An improved machine learning based control system for a crystallization softening device

Abstract

This invention belongs to the field of water treatment and automation control technology. Specifically, it is a machine learning-based improved control system for a crystal-inducing softening device. The system includes a multi-dimensional parameter sensing module for real-time acquisition of water quality and operating parameters; a seed kinetics feature extraction module for deducing the microscopic state variables of the seed crystals; a machine learning prediction and decision engine that uses a neural network model to output optimal reagent dosing and replenishment commands; and a precision execution closed-loop feedback module for high-frequency closed-loop regulation. This achieves digital reconstruction and transparent control of the crystallization process, significantly improving reagent utilization and system resistance to shock loads while suppressing the risk of crystal bursting, thus ensuring the continuous stability of the effluent water quality.

Description

Technical Field

[0001] This invention belongs to the field of water treatment and automation control technology, specifically a machine learning-based improved crystal-inducing softening device control system. Background Technology

[0002] Due to its advantages of low reagent consumption, low sludge production, and high degree of by-product particle size, crystallization softening technology is widely used in the pretreatment of industrial circulating water in many industries. Its core is to remove hardness ions in water by adding seed crystals and using the principle of interface-induced crystallization, and to achieve efficient solid-liquid separation with the help of fluidized bed. However, the limitations of the existing crystallization softening control system have become the core bottleneck restricting its evolution towards intelligence and precision. The control logic of existing crystal-inducing softening devices is based on empirical constant dosing ratio or simple feedback control. The agent is added according to a fixed metering ratio based on the hardness of the influent and supplemented by PID regulation of the effluent. This control mode ignores the nonlinear dynamic system nature of the crystal-inducing reactor and does not consider that the physical properties of the crystal seed as a dynamic growth entity will change significantly during operation.

[0003] Existing sensing technologies struggle to acquire the microscopic morphological parameters of deep seed crystals in real time, resulting in the control system operating in a black box state. This leads to discrepancies between control commands and the actual dynamic requirements inside the reactor. Seed aging can easily cause crystal bursting, and traditional models cannot capture the cross-scale transient coupling changes between fluid dynamics and crystallization kinetics. The core technical challenge is to construct an intelligent and precise control system based on the interface dynamics characteristics of the entire seed crystal life cycle.

