CN114819743A - Chemical enterprise energy consumption diagnosis and analysis method - Google Patents
Chemical enterprise energy consumption diagnosis and analysis method Download PDFInfo
- Publication number
- CN114819743A CN114819743A CN202210618538.3A CN202210618538A CN114819743A CN 114819743 A CN114819743 A CN 114819743A CN 202210618538 A CN202210618538 A CN 202210618538A CN 114819743 A CN114819743 A CN 114819743A
- Authority
- CN
- China
- Prior art keywords
- energy consumption
- parameter
- process parameters
- parameters
- target
- 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
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/04—Manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Software Systems (AREA)
- Educational Administration (AREA)
- Tourism & Hospitality (AREA)
- Development Economics (AREA)
- General Business, Economics & Management (AREA)
- Game Theory and Decision Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Manufacturing & Machinery (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Artificial Intelligence (AREA)
- Quality & Reliability (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Operations Research (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The invention discloses a chemical enterprise energy consumption diagnosis and analysis method, which relates to the technical field of energy consumption diagnosis and analysis and comprises the steps of obtaining a data set of each process parameter through a database; determining a process parameter related to the target energy consumption amount and a corresponding parameter interval through the data set; acquiring weights corresponding to various process parameters related to the target energy consumption amount, and determining combined parameters related to the target energy consumption amount and corresponding parameter intervals; and comparing the acquired real-time data of the process parameters with the parameter interval of the combined parameters, and determining the process parameters influencing the target energy consumption. The invention solves the problem of the industrial scene through machine learning and realizes the planning of energy-saving measures.
Description
Technical Field
The invention relates to the technical field of energy consumption diagnosis and analysis, in particular to an energy consumption diagnosis and analysis method for chemical enterprises.
Background
The ammonia distillation system of a part of alkali making processes in chemical production has large energy consumption, short continuous operation time and insufficient ammonia water liquid phase separation, thereby causing the waste of steam heat energy, having high ammonia content in wastewater and increasing the sewage treatment cost. The measurement shows that the temperature of the ammonia distillation waste liquid is higher than 1 ℃, and the heat loss is about 16.25kg/t steam amount. Production personnel often judge when the pressure is reduced, the degree of the reduced pressure and the opening of the regulating valve according to experience values, abundant experience cannot be passed, and meanwhile, the general estimation of operation instead of accurate execution exists, and data support cannot be provided for energy conservation.
At present, when the liquid level of the flow meter is usually measured by an enterprise to a half, the regulating valve is used for controlling the steam supply amount, the current working condition and the energy consumption condition are not connected together, the energy consumption fluctuation can only know whether special operation is carried out or not afterwards, but only the general condition is that actual data support cannot be obtained, and energy-saving measures cannot be carried out.
Disclosure of Invention
The invention aims to solve the technical problem of overcoming the defects of the prior art and provides a method for diagnosing and analyzing the energy consumption of chemical enterprises.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a method for diagnosing and analyzing the energy consumption of chemical enterprises includes,
acquiring a data set of each process parameter through a database;
determining a process parameter related to the target energy consumption amount and a corresponding parameter interval through the data set;
acquiring weights corresponding to various process parameters related to the target energy consumption amount, and determining combined parameters related to the target energy consumption amount and corresponding parameter intervals;
and comparing the acquired real-time data of the process parameters with the parameter interval of the combined parameters, and determining the process parameters influencing the target energy consumption.
As a preferred scheme of the chemical industry enterprise energy consumption diagnosis and analysis method, the method comprises the following steps: the determining of the process parameter associated with the target amount of energy consumption and the corresponding parameter interval from the data set comprises,
determining an abnormal threshold value of the energy consumption target quantity by using a 3 sigma model based on Gaussian distribution, and determining a threshold value range of the energy consumption target quantity according to the abnormal threshold value;
regarding any process parameter, taking 3 sigma in a 3 sigma model as a threshold, setting data larger than the threshold in a data set as a label 1, setting data smaller than the threshold in the data set as a label 0, and grouping the data in a chi-square binning mode;
and for the grouped data, woe is adopted to calculate the correlation between the independent variable and the dependent variable, and the IV value of each group is obtained, so that the process parameter related to the target energy consumption amount and the corresponding parameter interval are determined.
