CN113050573B - Production rhythm-based energy-saving method for air compressor - Google Patents
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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Abstract
The invention discloses an energy-saving method of an air compressor based on production rhythm, wherein a data acquisition layer comprises a hot data source and a cold data source; the data calculation layer is a calculation mechanism of a hot data source and a cold data source and comprises a real-time database and a data warehouse; the data service layer comprises a micro service architecture, the micro service architecture comprises a plurality of use case databases, supports a plurality of intelligent use case services, and simultaneously supports platform module use cases for providing aggregation portal services for each use case; the data application layer is used for providing an aggregation portal, providing UI interfaces of all use cases in the data service layer, and interacting with the micro service architecture through the API gateway; the invention relates to centralized monitoring and consumption prediction of an air compressor, which comprises the following steps: realize personnel and adjust accurate and unified: reduce the unit and frequently open and stop and the undulant frequency of load, prolong the unit maintenance cycle: and by adopting accurate adjustment, the unit operates in a relatively stable working condition range, the failure rate is reduced, and the maintenance period is prolonged.
Description
Technical Field
The invention relates to the technical field of automatic control of air compressors, in particular to an energy-saving method of an air compressor based on production rhythm.
Background
Compressed air is widely used in many occasions in steel enterprises because of its excellent characteristics, and is the third largest energy source following fuels and electric power. The air compressor machine is the key energy consumption equipment in the enterprise as the equipment of compressed air production, possesses huge energy-conserving potentiality. According to investigation, in the using cost of the air press in the production process, the equipment maintenance cost only accounts for about 23 percent, and the power consumption accounts for about 77 percent. The energy saving of the air compressor is implemented, so that the energy consumption cost of enterprises can be obviously reduced.
At present, no technology exists for monitoring the running condition of each air compressor in real time, the energy consumption sequence of the air compressors, comparing the difference between the output of the air compressors and the future predicted demand in real time, and automatically adjusting the air compressors to carry out corresponding operation according to the energy consumption sequence of the air compressors to realize the balance of compressed air. Under a common extensive operation mode, the gas-electricity ratio of the system cannot be guaranteed, and the whole operation performance fluctuates, so that the waste of compressed air is caused. Therefore, an energy-saving method of the air compressor based on the production rhythm is urgently needed to be designed so as to solve the problem of energy waste of the existing air compressor.
Disclosure of Invention
In view of the problems in the prior art, the invention aims to provide a method for saving energy of an air compressor based on production rhythm.
The technical scheme adopted by the invention for solving the technical problems is as follows: a method of air compressor energy conservation based on production cadence, comprising: the system comprises a data acquisition layer, a data calculation layer, a data service layer and a data application layer;
the data acquisition layer comprises a hot data source and a cold data source, the hot data source acquires real-time data through a PLC/DCS directly connected with the on-site industrial gateway and various industrial sensors and equipment below the hot data source, the cold data source comprises an ERP system, an energy management system, an MES system and a secondary system, the cold data source acquires business system data of each department as required, and meanwhile, a part of data is uploaded in a data file mode;
the data calculation layer is a calculation mechanism of a hot data source and a cold data source and comprises a real-time database and a data warehouse;
the data service layer comprises a micro service architecture, the micro service architecture comprises a plurality of use case databases, supports a plurality of intelligent use case services, and simultaneously supports platform module use cases for providing aggregated portal services for each use case;
the data application layer is used for providing an aggregation portal, providing UI interfaces of all use cases in the data service layer, and interacting with the micro service architecture through the API gateway.
Specifically, the real-time database is connected with a thermal data source, collects, processes and stores real-time thermal data, and provides a high-speed real-time and historical data interface for an upper data service layer case; the data warehouse is used for collecting cold data from the business system and hot data from the real-time database.
Specifically, the intelligent use case service comprises a company-level DPM use case service, an intelligent iron-making use case service, an intelligent steel-rolling use case service and an intelligent power use case service.
Specifically, the UI interfaces comprise a company-grade DPM use case UI, an intelligent ironmaking use case UI, an intelligent steelmaking use case UI, an intelligent rolling use case UI, an intelligent power use case UI and a platform module UI.
An energy-saving method of an air compressor based on production rhythm comprises the following steps:
1) air compressor machine energy consumption sequencing:
a) collecting the annual output and annual electric quantity consumption of each air compressor, and calculating the gas-electricity ratio, namely the unit consumption of the air compressor;
b) sequencing the energy consumption of the air compressors from low to high according to the gas-electricity ratio;
2) centralized information monitoring and real-time production difference loss calculation:
a) data acquisition of each data point: in order to reduce data acquisition delay, a data acquisition server is deployed on the site, professional data acquisition software is installed in the server, data are transmitted to a PI real-time database in real time through a PI client, data query is carried out on the system through Api provided by the PI, the data are cleaned for the first time according to a preset data range, and the accuracy of the data is guaranteed;
b) data processing logic: through message queue service, reading PI database data, storing the data into a Redis database after a subscriber acquires the data, grouping the data points into user groups and official network groups after keys correspond to point location names and values correspond to the latest values of the points, changing all the points into groups after the points are updated in each grouping, taking the latest data out of the Redis, accumulating and acquiring the summarized data of the user side and the client side;
c) calculating yield difference loss: the calculation formula is as follows: sum Diff ═ Σ prod- Σ cons;
3) prediction of compressed air consumption:
a) collecting a production shutdown plan: through a data import or API interface mode, a production shutdown plan is synchronized, and equipment shutdown time intervals are obtained according to equipment corresponding to a production line;
b) and (3) calculating the average consumption of each device: calculating the average consumption per minute of the equipment in the starting state through the annual flow and annual starting time of the equipment;
c) compressed air consumption prediction: dividing the prediction curve into 1440 points according to the time of the day, namely one point per minute, judging whether the equipment is in the equipment shutdown time interval per minute to distinguish the shutdown state of the equipment, accumulating the average consumption of the equipment in the startup state, and predicting the future consumption;
d) prediction curve: the prediction formula is:
sunmCons＝ΣdeviceVal
allDatas＝ΣsumCons；
4) the current and the opening degree of the static blade of the air compressor suggest operation:
a) tissue cold data: the method comprises the steps of sequencing the energy consumption of a centrifuge, maximizing the capacity of each air compressor, and increasing the current and the opening parameter of a static blade required by each cubic of compressed air;
b) the operation logic: according to the compressed air demand in the next 5 minutes, the air compressors in the starting state are preferentially adjusted according to the current air compressor sequence, the adjustment sequence is adjusted according to the energy consumption sequence, namely the air compressors with low energy consumption are preferentially increased to the maximum load, if the air compressors in the starting state cannot meet the state, the current maximum output is less than the future consumption, the output of the air compressors with low energy consumption is preferentially adjusted to the maximum load, and if the output of the air compressors with low energy consumption is still not met, the air compressors with high energy consumption are recommended to be started, the current maximum output is greater than the future consumption, the output of the air compressors with high energy consumption is preferentially reduced, and when the output of the air compressors with high energy consumption is reduced to 0, the shutdown is recommended.
Specifically, the frequency of data acquisition in step 2 is 50ms, and the point of data acquisition includes the pressure and flow rate of the user side and the flow rate and pressure of the air compressor station.
Specifically, sumDiff in the step 2 is to identify the generation difference loss, prod is all point location traffic at the exit of the air compressor station, and Cons is all point location traffic at the exit of the user side.
Specifically, in step 3, deviceyearencons represents the annual consumption of the device, deviceOpenTime represents the annual power-on time of the device, deviceVal represents the one-minute consumption of the device in the power-on state, sumcos represents the total consumption per minute, and allDatas represents the data set of the total consumption per minute.
The invention has the following beneficial effects:
the invention relates to a production rhythm-based energy-saving method for an air compressor, which comprises the following steps of 1) centralized monitoring and consumption amount prediction of the air compressor:
the method comprises the steps of integrating the metering points of the required gas consumption and the operation control information of the air compressor into a picture, performing background calculation, predicting the demand change, prompting the adjustment means in time, and solving the problems of monitoring dispersion and adjustment randomness;
2) realize personnel and adjust accurate and unified:
staff accurately adjust or timely reduce the number of units in operation according to the predicted information, so that the problems of adjustment randomness and overlarge emission are avoided, pressure fluctuation of a pipe network is reduced, economic benefits are improved, and the labor intensity of the staff is reduced;
3) reduce the unit and frequently open and stop and load fluctuation frequency, prolong the unit maintenance cycle:
and by adopting accurate adjustment, the unit operates in a relatively stable working condition range, the failure rate is reduced, and the maintenance period is prolonged.
