checks before control operations and real-time monitoring during control operations improve the reliability of system operation. The overall design realizes refined control of simultaneous intelligent dripping of multiple raw materials, optimizes the production process, improves production efficiency, and provides strong support for the large-scale and high-quality production of water-reducing agents.

[0016] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below.

[0018] Figure 1 A flowchart of a multi-raw material synchronous intelligent dripping system for the production of water-reducing agents; Figure 2 This is a schematic diagram of data transmission in a multi-raw material synchronous intelligent dripping system for the production of water-reducing agents. Figure 3 This is a schematic diagram of data transmission from a dual-factor coupling module in a multi-raw material synchronous intelligent dripping system for water-reducing agent production. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0020] In the description of this invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0021] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. Furthermore, the technical features involved in the different embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0022] Example 1: Implementation of large-scale production of conventional water-reducing agents This embodiment is applied to the large-scale continuous production of conventional water-reducing agents. This production mode features stable output, fixed production cycle, and mature raw material formulation. The production process requires the simultaneous addition of three core raw materials in a specific ratio. The reaction system has stringent requirements for temperature stability; excessive temperature fluctuations directly affect the dispersion performance and homogeneity of the water-reducing agent, leading to batch-to-batch variations in product quality. To achieve efficient and stable large-scale production, the multi-raw material synchronous intelligent dripping system for water-reducing agent production of this invention fully integrates into the entire production process, with each module operating collaboratively to precisely control every production stage.

[0023] After the system starts up, the partitioned thermal acquisition module is activated first. This module serves as the data sensing core of the entire system. It simultaneously activates high-precision temperature sensors at preset precise positions in the upper, middle and lower parts of the reactor jacket. These sensors are pre-calibrated and have the characteristics of fast response speed, high measurement accuracy and strong anti-interference ability, enabling them to work stably in complex reaction environments. The temperature sensor at the top of the reactor jacket primarily monitors the surface area of ​​the reaction system within the reactor, continuously collecting real-time temperature data, instantaneous temperature fluctuation data, and temperature trend data over time. Temperature changes on the surface area often reflect the initial reaction state during the initial addition of raw materials, serving as a crucial basis for determining whether the reaction start-up is stable. The temperature sensor in the middle of the jacket focuses on the main reaction area within the reactor, the primary site of the chemical reaction. This sensor focuses on collecting real-time temperature data, temperature difference data at different points within the main area, and temperature gradient data. Analyzing this data allows for precise understanding of the core reaction process and assessment of whether the reaction is proceeding uniformly. The temperature sensor at the bottom of the jacket collects real-time temperature data from the bottom reaction area, the bottom raw material mixing area, and the temperature difference between the bottom reaction area and the raw material addition temperature. The bottom area directly receives the newly added raw materials, and its temperature changes directly reflect the mixing effect and initial reaction intensity, providing an important reference for subsequent adjustments to the raw material dripping rate.

[0024] The raw temperature data collected by temperature sensors contains certain environmental interference signals. The zoned thermal acquisition module preprocesses this data, using filtering, noise reduction, and data smoothing techniques to remove invalid interference information and extract valid data that truly reflects the reaction state. This data is then used to calculate and output the real-time rate of change of reaction temperature in each zone. This process ensures the accuracy and reliability of the data, providing precise and comprehensive data support for subsequent reaction state assessment and dynamic control. It allows the system to clearly and in real-time monitor the temperature dynamics of different zones within the reactor, avoiding control errors caused by data deviations.

[0025] After receiving the real-time temperature change rate data of each region from the zoned thermal acquisition module, the dual-factor coupling module simultaneously acquires the real-time dripping acceleration rate data of the three raw materials. This dripping acceleration rate data is collected and transmitted in real time by the flow monitoring device in the raw material dripping pipeline. Subsequently, the dual-factor coupling module calls the preset zone-raw material dual-factor coupling algorithm for in-depth data processing. This algorithm is the core of the system's accurate assessment of the reaction state, and its mathematical expression is: in, For the first Real-time rate of change of actual reaction temperature in each region; For the first Weighting coefficients for each region; This represents the total number of reactants involved in the reaction. For the first Characteristic coefficients of the raw materials; For the first The real-time dripping acceleration rate of the raw materials; For the first In the algorithm's calculation process, the system fully integrates the weight coefficients of each region, assigning corresponding weight proportions based on the importance of different regions in the reaction process, ensuring that the evaluation results highlight the state of the core reaction region. At the same time, it accurately incorporates the characteristic coefficients of each raw material, which are pre-set based on the inherent properties of the raw material such as chemical properties, reactivity, and concentration, and can reflect the degree of influence of different raw materials on the reaction temperature change. By coupling the region weights, raw material characteristics, and real-time dropping acceleration rate in a multi-dimensional manner, the algorithm comprehensively integrates various key factors to generate detailed and comprehensive reaction state evaluation results.