As a preferred scheme of the chemical industry enterprise energy consumption diagnosis and analysis method, the method comprises the following steps: the abnormal threshold value of the energy consumption target quantity is determined by utilizing a 3 sigma model based on Gaussian distribution, and the threshold value range of the energy consumption target quantity is determined by the abnormal threshold value,
a Gaussian distribution function of
Where μ is desired and σ is standard deviation;
when the threshold exceeds 3 sigma, the threshold is regarded as an abnormal threshold, and the threshold range of the energy consumption target amount is determined to be [ mu-3 sigma, mu +3 sigma ].
As a preferred scheme of the chemical industry enterprise energy consumption diagnosis and analysis method, the method comprises the following steps: the correlation between the independent variable and the dependent variable is calculated by adopting woe for the grouped data, and the IV value of each group is obtained, so as to determine the process parameter related to the target energy consumption and the corresponding parameter interval,
by the formula:
calculating the correlation of independent variable and dependent variable, wherein # y i For the number of individuals labeled 1 in this group, # n i For the number of individuals labeled 0 in this group, # y T For the number of individuals with all tags 1 in the corresponding process parameters, # n T For the number of individuals, py, corresponding to a 0 tag for all process parameters i For a tag of 1 in the packetThe proportion of the individuals to the label of 1 in all the process parameters, pn i The proportion of the individuals with the labels of 0 in the group in all the process parameters with the labels of 0 is calculated;
by the formula:
calculating IV value of the ith group;
and determining the process parameters related to the energy consumption target amount and the corresponding parameter intervals according to the IV values of the groups in the calculated process parameters.
As a preferred scheme of the chemical industry enterprise energy consumption diagnosis and analysis method, the method comprises the following steps: the weight corresponding to each process parameter related to the target amount of energy consumption is obtained, and the combined parameter related to the target amount of energy consumption and the corresponding parameter interval are determined according to the weight,
carrying out correlation verification on various process parameters related to the energy consumption target quantity, and rejecting part of process parameters with correlation larger than a set value;
training the rest process parameters by adopting an LASSO regression model, and determining the weight corresponding to each process parameter;
by the formula X ═ W 1 X 1 +W 2 X 2 +…+W n Xn + b determines a combination parameter related to the target amount of energy consumption, wherein X is the combination parameter, Xn is the nth process parameter, and W is the n The weight corresponding to the nth process parameter is used, and b is a bias term;
taking 3 sigma in the 3 sigma model as a threshold, determining data larger than the threshold in the combined parameters as a label 1, determining data smaller than the threshold in the combined parameters as a label 0, and then grouping the data by using a chi-square binning mode;
for the grouped data, woe is adopted to calculate the correlation between independent variable and dependent variable, and the IV value of each group is obtained, so as to determine the parameter interval of the combined parameter.
As a preferred scheme of the chemical industry enterprise energy consumption diagnosis and analysis method, the method comprises the following steps: the correlation check is carried out between each technical parameter related to the energy consumption target quantity, and the part of technical parameters with the correlation larger than a set value is rejected,
according to the formula:
calculating a correlation coefficient between two process parameters, wherein X i In order to obtain the process parameters of item i,
is X i Average value of (d);
and when the correlation coefficient between the two process parameters is larger than a set value, removing one process parameter.
As a preferred scheme of the chemical industry enterprise energy consumption diagnosis and analysis method, the method comprises the following steps: the cost function of the LASSO regression model is
Where w is a vector of length n, excluding the coefficient θ of the intercept term 0 Theta is a vector of length n +1, including the coefficient theta of the intercept term 0 M is the number of technological parameters, n is a characteristic number, | w | | luminance 1 Is the L1 norm of the process parameter w.
As a preferred scheme of the chemical industry enterprise energy consumption diagnosis and analysis method, the method comprises the following steps: after comparing the acquired real-time data of the process parameters with the parameter interval of the combined parameters and determining the process parameters influencing the target energy consumption, the method also comprises the following steps,
and collecting process parameters periodically, training through a regression model, and updating the combination parameters and the corresponding parameter intervals.
