Electrolytic manganese hydrogen-chlor-alkali chemical coupling system planning and design verification method

Abstract

The present application relates to the technical field of chemical system optimization, and discloses a kind of electrolytic manganese hydrogen production-chlor-alkali chemical coupling system planning design verification method.The method obtains the current density of electrolytic manganese hydrogen production unit, tank voltage and other first real-time operating parameters, and the second real-time operating parameters such as alkali concentration, hydrogen purity of chlor-alkali chemical unit;Matching operating section matching current hydrogen fluctuation characteristics is screened from historical coupling operation data;Determine the coupling response action point by analyzing the tank voltage change on the time sequence of matching operating section, calculate the coupling delay from the starting point to the action point;Then compare the change trend of current density and hydrogen purity data sequence before and after the coupling response action point, determine the parameter matching degree;Determine the coupling weight coefficient by combining coupling delay, parameter matching degree and fluctuation characteristic difference;The dynamic efficiency coefficient generated is input into system verification controller with the first real-time operating parameter, and output system adjustment instruction, accurate planning verification of coupling system can be realized.

Description

Technical Field

[0001] This invention relates to the field of chemical system optimization technology, specifically to a planning, design and verification method for an electrolytic manganese hydrogen production-chlor-alkali chemical coupled system. Background Technology

[0002] In the chemical production field, both electrolytic manganese production and chlor-alkali chemical processing are important basic chemical processes. Both involve energy conversion and metabolism during operation, making coupled operation potentially feasible. Electrolytic manganese production generates hydrogen. If this hydrogen can be rationally integrated into the chlor-alkali chemical system, it can achieve cascaded resource utilization, reduce overall production energy consumption, and align with the current trend of green and low-carbon development in the chemical industry. However, the operating characteristics of the electrolytic manganese hydrogen production unit and the chlor-alkali chemical unit differ significantly. The current density of the electrolytic manganese hydrogen production unit is easily affected by factors such as raw material purity and electrolytic cell temperature, leading to unstable cell voltage data. In the chlor-alkali chemical unit, the alkali concentration is closely related to the brine purification effect and the stability of the electrolytic current, while hydrogen purity is directly constrained by multiple factors such as impurity content and system sealing. These dynamic changes in parameters pose numerous challenges to the coupled operation of the two units. The planning and design of coupled systems for electrolytic manganese hydrogen production and chlor-alkali chemical production often rely on empirical parameter settings, lacking dynamic response and precise analysis of real-time operating data. In actual operation, the failure to promptly acquire and integrate real-time operating parameters from both units (such as current density and cell voltage in the electrolytic manganese hydrogen production unit, and alkali concentration and hydrogen purity in the chlor-alkali chemical unit) makes it difficult to accurately determine the coupled operating status of the system. Furthermore, existing technologies do not consider operating segments in historical coupled operating data that match the hydrogen production fluctuation characteristics of the current stage, making it impossible to extract effective coupled operating patterns from historical data, resulting in a lag in the judgment of system coupled response. In addition, when evaluating the operating efficiency of the coupled system, the failure to construct reasonable weighting coefficients by combining key indicators such as coupling delay and parameter matching degree makes it difficult to generate dynamic efficiency coefficients that truly reflect the current operating stage. This results in a lack of scientific basis for the output of system adjustment commands, thus affecting the operational stability and resource utilization efficiency of the coupled system. These problems prevent the coupled system for electrolytic manganese hydrogen production and chlor-alkali chemical production from fully leveraging its resource integration advantages, limiting the achievement of energy conservation and emission reduction targets in chemical production processes. Summary of the Invention

[0003] The purpose of this invention is to provide a planning, design and verification method for an electrolytic manganese hydrogen production-chlor-alkali chemical coupled system, so as to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, this invention provides a planning, design, and verification method for an electrolytic manganese hydrogen production-chlor-alkali chemical coupled system, the method comprising: The first real-time operating parameters of the electrolytic manganese hydrogen production unit and the second real-time operating parameters of the chlor-alkali chemical unit are obtained. The first real-time operating parameters include current density and cell voltage data, and the second real-time operating parameters include alkali concentration and hydrogen purity data. Select matching operating segments from historical coupled operation data that match the hydrogen production fluctuation characteristics of the current operating phase; Analyze the changes in slot pressure data in the timing sequence of the matched operation segment to determine the coupling response point of the matched operation segment; use the time difference between the start point of each matched operation segment and the coupling response point as the coupling delay of each matched operation segment. By comparing the changing trends of the current density data sequence before the coupling response point with the hydrogen purity data sequence after the coupling response point, the parameter matching degree of each matching operation segment is determined. The coupling weight coefficient of the matching operation segment is determined by combining the coupling delay, the parameter matching degree, and the difference in hydrogen production fluctuation characteristics between the matching operation segment and the current operation stage. The electrolysis efficiency coefficients of all matching operating segments corresponding to the current operating stage are weighted by the coupling weight coefficient to generate the dynamic efficiency coefficient of the current operating stage; the dynamic efficiency coefficient and the first real-time operating parameter are input into the system verification controller, and the system verification controller outputs the adjustment command of the coupling system.