[0026] The assessment results specifically include information on the severity of the reaction in each region, the matching degree between the droplet acceleration rate of each raw material and the corresponding region's heat release, the determination of the overall reaction process stage, the reaction synergy parameters in different regions, and the distribution of reaction heat release intensity. The system's reaction intensity classification is determined based on the real-time temperature change rate of each zone, clearly distinguishing between mild, moderate, and severe levels, allowing operators to intuitively understand the reaction intensity of each zone. The matching degree between the droplet acceleration rate of each feedstock and the corresponding zone's exothermic reaction is directly derived from the zone-feedstock dual-factor coupling algorithm, accurately determining whether the current droplet acceleration rate is suitable for the exothermic reaction requirements, providing a clear direction for subsequent control. The overall reaction process stage determination information is derived through comprehensive analysis of the reaction status of each zone, clearly defining key stages such as the reaction initiation phase, peak phase, and decline phase, providing a basis for production process control. The reaction synergy parameter reflects the consistency of the reaction rhythm in each zone; if the synergy parameter is low, it indicates inconsistent reaction progress in each zone, requiring timely adjustment. The exothermic reaction intensity distribution information details the differences in exothermic distribution in different zones within the reactor, providing precise guidance for the waste heat recovery module. Through this comprehensive series of evaluation results, the system can form a clear and accurate understanding of the entire reaction process, avoiding blind control in subsequent adjustments. Figure 3 As shown.

[0027] After receiving the reaction state assessment results, the dynamic control module uses a deviation feedback dynamic control algorithm for calculation. The mathematical expression is as follows: in, For the first The drip rate was accelerated after the raw material was regulated; For the first The current drip rate of the raw material; This is the feedback adjustment coefficient; For the first Real-time rate of change of actual reaction temperature in each region; For the first The algorithm first compares the actual real-time temperature change rate of each region with the preset value. The preset real-time temperature change rate is the optimal value determined based on a large amount of production experimental data and theoretical calculations, which can ensure the smooth progress of the reaction and guarantee product quality. When the actual real-time temperature change rate is greater than the preset value, it indicates that the current reaction is too vigorous and the corresponding raw material dropping rate needs to be reduced; when the actual real-time temperature change rate is less than the preset value, it indicates that the reaction progress is too slow and the raw material dropping rate needs to be increased; when the deviation is within the preset reasonable range, the current dropping rate is maintained unchanged.

[0028] After determining the direction of regulation, the system prioritizes regulation based on the magnitude of the raw material characteristic coefficients. Raw materials with higher characteristic coefficients have a more significant impact on reaction temperature changes; therefore, regulation commands are generated preferentially for these raw materials to ensure rapid and effective adjustment of the reaction state. Simultaneously, the system clearly defines the range of each regulation, strictly controlling the magnitude of each regulation to not exceed the limit of the current raw material dropping rate, avoiding drastic fluctuations in the reaction system due to excessive regulation and ensuring a smooth transition of the reaction process. Furthermore, the system has set a scientific execution sequence for regulation commands to ensure the orderly execution of subsequent regulation operations.

[0029] After the control command is generated, it is not executed immediately. Instead, a comprehensive pre-check is performed on the unobstructed flow of each raw material dripping pipeline and the operating status of the corresponding metering pump. Pipeline pressure monitoring and flow detection are used to confirm that the dripping pipeline is free of blockages and leaks, ensuring smooth raw material delivery. The operating status, speed feedback, and sealing performance of the metering pumps are checked one by one to ensure that the metering pumps can accurately respond to the control command. After the pre-check is passed, the system initiates the control program for the metering pump corresponding to the high-priority raw material. Based on the control range determined by the control command, the system precisely adjusts the drip rate by controlling the speed of the metering pump's drive motor.

[0030] During the control process, the system collects the actual output flow data of each metering pump in real time. This data is transmitted to the dynamic control module via flow sensors and compared in real time with the drip rate required by the control command. If a deviation is detected between the actual output flow and the command requirement, fine-tuning is immediately performed to ensure control accuracy. After a single round of control operation, the system collects the real-time temperature change rate data of the corresponding area again at preset time intervals. The newly collected data is compared with the preset value, and combined with the reaction state evaluation results, it determines whether the current control effect has met expectations and whether a secondary control command needs to be generated, forming a closed-loop control mechanism to ensure the reaction system is always maintained in an optimal state.

