CN113741200B - Intelligent optimization calcination control system for lime sleeve kiln - Google Patents
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- 238000005457 optimization Methods 0.000 title claims abstract description 53
- 238000001354 calcination Methods 0.000 title claims abstract description 33
- 235000008733 Citrus aurantifolia Nutrition 0.000 title claims abstract description 26
- 235000011941 Tilia x europaea Nutrition 0.000 title claims abstract description 26
- 239000004571 lime Substances 0.000 title claims abstract description 26
- 238000000034 method Methods 0.000 claims abstract description 27
- 230000008569 process Effects 0.000 claims abstract description 20
- 238000005516 engineering process Methods 0.000 claims abstract description 15
- 230000008901 benefit Effects 0.000 claims abstract description 6
- 238000002485 combustion reaction Methods 0.000 claims description 22
- 238000010438 heat treatment Methods 0.000 claims description 8
- 230000008859 change Effects 0.000 claims description 7
- 230000009471 action Effects 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 230000000750 progressive effect Effects 0.000 claims description 5
- 230000000087 stabilizing effect Effects 0.000 claims description 5
- 238000011217 control strategy Methods 0.000 claims description 4
- 238000013461 design Methods 0.000 claims description 3
- 230000002708 enhancing effect Effects 0.000 claims description 3
- 238000012360 testing method Methods 0.000 claims description 3
- 230000009466 transformation Effects 0.000 claims description 3
- 238000011156 evaluation Methods 0.000 claims description 2
- 238000012937 correction Methods 0.000 claims 1
- 230000008878 coupling Effects 0.000 abstract description 6
- 238000010168 coupling process Methods 0.000 abstract description 6
- 238000005859 coupling reaction Methods 0.000 abstract description 6
- 239000007789 gas Substances 0.000 description 39
- 101100399296 Mus musculus Lime1 gene Proteins 0.000 description 15
- 238000010586 diagram Methods 0.000 description 7
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 239000003546 flue gas Substances 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 239000001301 oxygen Substances 0.000 description 3
- 229910052760 oxygen Inorganic materials 0.000 description 3
- 238000013473 artificial intelligence Methods 0.000 description 2
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 2
- 230000005284 excitation Effects 0.000 description 2
- 239000000446 fuel Substances 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000001105 regulatory effect Effects 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- BRPQOXSCLDDYGP-UHFFFAOYSA-N calcium oxide Chemical compound [O-2].[Ca+2] BRPQOXSCLDDYGP-UHFFFAOYSA-N 0.000 description 1
- ODINCKMPIJJUCX-UHFFFAOYSA-N calcium oxide Inorganic materials [Ca]=O ODINCKMPIJJUCX-UHFFFAOYSA-N 0.000 description 1
- 239000000292 calcium oxide Substances 0.000 description 1
- 239000000571 coke Substances 0.000 description 1
- 230000001276 controlling effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000013401 experimental design Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000013441 quality evaluation Methods 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
-
- C—CHEMISTRY; METALLURGY
- C04—CEMENTS; CONCRETE; ARTIFICIAL STONE; CERAMICS; REFRACTORIES
- C04B—LIME, MAGNESIA; SLAG; CEMENTS; COMPOSITIONS THEREOF, e.g. MORTARS, CONCRETE OR LIKE BUILDING MATERIALS; ARTIFICIAL STONE; CERAMICS; REFRACTORIES; TREATMENT OF NATURAL STONE
- C04B2/00—Lime, magnesia or dolomite
- C04B2/10—Preheating, burning calcining or cooling
- C04B2/12—Preheating, burning calcining or cooling in shaft or vertical furnaces
Landscapes
- Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Ceramic Engineering (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Materials Engineering (AREA)
- Structural Engineering (AREA)
- Organic Chemistry (AREA)
- Thermal Sciences (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Feedback Control In General (AREA)
Abstract
The invention discloses an intelligent optimization calcination control system of a lime sleeve kiln, which belongs to the technical field of automatic control and intelligent prediction models, and solves the problem that complex working conditions facing multiple variables, strong coupling and strong interference in the existing lime sleeve kiln calcination cannot be treated, and the technical key points are as follows: comprising a control-multivariable controller, the control-multivariable controller steps comprising: multivariate recognition modeling: identifying a model of the controlled object by adopting a test-modeling scheme; model predictive control, wherein the model predictive control adopts a multivariable optimization control technology; multiple constraint planning, the multiple constraint planning is used for preferentially associating key process limiting conditions; the multi-objective optimization has the advantage of being capable of handling complex working conditions of multiple variables, strong coupling and strong interference.