[0004] Therefore, the present invention provides an improved control system for a crystal-inducing softening device based on machine learning. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0006] The technical solution adopted by the present invention to solve its technical problem is: the improved crystal-inducing softening device control system based on machine learning described in the present invention includes a multi-dimensional parameter sensing module, a seed dynamics feature extraction module, a machine learning prediction decision engine, and a precision execution closed-loop feedback module. The multi-dimensional parameter sensing modules are deployed at the inlet, reaction zone, and outlet of the crystal-inducing softening device. They are used to acquire in real-time basic physicochemical parameters, including inlet water hardness, inlet water alkalinity, instantaneous flow rate, inlet water hydrogen ion concentration index, reactor internal temperature, and bed pressure drop. The sensing modules transmit the collected electrical signals to the central processing unit via a high-speed industrial bus, providing raw data support for subsequent digital modeling. The seed kinetics feature extraction module is a key link between physical entities and digital models. Its function is to transform the macroscopic parameters obtained by the sensing module into microscopic state variables that describe the crystallization process. The module has a built-in calculation model based on heterogeneous crystallization kinetics, which can reverse the dynamic changes of bed pressure drop and fluidization height to deduce the average equivalent particle size, bed porosity and effective specific surface area per unit volume of the seeds in the current bed. The module pays special attention to the evolution of interfacial activity of the seeds during the growth process. By integrating the historical cumulative crystallization amount, the life cycle stage of the seed crystal is determined. This feature extraction of the internal state of the black box eliminates the information asymmetry caused by the increase in seed crystal size in traditional systems, providing a logical premise for precise control. The machine learning prediction and decision engine, as the logical core of the entire control system, adopts a deep neural network architecture, specifically consisting of an input layer, multiple hidden feature extraction layers, and a nonlinear output layer. By learning from massive amounts of historical operating data, the engine establishes a deep causal mapping relationship between influent load, seed crystal state variables, and target effluent hardness. The decision engine can calculate the width of the metastable region under the current operating conditions in real time and predict the critical supersaturation threshold for homogeneous nucleation at a specific reagent dosage. The machine learning prediction decision engine has a self-learning function and can dynamically correct internal weight parameters based on real-time feedback of effluent turbidity and hardness data, thereby compensating for system deviations caused by sensor drift or equipment aging. During the decision-making stage, the engine outputs optimal alkali dosage instructions, circulation flow adjustment instructions, and crystal seed replenishment instructions to ensure that the system always operates within a steady-state range with the highest crystallization efficiency and no risk of crystal bursting. Preferably, the precision execution closed-loop feedback module is directly coupled to the hardware actuator of the crystal-inducing softening device. It includes a high-precision variable frequency dosing pump, an electric regulating valve, and an automated seed crystal replenishment device. This module receives digital instructions from a machine learning predictive decision engine and converts them into mechanical actions using a proportional-integral-derivative (PID) control algorithm. The precision execution closed-loop feedback module has an extremely high response frequency, enabling instantaneous compensation for sudden shocks in the influent water quality. Simultaneously, this module forms a local closed loop with the dosing flow meter and valve position feedback sensor, ensuring absolute accuracy in instruction execution, thereby achieving microsecond-level control of the chemical driving force inside the reactor. Preferably, the multi-dimensional parameter sensing module further includes an online particle size analysis unit, which is installed on the reflux branch of the reactor. This online particle size analysis unit uses the photoelectric blocking principle to capture the particle size distribution frequency of suspended particles in the circulating water flow in real time. This feature provides direct physical evidence for the machine learning prediction decision engine, enabling it to accurately identify the presence of signs of microcrystalline nuclei formation. Once an abnormal increase in the concentration of submicron-sized particles is detected, the system will determine it as a precursor to crystal bursting. The decision engine will immediately reduce the alkali dosage and increase the circulating dilution factor through a precise execution closed-loop feedback module, forcibly pulling the supersaturation back to the safe metastable region, thereby physically blocking the chain reaction of large-area crystal bursting. Preferably, the seed crystal kinetic feature extraction module has a self-compensation logic for bed expansion rate. Since the settling velocity of the seed crystals changes non-linearly as the particle size increases, this module dynamically corrects the prediction model for bed expansion rate by fitting the correlation curve between flow rate and pressure drop in real time. This improvement ensures that the contact strength between the dosing point and the seed crystal bed remains within the optimal kinetic range, avoiding spontaneous crystallization induced by localized accumulation of reagents at the bottom