As a preferred scheme of the chemical industry enterprise energy consumption diagnosis and analysis method, the method comprises the following steps: the method comprises the steps of periodically collecting process parameters, training through a regression model, updating combination parameters and corresponding parameter intervals,
periodically acquiring real-time data of each process parameter;
and judging whether the real-time data of the process parameters are within the threshold range of the target energy consumption amount, if so, comparing the real-time data of the process parameters with the parameter interval of the combined parameters to determine the process parameters influencing the target energy consumption amount, and if not, updating the real-time data of the process parameters and the energy consumption data to a database.
As a preferred scheme of the chemical industry enterprise energy consumption diagnosis and analysis method, the method comprises the following steps: the target energy consumption amount is the steam instantaneous flow.
The invention has the beneficial effects that:
the invention solves the problem of the industrial scene through machine learning and realizes the planning of energy-saving measures. The parameter sample data is used as the input of machine learning, the training of the model can be realized, and the trained model can be used for diagnosing the ammonia distillation process. Compared with manual analysis, after various production processes are optimized and adjusted, machine learning continuously performs data learning capacity through continuous data acquisition, knowledge base enrichment and model training, and abnormal energy consumption can be diagnosed more quickly and accurately.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a schematic flow chart of a chemical industry enterprise energy consumption diagnosis and analysis method provided by the present invention;
fig. 2 is a schematic specific flowchart of step S102 in the chemical industry enterprise energy consumption diagnosis and analysis method provided by the present invention.
Detailed Description
In order that the present invention may be more readily and clearly understood, a more particular description of the invention briefly described above will be rendered by reference to specific embodiments that are illustrated in the appended drawings.
Fig. 1 is a schematic flow chart of a chemical industry enterprise energy consumption diagnosis and analysis method provided in an embodiment of the present application. The method comprises the following steps of S101 to S105, wherein the specific steps are as follows:
step S101: and acquiring a data set of each process parameter through a database.
Specifically, taking an ammonia distillation system as an example, when the ammonia distillation system works, the data of each process parameter under the working condition is collected, the collected data is stored in a database, and the stored data set of each process parameter can be directly called from the database at the later stage. It will be appreciated that the data set of process parameters includes process parameters collected at different time periods.
Step S102: the process parameters related to the instantaneous flow of steam (target amount of energy consumption) and the corresponding parameter intervals are determined from the data set.
Referring to fig. 2, the steps specifically include the following steps:
step S102 a: and determining an abnormal threshold value of the steam instantaneous flow by using a 3 sigma model based on Gaussian distribution, and determining a threshold value range of the steam instantaneous flow.
Specifically, assuming each time is independent and has no context, a 3 σ model may be employed to detect anomalous intervals. It should be noted that the data need to follow a normal distribution.
A Gaussian distribution function of
Where μ is desired and σ is the standard deviation. Under the 3 σ rule, an outlier, such as one exceeding 3 times the standard deviation, can be considered to be an outlier. In fact, the values are distributed over [ mu-3 sigma, [ mu +3 sigma ]]Is 99.7%, the probability of occurrence of values other than the 3 σ distance average value is P (| x- μ>3 σ) is 0.03, which is a very individual small probability time. The threshold range of the instantaneous flow of the steam is determined to be [ mu-3 sigma, mu +3 sigma ] from the above]。
Step S102 b: for any process parameter, 3 sigma in the 3 sigma model is used as a threshold, data larger than the threshold in the data set is determined as a label 1, data smaller than the threshold in the data set is determined as a label 0, and then the data are grouped in a chi-square binning mode.
Specifically, the Chi-Square binning mode is a binning method depending on Chi-Square test, Chi-Square statistics (Chi-Square) is selected on statistical indexes for judgment, the basic idea of binning is to judge whether two adjacent intervals have distribution difference, and merging is performed from bottom to top based on the result of Chi-Square statistics until the binning limit condition is met.
