Method for producing carbon source through co-fermentation of sludge and kitchen waste

Abstract

The invention discloses a method for producing a carbon source through co-fermentation of sludge and kitchen waste, and relates to the technical field of organic waste recycling, and the method comprises the following steps: collecting a bubble generation time sequence, a liquid phase pressure fluctuation signal and an acoustic scattering signal in a sludge and kitchen waste co-fermentation system, and constructing a coupling distribution model of a bubble size and an oscillation frequency; generating an initial energy evolution spectrum; and under the constraint of the initial energy evolution spectrum, identifying a coherent feature sequence of bubble oscillation, calculating the energy concentration intensity of a multi-scale bubble group, positioning a high-frequency energy concentration area and generating a nonlinear resonance fingerprint. By means of dynamic monitoring and intelligent regulation and control, the energy gathering area is accurately positioned, the resonance process is intervened in real time, suppression of shock wave propagation and predictive control over the structural fatigue risk are achieved, and the fermentation system stability and the carbon source release efficiency are improved.

Description

Technical Field

[0001] This invention relates to the field of organic waste resource utilization technology, specifically to a method for co-fermenting sludge and kitchen waste to produce a carbon source. Background Technology

[0002] "Co-fermentation of sludge and food waste to produce carbon sources" refers to mixing residual sludge from urban wastewater treatment with food waste from residential or catering industries in a certain proportion and co-fermenting them under anaerobic conditions. Through the metabolic action of microorganisms, the organic matter (such as proteins, fats, and carbohydrates) is decomposed and converted into soluble organic carbon sources (such as volatile fatty acids, acetic acid, propionic acid, and butyric acid). These carbon sources can serve as electron donors or energy sources in subsequent biological denitrification, phosphorus removal, or fermentation energy production (such as methanogenesis and hydrogen production), achieving waste reduction, harmlessness, and resource utilization. In short, this process not only reduces the burden of sludge and food waste emissions but also produces highly valuable liquid carbon sources, providing low-cost, renewable biological carbon resources for wastewater treatment plants and environmental governance systems.

[0003] The existing technology has the following shortcomings: In existing technologies, mechanical stirring and natural gas release are commonly used in the co-fermentation of sludge and kitchen waste to achieve gas release. However, due to the dynamic fluctuations in the viscosity, surface tension, and loading rate of the materials in the fermentation system, uneven bubble size often occurs during bubble formation, resulting in a multi-peaked superposition distribution of bubble groups in the liquid phase. When the oscillation frequencies of bubbles of different scales couple under specific conditions, a nonlinear resonance effect is formed, causing localized energy accumulation. At this time, micro-scale bubbles will undergo high-frequency collapse and localized concentrated energy release under the action of transient pressure field, forming a microsecond-level shock wave field. When this shock wave propagates inside the tank, it will cause periodic mechanical impacts on the stirring blades and pipe connections, leading to the accumulation of microcracks on the structural surface, accelerated metal fatigue, and even fracture failure. In severe cases, it can also cause disordered gas-liquid distribution and a sharp drop in reaction efficiency in the fermentation system.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method for co-fermenting sludge and kitchen waste to produce a carbon source, so as to solve the problems in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for co-fermenting sludge and kitchen waste to produce a carbon source, comprising the following steps: The bubble generation time sequence, liquid phase pressure fluctuation signal and acoustic scattering signal in the sludge and kitchen waste co-fermentation system were collected. A coupled distribution model of bubble size and oscillation frequency was constructed to generate an initial energy evolution spectrum containing time, frequency and intensity information. Under the constraint of the initial energy evolution spectrum, the coherent characteristic sequence of bubble oscillation is identified, the energy accumulation intensity of multi-scale bubble swarms is calculated, the high-frequency energy accumulation region is located, and a nonlinear resonance fingerprint is generated. Based on nonlinear resonant fingerprints, a counterfactual replay chain is constructed to simulate the entire process of bubble merging and collapse, outputting the spatiotemporal distribution trajectory of shock wave propagation and generating a causal residual density map characterizing the energy transfer path. Based on the analysis of the mechanical response relationship between the stirring blade and the tank, the energy concentration area and the location of structural fatigue points are identified, a trigger threshold window is generated, and a controllable suppression bandwidth model is constructed. A feedforward constraint field is established based on the controllable suppression bandwidth model. A phase conjugate correction operation is applied to the gas-liquid resonance region in the fermentation system, and cavitation damping is injected to form an energy dissipation path and reduce shock wave accumulation. Under the action of the feedforward constraint field, a self-suppressed interference control instruction set is generated, and a linkage mechanism including time-frequency inverse diffusion mechanism and amplitude limiting write-back strategy is implemented to reconstruct the gas-liquid circulation path and vortex intensity distribution, so as to achieve rapid dissipation of abnormal energy and steady-state recovery of the system.

[0007] Preferably, the initial energy evolution spectrum generation steps are as follows: After the fermentation unit is running stably, the optical interferometry detection device, the piezoelectric hydraulic response acquisition detector and the photoacoustic wave detection device are started to collect the bubble generation and rupture time series, liquid phase micro-pressure disturbance signal and acoustic wave scattering intensity change signal, respectively. By using a high-frequency acquisition circuit to perform synchronous calibration with a unified time reference, the three types of signals are reconstructed and matched according to a unified time base, and the size change, main oscillation frequency and energy response value of each bubble are extracted. Based on the extracted data, a coupled distribution map containing bubble size, oscillation frequency and energy response intensity is constructed, and the energy characteristics are superimposed and analyzed with a unified time axis to generate an initial energy evolution spectrum containing time, frequency and intensity information.

[0008] Preferably, the nonlinear resonant fingerprint generation steps are as follows: The bubble oscillation trajectories in each time unit recorded in the initial energy evolution spectrum are extracted and numbered. Bubble trajectories with consistent frequency change trends, synchronously enhanced acoustic response, and the same pressure fluctuation direction are identified to construct a coherent oscillation sequence group. Multiple coherent oscillation sequences are longitudinally merged and divided into frequency bands. Frequency regions with enhanced energy density and multi-scale characteristics are selected and identified as high-frequency energy coupling regions. A composite identification spectrum is established based on five types of data: frequency, energy, bubble volume density, acoustic response, and hydraulic directional field, and encoded as a nonlinear resonant fingerprint.