of the reactor. Preferably, the machine learning prediction decision engine incorporates a long short-term memory network algorithm specifically designed for processing water quality data with time-series characteristics. This algorithm extracts the trend term of influent load fluctuations, enabling it to predict hardness changes over a preset time period. This provides the system with feedforward control capabilities, allowing it to adjust the bed fluidization state and preset reagent ratio before water quality deterioration signals reach the reactor. This significantly enhances the system's resistance to shock loads and ensures the continuous stability of the effluent water quality. Preferably, the precision execution closed-loop feedback module further includes a seed crystal life cycle management subsystem, which automatically triggers the sand discharge valve and sand replenishment device based on the seed crystal aging index output by the machine learning prediction decision engine. When the prediction engine determines that the effective specific surface area of ​​the current seed crystal is lower than the set lower limit, the management subsystem executes a directional sand removal action to discharge the large-diameter seed crystals at the bottom and simultaneously replenishes fresh seed crystals of the preset size. This automated maintenance mode based on machine learning prediction maintains the constant interfacial activity inside the reactor and completely solves the technical problem of performance drift caused by long-term seed crystal operation in traditional processes. The improved crystal-inducing softening device control system based on machine learning provided by this invention works by deeply digitally reconstructing the physical process. After system startup, the multi-dimensional parameter sensing module continuously inputs the reactor's operating environment parameters into the seed crystal dynamics feature extraction module. The extraction module calculates the current bed's micromechanical and crystallization kinetic parameters and aggregates them along with water quality parameters into the machine learning prediction and decision engine. The decision engine performs multi-dimensional correlation analysis in the digital space, identifies the similarity between the current state and historical successful operating conditions, and calculates the optimal set of control parameters by combining the real-time predicted crystal burst risk curve. The precision execution closed-loop feedback module then translates these parameters into physical actions. Through the coordinated adjustment of chemical dosing energy levels and fluid kinetic energy, the crystallization process occurs precisely at the seed crystal interface, maximally suppressing the generation of free microcrystals in the water. The beneficial effects of this invention are as follows: 1. The improved crystal-inducing softening device control system based on machine learning described in this invention utilizes a machine learning predictive decision engine for in-depth analysis of crystal dynamics characteristics. This eliminates the black-box operation risk caused by the inability to observe internal evolution in traditional control systems. The system can perceive the shrinkage of the crystal specific surface area in real time and make compensatory decisions, fundamentally curbing the crystal bursting phenomenon caused by uncontrolled local supersaturation. This significantly reduces the effluent turbidity compared to traditional systems, ensuring the safe operation of downstream membrane treatment systems. 2. The improved crystal-inducing softening device control system based on machine learning described in this invention uses a machine learning decision engine to accurately calculate the matching degree between the reagent dosage and the activity of the crystal interface, avoiding ineffective reagent loss caused by empirical overdosing. While ensuring the hardness removal rate, this invention can dynamically optimize alkali consumption according to the crystal growth state, significantly reducing the system's cost per ton of water treated, and also reducing the chemical load on the discharged wastewater. 3. The improved crystal-inducing softening device control system based on machine learning described in this invention, by leveraging the predictive capabilities of the Long Short-Term Memory (LSTM) network algorithm, can effectively cope with the drastic water quality fluctuations commonly seen in industrial circulating water systems. The system transforms from traditional passive feedback to active predictive intervention, significantly shortening the system's response time to load changes. This reduces the range of water quality fluctuations during non-steady-state processes such as startup, sand removal / replenishment, and water flow switching to an extremely low level. 4. The improved crystal-inducing softening device control system based on machine learning described in this invention, through the automated operation of the seed crystal lifecycle management subsystem, ensures that the crystallization medium in the reactor is always in a highly active state. The system performs precise replenishment and drainage based on the kinetic indicators output by the machine learning algorithm, avoiding seed crystal waste caused by blindly draining sand or efficiency degradation caused by insufficient sand drainage, and extending the effective operating cycle of a single seed crystal loading. 5. The improved crystallization softening device control system based on machine learning described in this invention ensures that the mass transfer and energy transfer inside the fluidized bed reach the optimal balance by coupling the control of the bed expansion rate and the crystallization rate. This not only improves the interface capture efficiency of ions, but also reduces the flow path short-circuiting phenomenon caused by uneven bed distribution, thereby maximizing the volume utilization rate of the reactor. Attached Figure Description

