CN116358112A - Control method of process air conditioning system - Google Patents
Control method of process air conditioning system Download PDFInfo
- Publication number
- CN116358112A CN116358112A CN202310383009.4A CN202310383009A CN116358112A CN 116358112 A CN116358112 A CN 116358112A CN 202310383009 A CN202310383009 A CN 202310383009A CN 116358112 A CN116358112 A CN 116358112A
- Authority
- CN
- China
- Prior art keywords
- air conditioning
- load
- process air
- conditioning unit
- fresh air
- 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.)
- Pending
Links
Images
Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
- F24F11/46—Improving electric energy efficiency or saving
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/56—Remote control
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
- F24F11/64—Electronic processing using pre-stored data
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
- F24F11/72—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
- F24F11/80—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/88—Electrical aspects, e.g. circuits
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2110/00—Control inputs relating to air properties
- F24F2110/10—Temperature
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2110/00—Control inputs relating to air properties
- F24F2110/20—Humidity
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B30/00—Energy efficient heating, ventilation or air conditioning [HVAC]
- Y02B30/70—Efficient control or regulation technologies, e.g. for control of refrigerant flow, motor or heating
Landscapes
- Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Combustion & Propulsion (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Signal Processing (AREA)
- Human Computer Interaction (AREA)
- Physics & Mathematics (AREA)
- Fuzzy Systems (AREA)
- Mathematical Physics (AREA)
- Air Conditioning Control Device (AREA)
Abstract
The invention discloses a control method of a process air conditioning system, which is used for regulating and controlling the cooling capacity of a source end refrigerating station or the heating capacity of a heat exchange station by acquiring historical data before a working day and predicting the predicted total load of an end process air conditioning unit through a load prediction model based on the historical data so as to realize the regulation and control of the end process air conditioning unit. The control method of the process air conditioning system uses the predicted total load when the source end is regulated and controlled, which is equivalent to the real-time total load of the air conditioning unit in the terminal production workshop, avoids the defect caused by thermal inertia, ensures that the cold supply or the heat supply of the source end is timely regulated and controlled according to the terminal working condition, and further ensures that the energy supply and the demand between the source end and the terminal are matched.
Description
Technical Field
The invention relates to the technical field of industrial energy conservation, in particular to a control method of a process air conditioning system.
Background
Along with the development of energy Internet, more and more large industrial enterprises build factory intelligent energy management and control platforms to improve the energy utilization efficiency and management efficiency of the enterprises, save the energy input cost and reduce the carbon emission.
The process air conditioner is energy equipment for a production workshop in an intelligent energy management and control platform, and generally consumes a large amount of cold or heat energy. When the process production plant needs fresh air with a certain temperature and humidity, the process air conditioner used by the terminal energy-saving equipment of the production plant is a fresh air combined air conditioning unit, the cold energy required by the air conditioning unit in summer working conditions is provided by a refrigerating station, and the heat required by winter working conditions is provided by a heat exchange station. Process air conditioning generally employs a decentralized control method, i.e. the source end controls the energy supply equipment of the source end and the end controls the energy utilization equipment of the end. The source end is a refrigerating station or a heat exchange station, the tail end is an air conditioning unit, and the control of an air conditioning process system can be realized by adopting a distributed control method.
However, because of thermal inertia of the air conditioning system pipe network, the load change of the air conditioning unit in the tail end production workshop obtained by the source end in the existing distributed control method is not real-time real load change of the air conditioning unit in the tail end production workshop, so that the output of the source end regulation energy is not timely, and the energy supply and demand between the source end and the tail end are not matched.
Disclosure of Invention
Based on the above, it is necessary to provide a process air conditioning system control method for timely adjusting the output of energy by the source end and matching the energy supply and demand between the source end and the tail end.
In a first aspect, the present invention provides a control method of a process air conditioning system, which is applied to a cloud platform, and includes:
acquiring historical parameters, wherein the historical parameters are terminal working condition data sets acquired by a process air conditioning system at intervals of delta T in a period of T before a working day, and the terminal working condition data comprise fresh air quantity of a process air conditioning unit, temperature and humidity of a fresh air inlet, a switching state and indoor air temperature;
carrying out data processing on the historical parameters to obtain historical processing parameters;
and inputting the historical processing parameters into a load prediction model to obtain a predicted total load of the air conditioning unit of the tail end process, wherein the predicted total load is used for calculating a control variable, and the control variable is used for regulating and controlling the cooling capacity of a source end refrigerating station or the heating capacity of a heat exchange station.