[0031] After the dual-factor coupling module outputs the reaction state assessment results, the waste heat recovery module immediately extracts the reaction exothermic data from the assessment results. Through a specialized data analysis algorithm, it deeply analyzes key information such as exothermic intensity and regional distribution. Based on preset standards, the exothermic intensity is divided into different recovery levels, each corresponding to a different heat recovery strategy, ensuring efficient and rational heat recovery. According to the defined recovery level, the system automatically adjusts the circulation rate of the heat exchange medium in the reactor jacket. The heat exchange medium circulates between the jacket and the waste heat recovery pipeline, absorbing the heat dissipated by the reactor through heat exchange. The circulation rate is adjusted in real time according to the exothermic intensity; the higher the exothermic intensity, the faster the circulation rate, maximizing the collection of reaction heat.

[0032] Meanwhile, the system precisely controls the flow rate of the heat exchange medium by adjusting the opening of pipeline valves, ensuring the stability and uniformity of heat transfer. When the exothermic reaction intensity is insufficient, and the recovered reaction heat alone cannot meet the temperature requirements of the raw material to be preheated, the system automatically activates the electric heating compensation mechanism. Based on the deviation between the real-time temperature of the raw material and the preset temperature, the system precisely adjusts the heating power of the electric heating device. When the deviation is large, the heating power is appropriately increased to accelerate the heating rate of the raw material; when the deviation is small, the heating power is reduced to prevent the raw material temperature from being too high and affecting its reactivity.

[0033] Throughout the preheating process, the system monitors key parameters in real time, including the temperature of the heat exchange medium, the temperature of the raw material to be preheated, and pipeline pressure. These parameters are fed back to the control unit of the waste heat recovery module via sensors. The control unit dynamically fine-tunes the circulation rate, valve opening, and heating power based on parameter changes, ensuring a stable and controllable preheating process. Once the raw material reaches the preset temperature, the system automatically switches to a heat preservation circulation mode to maintain a stable temperature and prevent temperature fluctuations from affecting subsequent reactions. Simultaneously, the waste heat recovery module accurately outputs real-time temperature data during the preheating process to the closed-loop optimization module, providing data support for dynamic correction of system parameters. This process not only achieves efficient recovery and utilization of reaction heat, reducing energy consumption in the production process, but also allows subsequent raw materials added to the reactor to quickly adapt to the reaction environment, improving reaction efficiency and reducing temperature fluctuations in the initial stages of the reaction.

[0034] The closed-loop optimization module, as the core optimization unit of the system, continuously receives key data from various modules, including the actual temperature change rate data output by the zoned heat acquisition module, the calculated reaction state evaluation results output by the dual-factor coupling module, the actual control data output by the dynamic control module, and the preheating temperature data output by the waste heat recovery module. This data covers key information about the reaction process, control process, and waste heat utilization process, providing a comprehensive basis for system parameter optimization. The actual temperature change rate data is compared with the calculated temperature change rate data from the dual-factor coupling module, the actual control data from the dynamic control module is compared with the calculated control data, and the actual preheating temperature data from the waste heat recovery module is compared with the calculated preheating temperature data. The deviation values ​​for each of the three sets of data are calculated. The system presets specific deviation thresholds to determine whether the data deviation is within a reasonable range. If any data deviation exceeds the corresponding threshold, it indicates a mismatch between the current system parameters and the actual production conditions, requiring timely correction. At this point, the closed-loop optimization module immediately generates a coefficient correction instruction to perform gradient correction on the raw material matching coefficient of the dual-factor coupling module. The coefficient value is adjusted according to the magnitude of the deviation to ensure that the algorithm can more accurately associate the region with the raw material factors. At the same time, the feedback adjustment coefficient of the dynamic control module is corrected stepwise to optimize the response sensitivity and control accuracy of the control algorithm.

[0035] If all data deviations do not exceed the preset threshold, it indicates that the current system parameters are well adapted to the production conditions, and the existing coefficients can be maintained unchanged. Through this continuous deviation comparison and parameter correction, the closed-loop optimization module achieves dynamic correction of system parameters, continuously optimizes the system's operating accuracy, ensures that the subsequent production process remains stable, effectively avoids parameter drift caused by long-term operation, and guarantees uniform product quality. Figure 1 As shown.