[0007] The invention will now be further described with reference to the accompanying drawings.

[0008] Figure 1 This is a structural block diagram of a machine learning-based improved crystal-inducing softening device control system in this invention. Detailed Implementation

[0009] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0010] like Figure 1 As shown in the embodiment of the present invention, an improved crystal-inducing softening device control system based on machine learning includes a multi-dimensional parameter sensing module, a seed kinetic feature extraction module, a machine learning prediction decision engine, and a precision execution closed-loop feedback module. The modules interact with each other at the millisecond level through industrial Ethernet and high-speed fieldbus, jointly supporting a dynamic feedback architecture with self-evolution capabilities.

[0011] The multi-dimensional parameter sensing module, as the sensing antenna of the system, is deployed at the water inlet, reaction zone and water outlet of the crystal-inducing softening device. During the project implementation, an electromagnetic flow meter and a multi-parameter online water quality analyzer are installed at the inlet end to obtain key chemical characteristics such as inlet hardness, inlet alkalinity, instantaneous flow rate, and inlet hydrogen ion concentration index in real time. Multi-stage pressure transmitters and high-precision platinum resistance temperature sensors are installed inside the reaction zone to capture minute disturbances in the reactor's internal temperature and bed pressure drop. These physicochemical parameters are collected to the field data acquisition station via standard shielded cables, and the collected electrical signals are converted into digital signals conforming to the protocol specifications and transmitted to the central processing unit using a high-speed industrial bus. The sampling frequency of the multi-dimensional parameter sensing module is set at the Hertz level, which enables it to capture instantaneous pressure fluctuations inside the fluidized bed caused by flow path short circuits or localized scaling, providing high-confidence raw data support for subsequent crystallization kinetic modeling. Furthermore, in order to solve the problem of lack of perception of the black box state inside the reactor in traditional control logic, this invention integrates a seed kinetic feature extraction module. This module is the key link between physical entities and digital models. Its function is to transform the macroscopic parameters obtained by the multidimensional parameter sensing module into microscopic state variables that describe the crystallization process. In the specific logic implementation, the module has a built-in calculation model based on heterogeneous crystallization kinetics. This model processes real-time data on bed pressure drop and fluidization height, and uses the principles of fluidization mechanics to reverse-engineer the average equivalent particle size of the seeds inside the current bed, the bed porosity, and the effective specific surface area per unit volume. As the surface of the seed crystal gradually enlarges due to the continuous deposition of calcium carbonate, the seed crystal settling velocity and interfacial mechanical characteristics change significantly. The seed crystal kinetics feature extraction module, through integral calculations of historically accumulated crystallization amounts, can accurately determine the current lifecycle stage of the seed crystal. Through this microscopic feature extraction, the system can assess the crystallization activity potential of the seed crystal in real time, eliminating the risk of selective failure caused by increased seed crystal size. This provides the necessary logical premise for machine learning predictive decision engines. The machine learning prediction and decision engine, serving as the intelligent brain of the entire control system, adopts a deep neural network architecture, which consists of an input layer, multiple hidden feature extraction layers, and a nonlinear output layer at the logical level. During the system training phase, the engine learns from massive amounts of historical operating data from industrial sites and establishes a deep causal mapping relationship between influent load (such as the product of hardness and flow rate), seed state variables (such as effective specific surface area), and target effluent hardness. The core advantage of the machine learning prediction decision engine lies in its ability to calculate the width of the metastable region under the current operating conditions in real time. During the crystallization and softening process, if the ion product of calcium ions and carbonate ions in the water exceeds the upper limit of the metastable region, homogeneous nucleation will occur in the system, producing a large number of fine particles that are difficult to settle, which is the so-called crystal bursting phenomenon. The decision engine can accurately predict the critical supersaturation threshold for homogeneous nucleation at a specific dosage of reagent through multidimensional mapping of real-time data, thereby providing early warning at the decision-making level. Furthermore, in a specific preferred embodiment, the machine learning predictive decision engine incorporates a Long Short-Term Memory (LSTM) network algorithm. This algorithm is specifically designed to process water quality data with significant time-series characteristics. Through its unique gating mechanism, the LTM network can identify the periodic patterns and stochastic trends of influent hardness fluctuations. By extracting the trend of influent load fluctuations, the system can predict the hardness change trend over the next 30 minutes to 2 hours. This enables the control system to possess feedforward control capabilities, meaning that the decision engine adjusts the bed fluidization state and preset reagent ratios in advance before the water quality deterioration signal reaches the reactor reaction zone. This significantly enhances the system's resistance to shock loads and ensures the continuous stability of effluent water quality under extremely complex operating conditions. The precision execution closed-loop feedback module is directly coupled to the hardware actuator of the crystal softening device, acting as a bridge to convert digital commands into physical actions. This module includes a high-precision variable frequency dosing pump, an electric regulating valve, and