In one embodiment, the historical processing parameters include fresh air load and production process load borne by the process air conditioning unit, and the data processing of the historical parameters to obtain the historical processing parameters includes:
and calculating fresh air load born by the process air conditioning unit according to the fresh air quantity of the process air conditioning unit and the temperature and humidity of a fresh air inlet and the indoor air temperature
Acquiring the production process load born by the process air conditioning unit according to the historical processing parameters
In one embodiment, the load prediction model is:
in which Q sys In order to predict the total load of the vehicle,
fresh air load borne by each process air conditioning unit, < >>
And the process production load born by each process air conditioning unit is eta, the process air conditioning system load correction coefficient considering heat exchange loss is eta, and n is the total number of the process air conditioning units.
In one embodiment, when the tail end working condition is refrigeration, the fresh air load borne by the process air conditioning unit is summer fresh air load, and the summer fresh air load is calculated according to the fresh air quantity of the process air conditioning unit and the temperature and humidity of the fresh air inlet;
when the tail end working condition is heating, the fresh air load borne by the process air conditioning unit is the winter fresh air load, and the winter fresh air load is calculated according to the fresh air quantity and the indoor air temperature of the process air conditioning unit.
In one embodiment, the summer fresh air load is calculated as:
Q c.o ＝M 0 (h o -h r ) (2)
in which Q c.o For fresh air cooling load in summer, M 0 Fresh air quantity h of process air conditioning unit r And h o And the indoor and outdoor air enthalpy values are respectively;
indoor and outdoor air enthalpy values are obtained through temperature and humidity rolling prediction of a fresh air inlet;
the calculation of the fresh air load in winter is as follows:
Q h.o ＝M 0 c p (t r -t o ) (3)
in which Q h.o C, a fresh air load value in winter p Specific heat of air, t r And t o The indoor and outdoor air temperatures are respectively.
In a second aspect, the present invention provides another control method of a process air conditioning system, applied to an edge side controller, including:
the method comprises the steps of obtaining a predicted total load of a process air conditioning unit at the tail end and real-time working condition parameters of a source end, wherein the predicted total load is predicted and obtained according to historical parameters, and the historical parameters are a tail end working condition data set which is acquired by a process air conditioning system at intervals of delta T in a period of time before a working day, and the tail end working condition data comprise fresh air quantity, temperature and humidity of a fresh air inlet, a switching state and indoor air temperature of the process air conditioning unit;
the predicted total load and the real-time working condition parameters of the source end are input into a feedforward control model to obtain control variables,
and transmitting the control variable to the source end for the source end to adjust the working condition.
In one embodiment, when the source end real-time working condition is refrigeration, the source end real-time working condition parameters are the water supply temperature of the cold supply station, the return water temperature of the cold supply station, the water supply temperature of a main pipeline of the process air conditioning unit and the rotation speed of a water pump, and the feedforward control model comprises a constant flow feedforward control model and a variable flow feedforward control model;
when the source end real-time working condition is heating, the source end real-time working condition parameter is the valve opening, and the feedforward control model comprises a fixed-error feedforward control model.
In one embodiment, the constant flow feedforward control model is
δ T ＝|T out -T g | （5）
Wherein T is g Supply water temperature for refrigerating station, T h For the return water temperature of the refrigerating station, T out Water supply temperature delta for main pipeline of process air conditioning unit T The temperature control zone is the total pipeline flow of the process air conditioning unit;
the variable flow feedforward control model is as follows
Q total /Q sys ＝n 1 /n 2 (6)
In which Q total To supply load to source end, n 1 To adjust the rotation speed of the front water pump, n 2 The rotating speed of the water pump is regulated; the fixed error feedforward control model is
δ Q ＝|Q total -Q sys | (7)
In delta Q Is the load control error.
In one embodiment, when the feedforward control model is a constant flow feedforward control model, the control variable is a refrigeration station water supply temperature;
when the feedforward control model is a variable flow feedforward control model, the control variable is the rotating speed of the water pump of the refrigeration station;
when the feedforward control model is a fixed-error feedforward control model, the control variable is the valve opening of the heating station.
In a third aspect, the present invention provides a control method of a process air conditioning system, applied to an air conditioning process system, where the air conditioning process system includes a cloud platform and an edge side controller, the method including:
the cloud platform acquires historical parameters, wherein the historical parameters are terminal working condition data sets acquired by the process air conditioning system at intervals of delta T in a period of time before a working day, and the terminal working condition data comprise fresh air quantity, temperature and humidity of a fresh air inlet, a switching state and indoor air temperature of the process air conditioning unit;
the cloud platform performs data processing on the historical parameters to obtain historical processing parameters;
the cloud platform inputs the historical processing parameters into a load prediction model to obtain a predicted total load of the air conditioning unit of the tail end process;
the cloud platform sends the predicted total load to an edge side controller;
the edge side controller receives the predicted total load and collects real-time working condition parameters of a source end;
the edge side controller inputs the predicted total load and the source end real-time working condition parameters to the feedforward control model to calculate a control variable, and the control variable is used for regulating and controlling the cooling capacity of the source end refrigeration station or the heating capacity of the heat exchange station.
The beneficial effects of the invention are as follows: according to the invention, the historical data before the working day is obtained, and the predicted total load of the terminal process air conditioning unit is predicted through the load prediction model based on the historical data, so that the terminal process air conditioning unit is regulated and controlled by regulating and controlling the cooling capacity of the source end refrigerating station or the heating capacity of the heat exchange station. The control method of the process air conditioning system uses the predicted total load when the source end is regulated and controlled, which is equivalent to the real-time total load of the air conditioning unit in the terminal production workshop, avoids the defect caused by thermal inertia, ensures that the source end is regulated and controlled timely according to the terminal working condition, and further ensures that the energy supply and demand between the source end and the terminal are matched.
Drawings
FIG. 1 is a schematic flow chart of a control method of a process air conditioning system according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a control method of a process air conditioning system according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart of a control method of a process air conditioning system according to an embodiment of the present invention;
FIG. 4 is a schematic flow chart of a control method of a process air conditioning system according to an embodiment of the present invention;
FIG. 5 shows an embodiment of the present invention providing an in-plant process air conditioning system;
FIG. 6 is a general architecture diagram for operation of a process air conditioning system according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of energy load of each air conditioning unit and return water temperature change of a process air conditioning system according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of a comparison result of a system load obtained by simulation and a system load obtained by prediction of a load prediction model according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In one embodiment, as shown in fig. 1, fig. 1 is one of the flow charts of the control method of the process air conditioning system provided in the embodiment of the present invention, and the control method of the process air conditioning system of the embodiment is applied to a cloud platform, and includes the following steps:
s101, acquiring historical parameters, wherein the historical parameters are terminal working condition data sets acquired by a process air conditioning system at intervals of delta T in a period of T before a working day, and the terminal working condition data comprise fresh air quantity, temperature and humidity of a fresh air inlet, a switching state and indoor air temperature of the process air conditioning unit.
For example, the historical parameter is end condition data collected every 15 minutes in a week prior to the current workday.
Preferably, the fresh air quantity, the temperature and the humidity of a fresh air inlet, the on-off state and the indoor air temperature of the process air conditioning unit are collected through the intelligent gateway, and historical data can be obtained by collecting operation data of sensors, terminal energy equipment and intelligent meters in a process workshop through the intelligent gateway.
S102, performing data processing on the historical parameters to obtain historical processing parameters.
S103, inputting the historical processing parameters into a load prediction model to obtain a predicted total load of the air conditioning unit of the tail end process, wherein the predicted total load is used for calculating a control variable, and the control variable is used for regulating and controlling the cooling capacity of a source end refrigerating station or the heating capacity of a heat exchange station.
In this embodiment, the predicted total load is equivalent to the real-time total load of the air conditioning unit in the terminal production workshop, the predicted total load is used to calculate the control variable, and the predicted total load is equivalent to the real-time total load of the terminal, so that the defect caused by thermal inertia is avoided in the process, the source terminal has synchronism with the terminal working condition when regulated according to the terminal working condition, the regulation is timely, and the energy supply and demand between the source terminal and the terminal are matched.
In one embodiment, the historical processing parameters include fresh air load and production process load carried by the process air conditioning unit. As shown in fig. 2, fig. 1 is a schematic flow chart of a control method of a process air conditioning system according to an embodiment of the present invention, and this embodiment relates to an alternative way how to perform data processing on historical parameters to obtain historical processing parameters. On the basis of the above embodiment, S102 includes the steps of:
s201, calculating fresh air load born by the process air conditioning unit according to fresh air quantity of the process air conditioning unit and temperature and humidity of a fresh air inlet and indoor air temperature
S202, acquiring production process load born by a process air conditioning unit according to historical processing parameters
The total load of the terminal process air conditioner comprises a fresh air load and a process production load, wherein the production process load changes relatively stably, the fresh air load is greatly influenced by outdoor weather parameters, and the fresh air load and the process production load calculated by the air conditioner need to be calculated when the terminal total load is calculated.
In one embodiment, the load prediction model is:
in which Q sys In order to predict the total load of the vehicle,
fresh air load borne by each process air conditioning unit, < >>
And the process production load born by each process air conditioning unit is eta, the process air conditioning system load correction coefficient considering heat exchange loss is eta, and n is the total number of the process air conditioning units.
In this embodiment, the sum of the fresh air load and the process production load of all air conditioning units is calculated as the predicted total load. The calculation of the predicted total load is not or less affected by external disturbances.