[0036] In this embodiment, during the large-scale production of conventional water-reducing agents, a zoned heat acquisition module comprehensively captures the temperature dynamics of each region, providing accurate data for reaction evaluation; a dual-factor coupling module correlates regional and raw material factors to generate comprehensive reaction status evaluation results, guiding the direction of regulation; a dynamic control module precisely adjusts the raw material dripping rate according to priority and scientific timing to ensure reaction stability; a waste heat recovery module efficiently utilizes reaction heat to preheat raw materials, reducing energy consumption; and a closed-loop optimization module continuously corrects system parameters, improving operational accuracy. These modules work together to construct a complete intelligent control system, achieving simultaneous and precise dripping of multiple raw materials, effectively ensuring a stable and controllable production process, improving product quality uniformity and production efficiency, and perfectly meeting the actual demands of large-scale continuous production.

[0037] Example 2: Implementation of customized production scenarios for high-performance water-reducing agents This embodiment applies to the customized production of high-performance water-reducing agents. This type of production focuses on the specific engineering needs of downstream customers, requiring adjustments to the product formulation based on personalized indicators such as concrete strength grade, construction environment, and durability requirements. The production process necessitates the simultaneous dripping of five raw materials with different chemical properties, reactivity, and concentration gradients. High-performance water-reducing agents have extremely high requirements for molecular structure regularity, dispersibility, and stability. The precision matching of the raw material dripping rate in the reaction system is far higher than that required for conventional water-reducing agent production. Furthermore, due to the variety of raw materials and their complex interactions, the exothermic reaction exhibits significant fluctuations, easily leading to problems such as localized overheating or insufficient reaction. To meet the high-precision control requirements of customized production, the multi-raw material synchronous intelligent dripping system for water-reducing agent production of this invention is fully integrated into the production process. Through the precise collaborative operation of each module, it achieves refined control of the reaction process.

[0038] Upon system startup, the zoned thermal acquisition module immediately enters operational mode. As the core unit for reaction status sensing, it rapidly activates high-precision temperature sensors located at preset positions in the upper, middle, and lower parts of the reactor jacket. These sensors are specially calibrated and possess characteristics of corrosion resistance, interference resistance, and rapid response, enabling them to stably acquire data in complex environments involving multiple raw material reactions. The temperature sensor at the top of the reactor jacket continuously focuses on the surface area of ​​the reaction system, collecting real-time temperature data, instantaneous temperature fluctuation data, and temperature trend data over time. As the initial contact area for raw material droplets, the surface area's temperature changes directly reflect the initial mixing and reaction initiation status, serving as a key basis for judging the initial reaction stability. The temperature sensor in the middle of the jacket monitors the main reaction area, the core site of the chemical reaction of the five raw materials. This sensor focuses on collecting real-time temperature data, temperature difference data at different points within the main area, and temperature gradient data. Analyzing this data allows for precise understanding of the core reaction process and uniformity, and timely detection of local reaction anomalies. The temperature sensor at the bottom of the jacket collects real-time temperature data from the bottom reaction area, the raw material mixing area, and the temperature difference between the bottom reaction area and the raw material addition temperature. The bottom area directly receives the newly added multiple raw materials, and its temperature changes directly reflect the mixing effect and initial reaction intensity, providing an important reference for subsequent differentiated adjustments to the droplet acceleration rate of multiple raw materials.

[0039] The raw temperature data collected by sensors contains a small amount of environmental interference signals. The zoned thermal acquisition module systematically preprocesses this data, using professional processing methods such as filtering, noise reduction, and data smoothing to remove invalid interference information and extract effective data that truly reflects the reaction state. This allows for the accurate calculation and output of the real-time temperature change rate in each zone. This process ensures the accuracy and completeness of the temperature data, meeting the high-precision temperature monitoring requirements of high-performance water-reducing agent production. It lays a solid data foundation for subsequent precise control of multiple raw materials, enabling the system to comprehensively and in real-time monitor the temperature dynamics of different zones within the reactor, avoiding control errors caused by data deviations.

[0040] After receiving the real-time temperature change rate data of each region from the zoned thermal acquisition module, the dual-factor coupling module simultaneously acquires the real-time dripping acceleration rate data of the five raw materials. This dripping acceleration rate data is collected in real time by high-precision flow monitoring devices in the dripping pipelines of each raw material and transmitted to the module. Subsequently, the dual-factor coupling module initiates a preset zone-raw material dual-factor coupling algorithm to perform deep data processing. This algorithm is the core technology for achieving accurate assessment of the reaction state of multiple raw materials, and it can fully integrate the regional reaction characteristics and the properties of the raw materials themselves to construct a multi-dimensional correlation model.

[0041] During the algorithm's computation, the system comprehensively integrates the weight coefficients of each region, assigning corresponding weights based on the importance of different regions in multi-raw material reactions, ensuring that the evaluation results highlight the state of the core reaction region. At the same time, it accurately incorporates the characteristic coefficients of each raw material, which are pre-set based on the inherent properties of the five raw materials, such as their chemical properties, reactivity, and concentration, and can accurately reflect the degree of influence of different raw materials on changes in reaction temperature. By coupling the region weights, raw material characteristics, and real-time dropping acceleration rate in a multi-dimensional manner, the algorithm comprehensively integrates various key factors to generate detailed and comprehensive reaction state evaluation results.

