CN116090358A - Method for production, storage and supply linkage optimization of semi-continuous biogas station - Google Patents

Method for production, storage and supply linkage optimization of semi-continuous biogas station Download PDF

Info

Publication number
CN116090358A
CN116090358A CN202310371555.6A CN202310371555A CN116090358A CN 116090358 A CN116090358 A CN 116090358A CN 202310371555 A CN202310371555 A CN 202310371555A CN 116090358 A CN116090358 A CN 116090358A
Authority
CN
China
Prior art keywords
gas
production
semi
biogas
continuous
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202310371555.6A
Other languages
Chinese (zh)
Other versions
CN116090358B (en
Inventor
朱炼
彭大江
蒋中宇
常关羽
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Chengdu Qianjia Technology Co Ltd
Original Assignee
Chengdu Qianjia Technology Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Chengdu Qianjia Technology Co Ltd filed Critical Chengdu Qianjia Technology Co Ltd
Priority to CN202310371555.6A priority Critical patent/CN116090358B/en
Publication of CN116090358A publication Critical patent/CN116090358A/en
Application granted granted Critical
Publication of CN116090358B publication Critical patent/CN116090358B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Artificial Intelligence (AREA)
  • Computer Hardware Design (AREA)
  • Geometry (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention relates to a method for regulating and optimizing production, storage and supply linkage of a semi-continuous biogas station, which comprises the following steps: learning biogas production capacity of a biogas station based on historical data, and constructing a semi-continuous instantaneous biogas production model of n times of feeding; training a semicontinuous instantaneous gas production model by using a time sequence; predicting future gas consumption of the user side, screening a production scheduling scheme through constraint conditions, and selecting an optimal production scheduling scheme as a production and storage linkage optimization scheme. The invention aims to establish a semi-continuous instantaneous gas production model through the linkage of the biogas production and biogas consumption capacity, and optimize the model, so as to obtain an optimal production, storage and supply linkage optimization scheme, and reduce biogas storage and methane emission on the premise of guaranteeing the gas consumption of users.