Description
Technical Field
The invention relates to the technical field of automatic control and intelligent prediction models, in particular to an intelligent optimization calcination control system for a lime sleeve kiln.
Background
At present, the traditional control theory of automatically controlling DCS/PLC to adjust single input and single output can not meet the requirements of energy conservation, optimal control and the like of various industries at present. The existing multivariable-based predictive control technology is developed immediately, so that not only can the running stability and consistency of the device be improved, but also the operation method can be unified, and the influence of human factors can be reduced; more importantly, the advanced control technology also provides support for the optimal operation of the device, so that the control of the device is more intelligent.
The calcination system of the lime sleeve kiln is used as a control object of strong coupling, strong interference and serious nonlinearity, and is always a control difficulty for lime production. The conventional PID control loop in the current DCS/PLC can only solve the control problem of a single loop or a cascade loop under stable working conditions, and is incapable of facing complex working conditions of multiple variables, strong coupling and strong interference.
Disclosure of Invention
Aiming at the defects existing in the prior art, the embodiment of the invention aims to provide an intelligent optimal calcination control system of a lime sleeve kiln, so as to solve the problems in the background art.
In order to achieve the above purpose, the present invention provides the following technical solutions:
an intelligent optimal calcination control system for a lime sleeve kiln, comprising a control-multivariable controller, the control-multivariable controller steps comprising:
1) Multivariate recognition modeling: identifying a model of the controlled object by adopting a test-modeling scheme;
11 Using optimal test signals, multi-channel excitation operation variables;
12 Identifying a multivariate model by adopting a progressive method;
13 Grade evaluating the model quality;
2) Model predictive control, wherein the model predictive control adopts a multivariable optimization control technology;
21 Predicting future action trends of the device through the model;
22 Feedback correcting the predicted trend according to the current data;
23 A) the total deviation between the actual value and the target value of the controlled variable CV is minimal;
24 The total amount of control variable adjustment is minimum, and the device reaches the steady-state optimal target according to the optimal dynamic scheme;
3) Multiple constraint planning, the multiple constraint planning is used for preferentially associating key process limiting conditions;
31 Determining a control strategy, taking all constraint conditions into account;
32 A control scheme that meets the limits of the manipulated variable upper and lower limits and the rate of change;
33 Upper and lower limits of all Controlled Variables (CVs) are met as much as possible, and when all CVs cannot be met, the process limits of the critical CVs are preferably met;
4) Multi-objective optimization;
41 Target optimization and maximum economic benefit;
42 Multi-priority processing function, realizing hierarchical processing design of process variables;
43 Economic optimization, supporting linear optimization and secondary optimization;
44 Supporting a model gain scheduling function and nonlinear transformation, and solving the nonlinear problem;
45 Self-adaptive disturbance estimation function, and overcomes the problem of undetectable disturbance.
As a further scheme of the invention, the multivariable identification modeling is provided with the gas heat value control optimization, and the gas heat value control optimization is based on the multivariable control, the gas quantity ratio control and the anti-interference, so that the gas heat value automatic control optimization is realized.
As a further scheme of the invention, the gas heat value control optimization achieves the aim of stabilizing the heat value and pressure of mixed gas by automatically setting the opening of the input gas valve and the frequency of the gas pressurizing machine.
As a further scheme of the invention, heat consumption control optimization is arranged in the multivariate recognition modeling, and is used for reducing manual intervention and adjustment, and steady-state targets are ensured through the multivariate system recognition, model predictive control and disturbance self-adaption technology.
As a further scheme of the invention, the Roots blower is adopted to supply driving air to the lime kiln for heating high-temperature gas of the driving air, so that the high-temperature gas is pumped into the air heat exchanger from the upper part of the calcining zone through the upper inner sleeve.
As a further aspect of the present invention, the heat consumption control preferably employs an air heat exchanger for enhancing circulation of heat in the calcining zone and improving physical sensible heat of the driving air to facilitate combustion of the flame in the combustion chamber.
As a further scheme of the invention, the control-multivariable controller adopts negative pressure control optimization, and the negative pressure control optimization is used for realizing interval control of a negative pressure measuring point of the lower combustion chamber and the exhaust gas fan, so that the negative pressure in the kiln is restored to the interval.
In summary, compared with the prior art, the embodiment of the invention has the following beneficial effects:
1. under normal conditions of the process and the equipment, the fluctuation of the heat value of the mixed gas is less than 1950+/-50 kcal/Nm3;
2. realizing automatic optimized calcination, stabilizing the temperature of the circulating gas, wherein the temperature fluctuation of the circulating gas is less than +/-5 ℃;
3. under normal working conditions, the automatic operation rate of the sleeve kiln for optimizing the calcination function reaches 97% -99%;
4. the index of the calcium oxide as a product component can be increased from the current 91.07 to be more than or equal to 92%, and the activity can be increased from the current 308ml to be more than or equal to 320ml;
5. the system is subjected to front-back calibration after being on line, so that the in-advance control of the lime production line is further realized, the product quality is greatly improved, the energy consumption is reduced, and the comprehensive benefit is considerable.