Step S102 c: and for the grouped data, woe is adopted to calculate the correlation between independent variables and dependent variables, and the IV value of each group is obtained, so that the process parameters related to the steam instantaneous flow and the corresponding parameter interval are determined.
Specifically, after grouping, for the ith group, by the formula:
calculating the correlation of independent variable and dependent variable, wherein # y i For the number of individuals labeled 1 in this group, # n i For the number of individuals labeled 0 in this group, # y T For the number of individuals with all tags 1 in the corresponding process parameters, # n T For the number of individuals, py, corresponding to a 0 tag for all process parameters i The ratio of the individuals labeled with 1 in the group to the labels of 1 in all the process parameters is represented by pn i The proportion of individuals labeled 0 in this group to all process parameters labeled 0.
Then by the formula:
IV values for the ith group are calculated. It will be appreciated that the greater the value of IV, the greater the predictive power of the dependent variable for that group (parameter interval). Therefore, the process parameters related to the steam instantaneous flow and the corresponding parameter intervals can be determined according to the IV values of the groups in the process parameters obtained through calculation.
By taking pressure parameters as an example, the pressure parameters are divided into three groups of 10-50 Pa, 50-100 Pa and 100-150 Pa, wherein the determined pressure parameter interval is the interval of 50-100 Pa when only 50-100 Pa influences the instantaneous flow of steam.
Step S103: and acquiring the weight corresponding to each process parameter related to the steam instantaneous flow, and determining the combined parameter related to the steam instantaneous flow and the corresponding parameter interval according to the weight.
Specifically, correlation verification is performed on various process parameters related to the steam instantaneous flow, and part of process parameters with correlation larger than a set value are removed.
According to the formula:
calculating a correlation coefficient between two process parameters, wherein X i In order to provide the process parameters of item i,
is X i Average value of (a).
If the two process parameters have strong correlation, multiple collinearity may exist, and part of the indexes need to be removed, and when the correlation coefficient between the two process parameters is larger than a set value, one process parameter is removed.
Then, considering that the Lasso regression can train parameters of some features with smaller effects to be 0, so as to obtain sparse solution, the Lasso regression model is adopted to train the remaining process parameters, and the weights corresponding to all the process parameters are determined. Wherein the cost function of the LASSO regression model is
Where w is a vector of length n, excluding the coefficient θ of the intercept term 0 Theta is a vector of length n +1, including the coefficient theta of the intercept term 0 M is the number of technological parameters, n is a characteristic number, | w | | luminance 1 Is the L1 norm of the process parameter w.
After the weights of all the process parameters are determined, the process parameters are processed according to the formula X ═ W 1 X 1 +W 2 X 2 +…+W n Xn + b determines a combination parameter related to the target amount of energy consumption, wherein X is the combination parameter, Xn is the nth process parameter, and W is the n Is the weight corresponding to the nth process parameter, and b is the offsetTerms, output by the model.
And then, taking 3 sigma in the 3 sigma model as a threshold, setting data which are larger than the threshold in the combined parameters as a label 1, setting data which are smaller than the threshold in the combined parameters as a label 0, grouping the data of the combined parameters by using a chi-square binning mode, calculating the correlation between independent variables and dependent variables of the grouped data by adopting woe, and acquiring IV values of all groups, thereby determining the parameter interval of the combined parameters.
Step S104: and comparing the acquired real-time data of the process parameters with the parameter interval of the combined parameters, and determining the process parameters influencing the target energy consumption.
Specifically, the acquired real-time data of the process parameters are compared with the parameter interval of the combined parameters, the comparison result is output, and the process parameters influencing the target energy consumption are determined according to the comparison result.
Step S105: and collecting process parameters periodically, training through a regression model, and updating the combination parameters and the corresponding parameter intervals.
Step S106: real-time data of various process parameters are periodically acquired.
Step S107: and judging whether the real-time data of the process parameters are within the threshold range of the target energy consumption amount, if so, comparing the real-time data of the process parameters with the parameter interval of the combined parameters to determine the process parameters influencing the target energy consumption amount, and if not, updating the real-time data of the process parameters and the energy consumption data to a database.