Description

Method for production, storage and supply linkage optimization of semi-continuous biogas station
Technical Field
The invention relates to the technical field of data processing, in particular to a method for regulating and optimizing production, storage and supply linkage of a semi-continuous biogas station.
Background
Rural biogas sites have been rapidly developed as a supply of renewable energy. On the one hand, the biogas station can convert agricultural wastes such as straw, pig manure and the like into organic fertilizer sustainable agricultural production; on the other hand, reliable renewable energy supply can be provided for residents. By the end of 2015, more than 10.3 ten thousand village-grade biogas stations are built in China, and the number of biogas stations and the number of treated wastes are continuously increasing.
Then, in commercial production environments, most decisions always tend to increase biogas production, guaranteeing gas usage for users, subject to technical conditions. This results in biogas production and user consumption that are not dynamically matched and overproduced biogas is discharged to the atmosphere, instead creating environmental pollution. Some theoretical studies have shown that methane (the main gas component in biogas) has a 25 times higher global warming potential than carbon dioxide. Therefore, there is an urgent need to limit biogas emissions from biogas sites to an acceptable range.
The typical biogas station at least comprises a batching pool, a fermentation tank, a gas storage tank and a plurality of monitoring devices of the Internet of things. Since fermentation gas is a continuous small volume of gas, and consumption gas is a centralized batch of gas (e.g., residential gas is smoothly concentrated in the midday and evening), a gas storage tank is typically used as a buffer for production/supply. Nevertheless, excessive gas storage exacerbates biogas escape.
Disclosure of Invention
The invention aims to provide a method for regulating and optimizing the production and storage of a semi-continuous biogas station in a linkage way by calculating the linkage biogas production and biogas consumption capacity and establishing a regulating and optimizing model, so that biogas storage and methane emission are reduced on the premise of guaranteeing the gas consumption of users.
In order to achieve the above object, the embodiment of the present invention provides the following technical solutions:
a method for regulating and optimizing production, storage and supply linkage of a semi-continuous biogas station comprises the following steps:
step 1, learning biogas production capacity of a biogas station based on historical data, and constructing a semi-continuous instantaneous biogas production model of n times of feeding; training a semicontinuous instantaneous gas production model by using a time sequence;
and 2, predicting future gas consumption of the user side, screening a production scheduling scheme through constraint conditions, and selecting an optimal production scheduling scheme as a production and storage linkage optimization scheme.
In the step 1, learning biogas production capacity of a biogas station based on historical data, and constructing a semi-continuous instantaneous biogas production model of n times of feeding, wherein the step comprises the following steps:
building a single-feeding biogas accumulation gas production model:
Figure SMS_1
（1）
wherein B (t) represents the accumulated gas yield at time t; b (B) 0 Representing the final gas yield given by the total amount of the fed materials and the fermentation formula; t represents the current time; t represents the feeding time; k represents a methane degradation constant; c represents the shape factor of the S-shaped curve,
Figure SMS_2
；
based on equation (1), a semi-continuous instantaneous gas production model is established for n charges:
Figure SMS_3
（3）
wherein b (t) represents the instantaneous gas production at the moment t in the semi-continuous feeding mode;
Figure SMS_4
，/>
Figure SMS_5
indicating the ith dosing time, +.>
Figure SMS_6
Indicating the current time t and the ith feeding time +.>
Figure SMS_7
Is a time difference of (2).
In the step 1, the training of the semi-continuous instantaneous gas production model by using a time sequence comprises the following steps:
time series
Figure SMS_8
The semi-continuous instantaneous gas production model is input, the instantaneous gas production y is taken as output, and the semi-continuous instantaneous gas production model is trained;