[0042] The assessment results clearly present the grading information of reaction intensity in each region, the matching degree information between the dripping acceleration rate of each raw material and the corresponding region's reaction exothermic effect, the overall reaction process stage information, the reaction synergy parameters of different regions, and the details of the reaction exothermic intensity distribution. Specifically, the grading information of reaction intensity in each region is determined based on the numerical range of the real-time change rate of the reaction temperature in each zone, clearly distinguishing different levels such as mild, moderate, and severe, allowing operators to intuitively grasp the reaction intensity in each region; the matching degree information between the dripping acceleration rate of each raw material and the corresponding region's reaction exothermic effect accurately determines whether the current dripping acceleration rate of each raw material is suitable for the reaction exothermic requirement, providing a clear direction for the differentiated control of five raw materials with different characteristics; the overall reaction process stage information is derived from a comprehensive analysis of the reaction status in each region, clearly dividing key stages such as the reaction start-up period, peak period, and stable period, providing a basis for production process control; the reaction synergy parameters of different regions reflect the consistency of the reaction rhythm in each region. If the synergy parameters are low, it indicates that the reaction progress in each region is inconsistent, and the dripping acceleration rate of the relevant raw materials needs to be adjusted in a timely manner; the details of the reaction exothermic intensity distribution characterize the differences in the exothermic distribution in different regions within the reactor, providing data support for the precise operation of the waste heat recovery module. Through this series of comprehensive and detailed evaluation results, the system can form a clear and accurate understanding of complex reaction processes involving multiple raw materials, providing detailed basis for subsequent precise control and ensuring that the dropping rate of each raw material is highly compatible with the reaction state.

[0043] After the dynamic control module receives the reaction state evaluation results output by the dual-factor coupling module, it immediately initiates the deviation feedback dynamic control algorithm for calculation. This algorithm is specifically designed for precise control of multiple raw materials and can generate targeted control strategies based on real-time changes in the reaction state. The algorithm first accurately judges the deviation between the real-time temperature change rate of each region and the preset value. The preset real-time temperature change rate is the optimal range determined based on the customized formulation and reaction mechanism of the high-performance water-reducing agent, ensuring stable reaction and guaranteeing the product's personalized performance indicators. When the actual real-time temperature change rate is higher than the preset value, it indicates that the reaction in the corresponding region is too vigorous, and the dropping rate of the relevant raw materials needs to be reduced; when the actual real-time temperature change rate is lower than the preset value, it indicates that the reaction progress is too slow, and the dropping rate of the corresponding raw materials needs to be increased; when the deviation is within the preset reasonable range, the current dropping rate remains unchanged.

[0044] After determining the control direction, the system prioritizes control based on the numerical values ​​of the raw material characteristic coefficients. Raw materials with higher characteristic coefficients have a more significant impact on reaction temperature changes; therefore, control commands are prioritized for these raw materials to ensure rapid and effective adjustment of the reaction state and prevent reaction imbalance due to untimely control of key raw materials. Simultaneously, the system strictly controls the amplitude of each control operation, ensuring that the amplitude does not exceed the limit of the current raw material dropping rate. This prevents drastic fluctuations in the reaction system caused by excessive control amplitude and ensures a smooth transition of the reaction process. Furthermore, the system has established a scientific sequence for the execution of control commands, providing assurance for the orderly operation of multi-raw material control.

[0045] After the control command is generated, the system first conducts a comprehensive pre-check on the unobstructedness of each raw material dripping pipeline and the operating status of the corresponding metering pump. Through pipeline pressure monitoring and flow detection, it confirms that each dripping pipeline is free of blockages and leaks, ensuring smooth and stable delivery of the five raw materials. The system then individually checks the motor operating status, speed feedback accuracy, and sealing performance of each metering pump to ensure accurate response to the control command. Once all pre-checks are passed, the system initiates the control program for the metering pump corresponding to the high-priority raw material. Based on the control range determined by the control command, the system precisely adjusts the drip rate by controlling the speed of the metering pump's drive motor.