an automated seed dispensing device. After receiving the optimal control command set from the machine learning prediction decision engine, the module uses a proportional-integral-derivative (PID) control algorithm to convert the dosing command into a frequency control signal for the frequency converter, driving the high-precision dosing pump to perform precise metering and dosing. The precision execution closed-loop feedback module has an extremely high response frequency and can perform millisecond-level dosing compensation for instantaneous changes in influent flow rate. Meanwhile, this module forms a local closed loop through the electromagnetic flowmeter on the dosing pipeline and the valve position feedback sensor of the electric valve, correcting execution errors in real time. This microsecond-level control of the chemical driving force inside the reactor ensures that the supersaturation is always maintained within the preset metastable region window. Furthermore, in a more engineering-supported implementation, the multi-dimensional parameter sensing module also includes an online particle size analysis unit, which is deployed on the reflux branch of the reactor or at the sampling point in the middle of the reaction zone. The online particle size analysis unit uses the photoelectric blocking principle to capture the particle size distribution frequency and particle concentration of suspended particles in the circulating water flow using high-frequency laser pulses. This physical characteristic provides direct feedback evidence for the machine learning prediction decision engine. In actual operation, once the online particle size analysis unit detects an abnormal increase in the concentration of submicron particles (such as 0.1 to 1 micrometer), the system will determine it as a precursor to crystal bursting or abnormal germination of tiny crystal nuclei. The decision engine immediately triggered an emergency intervention procedure through a precise closed-loop feedback module: reducing the dosage of alkali (such as sodium hydroxide or lime) and simultaneously increasing the circulating dilution flow rate. By rapidly reducing supersaturation, the crystallization logic was forcibly pulled back to the heterogeneous crystallization track, thereby effectively blocking the chain reaction of large-area crystal bursting at the physical level and protecting the safety of the downstream membrane system. The seed kinetics feature extraction module, when handling fluid dynamic equilibrium, possesses a self-compensating logic for bed expansion rate. During the operation of the crystallization softening device, as the seed particle size continuously grows, its single particle mass increases, leading to a gradual decrease in the bed expansion rate at the same upward flow rate. Traditional linear control logic often ignores this change, resulting in insufficient mixing intensity near the reagent dosing point. The seed kinetics feature extraction module dynamically corrects the bed expansion rate prediction model by real-time fitting of the nonlinear correlation curve between flow rate and pressure drop, and outputs the result to the decision engine. The engine adjusts the circulation pump frequency accordingly to ensure that the seed crystals at the dosing point are in the optimal fluidization state, greatly enhancing the contact strength between the reagent and the seed interface and avoiding spontaneous homogeneous crystallization induced by local accumulation of reagent at the bottom of the reactor. Another core technical feature of the control system of this invention lies in its integrated seed crystal lifecycle management subsystem. This subsystem is a functional subset of the precision execution closed-loop feedback module. Its operation is controlled by the seed crystal aging index output by the machine learning prediction decision engine. When the machine learning decision engine determines, based on the historical total crystal volume and the real-time pressure drop model, that the effective specific surface area of ​​the current seed crystal is lower than the set lower limit (i.e., the seed crystal is severely aged and the interfacial active center is completely covered), the seed crystal lifecycle management subsystem automatically triggers the sand discharge valve. The system then performs a directional sand discharge action, using the bottom sand discharge pipeline to discharge large-diameter, low-activity crystals. Based on the sand replenishment command calculated by the decision engine, it drives the sand replenishment pump to replenish fresh seed crystals with a preset particle size (e.g., 0.2 to 0.5 mm). This automated maintenance mode based on machine learning prediction maintains the dynamic constancy of the interfacial activity inside the reactor, completely solving the technical problems of performance drift and decreased hardness removal rate caused by long-term seed crystal operation in traditional processes. Example In this embodiment, a machine learning-based improved crystal-inducing softening device control system is applied to the circulating water wastewater softening treatment section of a large thermal power plant. The device has a water treatment capacity of 300 cubic meters per hour, and the influent hardness fluctuates between 500 and 1200 mg / L (calculated as calcium carbonate). The system uses a multi-dimensional parameter sensing module to monitor water quality in real time, and the machine learning prediction decision engine uses a deep neural network model containing three hidden layers. During system operation, the seed crystal lifecycle management subsystem automatically replenishes and removes sand every 24 hours based on the output of the prediction engine, maintaining the bed porosity at approximately 0.65. Comparative Example The comparative example uses the exact same crystallization softening reactor, but the control system employs a traditional manual mode combined with a simple online flow-ratio dosing control scheme. Seed management involves periodically taking manual samples to determine the particle size and then manually draining and removing sand. Operators set the dosing ratio based on experience, and the system lacks machine learning-based prediction and adaptive adjustment capabilities. During a 60-day continuous comparative test, operational data from both systems were collected and analyzed. The results are shown in Table 1. Table 1: Comparison of Operational Performance between the Control System of the Invention and the Traditional Control System

[0012] According to the comparative data in Table 1, it can be found that the technical effect produced by the present invention not only has substantial progress, but also shows unexpected improvements in multiple dimensions. First, in terms of effluent water quality, the control system of the present invention, with its machine learning predictive decision engine, achieves precise control of the metastable zone, resulting in a 41.3% reduction in the average total hardness of the effluent compared to the comparative example. More importantly, the standard deviation of the effluent hardness fluctuation decreased dramatically from 28.7 to 5.2. This directly proves that the feedforward prediction capability of the Long Short-Term Memory (LSTM) network algorithm can effectively smooth out water quality fluctuations and achieve extremely high operational determinism. In terms of safety, the number of times the crystal explosion event was triggered was reduced from 12 times in 60 days to only 1 time. This significant improvement is due to the instantaneous linkage between the online granularity analysis unit and the precision execution closed-loop feedback module in the multi-dimensional parameter sensing module. Once the precursor to crystal bursting was identified, the system intervened with microsecond-level dosing adjustments, successfully bringing the supersaturation back to a safe range. The effluent turbidity dropped from 4.20 NTU to 0.85 NTU, indicating extremely low levels of free microcrystals in the effluent. This significantly reduced the physical retention load on subsequent ultrafiltration or reverse osmosis membrane systems. In terms of economics, the machine learning decision engine accurately matches the dosage with the activity state of the seed crystal interface, avoiding blind surplus in the addition of chemicals, thus reducing the consumption of alkali per ton of water by 27.1%. The seed crystal lifecycle management subsystem maintains the seed crystal bed at a high activity level through automated sand replenishment, extending the effective operating cycle by three times and significantly reducing manual maintenance costs and substrate loss. The operating logic of the control system in this invention is not a simple linear accumulation, but is achieved through a deep digital reconstruction of the physical process. A multi-dimensional parameter sensing module feeds real-time physical quantities to a seed crystal dynamics feature extraction module. The extraction module uses feature variables to eliminate black-box effects and outputs the results to a machine learning prediction and decision engine. The decision engine performs causal correlation analysis within a digital twin space, identifies risk trends, and determines the optimal parameter set. Finally, a precision execution closed-loop feedback module translates this into physical execution actions. This closed-loop feedback logic of sensing-extraction-decision-execution transforms the crystal-inducing softening reactor from a passive device affected by random disturbances into an intelligent entity with self-correcting capabilities. Furthermore, in response to the complex electromagnetic environment of industrial sites, the multi-dimensional parameter sensing module employs redundant verification logic during signal transmission to ensure the purity of the data source. The machine learning prediction and decision engine has a self-learning function and can continuously optimize the weight matrix inside the neural network based on the feedback of the effluent water quality. For example, when a sensor experiences slight drift due to prolonged exposure to highly alkaline water, the decision engine can automatically calculate and compensate for this system deviation by identifying the discrepancy between the actual hardness of the effluent and the theoretical hardness. This adaptive compensation feature ensures that the system maintains its initial control accuracy throughout its long operating cycle, which can last for several years. The variable frequency controller for the dosing pump in the precision-executed closed-loop feedback module employs a vector control algorithm, ensuring extremely high metering accuracy even at very low flow rates. The seed crystal lifecycle management subsystem achieves gradient seed replacement through precise control of the sand discharge valve opening. This means only the largest, failed seed crystals located at the bottom of the reactor are discharged, while the moderately active growth seed crystals in the middle layer are retained. This refined management strategy is simply impossible to achieve using traditional manual methods. In summary, the improved crystal-inducing softening device control system provided by this invention, through the deep coupling of multi-dimensional parameter perception, in-depth analysis of seed kinetics, deep learning prediction and decision-making, and precise execution feedback, constructs a water treatment control paradigm with industry-leading significance. This invention not only solves the essential technical contradiction of dynamic imbalance between seed growth and control logic during the crystal-inducing softening reaction, but also endows the device with resilience to cope with extreme dynamic conditions by introducing crystal burst early warning and full life cycle management of seed crystals.