Therefore, the technical scheme of the application solves the problem of the industrial scene through machine learning, and realizes the planning of energy-saving measures. The parameter sample data is used as the input of machine learning, the training of the model can be realized, and the trained model can be used for diagnosing the ammonia distillation process. Compared with manual analysis, after various production processes are optimized and adjusted, machine learning continuously performs data learning capacity through continuous data acquisition, knowledge base enrichment and model training, and abnormal energy consumption can be diagnosed more quickly and accurately.
In addition to the above embodiments, the present invention may have other embodiments; all technical solutions formed by adopting equivalent substitutions or equivalent transformations fall within the protection scope of the claims of the present invention.
Claims (10)
1. A chemical industry enterprise energy consumption diagnosis analysis method is characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
acquiring a data set of each process parameter through a database;
determining a process parameter related to the target energy consumption amount and a corresponding parameter interval through the data set;
acquiring weights corresponding to various process parameters related to the target energy consumption amount, and determining combined parameters related to the target energy consumption amount and corresponding parameter intervals;
and comparing the acquired real-time data of the process parameters with the parameter interval of the combined parameters, and determining the process parameters influencing the target energy consumption.
2. The chemical industry enterprise energy consumption diagnosis and analysis method according to claim 1, wherein: the determining of the process parameter associated with the target amount of energy consumption and the corresponding parameter interval from the data set comprises,
determining an abnormal threshold value of the energy consumption target quantity by using a 3 sigma model based on Gaussian distribution, and determining a threshold value range of the energy consumption target quantity according to the abnormal threshold value;
regarding any process parameter, taking 3 sigma in a 3 sigma model as a threshold, setting data larger than the threshold in a data set as a label 1, setting data smaller than the threshold in the data set as a label 0, and grouping the data in a chi-square binning mode;
and for the grouped data, woe is adopted to calculate the correlation between the independent variable and the dependent variable, and the IV value of each group is obtained, so that the process parameter related to the target energy consumption amount and the corresponding parameter interval are determined.
3. The chemical industry enterprise energy consumption diagnosis and analysis method according to claim 2, wherein: the abnormal threshold value of the energy consumption target quantity is determined by utilizing a 3 sigma model based on Gaussian distribution, and the threshold value range of the energy consumption target quantity is determined by the abnormal threshold value,
a Gaussian distribution function of
Where μ is desired and σ is standard deviation;
when the threshold exceeds 3 sigma, the threshold is regarded as an abnormal threshold, and the threshold range of the energy consumption target amount is determined to be [ mu-3 sigma, mu +3 sigma ].
4. The chemical industry enterprise energy consumption diagnosis and analysis method according to claim 2, wherein: the correlation between the independent variable and the dependent variable is calculated by adopting woe for the grouped data, and the IV value of each group is obtained, so as to determine the process parameter related to the target energy consumption and the corresponding parameter interval,
by the formula:
calculating the correlation of independent variable and dependent variable, wherein # y i For the number of individuals labeled 1 in this group, # n i For the number of individuals labeled 0 in this group, # y T For the number of individuals with all tags 1 in the corresponding process parameters, # n T For the number of individuals, py, corresponding to a 0 tag for all process parameters i The ratio of the individuals labeled with 1 in the group to the labels of 1 in all the process parameters is represented by pn i The proportion of the individuals with the labels of 0 in the group in all the process parameters with the labels of 0 is calculated;
by the formula:
calculating IV value of the ith group;
and determining the process parameters related to the energy consumption target amount and the corresponding parameter intervals according to the IV values of the groups in the calculated process parameters.
5. The chemical industry enterprise energy consumption diagnosis and analysis method according to claim 1, wherein: the weight corresponding to each process parameter related to the target amount of energy consumption is obtained, and the combined parameter related to the target amount of energy consumption and the corresponding parameter interval are determined according to the weight,
carrying out correlation verification on various process parameters related to the energy consumption target quantity, and rejecting part of process parameters with correlation larger than a set value;
training the rest process parameters by adopting an LASSO regression model, and determining the weight corresponding to each process parameter;
by the formula X ═ W 1 X 1 +W 2 X 2 +…+W n Xn + b determines a combination parameter related to the target amount of energy consumption, wherein X is the combination parameter, Xn is the nth process parameter, and W is the n The weight corresponding to the nth process parameter is used, and b is a bias term;
taking 3 sigma in the 3 sigma model as a threshold, determining data larger than the threshold in the combined parameters as a label 1, determining data smaller than the threshold in the combined parameters as a label 0, and then grouping the data by using a chi-square binning mode;
for the grouped data, woe is adopted to calculate the correlation between independent variable and dependent variable, and the IV value of each group is obtained, so as to determine the parameter interval of the combined parameter.