[0046] During the control process, the system collects the actual output flow data of each metering pump in real time, transmits the data to the dynamic control module via flow sensors, and compares it in real time with the dripping acceleration rate required by the control command. If a deviation is detected between the actual output flow and the command requirement, fine-tuning is immediately performed to ensure control accuracy. After a single round of control operation, the system collects the real-time temperature change rate data of the corresponding area again at preset time intervals. Combined with the latest reaction state evaluation results, it determines whether the current control effect has met expectations and whether a secondary control command needs to be generated, forming a closed-loop control mechanism. This prevents excessive fluctuations in the raw material dripping acceleration rate from affecting reaction stability, ensures the timeliness and accuracy of high-performance coefficient raw material control, and ensures that multi-raw material reactions are always maintained in an optimal state.

[0047] After the reaction state assessment results are output by the dual-factor coupling module, the waste heat recovery module quickly extracts the reaction exothermic data from the assessment results. Through specialized data analysis algorithms, it deeply analyzes key information such as the magnitude of the exothermic intensity and regional distribution characteristics. Based on preset standards, the exothermic intensity is divided into three recovery levels: high, medium, and low. Different levels correspond to different heat recovery strategies to ensure efficient and rational heat recovery. According to the defined recovery level, the system automatically adjusts the circulation rate of the heat exchange medium in the reactor jacket. The heat exchange medium circulates between the jacket and the waste heat recovery pipeline, absorbing the heat dissipated by the reactor through heat exchange. The circulation rate is adjusted in real time according to the exothermic intensity; the higher the exothermic intensity, the faster the circulation rate, to maximize the collection of reaction heat.

[0048] Meanwhile, the system precisely controls the flow rate of the heat exchange medium by adjusting the opening of pipeline valves, ensuring the stability and uniformity of heat transfer. When the exothermic reaction intensity is insufficient, and the recovered reaction heat alone cannot meet the temperature requirements of the raw materials to be preheated, the system automatically activates the electric heating compensation mechanism. Based on the deviation between the real-time temperature of the raw materials and the preset temperature, the system precisely adjusts the heating power of the electric heating device. When the deviation is large, the heating power is appropriately increased to accelerate the heating rate of the raw materials; when the deviation is small, the heating power is reduced to prevent the raw material temperature from being too high and affecting its reactivity, ensuring that the preheating temperatures of all five raw materials can be accurately met.

[0049] Throughout the preheating process, the system monitors key parameters such as the temperature of the heat exchange medium, the temperature of the raw material to be preheated, and pipeline pressure in real time. These parameters are fed back to the control unit of the waste heat recovery module via sensors. The control unit dynamically fine-tunes the heat exchange medium circulation rate, valve opening, and heating power based on parameter changes, ensuring a stable and controllable preheating process. Once the raw material reaches the preset temperature, the system automatically switches to a heat preservation circulation mode to maintain a stable raw material temperature and prevent temperature fluctuations from affecting the synergistic effect of subsequent multi-raw material reactions. Simultaneously, the waste heat recovery module accurately synchronizes real-time temperature data during the raw material preheating process to the closed-loop optimization module, providing data support for the dynamic correction of system parameters. This process not only maximizes the utilization of reaction waste heat and reduces energy consumption but also ensures that the raw material preheating temperature meets the target, providing stable raw material temperature conditions for the synthesis of high-performance water-reducing agents and effectively reducing the risk of reaction anomalies caused by raw material temperature fluctuations.

[0050] The closed-loop optimization module, as the core optimization unit of the system, continuously aggregates key data from various modules, including actual temperature change rate data output by the zoned heat acquisition module, reaction state assessment calculation data output by the dual-factor coupling module, actual control parameters output by the dynamic control module, and preheating temperature data output by the waste heat recovery module. This data comprehensively covers key information about the reaction process, control process, and waste heat utilization process, providing a complete basis for the precise optimization of system parameters.

[0051] The closed-loop optimization module compares the actual temperature change rate data with the calculated temperature change rate data from the dual-factor coupling module, the actual control data from the dynamic control module with the calculated control data, and the actual preheating temperature data from the waste heat recovery module with the calculated preheating temperature data, accurately calculating the deviation values ​​for each of the three sets of data. The system presets specific deviation thresholds to determine whether the data deviations are within a reasonable range. If any data deviation exceeds the corresponding threshold, it indicates a mismatch between the current system parameters and the actual operating conditions of customized production of high-performance water-reducing agents, requiring timely correction. At this point, the closed-loop optimization module immediately generates a coefficient correction command, performing gradient correction on the raw material matching coefficient of the dual-factor coupling module. The coefficient value is precisely adjusted according to the magnitude of the deviation, ensuring that the algorithm can more accurately correlate regions and multiple raw material factors. Simultaneously, a step correction is performed on the feedback adjustment coefficient of the dynamic control module, optimizing the response sensitivity and control accuracy of the control algorithm.

