Leucine enrichment process control system and control method thereof

Abstract

The invention relates to the field of chemical control systems, and discloses a leucine enrichment process control system and a control method thereof.The system comprises a data acquisition module used for reading original data of a process sensor; the processing module is used for filtering the original data; the noise monitoring module is used for dynamically adjusting a measurement threshold value and updating reference data; the judgment and generation module is used for judging the process state, calculating parameters and target values which need to be adjusted based on the process state and generating a regulation and control instruction; and the instruction sending module is used for sending a regulation and control instruction to the execution mechanism to complete one-time control circulation. According to the invention, the differential operation is carried out on the original data collected in real time and the dynamically updated reference data, the dynamic signal representing the process change can be separated, and the common-mode filtering is carried out on the dynamic signal, so that the signal-to-noise ratio of the effective process parameter signal can be improved for suppressing noise components such as electromagnetic interference and the like co-existing on multiple sensing channels; and the accuracy of control decision is improved.

Description

Technical Field

[0001] This invention relates to the field of chemical control systems, specifically to a leucine enrichment process control system and its control method. Background Technology

[0002] Leucine is one of the eight essential amino acids for the human body and belongs to the branched-chain amino acid family. It plays a crucial physiological role in promoting protein synthesis, regulating muscle repair and growth, and maintaining stable blood glucose levels. Due to its significant nutritional value, the efficient and precise extraction and enrichment of leucine in special medical purpose foods, sports nutrition supplements, and functional health products has important industrial significance and application value.

[0003] In existing technologies, the enrichment process of amino acids such as leucine generally employs a traditional process control method centered on programmable logic controllers (PLCs) and industrial touchscreens (HMIs). Basic process control is achieved through periodic sampling and simple signal feedback. PLCs and general-purpose industrial touchscreens are widely used automation control methods in food processing and other industrial production. Typically, a PLC serves as the underlying control core, connecting sensors and actuators (such as weighing sensors, moisture sensors, and temperature sensors) via digital or analog input / output interfaces. The touchscreen acts as the human-machine interface (HMI), used for parameter input, operation monitoring, and alarm display. The PLC processes the collected sensor signals according to a preset logic program and automatically controls the relevant equipment to perform corresponding actions. When the monitored value exceeds the set range, the system triggers an alarm or stops. Existing PLC and touchscreen software mostly adopts sequential control and setpoint feedback modes. Their main functions include: parameter setting and real-time display; single-point detection and data storage of key parameters; over-limit alarms and simple safety protection logic. While PLC+touchscreen systems offer advantages such as high stability and versatility in food processing, their software primarily relies on fixed logic and simple signal processing, lacking adaptive optimization capabilities and failing to address the control of nutritional factors in special medical foods (e.g., leucine enrichment). Data acquisition accuracy is low, failing to meet the high-sensitivity monitoring requirements of amino acid enrichment processes. Specifically, data acquisition is primarily single-point, hindering multi-point synchronous monitoring and real-time noise filtering. System response depends on a fixed sampling period, resulting in delays and an inability to quickly respond to parameter fluctuations. The lack of intelligent data analysis and closed-loop optimization functions, coupled with the absence of dedicated data and solutions for leucine enrichment processes, prevents precise dynamic control of leucine and other amino acids. Furthermore, in the specific process of leucine enrichment, existing technologies lack the ability to directly, accurately, and synchronously monitor key process parameters (such as reactor temperature, material pH, real-time leucine concentration, and stirring rate). The control system cannot dynamically and accurately adjust the actuators (such as heaters, peristaltic pumps, and stirring motors) based on these process parameters, thus hindering optimized control of the leucine enrichment process.

[0004] Therefore, such solutions have significant limitations. First, at the data acquisition level, they primarily rely on discrete monitoring of single points or a few key points, failing to acquire global state distribution information of the process. Furthermore, the acquired raw signals typically lack effective real-time noise filtering, resulting in a low signal-to-noise ratio and affecting monitoring accuracy. Second, at the control logic level, these systems heavily depend on manual experience for parameter presets. Once key parameters such as temperature, pH, and material addition rate are set, they operate in a fixed sequence control and setpoint feedback mode, lacking the ability to dynamically adjust based on real-time operating conditions. This leads to inherent system response delays, making it difficult to quickly respond to batch variations in raw materials or instantaneous fluctuations in the process. In addition, their control software functions are relatively basic, mainly limited to simple data recording and fixed threshold alarms, lacking intelligent functions for in-depth analysis, self-learning, and closed-loop optimization of process data. Therefore, existing control solutions cannot meet the complex requirements of precise and dynamic regulation of nutritional factors, such as leucine enrichment. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a leucine enrichment process control system and its control method, which solves the problems of low monitoring accuracy, slow response, inability to adapt to changes in operating conditions, and lack of intelligent closed-loop optimization capabilities in existing control systems.

[0006] To achieve the above objectives, the first aspect of the present invention provides a leucine enrichment process control system, the system comprising:

[0007] The data acquisition module is used to cyclically read raw data from process sensors by driving multiple excitation channels and multiple sensing channels;

[0008] The processing module is used to filter the raw data to obtain effective process parameter signals after common-mode filtering.

[0009] The noise monitoring module is used to dynamically adjust the measurement threshold according to the environmental noise conditions and update the reference data to obtain the adjusted measurement threshold and the updated reference data.

[0010] The determination and generation module is used to combine the effective process parameter signals after common-mode filtering, the adjusted measurement threshold, and the updated reference data to determine the process state of leucine enrichment, calculate the parameters to be adjusted and the target values ​​based on the process state, and generate control instructions.

[0011] The instruction sending module is used to send control instructions to the actuator to complete one control cycle.

[0012] Preferably, the process sensors include a temperature sensor, a concentration sensor, a pH sensor, an online concentration detector, and a flow meter; the actuators include a heating device, a cooling device, a metering pump, a solenoid valve, and a stirrer for mixing materials, wherein the heating device and the cooling device are used to regulate the process temperature, the metering pump is used to add acid or alkali solutions or nutrient solutions, the solenoid valve is used to control the flow of fluid lines, and the stirrer is used to mix materials.

[0013] Preferably, the processing module is specifically used for:

[0014] S3-1, Based on the original data difference calculation formula, the original data and the dynamically updated reference data are differencing each other, and the absolute value is taken to extract the effective process parameter signal characterizing the change of process parameters. The original data difference calculation formula is as follows: ;

[0015] In the formula: This is a matrix of effective process parameter signals, where the magnitude of the effective process parameter signals directly corresponds to the monitoring nodes. The degree of deviation between the process parameters and the dynamically updated baseline data; Provided by the noise monitoring module and the node The corresponding updated matrix of baseline data; The nodes read by the data acquisition module in this acquisition cycle The original data;

[0016] S3-2, Based on the common-mode noise component formula, calculate the common-mode noise component of the signals of all sensing channels associated with the same excitation channel, wherein the common-mode noise component formula is:

[0017] ;

[0018] In the formula, In order to be with the first The common-mode noise components associated with each excitation channel, This refers to the total number of sensing channels corresponding to the process sensors. and All are positive numbers equal to or greater than;

[0019] Preferably, the processing module is further configured to:

[0020] Based on the common-mode noise component For the same excitation channel, the effective process parameter signal Common-mode filtering is performed to correct the signal at each node and obtain the effective process parameter signal after common-mode filtering. Among them, the effective process parameter signal after common-mode filtering. .

[0021] Preferably, the noise monitoring module is further configured to scan the process sensor under conditions without an excitation signal to acquire background noise data, determine the current noise level of the process sensor based on the background noise data, and dynamically adjust the adjusted measurement threshold used to determine the process state based on the background noise data and the noise level calculation formula; wherein,

[0022] The formula for calculating the noise level is: ;

[0023] In the formula: The adjusted measurement threshold is used to determine whether the process parameter signal is valid. Specifically, it includes: using the adjusted measurement threshold as a judgment benchmark, and when the value of the valid process parameter signal is greater than the judgment benchmark, it is confirmed as a valid process deviation signal. This represents the current noise level of the process sensor. The preset initial reference threshold characterizes the noise level. The system's baseline sensitivity when the value equals 1; A preset noise impact coefficient, which is adjusted according to the specific process environment, characterizes the sensitivity of the measurement threshold to changes in noise level.

[0024] Preferably, the noise monitoring module is further used for:

[0025] When the process state is determined to be stable and there are no effective process parameter signals, the reference data is adaptively updated based on the deviation between the original data and the current reference data, according to the reference data adaptive update formula, to compensate for the drift of the process sensor. The reference data adaptive update formula is as follows: ;

[0026] In the formula, For nodes The latest baseline data after adaptive updates; For nodes Old baseline data before the update; For nodes within the current collection period The original data, As the baseline update rate coefficient, The value is a preset positive integer that is much smaller than 1.

[0027] Preferably, the determination generation module is specifically used for:

[0028] Effective process parameter signals With the adjusted measurement threshold Perform comparisons one by one, and based on the comparison results, identify spatially connected elements that exceed the adjusted measurement threshold. The node corresponding to the valid process parameter signal is identified as an independent process parameter deviation event;

[0029] A weighted coordinate positioning algorithm is used to calculate one or more valid process parameter signals in the process parameter deviation event to determine the center coordinates of the process parameter deviation event. , The center coordinates are used to characterize the process parameter that has deviated and the magnitude of the deviation.

[0030] ;

[0031] ;

[0032] In the formula, and These are the X-axis and Y-axis coordinates of the center coordinates of the process parameter deviation event, respectively. and All are continuous floating-point values; The set of all valid signal points that constitute the process parameter deviation event; For set The index node of the valid signal points in the data; , For nodes Preset physical or logical coordinates in the sensor array; For nodes The effective process parameter signal value at the location, after common-mode filtering.

[0033] Preferably, when multiple process parameter deviation events exist simultaneously, a unique event ID is assigned to each process parameter deviation event; and the event ID is updated by comparing the distance between the center coordinates of each event in the current acquisition cycle and the previous acquisition cycle, so as to continuously track multiple concurrent process parameter deviation events.

[0034] Preferably, the system further includes a multi-parameter control priority allocation module, which is used to allocate execution priorities to the control instructions corresponding to the multiple process parameter deviation events according to preset process logic and safety requirements when the determination generation module determines that there are multiple process parameter deviation events, so as to determine the order in which the instruction sending module sends the control instructions;

[0035] The instruction sending module is also used to retrieve control instructions one by one from the instruction execution queue in sequence, translate the control instructions into electrical signals that the corresponding actuators can recognize and send them to drive the actuators of the heating device, cooling device, metering pump, solenoid valve and stirrer to operate, thereby completing the closed-loop control of process parameters.

[0036] A second aspect of the present invention provides a method for controlling the leucine enrichment process in a leucine enrichment process control system, the method comprising the following steps:

[0037] Based on multiple excitation channels and multiple sensing channels, the raw data of the process sensors are read.

[0038] The original data is filtered to obtain the effective process parameter signal after common-mode filtering.

[0039] The measurement threshold is dynamically adjusted according to the environmental noise level, and the baseline data is updated to obtain the adjusted measurement threshold and the updated baseline data.

[0040] By combining the effective process parameter signals after common-mode filtering, the adjusted measurement threshold, and the updated baseline data, the process state of leucine enrichment is determined, and the parameters to be adjusted and the target values ​​are calculated based on the process state, and control instructions are generated.

[0041] Send control instructions to the implementing agency to complete one control cycle.

[0042] This invention provides a control system and method for a leucine enrichment process. It has the following beneficial effects:

[0043] 1. This invention separates dynamic signals representing process changes by performing differential operations on real-time acquired raw data and dynamically updated reference data. Furthermore, by performing common-mode filtering on these signals, it can specifically suppress noise components such as electromagnetic interference that coexist in multiple sensing channels, thereby improving the signal-to-noise ratio of effective process parameter signals and ensuring that subsequent control decisions are based on more realistic data.

