CN115074776B - Intelligent self-adaptive control system and method for hydrogen production by water electrolysis and adaptive to wide power fluctuation - Google Patents
Intelligent self-adaptive control system and method for hydrogen production by water electrolysis and adaptive to wide power fluctuation Download PDFInfo
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- 239000001257 hydrogen Substances 0.000 title claims abstract description 162
- 229910052739 hydrogen Inorganic materials 0.000 title claims abstract description 162
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 title claims abstract description 135
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 39
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 36
- 238000000034 method Methods 0.000 title claims abstract description 28
- 238000005868 electrolysis reaction Methods 0.000 title claims abstract description 22
- 230000003044 adaptive effect Effects 0.000 title claims description 7
- 239000001301 oxygen Substances 0.000 claims description 103
- 229910052760 oxygen Inorganic materials 0.000 claims description 103
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 100
- 239000003513 alkali Substances 0.000 claims description 60
- 239000007788 liquid Substances 0.000 claims description 51
- 239000002585 base Substances 0.000 claims description 45
- 150000002431 hydrogen Chemical class 0.000 claims description 27
- 230000001105 regulatory effect Effects 0.000 claims description 12
- 238000003860 storage Methods 0.000 claims description 9
- 238000012937 correction Methods 0.000 claims description 8
- 230000001276 controlling effect Effects 0.000 claims description 7
- 238000011156 evaluation Methods 0.000 claims description 7
- 238000013528 artificial neural network Methods 0.000 claims description 6
- 230000001052 transient effect Effects 0.000 claims description 6
- 238000001514 detection method Methods 0.000 claims description 4
- 238000005259 measurement Methods 0.000 claims description 3
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- 238000004364 calculation method Methods 0.000 description 3
- 238000002474 experimental method Methods 0.000 description 3
- 238000004880 explosion Methods 0.000 description 3
- 239000007789 gas Substances 0.000 description 3
- 238000010248 power generation Methods 0.000 description 3
- 239000000243 solution Substances 0.000 description 3
- 239000012670 alkaline solution Substances 0.000 description 2
- 229910052799 carbon Inorganic materials 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005265 energy consumption Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
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- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
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- 230000005611 electricity Effects 0.000 description 1
- 239000002803 fossil fuel Substances 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 239000003031 high energy carrier Substances 0.000 description 1
- 239000012535 impurity Substances 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
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- C25B—ELECTROLYTIC OR ELECTROPHORETIC PROCESSES FOR THE PRODUCTION OF COMPOUNDS OR NON-METALS; APPARATUS THEREFOR
- C25B15/00—Operating or servicing cells
- C25B15/02—Process control or regulation
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- C—CHEMISTRY; METALLURGY
- C25—ELECTROLYTIC OR ELECTROPHORETIC PROCESSES; APPARATUS THEREFOR
- C25B—ELECTROLYTIC OR ELECTROPHORETIC PROCESSES FOR THE PRODUCTION OF COMPOUNDS OR NON-METALS; APPARATUS THEREFOR
- C25B1/00—Electrolytic production of inorganic compounds or non-metals
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- C25B15/00—Operating or servicing cells
- C25B15/02—Process control or regulation
- C25B15/023—Measuring, analysing or testing during electrolytic production
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- C25B15/00—Operating or servicing cells
- C25B15/02—Process control or regulation
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Abstract
The invention relates to an intelligent self-adaptive control system and method for preparing hydrogen by electrolyzing water, which is suitable for wide power fluctuation, wherein the system comprises: expert experience knowledge base module: the method comprises the steps of obtaining expected output parameters of the electrolytic cell under the condition that the limit of boundary conditions is met; feedback compensation module: the method comprises the steps of detecting a deviation value between an output parameter and a boundary condition after the operation of the electrolytic cell, and outputting an expert experience compensation value; and (3) a water electrolysis hydrogen production module: the method is used for controlling the stable operation of the electrolytic tank under the condition that the input power fluctuates according to the expert experience compensation value and the output value of the expert experience knowledge base. According to the invention, under the premise of adapting to wide power fluctuation, the parameter set value of the hydrogen production system can be obtained through the cooperation of each module, so that the electrolytic water hydrogen production module can safely and stably operate under the influence of renewable energy fluctuation, the hydrogen production efficiency is improved, and the prepared hydrogen is high in purity.
Description
Technical Field
The invention relates to the technical field of hydrogen production by water electrolysis, in particular to an intelligent self-adaptive control system and method for hydrogen production by water electrolysis, which are suitable for wide power fluctuation.
Background
The hydrogen energy is taken as clean low-carbon energy, has various advantages of cleanliness, storability, high energy carrier and the like, is considered as the most promising secondary energy in the 21 st century, and is also one of key paths for promoting the structural transformation of the energy and realizing the aim of double carbon. However, at present, fossil energy hydrogen production is the mainstream hydrogen production mode, the purity of the produced hydrogen is low, in order to reduce the harm of using a large amount of fossil fuel to human health and environment, the country encourages renewable energy sources to produce hydrogen, the produced hydrogen is green hydrogen, the purity is up to more than 99.95%, and the green hydrogen is the mainstream in the future.