In order to more clearly illustrate the structural features and efficacy of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and examples.
Drawings
Fig. 1 is a schematic diagram of a system architecture according to an embodiment of the invention.
FIG. 2 is a schematic diagram of a multivariate coupling model according to an embodiment of the present invention.
FIG. 3 is a schematic diagram of a measurable disturbance of a measurable signal according to an embodiment of the invention.
FIG. 4 is a schematic diagram of quality index of lime kiln according to an embodiment of the invention.
FIG. 5 is a schematic diagram of the optimization of heat control according to an embodiment of the invention.
FIG. 6 is a schematic diagram of an inventive embodiment of a drive fan MPC control system.
FIG. 7 is a schematic diagram of negative pressure control optimization according to an embodiment of the invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Specific implementations of the invention are described in detail below in connection with specific embodiments.
In one embodiment, an intelligent optimal calcination control system for lime sleeve kiln, see fig. 1-3, comprises a control-multivariable controller comprising the steps of:
1) Multivariate recognition modeling: identifying a model of the controlled object by adopting a test-modeling scheme;
11 Using optimal test signals, multi-channel excitation operation variables;
12 Identifying a multivariate model by adopting a progressive method;
13 Grade evaluating the model quality;
2) Model predictive control, wherein the model predictive control adopts a multivariable optimization control technology;
21 Predicting future action trends of the device through the model;
22 Feedback correcting the predicted trend according to the current data;
23 A) the total deviation between the actual value and the target value of the controlled variable CV is minimal;
24 The total amount of control variable adjustment is minimum, and the device reaches the steady-state optimal target according to the optimal dynamic scheme;
3) Multiple constraint planning, the multiple constraint planning is used for preferentially associating key process limiting conditions;
31 Determining a control strategy, taking all constraint conditions into account;
32 A control scheme that meets the limits of the manipulated variable upper and lower limits and the rate of change;
33 Upper and lower limits of all Controlled Variables (CVs) are met as much as possible, and when all CVs cannot be met, the process limits of the critical CVs are preferably met;
4) Multi-objective optimization;
41 Target optimization and maximum economic benefit;
42 Multi-priority processing function, realizing hierarchical processing design of process variables;
43 Economic optimization, supporting linear optimization and secondary optimization;
44 Supporting a model gain scheduling function and nonlinear transformation, and solving the nonlinear problem;
45 Self-adaptive disturbance estimation function, and overcomes the problem of undetectable disturbance.
In this embodiment, the model of the controlled object is the key to success or failure of Model Predictive Control (MPC). The model quality can be effectively ensured by adopting a multivariate system identification technology based on a progressive identification method (ASYM).
The system identification is to consider the system as a black box, a certain experimental signal is added at the input end of the system, and a mathematical model reflecting the input-output relation of the object is identified through a certain algorithm (such as a progressive identification method) by the measured input-output data. The identification modeling mainly comprises four steps: experimental design, model structure selection, model parameter estimation and model inspection.
The software automatically selects model structure parameters according to the process data to obtain a multivariable coupling model; the quality evaluation of the identification model can be carried out, and A, B, C, D four grades of evaluation are provided.
The model predictive control algorithm predicts the future output of the controlled object by using the dynamic model of the controlled object, and determines a series of future control actions by adding the optimization of a certain open-loop performance index of the time of the prediction time domain to the current time. But each control cycle only performs the current control action, and the above steps are repeated to recalculate the control action after correcting the next control cycle based on the new measured value.
The disturbance of the measurable signal to the controlled variable is fully considered, so that good control quality can be kept under the condition of frequent and large-amplitude measurable disturbance. Various constraints can be considered, and the operating point of the device can be made to approach the optimal operating point, so that the 'clamping' control of the device is realized.
Further, referring to fig. 4, the multivariate recognition modeling is provided with gas heat value control optimization, and the gas heat value control optimization is based on multivariate control, gas quantity ratio control and anti-interference, so that gas heat value automatic control optimization is realized.
