Method and system for dynamically regulating and controlling fermentation process parameters of citrus pulp feed

Abstract

The invention provides a dynamic regulation and control method and system for fermentation process parameters of citrus pulp feed, and relates to the technical field of automatic control, and the method comprises the following steps: obtaining initial physical property data of a citrus pulp raw material and initial state data of a fermentation environment; performing matrix viscoelasticity prediction according to the pectin content and the cellulose-hemicellulose ratio to obtain matrix initial viscoelasticity parameters; quantifying the mass transfer limiting effect in the fermentation process according to the initial viscoelastic parameters of the substrate to obtain the effective nutrient concentration of the microorganisms; performing microbial agent metabolic pathway dynamic distribution according to the effective nutrient concentration of the microorganisms to obtain a metabolic pathway switching signal; performing fermentation direction regulation and control according to the metabolic pathway switching signal to obtain a critical temperature and a pH intervention threshold value; and performing multi-parameter coupling dynamic regulation and control according to the critical temperature and the pH intervention threshold value to obtain dynamic regulation and control parameters. The intelligent control level of the fermentation process of the citrus pulp feed production line is remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of automatic control technology, and more specifically, to a method and system for dynamic control of fermentation process parameters for citrus pomace feed. Background Technology

[0002] With the development of animal husbandry and the increasing prominence of food security issues, the development of non-grain feed resources has become an important industry trend, and the demand for intelligent processing and control technologies to support this is becoming increasingly urgent. Citrus pomace, as a major byproduct of the juice processing industry, is rich in cellulose and other components, making it a potentially high-quality feed ingredient with enormous development potential.

[0003] However, the high pectin content and complex fiber structure of citrus pomace, combined with the high viscosity matrix formed during fermentation, pose unique challenges to the automatic control of the fermentation process. This manifests as hindered nutrient transfer and uneven microbial growth, severely impacting process stability and product consistency. Existing control strategies often employ setpoint control based on fixed time or single parameter thresholds, or open-loop control relying on operator experience. These simple control systems struggle to handle the multivariate coupling, large time delays, and nonlinear dynamic processes in fermentation systems. They cannot achieve coordinated regulation of key parameters such as temperature and pH, and they fail to establish a dynamic model feedforward mechanism from raw material properties to metabolic activities. This results in poor anti-interference capabilities of the control system, insufficient process stability, and ultimately, restricts precise control of product quality.

[0004] Based on the shortcomings of the existing technologies, there is an urgent need for a method and system for dynamic control of fermentation process parameters of citrus pomace feed. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for dynamically controlling the fermentation process parameters of citrus pomace feed, so as to improve the above-mentioned problems. To achieve the above objective, the technical solution adopted by this invention is as follows: In a first aspect, this application provides a method and system for dynamically controlling the fermentation process parameters of citrus pomace feed, including: The initial physical properties of the citrus pomace raw material and the initial state data of the fermentation environment were obtained. The initial physical properties included pectin content and cellulose-hemicellulose ratio, and the initial state data included initial temperature, initial pH value, and initial inoculum concentration of the fermentation agent. The matrix viscoelasticity is predicted based on the pectin content and the cellulose-hemicellulose ratio to obtain the initial viscoelastic parameters of the matrix. The mass transfer limitation effect during fermentation was quantified based on the initial viscoelastic parameters of the substrate. The effective nutrient concentration of the microorganism was obtained by simulating the nutrient diffusion obstruction by introducing a pseudo-substrate competitive inhibition effect. The metabolic pathway of the microbial agent is dynamically allocated based on the effective nutrient concentration of the microorganism and the initial inoculation concentration. By identifying the metabolic diversion critical point of the microbial community under carbon source limitation, the metabolic pathway switching signal is obtained. Fermentation direction is regulated based on the metabolic pathway switching signal to obtain the critical temperature and pH intervention threshold for maintaining the target metabolic pathway. Based on the critical temperature and the pH intervention threshold, and using the initial temperature and the initial pH value as the control benchmark, multi-parameter coupled dynamic control is performed. By analyzing the interaction between temperature and pH on enzyme activity and microbial community structure, dynamic control parameters are obtained. The dynamic control parameters include temperature adjustment amount, pH adjustment amount, and intervention timing based on metabolic pathway switching signals.

[0006] Secondly, this application also provides a method and system for dynamic control of fermentation process parameters for citrus pomace feed, including: The acquisition module is used to acquire the initial physical property data of citrus pomace raw material and the initial state data of the fermentation environment. The initial physical property data includes pectin content and cellulose-hemicellulose ratio, and the initial state data includes initial temperature, initial pH value, and initial inoculation concentration of fermentation agent. The prediction module is used to predict the matrix viscoelasticity based on the pectin content and the cellulose-hemicellulose ratio, and obtain the initial viscoelastic parameters of the matrix. The quantification module is used to quantify the mass transfer limitation effect in the fermentation process based on the initial viscoelastic parameters of the substrate. By introducing a pseudo-substrate competition inhibition effect to simulate the situation of nutrient diffusion obstruction, the effective nutrient concentration of microorganisms is obtained. The allocation module is used to dynamically allocate the metabolic pathway of the microbial agent according to the effective nutrient concentration of the microorganism, and obtain the metabolic pathway switching signal by identifying the metabolic diversion critical point of the microbial community under carbon source limitation. The regulation module is used to regulate the fermentation direction according to the metabolic pathway switching signal to obtain the critical temperature and pH intervention threshold for maintaining the target metabolic pathway. The output module is used to perform multi-parameter coupled dynamic regulation based on the critical temperature and the pH intervention threshold, and with the initial temperature and the initial pH value as the regulation benchmark. By analyzing the interaction between temperature and pH on enzyme activity and microbial community structure, dynamic regulation parameters are obtained. The dynamic regulation parameters include temperature adjustment amount, pH adjustment amount, and intervention timing based on metabolic pathway switching signals.

[0007] The beneficial effects of this invention are as follows: This invention establishes a dynamic process model from raw material properties to metabolic activities, and adopts a multi-parameter collaborative control strategy to achieve dynamic optimization of key operating variables such as temperature and pH during fermentation. This method uses a model predictive control algorithm, taking raw material characteristics as feedforward signals and metabolic state as feedback variables, to effectively overcome the mass transfer control problem caused by high-viscosity materials. It significantly improves the intelligent control level of the fermentation process in the citrus pomace feed production line, achieves stable control of the entire process from feeding to output, and ultimately ensures the consistency of feed product quality and the improvement of production efficiency. Attached Figure Description

[0008] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a flowchart illustrating a method for dynamically controlling fermentation process parameters of citrus pomace feed as described in an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of a dynamic control system for fermentation process parameters of citrus pomace feed as described in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a dynamic control device for fermentation process parameters of citrus pomace feed as described in an embodiment of the present invention.

[0010] The diagram is labeled as follows: 800, a method and system for dynamic control of fermentation process parameters of citrus pomace feed; 801, processor; 802, memory; 803, multimedia component; 804, I / O interface; 805, communication component; 901, acquisition module; 902, prediction module; 903, quantification module; 904, allocation module; 905, control module; 906, output module. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0012] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0013] Example 1:

[0014] This embodiment provides a method and system for dynamically controlling the fermentation process parameters of citrus pomace feed.

[0015] See Figure 1 The figure shows that the method includes steps S100 to S600.

[0016] Step S100: Obtain the initial physical property data of citrus pomace raw material and the initial state data of fermentation environment. The initial physical property data includes pectin content and cellulose-hemicellulose ratio. The initial state data includes initial temperature, initial pH value, and initial inoculation concentration of fermentation agent. Step S100 serves as the data foundation and logical starting point for the dynamic control method of this invention. Its core lies in proactively extending the control basis from environmental parameters during fermentation to the initial state of the system determined by the inherent characteristics of the raw materials. Typical citrus pomace fermentation includes raw material pretreatment, inoculation with microbial agents, controlled-environment fermentation, and post-treatment. Temperature and pH are two core and optimizable process parameters that directly affect microbial metabolism. This method aims to break through the static control mode of setting fixed thresholds for these parameters and achieve synergistic adaptive adjustment based on dynamic changes within the system. Therefore, step S100 requires systematically acquiring the unique characteristics of the fermentation system. Initial data representing the core physical properties of the raw materials include pectin content (which can be determined through chemical analysis such as the carbazole sulfuric acid method) and cellulose-hemicellulose ratio (often obtained through the van der Waals fiber analysis method), which together determine the initial physical structure of the matrix; as well as the environmental and biological conditions reflecting the fermentation start-up—initial temperature and initial pH value (read in real time by tank sensors) and initial inoculum concentration of the fermentation agent (determined by feeding operations and agent calibration). This step, by integrating raw material laboratory analysis, online monitoring, and known process parameters, provides precise and personalized input for subsequent derivation and is the foundation of the entire dynamic control chain.

[0017] Step S200: Based on the pectin content and cellulose-hemicellulose ratio, predict the viscoelasticity of the matrix to obtain the initial viscoelastic parameters of the matrix; Understandably, given the unique challenge of citrus pomace's high pectin content leading to gel network formation, this step does not rely on empirical judgment. Instead, it uses a theoretical model based on the ratio of pectin to fiber to predict the potential spatial structural stiffness after mixing—that is, the initial viscoelastic parameters of the matrix. This process transforms the chemical composition of the raw materials into quantifiable rheological indicators that influence mass transfer and mixing efficiency, laying a theoretical foundation for subsequent analysis of physical limitations during fermentation.

[0018] Step S300: Quantify the mass transfer limitation effect during fermentation based on the initial viscoelastic parameters of the substrate. Simulate the nutrient diffusion obstruction by introducing a pseudo-substrate competitive inhibition effect to obtain the effective nutrient concentration of the microorganism. It should be noted that this step links the viscoelastic parameter, a physical property, to nutrient acquisition by microorganisms, and introduces a biological model of "pseudo-substrate competitive inhibition" to simulate the physiological effects caused by impeded physical diffusion. This allows the local nutrient concentration, which is actually perceived by microorganisms but is difficult to measure directly, to be indirectly calculated, i.e., the effective nutrient concentration of microorganisms, thus transforming the physical barrier problem into a bioavailability problem that can be handled by a model.

[0019] Step S400: Dynamically allocate the metabolic pathway of the microbial agent according to the effective nutrient concentration of the microorganism and the initial inoculation concentration. By identifying the metabolic diversion critical point of the microbial community under carbon source limitation, the metabolic pathway switching signal is obtained. Understandably, based on the effective nutrient concentration obtained from the aforementioned steps, this step dynamically assesses the metabolic activity of the microbial community and accurately identifies the critical point at which its metabolic pathways diverge. It can generate metabolic pathway switching signals in advance before microorganisms shift from target metabolic pathways (such as efficient synthesis of microbial proteins) to non-target pathways (such as the production of excessive organic acids), thus realizing the transformation from passive monitoring to active prediction.

[0020] Step S500: Regulate the fermentation direction based on the metabolic pathway switching signal to obtain the critical temperature and pH intervention threshold for maintaining the target metabolic pathway. It should be noted that after obtaining the metabolic pathway switching signal, this step does not involve direct empirical adjustments, but rather reverse engineering: to maintain the ideal target metabolic pathway, what critical ranges should the key parameters (temperature and pH) in the fermentation environment be controlled within? This process determines the target threshold for intervention, giving subsequent regulation a clear direction and theoretical basis, aiming to stabilize the fermentation process within the target metabolic pathway.

[0021] Step S600: Based on the critical temperature and pH intervention threshold, and using the initial temperature and initial pH value as the control benchmark, perform multi-parameter coupled dynamic control. By analyzing the interaction between temperature and pH on enzyme activity and microbial community structure, obtain dynamic control parameters. The dynamic control parameters include temperature adjustment amount, pH adjustment amount, and intervention timing based on metabolic pathway switching signals.

[0022] Understandably, during fermentation, temperature and pH do not act independently but rather have complex interactive effects. Therefore, by analyzing their coupled influence on enzyme activity and microbial community structure, a synergistic adjustment scheme can be formulated. The final output dynamic control parameters (adjustment amount, intervention timing) is a comprehensive set of control instructions that considers the coupling relationship between parameters and the time dimension, thereby achieving refined and adaptive control of the fermentation process, ensuring process stability and product consistency.

[0023] Further, step S200 includes steps S210 to S230.

[0024] Step S210: Based on the pectin content and the cellulose-hemicellulose ratio, perform pectin-fiber complex structure modeling. By analyzing the intermolecular forces between the pectin molecular side chains and cellulose microfibers, obtain the structural parameters characterizing the spatial configuration of the complex. Step S220: Based on the structural parameters, perform composite network stiffness calculation and obtain the contribution factor of the composite to the overall stiffness of the matrix by simulating the synergistic effect of the pectin gel network and the rigid fiber skeleton. Step S230: Based on the contribution factor, perform macroscopic viscoelastic mapping processing on the matrix. By converting the microstructure stiffness into macroscopic rheological properties, the initial viscoelastic parameters of the matrix are obtained.

[0025] Specifically, step S210, the pectin-fiber composite structure modeling process, analyzes the intermolecular forces such as hydrogen bonds and electrostatic interactions between the functional groups on the side chains of pectin molecules and the surface of cellulose microfibers. This theoretically constructs a spatial network configuration where pectin encapsulates or intertwines with fibers, and outputs the characteristic structural parameters of this three-dimensional network. Step S220, based on the above structural parameters, performs composite network stiffness calculation. Its core is to simulate the synergistic enhancement effect produced by the spatial coupling of the flexible pectin gel and the rigid fiber skeleton. In this embodiment, the fiber skeleton serves as a physical support point, significantly enhancing the overall stiffness of the pectin gel network. This process calculates the composite structure's resistance to deformation through a mechanical model, thereby obtaining a contribution factor reflecting its contribution to the overall stiffness of the fermentation substrate. The formula for calculating the composite network stiffness is: ; In the formula, E c E represents the equivalent elastic modulus of the complex network, i.e., the contribution factor, indicating the overall stiffness of the complex against deformation; p E represents the elastic modulus of pectin gel, indicating the softness of the pectin network; f Φ represents the elastic modulus of the fiber skeleton, indicating the rigidity of the fiber network composed of cellulose and hemicellulose; p Φ represents the volume fraction of pectin in the complex; f Let be the volume fraction of the fiber in the composite; ξ is the cooperative interaction parameter.

[0026] Step S230 performs macroscopic viscoelastic mapping processing on the matrix based on the contribution factor, and converts the structural stiffness characteristics at the microscale into viscoelastic parameters that can directly characterize the flowability and deformability of the macroscopic fermentation matrix through a constitutive relation model based on rheological principles. The effect of this process is that it enables the present invention to quantitatively predict in advance the high viscosity and high elasticity behavior of the citrus pomace fermentation system caused by high pectin content based on easily measurable raw material chemical composition.

[0027] Further, step S300 includes steps S310 to S330.

[0028] Step S310: Based on the initial viscoelastic parameters of the matrix, the effective diffusion coefficient of nutrients is calculated. The diffusion behavior of small molecule nutrients in non-Newtonian fluid media is described by modifying the Stokes-Einstein relationship based on the viscoelastic parameters, and the effective diffusion coefficient of nutrients in the matrix is ​​obtained. Step S320: Based on the effective diffusion coefficient, construct the microbial colony-scale nutrient gradient field. By solving the unsteady diffusion equation and combining it with the spatial distribution characteristics of the microbial colony, obtain the time-varying nutrient concentration distribution in the colony microenvironment. Step S330: Based on the time-varying nutrient concentration distribution, perform pseudo-substrate competition inhibition effect integration processing. By equating the mass transfer limitation effect in the low nutrient concentration region with the existence of virtual competing substrates to simulate the actual nutrient uptake resistance of microorganisms, the effective nutrient concentration of microorganisms is obtained.

[0029] Specifically, in step S310, the initial viscoelastic parameters (such as viscosity and elastic modulus) of the matrix obtained in step S200 are used to modify the classical Stokes-Einstein relation. This modification is necessary because the classical theory only applies to the diffusion of molecules in simple Newtonian fluids, while the citrus pomace fermentation matrix is ​​a viscoelastic non-Newtonian fluid, and its network structure significantly hinders the free movement of small molecule nutrients (such as sugars). This modification allows for the calculation of an effective nutrient diffusion coefficient that better reflects the actual fermentation system. The modified Stokes-Einstein relation is expressed as follows: ; In the formula, D eff k is the effective diffusion coefficient of nutrients. B η is Boltzmann constant; T is the absolute temperature of fermentation; γ is the hydrodynamic radius of the nutrient molecule; η is the η of the nutrient molecule. o α is the zero-shear viscosity of the matrix, representing the viscosity of the material at extremely low shear rates (near rest); α is the structural hindrance factor, representing the additional geometric hindrance to diffusion caused by the density of the pectin-fiber network spatial structure; D e For Deborah number.

[0030] Step S320, based on this effective diffusion coefficient, constructs a microbial colony-scale nutrient gradient field. This is achieved by solving a non-steady-state diffusion equation that incorporates a nutrient consumption source term. This equation simulates the dynamic process of nutrients diffusing from high-concentration areas to the colony surface while being consumed by microorganisms, thereby calculating the spatial distribution of nutrient concentration at the micrometer scale around the colony, which evolves over time—that is, the i.e., the time-varying nutrient concentration distribution. The non-steady-state diffusion equation is: ; In the formula, C is the nutrient concentration; t is time; The Laplace operator is used to describe the diffusion of nutrients due to concentration gradients (i.e., from high concentration regions to low concentration regions). This is a nutrient consumption rate function, representing the amount of nutrients consumed by microorganisms per unit time and unit volume. It is a partial derivative.

[0031] Step S330 further couples the concentration distribution in this physical space with the physiological response of microorganisms, integrating the pseudo-substrate competition inhibition effect, and transforming the macroscopic problem of physical mass transfer limitation into a biological signal that microorganisms can "sense". Preferably, this method equates the local low nutrient concentration environment of the colony caused by diffusion obstruction to the existence of a virtual "pseudo-substrate" that competes with the real nutrient substrate for microbial transport proteins. Thus, a mature enzyme kinetic competition inhibition model is used to quantify the actual available nutrient concentration of microorganisms, i.e., the effective nutrient concentration of microorganisms. This process bypasses the difficulty of directly measuring the instantaneous nutrient concentration on the surface of microorganisms, and integrates the physical limitation effect into a physiological parameter that reflects the real nutrient pressure of microorganisms through the model.

[0032] Further, step S400 includes steps S410 to S430.

[0033] Step S410: Based on the effective nutrient concentration of microorganisms, conduct a cell growth potential assessment. Calculate the potential specific growth rate of the cells under the preset carbon source availability using a substrate-limited growth kinetic model to obtain the cell metabolic activity level. Step S420: Based on the level of bacterial cell metabolic activity, the critical point of metabolic flux allocation is solved. By analyzing the energy trade-off between the tricarboxylic acid cycle and the fermentation pathway in terms of carbon source competition, the critical specific growth rate that characterizes the switching of metabolic flow direction is obtained. Step S430: Based on the critical specific growth rate, perform metabolic pathway switching signal generation and processing. By comparing the deviation between the actual specific growth rate and the critical value in real time, a warning signal is dynamically generated to obtain the metabolic pathway switching signal.

[0034] Specifically, the core logic of steps S410 to S430 is to transform the nutrient stress state of the microorganisms into an actionable, advanced early warning signal regarding their metabolic pathway direction. This process begins with the bacterial growth potential assessment in step S410. This step uses the effective nutrient concentration of the microorganisms as the core input and substitutes it into a substrate-limited growth kinetic model (such as the Mono equation) for calculation. This process does not directly measure the growth rate, but rather theoretically calculates the potential specific growth rate that the bacteria can achieve without other limiting factors based on the current carbon source availability level. This rate is defined as the bacterial metabolic activity level, which quantifies the theoretical upper limit of the current nutrient environment's support for the bacterial community's proliferation capacity. Preferably, in this embodiment, the substrate-limited growth kinetic model is the extended Mono equation, expressed as: ; In the formula, μ p Potential specific growth rate, or "microbial metabolic activity level" as defined in step S410, represents the theoretically maximum proliferation rate that a microbial community can achieve under current nutrient stress; it is the theoretical upper limit for measuring its metabolic activity. max S represents the maximum specific growth rate, indicating the absolute maximum growth rate that a microorganism can achieve under optimal conditions (sufficient nutrients, no inhibition); eff K is the effective nutrient concentration for microorganisms. s K is the half-saturation constant, whose value is equal to the substrate concentration required to achieve half of the maximum specific growth rate; I is the inhibition constant, used to describe the growth inhibition effect that may occur at high substrate concentrations (i.e., high-concentration substrate inhibition); n is the inhibition exponent.

[0035] Step S420, based on this metabolic activity level, performs a critical point calculation for metabolic flux allocation. By applying the principles of metabolic flux analysis, it analyzes the cell's energy metabolism strategy: when nutrients are abundant and the specific growth rate is high, carbon flux tends to enter the tricarboxylic acid cycle to efficiently generate energy; however, when nutrients are limited and the specific growth rate is below a certain threshold, to maintain survival, the metabolic network allocates more carbon flux to fermentation pathways (such as lactic acid fermentation) to rapidly obtain energy through substrate-level phosphorylation. This step quantifies this energy trade-off using a mathematical model, solving for the critical specific growth rate that signifies a fundamental shift in the metabolic mainstream. Step S430 finally performs metabolic pathway switching signal generation. This is achieved by real-time monitoring or estimation of the cell's actual specific growth rate and continuous comparison with the critical rate obtained in step S420. Once the actual rate approaches or deviates from the critical value, the model dynamically generates a warning signal. This metabolic pathway switching signal does not indicate an already occurred change, but rather provides a forward-looking indication of the risk of impending metabolic imbalance, offering crucial decision-making basis and a time window for subsequent process parameter intervention.

[0036] Further, step S500 includes steps S510 to S530.

[0037] Step S510: Based on the metabolic pathway switching signal, perform reverse solution processing of the target metabolic pathway steady-state conditions. By constructing a metabolic network steady-state model with the enzyme activities corresponding to the rate-limiting steps as nodes, the target values ​​of each rate-limiting enzyme activity required to maintain the target metabolic flux are derived. Step S520: Based on the target values ​​of each rate-limiting enzyme activity, construct the environmental parameter-enzyme activity mapping relationship. By establishing a response surface model of the dynamic influence of temperature and pH on the conformation of the rate-limiting enzyme, obtain the temperature-pH matching range for maintaining the activity of the target enzyme. Step S530: Based on the temperature-pH matching range, perform intervention threshold optimization extraction processing, taking the maximization of fermentation rate as the objective function and the stability of metabolic network structure as the constraint condition, determine the operation boundary, and obtain the critical temperature and pH intervention thresholds for maintaining the target metabolic pathway.

[0038] Specifically, the reverse solution of the steady-state conditions of the target metabolic pathway in step S510 involves constructing a steady-state model with the enzyme activity corresponding to the rate-limiting step (i.e., the key biochemical reaction controlling the overall flux) in the metabolic network as the key node. When a metabolic pathway switching signal is received (indicating a risk of deviation in the metabolic flow), the process does not perform forward simulation but reverse calculation: taking the desired target metabolic flux (such as the desired protein synthesis rate) as the given target, it reverse-calculates the activity level that each rate-limiting enzyme in the network needs to be maintained at in order to achieve this target, thereby obtaining a set of enzyme activity target values. Step S520 involves constructing an environmental parameter-enzyme activity mapping relationship to find achievable external operating conditions for the aforementioned enzyme activity targets. This is achieved by establishing a response surface model reflecting the combined influence of temperature and pH on the enzyme's molecular conformation dynamics for each key rate-limiting enzyme identified in step S510. This model depicts the temperature-pH combination that maintains the enzyme within its target activity range. The intersection of the required activity ranges for all key enzymes constitutes a "temperature-pH matching interval" that synergistically supports the target metabolic pathway. Step S530 finally performs intervention threshold optimization extraction. Its core is to further determine the optimal operating boundary within the matching interval obtained in step S520. This method aims to maximize the fermentation rate, but simultaneously considers the structural stability of the metabolic network (avoiding metabolic collapse due to drastic parameter fluctuations) as a necessary constraint. An optimization algorithm is used to find a balance point within this interval that ensures both efficiency and system robustness, ultimately determining the specific critical temperature and pH intervention thresholds for real-time control, thus completing the conversion from biochemical signals to executable process parameters.

[0039] Further, step S600 includes steps S610 to S630.

[0040] Step S610: Based on the critical temperature and pH intervention threshold, construct the temperature-pH synergistic effect field. By quantifying the amplification or weakening effect of temperature change on pH regulation rate and effect, obtain the dynamic interaction influence coefficient between parameters. Step S620: Based on the dynamic interaction influence coefficient and metabolic pathway switching signal, and with the initial temperature and initial pH as the control benchmark, perform multi-step predictive control sequence generation processing, and use the model predictive control principle to solve the optimal coordinated adjustment trajectory of temperature and pH in the finite time domain to obtain the optimized control sequence. Step S630: Based on the optimized control sequence, perform executable control command conversion processing. By introducing actuator response characteristic constraints, the continuous trajectory is discretized into specific operation commands to obtain dynamic control parameters.

[0041] Specifically, the temperature-pH synergistic effect field construction in step S610 is based on the recognition that these two parameters do not act independently on the fermentation system, but rather have a strong coupling relationship. The specific technical approach is to quantify the indirect impact of temperature changes on the pH adjustment process. For example, increased temperature alters the ionization balance of the fermentation broth, thus affecting the "apparent value" of pH measurement. Simultaneously, it accelerates the rate of microbial acid production, thereby changing the actual dynamic pH change process. By modeling and quantifying the amplification or weakening effect of one parameter on the rate and effect of another, coefficients accurately describing their dynamic interaction are obtained. Step S620 then performs multi-step predictive control sequence generation. It combines the aforementioned dynamic interaction coefficients with metabolic pathway switching signals from the front end (as feedback reflecting the internal state of the system). Based on the principle of model predictive control, rolling optimization is performed: that is, in each control cycle, not only the current state is considered, but the behavior of the system within a finite future time is predicted, and an optimal temperature and pH synergistic adjustment trajectory is solved to smoothly guide the process parameters to near the intervention threshold in advance, while effectively avoiding metabolic switching, thus obtaining a forward-looking optimized control sequence. Step S630 ultimately performs executable control command conversion processing. Its necessity lies in the fact that the optimized control sequence generated in step S620 is a theoretically continuous trajectory, while the actual control actuators (such as heating tanks, acid / alkali pumps) have physical constraints such as response delays and switching characteristics. This processing introduces these actuator characteristics as constraints to discretize the smooth continuous trajectory into a series of specific, equipment-executable operation commands issued at specific time points (such as "turn on the heating power to X% at time T" or "inject Y ml of alkali solution at time T+Δt"). The final output dynamic control parameters are precisely this set of specific action plans that integrate timing, quantity, and equipment limitations.

[0042] Example 2:

[0043] like Figure 2 As shown in the figure, this embodiment provides a dynamic control system for the fermentation process parameters of citrus pomace feed. The system includes: The acquisition module 901 is used to acquire the initial physical property data of citrus pomace raw material and the initial state data of the fermentation environment. The initial physical property data includes pectin content and cellulose-hemicellulose ratio, and the initial state data includes initial temperature, initial pH value, and initial inoculation concentration of fermentation agent. Prediction module 902 is used to predict the viscoelasticity of the matrix based on the pectin content and the cellulose-hemicellulose ratio, and to obtain the initial viscoelastic parameters of the matrix. The quantification module 903 is used to quantify the mass transfer limitation effect in the fermentation process based on the initial viscoelastic parameters of the substrate. By introducing a pseudo-substrate competitive inhibition effect to simulate the situation of nutrient diffusion obstruction, the effective nutrient concentration of microorganisms is obtained. The allocation module 904 is used to dynamically allocate the metabolic pathway of the microbial agent based on the effective nutrient concentration of the microorganism and the initial inoculation concentration. By identifying the metabolic diversion critical point of the microbial community under carbon source limitation, the metabolic pathway switching signal is obtained. The regulation module 905 is used to regulate the fermentation direction based on the metabolic pathway switching signal to obtain the critical temperature and pH intervention threshold for maintaining the target metabolic pathway. The output module 906 is used to perform multi-parameter coupled dynamic regulation based on the critical temperature and the pH intervention threshold, and with the initial temperature and initial pH value as the regulation benchmark. By analyzing the interaction between temperature and pH on enzyme activity and microbial community structure, dynamic regulation parameters are obtained. The dynamic regulation parameters include temperature adjustment amount, pH adjustment amount, and intervention timing based on metabolic pathway switching signals.

[0044] In one specific embodiment of this application, the prediction module 902 includes: The first prediction unit is used to model the structure of the pectin-fiber complex based on the pectin content and the cellulose-hemicellulose ratio. By analyzing the intermolecular forces between the pectin molecular side chains and the cellulose microfibers, structural parameters characterizing the spatial configuration of the complex are obtained. The second prediction unit is used to calculate the stiffness of the composite network based on the structural parameters. By simulating the synergistic effect of the pectin gel network and the rigid fiber skeleton, the contribution factor of the composite to the overall stiffness of the matrix is ​​obtained. The third prediction unit is used to perform macroscopic viscoelastic mapping of the matrix based on the contribution factor. By converting the microstructural stiffness into macroscopic rheological properties, the initial viscoelastic parameters of the matrix are obtained.

[0045] In one specific embodiment of this application, the quantization module 903 includes: The first quantization unit is used to calculate the effective diffusion coefficient of nutrients based on the initial viscoelastic parameters of the matrix. By modifying the Stokes-Einstein relationship based on the viscoelastic parameters to describe the diffusion behavior of small molecule nutrients in non-Newtonian fluid media, the effective diffusion coefficient of nutrients in the matrix is ​​obtained. The second quantification unit is used to construct a microbial colony-scale nutrient gradient field based on the effective diffusion coefficient. By solving the unsteady diffusion equation and combining it with the spatial distribution characteristics of the microbial colony, the time-varying nutrient concentration distribution in the colony microenvironment is obtained. The third quantification unit is used to integrate the pseudo-substrate competition inhibition effect based on the time-varying nutrient concentration distribution. By equating the mass transfer limitation effect in the low nutrient concentration region with the existence of virtual competing substrates to simulate the actual nutrient uptake resistance of microorganisms, the effective nutrient concentration of microorganisms is obtained.

[0046] In one specific embodiment of this application, the allocation module 904 includes: The first allocation unit is used to assess the growth potential of microorganisms based on the effective nutrient concentration and the initial inoculation concentration. It calculates the potential specific growth rate of microorganisms under the preset carbon source availability by using a substrate-limited growth kinetic model to obtain the level of microbial metabolic activity. The second allocation unit is used to solve the critical point of metabolic flux allocation based on the level of cell metabolic activity. By analyzing the energy trade-off between the tricarboxylic acid cycle and the fermentation pathway in terms of carbon source competition, the critical specific growth rate that characterizes the switching of metabolic flow direction is obtained. The third allocation unit is used to generate and process metabolic pathway switching signals based on the critical specific growth rate. It dynamically generates early warning signals by comparing the deviation between the actual specific growth rate and the critical value in real time, thus obtaining the metabolic pathway switching signal.

[0047] In one specific embodiment of this application, the control module 905 includes: The first regulatory unit is used to reverse solve the steady-state conditions of the target metabolic pathway based on the metabolic pathway switching signal. By constructing a metabolic network steady-state model with the enzyme activities corresponding to the rate-limiting steps as nodes, the target values ​​of each rate-limiting enzyme activity required to maintain the target metabolic flux are deduced. The second regulation unit is used to construct the environmental parameter-enzyme activity mapping relationship based on the target values ​​of each rate-limiting enzyme activity. By establishing a response surface model of the dynamic influence of temperature and pH on the conformation of the rate-limiting enzyme, the temperature-pH matching range for maintaining the activity of the target enzyme is obtained. The third regulation unit is used to optimize the extraction process based on the temperature-pH matching range, with the fermentation rate as the objective function and the stability of the metabolic network structure as the constraint, to determine the operating boundary and obtain the critical temperature and pH intervention thresholds for maintaining the target metabolic pathway.

[0048] In one specific embodiment of this application, the output module 906 includes: The first output unit is used to construct a temperature-pH synergistic effect field based on the critical temperature and pH intervention threshold. By quantifying the amplification or weakening effect of temperature change on pH regulation rate and effect, the dynamic interaction influence coefficient between parameters is obtained. The second output unit is used to generate a multi-step predictive control sequence based on the dynamic interaction influence coefficient and metabolic pathway switching signal, and with the initial temperature and initial pH value as the control benchmark. Based on the model predictive control principle, it continuously solves the optimal coordinated adjustment trajectory of temperature and pH in the finite time domain to obtain the optimized control sequence. The third output unit is used to perform executable control command conversion processing based on the optimized control sequence. By introducing actuator response characteristic constraints, the continuous trajectory is discretized into specific operation commands to obtain dynamic control parameters. Example

[0049] Corresponding to the above method embodiments, this embodiment also provides a device for dynamic control of fermentation process parameters of citrus pomace feed. The device for dynamic control of fermentation process parameters of citrus pomace feed described below and the method for dynamic control of fermentation process parameters of citrus pomace feed described above can be referred to in correspondence.

[0050] Figure 3 This is a block diagram illustrating a dynamic control device 800 for the fermentation process parameters of citrus pomace feed, according to an exemplary embodiment. Figure 3 As shown, the dynamic control device 800 for fermentation process parameters of citrus pomace feed may include: a processor 801 and a memory 802. The dynamic control device 800 may also include one or more of the following: a multimedia component 803, an I / O interface 804, and a communication component 805.

[0051] The processor 801 controls the overall operation of the dynamic control device 800 for citrus waste feed fermentation process parameters to complete all or part of the steps in the aforementioned method for dynamic control of citrus waste feed fermentation process parameters. The memory 802 stores various types of data to support the operation of the dynamic control device 800 for citrus waste feed fermentation process parameters. This data may include, for example, instructions for any application or method operating on the device 800, as well as application-related data such as contact data, sent and received messages, images, audio, video, etc. The memory 802 can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical. Communication component 805 is used for wired or wireless communication between the method and system 800 for dynamic control of fermentation process parameters of citrus pomace feed and other devices. Wireless communication includes, for example, Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, and an NFC module.

[0052] In an exemplary embodiment, a dynamic control device 800 for citrus pomace fermentation process parameters can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to execute the aforementioned dynamic control method for citrus pomace fermentation process parameters.

[0053] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided. When executed by a processor, these program instructions implement the steps of the above-described method for dynamically controlling the fermentation process parameters of citrus pomace feed. For example, the computer-readable storage medium may be the memory 802 including the program instructions, which may be executed by a processor 801 of a device 800 for dynamically controlling the fermentation process parameters of citrus pomace feed to complete the above-described method for dynamically controlling the fermentation process parameters of citrus pomace feed.

[0054] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for dynamically controlling the fermentation process parameters of citrus pomace feed, characterized in that, include: The initial physical properties of the citrus pomace raw material and the initial state data of the fermentation environment were obtained. The initial physical properties included pectin content and cellulose-hemicellulose ratio, and the initial state data included initial temperature, initial pH value, and initial inoculum concentration of the fermentation agent. The matrix viscoelasticity is predicted based on the pectin content and the cellulose-hemicellulose ratio to obtain the initial viscoelastic parameters of the matrix. The mass transfer limitation effect during fermentation was quantified based on the initial viscoelastic parameters of the substrate. The effective nutrient concentration of the microorganism was obtained by simulating the nutrient diffusion obstruction by introducing a pseudo-substrate competitive inhibition effect. The metabolic pathway of the microbial agent is dynamically allocated based on the effective nutrient concentration of the microorganism and the initial inoculation concentration. By identifying the metabolic diversion critical point of the microbial community under carbon source limitation, the metabolic pathway switching signal is obtained. Fermentation direction is regulated based on the metabolic pathway switching signal to obtain the critical temperature and pH intervention threshold for maintaining the target metabolic pathway. Based on the critical temperature and the pH intervention threshold, and using the initial temperature and the initial pH value as the control benchmark, multi-parameter coupled dynamic control is performed. By analyzing the interaction between temperature and pH on enzyme activity and microbial community structure, dynamic control parameters are obtained. The dynamic control parameters include temperature adjustment amount, pH adjustment amount, and intervention timing based on metabolic pathway switching signals.

2. The method for dynamic control of fermentation process parameters of citrus pomace feed according to claim 1, characterized in that, The matrix viscoelasticity is predicted based on the pectin content and the cellulose-hemicellulose ratio, including: Based on the pectin content and the cellulose-hemicellulose ratio, a pectin-fiber complex structure modeling process was performed. By analyzing the intermolecular forces between the pectin molecular side chains and the cellulose microfibers, structural parameters characterizing the spatial configuration of the complex were obtained. Based on the structural parameters, the stiffness of the composite network is calculated. By simulating the synergistic effect of the pectin gel network and the rigid fiber skeleton, the contribution factor of the composite to the overall stiffness of the matrix is ​​obtained. Based on the contribution factor, the matrix is ​​subjected to macroscopic viscoelastic mapping processing. By converting the microstructural stiffness into macroscopic rheological properties, the initial viscoelastic parameters of the matrix are obtained.

3. The method for dynamically controlling the fermentation process parameters of citrus pomace feed according to claim 1, characterized in that, The mass transfer limitation effect during fermentation is quantified based on the initial viscoelastic parameters of the matrix, including: Based on the initial viscoelastic parameters of the matrix, the effective diffusion coefficient of nutrients is calculated. By modifying the Stokes-Einstein relationship based on the viscoelastic parameters to describe the diffusion behavior of small molecule nutrients in non-Newtonian fluid media, the effective diffusion coefficient of nutrients in the matrix is ​​obtained. Based on the effective diffusion coefficient, a microbial colony-scale nutrient gradient field is constructed. By solving the unsteady diffusion equation and combining it with the spatial distribution characteristics of the microbial colony, the time-varying nutrient concentration distribution in the colony microenvironment is obtained. Based on the time-varying nutrient concentration distribution, a pseudo-substrate competition inhibition effect integration process is performed. By equating the mass transfer limitation effect in the low nutrient concentration region with the existence of virtual competing substrates to simulate the actual nutrient uptake resistance of microorganisms, the effective nutrient concentration of microorganisms is obtained.

4. The method for dynamically controlling the fermentation process parameters of citrus pomace feed according to claim 1, characterized in that, Dynamic allocation of microbial agent metabolic pathways based on the effective nutrient concentration of the microorganism and the initial inoculation concentration includes: Based on the effective nutrient concentration of the microorganism and the initial inoculation concentration, the growth potential of the bacteria is evaluated. The potential specific growth rate of the bacteria under the preset carbon source availability is calculated by using a substrate-limited growth kinetic model to obtain the metabolic activity level of the bacteria. Based on the level of bacterial metabolic activity, the critical point of metabolic flux allocation was solved. By analyzing the energy trade-off between the tricarboxylic acid cycle and the fermentation pathway in terms of carbon source competition, the critical specific growth rate that characterizes the switching of metabolic flow was obtained. Based on the critical specific growth rate, a metabolic pathway switching signal generation process is performed. By comparing the deviation between the actual specific growth rate and the critical value in real time, an early warning signal is dynamically generated, thus obtaining the metabolic pathway switching signal.

5. The method for dynamically controlling the fermentation process parameters of citrus pomace feed according to claim 1, characterized in that, Fermentation direction regulation based on the aforementioned metabolic pathway switching signals includes: Based on the metabolic pathway switching signal, the steady-state conditions of the target metabolic pathway are solved in reverse. By constructing a steady-state model of the metabolic network with the enzyme activity corresponding to the rate-limiting step as the node, the target values ​​of each rate-limiting enzyme activity required to maintain the target metabolic flux are deduced. Based on the target activity values ​​of each rate-limiting enzyme, the mapping relationship between environmental parameters and enzyme activity is constructed. By establishing a response surface model of the dynamic influence of temperature and pH on the conformation of the rate-limiting enzyme, the temperature-pH matching range for maintaining the activity of the target enzyme is obtained. Based on the temperature-pH matching range, an intervention threshold optimization extraction process is performed, with the fermentation rate as the objective function and the stability of the metabolic network structure as the constraint condition, to determine the operational boundary and obtain the critical temperature and pH intervention thresholds for maintaining the target metabolic pathway.

6. A dynamic control system for fermentation process parameters of citrus pomace feed, characterized in that, include: The acquisition module is used to acquire the initial physical property data of citrus pomace raw material and the initial state data of the fermentation environment. The initial physical property data includes pectin content and cellulose-hemicellulose ratio, and the initial state data includes initial temperature, initial pH value, and initial inoculation concentration of fermentation agent. The prediction module is used to predict the matrix viscoelasticity based on the pectin content and the cellulose-hemicellulose ratio, and obtain the initial viscoelastic parameters of the matrix. The quantification module is used to quantify the mass transfer limitation effect in the fermentation process based on the initial viscoelastic parameters of the substrate. By introducing a pseudo-substrate competition inhibition effect to simulate the situation of nutrient diffusion obstruction, the effective nutrient concentration of microorganisms is obtained. The allocation module is used to dynamically allocate the metabolic pathway of the microbial agent according to the effective nutrient concentration of the microorganism and the initial inoculation concentration, and to obtain the metabolic pathway switching signal by identifying the metabolic diversion critical point of the microbial community under carbon source limitation. The regulation module is used to regulate the fermentation direction according to the metabolic pathway switching signal to obtain the critical temperature and pH intervention threshold for maintaining the target metabolic pathway. The output module is used to perform multi-parameter coupled dynamic regulation based on the critical temperature and the pH intervention threshold, and with the initial temperature and the initial pH value as the regulation benchmark. By analyzing the interaction between temperature and pH on enzyme activity and microbial community structure, dynamic regulation parameters are obtained. The dynamic regulation parameters include temperature adjustment amount, pH adjustment amount, and intervention timing based on metabolic pathway switching signals.

7. The dynamic control system for fermentation process parameters of citrus pomace feed according to claim 6, characterized in that, The prediction module includes: The first prediction unit is used to perform pectin-fiber complex structure modeling based on the pectin content and the cellulose-hemicellulose ratio. By analyzing the intermolecular forces between the pectin molecular side chains and cellulose microfibers, structural parameters characterizing the spatial configuration of the complex are obtained. The second prediction unit is used to perform composite network stiffness calculation based on the structural parameters, and obtain the contribution factor of the composite to the overall stiffness of the matrix by simulating the synergistic effect of the pectin gel network and the rigid fiber skeleton. The third prediction unit is used to perform macroscopic viscoelastic mapping processing of the matrix based on the contribution factor, and obtain the initial viscoelastic parameters of the matrix by converting the microstructure stiffness into macroscopic rheological properties.

8. The dynamic control system for fermentation process parameters of citrus pomace feed according to claim 6, characterized in that, The quantization module includes: The first quantization unit is used to calculate the effective diffusion coefficient of nutrients based on the initial viscoelastic parameters of the matrix. By modifying the Stokes-Einstein relationship based on the viscoelastic parameters to describe the diffusion behavior of small molecule nutrients in non-Newtonian fluid media, the effective diffusion coefficient of nutrients in the matrix is ​​obtained. The second quantification unit is used to construct a microbial colony-scale nutrient gradient field based on the effective diffusion coefficient. By solving the unsteady diffusion equation and combining it with the spatial distribution characteristics of the microbial colony, the time-varying nutrient concentration distribution in the colony microenvironment is obtained. The third quantification unit is used to perform pseudo-substrate competition inhibition effect integration processing based on the time-varying nutrient concentration distribution. By equating the mass transfer restriction effect in the low nutrient concentration region with the existence of virtual competing substrates to simulate the actual nutrient uptake resistance of microorganisms, the effective nutrient concentration of microorganisms is obtained.

9. The dynamic control system for fermentation process parameters of citrus pomace feed according to claim 6, characterized in that, The allocation module includes: The first allocation unit is used to perform a cell growth potential assessment based on the effective nutrient concentration of the microorganism and the initial inoculation concentration, and to calculate the potential specific growth rate of the cell under the preset carbon source availability by using a substrate-limited growth kinetic model to obtain the cell metabolic activity level. The second allocation unit is used to solve the critical point of metabolic flux allocation based on the metabolic activity level of the cells. By analyzing the energy trade-off between the tricarboxylic acid cycle and the fermentation pathway in terms of carbon source competition, the critical specific growth rate characterizing the switching of metabolic flow direction is obtained. The third allocation unit is used to generate a metabolic pathway switching signal based on the critical specific growth rate. It dynamically generates an early warning signal by comparing the deviation between the actual specific growth rate and the critical value in real time, thereby obtaining the metabolic pathway switching signal.

10. The dynamic control system for fermentation process parameters of citrus pomace feed according to claim 6, characterized in that, The control module includes: The first regulation unit is used to perform reverse solution processing of the target metabolic pathway steady-state conditions according to the metabolic pathway switching signal. By constructing a metabolic network steady-state model with the enzyme activities corresponding to the rate-limiting steps as nodes, the target values ​​of each rate-limiting enzyme activity required to maintain the target metabolic flux are deduced. The second regulation unit is used to construct the environmental parameter-enzyme activity mapping relationship based on the target values ​​of each rate-limiting enzyme activity. By establishing a response surface model of the dynamic influence of temperature and pH on the conformation of the rate-limiting enzyme, the temperature-pH matching range for maintaining the activity of the target enzyme is obtained. The third regulation unit is used to perform intervention threshold optimization extraction processing based on the temperature-pH matching range, with the fermentation rate as the objective function and the stability of the metabolic network structure as the constraint condition, to determine the operation boundary and obtain the critical temperature and pH intervention threshold for maintaining the target metabolic pathway.

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