Among them, in the hydrogen production process, alkaline water electrolysis is the most promising method for industrially producing green hydrogen, but because renewable energy sources such as wind, light and the like have uncertainty in power generation, a given power of an electrolysis cell has large fluctuation, and some key parameters in the electrolysis cell system can be caused to exceed safety boundary conditions, so that serious consequences are caused. For example, when the input power of the electrolytic cell system fluctuates in a large range at a certain moment, the hydrogen content in the oxygen scrubber is not in a safety range (below 2 percent), and in order to avoid explosion danger, the system can automatically and safely alarm and stop the electrolytic cell system, thereby further influencing the hydrogen production efficiency and the safety of the system. Therefore, the method for controlling the electrolytic cell system, which is suitable for wide power fluctuation, is researched, and the method ensures continuous and stable operation of the electrolytic cell and maximally improves the hydrogen production under the premise of safe operation of the system, and is of great significance in the application.
Disclosure of Invention
The invention aims to provide an intelligent self-adaptive control system and method for water electrolysis hydrogen production, which are suitable for wide power fluctuation, and through mutual cooperation between a feedback compensation module and a water electrolysis hydrogen production module by an expert experience knowledge base model, an electrolytic tank can adapt to different power fluctuation, safe and stable operation of the electrolytic tank is ensured, and hydrogen production efficiency of the electrolytic tank is improved.
In order to achieve the above object, the present invention provides the following solutions:
intelligent self-adaptive control system for preparing hydrogen by electrolyzing water, which adapts to wide power fluctuation, comprises:
expert experience knowledge base module: the method comprises the steps of acquiring expected output parameters of the electrolytic tank under the condition that boundary conditions are met, wherein the boundary conditions are used for limiting system parameters and guaranteeing safe operation of the system, and the expected output parameters are used for enabling the parameters during the operation of the system to meet the set boundary conditions;
feedback compensation module: the method comprises the steps of detecting a deviation value between an output parameter of the electrolytic tank after operation and the boundary condition, and outputting an expert experience compensation value;
and (3) a water electrolysis hydrogen production module: and the system is used for controlling the stable operation of the electrolytic tank under the condition that the input power fluctuates according to the expert experience compensation value and the output value of the expert experience knowledge base.
Preferably, the expert experience knowledge base module comprises:
expert experience knowledge base model: for determining, based on the power input by the current electrolytic cell and the current measured operating parameter Y of the electrolytic cell k Under the limitation of the boundary condition, the expected output parameter of the electrolytic cell under the limitation meeting the boundary condition is obtained.
Preferably, the measured operating parameter Y of the electrolyzer k Comprising the following steps: system pressure, alkali liquor flow, electrolyzer temperature, liquid level difference of an oxygen separator and a hydrogen separator, content of hydrogen in oxygen in an oxygen scrubber and content of oxygen in hydrogen in the hydrogen scrubber.
Preferably, the expert experience knowledge base module further comprises a query and matching unit, wherein the query and matching unit is used for computing and querying and matching expert experience knowledge in a knowledge base conforming to the boundary conditions through the current operation characteristic parameters of the electrolytic tank based on a multi-attribute similarity algorithm, and screening out expected output values closest to the current working conditions of the electrolytic tank in the knowledge base.
Preferably, the multi-attribute similarity algorithm comprises a nearest neighbor algorithm, euclidean distance and structural similarity, the overall similarity between the current operation parameters of the electrolytic tank and the expert experience knowledge is obtained based on the multi-attribute similarity algorithm, and query and matching are performed based on the similarity.
Preferably, the expert experience knowledge base module further includes an evaluation and correction unit and a storage and addition unit, where the evaluation and correction unit is configured to evaluate and correct a reuse result of expert experience knowledge in the expert experience knowledge base, determine whether to correct the expert experience knowledge according to whether an important parameter during operation of the electrolytic tank meets the boundary condition, and if the current important parameter exceeds the boundary condition, need to correct the expert experience knowledge, and if the important parameter is within the boundary condition, need not correct the expert experience knowledge; wherein the important parameters comprise the content of hydrogen in oxygen, the content of oxygen in hydrogen, the liquid level difference and the alkali liquor temperature; the storage and addition unit is used for adding new expert experience knowledge.
Preferably, the feedback compensation module includes:
expert rule establishing unit: the expert gives a correlation coefficient of parameter compensation for each deviation according to experience and rules according to the deviation setting rule between the important parameter and the boundary value which are output after the operation of the electrolytic tank; wherein the rule comprises: an error between the hydrogen content in the oxygen in the operation of the electrolytic cell and the threshold value of the boundary value, an error between the oxygen content in the hydrogen in the operation of the electrolytic cell and the threshold value of the boundary value, an error between the liquid level difference in the operation of the electrolytic cell and the threshold value of the boundary value, and an error between the alkali liquid temperature in the operation of the electrolytic cell and the threshold value of the boundary value;
an inference engine unit: and the expert experience compensation value is deduced by adopting an exhaustive item-by-item search algorithm according to the difference value between the important parameter output in the running of the electrolytic tank and the boundary value through expert experience rules, and the expert experience compensation value is output.
Preferably, the electrolytic water hydrogen production module is used for performing transient PID control on a pressure regulating valve and a pneumatic regulating valve in the electrolytic tank through combination of fuzzy rules and a neural network, so as to ensure that the response of the pressure of the electrolytic tank at the current moment, the liquid levels of the oxygen separator and the hydrogen separator, and the alkali liquor temperature and flow rate tend to be given values of important parameters.
An intelligent self-adaptive control method for preparing hydrogen by electrolyzing water, which adapts to wide power fluctuation, comprises the following steps:
constructing an expert experience knowledge base model, and obtaining expected output parameters of the electrolytic tank system under the condition that boundary conditions are met according to the input power and actual measurement parameters of the current electrolytic tank, wherein the boundary conditions comprise: the expected output parameters comprise a system pressure set value, an alkali liquor temperature set value and an alkali liquor flow set value;
operating the electrolytic tank system based on the expected output parameters, detecting a deviation value between the output parameters of the electrolytic tank system after operation and the boundary conditions through a feedback compensation model, and outputting expert experience compensation values;
and controlling a pressure regulating valve and a pneumatic regulating valve in the electrolytic tank to carry out transient PID control through combination of a fuzzy rule and a neural network according to the expert experience compensation value and the expected output parameter, and simultaneously monitoring and correcting the output boundary important parameter detection value to a feedback compensation model and an expert experience knowledge base model at the same time.
The beneficial effects of the invention are as follows:
on the basis of adapting to wide power fluctuation of the alkaline electrolytic tank caused by uncertainty of renewable energy power generation, the invention can adaptively control given parameters (system pressure, alkali liquor flow, alkali liquor temperature) of the electrolytic tank by an intelligent control method to ensure that important parameters (hydrogen content in oxygen and the like) of an electrolytic tank system are kept in a safe range, thereby effectively improving hydrogen production efficiency and purity of produced hydrogen of the electrolytic tank under the condition of ensuring safe and stable operation of the electrolytic tank system.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a module structure of an intelligent adaptive control system for producing hydrogen by electrolysis of water, which is adaptive to wide power fluctuation, according to an embodiment of the invention;
FIG. 2 is a graph of hydrogen content in oxygen for different cell input power ranges and different system pressures for embodiments of the present invention;
FIG. 3 is a schematic diagram of a safe zone of hydrogen content in oxygen at different power ranges and different system pressures in accordance with an embodiment of the present invention;
FIG. 4 is a graph of the oxygen content of hydrogen at various power ranges and various system pressures for an embodiment of the present invention;
FIG. 5 is a schematic diagram of separator head at different power ranges and different system pressures for an embodiment of the present invention;
FIG. 6 is a schematic diagram of energy consumption and energy efficiency of different power pressures according to an embodiment of the present invention;
FIG. 7 is a flow chart of an intelligent self-adaptive control method for producing hydrogen by electrolyzing water, which is adaptive to wide power fluctuation and is provided by the embodiment of the invention;
FIG. 8 is a schematic workflow diagram of an expert experience knowledge base module according to an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
After renewable energy sources generate electricity, power is output to an alkaline electrolytic tank through a direct current micro-grid, and power fluctuation provided to the electrolytic tank is large, so that important parameters of a hydrogen production system can exceed safe operation boundary conditions. For example:
1) The hydrogen content in the oxygen scrubber exceeds 2%, and once it exceeds 2%, there is an explosion hazard. In addition, the hydrogen produced by alkaline electrolysis of water is required to have high purity, and the oxygen content in the hydrogen scrubber is limited to be not more than 0.5%;
2) If the deviation between the liquid level of the oxygen separator and the liquid level of the hydrogen separator is larger, alkali liquid can enter the scrubber, the alkali liquid is sprayed out from the vent through the scrubber, if someone passes through the vent, alkali burn accidents can occur, in addition, if the liquid level on one side is too low, gas and alkali liquid in the separator can enter the circulating pump at the same time, so that the circulation volume of the alkali liquid can be greatly fluctuated, even the circulation of the alkali liquid is stopped, if the circulation of the alkali liquid is stopped, the liquid level deviation can be increased continuously, the gas in the separator on one side can enter the separator on the other side, the hydrogen-oxygen mixing phenomenon occurs in the separation, the explosion in the separator is very easy, and serious safety accidents occur, so that the liquid level difference between the oxygen separation and the hydrogen separator cannot exceed 5cm;
3) In the operation process of the alkaline electrolytic tank, the temperature of the tank body is mainly controlled through alkali liquor, wherein the alkali liquor temperature is required to be controlled at about 65 ℃, the fluctuation is less than 1.1 ℃, the larger the alkali liquor flow is, the lower the temperature of the hydrogen tank and the oxygen tank temperature is, because the temperature of a diaphragm between a cathode and an anode of the electrolytic tank is required to be certain, the temperature of the hydrogen tank and the oxygen tank temperature is required to be less than 85 ℃ when the electrolytic tank is operated, if the alkali liquor flow is too low, the temperature of a separator exceeds the boundary temperature, the diaphragm between the cathode and the anode of the electrolytic tank is damaged, and hydrogen and oxygen on two sides are mixed, so that serious consequences are caused.
4) In addition, the flow rate of the alkali liquor also affects the purity of the hydrogen and the oxygen, and if the flow rate of the alkali liquor is too large, the more impurity gas carried by the gas-liquid mixture generated by electrolysis in the separator, the purity of the oxygen and the hydrogen in the oxygen separator and the hydrogen separator is reduced. The embodiment sets the alkali liquor flow range to be 3.0-4.5m 3 And/h.
In addition, if the content of hydrogen in oxygen, the content of oxygen in hydrogen, the liquid level difference between the oxygen separator and the hydrogen separator, the flow rate of lye and the temperature of lye exceed the boundary conditions, the system safety is seriously affected, and the hydrogen production efficiency and the hydrogen production amount are reduced.
Referring to fig. 1, the embodiment provides an intelligent self-adaptive control system for preparing hydrogen by electrolysis of water, which adapts to wide power fluctuation, and comprises:
expert experience knowledge base module: for obtaining desired output parameters of an electrolytic cell under satisfaction of boundary condition constraints, wherein the boundary conditions include: the expected output parameters comprise a system pressure given value, an alkali liquor concentration given value and an alkali liquor flow given value;
feedback compensation module: the method comprises the steps of detecting a deviation value between an output parameter of the electrolytic tank after operation and the boundary condition, and outputting an expert experience compensation value;
and (3) a water electrolysis hydrogen production module: and the system is used for controlling the stable operation of the electrolytic tank under the condition that the input power fluctuates according to the expert experience compensation value and the output value of the expert experience knowledge base.
(1) Expert experience knowledge base model
The expert experience knowledge base model comprehensively considers the actual measurement parameter Y of the current operation of the current electrolytic tank system according to the input power of the current electrolytic tank system k In boundary condition B k Under the limit of (1), according to the expert experience knowledge base model, the closed-loop given value Z of the electrolytic tank system under the limit of meeting the boundary condition is coordinated and given k 。
Wherein the respective measured parameters (Y k ) The system pressure, the alkali liquor flow, the temperature of the electrolytic cell, the liquid level difference of the oxygen separator and the hydrogen separator, the content of hydrogen in oxygen in the oxygen scrubber and the content of oxygen in hydrogen in the hydrogen scrubber are respectively. And obtaining expected output parameters of the electrolytic tank system, namely a system pressure given value, an alkali liquor temperature given value and an alkali liquor flow given value, according to a method combining knowledge base reasoning and a mathematical model under the constraint of boundary conditions.
The method comprises the following steps:
(1) establishing a basic knowledge base of a hydrogen production system by water electrolysis
Through a large number of experiments, under the condition that the power (20%, 40%,60%, 80% and 100%) of different ranges is input into the electrolytic tank, the electrolytic tank system can stably operate under the limit of boundary conditions, namely that the hydrogen content in oxygen in an oxygen scrubber is less than 2% (50% LFL), the oxygen content in hydrogen in the hydrogen scrubber is less than 0.5%, and the liquid level of an oxygen separator and the liquid level of the hydrogen separator are changed by changing the pressure, the alkali liquid temperature and the alkali liquid flow parameters of the systemThe difference is within 5cm, the alkali liquor flow is 3-4.5m 3 Within/h, the temperature of the alkaline solution is kept at about 65 ℃, the fluctuation range is kept at 1.1 ℃, the temperature of the electrolytic tank is less than 85 ℃, and the like. Fig. 2 to 6 are each experimentally obtained, and representative meanings thereof are described in order below.
FIG. 2 is a graph of the variation of the hydrogen content in oxygen at different ranges of power input to the electrolyzer and by varying the system pressure, it can be seen that at the present system pressure (pressure in the oxygen separator) the hydrogen content in oxygen can be brought back into the safe range by properly varying the system pressure if the hydrogen content in oxygen exceeds 2% of the safe range (50% LFL) at the present system pressure.
At each power test, the cell was run continuously for more than two hours to ensure that the cell could be run for a long period of time at that power. For example, in the case that the input power of the electrolytic cell is 40% of the rated power, if the system pressure is 1.6Mpa, and the hydrogen content in the oxygen scrubber is 2% above, by changing the system pressure to 1.0Mpa, the hydrogen concentration in the oxygen can be seen to be lower than 2%, and the system returns to the safe range, so that when the hydrogen concentration in the oxygen is not in the safe range, the safe and stable operation of the system can be ensured by properly changing the pressure in the system, and a safe region of the hydrogen concentration in the oxygen under different input powers and different system pressures of the electrolytic cell can be established based on the safe region.
As shown in fig. 3, the red region is a region where the hydrogen content in oxygen is within the safe range when LFL takes 50%; the LFL takes red and blue as areas with the hydrogen content in oxygen within a safe range when 75 percent; the 3 area is an undetectable area; area 4 is an unsafe area; the 5 area is a temporarily available area.
Fig. 4 shows the change of oxygen content in hydrogen in the hydrogen scrubber under the condition that the power input to the electrolyzer is different and the pressure of the system is changed, and as can be seen from fig. 4, the concentration of oxygen in hydrogen can be well kept within the safety range, and the oxygen content in hydrogen in the hydrogen scrubber can be reduced by changing the pressure of the system when the power input to the electrolyzer is determined. In order to increase the concentration of hydrogen generated by the electrolytic water hydrogen production system, when the oxygen content in the hydrogen in the electrolytic tank system exceeds the hydrogen purity requirement (< 0.5%), the purpose of adjusting the oxygen content in the hydrogen can be achieved by adjusting the pressure of the system, so that the purity of the hydrogen produced by the system is increased.
FIG. 5 shows the change between the liquid level difference of the oxygen separator and the hydrogen separator under different power ranges and different pressure of the electrolytic tank, the liquid level difference of the hydrogen-oxygen separator of the electrolytic water hydrogen production system is required to be close to zero, the deviation is not more than 0.7cm, the maximum liquid level deviation shown in FIG. 5 is not more than 0.7cm, and the requirements are met, so that the input power of the electrolytic tank and the system pressure have no obvious correlation on the liquid level difference of the hydrogen-oxygen separator, and the safety range of the hydrogen content in oxygen can be met by adjusting the pressure of the system under different power. FIG. 6 shows the power and energy efficiency consumed by the system at different power and system pressures, with the stack energy consumption trending upward and the energy efficiency trending downward as power increases; the change of the system pressure has no obvious influence on the two indexes, so that the safety range of the hydrogen content in oxygen can be met by adjusting the system pressure.
Tables 1 to 2 show the change in the temperature of the oxygen tank and the temperature of the hydrogen tank in the electrolytic cell and the change in the purity of hydrogen and the purity of oxygen in the separator at different alkali liquid flows, and it can be seen from Table 1 that the larger the alkali liquid flow rate, the lower the temperature of the oxygen tank and the temperature of the hydrogen tank, and the alkali liquid flow rate is 2.6m 3 When the temperature of the electrolytic tank exceeds 85 ℃, the electrolytic tank is not in the safe range of the working temperature of the electrolytic tank, and the safe operation of the electrolytic tank system is not facilitated; in Table 2, as the flow rate of the alkali liquor increases, the purity of oxygen and the purity of hydrogen in the oxygen separator and the hydrogen separator slightly decrease, so that the effect of adjusting the temperature of the electrolytic cell and the purity of oxygen and hydrogen can be achieved by controlling the flow rate of the alkali liquor. Therefore, the hydrogen content in the oxygen can be changed by adjusting the alkali liquor flow under different working powers, and the oxygen content can be controlled to be 2 percent (50 percent LFL). Therefore, the hydrogen content in the oxygen can be controlled by changing the system pressure, and the power adjusting range can be widened by combining the system pressure and the alkali liquor flow.
TABLE 1
TABLE 2
Based on this, an original expert experience knowledge base is built from experiences obtained by a large number of experiments, and the expert experience knowledge base can be expressed as follows:
E k ＝{T k ,P k ,Y k ,B k ,Z k ,S k }
wherein E is k For the kth expert knowledge (k=1, 2,3,., m, m is the expert knowledge quantity); t (T) k For expert experience knowledge E k Storage time, P of k For expert experience knowledge E k The ratio of the input power of (2) to the rated power, Y k ＝{y k1 ,y k2 ,y k3 ,y k4 ,y k5 ,y k6 Is expert experience knowledge E k Characteristic parameter, y, of the cell system during operation k1 ,y k2 ,…,y k6 Respectively represent the system pressure, alkali liquor flow, the temperature of the electrolytic cell, the liquid level difference of the oxygen separator and the hydrogen separator, the hydrogen content in oxygen and the oxygen content in hydrogen under the safe and stable operation of the electrolytic cell system in the K expert experience knowledge. B (B) k ＝{b k1 ,b k2 ,b k3 ,b k4 ,b k5 Is expert experience knowledge E k Boundary conditions of important parameters in the stable operation of the cell system, b k1 ,b k2 ,,b k5 Respectively represent the hydrogen content in oxygen in the oxygen scrubber<2% oxygen content in Hydrogen scrubber<0.5％、3m 3 The alkali liquor flow rate per h is less than or equal to 4.5m 3 Level difference of/h, oxygen separator and hydrogen separator<5cm, the temperature of the alkaline solution is more than or equal to 63.9 ℃ and less than or equal to 66.1 ℃. Z is Z k ＝{z k1 ,z k2 ,z k3 Desired value z output by expert experience knowledge k1 ,z k2 ,z k3 Respectively denoted as systemsThe pressure set point, the alkali liquor temperature set point and the alkali liquor flow set point are unified. S is S k The comprehensive similarity between the current characteristic parameter conditions of the electrolytic tank system and the experience knowledge of the Kth expert. Expert experience knowledge E k The inputs and outputs of (a) are shown in table 3.
TABLE 3 Table 3
Query and match unit:
in the embodiment, multi-attribute similarity calculation is adopted, expert experience knowledge is queried and matched in a knowledge base conforming to a safety boundary condition through the operation characteristic parameters of the current electrolytic tank system, and an expected output value (shown in figure 8) which is most similar to the working condition of the current electrolytic tank in the knowledge base is screened. Firstly, setting the condition of the current electrolytic tank system as expert experience knowledge E n ， P n I.e. the power input duty ratio of the electrolytic cell under the current condition, Y n ＝{y n1 ,y n2 ,…,y n6 And the input power attribute of the electrolytic cell is calculated according to the optimized nearest neighbor algorithm, so that the expert experience knowledge in the adjacent power range where the current input power of the electrolytic cell is positioned is determined. The optimized nearest neighbor algorithm is as follows:
the exponential form can make the similarity calculation more accurate, sim (P i,k ,P i,n ) Representing expert experience knowledge E k Similarity of the input power of (2) to the current case input power. And (5) finding out expert experience knowledge of the adjacent range of the input power under the current condition. The characteristic parameters of the electrolytic tank system may be missing or the characteristic attribute value is 0, so that the characteristic information is incomplete, the similarity calculation is influenced, and the structural similarity is added to reduce the influence.
The structural similarity only calculates the attribute similarity of the current situation and the attribute value which is not 0 in the historical expert experience knowledge, the problem of incomplete information can be effectively avoided, and the structural similarity S of P and Q is expressed as follows, assuming that P= { all non-empty attribute sets of the electrolytic cell system in the current situation }, Q= { all non-empty attribute sets in the historical expert experience knowledge Q }:
wherein omega ∩ Is the sum, ω, of the weight values of all attributes in the intersection of sets P and Q ∪ The sum of the weight values of all the attributes is the union of the sets P and Q.
The Euclidean distance formula of the current parameter condition of the electrolytic tank system and the experience knowledge of the past expert is as follows:
wherein omega i The characteristic weight value representing expert experience knowledge can be used for obtaining that the system pressure characteristic has great influence on important parameters of the electrolytic tank system through expert experiment experience, under the condition that the power of the electrolytic tank is determined, the hydrogen content in oxygen in the electrolytic tank system is not in a safety range under the condition of the current system pressure, and the hydrogen content in the oxygen can be in the safety range through moderately adjusting the system pressure, as shown in fig. 2. The alkali liquor flow and the system liquid level have little influence on important parameters compared with the system pressure, and omega is based on the influence degree on the safe operation of the electrolysis system i The method comprises the following steps: omega i ＝{0.9，0.05，0.05，0，0，0}。
Combining the nearest neighbor algorithm, euclidean distance and structural similarity to obtain the overall similarity of the current condition of the electrolytic cell and the experience knowledge of the historical expert:
assume sim max The maximum similarity between the current electrolytic tank system condition and the experience knowledge of the historical expert is as follows:
comprehensive similarity threshold sim yz Can be set as follows:
wherein the threshold value Y YZ Given by expert experience, set to 0.9 here. Retrieving all overall similarity sim (E n ,E k ≥sim yz ) Expert knowledge of expert knowledge and then recording the solution { Z } of expert knowledge k Time T k Overall similarity sim (E n ,E k ) Then, the attribute values are arranged in descending order according to the overall similarity and the expert experience knowledge storage time, and the next processing is waited.
Expert experience knowledge reuse:
selecting the sim with the greatest similarity from the expert experience knowledge of the matching max Expert experience knowledge of (1) and determine the number Num thereof.
If num=1, only one expert experience knowledge representing the maximum similarity is given as E k K is more than or equal to 1 and less than or equal to m, so that expert experience knowledge E in the expert experience knowledge data table is matched k The next expert experience knowledge of (a) is E h H is more than or equal to 1 and less than or equal to m, and E is arranged in descending order according to attribute values of 'overall similarity' and 'expert experience knowledge storage time' when matching expert experience knowledge is searched out h The time most recent should be stored for the second highest similarity and for expert empirical knowledge. Expert knowledge E h Is Z h Overall similarity is sim h Expert experience knowledge E k Is Z k Then the desired output Z of the description in the present case hk The method comprises the following steps:
if Num>1, a plurality of expert experience knowledge having the same maximum overall similarity are described, f are provided, and the expert experience knowledge E is assumed i I= … f is arranged in descending order of expert knowledge storage time attribute values: e (E) 1 ,E 2 …E f ，Z 1 ,Z 2 …Z f For its corresponding desired output, then the desired output of the description in the present case is:
wherein θ is i The time weighting coefficient reused for the expert experience knowledge meets theta 1 ≥θ 2 ≥…≥θ l May be determined on a case-by-case or empirically.
Evaluation and correction unit:
in order to verify the validity of the expert experience knowledge reuse result, expert experience knowledge evaluation and correction must be performed. The first step is to evaluate the reuse result, if successful, the reuse result is not corrected, otherwise, expert experience knowledge correction is performed to improve the accuracy of the setting module. In this embodiment, the expert experience knowledge evaluation is based on feedback of the operation effect in the actual environment, and the expert experience knowledge correction is performed on the basis of problems occurring in the execution process.
After the electrolytic cell system is operated, the hydrogen content in oxygen, the oxygen content in hydrogen, the liquid level difference and the alkali liquor temperature have certain hysteresis, so that four important parameters are detected every other hour, four important parameter values are fed back to an expert experience knowledge base to judge whether the important parameters in the current condition operation state meet the boundary conditions or not, whether the expert experience knowledge is corrected or not is judged according to whether the boundary conditions are met, if the current important parameters exceed the boundary conditions, the expert experience knowledge is required to be corrected, for example, the operation of the system is influenced and the expert experience knowledge is corrected if the important parameters are all within the boundary conditions, and the expert experience knowledge is not required to be corrected.
Storage and addition unit:
for the case that the new expert experience knowledge is added into the historical expert experience knowledge base, firstly, calculating the overall similarity between the new expert experience knowledge and all expert experience knowledge stored in the historical expert experience knowledge base, if all the obtained similarity is smaller than or equal to a given threshold (taking the threshold to be 0.8), adding the new expert experience knowledge, and if at least one similarity is larger than the threshold, not storing.
(2) Feedback compensation model
After the desired output parameters selected from the expert experience knowledge base are given to the electrolyzer, variations in some parameters during operation of the electrolyzer may have a serious impact on the safety issues of the system. Among the four important parameters that have the most impact on system safety are the hydrogen content in oxygen in the oxygen scrubber, the oxygen content in hydrogen in the hydrogen scrubber, the level difference between the oxygen separator and the hydrogen separator, and the lye temperature. The hydrogen content in the oxygen is strictly required to be below 2%, the oxygen content in the hydrogen is required to be below 0.5%, the liquid level difference between the oxygen separator and the hydrogen separator cannot exceed 5cm, and the temperature of the alkali liquor is required to be kept at about 65 ℃.
In this embodiment, in order to cope with the situation that the important parameters exceed the boundary conditions, a feedback compensation model is added in the intelligent control model, after the operation of the electrolytic cell, four important parameters are detected every other hour, and the feedback compensation model corrects each parameter given value of the intelligent control system through a set expert rule according to the deviation between the four important parameters detected after the operation of the electrolytic cell and the boundary conditions, so that the parameters affecting the safety of the system and the purity of hydrogen production are returned to the safety range.
The feedback compensation model is mainly divided into three parts, namely:
expert rule establishing unit:
expert through actual experience and a large number of experimentsRules are set for deviations (Δe1, Δe2, Δ0e3, Δe4) between four important parameters outputted after operation of the electrolytic cell and the boundary values, and the rules are shown in table 4. Wherein Δe1, Δe2, Δe3, and Δe4 are respectively an error between the hydrogen content in oxygen and 2% of the boundary value in the operation of the electrolytic cell, an error between the oxygen content and 0.5% of the boundary value in the hydrogen in the operation of the electrolytic cell, an error between the liquid level difference and 5cm of the boundary value in the operation of the electrolytic cell, and an error Δp, Δf, and ΔT between the alkaline liquid temperature and 65 ℃ of the boundary value in the operation of the electrolytic cell, respectively represent a system pressure compensation value, an alkaline liquid flow compensation value, and an alkaline liquid temperature compensation value k obtained according to expert experience 1,1 ,k 1,2 ,...,k 4,1 ,k 4,2 The correlation coefficients of the compensation of the given parameters of the three electrolytic cells are respectively shown for four different deviations according to experience and law by an expert.
TABLE 4 Table 4
An inference engine unit:
the inference engine infers the corresponding expert experience compensation value by adopting an exhaustive item-by-item search algorithm according to the difference value between the four important parameters output in the running of the electrolytic tank and the boundary value through expert experience rules. And outputting the expert experience compensation value inferred by the inference engine.
And (3) a water electrolysis hydrogen production module:
the electrolytic water hydrogen production module carries out transient PID control on a pressure regulating valve, a pneumatic regulating valve and the like in the electrolytic tank system by combining a fuzzy rule and a neural network according to various given parameters of the electrolytic tank system, including a system pressure given value, an alkali liquor temperature given value and an alkali liquor flow given value, corrected by an expert experience knowledge base model system and a feedback compensation model, so that the liquid level of the electrolytic tank system, the liquid level of an oxygen separator and a hydrogen separator, the alkali liquor temperature and the flow at the moment can be quickly responded to tend to the given values of various parameters, and the safe and stable operation of the electrolytic tank hydrogen production system under the fluctuation of input power is ensured. Meanwhile, after the electrolytic tank operates, because of hysteresis of parameter change, four parameters are detected every hour, and the detected four parameter values are output to an expert experience knowledge base model, wherein the four parameters are the hydrogen content in oxygen in an oxygen scrubber, the oxygen content in hydrogen in a hydrogen scrubber, the liquid level difference between an oxygen separator and a hydrogen separator and the alkali liquid temperature respectively. The reason for outputting these four parameters to the expert experience knowledge base model is: judging whether the system is within the boundary range, if so, the system operates normally, and the expert experience knowledge does not need to be corrected. If not within the boundary, then expert experience knowledge needs to be modified.
As shown in fig. 7, the embodiment also provides an intelligent self-adaptive control method for preparing hydrogen by electrolyzing water, which is suitable for wide power fluctuation, and comprises the following steps:
the electrolytic water hydrogen production system carries out transient PID control on a pressure regulating valve, a pneumatic regulating valve and the like in the electrolytic tank system through combination of fuzzy rules and a neural network according to given parameters of the electrolytic tank system corrected by the expert experience knowledge base model system and the feedback compensation model, and simultaneously outputs detection values of four boundary important parameters to the feedback compensation model and the expert experience knowledge base model every hour. The purpose of the output to the feedback compensation model is to obtain compensation values of four important parameters according to the deviation between the four important parameters and the boundary conditions and the set expert experience rules; the purpose of outputting to the expert experience knowledge base model is to judge whether the current parameter detection value is in the boundary range, if so, the system operates normally, and the expert experience knowledge does not need to be corrected. If not within the boundary, then expert experience knowledge needs to be modified.
The intelligent self-adaptive control method for the electrolytic water hydrogen production adapting to wide power fluctuation can adaptively control given parameters (system pressure, alkali liquid flow, alkali liquid temperature) of the electrolytic tank by the intelligent control method on the basis of wide power fluctuation of the alkaline electrolytic tank due to the uncertainty of power generation adapting to renewable energy sources, so that important parameters (hydrogen content in oxygen and the like) of an electrolytic tank system are kept in a safe range, and the hydrogen production efficiency and the hydrogen production purity of the electrolytic tank are effectively improved under the condition that the electrolytic tank system is ensured to operate safely and stably.
The above embodiments are merely illustrative of the preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, but various modifications and improvements made by those skilled in the art to which the present invention pertains are made without departing from the spirit of the present invention, and all modifications and improvements fall within the scope of the present invention as defined in the appended claims.
Claims (7)
1. Intelligent self-adaptive control system for preparing hydrogen by electrolyzing water and adapting to wide power fluctuation is characterized by comprising:
expert experience knowledge base module: the method comprises the steps of acquiring expected output parameters of the electrolytic tank under the condition that boundary conditions are met, wherein the boundary conditions are used for limiting system parameters and guaranteeing safe operation of the system, and the expected output parameters are used for enabling the parameters during the operation of the system to meet the set boundary conditions;
feedback compensation module: the method comprises the steps of detecting a deviation value between an output parameter of the electrolytic tank after operation and the boundary condition, and outputting an expert experience compensation value;
and (3) a water electrolysis hydrogen production module: the system is used for controlling the stable operation of the electrolytic tank under the condition that the input power fluctuates according to the expert experience compensation value and the output value of the expert experience knowledge base;
the expert experience knowledge base module comprises:
expert experience knowledge base model: for determining, based on the power input by the current electrolytic cell and the current measured operating parameter Y of the electrolytic cell k Under the limitation of the boundary condition, acquiring an expected output parameter of the electrolytic tank under the limitation meeting the boundary condition; the boundary conditions include: the hydrogen content in oxygen, the oxygen content in hydrogen, the alkali liquor flow, the alkali liquor temperature and the liquid level difference of the oxygen separator and the hydrogen separator; the expected output parameters comprise a system pressure given value, an alkali liquor concentration given value and an alkali liquor flow given value;
the operation measured parameter Y of the electrolytic tank k Comprising the following steps: system pressure, alkali liquor flow, electrolyzer temperature, liquid level difference of an oxygen separator and a hydrogen separator, content of hydrogen in oxygen in an oxygen scrubber and content of oxygen in hydrogen in the hydrogen scrubber.
2. The intelligent self-adaptive control system for water electrolysis hydrogen production adapting to wide power fluctuation according to claim 1, wherein the expert experience knowledge base module further comprises a query and matching unit, the query and matching unit is used for computing and querying and matching expert experience knowledge in a knowledge base conforming to the boundary conditions through the current operation characteristic parameters of the electrolytic tank based on a multi-attribute similarity algorithm, and screening out expected output values closest to the current working conditions of the electrolytic tank in the knowledge base.
3. The intelligent self-adaptive control system for water electrolysis hydrogen production adapting to wide power fluctuation according to claim 2, wherein the multi-attribute similarity algorithm comprises a nearest neighbor algorithm, euclidean distance and structural similarity, the overall similarity between the current operation parameters of the electrolytic tank and the expert experience knowledge is obtained based on the multi-attribute similarity algorithm, and query and matching are performed based on the similarity.
4. The intelligent self-adaptive control system for the electrolytic water hydrogen production adapting to wide power fluctuation according to claim 1, wherein the expert experience knowledge base module further comprises an evaluation and correction unit and a storage and addition unit, the evaluation and correction unit is used for evaluating and correcting an expert experience knowledge reuse result in the expert experience knowledge base, judging whether to correct the expert experience knowledge according to whether important parameters meet the boundary conditions when the electrolytic tank operates, if the important parameters exceed the boundary conditions, the expert experience knowledge needs to be corrected, and if the important parameters are all within the boundary conditions, the expert experience knowledge does not need to be corrected; wherein the important parameters comprise the content of hydrogen in oxygen, the content of oxygen in hydrogen, the liquid level difference and the alkali liquor temperature; the storage and addition unit is used for adding new expert experience knowledge.
5. The intelligent adaptive control system for producing hydrogen from electrolyzed water adapted to wide power fluctuation according to claim 4, wherein the feedback compensation module comprises:
expert rule establishing unit: the expert gives a correlation coefficient of parameter compensation for each deviation according to experience and rules according to the deviation setting rule between the important parameter and the boundary value which are output after the operation of the electrolytic tank; wherein the rule comprises: an error between the hydrogen content in the oxygen in the operation of the electrolytic cell and the threshold value of the boundary value, an error between the oxygen content in the hydrogen in the operation of the electrolytic cell and the threshold value of the boundary value, an error between the liquid level difference in the operation of the electrolytic cell and the threshold value of the boundary value, and an error between the alkali liquid temperature in the operation of the electrolytic cell and the threshold value of the boundary value;
an inference engine unit: and the expert experience compensation value is deduced by adopting an exhaustive item-by-item search algorithm according to the difference value between the important parameter output in the running of the electrolytic tank and the boundary value through expert experience rules, and the expert experience compensation value is output.
6. The intelligent self-adaptive control system for preparing hydrogen by electrolyzing water, which is adaptive to wide power fluctuation, according to claim 1, wherein the electrolytic water hydrogen preparation module is used for performing transient PID control on a pressure regulating valve and a pneumatic regulating valve in the electrolytic tank through combination of fuzzy rules and a neural network, so as to ensure that the pressure, the liquid levels of an oxygen separator and a hydrogen separator and the alkali liquid temperature and flow rate of the electrolytic tank at the current moment respond to given values tending to important parameters.
7. The intelligent self-adaptive control method for preparing hydrogen by electrolyzing water, which is suitable for wide power fluctuation, is characterized by comprising the following steps:
constructing an expert experience knowledge base model, and obtaining expected output parameters of the electrolytic cell system under the limit of meeting boundary conditions according to the input power and the actual measurement parameters of the current electrolytic cell, wherein the boundary conditions comprise: the expected output parameters comprise a system pressure set value, an alkali liquor temperature set value and an alkali liquor flow set value;
operating the electrolytic tank system based on the expected output parameters, detecting a deviation value between the output parameters of the electrolytic tank system after operation and the boundary conditions through a feedback compensation model, and outputting expert experience compensation values;
and controlling a pressure regulating valve and a pneumatic regulating valve in the electrolytic tank to carry out transient PID control through combination of a fuzzy rule and a neural network according to the expert experience compensation value and the expected output parameter, and simultaneously monitoring and correcting the output boundary important parameter detection value to a feedback compensation model and an expert experience knowledge base model at the same time.
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