Based on the means of multivariable control, gas quantity ratio control and anti-interference self-adaption, the automatic control and optimization of the gas heat value are realized. According to the working condition of the whole system, the information such as temperature change, valve operation and the like is judged, the external factors which interfere with the stability of the heat value are overcome, the process characteristics are improved, the control quality of the execution process is improved, the flow control of coke oven gas is optimized, and then the heat value balance optimizing is realized. The control related variables are shown in the following table:
the aim of stabilizing the heat value and pressure of the mixed gas is achieved by automatically setting the opening of the input gas valve and the frequency of the gas pressurizing machine. The multivariable MPC controller is planned to be adopted, and no additional hardware equipment is needed to be modified in the implementation process of the scheme.
Further, referring to fig. 1, the gas heating value control optimization achieves the aim of stabilizing the mixed gas heating value and pressure by automatically setting the opening of the input gas valve and the frequency of the gas pressurizing machine.
By introducing multivariable model predictive control, the quality indexes of the lime kiln such as gas heat value and pressure, measurement of oxygen content in flue gas, CO2 and NOx, circulating gas temperature and the like are comprehensively considered, the automatic adjustment of heat consumption, upper and lower burner gas ratio and air-fuel ratio is realized, manual intervention and adjustment are reduced, and steady-state targets are ensured through the technologies of multivariable system identification, model predictive control and disturbance self-adaption.
Further, referring to fig. 5, heat consumption control optimization is set in the multivariate recognition modeling, and the heat consumption control optimization is used for reducing manual intervention adjustment, and steady-state targets are ensured through the techniques of multivariate system recognition, model predictive control and disturbance self-adaption.
The heat consumption control is optimized by adopting a Roots blower to supply driving air to the lime kiln, and high-temperature gas for heating the driving air is pumped into the air heat exchanger from the upper part of the calcining zone through the upper inner sleeve.
The heat consumption control optimization adopts an air heat exchanger, and the air heat exchanger is used for enhancing the circulation of heat in the calcining zone and improving the physical sensible heat of driving air so as to facilitate the combustion of flame in the combustion chamber.
The Roots blower supplies driving air to the lime kiln, the driving air is heated to about 450 ℃ in the air heat exchanger, and the driving air is conveyed to the ejector through the driving air loop; during the transfer from the air heat exchanger to the ejector, a portion of the heat is dissipated and the temperature of the drive air is reduced before entering the ejector. The high-temperature gas for heating the driving air is pumped into the air heat exchanger from the upper part of the calcining zone through the upper inner sleeve, the main functions of the air heat exchanger are to strengthen the circulation of heat in the calcining zone and improve the physical sensible heat of the driving air so as to facilitate the combustion of flame in the combustion chamber, but less than 30% of the heat in the high-temperature flue gas is recovered after the circulation, and more than 70% of the heat is lost. Although the combustion of the lower combustion chamber is favored to some extent after the drive air is heated, the higher the temperature is, the more favored the combustion. However, the heat energy used for heating the driving air is effective heat energy extracted from a high-temperature area in the kiln, so that considerable heat energy loss exists in the process of heating the driving air, and the higher the temperature of the driving air is, the larger the flow is, and the larger the heat loss is. If the heat of the flue gas entering the air heat exchanger is high, the temperature of the heated driving air is also high. The heat loss is also large during the transport of the relatively high-temperature drive air to the ejector, because of the large temperature difference from the environment. If the heat quantity entering the air heat exchanger is properly reduced and the temperature reduction of the driving air caused by the air flow and the gas flow of the lower combustion chamber is compensated by adjusting, the combustion effect can not be influenced.
Further, referring to fig. 7, the control-multivariable controller adopts negative pressure control optimization, and the negative pressure control optimization is used for realizing interval control of a negative pressure measuring point of the lower combustion chamber and the exhaust gas fan, so that the negative pressure in the kiln is restored to be in an interval.
In the actual production process, the conditions of fuel and raw materials inevitably change, the negative pressure in the kiln is abnormal, and the negative pressure of the lower combustion chamber fluctuates. The interval control of the negative pressure measuring point of the lower combustion chamber and the exhaust gas fan is realized, and once negative pressure fluctuation exceeding the interval occurs, the rotating speed of the exhaust gas fan is automatically regulated, so that the negative pressure in the kiln is restored to the interval. The influence of total air quantity and oxygen content (air leakage) on negative pressure is considered in the adjusting process.
The control strategy technologies such as classical control theory, modern control theory, model predictive control, system identification and artificial intelligence are combined by utilizing the computer software and hardware technology, so that the purposes of gas heat value balance adjustment, heat consumption control, driving fan outlet flow control and negative pressure control optimization are achieved, the purposes of self-adaptive adjustment and calcination optimization of the sleeve kiln are achieved, the process of reducing personnel intervention is achieved, and the automation and intelligent control level of the sleeve kiln is further improved. Aiming at observing flame combustion conditions and material tumbling scenes in a kiln through an observation hole, no mature video intelligent analysis technology and successful cases exist at present.
Based on the optimized calcination control technology of the sleeve kiln, the required process data (such as temperature, flow, pressure, valve position and the like) are acquired and processed, and modification and maintenance of optimized calcination control parameters are realized through an HMI interface. The calcination temperature of the lime kiln is fast and stable, the robustness is strong, and the overshoot is small.
The sleeve kiln optimizing and calcining system is used for continuously memorizing the characteristics of each sleeve kiln through on-site debugging in the combustion process, and continuously and automatically calculating and regulating the optimal gas quantity by judging different parameter changes and calcining conditions and utilizing modern control technologies such as fuzzy control, artificial intelligence and the like, so that the automatic optimizing control of the whole process of the sleeve kiln is realized.
According to the material, heat value change, in-kiln under-oxygen combustion and complete combustion process, the whole system optimizes calcination control, and aims to ensure the related self-adaptive change of system parameters so that the lime activity reaches the qualified standard of products.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (7)
1. An intelligent optimal calcination control system for a lime sleeve kiln, comprising a control-multivariable controller, characterized in that the control-multivariable controller steps comprise:
multivariate recognition modeling: identifying a model of the controlled object by adopting a test-modeling scheme;
adopting an optimal test signal and exciting an operation variable by multiple channels;
identifying a multivariate model by adopting a progressive method;
performing grade evaluation on the model quality;
model predictive control, wherein the model predictive control adopts a multivariable optimization control technology;
predicting future action trend of the device through a model;
according to the current data, feedback correction prediction trend is carried out;
the total deviation between the actual value and the target value of the controlled variable CV is minimal;
the total amount of control variable adjustment is minimum, and the device reaches the steady-state optimal target according to the optimal dynamic scheme;
multiple constraint planning, the multiple constraint planning is used for preferentially associating key process limiting conditions;
determining a control strategy, and considering all constraint conditions;
the control scheme meets the limits of the upper limit and the lower limit of the manipulated variable and the change rate;
the upper and lower limits of all Controlled Variables (CVs) are met as much as possible, and when all CVs cannot be met, the process limits of the key CVs are preferably met;
multi-objective optimization;
target optimization and maximum economic benefit;
a multi-priority processing function for realizing hierarchical processing design of the process variables;
economic optimization, supporting linear optimization and secondary optimization;
the model gain scheduling function and the nonlinear transformation are supported, and the nonlinear problem is solved;
and the self-adaptive disturbance estimation function overcomes the problem of undetectable disturbance.
2. The intelligent optimal calcination control system of the lime sleeve kiln according to claim 1, wherein the multivariable identification modeling is provided with gas heat value control optimization, and the gas heat value control optimization is based on multivariable control, gas quantity ratio control and anti-interference, so that gas heat value automatic control optimization is realized.
3. The intelligent optimal calcination control system of the lime sleeve kiln according to claim 2, wherein the gas heat value control optimization achieves the aim of stabilizing the heat value and the pressure of the mixed gas by automatically setting the opening degree of an input gas valve and the frequency of a gas pressurizing machine.
4. The intelligent optimal calcination control system of the lime sleeve kiln according to claim 1, wherein heat consumption control optimization is arranged in the multivariate recognition modeling, and is used for reducing manual intervention adjustment, and steady-state targets are ensured through the technology of multivariate system recognition, model predictive control and disturbance self-adaption.
5. The intelligent optimal calcination control system for lime sleeve kiln according to claim 4, wherein the optimal heat consumption control adopts a Roots blower to supply driving air to the lime kiln for heating high-temperature gas of the driving air, so that the high-temperature gas is pumped into the air heat exchanger from the upper part of the calcination zone through the upper inner sleeve.
6. The intelligent optimal calcination control system for lime sleeve kiln according to claim 5, wherein the optimal heat consumption control adopts an air heat exchanger for enhancing circulation of heat in the calcination zone and improving physical sensible heat of driving air so as to facilitate combustion of flame in the combustion chamber.
7. The intelligent optimal calcination control system of the lime sleeve kiln according to claim 1, wherein the control-multivariable controller adopts negative pressure control optimization, and the negative pressure control optimization is used for realizing interval control of a negative pressure measuring point of a lower combustion chamber and an exhaust fan so as to restore the negative pressure in the kiln to be in an interval.
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| CN104950681A (en) * | 2015-06-25 | 2015-09-30 | 安徽工业大学 | Fuzzy predictive control system for combustion process of lime kiln as well as control method of system |
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