A waste heat recovery system and method based on steam condensate

Abstract

This invention relates to the field of waste heat recovery technology, and discloses a waste heat recovery system and method based on steam condensate. The method utilizes high-precision sensors to collect temperature distribution data and pressure fluctuation data of steam condensate during its flow in a pipeline, forming a dynamic thermodynamic parameter set. Multi-scale decomposition technology is applied to process the dynamic thermodynamic parameter set, extracting high-frequency and low-frequency components of the temperature distribution data, and separating the periodic and non-periodic characteristics of the pressure fluctuation data to generate a thermodynamic feature matrix. Based on the thermodynamic feature matrix, flow stability and waste heat transfer efficiency indices for steam condensate are constructed. Based on the dynamic relationship between these two indices, the heat recovery stages of steam condensate are divided, and the corresponding temperature and pressure thresholds for each stage are marked. Based on the marking results of the heat recovery stages, the heat exchange rate of the waste heat recovery device and the condensate flow path are adjusted to generate an optimized control command set.

Description

Technical Field

[0001] This invention relates to the field of waste heat recovery technology, specifically to a waste heat recovery system and method based on steam condensate. Background Technology

[0002] In industrial production, steam, as an important heat energy carrier, is widely used in many fields such as power generation, chemical industry, textiles, and food processing. After steam completes its heating or work tasks, it releases latent heat and condenses into high-temperature condensate, which usually still contains a considerable amount of heat energy. Traditionally, this condensate is either directly discharged or reused after simple cooling, and its waste heat is not effectively utilized, resulting in huge energy waste. Against the backdrop of global energy shortages and increasing pressure to reduce carbon emissions, recovering and utilizing the waste heat in steam condensate is of great significance for improving overall energy efficiency, reducing fuel consumption, and lowering production costs.

[0003] The basic principle of steam condensate waste heat recovery is to transfer the heat from the condensate to the working fluid that needs preheating through a heat exchanger, thereby reducing the consumption of fresh steam or primary energy. Although this concept has been proposed for some time, and various forms of waste heat recovery devices have been applied in practice, existing recovery methods and systems still face many challenges in terms of operational efficiency and stability. A core issue is that the thermodynamic state of the steam condensate system is not constant. It is dynamically affected by various factors such as fluctuations in the load of upstream steam-using equipment, changes in steam pressure, pipeline distance, and insulation conditions. This causes the temperature, flow rate, and pressure of the condensate to fluctuate continuously during transportation, exhibiting non-stationary characteristics.

[0004] Existing waste heat recovery systems mostly employ relatively simple and crude control strategies. Common methods include setting a fixed target temperature or using simple on / off control. For example, the recovery process is initiated when the condensate temperature exceeds a certain set value, and stopped otherwise. This control method struggles to adapt to dynamic changes in condensate thermodynamic parameters. When condensate temperature, pressure, or flow rate fluctuates significantly, fixed control parameters can lead to suboptimal heat exchange conditions. For instance, during periods of high condensate temperature and stable flow, the heat exchange rate could be increased to recover more heat, but the system may still operate at the conventional rate, resulting in underutilization of the recovery potential. Conversely, during periods of unstable parameters, such as sudden pressure drops potentially accompanied by flash evaporation, continuing at the original rate could trigger system vibration or water hammer risks, impacting equipment safety. Furthermore, the system lacks the ability to precisely sense and proactively adjust the condensate thermodynamic state.

[0005] The flow stability of condensate in pipelines directly affects the effectiveness of waste heat recovery and equipment lifespan. Periodic pressure fluctuations and aperiodic shocks, as well as uneven temperature distribution, all have complex impacts on the operating state of heat exchangers. Current systems often only focus on average temperature or pressure, lacking in-depth analysis of parameter fluctuation characteristics. This makes it impossible to accurately determine whether the current flow state is suitable for efficient and safe heat recovery, let alone adjust operating strategies in advance based on the evolving trends. A disconnect exists between the heat recovery process and the dynamic characteristics of condensate generation, leading to unstable recovery efficiency and potentially causing interference to the pipeline system due to improper control. Summary of the Invention

[0006] The purpose of this invention is to provide a waste heat recovery system and method based on steam condensate to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides a method for recovering waste heat from steam condensate, the method comprising:

[0008] Collect temperature distribution and pressure fluctuation data of steam condensate during pipeline flow to form a dynamic thermodynamic parameter set;

[0009] Multi-scale decomposition of dynamic thermodynamic parameter set is performed to extract high-frequency and low-frequency components of temperature distribution data, while separating periodic and non-periodic features of pressure fluctuation data to generate thermodynamic feature matrix.

[0010] Based on the high-frequency components and periodicity of the thermodynamic characteristic matrix, a flow stability index for steam condensate is constructed. Combined with the low-frequency components and non-periodicity, a waste heat transfer efficiency index is calculated.

[0011] Based on the dynamic relationship between flow stability index and waste heat transfer efficiency index, the heat recovery stages of steam condensate are divided, and the corresponding temperature threshold and pressure threshold for each stage are marked.

[0012] Based on the marking results of the heat recovery stage, the heat exchange rate and condensate flow path of the waste heat recovery device are adjusted to generate an optimized control instruction set;

[0013] By optimizing the control instruction set to drive the valve opening and pumping frequency of the heat exchanger, and matching the thermodynamic state of the steam condensate in real time, the closed-loop regulation of the waste heat recovery process is completed.

[0014] Preferably, the multi-scale decomposition of the dynamic thermodynamic parameter set includes:

[0015] Wavelet packet transform is used to decompose the temperature distribution data into layers according to a preset frequency band, and the high-frequency and low-frequency components of temperature at different scales are obtained.

[0016] Empirical mode decomposition is applied to pressure fluctuation data to extract intrinsic mode function components. The first three intrinsic mode function components are used as periodic features, and the remaining components are used as non-periodic features.

[0017] The high-frequency components of temperature, the low-frequency components of temperature, the periodic features, and the non-periodic features are aligned by timestamps and combined into a four-dimensional thermodynamic feature matrix.

[0018] Preferably, the flow stability index of the constructed steam condensate includes:

[0019] The ratio of the energy entropy of the high-frequency temperature component to the variance of the periodic characteristics is calculated, and the weighted sum of the energy entropy and variance ratios is used as an index of flow stability.

[0020] The calculation of waste heat transfer efficiency index includes:

[0021] By performing a sliding window integration on the low-frequency temperature component, the cumulative heat capacity value is obtained. Combined with the amplitude mean of the non-periodic characteristics, a waste heat transfer efficiency index is generated.

[0022] Preferably, the step of dividing the heat recovery stage of steam condensate includes:

[0023] When the flow stability index exceeds the preset threshold and the waste heat transfer efficiency index is lower than the critical value, it is marked as an unstable stage.

[0024] When both the flow stability index and the waste heat transfer efficiency index are within the preset range, it is marked as the high-efficiency recovery stage.

[0025] The temperature and pressure thresholds are dynamically updated based on the switching frequency between the unstable phase and the efficient recovery phase.

[0026] Preferably, adjusting the heat exchange rate and condensate flow path of the waste heat recovery device includes:

[0027] During the unstable phase, the heat exchange rate is controlled below the baseline value, and the residence time of the condensate flow path is shortened.

[0028] In the high-efficiency recovery phase, the heat exchange rate is increased to a value higher than the baseline, and the residence time of the flow path is extended accordingly.

[0029] Preferably, the generation of the optimized control instruction set includes:

[0030] The heat exchange rate adjustment is converted into a pulse width modulation signal to control the valve opening of the heat exchanger.

[0031] Based on the change in residence time along the flow path, an incremental command for pumping frequency is generated to drive the speed adjustment of the centrifugal pump.

[0032] Preferably, the real-time matching of the thermodynamic state of the steam condensate includes:

[0033] Monitor the real-time temperature and pressure data at the heat exchanger outlet. When the deviation from the temperature or pressure threshold of the marking stage exceeds the tolerance, trigger the stage recalibration process.

[0034] The control instruction set is updated and optimized based on the recalibration results, and the adjustment weights of valve opening and pumping frequency are reallocated.

[0035] Preferably, the trigger phase recalibration process includes:

[0036] Extract the ten most recent sampling points of real-time temperature and pressure data, and calculate their root mean square error with the current stage threshold;

[0037] If the root mean square error exceeds the tolerance three times consecutively, the heat recovery stage division process will be re-executed, and the output of the optimization control instruction set will be frozen until the new stage is marked.

[0038] Preferably, the closed-loop regulation of the waste heat recovery process includes:

[0039] The real-time adjustment data of valve opening and pumping frequency are fed back to the dynamic thermodynamic parameter set to form the input for the next round of multi-scale decomposition;

[0040] The consistency between the calculated flow stability index and the waste heat transfer efficiency index is verified at fixed intervals, and the decomposition parameters of the thermodynamic characteristic matrix are corrected.

[0041] Preferably, the present invention also includes a steam condensate waste heat recovery system, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above-described steam condensate waste heat recovery method.

[0042] Compared with the prior art, the beneficial effects of the present invention are:

[0043] Traditional waste heat recovery methods often fail to accurately assess the thermodynamic state of steam condensate, resulting in low recovery efficiency and significant waste of heat. This invention, however, utilizes high-precision sensors to comprehensively collect temperature distribution and pressure fluctuation data of steam condensate during its flow through pipelines, creating a dynamic thermodynamic parameter set. Advanced multi-scale decomposition technology is employed to deeply analyze the data, extracting key features and constructing flow stability and waste heat transfer efficiency indices. Based on these precise indices, the heat exchange rate and condensate flow path of the waste heat recovery device are adjusted in real time, ensuring a precise match between the waste heat recovery process and the thermodynamic state of the steam condensate. In a certain industrial production scenario, the previous waste heat recovery rate of steam condensate was less than 30%. After adopting this invention, the waste heat recovery rate has significantly improved, fully utilizing the waste heat carried by the steam condensate, reducing the need for additional energy, and maximizing energy utilization. Compared to traditional methods, this represents a qualitative leap in energy efficiency.

[0044] In waste heat recovery systems, the unstable flow state of steam condensate, such as excessive pressure fluctuations or inconsistent flow rates, can affect the normal operation of the system and even lead to equipment failure. This invention, based on flow stability indicators, monitors the flow state of steam condensate in real time. When abnormal fluctuations in flow stability indicators are detected, such as the standard deviation of high-frequency temperature components exceeding the normal range, or sudden changes in pressure periodic characteristics, the operating parameters of the waste heat recovery device are adjusted promptly. By optimizing the valve opening of the heat exchanger, the flow rate of the heat exchange medium is precisely controlled, restoring the temperature and pressure of the steam condensate to stability; the pumping frequency is adjusted to stabilize the flow rate and velocity of the steam condensate, ensuring smooth flow in the pipeline. Taking the waste heat recovery system of a large chemical enterprise as an example, before applying this invention, the system experienced an average of 3-5 failures per month due to unstable steam condensate flow, leading to production interruptions and economic losses. After adopting this invention, the system's operational stability was significantly improved, the number of failures was greatly reduced, ensuring production continuity, and also reducing energy consumption caused by frequent equipment start-ups and shutdowns and maintenance failures, thus improving the overall system's operating efficiency and reliability.

[0045] Highly efficient waste heat recovery fully utilizes the waste heat of steam condensate, reducing enterprises' reliance on external energy purchases. With rising energy prices, this saves companies significant energy costs. For example, a dyeing and printing company reduced its annual steam purchase costs by hundreds of thousands of yuan after using the waste heat recovery technology of this invention. Precise control of the waste heat recovery device avoids excessive wear caused by unreasonable operating parameters. In traditional waste heat recovery systems, the valves and pumping equipment of the heat exchanger are easily subjected to significant impact and wear due to the inability to precisely adjust according to the actual state of the steam condensate, requiring frequent parts replacement and resulting in high maintenance costs. This invention, by adjusting valve opening and pumping frequency in real time, ensures the equipment operates at its optimal state, greatly extending its service life and reducing maintenance frequency and costs. Statistics show that after adopting this technology, equipment maintenance costs have decreased by approximately 30%-50%, significantly reducing operating costs for enterprises in terms of both energy consumption and equipment maintenance. Attached Figure Description

[0046] Figure 1 This is a schematic diagram illustrating the working principle of the steam condensate waste heat recovery method described in this invention.

[0047] Figure 2 A flowchart for the multi-scale decomposition of dynamic thermodynamic parameter sets;

[0048] Figure 3 A flowchart illustrating the stages of steam condensate heat recovery. Detailed Implementation

[0049] 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.

[0050] Please see Figure 1This invention provides a system and method for recovering waste heat from steam condensate. The method integrates multiple modules, including data acquisition, signal processing, index calculation, and closed-loop control, to achieve efficient recovery of waste heat from steam condensate. During the flow of steam condensate in the pipeline, temperature distribution data and pressure fluctuation data are collected in real time, forming a dynamic thermodynamic parameter set. This dynamic thermodynamic parameter set undergoes multi-scale decomposition processing to extract high-frequency and low-frequency components of the temperature distribution data, and simultaneously separates the periodic and non-periodic features of the pressure fluctuation data, generating a thermodynamic feature matrix. The high-frequency and periodic features in the thermodynamic feature matrix are used to construct the flow stability index of the steam condensate, while the low-frequency and non-periodic features are used to calculate the waste heat transfer efficiency index. The dynamic relationship between the flow stability index and the waste heat transfer efficiency index is analyzed, dividing the steam condensate into heat recovery stages, and marking the corresponding temperature and pressure thresholds for each stage. Based on the marking results of the heat recovery stages, the heat exchange rate of the waste heat recovery device and the condensate flow path are adjusted to generate an optimized control command set. The optimized control command set drives the valve opening and pumping frequency of the heat exchanger, matching the thermodynamic state of the steam condensate in real time to complete the closed-loop regulation of the waste heat recovery process. The entire process ensures the system adapts to the dynamic changes in the steam condensate through continuous data feedback and parameter updates.

[0051] Example 1: See Figure 2 The dynamic thermodynamic parameter set originates from synchronously acquired data from temperature sensors and pressure transmitters deployed in the steam condensate pipeline. The temperature sensors utilize PT100 platinum resistance thermometers, and the pressure transmitters employ piezoresistive sensing units. The sampling frequency is set to 10Hz to ensure the capture of transient characteristics in the steam condensate flow. The raw data undergoes anti-aliasing filtering and amplification processing via signal conditioning circuitry, and is then transmitted to the edge computing module via industrial Ethernet to form a time-series format dynamic thermodynamic parameter set. Wavelet packet transform is used to decompose the temperature distribution data, selecting the Db4 wavelet basis function as the mother wavelet. The Db4 wavelet possesses tight support and a suitable vanishing moment, making it suitable for analyzing local abrupt changes and continuous trends in the steam condensate temperature signal. The preset frequency band division is based on the physical characteristics of the steam condensate system, dividing the 0-5Hz frequency range into eight uniform sub-bands, each corresponding to a thermodynamic process. The decomposition layer is set to three levels: the first level decomposes the signal into two sub-bands, 0-2.5Hz and 2.5-5Hz; the second level recursively subdivides each sub-band; and the third level generates eight final frequency band components. The high-frequency temperature components are taken from the 7th and 8th subbands (3.75-5Hz), corresponding to rapid temperature fluctuations caused by bubble bursts or flow disturbances; the low-frequency temperature components are taken from the 1st and 2nd subbands (0-1.25Hz), reflecting the system's thermal inertia and steady-state heat transfer characteristics.

[0052] The implementation of the wavelet packet decomposition algorithm using the Mallat pyramid algorithm is based on wavelet multiresolution analysis theory. The algorithm starts with a dual-channel filter bank, employing a pre-designed set of decomposition filter coefficients. The low-pass filter coefficients are selected using specific numerical combinations, while the high-pass filter coefficients are generated through orthogonal mirror relationships. After the temperature distribution signal is input, it undergoes multi-level decomposition. The first level of decomposition convolves the original signal through both low-pass and high-pass filters. The convolution results are then subjected to a binary downsampling operation to generate two sets of sub-band signals: approximation coefficients and detail coefficients. The second level of decomposition simultaneously applies the approximation and detail coefficients generated in the previous level, forming four sets of sub-band signals. This decomposition mode recursively advances using a binary tree structure, with each parent node generating two child nodes, ultimately constructing a complete wavelet packet decomposition tree. The decomposition depth is determined based on the signal sampling frequency and feature preservation requirements, with the number of terminal nodes exponentially related to the decomposition depth. The frequency bandwidth corresponding to each node is calculated using the sampling theorem to ensure complete coverage of the signal spectrum. Threshold denoising of the coefficient matrix employs the Stein unbiased risk estimation criterion. The process begins with coefficient magnitude statistics, calculating and sorting the absolute value sequence of coefficients for each terminal node. Risk function evaluation is based on the coefficient sorting results, calculating the corresponding risk value by traversing candidate thresholds. Optimal threshold selection is achieved by minimizing the risk function, finding the threshold point that minimizes the estimated risk. Threshold processing employs a hard threshold rule, setting coefficients with magnitudes below the threshold to zero while retaining significant coefficients.

[0053] The denoised signal reconstruction is achieved through inverse wavelet packet transform. The reconstruction process starts from the terminal node and performs upsampling and filtering operations step by step. Upsampling inserts zero values ​​between coefficients, followed by convolution calculations using the reconstruction filter bank. The reconstruction results at each stage are synthesized along the path of the decomposition tree to finally recover the denoised temperature signal. The entire reconstruction process maintains the same filter bank parameters as the decomposition process to ensure complete signal reconstruction. Boundary handling in the algorithm implementation employs a symmetric extension method, adding mirror samples at both ends of the signal to avoid boundary effects. Computational efficiency is guaranteed by the fast wavelet transform algorithm, utilizing the tight support characteristics of the filter coefficients to reduce computational load. Real-time processing uses a sliding window mechanism, calculating only the wavelet packet coefficients of the changed parts when new data arrives. The denoising effect is verified through simulated signal testing, generating a test signal containing known features and adding Gaussian white noise. The signal-to-noise ratio and mean square error are used to compare the signals before and after denoising, while also checking the degree of feature preservation. In practical applications, the physical characteristics of the temperature signal are also used to verify the rationality of denoising, ensuring that the true temperature fluctuation characteristics are preserved while removing noise. Regarding parameter selection, the number of decomposition layers is determined based on the characteristic time scale of the temperature signal, and the threshold selection factor is dynamically adjusted according to the noise level. The algorithm implementation adopts a modular design, including four functional modules: signal preprocessing, wavelet packet decomposition, threshold denoising, and signal reconstruction. Each module includes parameter verification and anomaly handling mechanisms to ensure algorithm robustness. Computational resource management employs a dynamic memory allocation strategy, automatically adjusting the buffer size based on the signal length. Parallel computing technology is applied to multi-channel signal processing, simultaneously processing data from multiple temperature measurement points. Algorithm optimization is tailored to the characteristics of embedded platforms, employing fixed-point arithmetic and lookup tables to accelerate convolution calculations.

[0054] Empirical Mode Decomposition (EMD) is used to process pressure fluctuation data using an adaptive sieving process. The sieving stopping criterion is set to a standard deviation of less than 0.001 between two consecutive sieving results. The pressure fluctuation data is decomposed into 10 intrinsic mode function (IMF) components, each satisfying the condition that the difference between the number of local extrema and the number of zero-crossings does not exceed 1. The first three IMF components contain the main frequency components of the pressure fluctuations: the first IMF component corresponds to the fundamental frequency oscillation (1-2Hz) caused by the pumping mechanism; the second IMF component captures the harmonic components of valve action (2-3Hz); and the third IMF component describes the high-frequency fluctuations (3-4Hz) generated by pipeline resonance. The remaining components are classified as aperiodic features, including low-frequency trends from environmental interference, sensor drift, and random noise. The endpoint effects of EMD are suppressed using a mirror continuation method to ensure the stability of the decomposition boundary. Timestamp alignment uses a linear interpolation algorithm to unify the high-frequency and low-frequency temperature components generated by wavelet packet decomposition with the periodic and aperiodic features generated by EMD to a 1ms time precision. Interpolation points are generated based on integer multiples of the sampling time, and missing data is padded using Lagrange interpolation to ensure consistent length of the four-dimensional feature sequence. The aligned data is combined in chronological order to form a four-dimensional thermal feature matrix. The matrix row index represents the sampling time point (each row is 100ms apart), and the column indices sequentially store the amplitudes of the high-frequency temperature component, the amplitudes of the low-frequency temperature component, the energy norm of the periodic feature, and the mean amplitude of the non-periodic feature. The four-dimensional thermal feature matrix is ​​stored as a floating-point array, with memory layout optimized for row-major order to improve cache access efficiency.

[0055] The frequency band division parameters of the wavelet packet transform were obtained through training on historical data. The training set included temperature distribution data of the steam condensate system under different loads, and cluster analysis was used to determine the optimal sub-band boundaries. The selection of the Db4 wavelet basis was based on frequency domain response testing, comparing the reconstruction errors of different wavelet functions. The Db4 wavelet exhibited the smallest mean square error in temperature signal decomposition. The number of sieving steps in the empirical mode decomposition was dynamically adjusted, adaptively increasing or decreasing the number of intrinsic mode function components based on signal complexity to avoid over-decomposition or under-decomposition. The timestamp-aligned interpolation algorithm introduced an error compensation mechanism, calculating the cumulative deviation between the interpolation point and the actual sampling point. When the cumulative deviation exceeded a threshold, a resynchronization process was triggered. The generation process of the four-dimensional thermal feature matrix included a quality inspection step, calculating the signal-to-noise ratio and kurtosis index of each feature dimension and removing abnormal channel data. The dimensional scalability of the feature matrix allows for the integration of additional sensor data, such as appending flow rate characteristics measured by a flow meter after the fourth dimension to form a five-dimensional thermal feature matrix. The matrix data is persistently stored in a circular buffer with a capacity of 1000 rows, supporting a sliding window update strategy. The computational load of multi-scale decomposition is balanced through parallel pipeline processing. Wavelet packet transform and empirical mode decomposition are assigned to independent computation threads, and the threads exchange data through shared memory.

[0056] The real-time requirement of the steam condensate system necessitates multi-scale decomposition within 100ms. The algorithm employs fixed-point arithmetic optimization to reduce floating-point computational overhead. The convolution kernels of the wavelet packet transform are pre-compiled into lookup tables, and the extreme point detection of the empirical mode decomposition uses a fast hill-climbing algorithm to improve computational efficiency. The output interface of the four-dimensional thermodynamic feature matrix is ​​standardized to the OPCUA protocol for easy integration with upper-level index calculation modules. Status monitoring during the decomposition process records the decomposition depth and energy distribution ratio of each signal for fault diagnosis and performance evaluation. The robustness of multi-scale decomposition using dynamic thermodynamic parameter sets is ensured through redundant design, with primary and backup dual-channel data acquisition. Automatic switching to the backup channel occurs when the primary channel decomposition fails. Online parameter calibration for wavelet packet transform and empirical mode decomposition dynamically adjusts the number of decomposition layers and screening thresholds based on real-time signal characteristics. The time consistency of the four-dimensional thermodynamic feature matrix is ​​guaranteed by a hardware timestamp synchronization mechanism, using the IEEE 1588 precision clock protocol to align the clock signals of multiple acquisition nodes. The reliability of the steam condensate waste heat recovery method depends on the accuracy of the multi-scale decomposition. Calibration is performed periodically using a standard signal source. The calibration signal contains a mixture of sine and square waves with known frequency components to verify the frequency response characteristics of the decomposition algorithm. Dimensionality compression techniques for the feature matrix are applied during long-term operation; principal component analysis retains over 95% of the energy feature dimensions, reducing storage and transmission overhead. A hot backup mechanism for the multi-scale decomposition module ensures continuous system operation; when processor overload is detected, computational tasks are automatically migrated to a standby node.

[0057] Example 2: See Figure 3The calculation process for constructing the flow stability index is based on the high-frequency temperature component and periodicity in the four-dimensional thermodynamic feature matrix. The energy entropy of the high-frequency temperature component is calculated using the discrete form of the Shannon entropy formula. The squared amplitudes of the wavelet packet coefficients of the high-frequency temperature component are normalized to a probability distribution, and the sum of entropy values ​​reflects the degree of disorder in temperature fluctuations. The variance ratio calculation for the periodicity feature selects the first three intrinsic mode function components, calculating the variance value of each component. The variance ratio is defined as the ratio of the sum of the variances of the three components to the total variance of the pressure fluctuation data. The weighted sum of the energy entropy and variance ratio uses fixed weighting coefficients α=0.6 and β=0.4. These weighting coefficients are determined through regression analysis of historical operating data of the steam condensate system. The weighted sum result is normalized to the 0-1 range to form the flow stability index. The waste heat transfer efficiency index is calculated by processing the low-frequency temperature component and aperiodic features in the four-dimensional thermodynamic feature matrix. For the low-frequency temperature component, the sliding window integration is set with a window length of 10 seconds and a sliding step of 1 second. The Simpson numerical integration method is used to calculate the area under the curve within each window, and the result is converted into a cumulative heat capacity value in thermodynamic units. The amplitude mean of the aperiodic features is calculated by taking the arithmetic mean of the absolute values ​​of the residual components from the empirical mode decomposition. The amplitude mean and the cumulative heat capacity value are linearly weighted to generate the waste heat transfer efficiency index. The weighting coefficient is set to a ratio of 0.7:0.3 based on the heat exchanger characteristics.

[0058] The heat recovery stages of steam condensate are divided based on the numerical relationship between flow stability and waste heat transfer efficiency indices. The criteria for an unstable stage are a flow stability index greater than 0.8 and a waste heat transfer efficiency index less than 0.3. The criteria for a high-efficiency recovery stage are a flow stability index between 0.4 and 0.6 and a waste heat transfer efficiency index greater than 0.7. The dynamic update mechanism for temperature and pressure thresholds is based on stage switching frequency statistics. The switching frequency is calculated by the number of stage changes within a sliding time window. When the frequency exceeds 3 times per minute, the threshold update process is initiated, and the new threshold is recalibrated based on the average index value over the most recent 10 minutes. The energy entropy calculation details for the flow stability index include a wavelet coefficient preprocessing step. The coefficient matrix is ​​Z-score standardized to eliminate dimensionality. The probability distribution is generated using histogram statistics, and the number of partitions is determined to be 16 intervals according to the Sturgess formula. The logarithm base in the entropy calculation is set to 2, and the result is converted to bits. The variance calculation for periodic features uses an unbiased estimator, with n-1 corrected degrees of freedom in the denominator. When calculating the variance ratio, the total variance includes the variance contributions of all intrinsic mode function components. The sliding window integration of the waste heat transfer efficiency index employs a circular buffer data structure, with the buffer capacity corresponding to 10 seconds of sampled data. During integration, the number of data points within the window is updated in real-time. The unit of the accumulated heat capacity value is converted to kilojoules per cubic meter, and Min-Max normalization is performed before combining it with the mean amplitude of the non-periodic features. The coefficient adjustment mechanism for the linear weighted combination has an adaptive function, automatically recalibrating the weighting coefficients when sensor drift is detected.

[0059] The logic for dividing the heat recovery stages is implemented using a finite state machine. State transition conditions include lag intervals to prevent frequent switching. Transition from an unstable stage to a high-efficiency recovery stage requires sustained stability of the indicator for at least 5 seconds. The update algorithm for temperature and pressure thresholds uses an exponentially weighted moving average method: new threshold = original threshold × 0.8 + real-time measurement value × 0.2. A smoothing factor is set based on the system response characteristics. Stage marking results are stored in state word format, with each state word containing stage type, timestamp, and confidence level fields. The real-time calculation of flow stability indicators is ensured using a lookup table method, and the entropy values ​​of common coefficient combinations are pre-calculated and stored in flash memory. The integral calculation of waste heat transfer efficiency indicators is implemented using a hardware accelerator, utilizing the multiply-accumulate instructions of the DSP processor to improve calculation speed. The stage division module and the indicator calculation module exchange data through dual-port RAM to ensure timing consistency. The stability check of the threshold update process includes variance checking; the new threshold only takes effect after passing the statistical significance test. An anomaly handling mechanism for the steam condensate system is integrated into the indicator calculation. When a sensor failure is detected, it automatically switches to backup calculation mode, using the most recent valid data to estimate the current indicator value. The visualization output of the phase division results supports a human-machine interface display, with different phases color-coded to assist operators in monitoring. The historical phase sequence storage function supports trend analysis, with a data retention period set to 30 days for subsequent optimization. The calibration process of the indicator calculation module is activated periodically; the calibration signal is injected by a standard signal generator to compare the deviation between the calculated indicators and theoretical values ​​and adjust the algorithm parameters. Boundary conditions for phase division are set with safety tolerances to prevent misjudgment of phases due to slight fluctuations in operating conditions.

[0060] Example 3: The marking results of the heat recovery stage are directly input into the control strategy selection module. The module pre-stores a set of reference parameters corresponding to different stages. The reference heat exchange rate is set to 200 kW / ℃·m², and the reference flow path residence time is set to 120 seconds. In the unstable stage, the control strategy adjusts the heat exchange rate to 70% of the reference value, i.e., 140 kW / ℃·m². The condensate flow path is switched by using a three-way valve to connect a short-path pipe with a diameter of 50 mm, shortening the residence time to 84 seconds. In the high-efficiency recovery stage, the control strategy increases the heat exchange rate to 130% of the reference value, i.e., 260 kW / ℃·m². The flow path is switched to a long-path pipe with a diameter of 80 mm, and a buffer tank is activated, extending the residence time to 156 seconds.

[0061] The process of converting the heat exchange rate adjustment into a pulse width modulation signal uses the following formula:

[0062]

[0063] in: This represents the duty cycle increment of the pulse width modulation signal. This represents the proportional gain coefficient (with a value of 0.05% / ℃). This represents the integral gain coefficient (with a value of 0.001% / ℃·s). This indicates the temperature setpoint corresponding to the target heat exchange rate. This indicates the real-time temperature measurement at the heat exchanger outlet. The fundamental frequency of the pulse width modulation signal is set to 1kHz, and the duty cycle increment δ is output to the positioner of the electric regulating valve through a 16-bit digital-to-analog converter.

[0064] The calculation of the pumping frequency increment command based on the change in residence time along the flow path is based on the fluid dynamics continuity equation. The relationship between the command value Δf and the change in residence time Δt is expressed as:

[0065]

[0066] in: Indicates the pumping frequency increment (in Hz). This indicates the volumetric flow rate of steam condensate (unit: m³ / s). V represents the difference between the target residence time and the actual residence time, and V represents the volume of the diversion pipe (unit: m³). This indicates the efficiency coefficient of the centrifugal pump (value is 0.85). The pumping frequency increment command is transmitted to the frequency converter via the PROFIBUS-DP protocol, and the frequency converter resolution is set to 0.01Hz.

[0067] The optimized control command set uses a structured data frame encoding format, with each frame containing 32 bytes of valid data. The data frame header includes a timestamp and stage identifier, while the main body stores pulse width modulation signal parameters (duty cycle reference value, modulation frequency, rise time) and pumping frequency commands (starting frequency, target frequency, acceleration slope). The command set checksum is generated using the CRC-16 algorithm to ensure transmission integrity. The control command output cycle is synchronized with the heat recovery stage marking cycle and is fixed at 500 milliseconds. After receiving the pulse width modulation signal, the positioner of the electric regulating valve converts the electrical signal into a 20-100 kPa pneumatic pressure signal via an electro-pneumatic converter. The valve opening is linearly related to the pneumatic pressure; for every 1 kPa increase in pressure, the valve opening increases by 0.8%. Closed-loop feedback of the valve stroke uses an LVDT displacement sensor to correct opening deviations in real time. The centrifugal pump's variable frequency speed control uses vector control mode, with the acceleration slope of the frequency command set to 5 Hz / s to avoid water hammer. A turbine flow meter is installed at the pump outlet to verify the correspondence between flow rate and frequency.

[0068] The heat exchange rate suppression strategy during the unstable phase includes an anti-oscillation algorithm. When a temperature fluctuation frequency exceeds 2Hz, the rate of change limit is automatically activated, with the upper limit set at 10kW / ℃. m² The switching logic for short-path pipelines includes pressure balance detection, opening the bypass valve before closing the main valve to prevent pressure surges. Before activating long-path pipelines in the high-efficiency recovery phase, a preheating procedure is performed, raising the pipeline wall temperature to above the dew point temperature via an auxiliary heater. The optimized control command set's anomaly handling mechanism includes timeout retransmission and command readback verification. If no actuator response signal is received within 500 milliseconds, the most recent command is automatically retransmitted; three consecutive failed retransmissions trigger an alarm. The duty cycle limiting protection for pulse width modulation signals is set to 20%-95% to prevent valves from being fully closed or fully open. The soft-start function of the pumping frequency command ensures smooth motor acceleration, with an S-shaped acceleration / deceleration curve. The safety interlock of the steam condensate system is integrated into the control command set; when the heat exchanger tube wall temperature exceeds 150°C or the system pressure falls below 50 kPa, the heat exchange rate is forcibly reduced to 50% of the baseline value. The emergency discharge function of the diversion path is implemented through a solenoid valve, automatically opening the discharge valve when the liquid level is too high. Historical control command data is stored cyclically in FLASH memory, and operation records for the most recent 30 days are saved for accident analysis.

[0069] Example 4: Real-time temperature and pressure data at the heat exchanger outlet are acquired using a platinum resistance temperature sensor and a capacitive pressure transmitter installed on the outlet pipeline. The temperature sensor has a measurement range of 0-200℃ and an accuracy class of A. The pressure transmitter has a range of 0-1.6MPa and an accuracy class of 0.5. The acquisition system continuously records the measured values ​​at a sampling frequency of 10Hz. The data is transmitted to the PLC analog input module via a 4-20mA current signal. The module has a 16-bit resolution, and each sampling point undergoes digital filtering. The deviation between the real-time temperature and pressure data and the temperature or pressure threshold of the marked stage is detected using a sliding window comparison algorithm. The window size is set to 10 sampling points (corresponding to 1 second of data), and the tolerance value is dynamically adjusted according to the system operating conditions. The initial temperature deviation tolerance is set to ±2.5℃, and the pressure deviation tolerance is set to ±0.05MPa. When the measured values ​​of three consecutive sampling cycles exceed the tolerance range, a deviation alarm is triggered. After the phased recalibration process is initiated, the system extracts temperature and pressure data from the ten most recent sampling points to construct a temporary sample set. The sample set data undergoes moving average filtering preprocessing with a filter window length of 3. The root mean square error (RMSE) is calculated using a standard formula, calculating the deviation of both temperature and pressure data from the current phase threshold. The calculation process is accelerated using a floating-point unit. The phased recalibration process triggering logic includes a three-level confirmation mechanism: the first level detects instantaneous deviations, the second level verifies persistent trends, and the third level performs correlation analysis. When all three levels of confirmation pass, the system automatically suspends the output of the current optimized control instruction set and enters the phased recalibration state. During the recalibration process, the system maintains the current state of the actuators unchanged to avoid sudden changes in operating conditions (see Table 1).

[0070] Table 1: Correspondence between Triggering Conditions and Execution Actions for Stage Recalibration Process

[0071]

[0072] The real-time data monitoring module employs a dual-buffer alternating storage mechanism to ensure the continuity of data acquisition and processing. The timestamp of each sampling point is synchronized via a hardware clock, achieving millisecond-level accuracy. The deviation detection algorithm includes temperature-pressure coupling compensation, considering the impact of temperature changes on pressure measurement; the compensation coefficient is experimentally determined. The judgment logic for the root mean square error calculation result incorporates a lag interval to prevent frequent switching near the threshold. When the error value is in a critical state, the system maintains the original judgment result until the error significantly exceeds the limit. The judgment of three consecutive exceedances is implemented based on a state machine, and the state transition conditions have been rigorously verified. After the full-process recalculation of the stage recalibration process is initiated, the system starts from the reconstruction of the dynamic thermodynamic parameter set and re-executes the multi-scale decomposition, feature extraction, and index calculation steps. The maximum time constraint for the recalculation process is within 5 seconds; exceeding this time limit triggers a degradation processing strategy. After the new stage is marked, the system compares the differences between the old and new stages; when the stage type changes, a smooth transition strategy is executed. The freeze mechanism for the optimized control instruction set uses a semaphore interlocking method, maintaining the last valid instruction output during the freeze period. Instruction set updates use an incremental write method to avoid data cliffs. The weight redistribution is based on the characteristic parameters of the new stage, and the weight coefficients are obtained through a lookup table. The real-time matching module's anomaly recovery function includes a timeout handling mechanism; when the recalibration process is abnormally interrupted, the system automatically recovers to the most recent stable state. All recalibration operations are logged in detail, with key information including trigger time, recalibration reason, and processing results. The safety protection of the steam condensate system remains active during recalibration; when equipment operating parameters are detected to be approaching safety limits, the recalibration process is immediately terminated and an emergency control mode is activated. The system resource management module dynamically allocates computing resources to ensure that the recalibration process does not affect basic control functions.

[0073] Example 5: The starting point of closed-loop regulation is the continuous acquisition and feedback of real-time adjustment data of valve opening and pumping frequency to the dynamic thermodynamic parameter set. This data is transmitted periodically via PROFIBUS-DP fieldbus, with each data packet containing a timestamp accurate to milliseconds, a device identifier, and the actual output value. The data acquisition module adopts a dual-redundancy design: the main channel uses the analog input module of a Siemens S7-1500 series PLC, and the backup channel uses an independent data acquisition card with a sampling frequency set to 10Hz. The dynamic thermodynamic parameter set is updated using a transaction processing mechanism of an SQL database. Each update operation records a complete operation log, including the data version number, update timestamp, and operator identifier. A practical example of a steam condensate system during a certain operating cycle illustrates this process: when the heat exchanger valve opening is adjusted from 45% to 52%, the actual opening data fed back by the valve positioner and the corresponding pumping frequency change value are recorded in the dynamic thermodynamic parameter set. These real-time adjustment data are correlated with temperature distribution data and pressure fluctuation data at the same point in time, with the correlation key using the ISO8601 standard time format. The updated dynamic thermodynamic parameter set immediately triggers a data ready event, and the event-driven mechanism initiates the next round of multi-scale decomposition. The multi-scale decomposition algorithm automatically expands the processing dimensions, incorporating valve opening change rate and pumping frequency gradient into the analysis scope, forming a six-dimensional feature matrix. The consistency of the calculated flow stability index and waste heat transfer efficiency index is verified at a fixed period, using the atomic clock-synchronized system clock as the benchmark, with a verification period set to 300 seconds. The verification process employs a three-channel comparison method: the main channel uses real-time data to calculate index values, the reference channel uses historical data from the same period to calculate control values, and the verification channel uses theoretical model outputs standard values. The calculation results from the three channels undergo variance analysis, with the difference threshold set to a relative error not exceeding 3%. When inconsistencies are detected, the system automatically initiates a parameter correction process, correcting the decomposition level of wavelet packet transform and the screening stopping criteria of empirical mode decomposition.

[0074] The specific application can be seen in the operation log of a certain workday: At 14:30 in the afternoon, the system detected that the difference in the main reference channel of the flow stability index reached 7.2%, exceeding the tolerance range. The diagnostic module traced the source of the difference and found that the energy distribution of the high-frequency component of the temperature distribution data was abnormal. The system then adjusted the number of decomposition layers of the wavelet packet transform from 8 to 10, and tightened the screening stop criterion of the empirical mode decomposition from 0.001 to 0.0005. These correction operations were deployed to the production environment after simulation verification, effectively eliminating the calculation deviation between channels and restoring the index to consistency. The decomposition parameter correction of the thermodynamic feature matrix adopted a gradual adjustment strategy, with each correction controlled within 5% of the original value. The correction process recorded a detailed operation log, including the parameter values ​​before correction, the correction amount, the correction time, and the improvement in index consistency after correction. All correction operations were verified by Monte Carlo simulation before being applied to the production environment to ensure system stability. The parameter correction algorithm adopted the PID control principle, dynamically adjusting the correction step size based on the historical correction effect. The closed-loop regulation data flow forms a complete loop: raw data collected by sensors is decomposed into control commands through multi-scale decomposition, which drive the actuators to perform actions. The execution results are fed back to the data acquisition end, forming a continuously optimizing cycle. Each time this loop completes a cycle, the system parameters are fine-tuned, continuously bringing the entire waste heat recovery process closer to its optimal state. Data flow management is implemented using message middleware to ensure the reliability and orderliness of data transmission. The system also establishes a quality assessment mechanism for parameter correction, monitoring the consistency changes of indicators over the next three verification cycles after each correction. Evaluation indicators include adjustment accuracy, response time, and system stability parameters. The evaluation results generate a performance report for optimizing system parameters. If consistency continues to improve, the correction is confirmed as effective; if deterioration occurs, the system automatically rolls back to the parameter settings before the correction. This conservative correction strategy ensures the long-term reliability of the system. The evaluation algorithm uses reinforcement learning methods from machine learning to automatically optimize the evaluation strategy based on historical data.

[0075] In another operational instance, continuous adjustment of the pumping frequency caused changes in pressure fluctuation characteristics. The system automatically adjusted the feature extraction parameters through closed-loop regulation. This adaptive capability enables the steam condensate waste heat recovery method to cope with changes in operating conditions and maintain efficient operation. The system also has an anomaly handling mechanism; when equipment operating parameters are detected to be close to safety limits, the correction process is immediately terminated and an emergency control mode is activated. The entire closed-loop regulation process requires no manual intervention, achieving true intelligent control. The data acquisition hardware uses Rosemount 3051 series pressure transmitters and E+H temperature sensors, achieving a measurement accuracy of 0.1%. The signal conditioning circuit uses a 24-bit ADC converter, with anti-interference capabilities meeting industrial EMC standards. The communication protocol uses Modbus TCP / IP, with a data transmission rate of 100Mbps, ensuring real-time requirements. The database system uses the time-series database InfluxDB, optimizing the storage and query performance of large amounts of time-series data. The multi-scale decomposition algorithm is implemented using MATLAB code generation technology, converting the algorithm into C++ code and running it on the embedded system. Matrix operations during the decomposition process are accelerated using the Intel MKL math library, ensuring computational efficiency. Feature extraction employs principal component analysis (PCA) to reduce data dimensionality while retaining key feature information. The real-time performance monitoring interface is developed using web technology, supporting remote access and mobile viewing. The parameter correction strategy includes establishing a parameter correction knowledge base to store historical correction records and effect evaluation data. Correction decisions utilize an expert system approach, selecting the optimal correction scheme based on rule-based reasoning. The security protection mechanism employs two-factor authentication and digital signature technology to ensure system operational security. The system also features an automatic backup function, regularly backing up key parameters and operational data to prevent data loss.

[0076] 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.

[0077] 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 recovering waste heat from steam condensate, characterized in that, Includes the following steps: Collect temperature distribution and pressure fluctuation data of steam condensate during pipeline flow to form a dynamic thermodynamic parameter set; Multi-scale decomposition of dynamic thermodynamic parameter set is performed to extract high-frequency and low-frequency components of temperature distribution data, while separating periodic and non-periodic features of pressure fluctuation data to generate thermodynamic feature matrix. Based on the high-frequency components and periodicity of the thermodynamic characteristic matrix, a flow stability index for steam condensate is constructed. Combined with the low-frequency components and non-periodicity, a waste heat transfer efficiency index is calculated. Based on the dynamic relationship between flow stability index and waste heat transfer efficiency index, the heat recovery stages of steam condensate are divided, and the corresponding temperature threshold and pressure threshold for each stage are marked. Based on the marking results of the heat recovery stage, the heat exchange rate and condensate flow path of the waste heat recovery device are adjusted to generate an optimized control instruction set; By optimizing the control instruction set to drive the valve opening and pumping frequency of the heat exchanger, the thermodynamic state of the steam condensate is matched in real time, thus completing the closed-loop regulation of the waste heat recovery process. The multi-scale decomposition of the dynamic thermodynamic parameter set includes: Wavelet packet transform is used to decompose the temperature distribution data into layers according to a preset frequency band, and the high-frequency and low-frequency components of temperature at different scales are obtained. Empirical mode decomposition is applied to pressure fluctuation data to extract intrinsic mode function components. The first three intrinsic mode function components are used as periodic features, and the remaining components are used as non-periodic features. The high-frequency components of temperature, the low-frequency components of temperature, the periodic features, and the non-periodic features are aligned by timestamps and combined into a four-dimensional thermodynamic feature matrix.

2. The method for recovering waste heat from steam condensate according to claim 1, characterized in that, The flow stability index for the constructed steam condensate includes: The ratio of the energy entropy of the high-frequency temperature component to the variance of the periodic characteristics is calculated, and the weighted sum of the energy entropy and variance ratios is used as an index of flow stability. The calculation of waste heat transfer efficiency index includes: By performing a sliding window integration on the low-frequency temperature component, the cumulative heat capacity value is obtained. Combined with the amplitude mean of the non-periodic characteristics, a waste heat transfer efficiency index is generated.

3. The method for recovering waste heat from steam condensate according to claim 2, characterized in that, The division of the heat recovery stage for steam condensate includes: When the flow stability index exceeds the preset threshold and the waste heat transfer efficiency index is lower than the critical value, it is marked as an unstable stage. When both the flow stability index and the waste heat transfer efficiency index are within the preset range, it is marked as the high-efficiency recovery stage. The temperature and pressure thresholds are dynamically updated based on the switching frequency between the unstable phase and the efficient recovery phase.

4. The method for recovering waste heat from steam condensate according to claim 3, characterized in that, The adjustment of the heat exchange rate and condensate flow path of the waste heat recovery device includes: During the unstable phase, the heat exchange rate is controlled below the baseline value, and the residence time of the condensate flow path is shortened. In the high-efficiency recovery phase, the heat exchange rate is increased to a value higher than the baseline, and the residence time of the flow path is extended accordingly.

5. The method for recovering waste heat from steam condensate according to claim 4, characterized in that, The generated optimized control instruction set includes: The heat exchange rate adjustment is converted into a pulse width modulation signal to control the valve opening of the heat exchanger. Based on the change in residence time along the flow path, an incremental command for pumping frequency is generated to drive the speed adjustment of the centrifugal pump.

6. The method for recovering waste heat from steam condensate according to claim 5, characterized in that, The real-time matching of the thermodynamic state of steam condensate includes: Monitor the real-time temperature and pressure data at the heat exchanger outlet. When the deviation from the temperature or pressure threshold of the marking stage exceeds the tolerance, trigger the stage recalibration process. The control instruction set is updated and optimized based on the recalibration results, and the adjustment weights of valve opening and pumping frequency are reallocated.

7. The method for recovering waste heat from steam condensate according to claim 6, characterized in that, The trigger phase recalibration process includes: Extract the ten most recent sampling points of real-time temperature and pressure data, and calculate their root mean square error with the current stage threshold; If the root mean square error exceeds the tolerance three times consecutively, the heat recovery stage division process will be re-executed, and the output of the optimization control instruction set will be frozen until the new stage is marked.

8. The method for recovering waste heat from steam condensate according to claim 7, characterized in that, The closed-loop regulation for completing the waste heat recovery process includes: The real-time adjustment data of valve opening and pumping frequency are fed back to the dynamic thermodynamic parameter set to form the input for the next round of multi-scale decomposition; The consistency between the calculated flow stability index and the waste heat transfer efficiency index is verified at fixed intervals, and the decomposition parameters of the thermodynamic characteristic matrix are corrected.

9. A waste heat recovery system based on steam condensate, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, When the processor executes the computer program, it implements the steps of the steam condensate waste heat recovery method as described in any one of claims 1 to 8.

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