In an alternative embodiment, when the end working condition is refrigeration, the fresh air load borne by the process air conditioning unit is summer fresh air load, and the summer fresh air load is calculated according to the fresh air quantity of the process air conditioning unit and the temperature and the humidity of the fresh air inlet.
The calculation of the fresh air load in summer is as follows:
Q c.o ＝M 0 (h o -h r ) (2)
in which Q c.o For fresh air cooling load in summer, M 0 Fresh air quantity h of process air conditioning unit r And h o And the indoor and outdoor air enthalpy values respectively.
The indoor and outdoor air enthalpy value is obtained through temperature and humidity rolling prediction of the fresh air inlet. The prediction process may be performed using intelligent algorithms LSTM, GRU, or ANN.
In an alternative embodiment, when the end working condition is heating, the fresh air load borne by the process air conditioning unit is a winter fresh air load, and the winter fresh air load is calculated according to the fresh air quantity and the indoor air temperature of the process air conditioning unit.
The calculation of the fresh air load in winter is as follows:
Q h.o ＝M 0 c p (t r -t o ) (3)
in which Q h.o C, a fresh air load value in winter p Specific heat of air, t r And t o The indoor and outdoor air temperatures are respectively.
In this embodiment, an equipment management information system is set between the terminal and the cloud platform, terminal working condition parameters acquired by the sensor are received through the equipment management information system, and then the sensor classifies, sorts and simply processes the data and sends the data to the cloud platform.
Based on the same inventive concept, the embodiment of the invention also provides a process air conditioning system control device for realizing the above-mentioned process air conditioning system control method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of the control device of the one or more point process air conditioning system provided below may refer to the limitation of the control method of the process air conditioning system hereinabove, and will not be repeated herein.
In one embodiment, a process air conditioning system control device includes:
the system comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring historical parameters, the historical parameters are terminal working condition data sets acquired by a process air conditioning system every delta T time period in a period T before a working day, and the terminal working condition data comprise fresh air quantity, temperature and humidity of a fresh air inlet, a switching state and indoor air temperature of the process air conditioning unit;
the data processing module is used for carrying out data processing on the historical parameters to obtain historical processing parameters;
the first calculation module is used for inputting the historical processing parameters into a load prediction model to obtain a predicted total load of the terminal process air conditioning unit, the predicted total load is used for calculating a control variable, and the control variable is used for regulating and controlling the cooling capacity of a source end refrigerating station or the heating capacity of a heat exchange station.
The invention also provides another control method of a process air conditioning system, as shown in fig. 3, fig. 3 is one of the flow charts of the control method of the process air conditioning system provided by the embodiment of the invention, the control method of the process air conditioning system of the embodiment is applied to an edge side controller, and the method comprises the following steps:
s301, obtaining a predicted total load of the process air conditioning unit at the tail end and real-time working condition parameters of a source end, wherein the predicted total load is predicted and obtained according to historical parameters, and the historical parameters are a tail end working condition data set acquired by the process air conditioning system at intervals of delta T in a period of time before a working day, and the tail end working condition data comprise fresh air quantity, temperature and humidity of a fresh air inlet, a switching state and indoor air temperature of the process air conditioning unit.
Specifically, the real-time working condition parameters of the source end are collected through the edge side controller.
S302, the predicted total load and the source end real-time working condition parameters are input into a feedforward control model to obtain control variables.
Specifically, the control variable is the water supply temperature, the water pump flow rate or the valve opening. The control variables are carried by command signals.
S303, the control variable is issued to the source end so as to adjust the working condition of the source end.
Preferably, intermittent regulation is adopted instead of real-time regulation in the regulation working condition, namely, when the load change exceeds the control error range, the executing mechanism in the energy station is regulated and controlled, so that the running stability of the process air conditioning system is ensured
According to the process air conditioning system control method, the total load of the tail end process air conditioning unit is obtained according to historical data prediction, the predicted total load is equivalent to the real-time total load of the air conditioning unit in a tail end production workshop, the predicted total load and the source end real-time working condition parameters are used for calculation to obtain the control variable, the real-time total load of the tail end and the source end real-time working condition parameters are equivalent to the control variable calculated, the defect caused by thermal inertia is avoided, the source end has synchronism with the tail end working condition when regulated and controlled according to the tail end working condition, the regulation and the control are timely, and then energy supply and demand between the source end and the tail end are matched.
In one embodiment, when the source-side real-time working condition is refrigeration, the source-side real-time working condition parameters are the water supply temperature of the cold supply station, the return water temperature of the cold supply station, the water supply temperature of the main pipeline of the process air conditioning unit and the rotation speed of the water pump, and the feedforward control model comprises a constant flow feedforward control model and a variable flow feedforward control model.
