CN101670184A - Coordination optimizing control system of multiple targets, such as output, quality, energy consumption of evaporation device - Google Patents
Coordination optimizing control system of multiple targets, such as output, quality, energy consumption of evaporation device Download PDFInfo
- Publication number
- CN101670184A CN101670184A CN200910181159A CN200910181159A CN101670184A CN 101670184 A CN101670184 A CN 101670184A CN 200910181159 A CN200910181159 A CN 200910181159A CN 200910181159 A CN200910181159 A CN 200910181159A CN 101670184 A CN101670184 A CN 101670184A
- Authority
- CN
- China
- Prior art keywords
- liquid level
- evaporator
- concentration
- energy consumption
- quality
- 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
- 238000001704 evaporation Methods 0.000 title claims abstract description 34
- 230000008020 evaporation Effects 0.000 title claims abstract description 34
- 238000005265 energy consumption Methods 0.000 title claims abstract description 16
- 239000007788 liquid Substances 0.000 claims abstract description 109
- 239000000463 material Substances 0.000 claims abstract description 50
- 238000005259 measurement Methods 0.000 claims abstract description 42
- 238000013528 artificial neural network Methods 0.000 claims abstract description 21
- 238000012937 correction Methods 0.000 claims abstract description 11
- 238000005096 rolling process Methods 0.000 claims abstract description 7
- 230000000694 effects Effects 0.000 claims description 41
- 238000000034 method Methods 0.000 claims description 18
- 238000005457 optimization Methods 0.000 claims description 15
- 230000008569 process Effects 0.000 claims description 14
- 238000003062 neural network model Methods 0.000 claims description 7
- 230000001105 regulatory effect Effects 0.000 claims description 3
- 238000004519 manufacturing process Methods 0.000 description 10
- 238000005516 engineering process Methods 0.000 description 7
- 238000004458 analytical method Methods 0.000 description 6
- 230000003750 conditioning effect Effects 0.000 description 4
- 230000000704 physical effect Effects 0.000 description 4
- 239000000243 solution Substances 0.000 description 4
- 238000012549 training Methods 0.000 description 4
- 230000008859 change Effects 0.000 description 3
- 238000002425 crystallisation Methods 0.000 description 3
- 230000008025 crystallization Effects 0.000 description 3
- 239000007791 liquid phase Substances 0.000 description 3
- 230000003044 adaptive effect Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 230000007774 longterm Effects 0.000 description 2
- 238000013178 mathematical model Methods 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 230000001151 other effect Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000000926 separation method Methods 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000008151 electrolyte solution Substances 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 230000014759 maintenance of location Effects 0.000 description 1
- 238000002156 mixing Methods 0.000 description 1
- 238000000746 purification Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 150000003839 salts Chemical class 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Images
Landscapes
- Feedback Control In General (AREA)
Abstract
The invention discloses a coordination optimizing control system of multiple targets, such as output, quality, energy consumption of an evaporation device, comprising an evaporator liquid level forecasting model with an online correction function, a high-precision evaporator materiel concentration neural network soft measurement model and a multivariable decoupling control model. The evaporator liquid level forecasting model is established based on evaporator material balance and heat quantity balance, and online rolling correction is carried out based on a liquid level signal actually measured by an evaporator liquid level switch meter so as to realize the prediction to continuous measurement values of the evaporator liquid level. The technological parameters having important degree of association with the material are introduced; the predicted value variable of the evaporator liquid level is used as an input variable of the neural network soft measurement model to realize the onlinesoft measurement with high precision of the materiel concentration. On the basis, the multivariable decoupling control is adopted to coordinate evaporator concentration and liquid level, thus realizing coordination optimizing control of multiple targets, such as output, quality, and energy consumption of the evaporation device.
Description
(1) Field of the invention
The invention relates to a coordinated optimization control system of an evaporation device, in particular to a multi-target coordinated optimization system of the yield, the quality and the energy consumption of the evaporation device.
