A real-time energy consumption-based control parameter self-tuning method for a water electrolysis hydrogen production system

Abstract

The application discloses a kind of water electrolysis hydrogen production system control parameter self-tuning method based on real-time energy consumption, by deploying minute level precision electric energy meter and hydrogen flowmeter, construct real-time power consumption closed-loop feedback system, innovation uses two-stage collaborative setting mechanism: first stage decouples the strong coupling relationship of temperature and current, determines the lowest ΔE combination by grid optimization+fine optimization;Second level optimization alkali flow and separator liquid level.The application breaks through the bottleneck of multi-parameter artificial optimization, realizes the optimal setting of energy efficiency in a short time, and the unit power consumption decreases by 5.8-8.2%, and integrates SIL2 level safety protection.

Description

TECHNICAL FIELD

[0001] The application relates to a control parameter self-tuning method, in particular to a water electrolysis hydrogen production system control parameter self-tuning method based on real-time energy consumption, and belongs to the technical field of water electrolysis hydrogen production control. BACKGROUND

[0002] The current water electrolysis hydrogen production system has the following defects:

[0003] 1. Parameter staticization: temperature and current depend on fixed values or manual adjustment, and cannot dynamically respond to working condition fluctuations;

[0004] 2. Energy efficiency monitoring lag: traditional power consumption evaluation relies on offline detection, and lacks minute-level real-time feedback capability;

[0005] 3. Strong coupling of multiple parameters: temperature and current interact (temperature increases ion conductivity and current efficiency; current increases ohmic heat and temperature); manual optimization is inefficient;

[0006] 4. Lack of safety boundary: parameter adjustment is not dynamically constrained by equipment safety thresholds. SUMMARY

[0007] The technical problem to be solved by the application is to provide a water electrolysis hydrogen production system control parameter self-tuning method based on real-time energy consumption, which dynamically optimizes the lowest unit power consumption within the safety threshold.

[0008] To solve the above technical problems, the technical solution adopted by the application is:

[0009] A water electrolysis hydrogen production system control parameter self-tuning method based on real-time energy consumption, comprising the following steps:

[0010] S1, collecting alkali temperature T, electrolysis current I, alkali flow rate F, separator liquid level H, electric energy E, and hydrogen flow rate V;

[0011] S2, generating a parameter grid within a preset safety threshold T [Tmin, Tmax] and I [Imin, Imax], forming N groups of primary parameter combinations;

[0012] S3, sequentially executing each group of primary parameter combinations and recording the corresponding unit power consumption ΔE of each group of primary parameter combinations, and taking the primary parameter combination with unit power consumption ΔE less than the preset power consumption threshold as a primary candidate solution combination;

[0013] S4, for the i-th primary candidate solution combination (Ti, Ii), set small temperature step p and small current step q, get the field small step primary candidate solution combination (Ti-p, Ii-q) and (Ti+p, Ii+q), calculate the unit power consumption ΔE corresponding to all primary candidate solution combinations and the unit power consumption ΔE corresponding to all field small step primary candidate solution combinations (Ti-p, Ii-q) and (Ti+p, Ii+q), select the primary candidate solution combination or the field small step primary candidate solution combination corresponding to the minimum potential power consumption ΔE as the primary optimal solution combination;

[0014] S5, generate parameter grid within the preset safety threshold F∈[Fmin, Fmax], H∈[Hmin, Hmax], form M groups of secondary parameter combinations;

[0015] S6, execute each group of secondary parameter combinations in order and record the hydrogen purity and unit power consumption ΔE corresponding to each group of secondary parameter combinations, under the premise that the hydrogen purity is ≥99.9%, select the secondary parameter combination with the minimum unit power consumption ΔE fluctuation and output it together with the primary optimal solution combination as the global optimal solution control parameter.

[0016] Further, a high-precision electric energy meter is installed at the power line inlet end of the water electrolysis hydrogen production system, and a hydrogen mass flow meter is installed at the hydrogen outlet of the water electrolysis hydrogen production system.

[0017] Further, in the step S2, the step length of the alkali solution temperature T is P, the number of temperature points a of the alkali solution temperature T is (Tmax-Tmin) / P, the step length of the electrolysis current I is Q, the number of current points b of the electrolysis current T is (Imax-Imin) / Q, and the number of primary parameter combinations N is a*b.

[0018] Further, in the step S3, the unit power consumption ΔE=E / V.

[0019] Further, if the alkali solution temperature T or the electrolysis current I exceeds the safety threshold range or the unit power consumption ΔE fluctuation Δe≥±10% in the unit time during the setting process of the steps S3 and S4, it is immediately terminated and returned to the previous safe state.

[0020] Further, in the step S5, the step length of the alkali flow F is X, the number of flow points c of the alkali flow F is (Fmax-Fmin) / X, the step length of the separator liquid level H is Y, the number of liquid level points d of the separator liquid level H is (Hmax-Hmin) / Y, and the number of secondary parameter combinations M is c*d.

[0021] Further, the unit power consumption ΔE fluctuation Δe in the unit time is ΔE t -ΔE t-1 ) / ΔEt-1 100%, wherein ΔE t is the unit power consumption at the current moment, ΔE t-1 is the unit power consumption at the previous moment.

[0022] Compared with the prior art, the present application has the following advantages and effects: the present application provides a water electrolysis hydrogen production system control parameter self-tuning method based on real-time energy consumption, a real-time power consumption closed-loop feedback system is constructed by deploying a minute-level precision electric energy meter and a hydrogen gas flowmeter, and a two-stage collaborative tuning mechanism is innovatively adopted: the first stage decouples the strong coupling relationship between temperature and current, and determines the lowest ΔE combination through grid optimization + fine optimization; the second stage optimizes the alkali flow and the separator liquid level. The present application breaks through the bottleneck of multi-parameter manual optimization, realizes the completion of energy efficiency optimal tuning in a short time, and the unit power consumption reduction reaches 5.8-8.2%, and integrates SIL2 level safety protection. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 is a flowchart of a water electrolysis hydrogen production system control parameter self-tuning method based on real-time energy consumption of the present application.

[0024] Figure 2 is a hardware architecture schematic diagram of the water electrolysis hydrogen production system of the present application. DETAILED DESCRIPTION

[0025] In order to clearly and completely describe the technical solutions adopted by the present application to achieve the predetermined technical purposes, the technical solutions in the embodiments of the present application will be described in detail below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all embodiments, and the technical means or technical features in the embodiments of the present application can be replaced without creative labor. The present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0026] As shown in Figure 1 , a water electrolysis hydrogen production system control parameter self-tuning method based on real-time energy consumption of the present application comprises the following steps:

[0027] S1, as shown in Figure 2 , a high-precision electric energy meter is installed at the power line inlet end of the water electrolysis hydrogen production system, and a hydrogen mass flowmeter is installed at the hydrogen outlet of the water electrolysis hydrogen production system. Collect alkali temperature T, electrolysis current I, alkali flow F, separator liquid level H, electric energy E and hydrogen flow V.

[0028] Among them, the hydrogen flow V is obtained by subtracting the flow cumulative value one minute ago from the current flow cumulative value, which ensures the accuracy of the data.

[0029] S2, generate a parameter grid within a preset safety threshold T ∈ [Tmin, Tmax], I ∈ [Imin, Imax], to form N groups of primary parameter combinations.

[0030] The step size of the lye temperature T is P, the number of temperature points of the lye temperature T is a = (Tmax-Tmin) / P, the step size of the electrolysis current I is Q, the number of current points of the electrolysis current T is b = (Imax-Imin) / Q, and the number of primary parameter combinations N = a*b.

[0031] For example, [Tmin, Tmax] is 65-80℃, [Imin, Imax] is 700-1200A, the step size of the lye temperature T is 2℃, the number of temperature points of the lye temperature T is 8, the step size of the electrolysis current I is 100A, the number of current points of the electrolysis current T is 6, and the number of primary parameter combinations is 48.

[0032] S3, execute each group of primary parameter combinations in order and record the unit power consumption ΔE corresponding to each group of primary parameter combinations, and take the primary parameter combination with a unit power consumption ΔE less than a preset power consumption threshold as a primary candidate solution combination. Wherein, the unit power consumption ΔE = E / V. Each group of primary parameter combinations is executed for 5 minutes.

[0033] S4, for the ith primary candidate solution combination (Ti, Ii), set a small temperature step size p and a small current step size q to obtain the field small step primary candidate solution combination (Ti-p, Ii-q) and (Ti+p, Ii+q), calculate the unit power consumption ΔE corresponding to all primary candidate solution combinations and the unit power consumption ΔE corresponding to all field small step primary candidate solution combinations (Ti-p, Ii-q) and (Ti+p, Ii+q), and select the primary candidate solution combination or the field small step primary candidate solution combination corresponding to the minimum potential power consumption ΔE as the primary optimal solution combination.

[0034] For example, the small temperature step size is 1℃, the small current step size is 50A, each group of primary candidate solution combinations is run for 3 minutes, and the unit power consumption ΔE is updated every minute.

[0035] If the lye temperature T or the electrolysis current I exceeds the safety threshold range or the fluctuation Δe of the unit power consumption ΔE within the unit time is greater than or equal to ±10% during the setting process of steps S3 and S4, immediately terminate and back to the last safe state.

[0036] S5, generate a parameter grid within a preset safety threshold F ∈ [Fmin, Fmax], H ∈ [Hmin, Hmax], to form M groups of secondary parameter combinations.

[0037] The step length of the lye flow F is X, the flow point number c of the lye flow F is (Fmax-Fmin) / X, the step length of the separator liquid level H is Y, and the liquid level point number d of the separator liquid level H is (Hmax-Hmin) / Y, and the number M of the secondary parameter combination is c*d.

[0038] For example, [Fmin, Fmax] is 2-5 m 3 / h, [Hmin, Hmax] is 30-70% tank height.

[0039] The step length of the lye flow F is 0.3 m 3 / h, the flow point number of the lye flow F is 11, the step length of the separator liquid level H is 5% tank height, the liquid level point number of the separator liquid level H is 9, and the number of the secondary parameter combination is 99.

[0040] S6, sequentially execute each group of secondary parameter combinations and record the hydrogen purity and unit power consumption ΔE corresponding to each group of secondary parameter combinations, under the premise that the hydrogen purity is greater than or equal to 99.9%, select the secondary parameter combination with the minimum fluctuation of unit power consumption ΔE, combine it with the primary optimal solution, and output as the global optimal solution control parameter. Each group of secondary parameter combinations runs for 2 minutes.

[0041] The fluctuation Δe of the unit power consumption ΔE in a unit time is (ΔE t -ΔE t-1 ) / ΔE t-1 *100%, wherein ΔE t is the unit power consumption at the current time, and ΔE t-1 is the unit power consumption at the last time.

[0042] The application provides a water electrolysis hydrogen production system control parameter self-tuning method based on real-time energy consumption, which deploys a minute-level precision electric energy meter and a hydrogen flow meter to construct a real-time power consumption closed-loop feedback system, and innovatively adopts a two-stage cooperative tuning mechanism: the first stage decouples the strong coupling relationship between temperature and current, and determines the lowest ΔE combination through grid optimization + fine optimization; and the second stage optimizes the lye flow and the separator liquid level. The application breaks through the bottleneck of manual optimization of multiple parameters, realizes optimal energy efficiency tuning in a short time, reduces the unit power consumption by 5.8-8.2%, and integrates SIL2-level safety protection.

[0043] The application improves the system energy efficiency, the full-parameter tuning response time is short, the hardware limit + software dynamic boundary double protection realizes safety enhancement, the power consumption mutation protection response speed is improved by 3 times, the compatibility is good, the OPC UA interface uploads the minute-level energy efficiency data to the energy management system, and the optimization algorithm supports online upgrade.

[0044] The above merely describes preferred embodiments of the present application, and is not intended to limit the present application in any form. Although the present application has been disclosed with preferred embodiments as above, it is not intended to limit the present application. Any person skilled in the art, without departing from the technical solution of the present application, can make some changes or modifications to the above disclosed technical content to obtain equivalent embodiments with equivalent changes. However, as long as it does not deviate from the technical solution of the present application, and is within the spirit and principles of the present application, any simple modification, equivalent replacement and improvement of the above embodiments are still within the protection scope of the technical solution of the present application.

Claims

1. A real-time energy consumption-based control parameter self-tuning method for a water electrolysis hydrogen production system, characterized by Comprising the following steps: S1, collecting the alkali temperature T, electrolysis current I, alkali flow F, separator level H, electric energy E and hydrogen flow V; S2, generating a parameter grid within the preset safety threshold T∈[Tmin, Tmax], I∈[Imin, Imax] to form N groups of primary parameter combinations; In the step S2, the step length of the alkali temperature T is P, then the number of temperature points of the alkali temperature T is a= (Tmax-Tmin) / P, the step length of the electrolysis current I is Q, then the number of current points of the electrolysis current I is b= (Imax-Imin) / Q, and the number of primary parameter combinations is N=a*b; S3, executing each group of primary parameter combinations in sequence and recording the corresponding unit power consumption ΔE of each group of primary parameter combinations, and taking the primary parameter combination with the unit power consumption ΔE less than the preset power consumption threshold as a primary candidate solution combination; S4, for the i-th primary candidate solution combination (Ti, Ii), setting a small temperature step p and a small current step q to obtain the small-step primary candidate solution combinations (Ti-p, Ii-q) and (Ti+p, Ii+q) in the field, calculating the unit power consumption ΔE corresponding to all primary candidate solution combinations and the unit power consumption ΔE corresponding to all small-step primary candidate solution combinations (Ti-p, Ii-q) and (Ti+p, Ii+q) in the field, and selecting the primary candidate solution combination or the small-step primary candidate solution combination corresponding to the minimum unit power consumption ΔE as a primary optimal solution combination; S5, generating a parameter grid within the preset safety threshold F∈[Fmin, Fmax], H∈[Hmin, Hmax] to form M groups of secondary parameter combinations; In the step S5, the step length of the alkali flow F is X, then the number of flow points of the alkali flow F is c= (Fmax-Fmin) / X, the step length of the separator level H is Y, then the number of level points of the separator level H is d= (Hmax-Hmin) / Y, and the number of secondary parameter combinations is M=c*d; S6, executing each group of secondary parameter combinations in sequence and recording the corresponding hydrogen purity and unit power consumption ΔE of each group of secondary parameter combinations, and selecting the secondary parameter combination with the minimum fluctuation of unit power consumption ΔE as the global optimal solution control parameter under the premise that the hydrogen purity is ≥99.9%.

2. The method according to claim 1, wherein: A high-precision electric energy meter is installed at the power line inlet end of the water electrolysis hydrogen production system, and a hydrogen mass flow meter is installed at the hydrogen outlet of the water electrolysis hydrogen production system.

3. The method according to claim 1, wherein: In the step S3, the unit power consumption ΔE=E / V.

4. The method according to claim 1, wherein: If the alkali temperature T or the electrolysis current I exceeds the safety threshold range or the fluctuation Δe of the unit power consumption ΔE in the unit time is ≥±10% during the setting process of the step S3 and the step S4, it is immediately interrupted and rolled back to the previous safe state.

5. The method for real-time energy consumption based control parameter auto-tuning of water electrolysis hydrogen generation system according to claim 1, characterized in that: The fluctuation Δe of the unit power consumption ΔE in unit time = (ΔE t - ΔE t-1 ) / ΔE t-1 * 100%, wherein ΔE t is the unit power consumption at the current time, and ΔE t-1 is the unit power consumption at the previous time.

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