[0005] Preferably, the step of selecting matching operating segments from historical coupled operating data that match the hydrogen production fluctuation characteristics of the current operating phase includes: Based on the system fluctuation state, the affected coupling sections are identified from the parameter sequence of the historical coupled operation data; The parameter sequence of the historical coupled operation data is divided into multiple continuous operation segments, and the hydrogen production fluctuation intensity characteristics of each continuous operation segment are extracted. From the affected coupling sections, a matching operating section that matches the hydrogen production fluctuation intensity characteristics of the current operating phase is selected.

[0006] Preferably, the step of identifying the affected coupling segments from the parameter sequence of the historical coupled operation data based on system fluctuation states includes: Calculate the gradient value of change corresponding to each data point in the parameter sequence of the historical coupled operation data; The data point where the maximum gradient value first appears is used as the splitting node; Using the segmentation node as the boundary, the parameter sequence is divided into a preceding segment and a subsequent segment, and the average change gradient of the preceding segment and the subsequent segment are calculated respectively. The segment with the largest average change gradient is taken as the segment affected by coupling.

[0007] Preferably, the step of selecting matching operating segments from the affected coupling segments that match the hydrogen production fluctuation intensity characteristics of the current operating phase includes: The difference between the hydrogen production fluctuation intensity characteristics of each continuous operating segment in the coupled affected segment and the hydrogen production fluctuation intensity characteristics of the current operating stage is negatively correlated and mapped to obtain a fluctuation similarity index. Based on the fluctuation similarity index, a matching running segment is determined from the coupling-affected segment.

[0008] Preferably, the step of analyzing the time-series changes in the slot pressure data of the matched operating segment and determining the coupling response action point of the matched operating segment includes: For any matching running segment, the timing node where the slot pressure data undergoes a jump is taken as the point of action of the coupling response.

[0009] Preferably, the step of comparing the changing trends of the current density data sequence before the coupling response point with the hydrogen purity data sequence after the coupling response point to determine the parameter matching degree of each matching operation segment includes: Calculate the average gradient change of adjacent data points in the current density data sequence before the point of action of the coupling response, and use it as the benchmark for current density change; The average gradient change of adjacent data points in the hydrogen purity data sequence after the point of action of the coupling response is calculated as the benchmark for hydrogen purity change. The absolute difference between the current density change benchmark and the hydrogen purity change benchmark is negatively correlated to generate the parameter matching degree.

[0010] Preferably, determining the coupling weight coefficient of the matching operating segment by combining the coupling delay, the parameter matching degree, and the difference in hydrogen production fluctuation characteristics between the matching operating segment and the current operating stage includes: The coordinate points of each matching running segment are generated by using the electrolysis efficiency coefficient of the coupling response point of each matching running segment as the abscissa and the coupling delay as the ordinate. Curve fitting is performed on the coordinate points of all matching segments in the current running phase to generate a fitted trajectory line; Calculate the residual between the coordinates of each matched running segment and the fitted trajectory line; Using the parameter matching degree as the numerator and the product of the coupling delay and the residual as the denominator, the ratio is calculated as an indicator of the effectiveness of the matching operation segment. The coupling weight coefficient is determined based on the effectiveness index and the difference in hydrogen production fluctuation characteristics; wherein the effectiveness index is positively correlated with the coupling weight coefficient, and the difference in hydrogen production fluctuation characteristics is negatively correlated with the coupling weight coefficient.

[0011] Preferably, the step of weighting the electrolysis efficiency coefficients of all matching operating segments corresponding to the current operating stage using the coupling weight coefficient to generate the dynamic efficiency coefficient of the current operating stage includes: The coupling weight coefficients of all matching running segments are normalized so that the sum of the normalized coupling weight coefficients is 1. The normalized coupling weight coefficient is multiplied by the electrolysis efficiency coefficient of the corresponding matching running segment and then summed to output the dynamic efficiency coefficient.

[0012] Preferably, after the system verification controller outputs the adjustment command for the coupled system, it further includes: Based on the real-time load status of the chlor-alkali chemical unit and the adjustment instructions, a pulse modulation signal sequence is generated; Based on the direction and amount of change of the value of the adjustment instruction, the duty cycle parameter of the pulse modulation signal sequence is adjusted; Based on the voltage waveform characteristics of the adjustment command, the trigger phase timing of the pulse modulation signal sequence is corrected.

[0013] Preferably, the step of correcting the trigger phase timing of the pulse modulation signal sequence according to the voltage waveform characteristics of the adjustment command includes: Identify the starting point of the voltage rising edge and the starting point of the falling edge in the adjustment command; Calculate the phase offset of each pulse unit in the pulse modulation signal sequence relative to the starting point of the rising edge and the starting point of the falling edge of the voltage; The initial trigger time of each pulse unit is adjusted according to the phase offset to generate a phase-synchronized modulation signal sequence.

[0014] Compared with the prior art, the beneficial effects of the present invention are: This method for planning, designing, and verifying a coupled system of electrolytic manganese hydrogen production and chlor-alkali chemical processing provides fundamental data support for the dynamic analysis of the coupled system by acquiring real-time operating parameters of the electrolytic manganese hydrogen production unit and the chlor-alkali chemical processing unit. The first real-time operating parameter, including current density and cell voltage data, directly reflects the energy conversion efficiency and operational stability of the electrolytic manganese hydrogen production unit. The second real-time operating parameter, including alkali concentration and hydrogen purity data, accurately reflects the metabolic state and product quality level of the chlor-alkali chemical processing unit. The combination of these two parameters makes the judgment of the coupled system's operating status more comprehensive and accurate, avoiding judgment biases caused by missing data or analysis of a single parameter. By selecting matching operating segments from historical coupled operation data that correspond to the hydrogen production fluctuation characteristics of the current operating phase, the coupled operation patterns contained in the historical data can be fully explored, providing a reference for the current system operation analysis. By analyzing the changes in cell pressure data over time during the matching operating segments to determine the point of action of the coupled response, and using the time difference from the starting point to the point of action of the coupled response as the coupling delay, the response timeliness of the two units during the coupling process can be clearly defined. This clarifies the time characteristics of system coupling under different operating phases, helps to deepen the understanding of the dynamic operating mechanism of the coupled system, and avoids problems of untimely system adjustments due to insufficient understanding of the coupling delay characteristics. By comparing the trends of current density data sequences and hydrogen purity data sequences before and after the coupling response point, the parameter matching degree can be determined. This quantifies the correlation between key operating parameters of the two units, assesses the impact of operational fluctuations in the electrolytic manganese hydrogen production unit on the hydrogen purity of the chlor-alkali chemical unit, and identifies the advantages and disadvantages of parameter synergy in the coupled system, providing clear guidance for subsequent system optimization. Combining coupling delay, parameter matching degree, and the differences in hydrogen production fluctuation characteristics between the matched operating segment and the current operating stage, the coupling weight coefficient is determined. This comprehensively considers multiple factors affecting the operation of the coupled system, ensuring that the weight coefficient accurately reflects the reference value of different matched operating segments for the current system, avoiding evaluation bias caused by a single factor dominating weight setting. By weighting the electrolysis efficiency coefficients of all matching operating segments in the current operating phase using coupling weighting coefficients, a dynamic efficiency coefficient for the current operating phase is generated. This allows for real-time updates to the system's operational efficiency assessment, ensuring the efficiency coefficient closely matches the actual operating state of the system. Inputting the dynamic efficiency coefficient and the first real-time operating parameters into the system verification controller to output adjustment commands enables precise control of the coupled system. This ensures the system can adjust operating parameters promptly based on real-time operating status and historical patterns, optimizing the coupling relationship between the two units, improving overall operational stability and resource utilization efficiency, promoting the synergistic development of electrolytic manganese hydrogen production and chlor-alkali chemical processes, and helping chemical production processes move towards a greener and more efficient direction. Attached Figure Description

[0015] Figure 1 This is a schematic diagram illustrating the working principle of a planning, design, and verification method for an electrolytic manganese hydrogen production-chlorine-alkali chemical coupling system as described in this invention. Figure 2 A flowchart for matching the running segment filtering; Figure 3 A flowchart for screening matching running segments within the affected area; Figure 4 A flowchart for determining parameter matching degree. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. 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.

[0017] Please see Figure 1 This invention provides a planning, design, and verification method for an electrolytic manganese hydrogen production-chlor-alkali chemical coupled system, the method comprising: The system continuously acquires the first real-time operating parameters of the electrolytic manganese hydrogen production unit and the second real-time operating parameters of the chlor-alkali chemical unit. The first real-time operating parameters include current density and cell voltage data, while the second real-time operating parameters include alkali concentration and hydrogen purity data. The system filters out matching operating segments from stored historical coupled operating data that match the hydrogen production fluctuation characteristics of the current operating stage. For each matching operating segment, the system analyzes the temporal changes in cell voltage data to determine the coupling response point of that segment, and uses the time difference from the starting point to the coupling response point as the coupling delay. Furthermore, by comparing the changing trends of the current density data sequence before the coupling response point with the hydrogen purity data sequence after the coupling response point, the parameter matching degree of each matching operating segment is determined. Combining the coupling delay, parameter matching degree, and the difference in hydrogen production fluctuation characteristics between the matching operating segment and the current operating stage, a coupling weight coefficient for each matching operating segment is determined. Using this coupling weight coefficient, the electrolysis efficiency coefficients of all matching operating segments corresponding to the current operating stage are weighted and calculated to generate the dynamic efficiency coefficient for the current operating stage. Finally, the dynamic efficiency coefficient and the first real-time operating parameters are input into the system verification controller, which then outputs adjustment commands for the coupled system to achieve optimized verification of the system's operating status.

[0018] Example 1: See Figure 2During the operation of the electrolytic manganese hydrogen production-chlor-alkali chemical coupled system, the system continuously records and stores a large amount of historical coupled operation data, which constitutes a time-series database containing multi-dimensional parameters. When the system enters a new operating phase and specific fluctuation characteristics are observed in the hydrogen production unit, it is necessary to search for similar operating segments, i.e., matching operating segments, from the historical database for subsequent prediction and verification. The implementation process begins with the assessment of the system's fluctuation state in the historical data. The system fluctuation state is not a single indicator, but is identified by comprehensively analyzing the synergistic change patterns of current density, cell voltage, alkali concentration, and hydrogen purity in the parameter series. This analysis aims to locate those segments in the history where parameters exhibit significant non-stationarity or drastic changes. These segments usually indicate periods of active or sensitive coupling interactions, and their data contain valuable information on how the system responds to internal and external disturbances.

[0019] The specific operation of identifying coupling-affected sections involves point-by-point scanning and gradient calculation of the historical parameter sequence. The system calculates the rate of change of each data point relative to the previous data point, thereby generating a gradient sequence describing the abruptness of parameter changes. A typical example is that, within a certain historical time period, current density data may show a steep upward peak in a short period, followed by a lagging but corresponding rise in cell pressure data, while hydrogen purity data may show a dip that first slightly decreases and then recovers. In this example, the gradient peak of the current density change will be marked. The system will use this gradient peak as a dividing point to divide the entire time series into two parts. The system will calculate the average gradient values ​​of all gradient values ​​in each part separately. Typically, the part containing this abrupt change point will have a significantly higher average gradient than the other part; this part of the sequence is therefore identified as the coupling-affected section because it most likely records the dynamic process of the system transitioning from one steady state to another.

[0020] After identifying these sensitive segments in the historical data, the system does not treat them as a whole, but further subdivides them into shorter and continuous operating segments. The duration of each operating segment is pre-set, such as a five-minute or ten-minute data window. For each such short period, the system extracts its hydrogen production fluctuation intensity characteristics. This characteristic is a comprehensive quantity that considers not only the average current density, but also the amplitude and frequency of its oscillations within that period. For example, one operating segment might exhibit high-frequency, small-amplitude fluctuations in current density, while another might exhibit low-frequency but large-amplitude jumps. The system calculates the variance and standard deviation of the current density data for each segment and, combined with its trend, forms a feature vector characterizing the "intensity and pattern" of the fluctuations in that segment.

[0021] After segmenting and extracting features from historical data, the system focuses on the current operating phase. The current operating phase is also divided into the same short time periods, and real-time hydrogen production fluctuation intensity features are extracted. The next task is to filter out the matching operating segments from all the short time periods contained in the historically coupled affected segments that most closely resemble the features of the current time period. This process is accomplished by calculating the similarity between feature vectors. The system calculates the Euclidean distance between the feature vector of each historical segment and the feature vector of the current time period. The closer the distance, the more similar the fluctuation patterns of the two time periods. The system sets a similarity tolerance, selecting only historical operating segments whose distance values ​​are below this tolerance. For example, if the current operating phase shows that the current density is experiencing a series of periodic, moderate-amplitude fluctuations, the system will filter out historical segments from the historically affected segments that also exhibit periodic, moderate-amplitude fluctuation patterns, while filtering out segments that show stable, violent, or irregular fluctuations. Ultimately, this filtering process produces a set of matching operating segments of varying numbers. These operational segments are derived from historical data of known periods when the system was in a sensitive coupling state, and their internal hydrogen production fluctuation characteristics are highly similar to the observed state of the current system. This set of matched operational segments is output, providing a reliable data foundation for subsequent calculations of coupling delay, parameter matching degree, and the final dynamic efficiency coefficient. The core of this implementation lies in using refined segmentation and pattern matching of historical data to find the most comparable cases from past experience to the current situation, thereby making predictions and verifications based on historical data more targeted and accurate.

[0022] Example 2: See Figure 3 In the historical operation data analysis of the electrolytic manganese hydrogen production-chlor-alkali chemical coupled system, identifying the core region with the most significant system fluctuations and best reflecting the coupling interaction is the foundation for subsequent precise matching. This identification process does not rely on abrupt changes in a single parameter, but rather achieves this through comprehensive calculation and comparison of the gradients of multiple key parameters. The system processes long-term historical data, which consists of parameters such as current density, cell voltage, alkali concentration, and hydrogen purity arranged by timestamps. For each data point in the sequence, the system calculates its instantaneous rate of change relative to the previous data point, i.e., the gradient value. This gradient value quantifies the drasticness of the parameter change at that moment; a positive gradient indicates an upward trend, and a negative gradient indicates a downward trend, while its absolute value reflects the intensity of the change. By calculating the gradients of all data points, the system obtains a gradient sequence of the same length as the original parameter sequence, which clearly outlines the dynamic changes throughout the system's entire operational history.

[0023] Scanning the entire gradient sequence to locate the global maximum gradient point is a crucial step; this point represents the instant during which the parameter change rate is fastest in the entire system's history. This point is found by iterating through and comparing the absolute values ​​of all gradients. Once located, this point is established as a critical dividing point, splitting the entire historical parameter sequence in two. The resulting pre-sequence segment contains all data from the beginning of the sequence to this maximum gradient point, while the subsequent segment contains all data from that point to the end of the sequence. The significance of this segmentation is that it divides the system's history into two phases: "before the change" and "after the change," using the moment of most dramatic change as the dividing line.

[0024] To determine which segment better represents the core region affected by coupling, the system needs to further calculate the average gradient changes in these two segments. The average gradient change is not simply an average of all gradient values ​​within the segment, but rather takes into account both the directionality and persistence of the gradient. The system calculates the average of the absolute gradient values ​​of all data points in both the preceding and subsequent segments. Comparing these two averages, the segment with the larger average value indicates a more drastic overall change in parameters, meaning the system experienced a more significant state transition or external disturbance during that period, and is therefore identified as the segment affected by coupling. For example, if the average gradient of the preceding segment is much larger than that of the subsequent segment, it indicates that the largest change was a cumulative burst, and the system state gradually moderated after reaching its peak; conversely, if the average gradient of the subsequent segment is larger, it indicates that the largest change was a starting point, after which the system entered a period of continuous fluctuation and adjustment.

[0025] After successfully identifying the coupling-affected segments in the historical data, the next step is to select the most comparable specific operational segments within these segments to the current operational phase. These operational segments are consecutive time windows within the segment, each containing a fixed number of data points. For each such consecutive operational segment within the segment, the system extracts its hydrogen production fluctuation intensity characteristics. This characteristic is a multi-dimensional comprehensive description, which includes not only the statistical characteristics of the current density within that time period (such as variance and range) but also the morphological information of its fluctuations (such as the slope characteristics of the rising and falling edges).

[0026] The current operational phase also performs the same feature extraction, resulting in a feature vector representing the current fluctuation state. The core of selecting matching operational segments lies in calculating the degree of difference between the feature vector of each operational segment within the historically affected segment and the current feature vector. This degree of difference is quantified by calculating the differences between the two vectors in each dimension and combining them into an overall difference value. However, directly using the difference value is not intuitive, so the system performs a negative correlation mapping, transforming it into a fluctuation similarity index. The mapping function ensures that the smaller the difference value, the higher the similarity index; and the larger the difference value, the lower the similarity index. This allows the index to intuitively reflect the degree of matching between historical segments and the current state. Finally, the system sorts all continuous operational segments within the historically coupled affected segment based on the calculated fluctuation similarity index. The system sets a similarity threshold; only operational segments with similarity indices higher than this threshold are selected as the final matching operational segments. These selected operational segments not only come from the most volatile and sensitive periods in the system's history, ensuring data relevance, but their detailed fluctuation patterns are also highly similar to the current system's state, ensuring data comparability. This refined screening mechanism ensures that subsequent delay calculations, matching degree analyses, and weight allocations based on historical data are grounded in the most reliable and relevant historical experience, thus laying a solid data foundation for improving the accuracy of system verification and adjustment instructions. The entire process embodies a progressively refined data processing approach, from macro-regional positioning to micro-segment matching.

[0027] Example 3: See Figure 4 In the operational analysis of the electrolytic manganese hydrogen production-chlor-alkali chemical coupled system, accurately determining the point of action of the coupling response for each matched operating segment selected from historical data is a crucial operation. Identifying this point relies on a detailed examination of the temporal changes in cell pressure data within the matched operating segment. The system loads complete time-series data for that operating segment, where cell pressure data is recorded at fixed sampling intervals, forming a curve that changes over time. The core of the analysis lies in detecting whether there is a significant jump on this curve, i.e., a significant and unconventional jump in cell pressure value within a very short period. Such a jump usually indicates a step change in the internal state of the system or external input, thereby triggering the response of the coupling mechanism.

[0028] The detection of jumps is achieved through a threshold-based algorithm. The system calculates the absolute value of the difference between two adjacent data points in the sequence (e.g., the tank pressure values ​​collected at times t and t+1). This absolute value reflects the magnitude of the tank pressure change within a unit sampling period. The system compares this magnitude of change with a preset dynamic threshold. This threshold is not a fixed value but is adaptively adjusted based on the overall fluctuation level of the tank pressure data within the matched running segment; for example, it can be set to several times the average fluctuation amplitude of the data in that segment. When the magnitude of the change between adjacent points first exceeds this dynamic threshold, the corresponding time point (i.e., time t+1) is initially marked as a potential jump point.

[0029] A single exceedance of the threshold might originate from noise interference. To confirm that this is a genuine jump rather than noise, the system examines the data behavior over a subsequent period (e.g., the next few sampling periods) from that potential point. A genuine jump typically means that the cell pressure level will stabilize around a new value, while noise may quickly subside. If it is confirmed that the cell pressure value fluctuates within a new level range after the jump, the system ultimately determines this time point as the point of action of the coupled response for that matched operating segment. This point marks the moment when the chlor-alkali chemical unit begins to produce a significant and observable response to the fluctuations in the electrolytic manganese hydrogen production unit.

[0030] After successfully locating the coupling response point, the next step is to assess the consistency of parameter change trends between the hydrogen production unit and the chlor-alkali chemical unit within this matched operating segment, i.e., to calculate the parameter matching degree. This analysis divides the time series at the response point, examining the current density data sequence before the point and the hydrogen purity data sequence after the point. The goal of the analysis is to determine whether the changes in the power input (current density) of the hydrogen production unit and the changes in the output quality (hydrogen purity) of the chlor-alkali chemical unit show a coordinated trend. To achieve quantitative assessment, the system first calculates the baseline change of the current density sequence, which reflects the average drasticness and direction of current density change before the response point. Similarly, the system calculates the baseline change of the hydrogen purity sequence, reflecting the average trend of purity change after the response point. The parameter matching degree is determined by comparing the closeness of these two baselines. The calculation formula is as follows:

[0031] in: The parameter matching degree is the final calculated result. It is a dimensionless value between 0 and 1. The higher the value, the better the matching degree. The current density variation is represented by amperes per square meter per minute (A / m²·min), and it is calculated by the arithmetic mean of the gradient values ​​of all adjacent data points in the current density data sequence before the point of action of the coupling response. The baseline for hydrogen purity change is expressed as a percentage per minute (% / min), and it is calculated from the arithmetic mean of the gradient values ​​of all adjacent data points in the hydrogen purity data sequence after the point of action of the coupled response. Calculate the absolute value of the difference between two benchmarks. (Exponential function) The function is to map this absolute difference to a range of 0 to 1; when the difference is 0, the matching degree is 1; as the difference increases, the matching degree approaches 0. Through the above process, the system calculates a quantified parameter matching degree for each matching segment. This value, together with the previously determined coupling delay, becomes an important basis for subsequently calculating the reliability of the historical segment's prediction of the current state, i.e., the basic input of the coupling weight coefficient. The entire implementation emphasizes the accurate capture of key turning points in time series data and the quantitative measurement of the trend consistency between different parameter sequences.

[0032] Example 4: After determining the coupling delay and parameter matching degree of multiple historical matching operation segments, the system needs to comprehensively consider these quantitative indicators and their differences from the fluctuation characteristics of the current operating state to calculate a coupling weight coefficient for each matching operation segment. This coefficient represents the importance or reliability of the historical data of that segment in predicting the current system behavior. The calculation process begins by projecting each matching operation segment into a two-dimensional analysis space with the electrolysis efficiency coefficient and coupling delay as coordinates. Each matching operation segment is represented by a specific coordinate point, with its horizontal axis taking the actual value of the electrolysis efficiency coefficient recorded at the moment of action of the coupling response of that segment, and the vertical axis being the previously calculated value of the coupling delay of that segment.

[0033] Once all matching segments are located in two-dimensional space, the system employs a curve fitting algorithm to process these scattered points. The goal of the fitting is to find a smooth trajectory that best describes the potential correlation between the electrolysis efficiency coefficient and the coupling delay. Generating this fitted trajectory allows the system to grasp the statistical patterns of efficiency and delay throughout the historical operation. Subsequently, the system calculates the vertical distance between the coordinates of each matching segment and this fitted trajectory; this distance is defined as the residual. The magnitude of the residual directly reflects whether a historical segment conforms to the overall trend in its efficiency-delay relationship or represents an outlier; a larger residual usually indicates that the segment may have been affected by certain special factors at the time.

[0034] Based on the residuals, the system further calculates an effectiveness index for each matched operating segment. The calculation logic for this index is as follows: using the parameter matching degree of a historical operating segment as the numerator, and the product of its coupling delay and residuals as the denominator, a ratio is calculated. This calculation method implies that the effectiveness of a historical operating segment is directly proportional to the consistency of its hydrogen production and chlor-alkali parameter changes (high parameter matching degree), and inversely proportional to its slow response (large coupling delay) and the degree of deviation from the overall trend (large residuals). Therefore, operating segments with higher effectiveness indices are usually those historical segments that respond quickly, conform to general trends, and have high data quality. The determination of the coupling weight coefficient is the result of the combined effect of the effectiveness index and the fluctuation characteristic differences. The fluctuation characteristic differences are obtained by comparing the similarity of the current density fluctuation patterns between historical and current operating segments; the smaller the difference, the more similar the two are. The system constructs a weight calculation function, designed so that the coupling weight coefficient changes positively with the magnitude of the effectiveness index, and negatively with the magnitude of the fluctuation characteristic differences. Through this function, the system assigns a final weight value to each candidate historical matching segment, with those historical segments that are more effective and closer to the current state being given higher weights.

[0035] Table 1: Calculation of weights for matching running segments.

[0036]

[0037] After obtaining the coupling weight coefficients for all matching running segments, the system proceeds to generate the dynamic efficiency coefficient for the current running stage. Since the weight coefficients vary in magnitude, directly using them for weighted averaging would bias the result towards individual segments with excessively large weights. Therefore, the system first normalizes all original coupling weight coefficients. Normalization aims to scale all weight coefficients proportionally so that their sum equals one, thus transforming each weight coefficient into a proportional value representing its relative importance. The processed weight coefficients are distributed between zero and one, and the sum of all coefficients is one. The system then performs a weighted calculation, multiplying the normalized weight coefficient of each matching running segment by the actual value of the electrolysis efficiency coefficient recorded at the coupling response point of that segment, obtaining a weighted efficiency contribution value. Finally, the system sums the weighted efficiency contribution values ​​of all matching running segments, and the final result is the dynamic efficiency coefficient for the current running stage. This dynamic efficiency coefficient is not a directly measurable physical quantity, but an estimated value obtained through weighted fusion prediction based on historical similar scenarios. It reflects the operating efficiency level that the system may achieve under the current fluctuating state and will be sent to the system verification controller as one of the key inputs for generating adjustment instructions.

[0038] Example 5: After the system verification controller generates adjustment commands based on the dynamic efficiency coefficient and real-time operating parameters, these commands need to be converted into control signals that can directly drive the actuators. This conversion process begins with the accurate perception of the real-time load status of the chlor-alkali chemical unit. The system acquires the current and voltage readings of the electrolyzer through high-frequency sampling and calculates its instantaneous power level, thereby accurately determining whether the unit's current load is under light load, rated load, or overload. The adjustment command itself carries the target control quantity that the system needs to achieve. The magnitude and direction of this command together determine the basic form of the subsequently generated pulse modulation signal sequence. The signal generation module within the system creates a pulse sequence with a fixed fundamental frequency. The initial amplitude and period of this sequence are initially set by the absolute value of the adjustment command, providing a basic signal framework for subsequent fine modulation.

[0039] Based on the direction and magnitude of the adjustment command's value change, the system dynamically adjusts the duty cycle parameter of the pulse modulation signal sequence. The duty cycle, the ratio of the high-level duration within one pulse cycle to the entire cycle, directly determines the average energy output to the load. If the adjustment command value significantly increases compared to the previous moment, it means the system needs to increase output. The control logic will then correspondingly increase the duty cycle of the pulse sequence, lengthening the high-level duration and thus delivering more energy to the actuator. Conversely, if the command value decreases, the duty cycle will be lowered to reduce energy output. This adjustment is a closed-loop process; the system continuously compares the command requirement value with the current duty cycle state and makes fine adjustments to ensure a high degree of match between the output energy and the command requirements.

[0040] The system needs to precisely correct the trigger phase timing of the pulse modulation signal sequence based on the voltage waveform characteristics contained in the adjustment command; this is crucial for achieving accurate synchronous control. The system first performs real-time analysis of the voltage waveform of the adjustment command, using digital signal processing algorithms to accurately identify key feature points in the waveform, primarily the starting points of the voltage rising and falling edges. These points pinpoint the precise moments of the waveform's periodic changes. The identification process requires filtering out high-frequency noise interference superimposed on the waveform to accurately locate the true positions of the feature points.

[0041] After identifying the rising and falling edges of the voltage waveform, the system calculates the time difference between the trigger time of each independent pulse unit in the generated pulse modulation sequence and these voltage characteristic points, i.e., the phase offset. Each pulse unit has a theoretically correct initial trigger time, but this time may not be aligned with the current phase of the voltage waveform. The system calculates these offsets one by one to assess whether the existing pulse sequence is leading or lagging behind the command voltage waveform in phase. Based on the calculated phase offset, the system dynamically adjusts the initial trigger time of each pulse unit. For pulses with phase lag, their trigger time is advanced appropriately; for pulses with phase lead, their trigger time is slightly delayed. This process is a comprehensive recalibration of the trigger timing of the entire pulse sequence, aiming to precisely synchronize the trigger times of all pulses with the rising and falling edges of the voltage waveform. After this phase correction, the final output modulation signal sequence maintains a high degree of phase consistency with the voltage waveform commanded by the system, thereby achieving precise, efficient, and stable control of the actuators in the chlor-alkali chemical unit, ensuring that the coupled system smoothly adjusts to the target operating state based on the verification results. The entire implementation process embodies a progressive control logic, from instruction parsing to signal generation and then to phase synchronization.

[0042] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0043] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for planning, designing, and verifying a coupled system for electrolytic manganese hydrogen production and chlor-alkali chemical production, characterized in that, include: The first real-time operating parameters of the electrolytic manganese hydrogen production unit and the second real-time operating parameters of the chlor-alkali chemical unit are obtained. The first real-time operating parameters include current density and cell voltage data, and the second real-time operating parameters include alkali concentration and hydrogen purity data. Select matching operating segments from historical coupled operation data that match the hydrogen production fluctuation characteristics of the current operating phase; Analyzing the changes in the slot pressure data of the matched running segment in time sequence to determine the coupling response action point of the matched running segment includes: for any matched running segment, taking the time node where the slot pressure data changes abruptly as the coupling response action point; and taking the time difference between the start point of each matched running segment and the coupling response action point as the coupling delay of each matched running segment. By comparing the changing trends of the current density data sequence before the coupling response point with the hydrogen purity data sequence after the coupling response point, the parameter matching degree of each matching operation segment is determined. The coupling weight coefficient of the matching operation segment is determined by combining the coupling delay, the parameter matching degree, and the difference in hydrogen production fluctuation characteristics between the matching operation segment and the current operation stage. The electrolysis efficiency coefficients of all matching operating segments corresponding to the current operating stage are weighted by the coupling weight coefficient to generate the dynamic efficiency coefficient of the current operating stage; the dynamic efficiency coefficient and the first real-time operating parameter are input into the system verification controller, and the system verification controller outputs the adjustment command of the coupling system.

2. The planning, design, and verification method for the electrolytic manganese hydrogen production-chlor-alkali chemical coupled system according to claim 1, characterized in that, The process of selecting matching operating segments from historical coupled operating data that match the hydrogen production fluctuation characteristics of the current operating phase includes: Based on the system fluctuation state, the affected coupling sections are identified from the parameter sequence of the historical coupled operation data; The parameter sequence of the historical coupled operation data is divided into multiple continuous operation segments, and the hydrogen production fluctuation intensity characteristics of each continuous operation segment are extracted. From the affected coupling sections, a matching operating section that matches the hydrogen production fluctuation intensity characteristics of the current operating phase is selected.

3. The planning, design, and verification method for the electrolytic manganese hydrogen production-chlor-alkali chemical coupling system according to claim 2, characterized in that, The identification of affected coupling segments from the parameter sequence of historical coupled operation data based on system fluctuation states includes: Calculate the gradient value of change corresponding to each data point in the parameter sequence of the historical coupled operation data; The data point where the maximum gradient value first appears is used as the splitting node; Using the segmentation node as the boundary, the parameter sequence is divided into a preceding segment and a subsequent segment, and the average change gradient of the preceding segment and the subsequent segment are calculated respectively. The segment with the largest average change gradient is taken as the segment affected by coupling.

4. The planning, design, and verification method for the electrolytic manganese hydrogen production-chlor-alkali chemical coupling system according to claim 2, characterized in that, The step of selecting matching operating segments from the affected coupling segments that match the hydrogen production fluctuation intensity characteristics of the current operating phase includes: The difference between the hydrogen production fluctuation intensity characteristics of each continuous operating segment in the coupled affected segment and the hydrogen production fluctuation intensity characteristics of the current operating stage is negatively correlated and mapped to obtain a fluctuation similarity index. Based on the fluctuation similarity index, a matching running segment is determined from the coupling-affected segment.

5. The planning, design, and verification method for an electrolytic manganese hydrogen production-chlor-alkali chemical coupled system according to claim 1, characterized in that, The process of comparing the changing trends of the current density data sequence before and after the coupling response point to determine the parameter matching degree for each matching operation segment includes: Calculate the average gradient change of adjacent data points in the current density data sequence before the point of action of the coupling response, and use it as the benchmark for current density change; The average gradient change of adjacent data points in the hydrogen purity data sequence after the point of action of the coupling response is calculated as the benchmark for hydrogen purity change. The absolute difference between the current density change benchmark and the hydrogen purity change benchmark is negatively correlated to generate the parameter matching degree.

6. The planning, design, and verification method for the electrolytic manganese hydrogen production-chlorine-alkali chemical coupling system according to claim 2, characterized in that, The determination of the coupling weight coefficient for the matched operating segment, by combining the coupling delay, the parameter matching degree, and the difference in hydrogen production fluctuation characteristics between the matched operating segment and the current operating stage, includes: Using the electrolysis efficiency coefficient at the point of action of the coupling response of each matched running segment as the abscissa and the coupling delay as the ordinate, the coordinate points of each matched running segment are generated. Perform curve fitting on the coordinate points of all matching segments in the current running phase to generate a fitted trajectory line; Calculate the residual between the coordinates of each matched running segment and the fitted trajectory line; Using the parameter matching degree as the numerator and the product of the coupling delay and the residual as the denominator, the ratio is calculated as an indicator of the effectiveness of the matching operation segment. The coupling weight coefficient is determined based on the effectiveness index and the difference in hydrogen production fluctuation characteristics; wherein the effectiveness index is positively correlated with the coupling weight coefficient, and the difference in hydrogen production fluctuation characteristics is negatively correlated with the coupling weight coefficient.

7. The planning, design, and verification method for an electrolytic manganese hydrogen production-chlor-alkali chemical coupled system according to claim 1, characterized in that, The step of weighting the electrolysis efficiency coefficients of all matching operating segments corresponding to the current operating stage using the coupling weight coefficient to generate the dynamic efficiency coefficient of the current operating stage includes: The coupling weight coefficients of all matching running segments are normalized so that the sum of the normalized coupling weight coefficients is 1. The normalized coupling weight coefficient is multiplied by the electrolysis efficiency coefficient of the corresponding matching running segment and then summed to output the dynamic efficiency coefficient.

8. The planning, design, and verification method for an electrolytic manganese hydrogen production-chlor-alkali chemical coupled system according to claim 1, characterized in that, After the system verification controller outputs the adjustment command for the coupled system, it also includes: Based on the real-time load status of the chlor-alkali chemical unit and the adjustment instructions, a pulse modulation signal sequence is generated; Based on the direction and amount of change of the value of the adjustment instruction, the duty cycle parameter of the pulse modulation signal sequence is adjusted. Based on the voltage waveform characteristics of the adjustment command, the trigger phase timing of the pulse modulation signal sequence is corrected.

9. The planning, design, and verification method for an electrolytic manganese hydrogen production-chlorine-alkali chemical coupled system according to claim 8, characterized in that, The step of correcting the trigger phase timing of the pulse modulation signal sequence according to the voltage waveform characteristics of the adjustment command includes: Identify the starting point of the voltage rising edge and the starting point of the falling edge in the adjustment command; Calculate the phase offset of each pulse unit in the pulse modulation signal sequence relative to the starting point of the rising edge and the starting point of the falling edge of the voltage; The initial trigger time of each pulse unit is adjusted according to the phase offset to generate a phase-synchronized modulation signal sequence.

Images