[0052] If all data deviations do not exceed the preset threshold, it indicates that the current system parameters are well adapted to the customized production conditions, and the existing coefficients can be kept unchanged. Through this continuous deviation comparison and dynamic parameter correction, the closed-loop optimization module continuously optimizes the adaptability of the system parameters, effectively addressing the large exothermic fluctuations in the production of high-performance water-reducing agents, ensuring the precise synergy of the five raw material additions, and ensuring that product performance strictly meets the customer's customized requirements, such as... Figure 2 As shown.

[0053] This embodiment addresses the high requirements of customized production of high-performance water-reducing agents. Based on high-precision zoned heat acquisition, a dual-factor coupling module generates detailed reaction evaluation results, providing precise control basis for five raw materials with different properties. A dynamic control module strictly controls the control amplitude and timing to address the requirements of raw material compatibility and reaction stability. A waste heat recovery module efficiently recovers heat and accurately preheats the raw materials, reducing the impact of temperature fluctuations. A closed-loop optimization module dynamically corrects parameters to adapt to the large exothermic fluctuations of the reaction. All modules work closely together to achieve precise and coordinated addition of multiple raw materials, effectively solving the problems of difficult reaction control and high raw material compatibility requirements in customized production. This ensures product performance meets standards, improves the level of intelligent production, and provides reliable support for customized production of high-performance water-reducing agents.

[0054] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A multi-raw material synchronous intelligent dripping system for water-reducing agent production, characterized in that, The system includes: Zoned heat acquisition module: Temperature sensors are installed in the upper, middle and lower parts of the reactor jacket to collect temperature data in each zone and preprocess the collected temperature data to obtain the real-time change rate of reaction temperature in each zone. The dual-factor coupling module receives real-time temperature change rate data and real-time dropping acceleration rate data of each raw material, processes the data through a partition-raw material dual-factor coupling algorithm, and generates reaction state assessment results. Dynamic control module: Receives the output reaction state evaluation results, uses the deviation feedback dynamic control algorithm to calculate the evaluation results, generates control commands for the acceleration rate of each raw material droplet, and executes the control operation; Waste heat recovery module: Receives reaction exothermic data from the output reaction state assessment results, adjusts the heat transfer process according to the reaction exothermic data, completes the preheating treatment of the raw materials to be added, and outputs the temperature data of the raw material preheating process to the closed-loop optimization module. Closed-loop optimization module: Receives actual temperature change rate data, reaction state assessment result calculation data, actual control data and preheating temperature data, compares the deviation between actual data and calculated data, generates coefficient correction instructions and outputs them to the two-factor coupling module and dynamic control module respectively, to complete the dynamic correction of system parameters.

2. The multi-raw material synchronous intelligent dripping system for water-reducing agent production according to claim 1, characterized in that, In the partitioned heat acquisition module, when the temperature sensor is installed in the upper part of the reactor jacket, it is used to collect real-time temperature data, instantaneous temperature fluctuation data, and temperature change trend data of the surface area of ​​the reaction system inside the reactor. When the temperature sensor is installed in the middle part of the reactor jacket, it is used to collect real-time temperature data of the main reaction area inside the reactor, temperature difference data of different points in the main area, and temperature change gradient data of the main reaction area. When the temperature sensor is installed in the lower part of the reactor jacket, it is used to collect real-time temperature data of the bottom reaction area inside the reactor, temperature data of the bottom raw material mixing area, and temperature difference data between the bottom reaction area temperature and the raw material addition temperature.

3. The multi-raw material synchronous intelligent dripping system for water-reducing agent production according to claim 1, characterized in that, In the two-factor coupling module, the mathematical expression of the partition-raw material two-factor coupling algorithm is: in, For the first Real-time rate of change of actual reaction temperature in each region; For the first Weighting coefficients for each region; This represents the total number of reactants involved in the reaction. For the first Characteristic coefficients of the raw materials; For the first The real-time dripping acceleration rate of the raw materials; For the first The basic correction items for each region.

4. The multi-raw material synchronous intelligent dripping system for water-reducing agent production according to claim 1, characterized in that, In the dual-factor coupling module, the reaction state evaluation results specifically include: information on the severity grading of reactions in each region, information on the matching degree between the acceleration rate of each raw material droplet and the corresponding region's reaction heat release, information on the overall reaction process stage determination, synergy parameters of reactions in different regions, and information on the distribution of reaction heat release intensity. The information on the severity grading of reactions in each region is determined based on the numerical range of the real-time change rate of the reaction temperature in each zone. The information on the matching degree between the acceleration rate of each raw material droplet and the corresponding region's reaction heat release is obtained based on the calculation results of the zone-raw material dual-factor coupling algorithm. The information on the overall reaction process stage determination is combined with a comprehensive assessment of the reaction state in each region. The synergy parameters of reactions in different regions reflect the consistency of the reaction rhythm in each region. The information on the distribution of reaction heat release intensity characterizes the differences in heat release distribution in different regions within the reactor.

5. The multi-raw material synchronous intelligent dripping system for water-reducing agent production according to claim 1, characterized in that, In the dynamic control module, the mathematical expression of the deviation feedback dynamic control algorithm is: in, For the first The drip rate was accelerated after the raw material was regulated; For the first The current drip rate of the raw material; This is the feedback adjustment coefficient; For the first Real-time rate of change of actual reaction temperature in each region; For the first The preset reaction temperature change rate for each region.

6. The multi-raw material synchronous intelligent dripping system for water-reducing agent production according to claim 1, characterized in that, In the dynamic control module, the generated instructions for controlling the acceleration rate of each raw material droplet are specifically as follows: based on the reaction state assessment results of each region... With preset The deviation relationship is used to determine the corresponding raw material control direction. Greater than When a command is generated to reduce the rate of acceleration of the raw material droplets, Less than When the raw material droplet acceleration rate is increased, an instruction is generated. and When the deviation is within the preset range of -0.1℃ / min to 0.1℃ / min, a raw material droplet acceleration rate maintenance command is generated; according to the raw material characteristic coefficient The magnitude of the values ​​determines the priority of regulation. For raw materials with higher values, the control command should be executed first; the range of each control should be clearly defined, and the range of each control should not exceed the current dropping rate of the raw material. 20%; Set the execution sequence of control commands, first execute the control operation of high-priority raw materials, and collect the corresponding area again after an interval of 30 seconds. The data is then used to determine whether to generate a secondary control command.

7. The multi-raw material synchronous intelligent dripping system for water-reducing agent production according to claim 1, characterized in that, In the dynamic control module, the control operation is as follows: before the control command is executed, the unobstructedness of each raw material dripping pipeline and the operating status of the corresponding metering pump are checked; after the check is passed, according to the priority order in the control command, the metering pump control program corresponding to the high-priority raw material is started, and the metering pump speed is adjusted according to the control range determined by the control command. During the control process, the actual output flow data of each metering pump is collected in real time and compared with the drip acceleration rate required by the control command. After a single round of control operation is completed, data acquisition signals are triggered according to a preset timing sequence. Combined with the real-time change rate data of the actual reaction temperature, the real-time change rate of the actual reaction temperature and the actual operating parameters of the metering pump are fed back to the deviation feedback dynamic control algorithm of the dynamic control module for the determination of subsequent secondary control commands.

8. The multi-raw material synchronous intelligent dripping system for water-reducing agent production according to claim 1, characterized in that, The specific heat transfer control process of the waste heat recovery module includes: analyzing the reaction exothermic data, extracting information on exothermic intensity and regional distribution, and classifying the recovery level according to exothermic intensity >5℃ / min (high level), 2-5℃ / min (medium level), and <2℃ / min (low level); adjusting the circulation rate of the heat exchange medium in the reactor jacket according to the level to collect heat; adjusting pipeline valves to control the flow rate, and using electric heating for temperature compensation when the exothermic effect is insufficient; adjusting the heating power according to the deviation between the real-time temperature of the raw material to be preheated and the preset temperature: when the deviation is greater than 2℃, the heating power is adjusted to 80-100% of the rated power; when the deviation is between 1-2℃, the heating power is adjusted to 40-60% of the rated power; and when the deviation is less than 1℃, the heating power is adjusted to 10-30% of the rated power; monitoring the temperature of the heat exchange medium and raw material, and pipeline pressure in real time, and dynamically fine-tuning the parameters; and switching to the heat preservation circulation mode after the raw material reaches the preset temperature of 33-37℃.

9. The multi-raw material synchronous intelligent dripping system for water-reducing agent production according to claim 1, characterized in that, In the closed-loop optimization module, the specific process of comparing the deviation between actual data and calculated data and generating coefficient correction instructions is as follows: the actual temperature change rate is compared with the calculated temperature change rate, the actual control data is compared with the calculated control data, and the preheating temperature data is compared with the calculated preheating temperature data, setting deviation thresholds of ±0.2℃ / min, ±15%, and ±1℃ respectively; when any data deviation exceeds the corresponding threshold, a coefficient correction instruction is generated, and the raw material matching degree coefficient of the dual-factor coupling module is graded by ±3% to ±8%, and the feedback adjustment coefficient k of the dynamic control module is graded by ±0.1 to ±0.3; when the deviation does not exceed the threshold, the existing coefficient remains unchanged.

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