Preferably, the constant flow feedforward control model is
δ T ＝|T out -T g | (5)
Wherein T is g Supply water temperature for refrigerating station, T h For the return water temperature of the refrigerating station, T out Water supply temperature delta for main pipeline of process air conditioning unit T And m is the total pipeline flow of the process air conditioning unit for the temperature control zone.
For example, for a constant flow feedforward control model, the error, i.e., the temperature control band, is assumed to be 0.5 ℃, and the set temperature of chilled water within the control error range remains unchanged.
In this embodiment, the variable flow feedforward control model is
Q total /Q sys ＝n 1 /n 2 (6)
In which Q total To supply load to source end, n 1 To adjust front water pump rotationSpeed, n 2 The rotating speed of the water pump is regulated;
in one embodiment, when the source-side real-time condition is heating, the source-side real-time condition parameter is a valve opening, and the feedforward control model includes a fixed-error feedforward control model.
In this embodiment, the fixed error feedforward control model is
δ Q ＝|Q total -Q sys | (7)
In delta Q Is the load control error.
For example, assuming a load control error of 3%, the valve opening remains unchanged within the control error range.
In one embodiment, when the feedforward control model is a constant flow feedforward control model, the control variable is the water supply temperature of the refrigeration station, when the feedforward control model is a variable flow feedforward control model, the control variable is the water pump speed of the refrigeration station, and when the feedforward control model is a fixed error feedforward control model, the control variable is the valve opening of the heating station.
Based on the same inventive concept, the embodiment of the invention also provides a process air conditioning system control device for realizing the above-mentioned process air conditioning system control method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of the control device of the one or more point process air conditioning system provided below may refer to the limitation of the control method of the process air conditioning system hereinabove, and will not be repeated herein.
In one embodiment, a process air conditioning system control device includes:
the second acquisition module is used for acquiring the predicted total load of the process air conditioning unit at the tail end and real-time working condition parameters of the source end, the predicted total load is obtained according to the prediction of the history parameters, the history parameters are a tail end working condition data set acquired by the process air conditioning system at intervals of delta T in a period of time before the working day, and the tail end working condition data comprise the fresh air quantity of the process air conditioning unit, the temperature and humidity of a fresh air inlet, the switching state and the indoor air temperature;
the second calculation module is used for inputting the predicted total load and the source end real-time working condition parameters into a feedforward control model to obtain control variables;
and the issuing module is used for issuing the control variable to the source end so as to adjust the working condition of the source end.
The invention also provides another control method of the process air conditioning system, as shown in fig. 4, fig. 4 is a schematic flow chart of the control method of the process air conditioning system provided by the embodiment of the invention, and the control method of the process air conditioning system is applied to an air conditioning process system. The air conditioning process system comprises a cloud platform and an edge side controller, and the method comprises the following steps:
s401, a cloud platform acquires historical parameters, wherein the historical parameters are terminal working condition data sets acquired by a process air conditioning system every deltat time period in a period T before a working day, and the terminal working condition data comprise fresh air quantity, temperature and humidity of a fresh air inlet, a switching state and indoor air temperature of the process air conditioning unit.
S402, the cloud platform performs data processing on the historical parameters to obtain historical processing parameters.
S403, the cloud platform inputs the historical processing parameters into a load prediction model to obtain the predicted total load of the air conditioning unit of the end process.
S404, the cloud platform sends the predicted total load to the edge side controller.
S405, the edge side controller receives the predicted total load and collects real-time working condition parameters of the source end.
S406, the edge side controller inputs the predicted total load and the source end real-time working condition parameters to the feedforward control model to calculate a control variable, wherein the control variable is used for regulating and controlling the cooling capacity of the source end refrigeration station or the heating capacity of the heat exchange station.
The cloud platform is used for realizing the daily prediction of the system load, and the daily operation strategy of the energy supply station is generated by predicting the load curve.
According to the control method of the process air conditioning system, the total load adopted by calculation of the control variable is the terminal prediction total load, which is equivalent to the calculation of the control variable by using the real-time total load of the terminal and the real-time working condition parameters of the source terminal, the process avoids the defects caused by thermal inertia, and the source terminal has synchronism with the terminal working condition when regulated according to the terminal working condition before disturbance does not influence the output control variable, so that the energy supply and demand between the source terminal and the terminal are matched. And the cloud platform and the edge side controller are adopted to integrally optimize and control, so that the energy efficiency of the whole process air conditioning system is improved, the system operation cost is reduced, and the energy is saved and the cost is reduced for users.
In a specific embodiment, the process air conditioning system control method of the present invention will be described by taking the general configuration of the process air conditioning system in the production plant as shown in fig. 5 and the operation of the process air conditioning system as shown in fig. 6 as an example. The control method of the process air conditioning system of the embodiment comprises the following steps:
(1) And monitoring and recording the on-off state, the operation parameters and the fresh air parameters of a combined air conditioning unit (AHU) of the tail end energy utilization equipment of the process air conditioning system, wherein the fresh air parameters comprise a fresh air inlet temperature and humidity value and the air quantity of each process section AHU, and the operation parameters comprise an indoor temperature set value and the like.
(2) And the workshop-level equipment management information system collects the on-off state, the operation parameters, the fresh air parameters and the terminal production condition of the terminal energy-consumption equipment combined air conditioning unit (AHU) through the intelligent gateway, classifies and simply processes the data, and then uploads the data to the cloud platform for storage in the database. End-of-line conditions include production plan, process segment run time, throughput, and process tact.
(3) The cloud platform carries out secondary processing on data of the terminal process workshop, the data are processed into input parameters required by a load calculation model, and the input parameters comprise fresh air load and process production load of each process air conditioning unit. The cloud platform can also transmit the tail end data and the source end data stored in the database to the equipment management information system to assist the equipment management information system in data classification and simple processing of the data.
(4) The cloud platform calls a load calculation model, inputs the input parameters into the load calculation model, calculates the total predicted load of the air conditioning system of the whole process, and transmits the total predicted load value of the system to the edge measurement controller. The calculation is performed by a large calculation engine, and the large calculation engine can also predict input parameters and correct model parameters.
(5) The edge side controller collects input parameters (start-stop state and operation parameters of source equipment) related to edge control in the source refrigeration station, calculates control variables by combining the collected parameters with a system prediction cold/hot load value received from the cloud platform, and sends control signals (carriers of the control variables) to the actuator. The actuator comprises an air conditioning unit, a valve and a variable frequency pump. The edge side controller also collects the actual load of the source end, and uploads the actual load to the cloud platform as historical data of the next predicted total load calculated by the cloud platform. The edge side controller is internally provided with a real-time database, a feedforward reaction model and a small calculation force calculation controller, wherein the small calculation force controller is used for solving the feedforward reaction model and correcting the feedforward reaction model.
For the refrigerating station, the actuator may be a refrigerating unit or a frequency converter, and for the heating station, the actuator may be an electric control valve.
In order to verify the accuracy of the load prediction model, in a specific embodiment, the process air conditioning system control method of the invention is used for controlling the process air conditioning system of the heavy truck coating production workshop, and the workshop wind system is subjected to simulation on a simulation platform according to specific parameters obtained by investigation to obtain the energy load of each AHU and the return water temperature change of the process air conditioning system; and then obtaining the total load of the process air conditioning system through a load prediction model, and comparing the system load obtained through simulation with the system load obtained through the load prediction model. The energy load of each AHU and the return water temperature change of the process air conditioning system are shown in fig. 7, wherein Qc1 to Qc5 respectively represent the energy load of one AHU, and the return water temperature of the T_cold_r air conditioning system. The comparison result of the system load obtained by simulation and the system load obtained by prediction of the load prediction model is shown in fig. 8. As can be seen from fig. 7 and 8, the system load Q/u obtained by the load prediction model in the present invention sys And the system load Q/u obtained by simulation total The average error of (a) is smaller, and the prediction effect is better。
Based on the same inventive concept, the embodiment of the invention also provides a process air conditioning system control device for realizing the above-mentioned process air conditioning system control method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of the control device of the one or more point process air conditioning system provided below may refer to the limitation of the control method of the process air conditioning system hereinabove, and will not be repeated herein.
In one embodiment, a process air conditioning system control device includes:
the third acquisition module is used for acquiring historical parameters by the cloud platform, wherein the historical parameters are terminal working condition data sets acquired by the process air conditioning system at intervals of delta T in a period of time before a working day, and the terminal working condition data comprise fresh air quantity, temperature and humidity of a fresh air inlet, a switching state and indoor air temperature of the process air conditioning unit;
the second data processing module is used for carrying out data processing on the historical parameters by the cloud platform to obtain historical processing parameters;
the third calculation module is used for inputting the historical processing parameters into a load prediction model by the cloud platform to obtain the predicted total load of the end process air conditioning unit;
the second sending module is used for sending the predicted total load to the edge side controller by the cloud platform;
the receiving module is used for receiving the predicted total load and collecting real-time working condition parameters of the source end by the edge side controller;
and the fourth settlement module is used for inputting the predicted total load and the source end real-time working condition parameters into the feedforward control model by the edge side controller to calculate a control variable, wherein the control variable is used for regulating and controlling the cooling capacity of the source end refrigeration station or the heating capacity of the heat exchange station.
It should be understood that, although the steps in the flowcharts according to the embodiments described above are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in accordance with the embodiments described above may include a plurality of steps or stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of execution of the steps or stages is not necessarily sequential, but may be performed in rotation or alternatively with at least some of the other steps or stages.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of the invention should be assessed as that of the appended claims.
Claims (10)
1. The control method of the process air conditioning system is characterized by being applied to a cloud platform and comprising the following steps of:
acquiring historical parameters, wherein the historical parameters are terminal working condition data sets acquired by a process air conditioning system at intervals of delta T in a period of T before a working day, and the terminal working condition data comprise fresh air quantity, temperature and humidity of a fresh air inlet, a switching state and indoor air temperature of the process air conditioning unit;
carrying out data processing on the historical parameters to obtain historical processing parameters;
and inputting the historical processing parameters into a load prediction model to obtain a predicted total load of the air conditioning unit of the tail end process, wherein the predicted total load is used for calculating a control variable, and the control variable is used for regulating and controlling the cooling capacity of a source end refrigerating station or the heating capacity of a heat exchange station.
2. The process air conditioning system control method of claim 1, wherein the historical processing parameters include fresh air load and production process load borne by a process air conditioning unit, and the data processing the historical parameters to obtain the historical processing parameters includes:
calculating fresh air load born by the process air conditioning unit according to fresh air quantity of the process air conditioning unit, temperature and humidity of a fresh air inlet and indoor air temperature
Acquiring the production process load born by the process air conditioning unit according to the historical processing parameters
3. The process air conditioning system control method according to claim 2, wherein the load prediction model is:
in the method, in the process of the invention, sy s in order to predict the total load of the vehicle,
fresh air load borne by each process air conditioning unit, < >>
For each workerThe process production load born by the process air conditioning unit is eta as a process air conditioning system load correction coefficient considering heat exchange loss, and n is the total number of process air conditioning units.
4. The control method of a process air conditioning system according to claim 3, wherein when the end working condition is refrigeration, the fresh air load borne by the process air conditioning unit is a summer fresh air load, and the summer fresh air load is calculated according to the fresh air quantity of the process air conditioning unit and the temperature and humidity of the fresh air inlet;
when the tail end working condition is heating, the fresh air load borne by the process air conditioning unit is the winter fresh air load, and the winter fresh air load is calculated according to the fresh air quantity and the indoor air temperature of the process air conditioning unit.
5. The process air conditioning system control method according to claim 4, wherein the calculation of the summer fresh air load is:
Q c.o ＝M 0 (h o -h r ) (2)
in which Q c.o For fresh air cooling load in summer, M 0 Fresh air quantity h of process air conditioning unit r And h o And the indoor and outdoor air enthalpy values are respectively;
the indoor and outdoor air enthalpy value is obtained by rolling prediction of the temperature and the humidity of the fresh air inlet;
the calculation of the fresh air load in winter is as follows:
Q h.o ＝M 0 c p (t r -t o ) (3)
in which Q h.o C, a fresh air load value in winter p Specific heat of air, t r And t o The indoor and outdoor air temperatures are respectively.
6. A process air conditioning system control method, wherein the method is applied to an edge side controller and comprises the following steps:
the method comprises the steps of obtaining a predicted total load of a process air conditioning unit at the tail end and real-time working condition parameters of a source end, wherein the predicted total load is predicted and obtained according to historical parameters, the historical parameters are a tail end working condition data set which is collected by a process air conditioning system at intervals of delta T in a period of time before a working day, and the tail end working condition data comprise fresh air quantity, temperature and humidity of a fresh air inlet, a switching state and indoor air temperature of the process air conditioning unit;
inputting the predicted total load and the real-time working condition parameters of the source end into a feedforward control model to obtain control variables,
and the control variable is issued to the source end so as to adjust working conditions of the source end.
7. The control method of the process air conditioning system according to claim 6, wherein when the source-side real-time working condition is refrigeration, the source-side real-time working condition parameters are a cooling station water supply temperature, a cooling station backwater temperature, a total pipeline water supply temperature of the process air conditioning unit and a water pump rotating speed, and the feedforward control model comprises a constant flow feedforward control model and a variable flow feedforward control model;
when the source end real-time working condition is heating, the source end real-time working condition parameter is the valve opening, and the feedforward control model comprises a fixed-error feedforward control model.
8. The process air conditioning system control method according to claim 7, wherein the constant flow feedforward control model is
δ T ＝|T out -T g | (5)
Wherein T is g Supply water temperature for refrigerating station, T h For the return water temperature of the refrigerating station, T out Water supply temperature delta for main pipeline of process air conditioning unit T The temperature control zone is the total pipeline flow of the process air conditioning unit;
the variable flow feedforward control model is as follows
Q total /Q sys ＝n 1 /n 2 (6)
In which Q total To supply load to source end, n 1 To adjust the rotation speed of the front water pump, n 2 The rotating speed of the water pump is regulated;
the fixed error feedforward control model is
δ Q ＝|Q total -Q sys | (7)
In delta Q Is the load control error.
9. The process air conditioning system control method of claim 8, wherein when the feedforward control model is a constant flow feedforward control model, the control variable is a refrigeration station water supply temperature;
when the feedforward control model is a variable flow feedforward control model, the control variable is the rotation speed of the water pump of the refrigeration station;
when the feedforward control model is a fixed-error feedforward control model, the control variable is the opening degree of a valve of the heating station.
10. A process air conditioning system control method applied to an air conditioning process system, the air conditioning process system comprising a cloud platform and an edge side controller, the method comprising:
the cloud platform acquires historical parameters, wherein the historical parameters are terminal working condition data sets acquired by a process air conditioning system at intervals of delta T in a period of time before a working day, and the terminal working condition data comprise fresh air quantity, temperature and humidity of a fresh air inlet, a switching state and indoor air temperature of the process air conditioning unit;
the cloud platform performs data processing on the historical parameters to obtain historical processing parameters;
the cloud platform inputs the historical processing parameters into a load prediction model to obtain a predicted total load of the air conditioning unit of the tail end process;
the cloud platform sends the predicted total load to the edge side controller;
the edge side controller receives the predicted total load and collects real-time working condition parameters of a source end;
and the edge side controller inputs the predicted total load and the source end real-time working condition parameters to a feedforward control model to calculate a control variable, wherein the control variable is used for regulating and controlling the cooling capacity of a source end refrigeration station or the heating capacity of a heat exchange station.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310383009.4A CN116358112A (en) | 2023-04-11 | 2023-04-11 | Control method of process air conditioning system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310383009.4A CN116358112A (en) | 2023-04-11 | 2023-04-11 | Control method of process air conditioning system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116358112A true CN116358112A (en) | 2023-06-30 |
Family
ID=86938393
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310383009.4A Pending CN116358112A (en) | 2023-04-11 | 2023-04-11 | Control method of process air conditioning system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116358112A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118534791A (en) * | 2024-07-23 | 2024-08-23 | 中国电子工程设计院股份有限公司 | Continuous energy consumption simulation device and method for discrete production |
-
2023
- 2023-04-11 CN CN202310383009.4A patent/CN116358112A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118534791A (en) * | 2024-07-23 | 2024-08-23 | 中国电子工程设计院股份有限公司 | Continuous energy consumption simulation device and method for discrete production |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11473796B2 (en) | Systems and methods to control energy consumption efficiency | |
| CN110440396B (en) | Cloud-side-end-coordinated global optimization energy-saving control method and system for central air conditioner | |
| CN111237989B (en) | Building ventilation air conditioner control method and device based on load prediction | |
| CN110410942B (en) | Energy-saving optimization control method and system for cold and heat source machine room | |
| CN114383299B (en) | Central air-conditioning system operation strategy optimization method based on big data and dynamic simulation | |
| CN101968250B (en) | Energy-saving optimized control system and method for refrigerator room | |
| CN115013861B (en) | Indoor temperature control method and device based on heating system | |
| US20210034024A1 (en) | Building hvac system with multi-level model predictive control | |
| CN116358112A (en) | Control method of process air conditioning system | |
| CN110220288A (en) | Central air-conditioning system intelligent optimized control method and device based on big data cloud platform | |
| CN114440409A (en) | Self-adaptive energy-saving control method for central air-conditioning system | |
| CN110186156A (en) | Refrigeration plant Fuzzy control system | |
| CN101738944A (en) | Energy-saving control system and energy-saving alarming processing unit integrated with same | |
| CN114264045A (en) | Energy efficiency regulation and control system and method for central air conditioner | |
| US20230088808A1 (en) | Building hvac system with multi-level model predictive control | |
| CN116592417A (en) | Centralized heating system optimal control method and system based on load prediction | |
| CN111750507A (en) | Air conditioner tail end wind and water linkage adjusting system and adjusting method | |
| CN116147128A (en) | Energy-saving control method and system for heating ventilation air conditioner | |
| CN113865018A (en) | Method and system for regulating and controlling power of main unit of water-cooling central air conditioner | |
| CN114543273A (en) | Self-adaptive deep learning optimization energy-saving control algorithm for central air conditioner cooling system | |
| CN117970798A (en) | Optimal control method for energy consumption of air conditioning air system and water system | |
| CN117450688B (en) | Internet of things-based flue gas waste heat recovery energy scheduling management method and system | |
| CN117346417B (en) | Water chiller temperature control system and method based on Internet of things technology | |
| Grishin et al. | Three Stage Approach for Demand Response | |
| CN118378523A (en) | Urban rail ventilation air conditioning system energy-saving control algorithm based on big data and cloud platform |
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 |