CN117075498A - Water electrolysis hydrogen production energy consumption monitoring and bionic optimizing system - Google Patents
Water electrolysis hydrogen production energy consumption monitoring and bionic optimizing system Download PDFInfo
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- 239000001257 hydrogen Substances 0.000 title claims abstract description 116
- 229910052739 hydrogen Inorganic materials 0.000 title claims abstract description 116
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 115
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 title claims abstract description 109
- 238000005265 energy consumption Methods 0.000 title claims abstract description 86
- 238000012544 monitoring process Methods 0.000 title claims abstract description 55
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 49
- 238000005868 electrolysis reaction Methods 0.000 title claims abstract description 36
- 239000011664 nicotinic acid Substances 0.000 title claims abstract description 23
- 238000000034 method Methods 0.000 claims abstract description 63
- 230000008569 process Effects 0.000 claims abstract description 46
- 238000004088 simulation Methods 0.000 claims abstract description 41
- 238000005457 optimization Methods 0.000 claims abstract description 17
- 230000000007 visual effect Effects 0.000 claims abstract description 8
- 238000004458 analytical method Methods 0.000 claims abstract description 5
- 238000001914 filtration Methods 0.000 claims description 42
- 238000004364 calculation method Methods 0.000 claims description 21
- 238000004422 calculation algorithm Methods 0.000 claims description 16
- 238000010586 diagram Methods 0.000 claims description 8
- 238000007781 pre-processing Methods 0.000 claims description 7
- 238000012360 testing method Methods 0.000 claims description 7
- 230000003592 biomimetic effect Effects 0.000 claims description 6
- 125000004435 hydrogen atom Chemical group [H]* 0.000 claims description 6
- 238000005192 partition Methods 0.000 claims description 6
- 239000000126 substance Substances 0.000 claims description 6
- 238000012417 linear regression Methods 0.000 claims description 5
- 238000012800 visualization Methods 0.000 claims description 5
- 238000001514 detection method Methods 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims 2
- 238000005070 sampling Methods 0.000 claims 1
- 238000011161 development Methods 0.000 abstract description 3
- 238000010276 construction Methods 0.000 description 4
- 239000000463 material Substances 0.000 description 3
- HEZMWWAKWCSUCB-PHDIDXHHSA-N (3R,4R)-3,4-dihydroxycyclohexa-1,5-diene-1-carboxylic acid Chemical compound O[C@@H]1C=CC(C(O)=O)=C[C@H]1O HEZMWWAKWCSUCB-PHDIDXHHSA-N 0.000 description 2
- 230000003197 catalytic effect Effects 0.000 description 2
- 238000012886 linear function Methods 0.000 description 2
- 230000000737 periodic effect Effects 0.000 description 2
- 239000000498 cooling water Substances 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 239000008151 electrolyte solution Substances 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 150000002431 hydrogen Chemical class 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 239000013067 intermediate product Substances 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000000746 purification Methods 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 230000027756 respiratory electron transport chain Effects 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B17/00—Systems involving the use of models or simulators of said systems
- G05B17/02—Systems involving the use of models or simulators of said systems electric
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Abstract
In order to make up for the needs of the prior art, the invention provides a monitoring and bionic optimizing system for hydrogen production by water electrolysis, which comprises the following components: the system comprises an energy consumption monitoring subsystem, a bionic optimizing subsystem and a visual display subsystem, wherein: the energy consumption monitoring subsystem is used for collecting operation parameter data of the operation process of the electrolytic water hydrogen production system and forming energy consumption monitoring; the visual display subsystem is used for visually displaying the bionic optimization result of the bionic optimization subsystem; the bionic optimizing subsystem comprises a microprocessor component for bionic optimizing analysis. The invention integrates the real-time data into the simulation model to reflect the actual running condition of the real system, and can use multi-scene simulation to predict and optimize the performance of the energy consumption system, thereby meeting the development needs of the prior art.
Description
Technical Field
The invention relates to the technical field of production simulation, in particular to an energy consumption monitoring and bionic optimizing system for hydrogen production by water electrolysis.
Background
At present, an effective energy supervision system is lacking in a water electrolysis hydrogen production system, and technological parameters, raw materials, intermediate products, energy transfer and the like of each technological link of the hydrogen production system cannot be quantitatively analyzed, optimized and adjusted. After the hydrogen production system is combined with renewable energy sources such as photovoltaic and wind power, the power input of the hydrogen production device can also fluctuate due to the fluctuation of the renewable energy source power, and even the hydrogen production device is started and stopped frequently. The hydrogen production device is large in energy loss such as pressure relief and temperature reduction during start-up and shutdown, and the hydrogen production device is used as a chemical device, so that the safety risk is large during start-up and shutdown.
The energy consumption system for water electrolysis and hydrogen production is simulated by the virtual power plant technology, so that an energy consumption monitoring system for water electrolysis and hydrogen production can be formed, and the load of the hydrogen production device can be timely regulated according to the fluctuation of renewable energy source power, thereby effectively relieving the problem when the renewable energy source is used as the energy source for water electrolysis and hydrogen production. However, the existing simulation of the water electrolysis hydrogen production energy consumption system generally aims at single equipment or single scene, and along with the diversification of the current renewable energy sources, the water electrolysis hydrogen production energy consumption system needs to be simulated in a multi-scene complex environment.
Disclosure of Invention
In order to make up for the needs of the prior art, the invention provides a monitoring and bionic optimizing system for hydrogen production by water electrolysis, which comprises the following components: the system comprises an energy consumption monitoring subsystem, a bionic optimizing subsystem and a visual display subsystem, wherein: the energy consumption monitoring subsystem is used for collecting operation parameter data of the operation process of the electrolytic water hydrogen production system and forming energy consumption monitoring; the visual display subsystem is used for visually displaying the bionic optimization result of the bionic optimization subsystem; the biomimetic optimization subsystem includes a microprocessor assembly to perform the following analysis:
s1, acquiring operation parameter data of an operation process of the electrolytic water hydrogen production system, and preprocessing the acquired operation parameters.
S2, establishing a simulation model based on the energy consumption monitoring model of each process equipment and structure unit of the electrolytic water hydrogen production system and the operation parameter data preprocessed in the step S1.
S3, simulating different operation parameter conditions, and performing simulation by using the simulation model established in the step S2 to evaluate the operation condition of the water electrolysis hydrogen production system under the different operation parameter conditions.
And S4, applying different energy consumption algorithms and simulation results of the step S3 to an optimization algorithm, and establishing an objective function and constraint conditions in the system to optimize so as to obtain an optimal operation parameter combination.
S5, displaying the energy consumption condition of the water electrolysis hydrogen production system under the optimal operation parameter combination to a user through the visualization subsystem.
Further, the data preprocessing includes a data filtering process and a curve fitting process. The data filtering process is used for eliminating noise in the data, and the curve fitting process is used for fitting a curve of the energy consumption data.
Further, the data filtering process includes the steps of:
(1) Program judgment filtering: the maximum deviation value deltat of the adjacent sampled data is determined. If the difference value of the last acquired data compared with the previous acquired data exceeds the maximum deviation value delta T, the acquired data is an interference signal and is not acquired. And if the difference value of the data acquired at the last time is smaller than the maximum deviation value delta T compared with the data acquired at the previous time, the acquired data is reserved.
(2) And adopting at least one filtering method of median filtering, mean filtering and weighted mean filtering to carry out secondary filtering on the data sequence { Ti } |i=0-n formed after program judgment filtering. Wherein T in the data sequence 0 For the data value acquired at 0 th second, T i For the data value acquired for the ith second.
Further, the mean filtering is performed using formula (one):
(one)
Where Tg is the mean filtering result.
Further, the weighted average filtering is performed using equation (two):
c in the formula i A weighted value for the data value acquired for the ith second,the result is weighted average filtering.
Further, the curve fitting process employs: at least one of polynomial fitting, linear regression fitting, nonlinear regression fitting, spline interpolation fitting, fourier series fitting methods to fit the curve of the energy consumption data.
Further, the simulation model in step S2 at least includes: a hydrogen production energy consumption monitoring model and a hydrogen production process overview model. The hydrogen production energy consumption monitoring model is used for monitoring the operation energy consumption of main equipment and key components of the system, and simulating and predicting various physical, chemical and dynamic phenomena in the process of producing hydrogen by using the collected or set data. The hydrogen production process overview model is used for constructing each process device in the hydrogen production workshop and analyzing energy substance flow venation and correlation among the devices.
Further, the method for constructing the hydrogen production energy consumption monitoring model comprises the following steps:
firstly, a detection simulation model is built based on each process equipment and structure unit of the electrolytic water hydrogen production system.
And secondly, forming different factory partitions and energy consumption monitoring system relations of different levels in each partition.
Finally, the energy consumption Sang Jitu of the hydrogen production process is obtained.
Further, the construction method of the hydrogen production process overview model comprises the following steps:
firstly, a process simulation model is constructed according to a process diagram and a factory equipment general diagram of the water electrolysis hydrogen production system.
And secondly, adding an energy context graph into the process simulation model according to the energy flow relation among the devices and the structure of the public and auxiliary devices.
And finally, marking energy consumption data information at the acquisition point.
Further, the different energy consumption algorithm in step S4 at least includes: a direct current test value calculation method and a flow calculation method, wherein:
the calculation method of the direct current test value is that the formula (III) is adopted for calculation:
(III)
Wherein Q is hydrogen production, I is current value, t is production time, and F is Faraday constant.
The calculation method of the flowmeter algorithm is that the calculation is carried out by adopting the formula (IV):
(IV)
Wherein Q is hydrogen production amount, L is hydrogen flow amount, and t is production time.
The invention has the main advantages that:
1. the real-time data are integrated into the simulation model to reflect the actual running condition of the real system.
2. The invention can use multi-scene simulation to predict and optimize the performance of the energy consumption system, and meets the requirement of the development of the prior art.
3. The invention is applied to the process simulation of the energy consumption monitoring system through the intelligent algorithm, and can automatically search the optimal scheme and optimize the energy utilization.
Drawings
The invention will be further described with reference to the drawings and examples.
FIG. 1 is a schematic diagram of a system architecture of the present invention;
FIG. 2 is an exemplary energy consumption Sang Jitu;
FIG. 3 is a schematic diagram of an overview model of a hydrogen production process;
FIG. 4 is a schematic illustration of an exemplary two primary loop topology routes;
FIG. 5 is an exemplary energy consumption algorithm comparison graph;
FIG. 6 is a schematic representation of content presented to a user by a visualization system.
Detailed Description
The invention will now be described in further detail with reference to the accompanying drawings. The drawings are simplified schematic representations which merely illustrate the basic structure of the invention and therefore show only the structures which are relevant to the invention.
An electrolytic water hydrogen production energy consumption monitoring and bionic optimizing system, as shown in fig. 1, comprises: the system comprises an energy consumption monitoring subsystem, a bionic optimizing subsystem and a visual display subsystem, wherein: the energy consumption monitoring subsystem collects operation parameter data of the operation process of the electrolytic water hydrogen production system through the collecting unit and forms energy consumption monitoring; the visual display subsystem is used for visually displaying the bionic optimization result of the bionic optimization subsystem; the bionic optimizing subsystem comprises a microprocessor component for bionic optimizing analysis, and specifically comprises the following steps:
s1, acquiring operation parameter data of an operation process of the water electrolysis hydrogen production system through a data interaction unit, and preprocessing the acquired operation parameters.
And carrying out process simulation, namely acquiring data of the water electrolysis hydrogen production process, wherein the data acquisition mainly acquires key parameters in the water electrolysis hydrogen production process, such as current, voltage, temperature and the like, in real time through a sensor, instrument equipment and the like. Because the invention needs to construct a simulation model based on real operation parameters, the accuracy and usability of the parameters need to be ensured, and therefore, the operation parameters need to be subjected to data preprocessing.
The data preprocessing includes a data filtering process and a curve fitting process. The data filtering process is used for eliminating noise in the data, and the curve fitting process is used for fitting a curve of the energy consumption data.
The data filtering process comprises the following steps:
(1) Program judgment filtering: the maximum deviation value deltat of the adjacent sampled data is determined. If the difference value of the last acquired data compared with the previous acquired data exceeds the maximum deviation value delta T, the acquired data is an interference signal and is not acquired. And if the difference value of the data acquired at the last time is smaller than the maximum deviation value delta T compared with the data acquired at the previous time, the acquired data is reserved.
When the sampled data is seriously distorted due to random interference, false detection or unstable transmitter, the method can reject the distorted data and avoid the interference of the distorted data on the simulation model construction.
(2) The data sequence { T) formed after program judgment filtering is subjected to one filtering method of median filtering, mean filtering and weighted mean filtering i Secondary filtering is performed by } |i=0-n. Wherein T in the data sequence 0 For the data value acquired at 0 th second, T i For the data value acquired for the ith second.
The mean filtering is performed using equation (one):
(one)
Where Tg is the mean filtering result.
The weighted average filtering is performed using equation (two):
(II)
C in the formula i A weighted value for the data value acquired for the ith second,the result is weighted average filtering.
By adopting the method, the acquired operation parameter data can be optimized, so that the operation parameter data has higher accuracy and usability, and the difficulty in curve fitting is reduced.
The curve fitting process adopts: polynomial fitting, linear regression fitting, nonlinear regression fitting, spline interpolation fitting, fourier series fitting methods to fit the curve of the energy consumption data.
Specific:
1) Polynomial fitting: polynomial fitting is a simple and common method by which data can be fitted using polynomial functions. The appropriate polynomial degree may be selected as needed to accommodate the characteristics of the data.
2) Linear regression: linear regression is a method of predicting data by fitting a straight line. The best fit straight line can be found using the least squares method as needed.
3) Nonlinear regression: nonlinear regression is applied to data that cannot be fitted with a linear function. Non-linear functions, such as exponential, logarithmic, power functions, etc., may be used to fit the data as desired.
4) Spline interpolation: spline interpolation is a smooth curve fitting method that can approximate data by using multiple piecewise functions. Spline interpolation may provide more accurate and smooth fitting results.
5) Fourier series fitting: the fourier series may decompose a periodic signal into a sum of a plurality of sine and cosine functions. Fourier series fits may be used to process periodic energy consumption data as desired.
The curve fitting is not exhaustive, and other curve equations can be selected for fitting according to the curve form of the parameters.
S2, establishing a simulation model based on the energy consumption monitoring model of each process equipment and structure unit of the electrolytic water hydrogen production system and the operation parameter data preprocessed in the step S1.
The simulation model of step S2 includes: a hydrogen production energy consumption monitoring model and a hydrogen production process overview model.
The construction method of the hydrogen production energy consumption monitoring model comprises the following steps:
firstly, a detection simulation model is built based on each process equipment and structure unit of the electrolytic water hydrogen production system.
And secondly, forming different factory partitions and energy consumption monitoring system relations of different levels in each partition.
Finally, the energy consumption Sang Jitu of the hydrogen production process is obtained.
Taking a certain power plant as an example, a hydrogen production energy consumption monitoring model is constructed, the hydrogen production energy consumption monitoring model comprises the monitoring of main equipment operation energy consumption and system key components, and various physical, chemical and dynamic phenomena in the water electrolysis hydrogen production process can be simulated and predicted based on the existing model by utilizing collected or set data.
Taking an IGBT hydrogen production power supply as an example, the hydrogen production energy consumption monitoring model has the energy consumption Sang Jitu of the hydrogen production power supply, as shown in fig. 2, and the input includes one power supply, three auxiliary power supplies and one cooling water supply. The method can be used for detecting the energy consumption of the multi-unit parallel large-scale alkaline electrolysis hydrogen production system, and a sensing system for total station monitoring is established.
The construction method of the hydrogen production process overview model comprises the following steps:
firstly, a process simulation model is constructed according to a process diagram and a factory equipment general diagram of the water electrolysis hydrogen production system.
And secondly, adding an energy context graph into the process simulation model according to the energy flow relation among the devices and the structure of the public and auxiliary devices.
And finally, marking energy consumption data information at the acquisition point.
Taking a certain power plant as an example, the constructed hydrogen production process overview model is shown in fig. 3, the hydrogen production process overview model shows the relation between energy circulation of the hydrogen production plant station and production and public and auxiliary equipment in a hydrogen production unit building, and can analyze the energy material flow venation and correlation of electricity, water, gas and the like in the hydrogen production plant station, and the hydrogen production plant station comprises equipment such as a rectifier transformer, an electrolytic tank, a gas-liquid separation device, a purification system, a compressor and the like, and can detect the energy consumption of the equipment.
S3, simulating different operation parameter conditions, and performing simulation by using the simulation model established in the step S2 to evaluate the operation condition of the water electrolysis hydrogen production system under the different operation parameter conditions.
Taking 5MW alkaline water electrolysis hydrogen production equipment as an example, comparing the structure, key parameters, performance differences and advantages and disadvantages of hydrogen production equipment of different manufacturers, comparing the actual operation parameter differences and advantages and disadvantages of the hydrogen production equipment of different technical routes, and carrying out performance comparison analysis on parts of the electrolytic tank of each alkaline electrolysis hydrogen production equipment manufacturer, including anode catalytic materials, cathode catalytic materials, diaphragms, bipolar plate runners, gaskets and the like. Two main loop topology routes were obtained as shown in fig. 4: IGBT full-control rectification and DCDC chopper step-down technical route and diode rectification and DCDC chopper step-down technical route. And establishing a corresponding hydrogen production energy consumption monitoring model, namely a power supply operation loss simulation model for steady-state operation hydrogen production. And then, the power supply operation loss simulation model for steady-state operation hydrogen production is used for carrying out simulation by giving and comparing different operation conditions, different electric energy quality and different controllable parameters.
And S4, applying different energy consumption algorithms and simulation results of the step S3 to an optimization algorithm, and establishing an objective function and constraint conditions in the system to optimize so as to obtain an optimal operation parameter combination.
The different energy consumption algorithms described in step S4 include: a direct current test value calculation method and a flow calculation method, wherein:
the calculation method of the direct current test value calculates the hydrogen yield by measuring the direct current in the electrolyzer based on Faraday's law of electrolysis. When current passes through the electrolyte solution, each electron transfers a corresponding electric quantity to generate hydrogen, and the calculation is specifically carried out by adopting a formula (III):
(III)
Wherein Q is hydrogen production, I is current value, t is production time, and F is Faraday constant.
The calculation method of the flow calculation method calculates the hydrogen production amount by measuring the flow of the hydrogen based on the relationship between the volume flow of the hydrogen and the hydrogen production amount, specifically, the calculation is performed by adopting the formula (IV):
(IV)
Wherein Q is hydrogen production amount, L is hydrogen flow amount, and t is production time.
As shown in FIG. 5, a comparison graph of the energy consumption algorithm of a direct current test value calculation method and a flowmeter algorithm is adopted for a certain 5WM alkaline water electrolysis hydrogen production device.
S5, displaying the energy consumption condition of the water electrolysis hydrogen production system under the optimal operation parameter combination to a user through the visualization subsystem.
By the simulation method, the multi-scenario data simulated by the actual monitoring data energy consumption calculation, statistics and history monitoring can be compared, and different capacity schemes with highest capacity, lowest capacity and optimal capacity can be obtained according to the capacity of the equipment under different conditions, and the content displayed to a user by a visualization system is shown in fig. 6 and comprises the following steps: hydrogen production process, hydrogen production amount comparison, energy consumption comparison and hydrogen production operation scheme comparison.
In summary, the invention reflects the actual running condition of the real system by integrating real-time data into the simulation model. Meanwhile, the invention can use multi-scene simulation to predict and optimize the performance of the energy consumption system, and meets the requirement of the development of the prior art. The invention can be applied to the process simulation of the energy consumption monitoring system through an intelligent algorithm so as to automatically search the optimal scheme and optimize the energy utilization.
With the above-described preferred embodiments according to the present invention as an illustration, the above-described descriptions can be used by persons skilled in the relevant art to make various changes and modifications without departing from the scope of the technical idea of the present invention. The technical scope of the present invention is not limited to the description, but must be determined according to the scope of claims.
Claims (10)
1. An electrolytic water hydrogen production energy consumption monitoring and bionic optimizing system, which is characterized by comprising: the system comprises an energy consumption monitoring subsystem, a bionic optimizing subsystem and a visual display subsystem, wherein: the energy consumption monitoring subsystem is used for collecting operation parameter data of the operation process of the electrolytic water hydrogen production system and forming energy consumption monitoring; the visual display subsystem is used for visually displaying the bionic optimization result of the bionic optimization subsystem; the biomimetic optimization subsystem includes a microprocessor assembly to perform the following analysis:
s1, acquiring operation parameter data of an operation process of an electrolytic water hydrogen production system, and preprocessing the acquired operation parameter data;
s2, establishing a simulation model based on the energy consumption monitoring model of each process equipment and structure unit of the electrolytic water hydrogen production system and the operation parameter data preprocessed in the step S1;
s3, simulating different operation parameter conditions, and performing simulation by using the simulation model established in the step S2 to evaluate the operation condition of the water electrolysis hydrogen production system under the different operation parameter conditions;
s4, applying different energy consumption algorithms and simulation results of the step S3 to an optimization algorithm, and establishing an objective function and constraint conditions in a system to optimize so as to obtain an optimal operation parameter combination;
s5, displaying the energy consumption condition of the water electrolysis hydrogen production system under the optimal operation parameter combination to a user through the visualization subsystem.
2. The system for monitoring and biomimetically optimizing hydrogen production energy consumption by water electrolysis according to claim 1, wherein the data preprocessing comprises data filtering processing and curve fitting processing; the data filtering process is used for eliminating noise in the data, and the curve fitting process is used for fitting a curve of the energy consumption data.
3. The system for monitoring and biomimetically optimizing hydrogen production energy consumption by water electrolysis according to claim 2, wherein the data filtering process comprises the following steps:
(1) Program judgment filtering: determining the maximum deviation value delta T of adjacent sampling data; if the difference value of the data acquired at the last time compared with the data acquired at the previous time exceeds the maximum deviation value delta T, the acquired data is an interference signal and is not acquired; if the difference value of the data acquired in the last time is smaller than the maximum deviation value delta T compared with the data acquired in the previous time, the acquired data is reserved;
(2) At least one filtering method of median filtering, mean filtering and weighted mean filtering is adopted to judge the data sequence { T ] formed after filtering the program i Secondary filtering is carried out on the } |i=0-n; wherein T in the data sequence 0 For the data value acquired at 0 th second, T i For the data value acquired for the ith second.
4. A water electrolysis hydrogen production energy consumption monitoring and biomimetic optimization system according to claim 3, wherein the mean filtering is performed by adopting (a):
(one)
T in g The result is the mean value filtering result.
5. A water electrolysis hydrogen production energy consumption monitoring and biomimetic optimization system according to claim 3, wherein the weighted average filtering is performed by adopting (two):
(II)
C in the formula i A weighted value for the data value acquired for the ith second,the result is weighted average filtering.
6. The system for monitoring and biomimetically optimizing hydrogen production energy consumption by water electrolysis according to claim 2, wherein the curve fitting process adopts: at least one of polynomial fitting, linear regression fitting, nonlinear regression fitting, spline interpolation fitting, fourier series fitting methods to fit the curve of the energy consumption data.
7. The system for monitoring and biomimetic optimization of hydrogen production energy consumption by water electrolysis according to claim 1, wherein the simulation model in step S2 at least comprises: the hydrogen production energy consumption monitoring model and the hydrogen production process overview model; the hydrogen production energy consumption monitoring model is used for monitoring the operation energy consumption of main equipment and key components of the system, and simulating and predicting various physical, chemical and dynamic phenomena in the process of producing hydrogen by using the collected or set data; the hydrogen production process overview model is used for constructing each process device in the hydrogen production workshop and analyzing energy substance flow venation and correlation among the devices.
8. The system for monitoring and biomimetically optimizing hydrogen production energy consumption by water electrolysis according to claim 7, wherein the method for constructing the hydrogen production energy consumption monitoring model comprises the following steps:
firstly, establishing a detection simulation model based on each process equipment and structure unit of an electrolytic water hydrogen production system;
secondly, forming different factory partitions and energy consumption monitoring system relations of different levels in each partition;
finally, the energy consumption Sang Jitu of the hydrogen production process is obtained.
9. The system for monitoring and biomimetically optimizing hydrogen production energy consumption by water electrolysis according to claim 7, wherein the method for constructing the hydrogen production process overview model comprises the following steps:
firstly, constructing a process simulation model according to a process diagram of an electrolytic water hydrogen production system and a total diagram of factory equipment;
secondly, adding an energy context graph into the process simulation model according to the energy flow relation among the devices and the structure of the public and auxiliary devices;
and finally, marking energy consumption data information at the acquisition point.
10. The hydrogen production energy consumption monitoring and biomimetic optimization system according to claim 1, wherein the different energy consumption algorithm in step S4 at least comprises: a direct current test value calculation method and a flow calculation method, wherein:
the calculation method of the direct current test value is that the formula (III) is adopted for calculation:
(III)
Wherein Q is hydrogen production, I is current value, t is production time, and F is Faraday constant;
the calculation method of the flowmeter algorithm is that the calculation is carried out by adopting the formula (IV):
(IV)
Wherein Q is hydrogen production amount, L is hydrogen flow amount, and t is production time.
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