Drawings
Fig. 1 is a schematic diagram of an air compressor energy-saving system based on production rhythm.
Fig. 2 is a schematic diagram of an adjustment process of an air compressor energy-saving system based on a production rhythm.
Detailed Description
The technical solutions in the embodiments of the present invention will be described in further detail in the following clearly and completely with reference to the accompanying drawings in the embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
As shown in fig. 1, an air compressor energy-saving regulation system based on production rhythm includes the following four-layer framework from bottom to top: the system comprises a data acquisition layer, a data calculation layer, a data service layer and a data application layer.
A data acquisition layer: the system comprises all data sources, wherein one part is a thermal data source, namely real-time data are acquired by a PLC/DCS (programmable logic controller/distributed control system) and various industrial sensors and equipment under the PLC/DCS which are directly connected through a field industrial gateway; the other part is a cold data source, namely an ERP system, an energy management system, an MES system, a secondary system and the like, and collects business data of each department as required, and meanwhile, part of the data is uploaded in a data file mode.
A data calculation layer: the system comprises a cold data source and a hot data source, wherein a PI real-time database is connected with the real-time hot data source, collects, processes and stores real-time hot data, and provides a high-speed real-time and historical data interface for upper-layer use cases; the data warehouse constructed based on GreenPlum collects cold data from a business system and hot data from a real-time database, performs ETL cleaning, calculation and storage in a unified manner, constructs an ETL multi-layer structure from a basic data layer to a characteristic data layer and then to an application data layer, and provides analysis results of various dimensions for upper-layer use cases.
A data service layer: the micro-service architecture constructed based on the Spring Cloud and the Spring Boot supports a plurality of intelligent use cases, simultaneously supports platform module use cases for providing aggregated portal services for each use case, realizes management functions of unified gateway, unified configuration and the like of multiple use cases, and all micro-services adopt standard RESTful API services to carry out data interaction in a unified mode.
A data application layer: and providing an aggregation portal, providing UI interfaces of all use cases, and interacting with the micro service structure through the API gateway.
The intelligent air compressor adjusting system is characterized in that a front-end client APP is constructed by using H5, Javascript and CSS3 technologies, a chart uses Highcharts and Echarts components, the pushing technology mainly comprises WebSocket, and the connection between the front end and the rear end uses RESTful Web service and JSF technologies; performing data interaction between all micro-services of the application logic terminal through RESTful, constructing by using a Java-based Spring Cloud and Spring Boot enterprise-level micro-service technology, and providing centralized log service by using an ELK framework; the relational database of the data storage end adopts Mysql, and the real-time database adopts PI.
As shown in fig. 2, the functions of the air compressor energy-saving regulation system include air compressor energy consumption sequencing, air compressor centralized monitoring and consumption prediction, and accurate and uniform regulation of personnel. The energy-saving method of the air compressor based on the production rhythm comprises the following steps:
1) the air compressor energy consumption sequencing method comprises the following steps:
a) annual power consumption and compressed air yield data acquisition: and summarizing and calculating the data by pulling historical data of an energy network or a PI database.
b) The data processing logic: and comparing the annual compressed air yield with the annual power consumption to obtain a gas-power ratio, and sequencing according to the gas-power ratio.
c) The formula for calculating the gas-electricity ratio is as follows:
2) the steps of centralized information monitoring and real-time production difference loss calculation are as follows:
a) data acquisition of each data point: in order to reduce data acquisition delay, a data acquisition server is deployed on the site, professional data acquisition software is installed in the server, data are transmitted to a PI real-time database in real time through a PI client, data query is carried out on the system through Api provided by the PI, the data acquisition frequency is 50ms, the data are cleaned for the first time according to a preset data range, the accuracy of the data is guaranteed, and data acquisition points comprise pressure and flow of the client and flow and pressure of an air compressor station;
b) the data processing logic: through message queue service, reading PI database data, storing the data into a Redis database after a subscriber acquires the data, grouping the data points into user groups and official network groups after keys correspond to point location names and values correspond to the latest values of the points, changing all the points into groups after the points are updated in each grouping, taking the latest data out of the Redis, accumulating and acquiring the summarized data of the user side and the client side;
c) calculating yield difference loss: the calculation formula is as follows:
sum Diff＝Σprod-Σcons
sumDiff is used for identifying the yield difference loss, prod is the flow of all point locations at the outlet of the air compressor station, and Cons is the flow of all point locations at the outlet of the user terminal.