[0044] 2. This invention dynamically adjusts the measurement threshold by measuring background noise in real time, which can avoid false alarms in noisy industrial environments and maintain high sensitivity in quiet environments. By adaptively updating the reference data, it can automatically compensate for signal drift caused by factors such as aging and temperature drift of the sensor, ensuring measurement accuracy and consistency in long-term operation and reducing the frequency of manual maintenance and calibration.

[0045] 3. This invention uses a weighted coordinate positioning algorithm to transform discrete signal points into quantifiable process parameter deviation events, and combines an event ID allocation and tracking mechanism to achieve continuous management of multiple concurrent events. Finally, it uses a multi-parameter control priority allocation module to intelligently sort the generated multiple control commands, thus avoiding execution conflicts and improving control efficiency and safety under complex operating conditions. Attached Figure Description

[0046] Figure 1 This is a schematic diagram of the system architecture of the present invention;

[0047] Figure 2 This is a schematic diagram of the working process of the noise monitoring module of the present invention;

[0048] Figure 3 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0049] The technical solutions in 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. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] To better understand the present invention, the above content will be described in detail below with reference to specific embodiments.

[0051] Please see the appendix Figure 1 and attached Figure 2 This invention provides a leucine enrichment process control system, which includes:

[0052] The data acquisition module drives each excitation channel (TX) sequentially in a time-division manner, and simultaneously senses signals on all sensing channels (RX) at the moment each excitation channel is activated, thereby achieving cyclic scanning. This allows it to capture the changes in capacitance or other physical quantities of the entire sensor array at the current moment, and aggregate these unprocessed raw measurements into a complete data matrix, providing a real-time and comprehensive data foundation for subsequent processing.

[0053] The processing module first performs a differential operation between the received raw data and dynamically updated reference data to extract the signal components representing changes in process parameters. Then, it calculates and removes noise (such as power supply interference) that exists across all sensing channels under the same excitation, thereby improving the signal-to-noise ratio of the effective process parameter signal and making the true process change signal stand out from the background noise.

[0054] The noise monitoring module actively measures background noise during silent periods without excitation signals to dynamically calculate and adjust the measurement threshold used to determine signal validity, making the system more robust in noisy environments and more sensitive in quiet environments. When the system determines that the current process state is stable, it gradually integrates the current raw data into the reference data through low-pass filtering and other methods to automatically compensate for long-term drift caused by sensor aging, temperature drift, and other factors.

[0055] The decision generation module compares the valid process parameter signals output by the processing module with the dynamic thresholds provided by the noise monitoring module to identify significant process deviation events. For events consisting of multiple sensor nodes, a weighted coordinate algorithm is used to calculate the event center location using signal strength as the weight. Simultaneously, a unique ID is assigned to and continuously tracked for each event, enabling the differentiation and management of multiple concurrent, moving process events. Finally, combining the event type, location, and deviation magnitude, specific control actions and target values ​​are calculated using PID and other control algorithms to generate initial control commands.

[0056] The multi-parameter control priority allocation module prioritizes multiple control commands for different process parameters generated by the decision generation module. Based on pre-defined process logic rules (e.g., temperature adjustment before material feeding) and safety requirements (e.g., emergency stop commands have the highest priority), this module prioritizes these concurrent commands, ensuring that all control operations are executed in the most reasonable and safest order, avoiding conflicts between actuators.

[0057] The instruction sending module receives an ordered queue of instructions from the priority allocation module and executes them sequentially. Each logical instruction (such as turning on the heater or setting the metering pump flow rate) is translated into an electrical signal that the corresponding actuator can recognize. This signal is then sent out through specific communication interfaces such as GPIO, I2C, and SPI to directly drive the actuators, including the heater, solenoid valve, and metering pump, ultimately achieving closed-loop control of the process parameters.

[0058] In a specific embodiment:

[0059] The data acquisition module continuously and cyclically reads raw data from all process sensors by driving a sensor array composed of multiple excitation channels (TX) and multiple sensing channels (RX). In one specific implementation, the excitation and sensing channels can be conductive traces on a printed circuit board, arranged in a matrix layout. Each intersection point of an excitation channel and a sensing channel, or the adjacent area, forms an independent sensing node, and the capacitance value of this node is correlated with the specific process parameter being monitored (e.g., local temperature, pH value, or material concentration).

[0060] To ensure the periodicity and high real-time performance of data acquisition, a single complete scan operation of the data acquisition module can be triggered by a timer interrupt. For example, a timer with a period of one cycle (e.g., 10ms) can be configured. Whenever a timer interrupt occurs, the data acquisition module is activated, starting a new data acquisition cycle. Therefore, by using hardware interrupts to drive the process, the time uncertainty caused by software polling is avoided, thus ensuring that the system can acquire the instantaneous state of the process at a constant rate in real time.

[0061] Within a complete data acquisition cycle, the data acquisition module can enable (drive) multiple excitation channels one by one in a predetermined order. Specifically, at any given time, only one excitation channel is given an excitation signal. When a certain excitation channel, denoted as the , is activated... When the excitation channel is active, the data acquisition module will synchronously sense signals on all N sensing channels and read the output value of each sensing channel. This process will yield a set of values ​​related to the excitation channel. The raw data associated with each incentive channel.

[0062] Complete the first After reading all sensor channels under one excitation channel, the data acquisition module will immediately switch to the next excitation channel (the first one). The data acquisition module (M channels) is then scanned, and this reading process is repeated until all M excitation channels have been scanned. Therefore, the data acquisition module can efficiently obtain a snapshot of the entire sensor array's state at the current moment.

[0063] In this way, the data acquisition module can output a raw data matrix containing the states of all sensor nodes. Each element in this matrix represents the unprocessed digital measurement value output by the sensor at the intersection of the excitation channel and the sensing channel. This value directly reflects the magnitude of the physical capacitance of the node, and its change corresponds to the change of the monitored process parameter. This raw data matrix is ​​then completely transmitted to the subsequent processing module for further filtering and signal extraction.