(2) Background of the invention
The multi-effect evaporation device is widely applied to the concentration process of inorganic chemical product solutions such as salts, alkalis and the like and organic chemical product solutions, in order to obtain products with certain concentration, the evaporation device is generally formed by serially connecting multi-effect evaporation devices, each effect of evaporation comprises one or more evaporators connected in parallel, and the main production control indexes comprise the quality, the yield and the energy consumption of the products. When the evaporation device operates, the materials need to keep a certain retention time in each effect evaporator, and a reasonable concentration gradient is formed on each effect evaporator so as to exert the effect of each effect evaporator; on the other hand, the liquid level of each effect evaporator is maintained to be stable as much as possible, the phenomenon that the evaporator is lifted and falls greatly in operation is avoided, so that an effective separation space is obtained, the separation effect is improved, the product loss caused by the fact that secondary steam carries materials is avoided, meanwhile, the crystallization of certain components in the materials at improper time can be prevented, the heat transfer coefficient reduction caused by evaporator scaling is avoided, and the economic operation problems of low equipment efficiency, high energy consumption and the like caused by frequent equipment cleaning are avoided.
Evaporation is a physical process of material concentration and purification, and in order to ensure that an evaporation device exerts the maximum production intensity, the product quality is stable and the product energy consumption is the lowest, full-automatic production of the whole production process is required to be realized, the efficiency of each evaporator is coordinated and improved, and the closed-loop control of the product quality is realized on the basis. The current control scheme for evaporation plants is mainly based on the following ideas: the existing technology comprises that the product quality is controlled according to a temperature difference method, so that the product quality deviation is large, secondary blending can be carried out only according to a manual analysis value of the material concentration in a product storage tank, and the product quality is extremely unstable; in the prior art, the on-line soft measurement of the material concentration is realized by adopting a soft measurement technology, the measurement precision is greatly improved, but the practical requirement of production is still difficult to meet, and particularly, when the working condition fluctuation is large, the measurement precision is greatly reduced; the prior art adopts a differential pressure transmitter to measure the liquid level of an evaporator, a measuring instrument is difficult to normally operate, and for electrolyte solution, the effect of adopting electrode type liquid level measurement is much better, but false liquid level is often generated, so that a signal conditioning plate capable of processing the false liquid level is invented in the prior art, and the accuracy of the electrode type liquid level measurement is greatly improved; the control strategy in the existing mature evaporation control technology is designed based on the level switch type measurement mode and the concentration soft measurement technology, and is just the limitation of the evaporator liquid level and concentration soft measurement technology, so that the application of the advanced control and optimization control technology in the evaporation device is limited.
The above scheme has an unsatisfactory effect in practical application, and mainly has the following reasons:
1. the problem of false liquid level caused by failure or measurement of a liquid level transmitter due to crystallization accompanying the evaporation process is the first problem that the automatic control of an evaporation device is difficult to keep long-term stable operation, the liquid level of the existing evaporator is usually measured by adopting a two-position electrode, and the continuous liquid level meter is difficult to operate stably for a long time. The two-position electrode measurement reflects the step change of the liquid level, which is discontinuous, thus bringing certain difficulty to the stable control of the liquid level and the concentration of the evaporator, failing to achieve fine adjustment and easily causing abnormal fluctuation of the liquid level and the concentration of the evaporator;
2. the measurement of material concentration is the key to realize the basic automation and the optimized control of the evaporation device. Because the on-line analysis instrument of the material concentration has the factors of high price, extreme instability, difficult maintenance, low precision and the like at present, the existing soft measurement technology of the material concentration of the evaporation device has poor precision and working condition adaptability of soft measurement because the influence of the liquid level of an evaporator with important relevance is not considered.
Due to the problems in the evaporation production process, it is very difficult to ensure the long-term stable operation of the control system of the evaporation device and obtain the effects of maximum yield, stable quality and minimum energy consumption of the device.
(3) The invention content is as follows:
the invention provides a multi-target coordinated optimization control system for yield, quality and energy consumption of an evaporation device.
According to the invention, an evaporator liquid level prediction model is established according to material balance and heat balance, and online correction is carried out according to a level type liquid level signal actually measured by an evaporator obtained by a liquid level switch, so as to obtain a continuous evaporator liquid level prediction value; introducing a process parameter which has important relevance with the material concentration, namely the evaporator liquid level, as an auxiliary variable for soft measurement of the material concentration, and realizing high-precision soft measurement of the material concentration by adopting a neural network model; and finally, according to the predicted value of the evaporator liquid level and the high-precision soft measurement of the material concentration, adopting multivariable decoupling control to realize multi-target coordinated optimization control of the yield, the quality and the energy consumption of the evaporation device.
The liquid level of the evaporator adopts a two-position electrode measuring instrument, a switching value level measuring signal conditioning plate capable of processing false liquid level is configured, the output of the signal conditioning plate is analog quantity, the size of the signal conditioning plate can reflect the conductivity of materials at the position where the electrode is measured, a control system can diagnose the false liquid level caused by steam fog and crystallization according to the analog quantity to accurately judge the position where the liquid level of the evaporator is located, and the evaporator is generally provided with 3-5 measuring electrodes, so that the liquid level of the evaporator can be detected in a position mode.
The adjusting instrument can select an adjusting valve, a frequency converter or a switch cut-off valve according to the material characteristics.
Evaporator liquid level prediction model
The evaporator liquid level prediction model is set up to continuously predict the liquid level of the evaporator. Establishing an evaporator liquid level prediction model based on an evaporator material balance and heat balance mathematical model, and performing online correction on a position type state signal of the evaporator liquid level obtained by measuring through a two-position type electrode to realize online prediction on the evaporator liquid level;
heat balance equation of evaporator
F1×H1+F2×H2＝F3×H3+F4×H4+C×(L×S×ρ+V0×ρ)×ΔT
Material balance equation of evaporator
F1-F4-F3＝dL/dt×S×ρ
Wherein,
f1 and F3 are respectively the inlet and outlet flow of the evaporator; h1 and H3 are corresponding enthalpy values respectively, and can be obtained from a physical property manual according to temperature or pressure;
f2 and F4 are respectively the primary steam flow and the secondary steam flow of the evaporator; h2 and H4 are corresponding enthalpy values respectively,
can be obtained from a physical property manual according to temperature or pressure;
l, S, rho and C are respectively the liquid level and the cross-sectional area of the evaporator and the density and specific heat of the solution;
v0 is the volume of solution at 0% evaporator level;
delta T is the variation of the material liquid phase temperature in the evaporator;
the evaporator liquid level prediction model expressed by the difference equation can be prepared according to the equation
L(k+1)＝a×L(k)+b
A secondary steam flow prediction model can also be obtained
F4(k)＝c×L(k)+d
Correction of evaporator liquid level prediction model
Due to various interference factors existing in the industrial production process and the possible problem of the accuracy of parameters in the mechanism model, the predicted value of the evaporator liquid level and the actual liquid level have certain deviation inevitably. According to the measured evaporator liquid level two-position type liquid level measuring signal, the prediction model is corrected in a rolling mode, namely when one liquid level electrode contact is in contact connection, the actual liquid level height represented by the electrode is used as the actual measured value of the liquid level, and the deviation E (k) between the measured liquid level value and the predicted value is used for correcting the liquid level prediction model.
L(k+1)＝a×L(k)+b+E(k)
E(k)＝(L’(k)-L(k))
Wherein, L' (k) is the actual liquid level height represented by a certain liquid level electrode contact which is attracted at the latest moment, and E (k) is calculated once at the moment until a new liquid level electrode contact is attracted. By the method, when a new liquid level electrode contact is attracted at intervals, the actual liquid level value is used for carrying out online rolling correction on the liquid level prediction model, so that the liquid level prediction model is adaptive to the change of production working conditions and overcomes the influence of various interference factors, and an accurate evaporator liquid level measurement value is obtained.
Wherein a, b, c, d are related to variables and parameters in the evaporator material balance and heat balance equations and are calculable.
Establishment of high-precision evaporator material concentration neural network soft measurement model
A Back Propagation (BP) neural network is adopted to establish a soft measurement model of the evaporator material concentration neural network.
Selecting process parameters influencing the material concentration of the evaporator as auxiliary variables of soft measurement, wherein the process parameters comprise primary steam pressure Pi1, secondary steam pressure Pi2, evaporator liquid phase temperature Ti and predicted value Li of the liquid level of the evaporator, taking current time values Pi1(k), Pi2(k), Ti (k) and Li (k) of the four variables into consideration as input variables of a material concentration neural network soft measurement model, and selecting an artificial analysis value of the material concentration as an output variable of the neural network soft measurement model.
In the neural network model, the node of an input layer is i (i is 4), the number of hidden layer layers of an intermediate layer is m (m is 1 to 100), the number of hidden layer nodes is n (n is 2 to 100), and the number of output layer nodes is j (j is 1 to 100).
The method comprises the steps of utilizing an evaporator material concentration process mechanism mathematical model, testing around an operation point of a process device, generating and obtaining 50 groups of evaporator production process data with a large operation range, and taking 200 groups of data in total as training samples of an evaporator material concentration soft measurement neural network model together with 150 groups of data acquired in an industrial field. A forward BP neural network with a 4 x 1 structure is selected, input nodes of the forward BP neural network correspond to Pi1(k), Pi2(k) and Ti (k) field values of the evaporator and predicted values of the liquid level Li (k) of the evaporator, and output nodes of the forward BP neural network correspond to a material concentration manual analysis value (see figure 1).
After offline training of the neural network, weight values and threshold values of the neural network are obtained, and soft measurement values of evaporator material concentration can be output by the neural network model by acquiring Pi1(k), Pi2(k), Ti (k), Li (k) field values and predicted values of evaporator liquid level Li (k), wherein the unit is volume percentage.
Establishment of multi-target coordination optimization control model of evaporation device
A concentration set value calculation unit of each effect evaporator is designed. The concentration of the last effect evaporator is controlled by adopting a fixed value, the concentration set value of the previous effect evaporator is set by taking the online soft measurement value of the last effect concentration as a base point according to the deviation of the process requirement, and the concentration set values of other effect evaporators are set according to the principle by analogy in sequence;
a4-input 1-output multivariable decoupling control system is designed. The input variables comprise the liquid level L (i) and the concentration A (i) of the front effect evaporator, the liquid level L (i +1) and the concentration A (i +1) of the rear effect evaporator, and the output is the discharge valve position of the front effect evaporator. If the discharge valve is a two-position type cut-off valve, the output signal is further processed, and multivariable decoupling control is realized by adopting a duty ratio control algorithm (see figure 2) so as to establish reasonable concentration gradient in each effect evaporator and stabilize the liquid level of each effect evaporator to the maximum extent on the basis.
The discharge valve of the front effect evaporator is also a feed valve of the back effect evaporator, the liquid level and the concentration of the front effect evaporator and the back effect evaporator can be simultaneously influenced by adjusting the valve, and the concentration and the liquid level of each effect evaporator can be coordinately and optimally controlled by adjusting the action strength and the output weight of the LIC (i), AIC (i), LIC (i +1) and AIC (i +1) regulators.
By using the method of the invention to carry out optimization control on evaporation, the yield of the device can be improved, the steam consumption can be reduced, the product quality can be stabilized, and the evaporation device is in the best operation condition.
(4) Description of the drawings
FIG. 1 is a neural network soft measurement model of material concentration of a high-precision evaporator;
FIG. 2 is a multi-objective coordination optimization control model of the evaporation plant;
(5) detailed description of the preferred embodiments
Evaporator liquid level prediction model
Obtaining measurement variables related to material balance and heat balance of an evaporator on a DCS, wherein the measurement variables comprise material flow rates F1 and F3 and temperatures T1 and T3 of an inlet and an outlet of the evaporator, and enthalpy values H1 and H3 of the inlet and the outlet of the evaporator are obtained from a physical property manual according to the temperatures; the variables to be acquired also comprise the liquid temperature T of the evaporator, the primary steam flow F2, the temperatures T1 and T2 of the primary steam and the secondary steam, the pressures P1 and P2, and the enthalpy values H2 and H4 of the primary steam and the secondary steam are calculated according to the temperature and the pressure and table lookup according to a physical property handbook.
The evaporator inlet and outlet material flow rates F1 and F3 can also be calculated by the following formula, where F is Qmax × F (L/L), where Qmax and F (L/L) are the maximum flow rate and valve flow rate characteristic curves of the valve, respectively, provided by the valve manufacturer. According to the following evaporator material balance and heat balance equations, namely:
heat balance equation of evaporator
F1×H1+F2×H2＝F3×H3+F4×H4+C×(L×S×ρ+V0×ρ)×ΔT
Material balance equation of evaporator
F1-F4-F3＝dL/dt×S×ρ
Obtaining an evaporator liquid level prediction model: l (k +1) ═ a × L (k) + b
A secondary steam flow prediction model can also be obtained: f4(k) ═ c × l (k) + d
Wherein a, b, c, d are related to variables and parameters in the evaporator material balance and heat balance equations and are calculable.
Correction of evaporator liquid level prediction model
Obtaining switch variables representing the evaporator liquid level on DCS, if 5 electrodes are adopted to measure the liquid level, obtaining 5 switch variables, respectively representing 90%, 70%, 50%, 30% and 10% of the evaporator liquid level from high to low, and performing 'rolling' on-line correction on the prediction model according to the state of an actually measured two-position liquid level measurement signal of the evaporator liquid level, namely when a certain liquid level electrode contact is absorbed, using the actual liquid level height represented by the liquid level switch (electrode) as an actual measured value of the liquid level, and correcting the liquid level prediction model by using the deviation E (k) between the actual measured value and a predicted value of the liquid level.
L(k+1)＝a×L(k)+b+E(k)
E(k)＝(L’(k)-L(k))
Wherein, L' (k) is the actual liquid level height represented by a certain liquid level electrode contact which is attracted at the latest moment, and E (k) is calculated once at the moment until a new liquid level electrode contact is attracted. In this way, when a new liquid level electrode contact is attracted at intervals, the actual liquid level value represented by the new liquid level electrode contact is used for carrying out online rolling correction on the liquid level prediction model, so that the liquid level prediction model is adaptive to the change of production working conditions and overcomes the influence of various interference factors, and an accurate evaporator liquid level measurement value is obtained (see fig. 2).
Evaporator liquid level prediction model
Fig. 1 is a structural diagram of a high-precision evaporator material concentration Neural network soft measurement model, which adopts a three-layer BP Neural network (ANN), where W1 and W2 represent weights of the Neural network, and b1 and b2 represent thresholds of the Neural network. Acquiring process parameters of a primary steam pressure Pi1, a secondary steam pressure Pi2, an evaporator liquid phase temperature Ti and a predicted value Li of an evaporator liquid level which influence the material concentration on a DCS, acquiring a material concentration manual analysis value, testing around an operation point of a process device, generating and acquiring 50 sets of evaporator production process data with a large operation range, and taking the data together with 150 sets of data acquired on an industrial field to count 200 sets of data as a training sample of an evaporator material concentration soft measurement neural network model. Selecting a forward BP neural network with a 4 x 1 structure, inputting Pi1(k), Pi2(k), Ti (k) and Li (k) of the evaporator corresponding to nodes, and outputting a material concentration manual analysis value corresponding to the nodes.
After offline training of the neural network, weight values and threshold values of the neural network are obtained, and soft measurement values of evaporator material concentration in volume percentage are output by the neural network model by collecting Pi1(k), Pi2(k), Ti (k) field values and predicted values of evaporator liquid level Li (k).
Multi-target coordination optimization control model of evaporation device
The material concentration set value calculation unit of each evaporator receives the material concentration soft measurement signal of each evaporator and calculates the material concentration set value of each evaporator according to the following algorithm. The concentration of the last effect evaporator is controlled by adopting a fixed value, the concentration set value of the previous effect evaporator is set by taking the online soft measurement value of the last effect concentration as a base point according to the deviation of the process requirement, and the concentration set values of other effect evaporators are set according to the principle by analogy in sequence;
input variables in the 4-input 1-output multivariable decoupling control system comprise liquid level L (i) and concentration A (i) of a front effect evaporator and liquid level L (i +1) and concentration A (i +1) of a rear effect evaporator, and the output is the discharge valve position of the front effect evaporator. By setting the algorithm change-over switch SW, the situation of adopting different types of discharge valves can be met. If the discharge valve is a two-position type cut-off valve, the selector switch is switched from a point B to a point C, multivariable decoupling control is realized by adopting a duty ratio control algorithm, wherein the period Tc of the duty ratio controller ranges from 3 seconds to 30 seconds; if a regulating valve or a frequency converter is adopted, a change-over switch is tangent from a point C to a point B, and multivariable decoupling control is realized (see figure 2);
the discharge valve of the front effect evaporator is also a feed valve of the rear effect evaporator, the liquid level and the concentration of the front effect evaporator and the rear effect evaporator can be simultaneously influenced by adjusting the discharge valve, AIC (i) and AIC (i +1) are respectively concentration controllers of the ith effect evaporator and the ith +1 effect evaporator, LIC (i +1) and LIC (i +1) are respectively liquid level controllers of the ith effect evaporator and the ith +1 effect evaporator, Ki1, Ki2, Ki3 and Ki4 are weights of outputs of the I effect evaporator and the ith +1 effect evaporator, Ki1 and Ki2 range is between 0.7 and 1.0, and Ki3 and Ki4 range is between 0.1 and 0.3. The multi-target coordinated optimization control of the yield, the quality and the energy consumption of the evaporation device can be realized.
Claims (3)
1. A multi-target coordinated optimization control system for yield, quality and energy consumption of an evaporator device is characterized in that an evaporator liquid level prediction model is established based on evaporator material balance and heat balance, and online rolling correction is carried out on an evaporator liquid level signal according to the actually measured liquid level signal of an evaporator liquid level switch, so that prediction of a continuous measured value of the evaporator liquid level is realized; introducing a process parameter-evaporator liquid level prediction value variable with important relevance with the material concentration as an input variable of a neural network soft measurement model, and realizing high-precision online soft measurement of the material concentration by adopting a BP neural network model with a three-layer structure; on the basis, multivariable decoupling control is adopted to coordinate the concentration and the liquid level of each effect evaporator, and multi-target coordinated optimization control of the yield, the quality and the energy consumption of the evaporation device is realized.
2. The system for multi-objective coordinated optimization control of yield, quality and energy consumption of the evaporation plant according to claim 1, further comprising: in the online rolling correction of the evaporator liquid level, the liquid level represented by a certain point liquid level switch in the process of closing can be used as an actual measured value, and the liquid level prediction model is corrected according to the deviation of the actual measured value and a predicted value.
3. The system for multi-objective coordinated optimization control of yield, quality and energy consumption of the evaporation plant according to claim 1, further comprising: the regulating instrument can be a regulating valve, a frequency converter or an on-off cut-off valve, and when the on-off cut-off valve is adopted, the multivariable decoupling control of the evaporation device can be realized by adopting a duty ratio controller.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 200910181159 CN101670184B (en) | 2009-10-13 | 2009-10-13 | Coordination optimizing control method of multiple targets, such as output, quality, energy consumption of evaporation device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 200910181159 CN101670184B (en) | 2009-10-13 | 2009-10-13 | Coordination optimizing control method of multiple targets, such as output, quality, energy consumption of evaporation device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN101670184A true CN101670184A (en) | 2010-03-17 |
| CN101670184B CN101670184B (en) | 2013-02-06 |
Family
ID=42017734
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN 200910181159 Active CN101670184B (en) | 2009-10-13 | 2009-10-13 | Coordination optimizing control method of multiple targets, such as output, quality, energy consumption of evaporation device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN101670184B (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102520617A (en) * | 2011-12-30 | 2012-06-27 | 杭州电子科技大学 | Prediction control method for unminimized partial decoupling model in oil refining industrial process |
| CN102520616A (en) * | 2011-12-30 | 2012-06-27 | 杭州电子科技大学 | Partial decoupling unminimized model prediction function control method in oil refining industrial process |
| CN103064289A (en) * | 2012-12-19 | 2013-04-24 | 华南理工大学 | Multiple-target operation optimizing and coordinating control method and device of garbage power generator |
| CN103185771A (en) * | 2011-12-27 | 2013-07-03 | 株式会社堀场Stec | Liquid sample vaporizing system, diagnosis system and diagnosis program |
| CN103676855A (en) * | 2013-11-27 | 2014-03-26 | 北京中润零碳节能技术有限公司 | Production control method and production control system |
| CN107544286A (en) * | 2017-08-30 | 2018-01-05 | 浙江力太科技有限公司 | A kind of system identifying method in evaporization process |
| CN107807518A (en) * | 2017-09-05 | 2018-03-16 | 昆明理工大学 | A kind of Multiobjective Optimal Operation method of Chemical Manufacture raw material still-process |
| CN109213229A (en) * | 2017-07-07 | 2019-01-15 | 深圳市辉宏技术有限公司 | A kind of HTM control method for improving efficiency, apparatus and system |
| CN114014395A (en) * | 2021-11-05 | 2022-02-08 | 中国船舶重工集团公司第七一八研究所 | Waste liquid heat pump evaporation system and liquid level control method, control equipment and medium thereof |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1818206A (en) * | 2005-02-07 | 2006-08-16 | 上海造纸机械电控技术研究所 | Paper-pulp washing process and optimizing control |
| CN200960409Y (en) * | 2006-09-29 | 2007-10-17 | 项文远 | Evaporator with liquid level automatic controlling device |
| US20070256432A1 (en) * | 2002-12-09 | 2007-11-08 | Kevin Zugibe | Method and apparatus for optimizing refrigeration systems |
-
2009
- 2009-10-13 CN CN 200910181159 patent/CN101670184B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070256432A1 (en) * | 2002-12-09 | 2007-11-08 | Kevin Zugibe | Method and apparatus for optimizing refrigeration systems |
| CN1818206A (en) * | 2005-02-07 | 2006-08-16 | 上海造纸机械电控技术研究所 | Paper-pulp washing process and optimizing control |
| CN200960409Y (en) * | 2006-09-29 | 2007-10-17 | 项文远 | Evaporator with liquid level automatic controlling device |
Non-Patent Citations (1)
| Title |
|---|
| 姜莹: "基于神经网络的多效并流蒸发过程的模拟方法", 《广东化工》 * |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103185771A (en) * | 2011-12-27 | 2013-07-03 | 株式会社堀场Stec | Liquid sample vaporizing system, diagnosis system and diagnosis program |
| CN102520617B (en) * | 2011-12-30 | 2014-05-07 | 杭州电子科技大学 | Prediction control method for unminimized partial decoupling model in oil refining industrial process |
| CN102520616A (en) * | 2011-12-30 | 2012-06-27 | 杭州电子科技大学 | Partial decoupling unminimized model prediction function control method in oil refining industrial process |
| CN102520616B (en) * | 2011-12-30 | 2014-04-02 | 杭州电子科技大学 | Partial decoupling unminimized model prediction function control method in oil refining industrial process |
| CN102520617A (en) * | 2011-12-30 | 2012-06-27 | 杭州电子科技大学 | Prediction control method for unminimized partial decoupling model in oil refining industrial process |
| CN103064289B (en) * | 2012-12-19 | 2015-03-11 | 华南理工大学 | Multiple-target operation optimizing and coordinating control method and device of garbage power generator |
| CN103064289A (en) * | 2012-12-19 | 2013-04-24 | 华南理工大学 | Multiple-target operation optimizing and coordinating control method and device of garbage power generator |
| CN103676855B (en) * | 2013-11-27 | 2016-07-06 | 北京中润零碳节能技术有限公司 | Production control method and system |
| CN103676855A (en) * | 2013-11-27 | 2014-03-26 | 北京中润零碳节能技术有限公司 | Production control method and production control system |
| CN109213229A (en) * | 2017-07-07 | 2019-01-15 | 深圳市辉宏技术有限公司 | A kind of HTM control method for improving efficiency, apparatus and system |
| CN107544286A (en) * | 2017-08-30 | 2018-01-05 | 浙江力太科技有限公司 | A kind of system identifying method in evaporization process |
| CN107807518A (en) * | 2017-09-05 | 2018-03-16 | 昆明理工大学 | A kind of Multiobjective Optimal Operation method of Chemical Manufacture raw material still-process |
| CN107807518B (en) * | 2017-09-05 | 2020-09-25 | 昆明理工大学 | Multi-objective optimization scheduling method for chemical production raw material distillation process |
| CN114014395A (en) * | 2021-11-05 | 2022-02-08 | 中国船舶重工集团公司第七一八研究所 | Waste liquid heat pump evaporation system and liquid level control method, control equipment and medium thereof |
| CN114014395B (en) * | 2021-11-05 | 2024-03-29 | 中国船舶重工集团公司第七一八研究所 | Liquid waste heat pump evaporation system and liquid level control method, control equipment and medium thereof |
Also Published As
| Publication number | Publication date |
|---|---|
| CN101670184B (en) | 2013-02-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101670184B (en) | Coordination optimizing control method of multiple targets, such as output, quality, energy consumption of evaporation device | |
| CN101433919B (en) | Control method for laminar cooling of medium plate | |
| CN102540879A (en) | Multi-target evaluation optimization method based on group decision making retrieval strategy | |
| CN103019097B (en) | Optimal control system for steel rolling heating furnace | |
| CN102147613B (en) | Model predictive control method and system for moisture and temperature of tobacco redryer | |
| CN104898433B (en) | A kind of blast furnace cooling intensity control method based on fuzzy-adaptation PID control | |
| CN109521813B (en) | System and method for stably controlling concentration ratio of circulating water of thermal power plant | |
| CN111142494B (en) | Intelligent control method and system for amine liquid regeneration device | |
| CN101441443B (en) | Data processing method and device of sintering control parameter | |
| CN104846306A (en) | Zincing thickness control system and method | |
| CN204595644U (en) | Based on the aluminum-bar heating furnace temperature of combustion automaton of neural network | |
| CN103019090A (en) | Intelligent sintering mixed water adding control method with prediction function | |
| CN106637026B (en) | A kind of galvanizing process air pressure method for real-time optimization control and system | |
| CN105116726A (en) | Parameter design method for nonlinear predictive controller based on mechanism model | |
| CN107887672A (en) | A kind of lithium ion battery temprature control method and device | |
| CN115095852A (en) | Intelligent blowdown system of power plant boiler | |
| CN111967181A (en) | Irrigation time calculation method based on APSO-ELM and fuzzy logic | |
| CN111256252A (en) | Dynamic ice storage machine room control system | |
| CN1417176A (en) | Intelligent control method for ethylene rectifying tower of ethylene equipment | |
| CN105318312A (en) | Method of controlling steam temperature of power station boiler through multi-step advance prediction of full working conditions | |
| CN111888788B (en) | Circulating neural network control method and system suitable for traditional Chinese medicine extraction and concentration | |
| CN210951273U (en) | Automatic liquid level control system for deaerator of supercritical unit | |
| CN102500626A (en) | Plate strip hot continuous rolling reeling temperature control method based on thermodetector | |
| CN102676818B (en) | Prediction method for zinc powder effectiveness coefficient in wet zinc smelting and copper removal process | |
| CN207541486U (en) | Dry vacuum mechanical pump electric control gear |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| C56 | Change in the name or address of the patentee |
Owner name: BEIJING HEROOPSYS CO., LTD. Free format text: FORMER NAME: BEIJING HEROOPSYS CONTROL TECHNOLOGY CO., LTD. |
|
| CP03 | Change of name, title or address |
Address after: 100096, Beijing, Haidian District Xisanqi Building Materials West Road No. 31, D building, three West Patentee after: Beijing Heroopsys Technology Co., Ltd. Address before: 100096, Beijing, Xisanqi, Haidian District Building Materials West Road No. 31, block D, three, 305 Patentee before: Beijing Heroopsys Control Technology Co.,Ltd. |
|
| TR01 | Transfer of patent right |
Effective date of registration: 20180404 Address after: 100096 Beijing Haidian District Xisanqi building material Chengxi Road No. 31 D seat four layer Patentee after: Beijing kaimi Optimization Technology Co. Ltd. Address before: 100096, Beijing, Haidian District Xisanqi Building Materials West Road No. 31, D building, three West Patentee before: Beijing Heroopsys Technology Co., Ltd. |
|
| TR01 | Transfer of patent right |