6. The chemical industry enterprise energy consumption diagnosis and analysis method according to claim 5, wherein: the correlation check is carried out between each technical parameter related to the energy consumption target quantity, and the part of technical parameters with the correlation larger than a set value is rejected,
according to the formula:
calculating a correlation coefficient between two process parameters, wherein X i In order to obtain the process parameters of item i,
is X i Average value of (d);
and when the correlation coefficient between the two process parameters is larger than a set value, removing one process parameter.
7. The chemical industry enterprise energy consumption diagnosis and analysis method according to claim 5, wherein: the cost function of the LASSO regression model is
Where w is a vector of length n, excluding the coefficient θ of the intercept term 0 Theta is a vector of length n +1, including the coefficient theta of the intercept term 0 M is the number of technological parameters, n is a characteristic number, | w | | luminance 1 Is the L1 norm of the process parameter w.
8. The chemical industry enterprise energy consumption diagnosis and analysis method according to claim 2, wherein: after comparing the acquired real-time data of the process parameters with the parameter interval of the combined parameters and determining the process parameters influencing the target energy consumption, the method also comprises the following steps,
and collecting process parameters periodically, training through a regression model, and updating the combination parameters and the corresponding parameter intervals.
9. The chemical industry enterprise energy consumption diagnosis and analysis method according to claim 8, wherein: the method comprises the steps of periodically collecting process parameters, training through a regression model, updating combination parameters and corresponding parameter intervals,
periodically acquiring real-time data of each process parameter;
and judging whether the real-time data of the process parameters are within the threshold range of the target energy consumption amount, if so, comparing the real-time data of the process parameters with the parameter interval of the combined parameters to determine the process parameters influencing the target energy consumption amount, and if not, updating the real-time data of the process parameters and the energy consumption data to a database.
10. The chemical industry enterprise energy consumption diagnosis and analysis method according to any one of claims 1 to 9, characterized in that: the target energy consumption amount is the steam instantaneous flow.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210618538.3A CN114819743B (en) | 2022-06-01 | 2022-06-01 | Energy consumption diagnosis and analysis method for chemical enterprises |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210618538.3A CN114819743B (en) | 2022-06-01 | 2022-06-01 | Energy consumption diagnosis and analysis method for chemical enterprises |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114819743A true CN114819743A (en) | 2022-07-29 |
| CN114819743B CN114819743B (en) | 2023-08-01 |
Family
ID=82519480
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210618538.3A Active CN114819743B (en) | 2022-06-01 | 2022-06-01 | Energy consumption diagnosis and analysis method for chemical enterprises |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114819743B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130080135A1 (en) * | 2011-09-28 | 2013-03-28 | Usa As Represented By The Administrator Of The National Aeronautics And Space Administration | System, tool and method for integrated circuit and component modeling |
| US20200132882A1 (en) * | 2018-10-26 | 2020-04-30 | Tata Consultancy Services Limited | Method and system for online monitoring and optimization of mining and mineral processing operations |
| CN113486520A (en) * | 2021-07-08 | 2021-10-08 | 内蒙古民族大学 | Digital twinning-based dynamic regulation and control method, system and device for cutting technological parameters |
-
2022
- 2022-06-01 CN CN202210618538.3A patent/CN114819743B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130080135A1 (en) * | 2011-09-28 | 2013-03-28 | Usa As Represented By The Administrator Of The National Aeronautics And Space Administration | System, tool and method for integrated circuit and component modeling |
| US20200132882A1 (en) * | 2018-10-26 | 2020-04-30 | Tata Consultancy Services Limited | Method and system for online monitoring and optimization of mining and mineral processing operations |
| CN113486520A (en) * | 2021-07-08 | 2021-10-08 | 内蒙古民族大学 | Digital twinning-based dynamic regulation and control method, system and device for cutting technological parameters |
Non-Patent Citations (5)
| Title |
|---|
| 李鑫等: "基于工艺参数扰动的IC参数成品率多目标优化算法", 《电子学报》 * |
| 李鑫等: "基于工艺参数扰动的IC参数成品率多目标优化算法", 《电子学报》, no. 12, 15 December 2016 (2016-12-15) * |
| 陈超;王楠;于海洋;陈敏;: "基于卡方分箱法和逻辑回归算法的转炉操作工艺评价模型", 材料与冶金学报, no. 02 * |
| 陈超等: "基于卡方分箱法和逻辑回归算法的转炉操作工艺评价模型", 《材料与冶金学报》 * |
| 陈超等: "基于卡方分箱法和逻辑回归算法的转炉操作工艺评价模型", 《材料与冶金学报》, no. 02, 15 May 2019 (2019-05-15) * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114819743B (en) | 2023-08-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113255795B (en) | Equipment state monitoring method based on multi-index cluster analysis | |
| JP4911055B2 (en) | Batch process data analysis apparatus and abnormality detection / quality estimation apparatus using the same | |
| CN111160776A (en) | Detection method of abnormal working conditions in sewage treatment process using block principal component analysis | |
| WO2005024794A3 (en) | Automated identification of anomalous conditions in supply chain processes | |
| CN104808648A (en) | Online and real-time batch process monitoring method based on k nearest neighbor | |
| CN117850375B (en) | Multi-dimensional monitoring system of production line | |
| CN118690306B (en) | Digital twin-based heat accumulating type thermal incinerator system fault prediction method and system | |
| CN102880809A (en) | Polypropylene melt index on-line measurement method based on incident vector regression model | |
| CN114169254A (en) | Abnormal energy consumption diagnosis method and system based on short-term building energy consumption prediction model | |
| CN112861350A (en) | Temperature overheating defect early warning method for stator winding of water-cooled steam turbine generator | |
| CN106845826B (en) | PCA-Cpk-based cold continuous rolling production line service quality state evaluation method | |
| CN113848307A (en) | Feature extraction principal component analysis online monitoring method for sludge bulking | |
| CN112884197B (en) | Water bloom prediction method and device based on double models | |
| CN108830006B (en) | Linear-nonlinear industrial process fault detection method based on linear evaluation factor | |
| CN108470699B (en) | intelligent control system of semiconductor manufacturing equipment and process | |
| CN119337241A (en) | A KL divergence minor fault detection method based on improved self-attention mechanism | |
| CN119600774A (en) | A farmland water level environment monitoring alarm device | |
| CN104133437B (en) | Continuous-type chemical-engineering device and performance indicator real-time evaluation method and device thereof | |
| CN118762776A (en) | A tap water quality detection and analysis instrument system | |
| CN113467434A (en) | CCA fermentation process KPI related fault monitoring method based on double-layer k neighbor standardization | |
| CN114819743A (en) | Chemical enterprise energy consumption diagnosis and analysis method | |
| TWI639908B (en) | Method for detecting and diagnosing an abnormal process | |
| CN116978498A (en) | Melt index soft measurement method based on two-dimensional integrated statistical analysis model | |
| CN112397404B (en) | Semiconductor process inspection method and inspection system | |
| CN108491995B (en) | A screening method for key control factors for identification of drinking water risk factors |
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 | ||
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Address after: 11-14 / F, tower a, Tengfei building, 88 Jiangmiao Road, yanchuangyuan, Jiangbei new district, Nanjing, Jiangsu Province 210000 Applicant after: Anyuan Technology Co.,Ltd. Address before: 11-14 / F, tower a, Tengfei building, 88 Jiangmiao Road, yanchuangyuan, Jiangbei new district, Nanjing, Jiangsu Province 210000 Applicant before: NANJING ANYUAN TECHNOLOGY Co.,Ltd. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |