Optimization regulation and control method for continuous fermentation process in different stages based on parameter reuse

Abstract

The invention relates to the technical field of process industrial production and processing, in particular to a parameter reuse-based different-stage continuous fermentation process optimization regulation and control method, which comprises the following steps of: establishing a state space model for each fermentation stage in a continuous state variable fermentation process, and determining a comprehensive benefit optimization index of each fermentation stage; calculating to obtain the target feeding substrate concentration of each fermentation stage under the condition that the comprehensive benefit optimization index is minimum, and in the process of solving the feeding substrate concentration of each fermentation stage after the initial fermentation stage, calculating the feeding substrate concentration of each fermentation stage by utilizing the similarity of each stage of cell growth in the fermentation tank; and solving the comprehensive benefit optimization index of the current fermentation stage by using the parameter matrix of the target feeding substrate concentration and the long-term comprehensive benefit of the previous fermentation stage. The control effect of the continuous fermentation process is optimized, and the control performance of the fermentation process is improved, so that the fermentation cost is reduced, the fermentation efficiency and the fermentation quality are improved, and the economic benefit is maximized.

Description

Technical Field

[0001] The invention relates to the technical field of process industry production and processing, and in particular to a method for optimizing and controlling continuous fermentation processes at different stages based on parameter reuse. Background Art

[0002] Fermentation engineering is a process technology for culturing cells and producing metabolites in large quantities in fermenters under the most suitable fermentation conditions. It is widely used in the fields of food fermentation and biopharmaceuticals. Fermentation engineering usually finds the general conditions for bacterial cell growth and metabolism in the shake bottle stage, and then finds the optimal conditions and methods for fermentation process control through experiments in small fermenters. Large-scale experiments are required before large-scale industrial production. Since many reactions and activities occur in the fermenter, there is a great deal of uncertainty in the fermentation process. Some subtle differences such as the difference in water used, the length of time the strains are stored, and the size of the fermenter will cause differences in microbial metabolism. The same strain and culture medium will also produce different results in different batches of production. Therefore, fermentation process control is a very important part of the fermentation process. In industry, the fermentation process is usually controlled by keeping various parameters such as pH, dissolved oxygen concentration, temperature and residual matrix at a certain level.

[0003] Continuous fermentation is a commonly used fermentation method in the fermentation process with relatively stable operating conditions. In continuous fermentation, fresh culture medium substrate and outflowing culture fluid are added at the same rate to achieve a constant liquid volume in the fermentation tank. The stability of its operating conditions is conducive to maintaining the stability of microbial growth metabolism, thereby obtaining stable productivity and product quality. Continuous fermentation initially relied on manual experience, but today's research is based on the mathematical model of cell culture operating conditions and production goals, using the concentration of feed substrate as a control variable, constructing a linear control model for the inherent nonlinear characteristics of the fermentation model, and designing a stable linear controller.

[0004] However, since the actual cell growth involves complex chemical and biological reactions and cell population distribution characteristics, its dynamic evolution is complex. At different stages of cell growth, the mathematical model of cell culture is different and difficult to describe accurately. If the fermentation model is rebuilt and solved at each fermentation stage, it will consume too many computing resources, resulting in high fermentation costs and reduced fermentation efficiency and quality. Summary of the invention

[0005] Therefore, the technical problem to be solved by the present invention is to overcome the problem in the prior art that each fermentation process requires the fermentation model to be rebuilt and solved, resulting in reduced fermentation efficiency and fermentation quality.

[0006] In order to solve the above technical problems, the present invention provides a method for optimizing and controlling continuous fermentation processes at different stages based on parameter reuse, comprising:

[0007] Establish a state space model of the initial fermentation stage in the continuous fermentation process and design a state feedback controller. Based on the optimal steady state of the cell state variables in the initial fermentation stage, establish the comprehensive benefit optimization index of the initial fermentation stage.

[0008] Design a long-term comprehensive evaluation index of the feed substrate concentration in the initial fermentation stage to obtain a parameter matrix of the long-term comprehensive benefits in the initial fermentation stage;

[0009] Solve the feed substrate concentration in the initial fermentation stage when the comprehensive benefit optimization index in the initial fermentation stage is minimized, and use it as the target feed substrate concentration in the initial fermentation stage;

[0010] For each fermentation stage after the initial fermentation stage, a state space model of the current fermentation stage is established and a state feedback controller is designed; based on the optimal steady state of the cell state variables in the current fermentation stage, the comprehensive benefit optimization index of the current fermentation stage is established;

[0011] Based on the deviation of the comprehensive benefit optimization index between the current fermentation stage and the previous fermentation stage, the comprehensive benefit optimization index of the current fermentation stage is solved by using the target feed substrate concentration of the previous fermentation stage and the parameter matrix of the long-term comprehensive benefit, and the parameter matrix of the long-term comprehensive benefit of the current fermentation stage and the target feed substrate concentration are obtained when the comprehensive benefit optimization index of the current fermentation stage is minimized, including:

[0012] Design a long-term comprehensive evaluation index of feed substrate concentration in the current fermentation stage;

[0013] Initialize the parameter matrix of the long-term comprehensive benefit of the current fermentation stage and the feed substrate concentration, and use the target feed substrate concentration of the previous fermentation stage and the parameter matrix of the long-term comprehensive benefit to calculate the deviation of the comprehensive benefit optimization index between the current fermentation stage and the previous fermentation stage;

[0014] Based on the deviation of comprehensive benefit optimization index between the current fermentation stage and the previous fermentation stage, the parameter matrix of the long-term comprehensive benefit of the current fermentation stage is estimated;

[0015] Optimizing the feed substrate concentration in the current fermentation stage based on the estimated value of the parameter matrix of the long-term comprehensive benefits in the current fermentation stage;

[0016] Based on the estimated value of the parameter matrix of the long-term comprehensive benefits of the current fermentation stage and the optimized feed substrate concentration of the current fermentation stage, return to recalculate the deviation of the comprehensive benefit optimization index between the current fermentation stage and the previous fermentation stage, and re-estimate the parameter matrix of the long-term comprehensive benefits of the current fermentation stage, and then optimize the feed substrate concentration of the current fermentation stage again until the estimated value of the parameter matrix of the long-term comprehensive benefits of the current fermentation stage no longer changes, and obtain the parameter matrix of the long-term comprehensive benefits of the current fermentation stage and the target feed substrate concentration.

[0017] Preferably, a state space model of the initial fermentation stage in the continuous fermentation process is established and a state feedback controller is designed; based on the optimal steady state of the cell state variables in the initial fermentation stage, the comprehensive benefit optimization index of the initial fermentation stage is established, including:

[0018] The state space model of the initial fermentation stage is:

[0019] x a (k+1)=A a x a (k)+B a u a (k)

[0020] Among them, A a and B a is the state space model parameter of the initial fermentation stage, x a (k) and x a (k+1) represents the cell state variables at the initial fermentation stage at time k and time k+1, respectively, u a (k) represents the feed substrate concentration at the initial fermentation stage at time k;

[0021] Based on the state space model of the initial fermentation stage, the state feedback controller is designed as:

[0022]

[0023] Among them, K a is the control amount in the initial fermentation stage, x 0,a Represents the optimal steady state of cell state variables during the initial fermentation phase.

[0024] Preferably, based on the optimal steady state of the cell state variables in the initial fermentation stage, the comprehensive benefit optimization index of the initial fermentation stage is established as:

[0025]

[0026] Among them, c a (k) represents the comprehensive benefit optimization index of the initial fermentation stage at time k, It represents the difference between the measured value of the cell state in the initial fermentation stage at time k and the optimal steady-state value. E and F are both weight parameter matrices.

[0027] Preferably, a long-term comprehensive evaluation index of the feed substrate concentration in the initial fermentation stage is designed to obtain a parameter matrix of the long-term comprehensive benefits in the initial fermentation stage, including:

[0028] Design a long-term comprehensive evaluation index for the feed substrate concentration in the initial fermentation stage, the formula is:

[0029]

[0030] Among them, J a (k) and J a (k+1) are the long-term comprehensive evaluation indicators of the feed substrate concentration in the initial fermentation stage at time k and time k+1, γ is the attenuation factor, is the parameter matrix of the long-term comprehensive benefits in the initial fermentation stage, and R is the weight parameter matrix.

[0031] Preferably, when the state space model parameters of the initial fermentation stage are unknown, solving the feed substrate concentration of the initial fermentation stage when the comprehensive benefit optimization index of the initial fermentation stage is minimized as the target feed substrate concentration of the initial fermentation stage includes:

[0032] First, the parameter matrix of the long-term comprehensive benefits of the initial fermentation stage is estimated, including:

[0033] The long-term comprehensive evaluation index of the feed substrate concentration in the initial fermentation stage is expressed as:

[0034]

[0035] in, is with x a (k),u a (k), x 0,a The associated parameter vector, V a Yes and W a The parameter matrix corresponding to the sub-matrix parameters of ;

[0036] Build V a The relationship with the comprehensive benefit optimization index in the initial fermentation stage is as follows:

[0037] c a (k) = z a (k)V a

[0038] Among them, z a (k) = h a (k)-γh a (k+1);

[0039] The system identification method is used to solve the minimum comprehensive benefit optimization index V in the initial fermentation stage. a The estimated value of W a An estimated value of

[0040] The parameter matrix based on the long-term comprehensive benefits of the initial fermentation stage optimizes the feed substrate concentration in the initial fermentation stage. The formula is:

[0041]

[0042] in, is the estimated value of the parameter matrix of the long-term comprehensive benefits of the initial fermentation stage at time k, The parameter matrix W is the long-term comprehensive benefit of the initial fermentation stage. a The sub-matrix of They are An estimated value of

[0043] Then the control quantity K of the initial fermentation stage at time k+1 is a (k+1) is

[0044] Based on the optimized feed substrate concentration in the initial fermentation stage, the parameter matrix for re-estimating the long-term comprehensive benefits of the initial fermentation stage is returned, and the feed substrate concentration in the initial fermentation stage is optimized again until the estimated value of the parameter matrix for the long-term comprehensive benefits of the initial fermentation stage no longer changes, thereby obtaining the parameter matrix for the long-term comprehensive benefits of the initial fermentation stage and the target feed substrate concentration.

[0045] Preferably, when the state space model parameters of the initial fermentation stage are known, the parameter matrix of the long-term comprehensive benefits of the initial fermentation stage is directly obtained; and the feed substrate concentration of the initial fermentation stage is solved when the comprehensive benefit optimization index of the initial fermentation stage is minimized, as the target feed substrate concentration of the initial fermentation stage, and the methods include minimum principle, dynamic programming method, numerical calculation method and gradient method.

[0046] Preferably, a long-term comprehensive evaluation index of the feed substrate concentration in the current fermentation stage is designed, including:

[0047]

[0048] Among them, J b (k) and J b (k+1) are the long-term comprehensive evaluation indicators of the feed substrate concentration in the current fermentation stage at time k and time k+1, respectively, c b (k) is the comprehensive benefit optimization index of the current fermentation stage at time k, γ is the attenuation factor, xb (k) is the cell state variable of the current fermentation stage at time k, u b (k) is the feed substrate concentration of the current fermentation stage at time k, is the optimal steady state of the cell state variables in the current fermentation stage; is the parameter matrix of the long-term comprehensive benefits of the current fermentation stage, A b and B b are the state space model parameters of the current fermentation stage, and E, F and R are weight parameter matrices.

[0049] Preferably, the parameter matrix of the long-term comprehensive benefit of the current fermentation stage and the feed substrate concentration are initialized, and the target feed substrate concentration of the previous fermentation stage and the parameter matrix of the long-term comprehensive benefit are used to calculate the comprehensive benefit optimization index deviation between the current fermentation stage and the previous fermentation stage, including:

[0050] Assuming that the initial time of the current fermentation stage is τ, when k = τ, let u b (k) = u a* , and initialize

[0051] Among them, u b (k) represents the initial feed substrate concentration of the current fermentation stage at time k, u a* represents the target feed substrate concentration in the previous fermentation stage, The estimated value of the parameter matrix representing the long-term comprehensive benefits of the current fermentation stage at time k;

[0052] When k>τ, based on the parameter matrix of the long-term comprehensive benefits of the previous fermentation stage, the deviation of the comprehensive benefit optimization index between the current fermentation stage and the previous fermentation stage is calculated, including:

[0053] The comprehensive benefit optimization index of the current fermentation stage at time k is: in, is the difference between the measured value of the cell state in the current fermentation stage at time k and the optimal steady-state value. E and F are both weight parameter matrices. u b (k) is the feed substrate concentration of the current fermentation stage at time k;

[0054] The comprehensive benefit optimization index of the previous fermentation stage at time k is: Among them, J a' (k) and J a' (k+1) are the long-term comprehensive evaluation indicators of the feed substrate concentration in the previous fermentation stage at time k and time k+1, respectively, γ is the attenuation factor, x a'(k) is the cell state variable of the previous fermentation stage at time k, u a' (k) is the feed substrate concentration of the previous fermentation stage at time k, x 0,a ' is the optimal steady state of the cell state variables in the previous fermentation stage, The parameter matrix of the long-term comprehensive benefits calculated for the previous fermentation stage; yes The parameter matrix estimates corresponding to the submatrix parameters;

[0055] The comprehensive profit deviation between the current fermentation stage and the previous fermentation stage at time k is δ(k) = c b (k)-c a' (k);

[0056] The comprehensive profit deviation between the current fermentation stage and the previous fermentation stage at time k-1 is δ(k-1) = c b (k-1)-c a (k-1).

[0057] Preferably, based on the deviation of the comprehensive benefit optimization index between the current fermentation stage and the previous fermentation stage, the parameter matrix of the long-term comprehensive benefit of the current fermentation stage is estimated, and the formula includes:

[0058]

[0059] in, and are the comprehensive income deviation vectors at time k-1 and time k, respectively, U(k) is the difference gain in the current fermentation stage, and are the estimated values ​​of the parameter matrix of the long-term comprehensive benefits of the current fermentation stage at time k-1 and time k, The parameter matrix of the long-term comprehensive benefits calculated for the previous fermentation stage.

[0060] Preferably, the feed substrate concentration in the current fermentation stage is optimized based on the estimated value of the parameter matrix of the long-term comprehensive benefits in the current fermentation stage, and the formula is:

[0061]

[0062] Among them, u b (k+1) is the feed substrate concentration of the current fermentation stage at time k+1, The parameter matrix W is the long-term comprehensive benefit of the current fermentation stage. b The sub-matrix of They are Estimated value.

[0063] Then the control quantity K of the current fermentation stage at time k+1 isb (k+1) is

[0064] The above technical solution of the present invention has the following beneficial effects compared with the prior art:

[0065] The present invention discloses a method for optimizing and controlling continuous fermentation processes at different stages based on parameter reuse, by establishing a state space model for each fermentation stage in the continuous state variable fermentation process, and establishing the comprehensive benefit optimization index of each fermentation stage, and calculating the feed substrate concentration of each fermentation stage when the comprehensive benefit optimization index is minimum, so that the cell culture state in the fermenter can be stabilized at the optimal steady-state value in each fermentation stage. In the process of solving the feed substrate concentration of each fermentation stage after the initial fermentation stage, the present invention utilizes the similarity of each stage of cell growth in the fermenter, and utilizes the target feed substrate concentration of the previous fermentation stage and the parameter matrix of the long-term comprehensive benefit to solve the comprehensive benefit optimization index of the current fermentation stage, making full use of the known cell growth state and empirical culture parameter information, improving the fast startup performance of the algorithm, and improving the efficiency of solving, so that the cell culture state in the fermenter can be stabilized at the optimal steady-state value more quickly. Therefore, the present invention optimizes the control effect of the continuous fermentation process, improves the control performance of the fermentation process, thereby reducing the fermentation cost, improving the fermentation efficiency and fermentation quality, and maximizing the economic benefit. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below according to specific embodiments of the present invention in conjunction with the accompanying drawings, wherein:

[0067] Figure 1 It is a diagram of a continuous fermentation tank system;

[0068] Figure 2 It is a flow chart of a method for optimizing and controlling a continuous fermentation process at different stages based on parameter reuse;

[0069] Figure 3 It is a schematic diagram of the optimized process of fermentation stage a and fermentation stage b in Example 2;

[0070] Figure 4 This is a comparison chart of control performance when the parameter reuse method is used in the fermentation process of utilis yeast. DETAILED DESCRIPTION

[0071] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it, but the embodiments are not intended to limit the present invention.

[0072] Embodiment 1

[0073] Figure 1 The present invention provides a method for optimizing and controlling the continuous fermentation process at different stages based on parameter reuse. Figure 2 As shown, the steps include:

[0074] S1. Establish a state space model of the initial fermentation stage in the continuous fermentation process and design a state feedback controller. Based on the optimal steady state of the cell state variables in the initial fermentation stage, establish the comprehensive benefit optimization index of the initial fermentation stage, including:

[0075] Establish the state space model of the initial fermentation stage in the continuous fermentation process and design the state feedback controller, including:

[0076] According to the cell culture dynamics, with t as the sampling period, the state space model of the continuous fermentation process at the initial fermentation stage about the sampling time k is established as follows:

[0077] x a (k+1)=A a x a (k)+B a u a (k)

[0078] Among them, A a and B a is the state space model parameter of the initial fermentation stage, x a (k) and x a (k+1) represents the cell state variables at the initial fermentation stage at time k and time k+1, respectively, u a (k) represents the feed substrate concentration at the initial fermentation stage at time k.

[0079] Based on the state space model of the initial fermentation stage, the state feedback controller is designed as:

[0080]

[0081] Among them, K a is the control amount in the initial fermentation stage, x 0,a Represents the optimal steady state of cell state variables during the initial fermentation phase.

[0082] Specifically, x a (k) = [X a ,S a ,P a ] T , where X a , S a , P aThey represent the biomass concentration, substrate concentration, and product concentration at time k, respectively. The corresponding x 0,a =[X 0,a ,S 0,a ,P 0,a ] T , where X 0,a , S 0,a , P 0,a They represent the optimal steady-state of biomass concentration, substrate concentration, and product concentration at time k respectively.

[0083] There are many aspects to consider in the fermentation process, and they need to be set according to actual production needs. In the continuous fermentation process, the optimal steady state of all state variables needs to be considered comprehensively, that is, the biomass concentration, substrate concentration, and product concentration are all stable at the corresponding optimal steady-state values ​​to obtain stable productivity and product quality.

[0084] Based on the optimal steady state of cell state variables in the initial fermentation stage, the comprehensive benefit optimization index of the initial fermentation stage is established as follows:

[0085]

[0086] Among them, C a (k) represents the comprehensive benefit optimization index of the initial fermentation stage at time k, It represents the difference between the measured value of the cell state in the initial fermentation stage at time k and the optimal steady-state value. E and F are both weight parameter matrices.

[0087] In order to maximize the benefits of the initial fermentation stage, it is necessary to find the comprehensive benefit optimization index c of the initial fermentation stage. a (k) Minimum target feed substrate concentration, i.e.

[0088] S2. Design a long-term comprehensive evaluation index of the feed substrate concentration in the initial fermentation stage to obtain a parameter matrix of the long-term comprehensive benefits of the initial fermentation stage, including:

[0089] Design a long-term comprehensive evaluation index for the feed substrate concentration in the initial fermentation stage, the formula is:

[0090] J a (k) = c a (k)+γJ a (k+1)

[0091] Among them, J a (k) and J a (k+1) are the long-term comprehensive evaluation indicators of the feed substrate concentration in the initial fermentation stage at time k and time k+1, respectively; γ is the attenuation factor, and its value is 0≤γ≤1.

[0092] When the feed substrate concentration u in the initial fermentation stage a When (k) is the optimal value, J a (k+1) can be approximately regarded as The relevant function, namely Then in the initial fermentation stage, the long-term comprehensive evaluation index of feed substrate concentration can be approximately expressed as:

[0093]

[0094] in, is the parameter matrix of the long-term comprehensive benefits in the initial fermentation stage, and R is the weight parameter matrix; is with x a (k),u a (k), x 0,a The associated parameter vector, V a Yes and W a The parameter matrix corresponding to the sub-matrix parameters of .

[0095] S3. Calculate the feed substrate concentration in the initial fermentation stage when the comprehensive benefit optimization index in the initial fermentation stage is minimized, and use it as the target feed substrate concentration in the initial fermentation stage.

[0096] When the state space model parameter A in the initial fermentation stage a and B a When it is known, the parameter matrix of the long-term comprehensive benefit of the initial fermentation stage can be directly obtained. In addition, this embodiment adopts the minimum principle to solve the feed substrate concentration in the initial fermentation stage when the comprehensive benefit optimization index of the initial fermentation stage is the minimum, that is, by solving the Riccati differential equation of the matrix, the quadratic objective equation To solve the optimization problem.

[0097] Preferably, the solution method for the known state space model parameters is not limited to analytical methods such as the minimum principle and dynamic programming method, but also includes numerical calculation method and gradient method.

[0098] When the state space model parameter A in the initial fermentation stage a and B aWhen it is unknown, the dynamic programming method is used to solve the comprehensive benefit optimization problem. This method requires the model structure to be known, and is also applicable to the case where the model parameters are known, and has low requirements for model parameters. The solution idea is: using a trial-and-error method, randomly select appropriate feed substrate concentrations during the fermentation process to repeatedly predict and optimize long-term comprehensive benefits. At the initial moment, that is, k=0, under the constraint of the feed substrate concentration range in the actual fermentation process, randomly select a suitable initial feed substrate concentration; perform short-term fermentation and sampling at this concentration, and use the collected cell growth status data, combined with the comprehensive benefit optimization index, to use the feed substrate concentration long-term comprehensive evaluation index to predict the long-term comprehensive benefits of the current feed substrate concentration. If the model parameters are unknown, first estimate the long-term comprehensive benefits, that is, estimate the parameter matrix W related to the long-term comprehensive benefits a ; and perform secondary optimization on the long-term comprehensive evaluation index of feed substrate concentration to obtain a better feed substrate concentration; and then perform long-term comprehensive benefit prediction and optimization on the optimized feed substrate concentration, and repeat this process to obtain the target feed substrate concentration. The specific steps include:

[0099] S301. Estimating the parameter matrix of the long-term comprehensive benefits of the initial fermentation stage, including:

[0100] The long-term comprehensive evaluation index of feed substrate concentration in the initial fermentation stage is expressed as:

[0101]

[0102] Build V a The relationship with the comprehensive benefit optimization index in the initial fermentation stage is as follows:

[0103] c a (k) = z a (k)V a

[0104] Among them, z a (k) = h a (k)-γh a (k+1);

[0105] Due to x a (k) and u a (k), x 0,a , E, F and other parameters are known, and the system identification method is used to solve the minimum comprehensive benefit optimization index V in the initial fermentation stage. a The estimated value of W a The estimated value of , the solution methods include but are not limited to recursive least squares method, maximum likelihood method, stochastic approximation method, multiple innovation identification method, auxiliary model identification, etc.

[0106] S302, the long-term comprehensive evaluation index J of the feed substrate concentration in the initial fermentation stage can be used a (k) for u a (k) The analytical method with zero partial derivative is used to optimize the feed substrate concentration. The formula is:

[0107]

[0108] in, is the estimated value of the parameter matrix of the long-term comprehensive benefits of the initial fermentation stage at time k, The parameter matrix W is the long-term comprehensive benefit of the initial fermentation stage. a The sub-matrix of They are An estimated value of

[0109] Then the control quantity K of the initial fermentation stage at time k+1 is a (k+1) is

[0110] S303, let k=k+1.

[0111] S304. Based on the optimized feed substrate concentration of the initial fermentation stage, return to S301 to re-estimate the parameter matrix of the long-term comprehensive benefits of the initial fermentation stage, and optimize the feed substrate concentration of the initial fermentation stage again until the estimated value of the parameter matrix of the long-term comprehensive benefits of the initial fermentation stage no longer changes, thereby obtaining the parameter matrix of the long-term comprehensive benefits of the initial fermentation stage and the target feed substrate concentration.

[0112] S4. For each fermentation stage after the initial fermentation stage, a state space model of the current fermentation stage is established and a state feedback controller is designed; based on the optimal steady state of the cell state variables in the current fermentation stage, the comprehensive benefit optimization index of the current fermentation stage is established, including:

[0113] If during the fermentation process, at time k = τ, the fermentation process transitions from the previous fermentation stage to the current fermentation stage, the model parameters change. At this time, the optimal feed substrate concentration u of the previous fermentation stage a* It is not the optimal solution in the current fermentation stage.

[0114] According to the cell culture dynamics, with t as the sampling period, the state space model of the continuous fermentation process at the current fermentation stage about the sampling time k is re-established:

[0115] x b (k+1)=A b x b (k)+B b u b (k)

[0116] Among them, A b and B b is the state space model parameter of the current fermentation stage, x b (k) and x b (k+1) represents the cell state variables of the current fermentation stage at time k and time k+1, respectively, u b (x) represents the feed substrate concentration of the current fermentation stage at time k.

[0117] Based on the state space model of the current fermentation stage, the state feedback controller is designed as:

[0118]

[0119] Among them, K b is the control amount of the current fermentation stage, x 0,b Represents the optimal steady state of the cell state variables at the current fermentation stage.

[0120] Specifically, x b (k) = [X b ,S b ,P b ] T , where X b , S b , P b They represent the biomass concentration, substrate concentration, and product concentration of the current fermentation stage at time k, respectively. The corresponding x 0,b =[X 0,b ,S 0,b ,P 0,b ] T , where X 0,b , S 0,b , P 0,b They represent the optimal steady-state of biomass concentration, substrate concentration and product concentration in the current fermentation stage at time k respectively.

[0121] Based on the optimal steady state of the cell state variables in the current fermentation stage, the comprehensive benefit optimization index of the current fermentation stage is established as follows:

[0122]

[0123] Among them, c b (k) represents the comprehensive benefit optimization index of the current fermentation stage at time k, It represents the difference between the measured value of the cell state in the current fermentation stage at time k and the optimal steady-state value. E and F are both weight parameter matrices.

[0124] In order to maximize the benefits of the current fermentation stage, it is necessary to find the comprehensive benefit optimization index c of the current fermentation stage.b (k) Minimum target feed substrate concentration, i.e.

[0125] S5. Based on the deviation of the comprehensive benefit optimization index between the current fermentation stage and the previous fermentation stage, the comprehensive benefit optimization index of the current fermentation stage is solved by using the target feed substrate concentration of the previous fermentation stage and the parameter matrix of the long-term comprehensive benefit, and the parameter matrix of the long-term comprehensive benefit of the current fermentation stage and the target feed substrate concentration are obtained when the comprehensive benefit optimization index of the current fermentation stage is minimized, specifically including:

[0126] S501. Design a long-term comprehensive evaluation index for the feed substrate concentration in the current fermentation stage. The formula is:

[0127]

[0128] in, It is the parameter matrix of the long-term comprehensive benefits of the current fermentation stage.

[0129] Since the model parameter A in the current fermentation stage b and B b If it is unknown, the estimation of the long-term comprehensive benefits of the current fermentation stage is transformed into the parameter matrix W b If the optimal feed substrate concentration is solved again, it will take a long time and the fermentation cost will be high. Therefore, in the control process of the current fermentation stage, the empirical control data of the previous fermentation stage is reused, that is, the target feed substrate concentration u of the previous fermentation stage is u a* Parameter matrix with long-term comprehensive benefits Construct and solve optimization problems based on parameter reuse, make full use of known cell growth states and empirical culture environment data, use the similarities of the two fermentation stages, improve the rapid startup performance of the algorithm, improve the optimization effect, and enable each cell culture state to quickly stabilize at the optimal steady-state value, thereby reducing fermentation costs and maximizing economic benefits.

[0130] S502, initializing the parameter matrix of the long-term comprehensive benefits and the feed substrate concentration of the current fermentation stage, and using the target feed substrate concentration of the previous fermentation stage and the parameter matrix of the long-term comprehensive benefits to calculate the deviation of the comprehensive benefit optimization index between the current fermentation stage and the previous fermentation stage.

[0131] When k = τ, let u b (k) = u a* , and initialize

[0132] Among them, u b (k) represents the initial feed substrate concentration of the current fermentation stage at time k, u a*represents the target feed substrate concentration in the previous fermentation stage, The estimated value of the parameter matrix representing the long-term comprehensive benefits of the current fermentation stage at time k.

[0133] When k>τ, based on the parameter matrix of the long-term comprehensive benefits of the previous fermentation stage, the deviation of the comprehensive benefit optimization index between the current fermentation stage and the previous fermentation stage is calculated, including:

[0134] The comprehensive benefit optimization index of the current fermentation stage at time k is:

[0135] The comprehensive benefit optimization index of the previous fermentation stage at time k is:

[0136] Among them, J a' (k) and J a' (k+1) are the long-term comprehensive evaluation indicators of the feed substrate concentration in the previous fermentation stage at time k and time k+1, respectively, γ is the attenuation factor, x a' (k) is the cell state variable of the previous fermentation stage at time k, u a' (k) is the feed substrate concentration of the previous fermentation stage at time k, x 0,a' is the optimal steady state of the cell state variables in the previous fermentation stage, The parameter matrix of the long-term comprehensive benefits calculated for the previous fermentation stage; yes The parameter matrix estimates corresponding to the submatrix parameters;

[0137] The comprehensive profit deviation between the current fermentation stage and the previous fermentation stage at time k is δ(k) = c b (k)-c a' (k);

[0138] The comprehensive profit deviation between the current fermentation stage and the previous fermentation stage at time k-1 is δ(k-1) = c b (k-1)-c a (k-1).

[0139] S503. Estimate the parameter matrix of the long-term comprehensive benefits of the current fermentation stage based on the deviation of the comprehensive benefit optimization index between the current fermentation stage and the previous fermentation stage.

[0140] At time k, the comprehensive benefit c known at time k-1 b (k-1), c a (k-1) and The estimated value of the parameter matrix of the long-term comprehensive benefits of the current fermentation stage at time k-1 by the design difference gain U(k) The correction is made by using the similarity between the previous fermentation stage and the current fermentation stage to make the estimated value of the parameter matrix of the long-term comprehensive benefit of the current fermentation stage Approximation to the true value W b , the formula includes:

[0141]

[0142] in, and are the comprehensive income deviation vectors at time k-1 and time k, respectively, U(k) is the difference gain in the current fermentation stage, and are the estimated values ​​of the parameter matrix of the long-term comprehensive benefits of the current fermentation stage at time k-1 and time k, The parameter matrix of the long-term comprehensive benefits calculated for the previous fermentation stage.

[0143] S504. Optimize the feed substrate concentration in the current fermentation stage based on the estimated value of the parameter matrix of the long-term comprehensive benefits in the current fermentation stage.

[0144] The algorithms for optimizing the feed substrate concentration in the current fermentation stage include but are not limited to the interior point method, exterior point method, sequential quadratic programming method, genetic algorithm, particle swarm algorithm, etc., but the optimization process is relatively complicated. Therefore, this embodiment adopts the long-term comprehensive evaluation index J of the feed substrate concentration in the current fermentation stage. b (k) for u b (k) The analytical method with zero partial derivative is used to optimize the feed substrate concentration in the current fermentation stage. The formula is:

[0145]

[0146] Among them, u b (k+1) is the feed substrate concentration of the current fermentation stage at time k+1, The parameter matrix W is the long-term comprehensive benefit of the current fermentation stage. b The sub-matrix of They are Estimated value.

[0147] Then the control quantity K of the current fermentation stage at time k+1 is b (k+1) is

[0148] S505. Let k=k+1.

[0149] S506. Based on the estimated value of the parameter matrix of the long-term comprehensive benefits of the current fermentation stage and the optimized feed substrate concentration of the current fermentation stage, return to S502 to recalculate the deviation of the comprehensive benefit optimization index between the current fermentation stage and the previous fermentation stage, and re-estimate the parameter matrix of the long-term comprehensive benefits of the current fermentation stage, and then optimize the feed substrate concentration of the current fermentation stage again until the estimated value of the parameter matrix of the long-term comprehensive benefits of the current fermentation stage no longer changes, and obtain the parameter matrix of the long-term comprehensive benefits of the current fermentation stage and the target feed substrate concentration.

[0150] The present invention aims at the situation that the amount of data on the initial cell culture status in each stage of cell growth in a fermentation tank during a continuous fermentation process is small, especially under the condition that the model parameters are unknown or inaccurately known, and is suitable for a continuous fermentation process in which the process model order and process requirements are known and the status is measurable.

[0151] In summary, the present invention discloses a method for optimizing and controlling continuous fermentation processes at different stages based on parameter reuse, by establishing a state space model for each fermentation stage in the continuous state variable fermentation process, and establishing the comprehensive benefit optimization index of each fermentation stage, and calculating the feed substrate concentration of each fermentation stage when the comprehensive benefit optimization index is minimum, so that the cell culture state in the fermenter can be stabilized at the optimal steady-state value in each fermentation stage. In the process of solving the feed substrate concentration of each fermentation stage after the initial fermentation stage, the present invention utilizes the similarity of each stage of cell growth in the fermenter, and uses the target feed substrate concentration of the previous fermentation stage and the parameter matrix of the long-term comprehensive benefit to solve the comprehensive benefit optimization index of the current fermentation stage, making full use of the known cell growth state and empirical culture parameter information, improving the fast startup performance of the algorithm, and improving the efficiency of solving, so that the cell culture state in the fermenter can be stabilized at the optimal steady-state value more quickly. Therefore, the present invention optimizes the control effect of the continuous fermentation process, improves the control performance of the fermentation process, thereby reducing the fermentation cost, improving the fermentation efficiency and fermentation quality, and maximizing the economic benefit.

[0152] Embodiment 2

[0153] To verify the effect of the present invention, this example applies a method for optimizing and controlling continuous fermentation processes at different stages based on parameter reuse to a fermentation process using Tola utilis.

[0154] First, the state space model of the initial fermentation stage in the continuous fermentation process, i.e., fermentation stage a, is established and the state feedback controller is designed:

[0155] x a (k+1)=A a x a (k)+B a u a(k)

[0156] Set the state space model parameters for fermentation stage a

[0157] Based on the state space model of fermentation stage a, the state feedback controller is designed as:

[0158]

[0159] The optimal steady-state x of the cell state variables in fermentation stage a 0,a =[X 0,a ,S 0,a ,P 0,a ] T =[7.038,2.404,25] T .

[0160] Based on the optimal steady state of cell state variables in fermentation stage a, the comprehensive benefit optimization index of fermentation stage a is established as follows:

[0161]

[0162] set up F=1.

[0163] At k=0, set the initial cell culture state concentration parameters x a (0) = [0, 8.5, 0] T , and randomly select the appropriate initial feed substrate concentration u a (0) = 5.1884. Solve the target feed substrate concentration u of fermentation stage a when the comprehensive benefit optimization index of fermentation stage a is minimized a* Parameter matrix with long-term comprehensive benefits

[0164] When the fermentation process enters the next fermentation stage b, the state space model of the continuous fermentation process of fermentation stage b at sampling time k is reestablished:

[0165] x b (k+1)=A b x b (k)+B b u b (k)

[0166] set up

[0167] Based on the state space model of fermentation stage b, the state feedback controller is designed as:

[0168]

[0169] The optimal steady-state x of the cell state variables in fermentation stage b 0,b =[X 0,b ,S 0,b ,P 0,b ] T =[7.038,2.404,25] T .

[0170] Based on the optimal steady state of cell state variables in fermentation stage b, the comprehensive benefit optimization index of fermentation stage b is established as follows:

[0171]

[0172] set up F=1.

[0173] In fermentation stage b, the comprehensive benefit optimization problem based on parameter reuse is solved, and the parameter matrix estimation value of fermentation stage a is reused. and the optimal feed substrate concentration u a* To optimize the solution of the feed substrate concentration of fermentation stage b. At the beginning of fermentation stage b, that is, when k = τ, set the initial parameter matrix of fermentation stage b Let u b (τ) = u a* , at this time, the concentration parameters of the cell culture state are x b (τ)=[0,8.5,0] T .

[0174] Based on the deviation of the comprehensive benefit optimization index between fermentation stage b and fermentation stage a, the comprehensive benefit optimization index of fermentation stage b was solved using the target feed substrate concentration of fermentation stage a and the parameter matrix of long-term comprehensive benefits, and the parameter matrix and target feed substrate concentration of long-term comprehensive benefits of fermentation stage b were obtained when the comprehensive benefit optimization index of fermentation stage b was minimized.

[0175] Figure 3 It is a schematic diagram of the optimized process of fermentation stage a and fermentation stage b.

[0176] The target feed substrate concentration of fermentation stage b is calculated using the solution method in S3 and the solution method based on parameter reuse in S5, respectively. Figure 4 The figure is a control performance comparison chart of the two methods for calculating the target feed substrate concentration in fermentation stage b. The time k used to obtain the optimal feed substrate concentration under the two methods is 392 and 100 respectively. The method for solving the comprehensive benefit optimization problem based on parameter reuse can quickly obtain the optimal feed substrate concentration while improving the control performance, so that each cell culture state can quickly stabilize at the optimal steady-state value.

[0177] According to the above, the method of the present invention realizes the optimization and regulation of the fermentation process of utilis yeast. It can be seen that the comprehensive benefit optimization problem solving based on parameter reuse makes full use of the known cell growth state and empirical culture parameter information. In the case of a small amount of cell culture state data at the beginning of a new fermentation stage, the similarity between the two is utilized to improve the optimization effect, improve the rapid startup performance of the algorithm, and enhance the control performance, thereby reducing the fermentation cost and maximizing the economic benefit.

[0178] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0179] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0180] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0181] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0182] Obviously, the above embodiments are merely examples for the purpose of clear explanation and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the present invention.

Claims

1. A method for optimizing and controlling continuous fermentation processes at different stages based on parameter reuse, characterized in that: include: Establish a state space model of the initial fermentation stage in the continuous fermentation process and design a state feedback controller. Based on the optimal steady state of the cell state variables in the initial fermentation stage, establish the comprehensive benefit optimization index of the initial fermentation stage. Design a long-term comprehensive evaluation index of the feed substrate concentration in the initial fermentation stage to obtain a parameter matrix of the long-term comprehensive benefits in the initial fermentation stage; Solve the feed substrate concentration in the initial fermentation stage when the comprehensive benefit optimization index in the initial fermentation stage is minimized, and use it as the target feed substrate concentration in the initial fermentation stage; For each fermentation stage after the initial fermentation stage, a state space model of the current fermentation stage is established and a state feedback controller is designed; based on the optimal steady state of the cell state variables in the current fermentation stage, the comprehensive benefit optimization index of the current fermentation stage is established; Based on the deviation of the comprehensive benefit optimization index between the current fermentation stage and the previous fermentation stage, the comprehensive benefit optimization index of the current fermentation stage is solved by using the target feed substrate concentration of the previous fermentation stage and the parameter matrix of the long-term comprehensive benefit, and the parameter matrix of the long-term comprehensive benefit of the current fermentation stage and the target feed substrate concentration are obtained when the comprehensive benefit optimization index of the current fermentation stage is minimized. include: Design a long-term comprehensive evaluation index of feed substrate concentration in the current fermentation stage; Initialize the parameter matrix of the long-term comprehensive benefit of the current fermentation stage and the feed substrate concentration, and use the target feed substrate concentration of the previous fermentation stage and the parameter matrix of the long-term comprehensive benefit to calculate the deviation of the comprehensive benefit optimization index between the current fermentation stage and the previous fermentation stage; Based on the deviation of comprehensive benefit optimization index between the current fermentation stage and the previous fermentation stage, the parameter matrix of the long-term comprehensive benefit of the current fermentation stage is estimated; Optimizing the feed substrate concentration in the current fermentation stage based on the estimated value of the parameter matrix of the long-term comprehensive benefits in the current fermentation stage; Based on the estimated value of the parameter matrix of the long-term comprehensive benefits of the current fermentation stage and the optimized feed substrate concentration of the current fermentation stage, return to recalculate the comprehensive benefit optimization index deviation between the current fermentation stage and the previous fermentation stage, and re-estimate the parameter matrix of the long-term comprehensive benefits of the current fermentation stage, and then optimize the feed substrate concentration of the current fermentation stage again. Until the estimated value of the parameter matrix of the long-term comprehensive benefits of the current fermentation stage no longer changes, the parameter matrix of the long-term comprehensive benefits of the current fermentation stage and the target feed substrate concentration are obtained.

2. The method for optimizing and controlling continuous fermentation processes at different stages based on parameter reuse according to claim 1, characterized in that: Establish the state space model of the initial fermentation stage in the continuous fermentation process and design the state feedback controller; Based on the optimal steady state of cell state variables in the initial fermentation stage, the comprehensive benefit optimization indicators of the initial fermentation stage are established, including: The state space model of the initial fermentation stage is: x a (k+1)=A a x a (k)+B a u a (k) Among them, A a and B a is the state space model parameter of the initial fermentation stage, x a (k) and x a (k+1) represents the cell state variables at the initial fermentation stage at time k and time k+1, respectively, u a (k) represents the feed substrate concentration at the initial fermentation stage at time k; Based on the state space model of the initial fermentation stage, the state feedback controller is designed as: u a (k)=K a (x a (k)-A a-1 x 0,a ) Among them, K a is the control amount in the initial fermentation stage, x 0,a Represents the optimal steady state of cell state variables during the initial fermentation phase.

3. The method for optimizing and controlling continuous fermentation processes at different stages based on parameter reuse according to claim 2, characterized in that ,Based on the optimal steady state of cell state variables in the initial fermentation stage, the comprehensive benefit optimization index of the initial fermentation stage is established as: Among them, c a (k) represents the comprehensive benefit optimization index of the initial fermentation stage at time k, It represents the difference between the measured value of the cell state in the initial fermentation stage at time k and the optimal steady-state value. E and F are both weight parameter matrices.

4. The method for optimizing and controlling continuous fermentation processes at different stages based on parameter reuse according to claim 3, characterized in that: Design a long-term comprehensive evaluation index of the feed substrate concentration in the initial fermentation stage to obtain a parameter matrix of the long-term comprehensive benefits of the initial fermentation stage, including: Design a long-term comprehensive evaluation index for the feed substrate concentration in the initial fermentation stage, the formula is: Among them, J a (k) and J a (k+1) are the long-term comprehensive evaluation indicators of the feed substrate concentration in the initial fermentation stage at time k and time k+1, γ is the attenuation factor, is the parameter matrix of the long-term comprehensive benefits in the initial fermentation stage, and R is the weight parameter matrix.

5. The method for optimizing and controlling continuous fermentation processes at different stages based on parameter reuse according to claim 4, characterized in that: When the state space model parameters of the initial fermentation stage are unknown, the feed substrate concentration in the initial fermentation stage is solved when the comprehensive benefit optimization index of the initial fermentation stage is minimized, as the target feed substrate concentration in the initial fermentation stage, including: First, the parameter matrix of the long-term comprehensive benefits of the initial fermentation stage is estimated, including: The long-term comprehensive evaluation index of the feed substrate concentration in the initial fermentation stage is expressed as: in, is with x a (k) and u a (k), x 0,a The associated parameter vector, V a Yes and W a The parameter matrix corresponding to the sub-matrix parameters of ; Build V a The relationship with the comprehensive benefit optimization index in the initial fermentation stage is as follows: c a (k)=z a (k)V a Among them, z a (k) = h a (k)-γh a (k+1); The system identification method is used to solve the minimum comprehensive benefit optimization index V in the initial fermentation stage. a The estimated value of W a An estimated value of The parameter matrix based on the long-term comprehensive benefits of the initial fermentation stage optimizes the feed substrate concentration in the initial fermentation stage. The formula is: in, is the estimated value of the parameter matrix of the long-term comprehensive benefits of the initial fermentation stage at time k, The parameter matrix W is the long-term comprehensive benefit of the initial fermentation stage. a The sub-matrix of They are An estimated value of Then the control quantity K of the initial fermentation stage at time k+1 is a (k+1) is Based on the optimized feed substrate concentration in the initial fermentation stage, the parameter matrix for re-estimating the long-term comprehensive benefits of the initial fermentation stage is returned, and the feed substrate concentration in the initial fermentation stage is optimized again until the estimated value of the parameter matrix for the long-term comprehensive benefits of the initial fermentation stage no longer changes, thereby obtaining the parameter matrix for the long-term comprehensive benefits of the initial fermentation stage and the target feed substrate concentration.

6. The method for optimizing and controlling continuous fermentation processes at different stages based on parameter reuse according to claim 4, characterized in that: When the state space model parameters of the initial fermentation stage are known, the parameter matrix of the long-term comprehensive benefits of the initial fermentation stage is directly obtained; The feed substrate concentration in the initial fermentation stage is solved when the comprehensive benefit optimization index of the initial fermentation stage is minimized, which is used as the target feed substrate concentration in the initial fermentation stage. The methods include minimum principle, dynamic programming method, numerical calculation method and gradient method.

7. The method for optimizing and controlling continuous fermentation processes at different stages based on parameter reuse according to claim 4, characterized in that: Design long-term comprehensive evaluation indicators of feed substrate concentration in the current fermentation stage, including: Among them, J b (k) and J b (k+1) are the long-term comprehensive evaluation indicators of the feed substrate concentration in the current fermentation stage at time k and time k+1, respectively, c b (x) is the comprehensive benefit optimization index of the current fermentation stage at time k, γ is the attenuation factor, x b (x) is the cell state variable of the current fermentation stage at time k, u b (x) is the feed substrate concentration of the current fermentation stage at time k, is the optimal steady state of the cell state variables in the current fermentation stage; is the parameter matrix of the long-term comprehensive benefits of the current fermentation stage, A b and B b are the state space model parameters of the current fermentation stage, and E, F and R are weight parameter matrices.

8. The method for optimizing and controlling continuous fermentation processes at different stages based on parameter reuse according to claim 7, characterized in that , initialize the parameter matrix of the long-term comprehensive benefit of the current fermentation stage and the feed substrate concentration, use the target feed substrate concentration of the previous fermentation stage and the parameter matrix of the long-term comprehensive benefit to calculate the deviation of the comprehensive benefit optimization index between the current fermentation stage and the previous fermentation stage, including: Assuming that the initial time of the current fermentation stage is τ, when k = τ, let u b (k) = a u* , and initialize Among them, u b (x) represents the initial feed substrate concentration of the current fermentation stage at time k, u a* represents the target feed substrate concentration in the previous fermentation stage, The estimated value of the parameter matrix representing the long-term comprehensive benefits of the current fermentation stage at time k; When k>τ, based on the parameter matrix of the long-term comprehensive benefits of the previous fermentation stage, the deviation of the comprehensive benefit optimization index between the current fermentation stage and the previous fermentation stage is calculated, including: the comprehensive benefit optimization index of the current fermentation stage at time k is in, is the difference between the measured value of the cell state in the current fermentation stage at time k and the optimal steady-state value. E and F are both weight parameter matrices. u b (k) is the feed substrate concentration of the current fermentation stage at time k; The comprehensive benefit optimization index of the previous fermentation stage at time k is: Among them, J a' (k) and J a' (k+1) are the long-term comprehensive evaluation indicators of the feed substrate concentration in the previous fermentation stage at time k and time k+1, respectively, γ is the attenuation factor, x a' (k) is the cell state variable of the previous fermentation stage at time k, u a' (k) is the feed substrate concentration of the previous fermentation stage at time k, x 0,a' is the optimal steady state of the cell state variables in the previous fermentation stage, The parameter matrix of the long-term comprehensive benefits calculated for the previous fermentation stage; yes The parameter matrix estimates corresponding to the submatrix parameters; The comprehensive profit deviation between the current fermentation stage and the previous fermentation stage at time k is δ(k) = c b (k)-c a' (k); The comprehensive profit deviation between the current fermentation stage and the previous fermentation stage at time k-1 is δ(k-1) = c b (k-1)-c a (k-1).

9. The method for optimizing and controlling continuous fermentation processes at different stages based on parameter reuse according to claim 8, characterized in that: Based on the deviation of the comprehensive benefit optimization index between the current fermentation stage and the previous fermentation stage, the parameter matrix of the long-term comprehensive benefit of the current fermentation stage is estimated. The formula includes: in, and are the comprehensive income deviation vectors at time k-1 and time k, respectively, U(k) is the difference gain in the current fermentation stage, and are the estimated values ​​of the parameter matrix of the long-term comprehensive benefits of the current fermentation stage at time k-1 and time k, The parameter matrix of the long-term comprehensive benefits calculated for the previous fermentation stage.

10. The method for optimizing and controlling continuous fermentation processes at different stages based on parameter reuse according to claim 9, characterized in that: Based on the estimated value of the parameter matrix of the long-term comprehensive benefits of the current fermentation stage, the feed substrate concentration of the current fermentation stage is optimized, and the formula is: Among them, u b (k+1) is the feed substrate concentration of the current fermentation stage at time k+1, The parameter matrix W is the long-term comprehensive benefit of the current fermentation stage. b The sub-matrix of They are Estimated value. Then the control quantity K of the current fermentation stage at time k+1 is b (k+1) is

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