let the real instantaneous gas yield be y, the semi-continuous instantaneous gas yield model output predicted gas yield y, the minimum objective function of the semi-continuous instantaneous gas yield model is:
Figure SMS_9
（4）
wherein ,B0 C, k are learning parameters, for B 0 C, k are subjected to numerical range limitation, B 0 ∈[B 1 ,B 2 ]，c∈[c 1 ,c 2 ]，k∈[k 1 ,k 2 ]And (3) making:
Figure SMS_10
（5-1）
Figure SMS_11
（5-2）
Figure SMS_12
（5-3）
bringing equations (5-1), (5-2) and (5-3) into equation (4) converts the minimized objective function into an unconstrained objective function:
Figure SMS_13
（6）
solving the optimal by adopting a gradient descent method
Figure SMS_14
、/>
Figure SMS_15
、/>
Figure SMS_16
：
Figure SMS_17
（7）
wherein ：
Figure SMS_18
Figure SMS_19
Figure SMS_20
Figure SMS_21
Figure SMS_22
Figure SMS_23
bringing the optimal solution obtained in the formula (7) into the formula (6), and calculating to obtain
Figure SMS_24
Presetting a lower error limit->
Figure SMS_25
；
If it is
Figure SMS_26
Optimal solution obtained by equation (7)>
Figure SMS_27
、/>
Figure SMS_28
、/>
Figure SMS_29
Carrying out formulas (5-1), (5-2) and (5-3) to solve the optimal B 0 、c、k；
If it is
Figure SMS_30
Calculating +.>
Figure SMS_31
、/>
Figure SMS_32
、/>
Figure SMS_33
For a given->
Figure SMS_34
、/>
Figure SMS_35
、/>
Figure SMS_36
Negative gradient adjustment is performed to form a new solution:
Figure SMS_37
Figure SMS_38
Figure SMS_39
wherein ,
Figure SMS_40
for a given learning rate; will beNew solution re-bring formula (6) calculate +.>
Figure SMS_41
Up to->
Figure SMS_42
。
In the step 2, the step of predicting the future gas consumption of the user terminal includes:
based on historical data, predicting future gas consumption of the user side by using an ARIMA model:
Figure SMS_43
Figure SMS_46
indicate->
Figure SMS_50
Tian (heaven)>
Figure SMS_53
Indicate->
Figure SMS_45
Air consumption in the days>
Figure SMS_49
Indicate->
Figure SMS_52
Air consumption in the days>
Figure SMS_55
Indicate->
Figure SMS_47
Air consumption in the day; c. and (2)>
Figure SMS_48
、...、/>
Figure SMS_51
、/>
Figure SMS_54
、...、/>
Figure SMS_44
Is the coefficient used for model fitting.
In the step 2, the production scheduling scheme is screened through constraint conditions, and the optimal production scheduling scheme is selected as a production, storage and supply linkage scheduling scheme, which comprises the following steps:
constructing production, storage and supply linkage constraint conditions:
constraint condition 1, wherein the sum of the gas yield of the previous day and the gas amount of the gas storage tank is larger than the sum of the gas consumption of the next day and the minimum gas amount of the gas storage tank;
constraint condition 2, the gas storage tank gas amount does not exceed the designed highest gas amount;
constraint condition 3, the air quantity of the air storage tank is not lower than the designed minimum air quantity;
when the current time t and the first feeding time
Figure SMS_56
Time difference of->
Figure SMS_57
Together with the total of 2 z A seed production scheduling scheme;
pair 2 according to constraint z Screening a production scheduling scheme, namely, enabling the production scheduling scheme meeting constraint conditions to be a set A, defining 2 metrics of the set A as (d 1, d 2), wherein the metric d1 is the shortest distance between two curves, the metric d2 is the farthest distance between the two curves, the first curve represents the sum of the gas production amount of the previous day and the gas storage tank, and the second curve represents the sum of the gas consumption amount of the next day and the minimum gas storage tank;
in the set A, d1 is the highest-level guaranteed gas consumption scheme, and d2 is the lowest-level optimized gas storage scheme; d1 descending order sorting is respectively carried out on the set A, d2 ascending order sorting is carried out on the set A, and the optimal production, storage and supply linkage optimization scheme with the smallest sum of two sorting serial numbers is adopted.
Compared with the prior art, the invention has the beneficial effects that:
the invention aims to establish a semi-continuous instantaneous gas production model through the linkage of the biogas production and biogas consumption capacity, and optimize the model, so as to obtain an optimal production, storage and supply linkage optimization scheme, and reduce biogas storage and methane emission on the premise of guaranteeing the gas consumption of users.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of the present invention;
FIG. 2 is a schematic diagram for predicting gas consumption of a user terminal for 10 days in the future according to an embodiment of the present invention;
FIG. 3 is a graph showing the sum of the gas production amount of the previous day and the gas storage tank and the sum of the gas consumption amount of the subsequent day and the minimum gas storage tank according to the 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. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present invention.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Also, in the description of the present invention, the terms "first," "second," and the like are used merely to distinguish one from another, and are not to be construed as indicating or implying a relative importance or implying any actual such relationship or order between such entities or operations. In addition, the terms "connected," "coupled," and the like may be used to denote a direct connection between elements, or an indirect connection via other elements.
Examples:
generally, the gas yield of a biogas station is related to the factors such as the total feeding amount, the feeding frequency, the fermentation temperature, the raw materials, the fermentation formula and the like, but the fluctuation of the total feeding amount, the fermentation temperature, the raw materials and the fermentation formula is small each time to a specific biogas station, and the gas yield is mainly related to the feeding frequency. The invention is realized by the following technical scheme, namely a method for adjusting and optimizing production, storage and supply linkage of a semi-continuous biogas station, wherein decision variables used for optimization are mainly feeding frequencies, and referring to fig. 1, the method comprises the following steps:
step 1, learning biogas production capacity of a biogas station based on historical data.
Building a single-feeding biogas accumulation gas production model:
Figure SMS_58
（1）
wherein B (t) represents the accumulated gas yield at time t; b (B) 0 Representing the final gas yield given by the total amount of the fed materials and the fermentation formula; t represents the current time; t represents the feeding time; k represents a methane degradation constant; c represents the shape factor of the S-shaped curve,
Figure SMS_59
。
deriving t from the formula (1) to obtain an instantaneous gas production model:
Figure SMS_60
（2）
based on equation (2), a semi-continuous instantaneous gas production model is established for n charges:
Figure SMS_61
(3)
wherein b (t) represents the instantaneous gas production at the moment t in the semi-continuous feeding mode;
Figure SMS_62
，/>
Figure SMS_63
indicating the ith dosing time, +.>
Figure SMS_64
Indicating the current time t and the ith feeding time +.>
Figure SMS_65
Is a time difference of (2).
Time series
Figure SMS_66
As the input of the semi-continuous instantaneous gas production model, taking the instantaneous gas production y as the output, B 0 And c, k are parameters which need to be learned by data, and training the semi-continuous instantaneous gas production model. Let the real instantaneous gas production be y, the semi-continuous instantaneous gas production model output predicted gas production y, the minimizing objective function of model (3) is:
Figure SMS_67
（4）
for parameter B 0 C, k are subjected to numerical range limitation, B 0 ∈[B 1 ,B 2 ]，c∈[c 1 ,c 2 ]，k∈[k 1 ,k 2 ]Establishing an equality transformation, and enabling:
Figure SMS_68
（5-1）/>
Figure SMS_69
（5-2）
Figure SMS_70
（5-3）
wherein ,
Figure SMS_71
，/>
Figure SMS_72
，/>
Figure SMS_73
，/>
Figure SMS_74
，/>
Figure SMS_75
，
Figure SMS_76
。
bringing equations (5-1), (5-2) and (5-3) into equation (4) modifies the minimization objective function to an unconstrained objective function:
Figure SMS_77
（6）
solving the optimal by adopting a gradient descent method
Figure SMS_78
、/>
Figure SMS_79
、/>
Figure SMS_80
：
Figure SMS_81
Figure SMS_82
Figure SMS_83
Figure SMS_84
Figure SMS_85
Figure SMS_86
/>
Figure SMS_87
（7）
Bringing the optimal solution obtained in the formula (7) into the formula (6), and calculating to obtain
Figure SMS_88
Presetting a lower error limit->
Figure SMS_89
。
If it is
Figure SMS_90
The model (3) can achieve higher prediction performance, and the optimal solution obtained by the formula (7) can be brought into the formulas (5-1), (5-2) and (5-3) to solve the optimal B 0 、c、k。
If it is
Figure SMS_91
Calculating +.>
Figure SMS_92
、/>
Figure SMS_93
、/>
Figure SMS_94
For a given->
Figure SMS_95
、/>
Figure SMS_96
、/>
Figure SMS_97
Negative gradient adjustment is performed to form a new solution:
Figure SMS_98
Figure SMS_99
Figure SMS_100
wherein ,
Figure SMS_101
for a given learning rate; re-bringing the new solution into equation (6) to calculate +.>
Figure SMS_102
Up to->
Figure SMS_103
。
And 2, optimizing the production, storage and supply of the biogas station according to the biogas production capacity.
Based on historical data, the ARIMA model is used for predicting the future (e.g. 10 days in the future) gas consumption of the user side:
Figure SMS_104
Figure SMS_106
indicate->
Figure SMS_112
Tian (heaven)>
Figure SMS_115
Indicate->
Figure SMS_108
Air consumption in the days>
Figure SMS_109
Indicate->
Figure SMS_113
Air consumption in the days>
Figure SMS_116
Indicate->
Figure SMS_107
Air consumption in the day; c. and (2)>
Figure SMS_111
、...、/>
Figure SMS_114
、/>
Figure SMS_117
、...、/>
Figure SMS_105
Is a coefficient for model fitting; predicting +.f using historical data from the previous days>
Figure SMS_110
Daily gas consumption.
Referring to fig. 2, the uppermost curve of the hatched portion represents the upper limit of the predicted gas consumption, the middle curve represents the predicted gas consumption, and the lowermost curve represents the lower limit of the predicted gas consumption.
Constructing production, storage and supply linkage constraint conditions:
constraint condition 1, wherein the sum of the gas yield of the previous day and the gas amount of the gas storage tank is larger than the sum of the gas consumption of the next day and the minimum gas amount of the gas storage tank;
constraint condition 2, the gas storage tank gas amount does not exceed the designed highest gas amount;
constraint condition 3, the air quantity of the air storage tank is not lower than the designed minimum air quantity.
The design of the highest gas volume and the design of the lowest gas volume refer to the design of the highest gas volume and the lowest gas volume of the gas storage tank.
Based on the constraint conditions, full space exploration of feasible solutions is performed to determine a feasible scheme of future gas consumption. Generally, when the current time t and the first feeding time
Figure SMS_118
Time difference of->
Figure SMS_119
On the day, the biogas generated by the batch is negligible. For a future 10 day schedule, if the day's feed is denoted as "1" and if no feed is denoted as "0", the data range is [0000000000 1111111111 ]]In total 2 10 =1024 possible production scheduling schemes. And (3) calling the formula, calculating the gas production amount of 10 days in the future for each production scheduling scheme, and predicting the feasible scheme of the gas consumption amount of 10 days in the future by combining the load.
Selecting an optimal scheme, screening 1024 production scheduling schemes according to constraint conditions, enabling the production scheduling schemes meeting the constraint conditions to be set A, referring to FIG. 3, wherein the upper curve represents the sum of the gas production amount of the previous day and the gas storage tank gas amount, the lower curve represents the sum of the gas consumption amount of the next day and the minimum gas amount of the gas storage tank, 2 measures of the set A are defined as (d 1, d 2), the measure d1 is the shortest distance between the two curves, and the measure d2 is the farthest distance between the two curves.
In the set A, d1 is the highest guaranteed gas consumption scheme, and d2 is the lowest optimized gas storage scheme. D1 descending order sorting is respectively carried out on the set A, d2 ascending order sorting is carried out on the set A, and the optimal production/storage/supply linkage optimization scheme with the minimum sum of two sorting serial numbers is adopted.
For example: the set a= [ (3, a), (2, b), (1, h), (4, g), (5, c) ] has 2 ordering means for ordering the numbers (i.e. d 1) in descending order [ (5, c), (4, g), (3, a), (2, b), (1, h) ], and for the letters (i.e. d 2) in ascending order [ (3, a), (2, b), (5, c), (4, g), (1, h) ]. It can be seen that the sequence numbers of (5, c) add to 5+3=8 (i.e. d1=5 is numbered 1 and d2=3 is numbered 3); the sequence number addition of (4, g) is 4+4=8; the sequence number addition of (3, a) is 3+1=4; the sequence number addition of (2, b) is 2+2=4; the sum of the sequence numbers of (1, h) is 1+5=6. And taking the smallest sequence number addition as (2, b) and (3, a), and randomly selecting one scheme as an optimal scheme if a plurality of schemes with the smallest sequence number addition are obtained.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (5)

1. A method for regulating and optimizing production, storage and supply linkage of a semi-continuous biogas station is characterized by comprising the following steps: the method comprises the following steps:
step 1, learning biogas production capacity of a biogas station based on historical data, and constructing a semi-continuous instantaneous biogas production model of n times of feeding; training a semicontinuous instantaneous gas production model by using a time sequence;
and 2, predicting future gas consumption of the user side, screening a production scheduling scheme through constraint conditions, and selecting an optimal production scheduling scheme as a production and storage linkage optimization scheme.
2. The method for generating, storing and supplying linkage optimization of the semi-continuous biogas station according to claim 1, which is characterized in that: in the step 1, learning biogas production capacity of a biogas station based on historical data, and constructing a semi-continuous instantaneous biogas production model of n times of feeding, wherein the step comprises the following steps:
building a single-feeding biogas accumulation gas production model:
Figure QLYQS_1
（1）
wherein B (t) represents the accumulated gas yield at time t; b (B) 0 Representing the final gas yield given by the total amount of the fed materials and the fermentation formula; t represents the current time; t represents the feeding time; k represents a methane degradation constant; c represents the shape factor of the S-shaped curve,
Figure QLYQS_2
；
based on equation (1), a semi-continuous instantaneous gas production model is established for n charges:
Figure QLYQS_3
（3）
wherein b (t) represents the instantaneous gas production at the moment t in the semi-continuous feeding mode;
Figure QLYQS_4
，/>
Figure QLYQS_5
indicating the ith dosing time, +.>
Figure QLYQS_6
Indicating the current time t and the ith feeding time +.>
Figure QLYQS_7
Is a time difference of (2).
3. The method for generating, storing and supplying linkage optimization of the semi-continuous biogas station according to claim 2, which is characterized in that: in the step 1, the training of the semi-continuous instantaneous gas production model by using a time sequence comprises the following steps:
time series
Figure QLYQS_8
The semi-continuous instantaneous gas production model is input, the instantaneous gas production y is taken as output, and the semi-continuous instantaneous gas production model is trained;
let the real instantaneous gas yield be y, the semi-continuous instantaneous gas yield model output predicted gas yield y, the unconstrained objective function of the semi-continuous instantaneous gas yield model is:
Figure QLYQS_9
（6）
wherein ,
Figure QLYQS_10
，/>
Figure QLYQS_11
，/>
Figure QLYQS_12
；
solving the optimal by adopting a gradient descent method
Figure QLYQS_13
、/>
Figure QLYQS_14
、/>
Figure QLYQS_15
：/>
Figure QLYQS_16
（7）
wherein ：
Figure QLYQS_17
Figure QLYQS_18
Figure QLYQS_19
Figure QLYQS_20
Figure QLYQS_21
Figure QLYQS_22
presetting a lower error limit
Figure QLYQS_23
；
If it is
Figure QLYQS_24
Obtain the optimal B 0 、c、k；
If it is
Figure QLYQS_25
Calculating +.>
Figure QLYQS_26
、/>
Figure QLYQS_27
、/>
Figure QLYQS_28
For a given->
Figure QLYQS_29
、/>
Figure QLYQS_30
、/>
Figure QLYQS_31
Negative gradient adjustment is performed to form a new solution:
Figure QLYQS_32
Figure QLYQS_33
Figure QLYQS_34
wherein ,
Figure QLYQS_35
for a given learning rate; up to->
Figure QLYQS_36
。/>
4. The method for generating, storing and supplying linkage optimization of the semi-continuous biogas station according to claim 2, which is characterized in that: in the step 2, the step of predicting the future gas consumption of the user terminal includes:
based on historical data, predicting future gas consumption of the user side by using an ARIMA model:
Figure QLYQS_37
Figure QLYQS_40
indicate->
Figure QLYQS_44
Tian (heaven)>
Figure QLYQS_47
Indicate->
Figure QLYQS_39
Air consumption in the days>
Figure QLYQS_43
Indicate->
Figure QLYQS_46
Air consumption in the days>
Figure QLYQS_49
Represent the first
Figure QLYQS_38
Air consumption in the day; c. and (2)>
Figure QLYQS_42
、...、/>
Figure QLYQS_45
、/>
Figure QLYQS_48
、...、/>
Figure QLYQS_41
Is the coefficient used for model fitting.
5. The method for generating, storing and supplying linkage optimization of the semi-continuous biogas station according to claim 4, which is characterized in that: in the step 2, the production scheduling scheme is screened through constraint conditions, and the optimal production scheduling scheme is selected as a production, storage and supply linkage scheduling scheme, which comprises the following steps:
constructing production, storage and supply linkage constraint conditions:
constraint condition 1, wherein the sum of the gas yield of the previous day and the gas amount of the gas storage tank is larger than the sum of the gas consumption of the next day and the minimum gas amount of the gas storage tank;
constraint condition 2, the gas storage tank gas amount does not exceed the designed highest gas amount;
constraint condition 3, the air quantity of the air storage tank is not lower than the designed minimum air quantity;
when the current time t and the first feeding time
Figure QLYQS_50
Time difference of->
Figure QLYQS_51
Together with the total of 2 z A seed production scheduling scheme;
pair 2 according to constraint z Screening a production scheduling scheme, namely, enabling the production scheduling scheme meeting constraint conditions to be a set A, defining 2 metrics of the set A as (d 1, d 2), wherein the metric d1 is the shortest distance between two curves, the metric d2 is the farthest distance between the two curves, the first curve represents the sum of the gas production amount of the previous day and the gas storage tank, and the second curve represents the sum of the gas consumption amount of the next day and the minimum gas storage tank;
in the set A, d1 is the highest-level guaranteed gas consumption scheme, and d2 is the lowest-level optimized gas storage scheme; d1 descending order sorting is respectively carried out on the set A, d2 ascending order sorting is carried out on the set A, and the optimal production, storage and supply linkage optimization scheme with the smallest sum of two sorting serial numbers is adopted.
CN202310371555.6A 2023-04-10 2023-04-10 Method for production, storage and supply linkage optimization of semi-continuous biogas station Active CN116090358B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310371555.6A CN116090358B (en) 2023-04-10 2023-04-10 Method for production, storage and supply linkage optimization of semi-continuous biogas station

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310371555.6A CN116090358B (en) 2023-04-10 2023-04-10 Method for production, storage and supply linkage optimization of semi-continuous biogas station

Publications (2)

Publication Number Publication Date
CN116090358A true CN116090358A (en) 2023-05-09
CN116090358B CN116090358B (en) 2023-06-23

Family

ID=86212375

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310371555.6A Active CN116090358B (en) 2023-04-10 2023-04-10 Method for production, storage and supply linkage optimization of semi-continuous biogas station

Country Status (1)

Country Link
CN (1) CN116090358B (en)

Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101701224A (en) * 2009-12-08 2010-05-05 湖南省土壤肥料研究所 Fermented material for producing methane
EP2559750A1 (en) * 2011-08-18 2013-02-20 Olaf Kujawski System-wide control and regulation method for biogas assemblies
CN103627627A (en) * 2013-11-29 2014-03-12 沈阳大学 Household biogas system
CN107058399A (en) * 2017-06-05 2017-08-18 合肥嘉仕诚能源科技有限公司 A kind of method that utilization market of farm produce abandoned vegetable mixed fermentation produces biogas
CN107967537A (en) * 2017-11-27 2018-04-27 湖南大学 The energy management method and device of a kind of micro- energy net in scene natural pond
CN109215743A (en) * 2018-09-04 2019-01-15 南京工业大学 A kind of prediction technique of the biogas production process based on New BP Neural neural net model establishing
US20200074307A1 (en) * 2018-09-05 2020-03-05 WEnTech Solutions Inc. System and method for anaerobic digestion process assessment, optimization and/or control
CN112215464A (en) * 2020-09-04 2021-01-12 北京天泽智云科技有限公司 Prediction balance scheduling system for blast furnace gas under multiple working conditions
CN114239224A (en) * 2021-11-19 2022-03-25 上海电力设计院有限公司 Marsh gas production rate optimization algorithm taking livestock and poultry manure as raw material
CN114492041A (en) * 2022-01-27 2022-05-13 河北农业大学 Optimization method and system for biogas cogeneration system of farm
CN115330021A (en) * 2022-07-12 2022-11-11 国网江苏省电力有限公司新沂市供电分公司 Comprehensive energy operation optimization system and method considering methane electric heat utilization ratio

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101701224A (en) * 2009-12-08 2010-05-05 湖南省土壤肥料研究所 Fermented material for producing methane
EP2559750A1 (en) * 2011-08-18 2013-02-20 Olaf Kujawski System-wide control and regulation method for biogas assemblies
CN103627627A (en) * 2013-11-29 2014-03-12 沈阳大学 Household biogas system
CN107058399A (en) * 2017-06-05 2017-08-18 合肥嘉仕诚能源科技有限公司 A kind of method that utilization market of farm produce abandoned vegetable mixed fermentation produces biogas
CN107967537A (en) * 2017-11-27 2018-04-27 湖南大学 The energy management method and device of a kind of micro- energy net in scene natural pond
CN109215743A (en) * 2018-09-04 2019-01-15 南京工业大学 A kind of prediction technique of the biogas production process based on New BP Neural neural net model establishing
US20200074307A1 (en) * 2018-09-05 2020-03-05 WEnTech Solutions Inc. System and method for anaerobic digestion process assessment, optimization and/or control
CN112215464A (en) * 2020-09-04 2021-01-12 北京天泽智云科技有限公司 Prediction balance scheduling system for blast furnace gas under multiple working conditions
CN114239224A (en) * 2021-11-19 2022-03-25 上海电力设计院有限公司 Marsh gas production rate optimization algorithm taking livestock and poultry manure as raw material
CN114492041A (en) * 2022-01-27 2022-05-13 河北农业大学 Optimization method and system for biogas cogeneration system of farm
CN115330021A (en) * 2022-07-12 2022-11-11 国网江苏省电力有限公司新沂市供电分公司 Comprehensive energy operation optimization system and method considering methane electric heat utilization ratio

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
XIAOHUI LI 等: "Prediction model of biogas production for anaerobic digestion process of food waste based on LM-BP neural network and particle swarm algorithm optimization", 2017 CHINESE AUTOMATION CONGRESS (CAC) *
刘轶鋆 等: "禽粪与麦秸协同厌氧发酵体系的需求导向产沼气性能及模型预测", 科学技术与工程, vol. 22, no. 32 *
吴财芳;姚帅;杜严飞;: "基于时间序列BP神经网络的煤层气井排采制度优化", 中国矿业大学学报, vol. 44, no. 01 *
花亚梅;赵贤林;王效华;滕昆辰;: "基于改进BP神经网络的厌氧发酵产气量预测模型", 环境工程学报, vol. 10, no. 10 *

Also Published As

Publication number Publication date
CN116090358B (en) 2023-06-23

Similar Documents

Publication Publication Date Title
KR101500304B1 (en) A control method of charging and discharging of energy storage and system for it
CN111030188B (en) Hierarchical control strategy containing distributed and energy storage
CN112467730B (en) Power system optimal scheduling method considering wind-solar output prediction error and demand response flexibility
CN111668877A (en) Active power distribution network distribution robust optimization scheduling method and system
CN107203855B (en) Robust two-layer optimization model containing real-time scheduling of wind power system and conversion equivalent method
CN111950808B (en) Comprehensive energy system random robust optimization operation method based on comprehensive demand response
CN112651634A (en) Active power distribution system source network load storage day-ahead active scheduling method based on sequence operation
CN109886446A (en) Based on the Electrical Power System Dynamic economic load dispatching method for improving Chaos particle swarm optimization algorithm
CN116090358B (en) Method for production, storage and supply linkage optimization of semi-continuous biogas station
CN117057491A (en) Rural area power supply optimization management method based on combination of MPC and energy storage system
CN115471074A (en) Planning model construction method of comprehensive energy microgrid
CN109995076A (en) A kind of photovoltaic based on energy storage collects system power and stablizes output cooperative control method
CN113869742A (en) Power dispatching system of comprehensive supply and demand side based on mobile home and critic network
CN111539549B (en) Water-energy-grain tie relation prediction method based on resource life cycle process
CN116776538A (en) Uncertainty-considered low-carbon planning method for electric power-natural gas-hydrogen coupling network
CN117013522A (en) Comprehensive energy system scheduling optimization method considering distributed power supply and gas-electricity cooperation
CN115829091A (en) Industrial process microgrid planning method considering renewable energy supply
CN115864409A (en) Power grid section power adjustment strategy based on deep reinforcement learning
CN114493090A (en) Intelligent control method and device for comprehensive energy of industrial park
CN112068516B (en) Gas boiler cooperative operation method based on precise control technology
CN111082442B (en) Energy storage capacity optimal configuration method based on improved FPA
CN107273619B (en) Steel enterprise static energy flow network optimization design method
CN117094507B (en) Method and system for planning agricultural industry building comprehensive energy based on biomass resources
CN116415740B (en) Two-stage robust optimization method for virtual power plant based on saddle uncertainty
CN115640894B (en) Comprehensive energy system optimal scheduling method based on double time scales

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant