CN104965409A - Industrial circulating water system energy consumption self-learning optimization control method - Google Patents
Industrial circulating water system energy consumption self-learning optimization control method Download PDFInfo
- Publication number
- CN104965409A CN104965409A CN201510345472.5A CN201510345472A CN104965409A CN 104965409 A CN104965409 A CN 104965409A CN 201510345472 A CN201510345472 A CN 201510345472A CN 104965409 A CN104965409 A CN 104965409A
- Authority
- CN
- China
- Prior art keywords
- cooling tower
- frequency
- cold
- water pump
- demand
- 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
Landscapes
- Feedback Control In General (AREA)
- Air Conditioning Control Device (AREA)
- Other Air-Conditioning Systems (AREA)
Abstract
The invention discloses an industrial circulating water system energy consumption self-learning optimization control method. Through automatic working condition recognition, when a working condition changes, matching is carried out in a working condition control parameter table according to a demand cooling quantity Qd and an outdoor enthalpy Eo, and equipment control parameters are set; if the working condition appears for the first time, through self-learning optimization adjustment, new equipment control parameters are set, and the equipment control parameters are optimized continuously in system operation. Cooling quantity supply and demand of the system are matched through automatic recognition, the system supply can be prevented from exceeding the demand, energy consumption waste can be avoided, or the supply can be prevented from being smaller than demand, and the production can be prevented from being influenced. According to the demand cooling quantity and the outdoor enthalpy, in a table looking-up method, the optimized control parameters in a historical general working condition are looked up, and stability of system control can be ensured; through continuously adjusting opening of an electric valve of a pipe network slightly in the case of operation, features of the pipeline are optimized, energy consumption can be ensured to be the minimal all the time in the case of operation, energy consumption self-learning control can be finally realized, and the industrial circulating water system optimization control is thus realized.
Description
Technical field
The present invention relates to industrial circulating water system power-saving technology, be specifically related to a kind of industrial circulating water system energy consumption self-learning optimization control method.
Background technology
Industrial circulating water system is generally main comprises water circulating pump, pipe network, cooling tower, electrically operated valve and cooled equipment, wherein, water circulating pump connects cooled equipment by pipeline, cooled equipment connects cooling tower by pipeline, cooling tower connects water pump by pipeline, pipeline arranges electrically operated valve, and all pipelines form pipe network, as shown in Figure 1.Main energy consumption equipment in industrial circulating water system is water circulating pump and cooling tower, and controlled parameter is water pump frequency, cooling tower frequency, electrically operated valve aperture, for cooled equipment provides enough colds.Conventional energy-saving control method is generally the pump variable frequency of single consideration return water temperature.Existing industrial circulating water system has following 4 features:
1. wide application: relate to sintering, coking, ironmaking, steel-making, Rolling production whole process;
2. energy consumption is huge: for mini-medium mill, and only power consumption just reaches more than 300,000,000 degree water pump year;
3. automaticity is lower: mostly use manual operation, even if adopt automatic control system, control overflow and level are not high yet;
4. the energy saving space is huge: circulation is as production backup system, as long as can meet cooling device temperature requirement, more extensive with in management in control technology, energy-saving potential is huge.
Summary of the invention
In view of the subordinate status of industrial circulating water system, energy consumption rich and influential family, equipment disperses, and the feature that automatization level is low, the present invention proposes a kind of industrial circulating water system energy consumption self-learning optimization control method.
The object of the present invention is to provide a kind of industrial circulating water system energy consumption self-learning optimization control method.
Industrial circulating water system energy consumption self-learning optimization control system of the present invention comprises: environmental sensor, controller and actuator; Wherein, environmental sensor comprises outdoor temperature humidity sensor, flow sensor, cooling-water temperature sensor and hydraulic pressure sensor; Actuator comprises: the frequency converter of water pump, the frequency converter of cooling tower and electrically operated valve.
Industrial circulating water system energy consumption self-learning optimization control method of the present invention, comprises the following steps:
1) demand cold Qd and outdoor enthalpy Eo and equipment controling parameters index value ID is stored, form " operating conditions parameter " form that is index with demand cold Qd and outdoor enthalpy Eo, in index value ID, store the equipment controling parameters corresponding with demand cold Qd and outdoor enthalpy Eo, equipment controling parameters comprises water pump frequency Fp, cooling tower frequency Ft and electrically operated valve aperture L;
2) actuator accepts the equipment controling parameters that controller issues, and realizes the control to the frequency converter of water pump, the frequency converter of cooling tower and electrically operated valve;
3) environmental sensor Real-time Collection sensing data, the supply water temperature Ts that sensing data comprises outdoor temperature Tw, outside humidity Dw, recirculated water enters cooling tower, recirculated water flow out the return water temperature Tb of cooling tower, circulation flow V, pump power Pb and cooling tower power P t, and are sent to controller;
4) controller automatic operating identification:
When the supply backwater temperature difference Dt (Dt=Tb-Ts) of circulation is at supply backwater temperature difference setting value Dt
0tolerance Δ Dt within the scope of fluctuation, and return water temperature Tb is no more than return water temperature limit value Tb
0time, namely meet Dt
0-Δ Dt≤Dt≤Dt
0+ Δ Dt, and Tb≤Tb
0then current cooled device requirement cold supplies cold with circulation and mates, judge that operating mode does not change, then equipment controling parameters is constant, return step 2), on the contrary supply cold does not mate with demand cold, then operating mode changes, then need identification demand cold Qd and enter step 5 after calculating real-time outdoor enthalpy Eo);
5) matching unit controling parameters:
When operating mode changes, search need cold Qd and outdoor enthalpy Eo in " operating conditions parameter " form, when demand cold and form have the minimum value min|Qd-Qd (i) of the absolute value of the difference of demand cold Qd (i) | < Δ Qd, the tolerance of Δ Qd demand cold, and the minimum value min|Eo-Eo (j) of the absolute value of the difference of outdoor enthalpy Eo and existing outdoor enthalpy Eo (j) | < Δ Eo, Δ Eo is the tolerance of outdoor enthalpy, then the match is successful, with index value ID (i, j) the equipment controling parameters value in is equipment controling parameters now, return step 2),
If search for the equipment controling parameters index value ID that whole form does not have to mate, show that this operating mode occurs first, then enter step 6) start recirculated water energy consumption Self-learning control, wherein, i and j is respectively natural number;
6) energy consumption Self-learning control:
I. equipment controling parameters initialization:
In order to ensure that system supply cold reaches demand cold before self study, initialization is carried out to equipment controling parameters (water pump frequency Fp, cooling tower frequency Ft, electrically operated valve aperture L);
Ii. self-learning optimization regulates:
Object is the while of ensureing supply cold, reduces system energy consumption, first by adjustment equipment controling parameters, carry out electrically operated valve aperture L to regulate, increase electrically operated valve aperture with constant speed, until return water temperature Tb no longer declines, keep electrically operated valve aperture L now constant;
Then, carry out the optimization of equipment frequency modulation, be divided into and set up standby adjustment, oppositely adjustment and regulate in the same way, under being recorded in five kinds of situations respectively, regulate the minimum P1 ~ P5 of the power sum of cooling tower power P t and pump power Pb in water pump frequency Fp and cooling tower frequency Ft process;
Finally, minimum P1 ~ the P5 of power sum in comparison five kinds of situations, select the minimum min (P1 of minimum power sum ... P5), equipment controling parameters (the water pump frequency Fp corresponding with it, cooling tower frequency Ft, electrically operated valve aperture L) be set to now equipment controling parameters, by work information demand cold Qd, outdoor enthalpy Eo and equipment controling parameters: water circulating pump frequency Fp, cooling tower frequency Ft and electrically operated valve aperture L adds " operating conditions parameter " form, and set up new equipment controling parameters index value ID, complete Self-learning control, return step 1).
Wherein, in step 4) in, supply cold does not mate with demand cold, then system condition changes, then need identification demand cold Qd and calculate real-time outdoor enthalpy Eo:
I. controller identification demand cold Qd, is divided into following two kinds of situations:
A) when working conditions change direction is that demand cold is greater than supply cold, namely supply backwater temperature difference is greater than the tolerance of supply backwater temperature difference setting value, or return water temperature is greater than return water temperature setting value, Dt>Dt
0+ Δ Dt or Tb>Tb
0, unaffected in order to ensure production, preferentially increase flow: increase electrically operated valve aperture, until aperture reaches 100% with constant speed, if supply backwater temperature difference or return water temperature change or ascendant trend, water pump frequency is increased with constant speed, until reach the upper limit of water pump frequency, increase cooling tower frequency with constant speed simultaneously, until reach the upper limit of cooling tower frequency, or supply backwater temperature difference and return water temperature reach limit, namely supply cold and be not less than demand cold, circulation supply cold curve and supply backwater temperature difference curve and return water temperature curve is constantly drawn in adjustment process, after adjustment completes, according to supply backwater temperature difference curve or return water temperature knee point moment query demand cold Qd, Qd=C*V*Td, wherein, C is the constant be directly proportional to fluid density,
B) when working conditions change direction is that demand cold is less than supply cold, namely backwater temperature difference is less than the tolerance of supply backwater temperature difference setting value, or return water temperature is less than return water temperature setting value, Dt<Dt
0-Δ Dt or Tb<Tb
0system also for ease of does not affect production, record the electrically operated valve aperture L (t0) of this moment t0, only with constant rate reduction electrically operated valve aperture, until supply backwater temperature difference and return water temperature reach limit, record supply backwater temperature difference curve or the demand cold Qd in return water temperature knee point moment simultaneously, identified that rear electrically operated valve aperture is oppositely adjusted to L (t0);
Ii. the low-temperature receiver of industrial circulating water system comes from air, and the change of weather is the primary outer factor affecting circulation supply cold and energy consumption, therefore needs while operating mode's switch to calculate real-time outdoor enthalpy Eo:
Eo=1.01Tw+ (2500+1.84Tw) Dw (1) completes the calculating of demand cold Qd and outdoor enthalpy Eo, then operating mode's switch work completes.
In step 6) the initialization of i. equipment controling parameters in, specifically comprise following two kinds of situations:
A) in " operating conditions parameter " form search need cold Qd and form existing demand cold Q (i) difference for negative and minimum and outdoor enthalpy Eo and form existing outdoor enthalpy Eo (j) difference are for bear and minimum, with this equipment controling parameters index value ID (i, j) the equipment controling parameters in is initial optimization parameter, i.e. water pump frequency Fp (t+0)=Fp (i, j), cooling tower frequency Ft (t+0)=Ft (i, j), electrically operated valve aperture L (t+0)=L (i, j), wherein Fp (i, j), Ft (i, j) with L (i, j) equipment controling parameters index value ID (i is respectively, j) the water pump frequency stored in, cooling tower frequency and electrically operated valve aperture,
If b) existing demand cold Qd (i) of form is all less than now demand cold Qd, or existing outdoor enthalpy Eo (j) is all less than outdoor enthalpy Eo now, then the value when initiation parameter of water pump frequency Fp, cooling tower frequency Ft, electrically operated valve aperture L being set to operating mode's switch when supply backwater temperature difference Td or return water temperature Tb flex point.
In step 6) ii. self-learning optimization regulate in, regulate the minimum P1 ~ P5 of the power sum of cooling tower power P t and pump power Pb in water pump frequency Fp and cooling tower frequency Ft process under being recorded in five kinds of situations respectively, specifically comprise following five kinds of situations:
A) standby adjustment is set up:
Independent adjustment water pump frequency Fp, make water pump frequency with constant rate reduction, until water pump frequency Fp reaches the lower limit of water pump frequency, or the power sum of cooling tower power P t and pump power Pb no longer reduces, or cold meets Qd-Δ Qt≤Qt≤Qd+ Δ Qt in real time, Δ Qt is the undulate quantity of demand cold, the minimum P1 of record power sum now;
Independent adjustment cooling tower frequency Ft, make cooling tower frequency Ft with constant rate reduction, until cooling tower frequency reaches the lower limit of cooling tower frequency, or the power sum of cooling tower power P t and pump power Pb no longer reduces, or cold meets Qd-Δ Qt≤Qt≤Qd+ Δ Qt in real time, the minimum P2 of record power sum now;
B) oppositely regulate:
Regulate water pump frequency Fp with constant rate reduction, cooling tower frequency Ft increases with constant speed simultaneously; Until water pump frequency Fp reaches the lower limit of water pump frequency, or cooling tower frequency Ft reaches the upper limit of cooling tower frequency, or the power sum of cooling tower power P t and pump power Pb no longer reduces, or real-time cold Qd-Δ Qt≤Qt≤Qd+ Δ Qt, Δ Qt is the undulate quantity of demand cold, the minimum P3 of recording power sum;
Regulate water pump frequency Fp to increase with constant speed, cooling tower frequency is with constant rate reduction simultaneously; Until water pump frequency Fp reaches the upper limit of water pump frequency, or cooling tower frequency reaches the lower limit of cooling tower frequency, or cooling tower power P t and pump power Pb sum no longer reduce, or cold meets Qd-Δ Qt≤Qt≤Qd+ Δ Qt, the minimum P4 of recording power sum in real time;
C) regulate in the same way:
Regulate water pump frequency Fp with constant rate reduction, cooling tower frequency is with constant rate reduction simultaneously; Until water pump frequency Fp reaches the lower limit of water pump frequency, or cooling tower frequency reaches the lower limit of cooling tower frequency, or cooling tower power P t and pump power Pb sum no longer reduce, or cold meets Qd-Δ Qt≤Qt≤Qd+ Δ Qt, the minimum P5 of recording power sum in real time.
Further, because Changes in weather and technique change cause working conditions change in system cloud gray model, but when being not enough to trigger new operating mode generation, for realizing inner Automatic parameter optimization, control method of the present invention also comprises self-optimizing control, specifically comprises the following steps:
I. by gathering intake pressure Pi, the top hole pressure Po of water pump, the flow V of circulation and pump power Pb, pump efficiency ρ is calculated
p:
Wherein, C is the constant be directly proportional to fluid density;
Ii. by gathering inlet water temperature degree Ti, outlet temperature To, the circulation flow V and cooling tower power P t of cooling tower, efficiency of cooling tower ρ is calculated
t:
Wherein, B is the constant be directly proportional to fluid density and specific heat capacity;
Iii. due to the linear relationship of relative discharge V/Vmax and relative opening degree L/Lmax, optimized algorithm by fine setting electrically operated valve aperture L, thus changes flow V, and fine setting amplitude, between current aperture allowed band, makes pump efficiency ρ
pwith efficiency of cooling tower ρ
treach maximal value, finally realize real time energy consumption self-optimizing control.Allowed band is ± 10%.
Advantage of the present invention:
The present invention, by automatic operating identification, works as working conditions change, and cold Qd and outdoor enthalpy Eo mates in " operating conditions parameter " form according to demand, arranges equipment controling parameters; If Qd and Eo do not matched in form, then operating mode occurs first, is regulated by self-learning optimization, arranges new equipment controling parameters, continues to optimize equipment controling parameters in system cloud gray model.By automatically identifying that guarantee system cold supply and demand is mated, avoiding system to supply and being greater than demand and causing energy consumption waste, or supply is less than demand and causes production influenced; Cold and the method for outdoor enthalpy by tabling look-up according to demand, search history lead to operating mode under optimal control parameter, ensure the stability of Systematical control; Be in operation constantly by the aperture of fine setting pipe network electrically operated valve, optimize pepeline characteristic, during guarantee runs, energy consumption is always minimum, finally realizes energy consumption Self-learning control, thus realizes the optimum control of industrial circulating water system.
Accompanying drawing explanation
Fig. 1 is the structured flowchart of existing industrial circulating water system;
Fig. 2 is the structured flowchart of industrial circulating water system energy consumption self-learning optimization control device of the present invention;
Fig. 3 is the process flow diagram of industrial circulating water system energy consumption self-learning optimization control method of the present invention.
Embodiment
Below in conjunction with accompanying drawing, by embodiment, the present invention will be further described.
As shown in Figure 2, the industrial circulating water system energy consumption self-learning optimization control system of the present embodiment comprises: environmental sensor, controller and actuator; Wherein, environmental sensor comprises outdoor temperature humidity sensor, line sensor, pump sensor and cooling tower sensor; Actuator comprises: the frequency converter of water pump, the frequency converter of cooling tower and electrically operated valve.
As shown in Figure 3, the industrial circulating water system energy consumption self-learning optimization of the present embodiment controls, and comprises the following steps:
1) demand cold Qd and outdoor enthalpy Eo and equipment controling parameters index value ID is stored, form " operating conditions parameter " form that is index with demand cold Qd and outdoor enthalpy Eo;
2) controller controls the equipment controling parameters of actuator, and equipment controling parameters comprises water pump frequency Fp, cooling tower frequency Ft and electrically operated valve aperture L;
3) environmental sensor Real-time Collection sensing data, the supply water temperature Ts that sensing data comprises outdoor temperature Tw, outside humidity Dw, recirculated water enters cooling tower, recirculated water flow out the return water temperature Tb of cooling tower, circulation flow V, pump power Pb and cooling tower power P t, and these sensing datas are sent to controller;
4) controller automatic operating identification:
As the supply backwater temperature difference Dt of circulation, Dt=Tb-Ts, at supply backwater temperature difference setting value Dt
0tolerance Δ Dt=1 ° within the scope of fluctuation, and return water temperature Tb is no more than return water temperature limit value Tb
0time, namely meet Dt
0-1 °≤Dt≤Dt
0+ 1 °, and Tb≤Tb
0then current cooled device requirement cold supplies cold with circulation and mates, judge that operating mode does not change, then equipment controling parameters is constant, return step 2), on the contrary supply cold does not mate with demand cold, then operating mode changes, then identify demand cold Qd and enter step 5 after calculating real-time outdoor enthalpy Eo);
I. controller identification demand cold Qd, is divided into following two kinds of situations:
A) when working conditions change direction is that demand cold is greater than supply cold, namely supply backwater temperature difference is greater than the tolerance of supply backwater temperature difference setting value, or return water temperature is greater than return water temperature setting value, Dt>Dt
0+ 1 ° or Tb>Tb
0, unaffected in order to ensure production, preferentially increase flow: the speed with 10% increases electrically operated valve aperture, until aperture reaches 100%, if supply backwater temperature difference or return water temperature change or ascendant trend, water pump frequency is increased with the speed of 5Hz/s, until reach 50Hz, increase cooling tower frequency with the speed of 7Hz/s simultaneously, until reach the upper limit 50Hz of cooling tower frequency, or supply backwater temperature difference and return water temperature reach limit, namely supply cold and be not less than demand cold, circulation supply cold curve and supply backwater temperature difference curve and return water temperature curve is constantly drawn in adjustment process, after adjustment completes, according to supply backwater temperature difference curve or return water temperature knee point moment query demand cold Qd, Qd=C*V*Td, wherein, C is the constant be directly proportional to fluid density,
B) when working conditions change direction is that demand cold is less than supply cold, namely backwater temperature difference is less than the tolerance of supply backwater temperature difference setting value, or return water temperature is less than return water temperature setting value, Dt<Dt
0-1 ° or Tb<Tb
0system also for ease of does not affect production, record the electrically operated valve aperture L (t0) of this moment t0, only with the rate reduction electrically operated valve aperture of 5%/s, until supply backwater temperature difference and return water temperature reach limit, record supply backwater temperature difference curve or the demand cold Qd in return water temperature knee point moment simultaneously, identified that rear electrically operated valve aperture is oppositely adjusted to L (t0);
Ii. outdoor enthalpy Eo:Eo=1.01Tw+ (2500+1.84Tw) Dw is calculated
Complete the calculating of demand cold Qd and outdoor enthalpy Eo, then operating mode's switch work completes;
5) matching unit controling parameters:
When operating mode changes, search need cold Qd and outdoor enthalpy Eo in " operating conditions parameter " form, when demand cold and form have demand cold difference lower limit min|Qd-Qd (i) | <Qd*10%, and the minimum value min|Eo-Eo (j) of the absolute value of the difference of outdoor enthalpy Eo and existing outdoor enthalpy Eo (j) | <Eo*8%, then the match is successful, with index value ID (i, j) the equipment controling parameters value in is equipment controling parameters (water pump frequency Fp now, cooling tower frequency Ft and electrically operated valve aperture L), return step 2),
If the whole table of traversal search does not have the equipment controling parameters index value ID that can mate, show that this operating mode occurs first, then enter step 6) start recirculated water energy consumption Self-learning control, wherein, i and j is respectively natural number;
6) energy consumption Self-learning control:
I. equipment controling parameters initialization:
A) in " operating conditions parameter " form search need cold Qd and form existing demand cold Q (i) difference for negative and minimum and outdoor enthalpy Eo and form existing outdoor enthalpy Eo (j) difference are for bear and minimum, with this equipment controling parameters index value ID (i, j) the equipment controling parameters in is initial optimization parameter, i.e. water pump frequency Fp (t+0)=Fp (i, j), cooling tower frequency Ft (t+0)=Ft (i, j), electrically operated valve aperture L (t+0)=L (i, j), wherein, Fp (t+0), Ft (t+0) and L (t+0) is respectively water pump frequency, cooling tower frequency and electrically operated valve aperture are at the initiation parameter of t,
If b) existing demand cold Qd (i) of form is all less than now demand cold Qd, or existing outdoor enthalpy Eo (j) is all less than outdoor enthalpy Eo now, then the value when initiation parameter of water pump frequency Fp, cooling tower frequency Ft, electrically operated valve aperture L being set to operating mode's switch when supply backwater temperature difference Td or return water temperature Tb flex point;
Ii. self-learning optimization regulates:
First, carry out electrically operated valve aperture L and regulate, increase electrically operated valve aperture with the speed of 5%/s, until return water temperature Tb no longer declines, keep electrically operated valve aperture L now constant;
Then, carry out the optimization of equipment frequency modulation, be divided into and set up standby adjustment, oppositely adjustment and regulate in the same way:
A) standby adjustment is set up:
Independent adjustment water pump frequency Fp, make water pump frequency with the rate reduction of 1Hz/s, until water pump frequency Fp reaches 35Hz, or the power sum of cooling tower power P t and pump power Pb no longer reduces, or cold meets Qd-Qd*0.5%≤Qt≤Qd+Qd*0.5% in real time, the minimum P1 of record power sum now;
Independent adjustment cooling tower frequency Ft, make cooling tower frequency Ft with the rate reduction of 2Hz/s, until cooling tower frequency reaches 35Hz, or the power sum of cooling tower power P t and pump power Pb no longer reduces, or cold meets Qd-Qd*0.5%≤Qt≤Qd+Qd*0.5% in real time, the minimum P2 of record power sum now;
B) oppositely regulate:
Regulate water pump frequency Fp with the rate reduction of 1Hz/s, cooling tower frequency Ft increases with the speed of 2Hz/s simultaneously; Until water pump frequency Fp reaches 35Hz, or cooling tower frequency Ft reaches 50Hz, or the power sum of cooling tower power P t and pump power Pb no longer reduces, or real-time cold Qd-Qd*0.5%≤Qt≤Qd+Qd*0.5%, the minimum P3 of recording power sum;
Regulate water pump frequency Fp to increase with the speed of 1Hz/s, cooling tower frequency is with the rate reduction of 2Hz/s simultaneously; Until water pump frequency Fp reaches 50Hz, or cooling tower frequency reaches 35Hz, or cooling tower power P t and pump power Pb sum no longer reduce, or cold meets Qd-Qd*0.5%≤Qt≤Qd+Qd*0.5%, the minimum P4 of recording power sum in real time;
C) regulate in the same way:
Regulate water pump frequency Fp with the rate reduction of 1Hz/s, cooling tower frequency is with the rate reduction of 2Hz/s simultaneously; Until water pump frequency Fp reaches 35Hz, or cooling tower frequency reaches 35Hz, or cooling tower power P t and pump power Pb sum no longer reduce, or cold meets Qd-Qd*0.5%≤Qt≤Qd+Qd*0.5%, the minimum P5 of recording power sum in real time;
Finally, minimum P1 ~ the P5 of power sum in comparison five kinds of situations, select the minimum min (P1 of minimum power sum ... P5), the equipment controling parameters (water pump frequency Fp, cooling tower frequency Ft, electrically operated valve aperture L) corresponding with it is set to equipment controling parameters now, work information demand cold Qd, outdoor enthalpy Eo and controling parameters water circulating pump frequency Fp, cooling tower frequency Ft, electrically operated valve aperture L are added " operating conditions parameter " form, complete Self-learning control, return step 1).
It is finally noted that, the object publicizing and implementing mode is to help to understand the present invention further, but it will be appreciated by those skilled in the art that: without departing from the spirit and scope of the invention and the appended claims, various substitutions and modifications are all possible.Therefore, the present invention should not be limited to the content disclosed in embodiment, and the scope that the scope of protection of present invention defines with claims is as the criterion.
Claims (5)
1. an industrial circulating water system energy consumption self-learning optimization control method, industrial circulating water system energy consumption self-learning optimization control system comprises: environmental sensor, controller and actuator; Wherein, environmental sensor comprises outdoor temperature humidity sensor, flow sensor, cooling-water temperature sensor and hydraulic pressure sensor; Actuator comprises: the frequency converter of water pump, the frequency converter of cooling tower and electrically operated valve, is characterized in that, described control method comprises the following steps:
1) demand cold Qd and outdoor enthalpy Eo and equipment controling parameters index value ID is stored, form " operating conditions parameter " form that is index with demand cold Qd and outdoor enthalpy Eo, in index value ID, store the equipment controling parameters corresponding with demand cold Qd and outdoor enthalpy Eo, equipment controling parameters comprises water pump frequency Fp, cooling tower frequency Ft and electrically operated valve aperture L;
2) actuator accepts the equipment controling parameters that controller issues, and realizes the control to the frequency converter of water pump, the frequency converter of cooling tower and electrically operated valve;
3) environmental sensor Real-time Collection sensing data, the supply water temperature Ts that sensing data comprises outdoor temperature Tw, outside humidity Dw, recirculated water enters cooling tower, recirculated water flow out the return water temperature Tb of cooling tower, circulation flow V, pump power Pb and cooling tower power P t, and are sent to controller;
4) controller automatic operating identification:
When the supply backwater temperature difference Dt (Dt=Tb-Ts) of circulation is at supply backwater temperature difference setting value Dt
0tolerance Δ Dt within the scope of fluctuation, and return water temperature Tb is no more than return water temperature limit value Tb
0time, namely meet Dt
0-Δ Dt≤Dt≤Dt
0+ Δ Dt, and Tb≤Tb
0then current cooled device requirement cold supplies cold with circulation and mates, judge that operating mode does not change, then equipment controling parameters is constant, return step 2), on the contrary supply cold does not mate with demand cold, then operating mode changes, then need identification demand cold Qd and enter step 5 after calculating real-time outdoor enthalpy Eo);
5) matching unit controling parameters:
When operating mode changes, search need cold Qd and outdoor enthalpy Eo in " operating conditions parameter " form, when demand cold and form have the minimum value min|Qd-Qd (i) of the absolute value of the difference of demand cold Qd (i) | < Δ Qd, the tolerance of Δ Qd demand cold, and the minimum value min|Eo-Eo (j) of the absolute value of the difference of outdoor enthalpy Eo and existing outdoor enthalpy Eo (j) | < Δ Eo, Δ Eo is the tolerance of outdoor enthalpy, then the match is successful, with index value ID (i, j) the equipment controling parameters value in is equipment controling parameters now, return step 2),
If search for the equipment controling parameters index value ID that whole form does not have to mate, show that this operating mode occurs first, then enter step 6) start recirculated water energy consumption Self-learning control, wherein, i and j is respectively natural number;
6) energy consumption Self-learning control:
I. equipment controling parameters initialization:
Initialization is carried out to equipment controling parameters;
Ii. self-learning optimization regulates:
First, carry out electrically operated valve aperture L and regulate, increase electrically operated valve aperture with constant speed, until return water temperature Tb no longer declines, keep electrically operated valve aperture L now constant;
Then, carry out the optimization of equipment frequency modulation, be divided into and set up standby adjustment, oppositely adjustment and regulate in the same way, under being recorded in five kinds of situations respectively, regulate the minimum P1 ~ P5 of the power sum of cooling tower power P t and pump power Pb in water pump frequency Fp and cooling tower frequency Ft process;
Finally, minimum P1 ~ the P5 of power sum in comparison five kinds of situations, select the minimum min (P1 of minimum power sum ... P5), the equipment controling parameters corresponding with it is set to equipment controling parameters now, by work information demand cold Qd, outdoor enthalpy Eo and equipment controling parameters: water circulating pump frequency Fp, cooling tower frequency Ft and electrically operated valve aperture L add " operating conditions parameter " form, and set up new equipment controling parameters index value ID, complete Self-learning control, return step 1).
2. control method as claimed in claim 1, is characterized in that, in step 4) in, identify demand cold Qd and calculate real-time outdoor enthalpy Eo:
I. controller identification demand cold Qd, is divided into following two kinds of situations:
A) when working conditions change direction is that demand cold is greater than supply cold, namely supply backwater temperature difference is greater than the tolerance of supply backwater temperature difference setting value, or return water temperature is greater than return water temperature setting value, Dt>Dt
0+ Δ Dt or Tb>Tb
0, unaffected in order to ensure production, preferentially increase flow: increase electrically operated valve aperture, until aperture reaches 100% with constant speed, if supply backwater temperature difference or return water temperature change or ascendant trend, water pump frequency is increased with constant speed, until reach the upper limit of water pump frequency, increase cooling tower frequency with constant speed simultaneously, until reach the upper limit of cooling tower frequency, or supply backwater temperature difference and return water temperature reach limit, namely supply cold and be not less than demand cold, circulation supply cold curve and supply backwater temperature difference curve and return water temperature curve is constantly drawn in adjustment process, after adjustment completes, according to supply backwater temperature difference curve or return water temperature knee point moment query demand cold Qd, Qd=C*V*Td, wherein, C is the constant be directly proportional to fluid density,
B) when working conditions change direction is that demand cold is less than supply cold, namely backwater temperature difference is less than the tolerance of supply backwater temperature difference setting value, or return water temperature is less than return water temperature setting value, Dt<Dt
0-Δ Dt or Tb<Tb
0system also for ease of does not affect production, record the electrically operated valve aperture L (t0) of this moment t0, only with constant rate reduction electrically operated valve aperture, until supply backwater temperature difference and return water temperature reach limit, record supply backwater temperature difference curve or the demand cold Qd in return water temperature knee point moment simultaneously, identified that rear electrically operated valve aperture is oppositely adjusted to L (t0);
Ii. real-time outdoor enthalpy Eo is calculated:
Eo＝1.01Tw+(2500+1.84Tw)Dw
Complete the calculating of demand cold Qd and outdoor enthalpy Eo, then operating mode's switch work completes.
3. control method as claimed in claim 1, is characterized in that, in step 6) the initialization of i. equipment controling parameters comprise following two kinds of situations:
A) in " operating conditions parameter " form search need cold Qd and form existing demand cold Q (i) difference for negative and minimum and outdoor enthalpy Eo and form existing outdoor enthalpy Eo (j) difference are for bear and minimum, with this equipment controling parameters index value ID (i, j) the equipment controling parameters in is initial optimization parameter, i.e. water pump frequency Fp (t+0)=Fp (i, j), cooling tower frequency Ft (t+0)=Ft (i, j), electrically operated valve aperture L (t+0)=L (i, j), wherein Fp (i, j), Ft (i, j) with L (i, j) equipment controling parameters index value ID (i is respectively, j) the water pump frequency stored in, cooling tower frequency and electrically operated valve aperture,
If b) existing demand cold Qd (i) of form is all less than now demand cold Qd, or existing outdoor enthalpy Eo (j) is all less than outdoor enthalpy Eo now, then the value when initiation parameter of water pump frequency Fp, cooling tower frequency Ft, electrically operated valve aperture L being set to operating mode's switch when supply backwater temperature difference Td or return water temperature Tb flex point.
4. control method as claimed in claim 1, it is characterized in that, in step 6) ii. self-learning optimization regulate in, regulate the minimum P1 ~ P5 of the power sum of cooling tower power P t and pump power Pb in water pump frequency Fp and cooling tower frequency Ft process under being recorded in five kinds of situations respectively, specifically comprise following five kinds of situations:
A) standby adjustment is set up:
Independent adjustment water pump frequency Fp, make water pump frequency with constant rate reduction, until water pump frequency Fp reaches the lower limit of water pump frequency, or the power sum of cooling tower power P t and pump power Pb no longer reduces, or cold meets Qd-Δ Qt≤Qt≤Qd+ Δ Qt in real time, Δ Qt is the undulate quantity of demand cold, the minimum P1 of record power sum now;
Independent adjustment cooling tower frequency Ft, make cooling tower frequency Ft with constant rate reduction, until cooling tower frequency reaches the lower limit of cooling tower frequency, or the power sum of cooling tower power P t and pump power Pb no longer reduces, or cold meets Qd-Δ Qt≤Qt≤Qd+ Δ Qt in real time, the minimum P2 of record power sum now;
B) oppositely regulate:
Regulate water pump frequency Fp with constant rate reduction, cooling tower frequency Ft increases with constant speed simultaneously; Until water pump frequency Fp reaches the lower limit of water pump frequency, or cooling tower frequency Ft reaches the upper limit of cooling tower frequency, or the power sum of cooling tower power P t and pump power Pb no longer reduces, or real-time cold Qd-Δ Qt≤Qt≤Qd+ Δ Qt, Δ Qt is the undulate quantity of demand cold, the minimum P3 of recording power sum;
Regulate water pump frequency Fp to increase with constant speed, cooling tower frequency is with constant rate reduction simultaneously; Until water pump frequency Fp reaches the upper limit of water pump frequency, or cooling tower frequency reaches the lower limit of cooling tower frequency, or cooling tower power P t and pump power Pb sum no longer reduce, or cold meets Qd-Δ Qt≤Qt≤Qd+ Δ Qt, the minimum P4 of recording power sum in real time;
C) regulate in the same way:
Regulate water pump frequency Fp with constant rate reduction, cooling tower frequency is with constant rate reduction simultaneously; Until water pump frequency Fp reaches the lower limit of water pump frequency, or cooling tower frequency reaches the lower limit of cooling tower frequency, or cooling tower power P t and pump power Pb sum no longer reduce, or cold meets Qd-Δ Qt≤Qt≤Qd+ Δ Qt, the minimum P5 of recording power sum in real time.
5. control method as claimed in claim 1, is characterized in that, comprise self-optimizing control further, comprise the following steps: i., by gathering intake pressure Pi, the top hole pressure Po of water pump, the flow V of circulation and pump power Pb, calculates pump efficiency ρ
p:
Wherein, C is the constant be directly proportional to fluid density;
Ii. by gathering inlet water temperature degree Ti, outlet temperature To, the circulation flow V and cooling tower power P t of cooling tower, efficiency of cooling tower ρ is calculated
t:
Wherein, B is the constant be directly proportional to fluid density and specific heat capacity;
Iii. the linear relationship of relative discharge V/Vmax and relative opening degree L/Lmax, optimized algorithm by fine setting electrically operated valve aperture L, thus changes flow V, and fine setting amplitude, between current aperture allowed band, makes pump efficiency ρ
pwith efficiency of cooling tower ρ
treach maximal value, finally realize real time energy consumption self-optimizing control.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510345472.5A CN104965409B (en) | 2015-06-19 | 2015-06-19 | A kind of industrial circulating water system energy consumption self-learning optimization control method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510345472.5A CN104965409B (en) | 2015-06-19 | 2015-06-19 | A kind of industrial circulating water system energy consumption self-learning optimization control method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104965409A true CN104965409A (en) | 2015-10-07 |
| CN104965409B CN104965409B (en) | 2017-06-09 |
Family
ID=54219447
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510345472.5A Active CN104965409B (en) | 2015-06-19 | 2015-06-19 | A kind of industrial circulating water system energy consumption self-learning optimization control method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104965409B (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109225640A (en) * | 2018-10-15 | 2019-01-18 | 厦门邑通软件科技有限公司 | A kind of wisdom electric precipitation power-economizing method |
| CN110471278A (en) * | 2019-07-23 | 2019-11-19 | 江苏天晟节能科技有限公司 | A kind of industrial circulating water system fixed-voltage altering-current amount control method |
| CN110470039A (en) * | 2019-09-06 | 2019-11-19 | 创新奇智(重庆)科技有限公司 | A kind of air conditioner water valve regulation method based on the theory of optimal control |
| CN110597207A (en) * | 2019-09-18 | 2019-12-20 | 中冶赛迪重庆信息技术有限公司 | Coking operation action amount identification method and system and computer readable storage medium |
| CN111829043A (en) * | 2020-07-23 | 2020-10-27 | 杭州源牌科技股份有限公司 | Control method and device |
| CN113790516A (en) * | 2021-09-18 | 2021-12-14 | 深圳达实智能股份有限公司 | Global optimization energy-saving control method and system for central air-conditioning refrigeration station and electronic equipment |
| CN117234071A (en) * | 2023-11-16 | 2023-12-15 | 中控技术股份有限公司 | Circulating water system energy-saving optimization method and device based on temperature and pressure control |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1622009A1 (en) * | 2004-07-27 | 2006-02-01 | Texas Instruments Incorporated | JSM architecture and systems |
| JP2012053505A (en) * | 2010-08-31 | 2012-03-15 | Hitachi Ltd | Plant control system and thermal power plant control system |
| CN102508474A (en) * | 2011-11-01 | 2012-06-20 | 杭州哲达科技股份有限公司 | Circulated cooling water operation optimization control system for industrial enterprise |
| CN102536774A (en) * | 2011-12-27 | 2012-07-04 | 酒泉钢铁(集团)有限责任公司 | Operation characteristics and operation energy efficiency analyzing device of industrial circulating water pump set |
| CN102562561A (en) * | 2011-12-27 | 2012-07-11 | 酒泉钢铁(集团)有限责任公司 | Method for analyzing operation energy efficiency of pump set of industrial circulating water system |
| CN103063080A (en) * | 2012-11-09 | 2013-04-24 | 中冶南方工程技术有限公司 | Temperature control method of cinder-flushing water of cooling tower outlet of blast furnace slag processing system |
| CN103718127A (en) * | 2011-05-31 | 2014-04-09 | 西门子工业公司 | Device and method for optimization of chilled water plant system operation |
-
2015
- 2015-06-19 CN CN201510345472.5A patent/CN104965409B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1622009A1 (en) * | 2004-07-27 | 2006-02-01 | Texas Instruments Incorporated | JSM architecture and systems |
| JP2012053505A (en) * | 2010-08-31 | 2012-03-15 | Hitachi Ltd | Plant control system and thermal power plant control system |
| CN103718127A (en) * | 2011-05-31 | 2014-04-09 | 西门子工业公司 | Device and method for optimization of chilled water plant system operation |
| CN102508474A (en) * | 2011-11-01 | 2012-06-20 | 杭州哲达科技股份有限公司 | Circulated cooling water operation optimization control system for industrial enterprise |
| CN102536774A (en) * | 2011-12-27 | 2012-07-04 | 酒泉钢铁(集团)有限责任公司 | Operation characteristics and operation energy efficiency analyzing device of industrial circulating water pump set |
| CN102562561A (en) * | 2011-12-27 | 2012-07-11 | 酒泉钢铁(集团)有限责任公司 | Method for analyzing operation energy efficiency of pump set of industrial circulating water system |
| CN103063080A (en) * | 2012-11-09 | 2013-04-24 | 中冶南方工程技术有限公司 | Temperature control method of cinder-flushing water of cooling tower outlet of blast furnace slag processing system |
Non-Patent Citations (1)
| Title |
|---|
| 李丹美，等: "纺织空调系统的整体节能控制", 《棉纺织技术》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109225640A (en) * | 2018-10-15 | 2019-01-18 | 厦门邑通软件科技有限公司 | A kind of wisdom electric precipitation power-economizing method |
| CN110471278A (en) * | 2019-07-23 | 2019-11-19 | 江苏天晟节能科技有限公司 | A kind of industrial circulating water system fixed-voltage altering-current amount control method |
| CN110470039A (en) * | 2019-09-06 | 2019-11-19 | 创新奇智(重庆)科技有限公司 | A kind of air conditioner water valve regulation method based on the theory of optimal control |
| CN110597207A (en) * | 2019-09-18 | 2019-12-20 | 中冶赛迪重庆信息技术有限公司 | Coking operation action amount identification method and system and computer readable storage medium |
| CN111829043A (en) * | 2020-07-23 | 2020-10-27 | 杭州源牌科技股份有限公司 | Control method and device |
| CN113790516A (en) * | 2021-09-18 | 2021-12-14 | 深圳达实智能股份有限公司 | Global optimization energy-saving control method and system for central air-conditioning refrigeration station and electronic equipment |
| CN117234071A (en) * | 2023-11-16 | 2023-12-15 | 中控技术股份有限公司 | Circulating water system energy-saving optimization method and device based on temperature and pressure control |
| CN117234071B (en) * | 2023-11-16 | 2024-01-26 | 中控技术股份有限公司 | Circulating water system energy-saving optimization method and device based on temperature and pressure control |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104965409B (en) | 2017-06-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104965409A (en) | Industrial circulating water system energy consumption self-learning optimization control method | |
| CN105020845B (en) | A kind of air-conditioning system linkage energy-saving control system and method | |
| CN108990383A (en) | A kind of data center's air-conditioning system forecast Control Algorithm | |
| CN100491852C (en) | Automatic control method for central cold supply system | |
| CN102367980B (en) | Central air-conditioning multi-dimension integrating optimization control system and method | |
| CN107421029B (en) | Tail end cold quantity balance control method | |
| CN109341010B (en) | Energy supply integrated control method and device for air conditioning system of electric refrigerator | |
| CN105571063A (en) | Shallow geothermal energy source management system and implementation method thereof | |
| CN105864016B (en) | A kind of more water pump distributing system variable water volume runing adjustment methods of open type | |
| CN115823706B (en) | Self-adaptive variable pressure difference energy-saving control system and method for primary pump | |
| CN204902127U (en) | Terminal coordinated control's of air conditioner water system and air conditioner economizer | |
| CN105444356A (en) | Intelligent energy efficiency optimizing control system for central air conditioning system and control method of intelligent energy efficiency optimizing control system | |
| CN103994553A (en) | Refrigerating system cooling water energy-saving control method, system and device | |
| CN104501421A (en) | Control method of variable-frequency two-stage compressive heat pump water heater | |
| CN113915719B (en) | Real-time frequency conversion control method and controller for central air-conditioning water pump | |
| CN203880881U (en) | Air-conditioner refrigeration water temperature self-adaptation regulating system | |
| CN113218040A (en) | Energy efficiency improvement control method for central air-conditioning system | |
| CN115049141A (en) | Control method and control device for refrigeration system and refrigeration system | |
| CN115903712A (en) | Energy-saving optimization method and optimization control system suitable for industrial refrigeration system | |
| CN201964577U (en) | Energy-saving control system for central air conditioner | |
| CN203857618U (en) | Self-adaptive throttle control equipment for water cooling unit freezing pump of central air conditioner | |
| CN203824002U (en) | Optimal control system for comprehensive electricity unit consumption of refrigeration station for central air conditioner | |
| CN108224632A (en) | Hotel Buildings central air conditioner room chilled water system comprehensive energy efficiency lifting control method | |
| CN209181068U (en) | Energy saving regulator control system of pipe network dynamic load intelligent pressure regulating difference | |
| CN112556258A (en) | Heat pump intelligent control method for compensating time delay |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20180920 Address after: 315400 Zhejiang Yuyao Economic Development Zone East New District Patentee after: Zhejiang wisdom Technology Co., Ltd. Address before: 100084 room 103, block B, research and research complex, Tsinghua University, Qinghua garden, Haidian District, Beijing. Patentee before: BEIJING GAN WEI TECHNOLOGY DEVELOPMENT CO., LTD. |