[0013] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A machine learning based modified crystallization softening device control system, characterized by, include: The multi-dimensional parameter sensing module is used to collect the physicochemical state parameters of the inlet, reaction zone and outlet of the crystal-inducing softening device in real time, and convert the physicochemical state parameters into electrical signals for digital transmission. The seed crystal kinetics feature extraction module is communicatively connected to the multidimensional parameter sensing module. It is used to convert the physicochemical state parameters into microscopic state variables describing the crystallization process based on the heterogeneous crystallization kinetics model, and to determine the life cycle stage of the seed crystal. The machine learning prediction and decision engine is connected in communication with the seed kinetic feature extraction module. It is used to process the microscopic state variables using a deep neural network architecture, calculate the width of the metastable region and the critical threshold of crystal burst risk under the current working condition, and output the optimal control instruction set. The precision execution closed-loop feedback module is coupled to the actuators of the machine learning prediction decision engine and the crystal softening device, respectively. It is used to receive the optimal control instruction set, drive the actuator to perform mechanical actions through the proportional-integral-derivative adjustment algorithm, and form a local closed-loop control based on the action feedback.

2. The machine learning based modified crystallization softening device control system of claim 1, wherein, The multidimensional parameter sensing module includes: The inlet sensing unit is used to collect inlet water hardness, inlet water alkalinity, instantaneous flow rate, and inlet water hydrogen ion concentration index. The reaction zone sensing unit is used to collect the temperature inside the reactor of the crystal softening device and the pressure drop of the seed bed. The water outlet sensing unit is used to collect water hardness, water alkalinity and water turbidity. The multi-dimensional parameter sensing module transmits parameters to the central processing unit via a high-speed industrial bus at a preset sampling frequency; the sampling frequency is set to capture instantaneous pressure fluctuations inside the fluidized bed.

3. The machine learning based modified crystallization softening device control system of claim 2, wherein, The seed crystal dynamics feature extraction module has built-in reverse deduction logic based on the principle of fluidization mechanics, and its feature processing steps include: The real-time data of the pressure drop and fluidization height of the seed bed collected by the multi-dimensional parameter sensing module are obtained. A nonlinear correlation curve between flow rate and pressure drop is established, and the average equivalent particle size and porosity of the seeds inside the seed bed are calculated in combination with fluid kinetic energy parameters. Calculate the effective specific surface area per unit volume based on the geometric model of seed particles; By integrating the historical cumulative crystallization amount, the change in the thickness of the deposition layer on the seed crystal surface is calculated, and the initial, growth, or aging life cycle stage of the seed crystal is determined accordingly.

4. The machine learning based modified crystallization softening device control system of claim 3, wherein, The machine learning prediction decision engine includes: The input layer is used to receive macroscopic physicochemical state parameters collected by the multidimensional parameter sensing module and microscopic state variables output by the seed kinetic feature extraction module. The hidden feature extraction layer is used to perform non-linear feature mapping on the input data through multi-layer neural connections, and to establish a deep causal mapping relationship between influent load, effective specific surface area and target effluent hardness. The output layer is used to output the optimal control instruction set, including the alkali dosage instruction, the circulation flow rate adjustment instruction, and the seed crystal replenishment action instruction. The machine learning prediction decision engine has a self-learning correction logic. By comparing the real-time feedback of the effluent turbidity and the effluent hardness data, it automatically calculates the system deviation and dynamically corrects the neuron weight parameters in the hidden feature extraction layer.

5. The machine learning based modified crystallization softening device control system of claim 4, wherein, The machine learning prediction decision engine incorporates a Long Short-Term Memory (LSTM) network algorithm. This LTM network algorithm processes water quality data with time-series characteristics through a gating mechanism, and its prediction control logic includes: Trend terms are extracted from the historical fluctuation data of the influent hardness and the instantaneous flow rate; Predict the trend of influent load changes within a preset time period; Before the water quality fluctuation signal reaches the reaction zone, a feedforward control command is output to the precision execution closed-loop feedback module to adjust the fluidization state of the seed bed and the reagent dosage ratio in advance.

6. The improved crystal-inducing softening device control system based on machine learning according to claim 4, characterized in that, The precision execution closed-loop feedback module includes: The dosing execution unit consists of a high-precision variable frequency dosing pump and a matching dosing flow meter, and is used to execute precise metering and dosing of the agent according to the alkali dosing command; The fluid regulation unit, consisting of an electric regulating valve and a circulating pump frequency converter, is used to regulate the upward flow rate inside the reactor according to the circulating flow rate regulation command. The automated sand removal and replenishment unit consists of an automated sand removal valve and a sand replenishment device, and is used to perform the physical replacement of the seed crystals. The precision execution closed-loop feedback module achieves real-time correction of execution accuracy through the dosing flow meter and the valve position feedback sensor of the electric regulating valve. The control response time is set to the microsecond level to maintain a constant supersaturation inside the reactor.

7. The improved crystal-inducing softening device control system based on machine learning according to claim 2, characterized in that, The multidimensional parameter sensing module also includes an online particle size analysis unit, which is installed on the reflux branch of the reactor in the crystallization softening device. Its monitoring and early warning logic includes: The particle size distribution frequency of suspended particles in circulating water is captured in real time using laser pulse signals through the principle of photoelectric interruption. When the concentration of suspended particles with a particle size in the range of 0.1 micrometers to 1 micrometer is detected to exceed the preset safe concentration threshold, a pre-crystal bursting signal is sent to the machine learning prediction decision engine. After receiving the precursor signal of crystal bursting, the machine learning prediction decision engine immediately outputs an intervention command to the precision execution closed-loop feedback module. The intervention command includes reducing the amount of alkali added and increasing the cyclic dilution ratio until the concentration of suspended particles returns to below the safe concentration threshold.

8. The improved crystal-inducing softening device control system based on machine learning according to claim 6, characterized in that, The seed kinetic feature extraction module has a bed expansion rate self-compensation logic, and its compensation execution steps include: Real-time acquisition of mass change data of the seed crystals as they grow during the crystallization process; Correct the settling velocity constant in the aforementioned nonlinear correlation curve; The mapping model between the flow rate and the pressure drop of the seed bed is refitted, and the corrected predicted value of the bed porosity is output to the machine learning prediction decision engine. The machine learning prediction decision engine adjusts the output power of the fluid regulation unit to compensate for the decrease in fluidization intensity caused by the increase in seed particle size, ensuring that the mixing intensity at the drug addition point is maintained within the preset kinetic range.

9. The improved crystal-inducing softening device control system based on machine learning according to claim 6, characterized in that, The precision execution closed-loop feedback module also includes a seed crystal lifecycle management subsystem, whose automated maintenance logic includes: The seed aging index is received from the machine learning prediction decision engine. The seed aging index is determined based on the shrinkage ratio of the effective specific surface area of ​​the seed relative to the initial specific surface area. When the effective specific surface area is determined to be lower than the set activity lower limit, the seed crystal life cycle management subsystem automatically opens the automated sand discharge valve; The directional sand discharge action removes the failed seed crystals from the bottom of the reactor and simultaneously drives the sand replenishment device to replenish fresh seed crystals of a preset particle size. The height of the bed after sand replenishment is verified by the seed kinetic feature extraction module to achieve dynamic balance of seed interface activity.

10. The improved crystal-inducing softening device control system based on machine learning according to claim 6, characterized in that, The machine learning prediction and decision engine has a built-in dynamic calculation model for the metastable region, and its risk control steps include: The reactor internal temperature and the influent hydrogen ion concentration index are acquired by the multi-dimensional parameter sensing module. Calculate the solubility product constant of calcium carbonate under the current reaction conditions; Based on the effective specific surface area among the microscopic state variables, the critical ion product threshold is calculated under the premise that spontaneous homogeneous nucleation does not occur. The real-time ion accumulation data is compared with the critical ion accumulation threshold. When the difference between the two is less than the preset safety margin, the alkali dosage instruction is forcibly corrected to block the homogeneous nucleation reaction.

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