A fully automatic intelligent control system and method for fermentation equipment

Abstract

The present invention discloses a fully automatic intelligent control system and method for fermentation equipment, and relates to the technical field of automatic control of fermentation equipment. The system obtains time series data of state parameters and controlled parameters during a historical fermentation process, classifies the fermentation state within the fermentation equipment according to the state parameter data, obtains a state space, defines adjustment of the controlled parameter as a single action, classifies the actions according to the adjustment amount of the controlled parameter, obtains an action space, extracts a sequence of time series data of the state parameters when any action in the action space is executed, constructs a Markov chain model of the fermentation state within the fermentation equipment, defines a conversion value between any two fermentation states, calculates the expected conversion value k time steps after time t when the action is executed, selects the action corresponding to the maximum value in the expected conversion value calculation result in the kth time step, and adjusts the controlled parameter in the kth time step.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic control of fermentation equipment, and in particular to a fully automatic intelligent control system and method for fermentation equipment. Background Art

[0002] Aerobic composting and anaerobic composting are two common forms of composting. Anaerobic composting is a simple process with low compost temperatures, but it also has a long composting period and a strong odor, making it less commonly used. Aerobic composting, carried out under aerated conditions, results in higher compost temperatures, shorter fermentation cycles, and a high degree of harmlessness. Therefore, many composting plants both domestically and internationally use aerobic composting. Aerobic composting of organic waste is an effective means of rendering organic solid waste harmless, stabilizing it, and utilizing it as a resource, making it a key solution to ecological and environmental issues.

[0003] Natural composting has problems such as incomplete degradation, unbalanced nutrition, slow fermentation speed, long composting time and odor pollution. The use of composting reactors can effectively solve the problems of waste treatment and discharge, thereby improving fermentation efficiency, shortening the production cycle and realizing mechanized production, making the composting process more environmentally friendly and controllable. At present, common types of aerobic composting reactors include: tower reactors, silo reactors, tunnel kiln composting reactors, drum reactors, tank reactors, etc. In the actual production process, it can be selected according to the production scale and production needs. In the process of industrial composting, the fermentation of organic matter is essentially the action process of microorganisms. Relevant studies have shown that controlling the temperature, humidity and oxygen supply of the fermentation process is the key to the whole process. Therefore, the above parameters need to be precisely controlled to ensure the efficient fermentation process.

[0004] Currently, the traditional PID control method for controlling the temperature and oxygen flow rate of aerobic composting reactors remains in two main categories: closed-loop control and compensation control. The existing control system is a nonlinear, time-delay system with multiple inputs, large disturbances, and strong coupling. Conventional PID control algorithms are virtually incapable of achieving stability. Therefore, to reduce overshoot, differential and intermediate feedback methods are typically employed. However, the control effect is unsatisfactory, with significant overshoot still present in the controlled variable and a very slow response speed.

[0005] To address the problems of conventional PID control, domestic and foreign experts and scholars have proposed methods that can change the parameters of the PID control system as the system operating environment changes. Among them, the more effective and simple ones are the fuzzy PID control method and the neural network PID control method. The PID parameters are automatically adjusted by learning the error signal of the system through the neural network, so that the system parameters have the ability to self-adjust as the operating environment changes. Although the above control methods can optimize the control effect of the traditional PID control algorithm, in the actual production process, different adjustment actions often affect multiple controlled parameters. For example, when adjusting the oxygen flow rate, the temperature inside the reactor will also be affected and change as the oxygen flow rate changes, and the system needs to further adjust the temperature. This cycle makes the formulation of the adjustment strategy and the execution of the adjustment action always in a passive state throughout the control process, and it is impossible to actively regulate the controlled parameters according to the real-time status and change trends inside the reactor. To this end, we propose a fully automatic intelligent control system and method for fermentation equipment. Summary of the Invention

[0006] The main purpose of the present invention is to provide a fully automatic intelligent control system and method for fermentation equipment, which can effectively solve the problems in the background technology.

[0007] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0008] A fully automatic intelligent control method for fermentation equipment, comprising:

[0009] Acquire time series data of state parameters and controlled parameters in the historical fermentation process; wherein, the state parameter includes at least one of the temperature, oxygen content, and moisture content in the fermentation equipment; and the controlled parameter is the ventilation volume in the fermentation equipment.

[0010] The fermentation state in the fermentation equipment is classified according to the state parameter data, and the state space S={s1, s2, ..., s u}, s u It is represented as the u-th fermentation state in the fermentation equipment; the adjustment of the controlled parameter is defined as an action, and the actions are classified according to the adjustment amount of the controlled parameter to obtain the action space A = {a1, a2, ..., a v}, a v It is expressed as the vth type of action;

[0011] The fermentation state classification process includes the following steps:

[0012] Step S11: Set the optimal value of the state parameter of the rth category at time t to The qth sampling data value of the rth type of state parameter in the historical fermentation process is

[0013] Step S12: Calculate the q sampling data values ​​of the state parameters respectively and the optimal value of the state parameter The distance between The calculation formula is: To obtain Construct a data sample set, denoted as

[0014] Step S13: Use the Sigmoid function to adjust the mapping of each element in the acquired data sample set to between [0,1], and obtain the Sigmoid function value corresponding to each element Using function values The elements are classified according to the following principles:

[0015] when hour, For the first category;

[0016] when hour, For the second category;

[0017] when hour, For the third category;

[0018] And so on,

[0019] when hour, For the μth category;

[0020] in, are the minimum and maximum values ​​of the Sigmoid function of the elements in the data sample set respectively;

[0021] Step S14: According to The classification of the state parameter is used to determine the qth state parameter sampling data value. The corresponding fermentation states are as follows:

[0022] when When it is the first type, the state parameter The corresponding fermentation state is the first category;

[0023] when For the second type, the state parameter The corresponding fermentation state is the second type;

[0024] when When it is the third type, the state parameter The corresponding fermentation state is the third category;

[0025] And so on,

[0026] when When it is μ type, the state parameter The corresponding fermentation state is the μth category.

[0027] The action classification process includes the following steps:

[0028] Step S21: Set the controlled parameter at time t to cp t , the adjustment step size is △cp;

[0029] Step S22: the adjustment of the controlled parameter is divided into three categories: increase, remain unchanged, and decrease. When the adjustment amount is within the range of an adjustment step Δcp, it is regarded as an adjustment to keep the controlled parameter unchanged. Each increase of an adjustment step Δcp is regarded as an adjustment to increase the controlled parameter. Each decrease of an adjustment step Δcp is regarded as an adjustment to decrease the controlled parameter.

[0030] Step S23: taking any of the adjustments to keep the controlled parameter unchanged, the adjustments to increase the controlled parameter, and the adjustments to decrease the controlled parameter in step S22 as one action, and obtaining the classification result of the action;

[0031] Step S24: Collect all the actions and obtain the action space A = {a1, a2, ..., a v}.

[0032] Extract any action a in the action space A λ State parameter time series data sequence when executed Using data series A Markov chain model of the fermentation state in the fermentation equipment is constructed, and the model is defined as: Represented as action a λ When executed, the probability that the fermentation state in the fermentation equipment is transferred from the i-th category to the j-th category in one step, where i, j∈u; λ∈v; S t Represented as action a λ The fermentation status in the fermentation equipment at the time t being executed; S t+1 Represented as action a λ The fermentation state in the fermentation equipment at time t+1 after a time step is executed; probability The calculation formula is:

[0033]

[0034] Where, Represented as a data sequence In the case of action a λWhen executed, the frequency of the fermentation state in the fermentation equipment being transferred from the i-th category to the j-th category in one step.

[0035] Defined in the state space S, any two fermentation states s i 、s j The conversion value between i →s j ), calculate action a λ The expected conversion value G after k time steps at time t is executed t+k , the calculation formula is: Where γ represents the attenuation factor, and γ∈(0,1); R t+k (s i →s j ) is expressed as the conversion value at time step t+k;

[0036] Select the kth time step, the expected value of the transformation G t+k The maximum value G in the calculation result (t+k)max The corresponding action is taken as an action at the k-th time step, and the determined action is performed to adjust the controlled parameter at the k-th time step.

[0037] A fully automatic intelligent control system for fermentation equipment, comprising:

[0038] Data acquisition module, used to obtain time series data of state parameters and controlled parameters during the historical fermentation process;

[0039] a first data processing module, configured to classify the fermentation state in the fermentation equipment according to the state parameter data and obtain a state space S;

[0040] A second data processing module is configured to define the adjustment of the controlled parameter as an action, classify the actions according to the adjustment amount of the controlled parameter, and obtain an action space A;

[0041] Model building module for extracting any action a in the action space A λ State parameter time series data sequence when executed Using data series Construct a Markov chain model of the fermentation state in the fermentation equipment;

[0042] The conversion value expectation calculation module is used to define in the state space S, any two fermentation states s i 、s j The conversion value between i →s j ), and calculate action a λ The expected conversion value G after k time steps at time t is executedt+k ;

[0043] An action determination module is used to select the expected conversion value G in the kth time step t+k The maximum value G in the calculation result (t+k)max The corresponding action is taken as an action at the kth time step;

[0044] The action execution module is used to execute a determined action and adjust the controlled parameter at the kth time step.

[0045] The system further includes a memory, a processor, and a computer program stored in the memory and executable on the processor.

[0046] The present invention has the following beneficial effects:

[0047] Compared with the existing technology, by obtaining the time series data of the state parameters and the controlled parameters in the historical fermentation process, the fermentation state in the fermentation equipment is classified according to the state parameter data, the state space S is obtained, the adjustment of the controlled parameter is defined as an action, the actions are classified according to the adjustment amount of the controlled parameter, the action space A is obtained, and any action a in the action space A is extracted. λ State parameter time series data sequence when executed Using data series Construct a Markov chain model of the fermentation state in the fermentation equipment, and define in the state space S that any two of the fermentation states s i 、s j The conversion value between i →s j ), calculate action a λ The expected conversion value G after k time steps at time t is executed t+k , select the kth time step, the expected conversion value G t+k The maximum value G in the calculation result (t+k)max The corresponding action is taken as an action at the kth time step, and the determined action is executed to adjust the controlled parameter at the kth time step. The statistical model is used to comprehensively consider the influence of the adjustment action on the various controlled parameters, and the controlled parameters are actively regulated according to the real-time status and change trend inside the reactor to improve the control effect and the control accuracy of the controlled parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a schematic flow chart of a fully automatic intelligent control method for fermentation equipment of the present invention;

[0049] Figure 2 This is a structural diagram of a fully automatic intelligent control system for fermentation equipment of the present invention;

[0050] Figure 3 Schematic diagram of the calculation process of conversion value expectation in the technical solution of the present invention. DETAILED DESCRIPTION

[0051] The present invention will be further described below in conjunction with specific embodiments. The accompanying drawings are for illustrative purposes only and represent only schematic diagrams rather than actual drawings. They should not be understood as limiting the present invention. In order to better illustrate the specific embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product.

[0052] The specific implementation process of the technical solution of the present invention includes the following steps:

[0053] Step 1: Obtain time series data of state parameters and controlled parameters during the historical fermentation process; wherein the state parameter includes at least one of the temperature, oxygen content, and moisture content in the fermentation equipment; and the controlled parameter is the ventilation volume in the fermentation equipment.

[0054] Step 2: Classify the fermentation state in the fermentation equipment according to the state parameter data and obtain the state space S = {s1, s2, ..., s u}, s u It represents the u-th fermentation state in the fermentation equipment;

[0055] The fermentation status classification process includes the following steps:

[0056] Step S21: Set the optimal value of the rth state parameter at time t to The qth sampling data value of the rth type of state parameter in the historical fermentation process is

[0057] Step S22: Calculate the q sampling data values ​​of the state parameters respectively and the optimal value of the state parameter The distance between The calculation formula is: To obtain Construct a data sample set, denoted as

[0058] Step S23: Use the Sigmoid function to adjust the mapping of each element in the acquired data sample set to between [0,1], and obtain the Sigmoid function value corresponding to each element Using function values The elements are classified according to the following principles:

[0059] when hour, For the first category;

[0060] when hour, For the second category;

[0061] when hour, For the third category;

[0062] And so on,

[0063] when hour, For the μth category;

[0064] in, are the minimum and maximum values ​​of the Sigmoid function of the elements in the data sample set respectively;

[0065] Step S24: According to The classification of the state parameter is used to determine the qth state parameter sampling data value. The corresponding fermentation states are as follows:

[0066] when When it is the first type, the state parameter The corresponding fermentation state is the first category;

[0067] when For the second type, the state parameter The corresponding fermentation state is the second type;

[0068] when When it is the third type, the state parameter The corresponding fermentation state is the third category;

[0069] And so on,

[0070] when When it is the μth type, the state parameter The corresponding fermentation state is the μth category.

[0071] Step 3: Define the adjustment of the controlled parameter as an action, classify the actions according to the adjustment amount of the controlled parameter, and obtain the action space A = {a1, a2, ..., a v}, a v It is expressed as the vth type of action;

[0072] The action classification process includes the following steps:

[0073] Step S31: Set the controlled parameter at time t to cp t , the adjustment step size is △cp;

[0074] Step S32: The adjustment of the controlled parameter is divided into three categories: increase, remain unchanged, and decrease. When the adjustment amount is within the range of an adjustment step Δcp, it is considered that the controlled parameter is kept unchanged. Each increase of the adjustment step Δcp is considered to be an increase of the controlled parameter. Each decrease of the adjustment step Δcp is considered to be a decrease of the controlled parameter.

[0075] Specifically,

[0076] When the controlled parameter is cp t Adjust to the interval [cp t -△cp,cp t +△cp], it is an adjustment to keep the controlled parameter unchanged, which is a single action;

[0077] When the controlled parameter is cp t Adjust to the interval (cp t +△cp,cp t +2×△cp], it is an adjustment to increase the adjustment step length of the controlled parameter, which is another action;

[0078] When the controlled parameter is cp t Adjust to the interval (cp t +2×△cp,cp t +3×△cp], it is another action to increase the adjustment steps of the controlled parameter;

[0079] And so on,

[0080] When the controlled parameter is cp t Adjust to the interval (cp t +p×△cp,cp t +(p+1)×△cp], it is an adjustment to increase the controlled parameter by p adjustment steps, which is another action;

[0081] Similarly,

[0082] When the controlled parameter is cp t Adjust to the interval (cp t -2×△cp,cp t -△cp], it is an adjustment to reduce the controlled parameter by one adjustment step, which is another action;

[0083] When the controlled parameter is cp t Adjust to the interval (cp t -3×△cp,cp t -2×△cp], it is an adjustment to reduce the controlled parameter by two adjustment steps, which is another action;

[0084] And so on,

[0085] When the controlled parameter is cp t Adjust to the interval (cp t -(p+1)×△cp,cp t -p×△cp], it is an adjustment to reduce the controlled parameter by p adjustment steps, which is another action.

[0086] Step S33: taking any adjustment of keeping the controlled parameter unchanged, increasing the controlled parameter, or decreasing the controlled parameter in step S32 as one action, obtaining the action classification result;

[0087] Step S34: Collect all the actions and obtain the action space A = {a1, a2, ..., a v}, in this embodiment, 2p+1=v.

[0088] Step 4: Extract any action a in the action space A λ State parameter time series data sequence when executed

[0089]

[0090] Step 5: Utilize the data series Construct a Markov chain model of the fermentation state in the fermentation equipment. The model is defined as: Represented as action a λ When executed, the probability that the fermentation state in the fermentation equipment is transferred from the i-th category to the j-th category in one step, where i, j∈u; λ∈v; S t Represented as action a λ The fermentation status in the fermentation equipment at the time t being executed; S t+1 Represented as action a λ The fermentation state in the fermentation equipment at time t+1 after a time step is executed; where the probability The calculation formula is:

[0091]

[0092] Where, Represented as a data sequence In the case of action a λ When executed, the frequency of the fermentation state in the fermentation equipment being transferred from the i-th category to the j-th category in one step.

[0093] Step 6: Define any two fermentation states s in the state space S i 、s j The conversion value between i →s j ),like Figure 3The calculation process of expected conversion value is shown in the diagram, calculating action a λ The expected conversion value G after k time steps at time t is executed t+k , the calculation formula is: Where γ represents the attenuation factor, and γ∈(0,1); R t+k (s i →s j ) is expressed as the conversion value at time step t+k;

[0094] Step 7: Select the expected conversion value G in the kth time step t+k The maximum value G in the calculation result (t+k)max The corresponding action is taken as an action at the k-th time step, and the determined action is executed to adjust the controlled parameters at the k-th time step.

[0095] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A fully automatic intelligent control method for fermentation equipment, characterized in that: include: Obtain the time series data of state parameters and controlled parameters during the historical fermentation process; The fermentation state in the fermentation equipment is classified according to the state parameter data, and the state space S={s1, s2, ..., s u }, s u It is represented as the u-th fermentation state in the fermentation equipment; the adjustment of the controlled parameter is defined as an action, and the actions are classified according to the adjustment amount of the controlled parameter to obtain the action space A = {a1, a2, ..., a v }, a v It is expressed as the vth type of action; Extract any action a in the action space A λ State parameter time series data sequence when executed Using data series A Markov chain model of the fermentation state in the fermentation equipment is constructed, and the model is defined as: Represented as action a λ When executed, the probability that the fermentation state in the fermentation equipment is transferred from the i-th category to the j-th category in one step, where i, j∈u; λ∈v; S t Represented as action a λ The fermentation state in the fermentation equipment at the time t being executed; S t+1 Represented as action a λ The fermentation state in the fermentation equipment at time t+1 after a time step is executed; Defined in the state space S, any two fermentation states s i 、s j The conversion value between i →s j ), calculate action a λ The expected conversion value G after k time steps at time t is executed t+k , the calculation formula is: Where γ represents the attenuation factor, and γ∈(0,1); R t+k (s i →s j ) is expressed as the conversion value at time step t+k; Select the kth time step, the expected value of the transformation G t+k The maximum value G in the calculation result (t+k)max The corresponding action is taken as an action at the k-th time step, and the determined action is performed to adjust the controlled parameter at the k-th time step. The fermentation state classification process includes the following steps: Step S11: Set the optimal value of the state parameter of the rth category at time t to The qth sampling data value of the rth type of state parameter in the historical fermentation process is Step S12: Calculate the q sampling data values ​​of the state parameters respectively and the optimal value of the state parameter The distance between The calculation formula is: To obtain Construct a data sample set, denoted as Step S13: Use the Sigmoid function to adjust the mapping of each element in the acquired data sample set to between [0,1], and obtain the Sigmoid function value corresponding to each element Using function values The elements are classified according to the following principles: when hour, For the first category; when hour, For the second category; when hour, For the third category; And so on, when hour, For the μth category; in, are the minimum and maximum values ​​of the Sigmoid function of the elements in the data sample set respectively; Step S14: According to The classification of the state parameter is used to determine the qth state parameter sampling data value. The corresponding fermentation states are as follows: when When it is the first type, the state parameter The corresponding fermentation state is the first category; when For the second type, the state parameter The corresponding fermentation state is the second type; when When it is the third type, the state parameter The corresponding fermentation state is the third category; And so on, when When it is the μth type, the state parameter The corresponding fermentation state is class μ.

2. A fully automatic intelligent control method for fermentation equipment according to claim 1, characterized in that: The state parameter includes at least one of the temperature, oxygen content, and moisture content in the fermentation equipment; and the controlled parameter is the ventilation volume in the fermentation equipment.

3. A fully automatic intelligent control method for fermentation equipment according to claim 1, characterized in that: The action classification process includes the following steps: Step S21: Set the controlled parameter at time t to cp t , the adjustment step size is Δcp; Step S22: the adjustment of the controlled parameter is divided into three categories: increase, remain unchanged, and decrease. When the adjustment amount is within an adjustment step size Δcp, it is regarded as an adjustment to keep the controlled parameter unchanged. Each increase of the adjustment step size Δcp is regarded as an adjustment to increase the controlled parameter. Each decrease of the adjustment step size Δcp is regarded as an adjustment to decrease the controlled parameter. Step S23: taking any of the adjustments to keep the controlled parameter unchanged, the adjustments to increase the controlled parameter, and the adjustments to decrease the controlled parameter in step S22 as one action, and obtaining the classification result of the action; Step S24: Collect all the actions and obtain the action space A = {a1, a2, ..., a v }.

4. A fully automatic intelligent control method for fermentation equipment according to claim 1, characterized in that: Action a λ When the function is executed, the probability that the fermentation state in the fermentation equipment is transferred from the i-th category to the j-th category in one step is The calculation formula is: Where, Represented as a data sequence In the case of action a λ When executed, the frequency of the fermentation state in the fermentation equipment being transferred from the i-th category to the j-th category in one step.

5. A system for the fully automatic intelligent control method of fermentation equipment according to any one of claims 1 to 4, characterized in that: include: Data acquisition module, used to obtain time series data of state parameters and controlled parameters during the historical fermentation process; a first data processing module, configured to classify the fermentation state in the fermentation equipment according to the state parameter data and obtain a state space S; A second data processing module is configured to define the adjustment of the controlled parameter as an action, classify the actions according to the adjustment amount of the controlled parameter, and obtain an action space A; Model building module for extracting any action a in the action space A λ State parameter time series data sequence when executed Using data series Construct a Markov chain model of the fermentation state in the fermentation equipment; The conversion value expectation calculation module is used to define in the state space S, any two fermentation states s i 、s j The conversion value between i →s j ), and calculate action a λ The expected conversion value G after k time steps at time t is executed t+k ; An action determination module is used to select the expected conversion value G in the kth time step t+k The maximum value G in the calculation result (t+k)max The corresponding action is taken as an action at the kth time step; The action execution module is used to execute a determined action and adjust the controlled parameters at the kth time step. The system also includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor can implement the steps of the fully automatic intelligent control method for fermentation equipment when executing the program.

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