3) The compressed air consumption prediction steps are as follows:
a) collecting a production shutdown plan: through a data import or API interface mode, a production shutdown plan is synchronized, and equipment shutdown time intervals are obtained according to equipment corresponding to a production line;
b) and (3) calculating the average consumption of each device: calculating the average consumption per minute of the equipment in the starting state through the annual flow and annual starting time of the equipment;
c) compressed air consumption prediction: dividing the prediction curve into 1440 points according to the time of the day, namely one point per minute, judging whether the equipment is in the equipment shutdown time interval per minute to distinguish the shutdown state of the equipment, accumulating the average consumption of the equipment in the startup state, and predicting the future consumption;
d) prediction curve: the prediction formula is:
sunmCons＝ΣdeviceVal
allDatas＝∑sumCons
wherein deviceYearCons represents the annual consumption of the device, deviceOpime represents the annual boot time (to the nearest minute) of the device, deviceVal represents the one-minute consumption of the device in the boot state, sumCons represents the total consumption per minute, and allDatas represents a data set of the total consumption per minute.
4) The method for realizing accurate adjustment and unification of the personnel comprises the following steps:
a) tissue cold data: the method comprises the steps of sequencing the energy consumption of a centrifuge, maximizing the capacity of each air compressor, and increasing the current and the opening parameter of a static blade required by each cubic of compressed air;
b) the operation logic: according to the compressed air demand in the next 5 minutes, the air compressors in the starting state are preferentially adjusted according to the current air compressor sequence, the adjustment sequence is adjusted according to the energy consumption sequence, namely the air compressors with low energy consumption are preferentially increased to the maximum load, if the air compressors in the starting state cannot meet the state, the current maximum output is less than the future consumption, the output of the air compressors with low energy consumption is preferentially adjusted to the maximum load, and if the output of the air compressors with low energy consumption is still not met, the air compressors with high energy consumption are recommended to be started, the current maximum output is greater than the future consumption, the output of the air compressors with high energy consumption is preferentially reduced, and when the output of the air compressors with high energy consumption is reduced to 0, the shutdown is recommended.
The present invention is not limited to the above embodiments, and any structural changes made under the teaching of the present invention shall fall within the scope of the present invention, which is similar or similar to the technical solutions of the present invention.
The techniques, shapes, and configurations not described in detail in the present invention are all known techniques.
Claims (5)
1. A method for saving energy of an air compressor based on production rhythm is characterized by comprising the following steps: the system comprises a data acquisition layer, a data calculation layer, a data service layer and a data application layer;
the data acquisition layer comprises a hot data source and a cold data source, the hot data source is directly connected with a PLC/DCS (programmable logic controller/distributed control system) through an on-site industrial gateway, various industrial sensors and equipment below the PLC/DCS acquire real-time data, the cold data source comprises an ERP (enterprise resource planning), an energy management system, an MES (manufacturing execution system) and a secondary system, acquires service system data of each department as required, and simultaneously uploads a part of data in a data file mode;
the data calculation layer is a calculation mechanism of a hot data source and a cold data source and comprises a real-time database and a data warehouse;
the data service layer comprises a micro service architecture, the micro service architecture comprises a plurality of use case databases, supports a plurality of intelligent use case services, and simultaneously supports platform module use cases for providing aggregated portal services for each use case;
the data application layer is used for providing an aggregation portal, providing UI interfaces of all use cases in the data service layer, and interacting with the micro service architecture through the API gateway;
the energy-saving method of the air compressor based on the production rhythm comprises the following steps:
1) air compressor machine energy consumption sequencing:
a) collecting the annual output and annual electric quantity consumption of each air compressor, and calculating the gas-electricity ratio, namely the unit consumption of the air compressor;
b) sequencing the energy consumption of the air compressors from low to high according to the gas-electricity ratio;
2) centralized information monitoring and real-time production difference loss calculation:
a) data acquisition of each data point: in order to reduce data acquisition delay, a data acquisition server is deployed on the site, professional data acquisition software is installed in the server, data are transmitted to a PI real-time database in real time through a PI client, data query is carried out on the system through Api provided by the PI, the data are cleaned for the first time according to a preset data range, and the accuracy of the data is guaranteed;
b) the data processing logic: through message queue service, reading PI database data, storing the data into a Redis database after a subscriber acquires the data, grouping the data points into user groups and official network groups after keys correspond to point location names and values correspond to the latest values of the points, changing all the points into groups after the points are updated in each grouping, taking the latest data out of the Redis, accumulating and acquiring the summarized data of the user side and the client side;
c) calculating yield difference loss: the calculation formula is as follows: sumDiff ═ Σ prod- Σ cons,
wherein: sumDiff is used for identifying the yield difference loss, prod is the flow of all point locations at the outlet of the air compressor station, and Cons is the flow of all point locations at the outlet of the user side;
3) compressed air consumption prediction:
a) collecting a production shutdown plan: through a data import or API interface mode, a production shutdown plan is synchronized, and equipment shutdown time intervals are obtained according to equipment corresponding to a production line;
b) and (3) calculating the average consumption of each device: calculating the average consumption per minute of the equipment in the starting state through the annual flow and annual starting time of the equipment;
c) compressed air consumption prediction: dividing the prediction curve into 1440 points according to the time of the day, namely one point per minute, judging whether the equipment is in the equipment shutdown time interval per minute to distinguish the shutdown state of the equipment, accumulating the average consumption of the equipment in the startup state, and predicting the future consumption;
d) prediction curve: the prediction formula is:
sunmCons＝ΣdeviceVal
allDatas＝ΣsumCons，
wherein: deviceYearCons represents equipment annual consumption, deviceOpTime represents equipment annual boot time, deviceVal represents equipment single-minute consumption in a boot state, sumCons represents overall consumption per minute, and allDatas represents a data set of overall consumption per minute;
4) the current and the opening degree of the static blade of the air compressor suggest operation:
a) tissue cold data: the method comprises the steps of sequencing the energy consumption of a centrifuge, maximizing the capacity of each air compressor, and increasing the current and the opening parameter of a static blade required by each cubic of compressed air;
b) the operation logic: according to the compressed air demand in the next 5 minutes, the air compressors in the starting state are preferentially adjusted according to the current air compressor sequence, the adjustment sequence is adjusted according to the energy consumption sequence, namely the air compressors with low energy consumption are preferentially increased to the maximum load, if the air compressors in the starting state cannot meet the state, the current maximum output is less than the future consumption, the output of the air compressors with low energy consumption is preferentially adjusted to the maximum load, and if the output of the air compressors with low energy consumption is still not met, the air compressors with high energy consumption are recommended to be started, the current maximum output is greater than the future consumption, the output of the air compressors with high energy consumption is preferentially reduced, and when the output of the air compressors with high energy consumption is reduced to 0, the shutdown is recommended.
2. The production cadence-based air compressor energy-saving method according to claim 1, wherein the real-time database is connected with a thermal data source, collects, processes and stores real-time thermal data, and provides a high-speed real-time and historical data interface for upper data service layer use cases; the data warehouse is used for collecting cold data from the business system and hot data from the real-time database.
3. The production cadence-based air compressor energy-saving method according to claim 1, wherein the intelligent use case service comprises a company-level DPM use case service, an intelligent iron-making use case service, an intelligent steel rolling use case service, and an intelligent power use case service.
4. The production cadence-based air compressor energy-saving method according to claim 1, wherein the UI interfaces comprise a company-grade DPM use case UI, an intelligent iron-making use case UI, an intelligent steel-rolling use case UI, an intelligent power use case UI, and a platform module UI.
5. The production cadence-based air compressor energy-saving method according to claim 1, wherein the frequency of data collection in step 2) is 50ms, and the points of data collection comprise pressure and flow at a user end and flow and pressure at an air compressor station.
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| CN112180816A (en) * | 2020-10-20 | 2021-01-05 | 河北匠心智联软件技术有限公司 | Management and control system of compressor air system based on Internet of things cloud platform |
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| CN109426205A (en) * | 2017-09-05 | 2019-03-05 | 万洲电气股份有限公司 | A kind of industrial intelligent Optimization of Energy Saving system |
| CN109710976A (en) * | 2018-11-28 | 2019-05-03 | 鞍钢集团自动化有限公司 | A kind of heater for rolling steel gas consumption amount prediction technique |
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