[0009] Preferably, the steps for generating the causal residual density map are as follows: Based on the multidimensional features recorded in the nonlinear resonant fingerprint, the bubble evolution sequence is extracted and physical boundary conditions are constructed; Simulate the entire process of bubble merging and collapse based on bubble size changes and hydraulic response behavior, and plot the energy release trajectory. A shock wave propagation path model was established and multi-source shock wave trajectories were superimposed to construct an energy coupling zone map; Residual analysis was performed on the theoretical propagation trajectory and the measured energy response, and a causal residual density map was generated. Based on the residual dense region, a causal relationship chain is constructed by backtracking and the concentrated path of energy transfer to the structural interface is identified.

[0010] The preferred method for constructing a controllable suppression bandwidth model is as follows: The energy concentration region is spatially registered with the structural model of the stirring device in a three-dimensional coordinate system, and a perturbation mapping relationship is established. Based on the registration results, energy input data of key parts are extracted and structural mechanical response curves are established; Based on the structural fatigue evolution trend, a joint triggering threshold window is constructed and a high-sensitivity triggering region is extracted; An energy bandwidth adjustment strategy is established based on the frequency range and structural response intensity, and a controllable suppression bandwidth model is generated.

[0011] Preferably, the target frequency range in the controllable suppression bandwidth model is obtained by expanding it up and down by a fixed ratio based on the center frequency of the trigger frequency band. In the adjustment strategy, the injection path, energy suppression amplitude, and action period are set collaboratively according to the structural resonance characteristics of the device and the acoustic parameters of the liquid medium.

[0012] Preferably, the steps for establishing a feedforward constraint field based on a controllable suppression bandwidth model, applying phase conjugate correction to the gas-liquid resonance region, and injecting cavitation damping to construct an energy dissipation path are as follows: Based on the frequency center value and energy change rate in the controllable suppression bandwidth model, a resonance risk space field is established and a basic framework for the feedforward constraint field is constructed. A broadband tunable sound source array is deployed in the resonant risk space field, and a phase conjugate control signal is applied to form an intervention sound field. Microbubble clusters within a particle size control range are injected into the target liquid layer and a microcavitation process is induced to form a dissipation path; Periodically extracting energy distribution and structural response data to form a control baseline and then correcting feedforward parameters to achieve a closed-loop intervention.

[0013] Preferably, the steps for generating a self-suppressive interference control command set under the action of a feedforward constraint field, implementing a linkage mechanism, and reconstructing the gas-liquid circulation path and vortex intensity distribution are as follows: The residual density of acoustic field energy and gas-liquid disturbance parameters are collected to generate an interference control command set; Based on the control instruction set, a reverse diffusion wave group is generated and a multi-directional intervention wave injection strategy driven by frequency misalignment is implemented; A limiting write-back mechanism is implemented to control the acoustic pressure amplitude in the disturbed area and correct the agitator drive parameters and the gas-liquid circulation main shaft path. Steady-state confirmation is completed based on real-time indicators after the disturbance, and the set of control strategy parameters is recorded for subsequent rapid response.

[0014] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention collects dynamic behavior and acoustic response data of bubbles, constructs a coupled distribution model, and generates an initial energy evolution spectrum. It then extracts the resonance characteristics of multi-scale bubbles, achieving precise positioning and dynamic tracking of energy focusing regions. By reconstructing the shock wave propagation path through a counterfactual playback chain, and establishing a structural response mapping and controllable suppression bandwidth model based on a causal residual density map, it enables the prediction and control of impeller and tank fatigue risks. Furthermore, it constructs a feedforward constraint field and a self-suppression interference linkage mechanism, enabling the fermentation system to perceive, interfere with, absorb, and reconstruct abnormal energy fluctuations, forming an integrated steady-state maintenance mechanism with real-time response, directional diffusion, amplitude limiting correction, and stable rearrangement functions. Compared to existing control methods relying on static stirring optimization and structural reinforcement, this invention can significantly reduce the risk of structural fatigue accumulation during equipment operation, improve gas-liquid mixing uniformity and carbon source release efficiency, effectively extend equipment service life, and improve the overall operational stability of the fermentation system. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0016] Figure 1 This is a flowchart of a method for co-fermenting sludge and kitchen waste to produce a carbon source according to the present invention. Detailed Implementation

[0017] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0018] This invention provides, for example Figure 1 The method for co-fermenting sludge and kitchen waste to produce a carbon source, as shown, includes the following steps: The bubble generation time sequence, liquid phase pressure fluctuation signal and acoustic scattering signal in the sludge and kitchen waste co-fermentation system were collected. A coupled distribution model of bubble size and oscillation frequency was constructed to generate an initial energy evolution spectrum containing time, frequency and intensity information. To achieve high-precision observation and energy anomaly identification of bubble behavior during the co-fermentation of sludge and kitchen waste, a bubble signal acquisition and energy spectrum reconstruction method based on nanosecond-level time synchronization is proposed, which includes the following steps: After the anaerobic fermentation unit stabilized, a high-precision signal acquisition device was activated. An optical interferometry detector was installed at the bottom of the fermenter, a piezoelectric hydraulic response detector was installed on the side wall, and a photoacoustic wave detector was installed vertically near the liquid surface at the top of the fermenter. These devices were used to acquire time-series signals of bubble formation and collapse, pressure disturbance signals generated by bubble behavior within the liquid phase, and acoustic wave scattering signals generated by bubble collapse or high-speed oscillation. The optical interferometry detector continuously acquired microscopic image frames within the liquid phase and used an inter-frame difference algorithm to analyze the bubble generation, expansion, merging, and collapse processes, extracting the specific time points and size change curves of each bubble from its birth to its disappearance. The piezoelectric response detector recorded the micro-pressure fluctuations in the liquid caused by rapid volume changes or collapse of bubbles, converted them into electrical signals, and output a transient pressure change time series. The photoacoustic wave detector recorded the acoustic wave scattering signals generated by bubble interface vibrations, and obtained acoustic intensity change curves reflecting the frequency and morphological characteristics of bubble surface motion through spectral analysis. All three types of signals are uniformly time-referenced and calibrated using a dedicated high-frequency acquisition circuit. All original signals are recorded with nanosecond-level synchronization accuracy, ensuring strict time comparability between the information acquired by each data channel. This avoids timing misalignment between different signal sources due to sampling rate or signal delay, thus achieving a data acquisition foundation with high spatiotemporal coupling.

[0019] The three types of signals are jointly processed using a unified time reference. First, a time series registration algorithm is used to reconstruct each data stream. The bubble morphology data in the optical image frames are precisely sorted according to the time axis and matched one-to-one with the pressure change data and acoustic wave scattering intensity data at the corresponding time points. After signal alignment, an image recognition algorithm is used to extract the dynamic size of each bubble and calculate the rate of size change per unit time. Simultaneously, the liquid phase disturbance intensity corresponding to the bubble is identified through pressure fluctuation signal analysis, and the main oscillation frequency of the bubble is extracted through acoustic wave frequency feature analysis. Under the condition of synchronization of the three types of information, a three-dimensional physical feature dataset for each bubble is constructed, including generation time, maximum diameter, collapse duration, oscillation frequency, and acoustic wave energy response. All datasets are clustered and categorized in two dimensions, bubble size and oscillation frequency, respectively, using a distribution fitting method. Finally, a coupled distribution map is generated with the maximum bubble diameter on the horizontal axis, the main oscillation frequency on the vertical axis, and the color level representing the corresponding energy response intensity. This map not only comprehensively shows the scale distribution characteristics of the bubble population formed under specific fermentation conditions but also reveals the correlation characteristics between size differences and oscillation frequency, which is a key foundation for analyzing the local energy accumulation trend and resonance possibility.

[0020] Based on the statistical results of the coupled distribution map, the energy characteristics of all individual bubbles are sequentially superimposed on a unified time axis to construct an initial energy evolution spectrum. The specific steps are as follows: First, the maximum oscillation frequency, strongest acoustic wave scattering amplitude, and liquid phase pressure response value corresponding to each bubble within its life cycle are extracted. These physical quantities are normalized and mapped to standardized energy contribution indicators. Then, using a unified time axis as a reference, the energy contribution values ​​of all bubbles within the same time window are accumulated to generate a total energy response curve per unit time. Next, the above accumulation calculation is repeated in a fixed-time-step sliding window manner throughout the complete fermentation cycle to form a continuous energy response spectrum. Finally, frequency and intensity information are superimposed onto the spectrum as dimensions to generate an initial energy evolution spectrum containing time, frequency, and energy dimensions. This evolution spectrum can clearly show the time points of abnormally rapid energy growth or local abnormal high-frequency surges during fermentation, providing a basis for subsequent high-frequency resonance identification, nonlinear response inversion, and structural risk prediction.

[0021] Under the constraint of the initial energy evolution spectrum, the coherent characteristic sequence of bubble oscillation is identified, the energy accumulation intensity of multi-scale bubble swarms is calculated, the high-frequency energy accumulation region is located, and a nonlinear resonance fingerprint is generated. After the initial energy evolution spectrum is constructed, in order to accurately identify potential high-frequency resonant energy sources, the following method is proposed to further analyze the time series characteristics, frequency coupling trends, and energy concentration behavior during bubble oscillation: Bubble data within each time unit recorded in the initial energy evolution spectrum were extracted item by item, and a single-bubble oscillation characteristic trajectory was constructed according to a unified time axis. This trajectory includes: the size change curve of the bubble from generation to rupture, the transient change trend of the oscillation frequency, the change of acoustic scattering signal intensity response over time, and the value of liquid phase micropressure change within the corresponding time period. The above four types of data were bound to the life cycle of each individual bubble by number, and an oscillation trajectory diagram with physical attribute labels was constructed on the time axis. Subsequently, by performing trajectory overlap analysis on all bubble trajectories, multiple bubble oscillation trajectories with similar frequency change rates, synchronous enhancement of acoustic energy response, and consistent pressure fluctuation direction were identified, and these trajectories with common behavioral characteristics were grouped into the first type of coherent oscillation sequence group. To ensure the stability and repeatability of the identified physical behavior correlation, the frequency phase offset difference, energy release synchronization index, and pressure response time overlap degree between each trajectory pair were calculated within the coherent sequence group, and trajectory pairs that did not meet the coupling strength threshold were screened out, retaining only highly coherent bubble behavior sequences with substantial physical correlation.

[0022] For the identified first-type coherent oscillation sequence group, multiple coherent sequences are longitudinally merged within a unified time frame and regrouped according to the distribution characteristics of the dominant oscillation frequency and the number of participating bubbles to construct a high-density oscillating energy cluster with multi-scale characteristics. This process first divides each sequence group into fixed frequency bands according to frequency division criteria, and statistically analyzes five indicators within each band: average bubble size, number density, oscillation duration, average acoustic intensity, and average hydraulic response. Then, using a frequency band-by-band time-series superposition method, energy density regression fitting is performed on frequency bands exhibiting increased frequency concentration, gradually increasing bubble number density, and rapidly increasing energy release per unit time within continuous time intervals to determine whether they are core frequency bands for energy accumulation. For frequency bands that simultaneously meet the three conditions of increased aggregation, reduced frequency convergence range, and improved energy release stability within two or more continuous time intervals, they are identified as multi-scale energy coupling regions. The regional characteristics obtained through this step not only include frequency band range, energy density trend, and the number of participating oscillating bubbles, but also reflect the dynamic synchronicity characteristics of bubble behavior at both the physical and spectral scales, providing a physical basis for the next step of generating an identification map.

[0023] After identifying multiple high-frequency energy coupling regions, a nonlinear resonance fingerprint is constructed based on their physical property data. The fingerprint construction includes five specific steps: frequency characteristic curve extraction, energy evolution trend encoding, generation of participating bubble volume distribution density maps, acoustic response mode sequence fitting, and hydraulic fluctuation direction field calculation. First, the frequency characteristic curve is polynomial-fitted and inflection point information is extracted as time anchors in the resonance fingerprint. Second, the energy change slope within the corresponding time window is added to the fingerprint encoding as a numerical vector, forming an energy evolution layer. Next, the maximum size of each participating bubble within the corresponding time period is converted to volume, and a number density map within a unit volume region is constructed, forming a scale distribution layer. Then, an acoustic response mode map is constructed using the change in acoustic wave scattering intensity over time and superimposed on the acoustic layer. Finally, based on the numerical change of the liquid phase pressure detection signal at the spatial detection point, a micro-pressure fluctuation direction field vector map is constructed, generating a pressure response layer. Ultimately, the five data layers are spatiotemporally registered and superimposed, encoding a composite identification map containing five attributes: time node, frequency range, energy accumulation, volume density, and pressure orientation—this is the nonlinear resonance fingerprint proposed in this invention. This fingerprint can not only uniquely identify the energy spatial distribution characteristics of a certain high-frequency resonance process, but also serve as a basis for the identification and control of the entire process of subsequent bubble collapse path reconstruction, shock wave propagation direction prediction, and stress concentration area location of mechanical structures.

[0024] Based on nonlinear resonant fingerprints, a counterfactual replay chain is constructed to simulate the entire process of bubble merging and collapse, outputting the spatiotemporal distribution trajectory of shock wave propagation and generating a causal residual density map characterizing the energy transfer path. After completing the nonlinear resonance fingerprint extraction, in order to further analyze the spatiotemporal propagation behavior of the shock wave caused by bubble collapse in the liquid phase and clarify the energy transfer path and intensity evolution process, a method based on physical measurement data for back-reconstruction is proposed, which includes the following steps: Based on the time anchor points, main oscillation frequency range, bubble volume distribution density, acoustic response mode, and hydraulic disturbance characteristics recorded in the nonlinear resonance fingerprint, bubble behavior data segments matching the time sequence are extracted from the established original energy evolution spectrum. All individual bubbles participating in the resonance within this time period are individually screened to identify their maximum diameter change, oscillation amplitude, collapse start and end times, peak energy release time, acoustic wave scattering slope change, and liquid phase pressure disturbance response intensity. These physical quantities are structurally assembled to form a bubble evolution sequence for spatiotemporal reconstruction, providing realistic physical boundary conditions for counterfactual reenactment. Unlike existing technologies that only detect pressure anomalies at the moment of rupture, this step preserves the continuity of bubble morphology evolution and energy release by tracing back the entire oscillation process, providing a high spatiotemporal resolution foundation for subsequent modeling.

[0025] Based on the size changes, collapse trends, expansion rates, and hydraulic response curves of each bubble in the bubble evolution sequence, a complete dynamic behavior process from bubble merger to rupture is constructed. Specifically, bubble surface tension, internal and external pressure difference, local liquid density, and temperature coupling are used as input parameters to simulate the volume growth during the merger phase, the neck compression process, and the transient volume change during critical point collapse. By calculating the morphological change rate, surface area shrinkage ratio, and energy released per unit volume for each bubble before and after collapse on a time-slice basis, each collapse event is precisely mapped to a three-dimensional liquid phase space coordinate system, forming a spatialized energy release lattice map. Subsequently, the evolution path of this lattice over continuous time is plotted using an overlay method, obtaining a preliminary energy release trajectory map. The start time, end time, energy value, and spatial location of each energy node are marked, constructing a high-density energy release field covering the entire resonance process.

[0026] Based on the energy release trajectory map, the shock wave propagation behavior of each energy node in the liquid medium is calculated. First, a shock wave propagation path model is established based on actual parameters such as the sound velocity, viscosity, density, temperature gradient, and interface damping coefficient of the liquid phase. For each impact point generated by bubble collapse, the propagation direction, velocity, and energy attenuation coefficient of its initial shock wave front are calculated. Using this point as the source, the propagation position of the shock wave front in space is deduced step by step in time sequence, forming a single-source shock wave propagation trajectory map. Subsequently, the shock wave trajectories corresponding to all bubble source points are superimposed to construct the total shock wave field of the entire resonance event. By tracking the locations where shock waves meet, superimpose, interfere, and focus during propagation, regions where the shock wave intensity increases simultaneously and the propagation paths highly overlap within a unit time and unit space are identified and defined as shock energy coupling zones. These regions are assigned unique identifiers, and their formation time, focusing intensity, spatial scale, and duration are recorded to construct a concentrated propagation path network of shock waves throughout the entire cycle.

[0027] Based on the aforementioned shock wave trajectory map, residual analysis was performed to assess the deviation between the actual energy evolution spectrum and the predicted propagation trajectory. The theoretical propagation path and the measured energy response trajectory were compared one-to-one for each energy release point, calculating the differences in three dimensions: propagation time offset, the difference between the actual and predicted shock wave intensity, and the distance deviation of the impact location in the spatial coordinate system. All deviation values ​​were standardized, and a residual density map was plotted in three-dimensional spatial coordinates. The density value of each spatial cell in the map represents the degree of deviation accumulation from the theoretical path during shock wave propagation. Subsequently, cluster analysis was performed on all high residual density points, identifying regions that persisted, exhibited steep density gradient changes, and repeatedly appeared at multiple time points as anomalous energy accumulation paths. This path represents the concentrated transmission channel caused by non-uniformity during the energy transfer from bubble oscillation to the structural interface.

[0028] Using high residual density regions identified in the causal residual density map as anchor points, a causal chain was traced back along the shock wave propagation path. The bubble collapse source, propagation path, interference source point, and structural response point corresponding to each residual density region were arranged sequentially along the time axis, forming a continuous causal event chain. Each node in the chain recorded the occurrence time, corresponding energy release, number of participating shock waves, and superposition intensity, thus establishing a causal relationship map between the energy transfer path and the structural stress outcome. At the end of each chain, combining the direction of liquid phase pressure transmission and the structural rigidity characteristics, the stress concentration points where the shock wave finally reached the impeller, tank wall, and interface connection area were spatially labeled, and the stress accumulation rate under continuous impact was calculated. This result provides basic data for the next stage of structural fatigue point identification and threshold trigger window construction, and also verifies the causal mapping relationship between resonance fingerprint and shock wave propagation.

[0029] Based on the analysis of the mechanical response relationship between the stirring blade and the tank, the energy concentration area and the location of structural fatigue points are identified, a trigger threshold window is generated, and a controllable suppression bandwidth model is constructed. After constructing the causal residual density map, in order to quantify the specific impact of shock wave energy on the structure of the mixing equipment, the stress response behavior of the impeller and the tank is further analyzed, fatigue risk areas are identified, and a precise triggering mechanism and dynamic suppression model are established. The specific steps include: High-density energy concentration areas are marked in the causal residual density map and spatially registered with the physical structure model of the stirring device in a three-dimensional coordinate system to establish a one-to-one correspondence between energy disturbance and structural morphology. During registration, based on the equipment CAD model, the residual point coordinates are converted to a structural surface coordinate system and marked at specific structural locations such as the upstream side of the stirring blade, the blade connection root, the arc transition area of ​​the inner wall of the tank, the junction line between the tank bottom and side wall, and the feed inlet fixing ring. Subsequently, the energy superposition trajectory of these structural parts over the entire shock wave propagation cycle is extracted, recording the cumulative number of energy inputs, input energy density, impact direction change vector, and duration of action over multiple consecutive time periods. This constructs a time-space-energy correlation map for structural units, achieving high-precision mapping of energy disturbance paths onto the actual equipment structure. This method transforms abstract energy residual data into a model of mechanical interference effects on specific components, significantly outperforming existing coarse methods based on static heatmaps to identify hotspot areas.

[0030] After completing the energy disturbance structure mapping, the dynamic analysis of the structural stress response begins. This stage uses key structural components as units, establishing corresponding mechanical response curves based on the maximum input energy, directional change amplitude, and impact frequency they experience in the energy disturbance spectrum. Representative response points are selected, including the blade leading edge, trailing edge, root bolt connection area, the central axis of the tank inner wall, and the bottom rounded transition section. Combining material properties (including elastic modulus, yield strength, fatigue limit, and Poisson's ratio), structural wall thickness, support method, and local stress concentration factor, the instantaneous stress response changes under shock wave action are simulated. The stress rise rate per unit time, repetitive loading frequency, peak stress duration, and strain hysteresis are extracted to construct a response spectrum reflecting the trend of mechanical performance changes after continuous impact. For regions where stress peaks have not completely subsided before reloading in multi-cycle impacts, the micro-strain accumulation rate and critical stress threshold are used to determine whether the fatigue evolution range has been entered. Compared to the traditional finite element method, which can only evaluate static limits step by step, this identification mechanism has the ability to analyze the continuity and dynamic evolution in the time dimension, and is closer to the structural damage development path under actual operating conditions.

[0031] After identifying the structural fatigue evolution point, a trigger threshold window is further constructed to enable early intervention in the abnormal evolution process. This window design unfolds along two dimensions: time and frequency. In the time dimension, based on the energy input rate curve of the area surrounding the fatigue point, time periods where the energy input slope exceeds a set threshold and the peak duration exceeds a set number of cycles are identified and marked as time trigger windows. In the frequency dimension, the shock wave frequency distribution is analyzed within the aforementioned time periods, and the main oscillation frequency segment that rapidly transitions from low to high frequency within a short period is extracted, constituting a frequency trigger window. The two-dimensional windows are cross-operated to extract a joint window that simultaneously satisfies time concentration and a large frequency transition rate as a high-sensitivity trigger zone. Each window definition includes: start time, end time, peak frequency range, corresponding energy change rate, structural response rate, and affected area coordinate number. Background energy and structural response state for one complete cycle before and after each window are extracted as baselines. By comparing the states within the window, trigger intensity factors and intervention demand factors are obtained, providing input for subsequent dynamic control strategies. This mechanism possesses high responsiveness, quantification, and predictability, surpassing the existing technical paradigm of passive response mechanisms based on fixed thresholds.

[0032] Based on the frequency range of the trigger window, energy change trends, and structural response intensity, a controllable suppression bandwidth model is established to achieve active adjustment of the shock wave propagation path. The specific construction method is as follows: First, the trigger frequency band is extended upwards and downwards by 10% of its width around the center frequency, forming a symmetrical frequency band as the target control frequency range. Then, the energy change curve and structural response curve within this frequency band are energy-coupled and fitted to identify the frequency group with the most concentrated energy and the most intense structural response within this band. Subsequently, combining the wave impedance, viscosity, sound velocity of the liquid medium, and the resonant frequency of the device structure, the magnitude and duration of the anti-phase modulation energy required to be applied to this frequency group are calculated to achieve effective intervention. Finally, based on the shock wave propagation direction field, frequency characteristics, and device geometric parameters, a list of bandwidth injection strategies for different parts is constructed, including the application point location, intervention medium injection path, target frequency band, energy suppression amplitude, and duration of action. This bandwidth model is not only suitable for real-time adjustment but can also be used as preset engineering parameters for material selection, structural optimization, and control strategy setting during the design phase, exhibiting high versatility and adaptability.

[0033] A feedforward constraint field is established based on the controllable suppression bandwidth model. A phase conjugate correction operation is applied to the gas-liquid resonance region in the fermentation system, and cavitation damping is injected to form an energy dissipation path and reduce shock wave accumulation. After establishing a controllable suppression bandwidth model that includes energy response frequency band, action time range, and structural response characteristics, in order to effectively intervene in and control the shock wave aggregation trend in the gas-liquid resonance region of the fermentation system in real time, further feedforward constraint acoustic field construction and injection measures based on resonance characteristic prediction are implemented, specifically including the following steps: Based on the frequency center value, frequency band extension range, spatial coordinates of the structural fatigue region, and energy slope change rate identified in the controllable suppression bandwidth model, a three-dimensional spatial mapping model of the area to be intervened in is constructed inside the fermenter. This mapping model is based on the actual equipment geometry, projecting the energy accumulation area onto the outer edges of the stirring blades, the vertical high bubble density zone in the center of the tank, and the intersection of the transition curve between the tank wall and bottom, respectively designated as key energy response zones. For these regions, the mesh is refined within the spatial range, with each spatial cell labeled with its current bubble oscillation dominant frequency, local liquid pressure amplitude, sound wave propagation direction, and the trend of the residual value change from the previous period. This yields the resonance risk spatial field, which is then used as the target input to construct a pre-emptive disturbance carrier capable of responding in advance before bubble resonance events occur, serving as the basic framework of the feedforward constraint field.

[0034] Based on the resonant risk space field, an active sound source array for acoustic phase interference is designed to project an intervention signal with phase reversal characteristics, achieving phase conjugate control of the resonant core frequency. A broadband tunable sound source array made of piezoelectric ceramic material is selected and deployed around the fermenter, above the impeller axis, and in the liquid layer area surrounding the vertical middle section of the tank to ensure a full-coverage disturbance projection path. The sound source driving signal generates an initial waveform based on the center frequency in the suppression bandwidth model. The signal is a continuous sine wave superimposed with a frequency-converted carrier wave, possessing a fixed phase delay value to match the periodic variation characteristics of the resonant waveform. To achieve precise phase reversal, a wave velocity correction coefficient is introduced into the signal propagation path to correct for sound velocity shifts caused by temperature gradients, fluid density changes, or stirring disturbances, ensuring that the arrival time of the intervention wave in the target area overlaps with the peak of the resonant wave and achieves a 180-degree phase reversal. In actual intervention, by periodically adjusting the frequency and amplitude of the input signal of the excitation source, the acoustic disturbance energy stably covers the resonant frequency band, dynamically following the slight drift of the main oscillation frequency of the bubble in the system, thereby achieving substantial interference with the resonant behavior and energy dispersion.

[0035] While establishing the phase-conjugate acoustic field, to enhance the energy dissipation capability within the perturbation region, a bubble field for inducing micro-cavitation is further injected into the resonant region. This passively weakens some of the high-frequency resonant energy through a cavitation dissipation mechanism. High-purity nitrogen is used in this step, injected into the target liquid layer through a precision microporous ceramic diffuser to form a micro-nano bubble cloud with a particle size controlled between 1 and 3 micrometers. The bubble concentration is controlled at 1 × 10⁻⁶ per cubic centimeter. 6 The goal is to achieve a threshold density of approximately [number missing] microbubbles to ensure effective cavitation induction without affecting the main reaction process. When the conjugate sound wave propagates in the target area, it interacts with the microbubble cluster. Local bubbles collapse early under the superposition of sound pressure waves, releasing trace amounts of energy and simultaneously disrupting the resonant integrity of the original sound field, creating micro-energy dissipation points. These dissipation points form multiple distributed energy absorption sections along the sound wave propagation path, thus achieving gradual energy decomposition before the sound waves aggregate. To prevent the cavitation process from spreading to the critical reaction zone of the fermentation system, sound wave energy detection arrays are deployed on both sides of the gas injection area. When the sound pressure drops to a set lower limit, bubble injection is paused to prevent excessive diffusion of the cavitation induction zone from disturbing the fermentation environment. This strategy differs from traditional passive vibration reduction measures; instead, it actively induces a controlled cavitation process, transforming microscale collapse into an energy dissipation path and enhancing the ability to fine-grainedly intervene in resonant energy.

[0036] During the continuous operation of the feedforward constraint field, the intervention response, which is a synergistic superposition of phase conjugate acoustic waves and cavitation damping, is periodically compared with the energy evolution spectrum and causal residual density map to form a closed-loop control baseline. After each time period, the rate of change of energy density, the average bubble size distribution, the amplitude of change of liquid-phase acoustic impedance, and the number of high-frequency aggregation points within the target area are extracted as indicators for judging the control effect. If the energy aggregation intensity fails to reach the preset suppression target within two consecutive periods, the sound source signal recalibration procedure is triggered to adjust the excitation frequency, signal morphology, injection phase delay value, and energy amplitude; simultaneously, the bubble injection rate and particle size distribution are optimized to reduce the cavitation-induced zone or increase the local density. Through this closed-loop correction process, the feedforward constraint field maintains a high degree of adaptability to the actual resonance behavior under dynamic evolution conditions, thereby achieving real-time identification, rapid response, and continuous dispersal of energy aggregation. Ultimately, the internal resonance wave of the fermentation system is intervened and deconstructed before it is formed, the shock wave is dissipated and decomposed at multiple points in the propagation path, the gas-liquid distribution is restored to uniformity, and the structural response is restored to the low intensity range, effectively avoiding the problems of increased structural fatigue, fluctuations in reaction efficiency, and increased operational risks.

[0037] Under the action of the feedforward constraint field, a self-suppressed interference control instruction set is generated, and a linkage mechanism including time-frequency inverse diffusion mechanism and amplitude limiting write-back strategy is implemented to reconstruct the gas-liquid circulation path and vortex intensity distribution, so as to achieve rapid dissipation of abnormal energy and system steady-state recovery. After achieving active phase correction and cavitation energy dissipation of the high-frequency resonance path within the fermentation system using a feedforward constraint field, a multi-level, interconnected interference control command set is further implemented to maintain the long-term stability and dynamic controllability of the gas-liquid circulation system. Disturbance dissipation and structural restoration are achieved through reverse diffusion and amplitude-limiting write-back measures, specifically including the following steps: During the continuous operation phase of the feedforward constraint field, time-series data are collected on parameters such as acoustic energy residual density, vortex velocity distribution in the liquid phase, bubble size variation rate, and pressure gradient change rate in the fermentation reactor, and these data are archived and analyzed according to a unified time reference. At the end of each energy residual cycle, the perturbation core region in the current cycle is compared with the corresponding region in the previous cycle to extract the expansion trend of frequency fluctuation range, the rate of change of average sound pressure amplitude, and the drift rate of local bubble collapse frequency. If the residual density does not decrease significantly, or if the center of the newly formed vortex structure shifts and superimposes on the original pressure hotspot region, it indicates that the current feedforward control strategy has not fully covered the energy transfer path. At this time, based on the above spatiotemporal distribution trends, a self-suppressive interference control instruction set is generated, which includes: the perturbation frequency band to be enhanced, the fine-tuning range of the perturbation wave input delay, the adjustment direction of the vortex reconstruction point coordinates, the variation amplitude of the gas injection cavitation trigger rate, and the cycle adjustment coefficient. This instruction set is highly targeted and correlated with structural feedback, and is a core prerequisite for the dynamic response of the subsequent linkage mechanism.

[0038] Based on the frequency adjustment data and disturbance path update points in the interference control command set, the reverse diffusion wave group generation process is initiated, implementing a multi-directional intervention wave injection strategy driven by frequency misalignment. Multiple acoustic wave intervention devices are set up along the main liquid-phase circulation path within a range of approximately 0.2 to 0.5 meters outside the resonance zone. These devices are driven by multiple independent controllers to emit disturbance waves in misaligned frequency bands, covering 5% to 15% above the original resonance band, with a phase delay controlled to around 180 degrees, achieving reverse acoustic energy release. This reverse diffusion wave group propagates in the spatial path in the opposite direction to the original acoustic pressure wave. Upon entering the energy focusing zone, it interferes with the acoustic wave focusing boundary, causing asymmetrical stretching of the acoustic pressure field and forcing local bubbles to collapse prematurely due to coherent field disturbance. To enhance the spatial deployment capability of the diffusion wave group, a variable amplitude envelope wave structure is introduced during the injection process. The wavefront intensity is 0.8 times lower than the peak value of the center frequency, ensuring that energy disturbance occurs in the edge distribution area outside the main transmission path. This method achieves instability and directional decomposition of energy transfer within the sound pressure field, effectively disrupting the high-energy focusing formation mechanism and transforming the original unidirectional aggregation into a multidirectional low-intensity dispersion form.

[0039] After the reverse diffusion wave group generates a local disturbance structure, to avoid the risk of structural fatigue accumulation caused by excessive disturbance leading to the generation of new vortices or uneven pressure rebound, a further amplitude limiting and write-back mechanism is implemented. This limits the upper limit of disturbance in the imbalance region of the gas-liquid circulation path and writes back the flow path according to the original design parameters. The amplitude limiting measures consist of two parts: First, the amplitude of the maximum sound pressure gradient inside the disturbance region is constrained and controlled, limiting it to above the lower limit pressure required to induce new cavitation behavior, and reducing the collapse risk by narrowing the cavitation-induced bubble particle size range (controlled between 0.5 micrometers and 1.2 micrometers); Second, the control signal of the stirring blade is linked and corrected. When the vortex center offset is detected to exceed 3 degrees of angular deviation or the velocity direction difference exceeds 10%, the angular velocity change period of the stirring blade is adjusted in real time to avoid the natural resonance frequency range that coincides with the sound wave focusing frequency. Simultaneously, referencing the velocity distribution in the liquid phase circulation path during the previous stable period, the main circulation axis of the gas-liquid fluid is recalibrated to an axisymmetric equilibrium distribution with the central vortex point as the axis of equilibrium. The proportion of liquid recirculation within the path is adjusted to maintain the equipotential diffusion state of the overall flow. Through the above amplitude limiting and path rewriting measures, it is ensured that the fermentation liquid can quickly return to a controlled state after disturbance, without secondary energy concentration.

[0040] After the self-suppression command is implemented, the reverse diffusion wave is injected, and the amplitude-limiting path is reconstructed, the fermentation reactor enters the steady-state recovery confirmation stage. This stage is based on five real-time observation indicators: the rate of change of bubble density per unit volume is less than a set value (e.g., less than 10³ bubbles per second); the stabilization time of the sound wave propagation path is greater than 5 consecutive seconds; the amplitude of liquid phase pressure fluctuation does not exceed ±15% of the previous cycle average; the vortex path offset fluctuates within 0.1 meters; and the rate of change of the vertical velocity gradient of the liquid phase is maintained within ±10%. Once these five conditions are met, it is confirmed that the fermentation reactor has returned from a state of strong disturbance resonance to a stable system state with balanced gas-liquid distribution, stable sound pressure energy, and structural response load within a low-stress range. Finally, the parameter combination used in this round of self-suppression control, including the disturbance initiation frequency, intervention delay duration, diffusion wave amplitude ratio, amplitude-limiting correction value, and path write-back strategy, is recorded as a "high-frequency resonance response strategy set" for rapid recall under subsequent similar disturbance triggers.

[0041] This invention collects dynamic behavior and acoustic response data of bubbles, constructs a coupled distribution model, and generates an initial energy evolution spectrum. It then extracts the resonance characteristics of multi-scale bubbles, achieving precise positioning and dynamic tracking of energy focusing regions. By reconstructing the shock wave propagation path through a counterfactual playback chain, and establishing a structural response mapping and controllable suppression bandwidth model based on a causal residual density map, it enables the prediction and control of impeller and tank fatigue risks. Furthermore, it constructs a feedforward constraint field and a self-suppression interference linkage mechanism, enabling the fermentation system to perceive, interfere with, absorb, and reconstruct abnormal energy fluctuations, forming an integrated steady-state maintenance mechanism with real-time response, directional diffusion, amplitude limiting correction, and stable rearrangement functions. Compared to existing control methods relying on static stirring optimization and structural reinforcement, this invention can significantly reduce the risk of structural fatigue accumulation during equipment operation, improve gas-liquid mixing uniformity and carbon source release efficiency, effectively extend equipment service life, and improve the overall operational stability of the fermentation system.

[0042] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for co-fermenting sludge and kitchen waste to produce a carbon source, characterized in that, Includes the following steps: The bubble generation time sequence, liquid phase pressure fluctuation signal and acoustic scattering signal in the sludge and kitchen waste co-fermentation system were collected to construct a coupled distribution model of bubble size and oscillation frequency and generate an initial energy evolution spectrum. Under the constraint of the initial energy evolution spectrum, the coherent characteristic sequence of bubble oscillation is identified, the energy accumulation intensity of multi-scale bubble swarms is calculated, the high-frequency energy accumulation region is located, and a nonlinear resonance fingerprint is generated. Based on nonlinear resonant fingerprints, a counterfactual replay chain is constructed to simulate the entire process of bubble merging and collapse, outputting the spatiotemporal distribution trajectory of shock wave propagation and generating a causal residual density map characterizing the energy transfer path. Based on the analysis of the mechanical response relationship between the stirring blade and the tank, the energy concentration area and the location of structural fatigue points are identified, a trigger threshold window is generated, and a controllable suppression bandwidth model is constructed. A feedforward constraint field is established based on the controllable suppression bandwidth model. A phase conjugate correction operation is applied to the gas-liquid resonance region in the fermentation system, and cavitation damping is injected to form an energy dissipation path. Under the action of the feedforward constraint field, a self-suppressive interference control command set is generated, and a linkage mechanism is implemented to reconstruct the gas-liquid circulation path and vortex intensity distribution.

2. The method for co-fermenting sludge and kitchen waste to produce a carbon source according to claim 1, characterized in that, The steps for generating the initial energy evolution spectrum are as follows: After the fermentation unit is running stably, the optical interferometry detection device, the piezoelectric hydraulic response acquisition detector and the photoacoustic wave detection device are started to collect the bubble generation and rupture time series, liquid phase micro-pressure disturbance signal and acoustic wave scattering intensity change signal, respectively. By using a high-frequency acquisition circuit to perform synchronous calibration with a unified time reference, the three types of signals are reconstructed and matched according to a unified time base, and the size change, main oscillation frequency and energy response value of each bubble are extracted. Based on the extracted data, a coupled distribution map containing bubble size, oscillation frequency and energy response intensity is constructed, and the energy characteristics are superimposed and analyzed with a unified time axis to generate an initial energy evolution spectrum.

3. The method for co-fermenting sludge and kitchen waste to produce a carbon source according to claim 2, characterized in that, The steps for generating a nonlinear resonant fingerprint are as follows: The bubble oscillation trajectories in each time unit recorded in the initial energy evolution spectrum are extracted and numbered. Bubble trajectories with consistent frequency change trends, synchronously enhanced acoustic response, and the same pressure fluctuation direction are identified to construct a coherent oscillation sequence group. Multiple coherent oscillation sequences are longitudinally merged and divided into frequency bands. Frequency regions with enhanced energy density and multi-scale characteristics are selected and identified as high-frequency energy coupling regions. A composite identification spectrum is established based on five types of data: frequency, energy, bubble volume density, acoustic response, and hydraulic directional field, and encoded as a nonlinear resonant fingerprint.

4. The method for co-fermenting sludge and kitchen waste to produce a carbon source according to claim 3, characterized in that, The steps for generating a causal residual density map are as follows: Based on the multidimensional features recorded in the nonlinear resonant fingerprint, the bubble evolution sequence is extracted and physical boundary conditions are constructed; Simulate the entire process of bubble merging and collapse based on bubble size changes and hydraulic response behavior, and plot the energy release trajectory. A shock wave propagation path model was established and multi-source shock wave trajectories were superimposed to construct an energy coupling zone map; Residual analysis was performed on the theoretical propagation trajectory and the measured energy response, and a causal residual density map was generated. Based on the residual dense region, a causal relationship chain is constructed by backtracking and the concentrated path of energy transfer to the structural interface is identified.

5. The method for co-fermenting sludge and kitchen waste to produce a carbon source according to claim 4, characterized in that, The process of constructing the controllable suppression bandwidth model is as follows: The energy concentration region is spatially registered with the structural model of the stirring device in a three-dimensional coordinate system, and a perturbation mapping relationship is established. Based on the registration results, energy input data of key parts are extracted and structural mechanical response curves are established; Based on the structural fatigue evolution trend, a joint triggering threshold window is constructed and a high-sensitivity triggering region is extracted; An energy bandwidth adjustment strategy is established based on the frequency range and structural response intensity, and a controllable suppression bandwidth model is generated.

6. The method for co-fermenting sludge and kitchen waste to produce a carbon source according to claim 5, characterized in that, In the controllable suppression bandwidth model, the target frequency range is obtained by expanding it up and down by a fixed ratio based on the center frequency of the trigger frequency band. In the adjustment strategy, the injection path, energy suppression amplitude, and action period are set collaboratively according to the resonant characteristics of the device structure and the acoustic parameters of the liquid medium.

7. The method for co-fermenting sludge and kitchen waste to produce a carbon source according to claim 5, characterized in that, The steps are as follows: A feedforward constraint field is established based on the controllable suppression bandwidth model; phase conjugate correction is applied to the gas-liquid resonance region; and cavitation damping is injected to construct the energy dissipation path. Based on the frequency center value and energy change rate in the controllable suppression bandwidth model, a resonance risk space field is established and a basic framework for the feedforward constraint field is constructed. A broadband tunable sound source array is deployed in the resonant risk space field and a phase conjugate control signal is applied to form an intervention sound field. Microbubble clusters within a particle size control range are injected into the target liquid layer and a microcavitation process is induced to form a dissipation path; Periodically extract energy distribution and structural response data to form a control baseline and perform feedforward parameter correction.

8. The method for co-fermenting sludge and kitchen waste to produce a carbon source according to claim 1, characterized in that, The steps for generating a self-suppressive interference control command set under the action of a feedforward constraint field, implementing a linkage mechanism, and reconstructing the gas-liquid circulation path and vortex intensity distribution are as follows: The residual density of acoustic field energy and gas-liquid disturbance parameters are collected to generate an interference control command set; Based on the control instruction set, a reverse diffusion wave group is generated and a multi-directional intervention wave injection strategy driven by frequency misalignment is implemented; A limiting write-back mechanism is implemented to control the acoustic pressure amplitude in the disturbed area and correct the agitator drive parameters and the gas-liquid circulation main shaft path. Steady-state confirmation is completed based on real-time indicators after the disturbance, and the set of control strategy parameters is recorded for subsequent rapid response.

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