[0064] The raw data matrix output by the data acquisition module is then transmitted to the processing module. The primary responsibility of the processing module is to convert this raw data into effective process parameter signals with clear physical meaning that can directly characterize changes in process parameters. This conversion forms the basis for subsequent state judgments and decisions.

[0065] Under stable conditions in the leucine enrichment process, i.e., when all process parameters are at their target setpoints, the raw data output by each process sensor will stabilize near a baseline level. This baseline data set is defined in this invention as a baseline data matrix. This baseline data matrix is ​​not a fixed constant, but is dynamically maintained by the noise monitoring module, which will be described in detail later, based on the system's operating status. It represents the system's "zero-point" reference when no process deviation events occur.

[0066] Therefore, the processing module extracts the deviation of the process parameters by performing differential operations. Specifically, for any node in the sensor array... Its effective process parameter signal matrix It can be calculated using the following formula:

[0067] ;

[0068] In the formula: This is an effective process parameter signal matrix, whose numerical values ​​directly correspond to the nodes. The degree of deviation of the monitored process parameters from the baseline state; Provided by the noise monitoring module, and the node The corresponding current baseline data matrix; The nodes read by the data acquisition module in this acquisition cycle The original data.

[0069] Therefore, through the above calculations, the processing module converts the absolute raw data readings into relative signal changes, which are close to zero. A zero value indicates that the process parameters at that point are stable at the baseline level; a non-zero value indicates that the process parameters have deviated from the baseline.

[0070] However, the effective process parameter signal matrix obtained through this difference operation Although static reference information has been removed, various noises from the industrial environment are still inevitably coupled into the signal matrix, such as common-mode interference caused by power supply fluctuations or the start-up and shutdown of large equipment. To ensure the accuracy and reliability of subsequent process status judgments, the processing module needs to perform further filtering on the signal matrix.

[0071] And on the effective process parameter signal matrix During further processing, the processing module performs common-mode filtering to address the issue that when a single excitation channel is driven, all associated sensing channels, in addition to sensing changes in their respective process parameters, are also highly susceptible to interference from common noise sources in the external environment, such as power supply ripple and electromagnetic field radiation in space. These interferences introduce noise signals with essentially the same amplitude and phase, i.e., common-mode noise components, into all sensing channels.

[0072] To eliminate common-mode noise components, the processing module independently executes a filtering algorithm for each row of the effective process parameter signal matrix (i.e., the dataset corresponding to the same excitation channel). The first step of this algorithm is to calculate the estimated value of the common-mode noise component in the current row. A specific and effective implementation is to calculate the arithmetic mean of all effective process parameter signals in that row; this average value is considered as the common-mode noise shared by all sensing channels under that excitation channel. Its mathematical expression is as follows:

[0073] ;

[0074] In the formula, Representative and the The common-mode noise component associated with each excitation channel;

[0075] This represents the total number of sensor channels;

[0076] The common-mode noise component of that row was calculated. Then, the processing module immediately executes the second step:

[0077] This involves subtracting this common noise component from each valid process parameter signal in that row. Through this operation, the signal at each sensing node is corrected, generating a signal that has undergone common-mode filtering. The expression is:

[0078] ;

[0079] in, That is, a node The signal is a purer, more effective process parameter signal after common-mode suppression.

[0080] Furthermore, the processing module repeats the above two steps for each row of the effective process parameter signal matrix, thus performing common-mode noise calculation and cancellation for the data of each excitation channel. Finally, after the entire matrix is ​​processed, a completely new signal matrix that has undergone common-mode filtering is generated. The matrix The data in this dataset eliminates common background noise interference, allowing the differential-mode signals representing local variations in actual process parameters to stand out, significantly improving the signal-to-noise ratio. Therefore, this purified signal matrix... This data will serve as a more reliable foundation and will be transmitted to the noise monitoring module and the judgment generation module for subsequent threshold judgment and process status analysis.

[0081] In one embodiment, to achieve accurate assessment of environmental noise, the noise monitoring module periodically monitors environmental noise. One specific implementation of this process involves inserting a specific quiet period between regular data acquisition cycles. During this quiet period, all excitation channels of the data acquisition module are not driven, i.e., no excitation signal is applied. Therefore, the noise monitoring module instructs the data acquisition module to scan all sensor channels one or more times; the data acquired during this process is pure background noise data, excluding process parameter response signals.

[0082] After acquiring the background noise data, the noise monitoring module quantifies it to calculate an index that can stably characterize the current ambient noise intensity, namely the noise level. One specific implementation involves calculating the standard deviation or peak-to-peak value of multiple sets of background noise data collected within a time window. To avoid drastic changes in noise levels due to instantaneous strong interference, this invention preferably uses the exponential moving average (EMA) algorithm to smooth the calculated instantaneous noise intensity. Its mathematical expression is as follows:

[0083] ;

[0084] in, It is the current moment. Noise level, It was the previous moment Noise level, It is the current moment. The standard deviation of the internal background noise data It is a smoothing coefficient between 0 and 1. Through this smoothing process, a noise level that changes gradually and can truly reflect the overall level of environmental noise can be obtained. .

[0085] To achieve a stable noise level The noise monitoring module can then dynamically adjust the measurement threshold based on this level. The mathematical formula for this dynamic adjustment process is as follows:

[0086] ;

[0087] In the formula: The dynamically adjusted measurement threshold will be used to determine whether the process parameter signal is valid. The preset initial reference threshold represents the noise level under ideal or reference noise conditions (i.e., The system's baseline sensitivity when the value is equal to 1; This refers to the current noise level calculated from the background noise data in the above steps. : This is a preset noise impact coefficient, which determines the sensitivity of the measurement threshold to changes in noise level. It is an empirical parameter that can be adjusted according to the specific process environment.

[0088] Through the calculation of this formula, the system can achieve adaptive adjustment of the measurement threshold. In a quiet environment, Approaching 1, making Approximately equal to The system maintains high sensitivity; in noisy environments, Increase, making This is correspondingly increased, effectively filtering out high-intensity noise interference and preventing misjudgments. Furthermore, this dynamically adjusted measurement threshold... It will then be provided to the determination and generation module as a dynamic reference for determining valid process parameter signals.

[0089] In addition to dynamically adjusting the measurement threshold to adapt to real-time environmental noise, the noise monitoring module is also used for adaptive updates of the reference data to solve the problem of long-term, slow signal drift caused by factors such as changes in ambient temperature and component aging of process sensors, thereby ensuring the long-term accuracy of effective process parameter signal extraction.

[0090] Specifically, the activation of this baseline data update mechanism depends on the assessment of the current process status. Specifically, the determination module will update the signal after common-mode filtering. With the dynamic measurement threshold provided by the noise monitoring module After comparison, if the signal value of any node in the entire sensor array exceeds the threshold, then the current process state is determined to be stable and without valid process parameter signals. Once the system is confirmed to be in this stable state, the noise monitoring module will trigger a check of the reference data matrix. The update operation. In one embodiment, the update employs a slow approximation approach to ensure that the reference value smoothly follows the long-term drift of the sensor while ignoring brief noise fluctuations that do not reach the trigger threshold. A preferred implementation uses a first-order infinite impulse response (IIR) low-pass filter, mathematically expressed as:

[0091] ;

[0092] In the formula, For the updated nodes Latest benchmark data; Before the update, the node The old baseline data; For the current collection period, the nodes The original data. The baseline update rate coefficient is a preset normal number that is much smaller than 1.

[0093] Therefore, this formula can be used to make minor adjustments to the baseline data based on the deviation between the current raw data and the current baseline data. (Baseline update rate coefficient) The value of directly determines the update rate. By setting it to a very small value (e.g., 0.01), it can be ensured that the magnitude of each update is very small, thus slowing down the update and allowing the reference data to effectively filter out high-frequency noise interference and only respond to persistent low-frequency changes that represent sensor drift.

[0094] By analyzing each node in the sensor array By repeating the above update calculations, the noise monitoring module eventually generates a completely new, adaptively updated baseline data matrix. At this point, this new reference matrix, which includes sensor drift compensation, will serve as the zero point (or baseline) reference for calculating effective process parameter signals in the next data acquisition and processing cycle, thereby ensuring the measurement accuracy and stability of the entire control system during long-term operation.

[0095] After the noise monitoring module provides dynamic measurement thresholds and adaptive reference data, the system's control flow enters the decision generation module.

[0096] Specifically, the determination and generation module receives a signal matrix from the processing module that has undergone common-mode filtering. In conjunction with the dynamic measurement threshold provided by the noise monitoring module The core task of performing substantive judgments on process status is to transform discrete sensor signal points into structured process parameter deviation events with clear physical meaning.

[0097] The determination and generation module first processes the signal matrix. Each element in the process is examined one by one. Specifically, for any given node... The generation module will compare its signal values. With the current dynamic measurement threshold If and only if Greater than Only when the node is identified as a valid signal point and marked is the node identified as such.

[0098] During a complete scan cycle, a localized change in a process parameter, such as the addition of an acid or alkali solution, can affect multiple adjacent process sensors, creating a connected region composed of multiple adjacent, marked as valid signal points. The decision generation module then aggregates these spatially adjacent or connected valid signal points, identifying and categorizing them as independent, complete process parameter deviation events.

[0099] For each identified process parameter deviation event, to determine its core location and intensity, the decision generation module executes a weighted coordinate positioning algorithm. Since the center of a deviation event should be more inclined towards sensor nodes with stronger signal responses, the weighted coordinate positioning algorithm uses the signal strength of each valid signal point as its weight when calculating the center coordinates. The specific mathematical expression of this algorithm is as follows:

[0100] ;

[0101] ;

[0102] In the formula, and The X-axis and Y-axis center coordinates of the process parameter deviation event are calculated. Due to the weighted average calculation characteristics, these coordinates are continuous floating-point values. This represents the set of all valid signal points that constitute the process parameter deviation event. For set The index node of the valid signal points in the data; , For nodes Preset physical or logical coordinates in the sensor array; For nodes The effective process parameter signal values ​​at the location, after common-mode filtering, are used as the weights in the calculation.

[0103] The center coordinates obtained by the above algorithm ( , ), the coordinate value ( , This can be identified by mapping the deviation to the process parameters; for example, a coordinate in a specific region might represent a change in pH, while falling into another region might represent a change in temperature. Secondly, this coordinate can be further correlated with the total signal strength of the event (i.e.,...). These factors, combined, are used to characterize the deviation of the process parameter. Thus, the decision generation module can transform a series of disordered signal data points into a structured event with location and quantization intensity, providing a data foundation for subsequent generation of control commands.

[0104] After transforming discrete signal points into structured process parameter deviation events using a weighted coordinate positioning algorithm, the decision generation module further needs to address situations where multiple process parameter deviation events may occur simultaneously in the leucine enrichment process. For example, during the feeding process, localized changes in concentration and temperature may occur simultaneously, requiring the system to accurately distinguish and track these concurrent events.

[0105] To distinguish and manage the lifecycle of multiple concurrent and independent process parameter deviation events, this invention introduces an event ID allocation and continuous tracking mechanism. This mechanism determines the continuity of events by comparing the distance between the center coordinates of each event in the current acquisition cycle and the previous acquisition cycle, thereby enabling stable inheritance and updating of event IDs.

[0106] Specifically, the determination and generation module will maintain the previous collection cycle. A list of valid events, containing a unique event ID and its center coordinates for each event. During the current collection period ( After identifying a new set of process parameter deviation events, the decision generation module calculates the spatial distance between each new event in the current cycle and each old event in the previous cycle's list. The preferred distance calculation method is Euclidean distance, mathematically expressed as:

[0107] ;

[0108] In the formula, For the current cycle number The event is related to the previous cycle. Euclidean distance between the center coordinates of the events; , Current collection period In the middle, the first A process parameter deviates from the center coordinates of the event; , For the previous collection cycle In the middle, the first The process parameter deviates from the center coordinates of the event.

[0109] The system uses this distance calculation to execute matching logic to update the event ID. Specifically, for a specific event in the current period... The system will search for events that are close to it in the previous cycle's event list. Minimum event If the minimum distance is less than a preset tracking distance threshold, and the event... If no other event has been matched, then the event is determined. It is an event The continuation of the event Inheritance event A unique event ID is used to enable updates through inheritance.

[0110] If an event in the current cycle cannot be matched with any other event in the previous cycle whose distance is less than the tracking distance threshold, then the event is determined to be a newly emerging process parameter deviation event. In this case, the determination generation module will assign a brand new, unused, unique event ID to it from the ID pool.

[0111] Conversely, if an event ID from the previous period does not match any event in the current period, that event ID will be placed in a brief observation state. If the ID continues to fail to match in the following several consecutive acquisition periods, the system confirms that the process parameter deviation event has ended, and its ID can be recycled to the ID pool for later use.

[0112] Therefore, through this ID allocation and continuous tracking mechanism, the decision generation module can stably and continuously track multiple concurrent, moving, or intensity-changing process parameter deviation events in a dynamically changing environment. This, in turn, provides a historically continuous, structured event data stream for the subsequent multi-parameter control priority allocation module.

[0113] After continuously tracking concurrent process parameter deviation events, the decision generation module transforms these identified, quantified, and continuously tracked events into specific, executable initial control commands. This allows the module to combine effective process parameter signals, adjusted measurement thresholds, and updated baseline data to determine the process status, calculate the parameters and target values ​​to be adjusted based on the process status, and generate control commands.

[0114] For each process parameter deviation event that has been assigned a unique event ID and continuously tracked, the determination generation module first determines its center coordinates ( , The module can determine the specific process parameter type corresponding to the event by consulting a preset parameter region mapping table, such as pH value, temperature, or concentration of a specific component. Simultaneously, the module will record the signal values ​​of all valid signal points constituting the event. By summing the results, we obtain a quantitative indicator that represents the overall deviation of the event. This indicator This refers to the process deviation at the current moment.

[0115] After determining the process parameters that need to be adjusted and their deviation ranges, the decision generation module can employ a closed-loop control algorithm to calculate the precise control target value. A preferred embodiment is a proportional-integral-derivative (PID) control algorithm, which allows the generation of control commands to consider not only the current deviation but also historical cumulative deviations and future trends, thus making the control more precise and stable. Its mathematical expression is as follows:

[0116] ;

[0117] In the formula: The control output value calculated at the current moment will be used directly or converted into the target value in the control command through mapping. Current collection period In this context, the process deviation of the event is determined by the overall deviation magnitude of the event; For the previous collection cycle In the process, the process deviation of the same event is tracked through the event ID; , , These are the proportional, integral, and derivative control coefficients, whose values ​​can be preset according to the characteristics of the specific process object. The system control cycle is the time interval between two data acquisitions.

[0118] Among them, the proportional term Responding to the current deviation; integral term Indicates the time since the system started running (time). From the current time (time) ), process deviation for each acquisition cycle The summation reflects the total historical deviations; the differential term By comparing the changes in deviation of the same event in two consecutive periods, the trend of change can be predicted, thereby suppressing overshoot.

[0119] After calculating the control output quantity Subsequently, the determination generation module combines the event with the process parameter type corresponding to the event to ultimately form a structured initial control instruction. This instruction includes at least the event ID, the target actuator (e.g., a metering pump associated with pH adjustment), the control action (e.g., turning on or adjusting the flow rate), and the parameters specified by the event. The specific target value is determined (e.g., start for 5 seconds or set the flow rate to 10 mL / min). These generated initial control commands, along with their event IDs, are sent to the multi-parameter control priority allocation module for subsequent execution order decisions.

[0120] In determining the set of one or more initial control commands output by the generation module, since there are conflicts between the actions of different actuators when multiple control commands are executed concurrently, in order to ensure that critical or urgent controls are executed first, the control priority allocation module needs to allocate priority to the multiple sets of initial control commands.

[0121] Priority settings must follow clear rules. First, instructions based on safety requirements have the highest execution priority. For example, emergency cooling or shutdown instructions used to address reactor overheating are set to the highest priority (e.g., priority 1) to ensure that system safety is guaranteed immediately under any circumstances.

[0122] Secondly, instructions based on preset process logic can be used. For example, in the leucine enrichment process, the pH value usually needs to be adjusted to a specific range before adding nutrient solution to ensure absorption efficiency. Therefore, the control instructions of the metering pump for adjusting the pH value will be set to have a higher priority than the control instructions of the metering pump for adding nutrient solution (for example, pH adjustment is priority 2, and feeding is priority 3).

[0123] After assigning priority values ​​to all concurrent initial control commands, if two or more commands have the same priority, the control priority allocation module can apply secondary sorting rules. A preferred implementation is to compare the deviation magnitudes of the process parameter deviation events corresponding to these commands with the same priority. Events with larger deviations will have their corresponding control commands assigned a higher temporary execution order to prioritize handling process conditions with more severe deviations.

[0124] Therefore, through the aforementioned priority allocation and sorting, the priority allocation module can divide the unordered initial set of control instructions into an ordered instruction execution queue. In this queue, the highest priority instruction is placed first, and the lowest priority instruction is placed last, and then passed to the instruction sending module. This ensures that all control actions are executed sequentially in the most reasonable and safest order. Furthermore, it can also regulate other actuators (such as a mixer) to speed up or slow down the mixing rate.

[0125] Finally, the instruction sending module receives the ordered instruction execution queue generated by the multi-parameter control priority allocation module, and then transforms the control instructions at the logical level into control behaviors that have a real effect on the physical world.

[0126] Specifically, the instruction sending module retrieves control instructions one by one from the first position of the instruction execution queue, processes them according to their order, and converts them into physical electrical signals to drive the corresponding actuators. The specific implementation method can vary depending on the interface type of the target actuator. For example, if the instruction target is to control a heater connected via a relay, the instruction sending module will locate the general purpose input / output (GPIO) pin that controls the relay. Subsequently, based on the on or off action in the instruction, the GPIO pin is set to a high or low level, thereby controlling the relay's on / off state and heating or stopping the reaction system.

[0127] For example, if the instruction target is to adjust the flow rate of a metering pump that supports digital communication, this metering pump is connected to the system via an internal integrated circuit (IIC) bus or a serial peripheral interface (SPI) bus. The instruction sending module packages the target value (such as the flow rate value) in the instruction into a complete data frame according to the IIC or SPI protocol. This data frame contains the slave device address, the target register address, and the data to be written. Subsequently, the module drives the corresponding communication bus controller to send the data frame to the metering pump to complete the precise setting of the flow rate.

[0128] After sending one instruction, the instruction sending module can process the next instruction in the queue until the entire instruction execution queue is exhausted. This completes the closed-loop control from data acquisition, through processing, judgment, priority allocation, to the final instruction sending. This enables rapid response and effective control of various parameter deviations throughout the leucine enrichment process.

[0129] Please see the appendix Figure 3 This invention provides a method for controlling the enrichment process of leucine, the method comprising:

[0130] Step 1: Read the raw data from the process sensors

[0131] First, each excitation channel is activated sequentially using a time-division multiplexing method. When a particular excitation channel is activated, signal sensing is performed synchronously on all sensing channels, and the output values ​​of all sensing channels are read. By scanning all excitation channels one by one, the state of the entire sensor array at the current moment is captured, and these unprocessed measurements are aggregated into a complete raw data matrix.

[0132] Step 2: Filter the raw data to obtain effective process parameter signals.

[0133] First, the acquired raw data matrix is ​​differentially analyzed with the baseline data matrix, and the absolute value of the difference is taken to obtain a preliminary signal matrix representing the deviation of process parameters. Then, common-mode filtering is performed on each row of this signal matrix (i.e., the data corresponding to the same excitation channel). Specifically, the average value of each row's signal is calculated as an estimate of the common-mode noise, and this average value is then subtracted from each signal in that row. After this processing, a more effective process parameter signal with a higher signal-to-noise ratio and better representing the actual local variations in the process is obtained.

[0134] Step 3: Dynamically adjust the measurement threshold and update the baseline data based on the ambient noise level.

[0135] First, utilizing the silent intervals between regular acquisition cycles, the sensor is scanned without driving any excitation channels to measure the pure background noise intensity. Based on the measured noise level, the measurement threshold used to determine signal validity is adjusted. In noisy environments, the threshold is increased to prevent false positives; in quiet environments, the threshold is decreased to maintain high sensitivity. This update is triggered when the current process state is determined to be stable without any significant parameter deviations. Then, through a slow approximation algorithm, the currently acquired raw data is gradually and incrementally incorporated into the reference data to compensate for long-term signal drift caused by factors such as sensor aging and temperature changes, ensuring that the zero-point reference for differential operations remains accurate.

[0136] Step 4: Determine the process status and generate control instructions.

[0137] First, the effective process parameter signals obtained in the second step are compared with the dynamically measured thresholds adjusted in the third step. When the signal strength exceeds the threshold, the signal point is identified as a valid deviation point. Then, spatially adjacent valid deviation points are combined into a complete process parameter deviation event. For each event, a weighted coordinate positioning algorithm is used, with signal strength as the weight, to calculate the center coordinates of the event. These center coordinates not only identify which process parameter (such as temperature or pH) has deviated, but are also related to the deviation magnitude. Furthermore, to manage and track multiple concurrent deviation events simultaneously (e.g., feed causing changes in concentration and temperature), a unique event ID is assigned to each newly occurring event. The continuity of events is determined by comparing the distance between the event center coordinates between consecutive control cycles, thus achieving stable inheritance and tracking of each event ID. Finally, for each tracked event, based on the type of process parameter it represents, the magnitude of the deviation, and the trend of change, a closed-loop control algorithm (such as PID) is used to calculate the parameter amount to be adjusted and the target value, ultimately generating an initial control command containing the actuator, control action, and target value.

[0138] Step 5: Send control commands to the actuators to complete one control cycle.

[0139] Before sending instructions to the actuators, the multiple initial control instructions generated in the previous step, which may occur concurrently, are prioritized. The prioritization is based first on preset safety requirements, ensuring that instructions such as emergency shutdowns have the highest priority; secondly, on preset process logic, such as ensuring pH adjustment before material addition; and for instructions with the same priority, those with larger deviations are processed first. After prioritization, an ordered instruction execution queue is obtained. Instructions are then retrieved one by one according to the queue's order, translated into electrical signals recognizable by the corresponding actuators (such as heaters, metering pumps, solenoid valves, and stirrers), and sent through the appropriate communication interface to drive the actuators to complete the corresponding physical operations. Once all instructions in the queue have been sent and executed, a complete closed-loop control cycle is completed, and the next control cycle begins, repeating continuously to achieve real-time, adaptive, and intelligent control of the entire leucine enrichment process.

Claims

1. A control system for leucine enrichment process, characterized in that, include: The data acquisition module is used to cyclically read raw data from process sensors by driving multiple excitation channels and multiple sensing channels; The processing module is used to filter the raw data to obtain effective process parameter signals after common-mode filtering. The noise monitoring module is used to dynamically adjust the measurement threshold according to the environmental noise conditions and update the reference data to obtain the adjusted measurement threshold and the updated reference data. The determination and generation module is used to combine the effective process parameter signals after common-mode filtering, the adjusted measurement threshold, and the updated reference data to determine the process state of leucine enrichment, calculate the parameters to be adjusted and the target values ​​based on the process state, and generate control instructions. The instruction sending module is used to send control instructions to the actuator to complete one control cycle.

2. The leucine enrichment process control system according to claim 1, characterized in that, The process sensors include a temperature sensor, a concentration sensor, a pH sensor, an online concentration detector, and a flow meter; the actuators include a heating device, a cooling device, a metering pump, a solenoid valve, and a mixer for mixing materials, wherein the heating device and the cooling device are used to regulate the process temperature, the metering pump is used to add acid or alkali solutions or nutrient solutions, the solenoid valve is used to control the flow of fluid pipelines, and the mixer is used to mix materials.

3. The leucine enrichment process control system according to claim 1, characterized in that, The processing module is specifically used for: S3-1, Based on the original data difference calculation formula, the original data and the dynamically updated reference data are differencing each other, and the absolute value is taken to extract the effective process parameter signal characterizing the change of process parameters. The original data difference calculation formula is as follows: ; In the formula: This is a matrix of effective process parameter signals, where the magnitude of the effective process parameter signals directly corresponds to the monitoring nodes. The degree of deviation between the process parameters and the dynamically updated baseline data; Provided by the noise monitoring module and the node The corresponding updated matrix of baseline data; The nodes read by the data acquisition module in this acquisition cycle The original data; S3-2, Based on the common-mode noise component formula, calculate the common-mode noise component of the signals of all sensing channels associated with the same excitation channel, wherein the common-mode noise component formula is: ； In the formula, In order to be with the first The common-mode noise components associated with each excitation channel, This refers to the total number of sensing channels corresponding to the process sensors. and All are positive numbers equal to or greater than.

4. The leucine enrichment process control system according to claim 3, characterized in that, The processing module is also used for: Based on the common-mode noise component For the same excitation channel, the effective process parameter signal Common-mode filtering is performed to correct the signal at each node and obtain the effective process parameter signal after common-mode filtering. Among them, the effective process parameter signal after common-mode filtering. .

5. The leucine enrichment process control system according to claim 1, characterized in that, The noise monitoring module is also used to scan the process sensor under no-excitation signal conditions, acquire background noise data, and determine the current noise level of the process sensor based on the background noise data. The adjusted measurement threshold used to determine the process status is dynamically adjusted based on the background noise data and the noise level calculation formula; wherein... The formula for calculating the noise level is: ; In the formula: The adjusted measurement threshold is used to determine whether the process parameter signal is valid. Specifically, it includes: using the adjusted measurement threshold as a judgment benchmark, and when the value of the valid process parameter signal is greater than the judgment benchmark, it is confirmed as a valid process deviation signal. This represents the current noise level of the process sensor. The preset initial reference threshold characterizes the noise level. The system's baseline sensitivity when the value equals 1; A preset noise impact coefficient, which is adjusted according to the specific process environment, characterizes the sensitivity of the measurement threshold to changes in noise level.

6. The leucine enrichment process control system according to claim 1, characterized in that, The noise monitoring module is also used for: When the process state is determined to be stable and there are no effective process parameter signals, the reference data is adaptively updated based on the deviation between the original data and the current reference data, according to the reference data adaptive update formula, to compensate for the drift of the process sensor. The reference data adaptive update formula is as follows: ; In the formula, For nodes The latest baseline data after adaptive updates; For nodes Old baseline data before the update; For nodes within the current collection period The original data, As the baseline update rate coefficient, The value is a preset positive integer that is much smaller than 1.

7. The leucine enrichment process control system according to claim 1, characterized in that, The determination generation module is specifically used for: Effective process parameter signals With the adjusted measurement threshold Perform comparisons one by one, and based on the comparison results, identify spatially connected elements that exceed the adjusted measurement threshold. The node corresponding to the valid process parameter signal is identified as an independent process parameter deviation event; A weighted coordinate positioning algorithm is used to calculate one or more valid process parameter signals in the process parameter deviation event to determine the center coordinates of the process parameter deviation event. , The center coordinates are used to characterize the process parameter that has deviated and the magnitude of the deviation. ； ； In the formula, and These are the X-axis and Y-axis coordinates of the center coordinates of the process parameter deviation event, respectively. and All are continuous floating-point values; The set of all valid signal points that constitute the process parameter deviation event; For set The index node of the valid signal points in the data; , For nodes Preset physical or logical coordinates in the sensor array; For nodes The effective process parameter signal value at the location, after common-mode filtering.

8. The leucine enrichment process control system according to claim 7, characterized in that, When multiple process parameter deviation events exist simultaneously, a unique event ID is assigned to each process parameter deviation event; and the event ID is updated by comparing the distance between the center coordinates of each event in the current acquisition cycle and the previous acquisition cycle, so as to continuously track multiple concurrent process parameter deviation events.

9. The leucine enrichment process control system according to claim 1, characterized in that, The system also includes a multi-parameter control priority allocation module, which is used to allocate execution priorities to the control instructions corresponding to the multiple process parameter deviation events according to preset process logic and safety requirements when the judgment generation module determines that there are multiple process parameter deviation events, so as to determine the order in which the instruction sending module sends the control instructions. The instruction sending module is also used to retrieve control instructions one by one from the instruction execution queue in sequence, translate the control instructions into electrical signals that the corresponding actuators can recognize and send them to drive the actuators of the heating device, cooling device, metering pump, solenoid valve and stirrer to operate, thereby completing the closed-loop control of process parameters.

10. A method for controlling a leucine enrichment process based on a leucine enrichment process control system according to any one of claims 1-9, characterized in that, The method includes the following steps: Based on multiple excitation channels and multiple sensing channels, the raw data of the process sensors are read. The original data is filtered to obtain the effective process parameter signal after common-mode filtering. The measurement threshold is dynamically adjusted according to the environmental noise level, and the baseline data is updated to obtain the adjusted measurement threshold and the updated baseline data. By combining the effective process parameter signals after common-mode filtering, the adjusted measurement threshold, and the updated baseline data, the process state of leucine enrichment is determined, and the parameters to be adjusted and the target values ​​are calculated based on the process state, and control instructions are generated. Send control instructions to the implementing agency to complete one control cycle.

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