CN113696371A - Intelligent control system applied to PVC drying fluidized bed - Google Patents
Intelligent control system applied to PVC drying fluidized bed Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29B—PREPARATION OR PRETREATMENT OF THE MATERIAL TO BE SHAPED; MAKING GRANULES OR PREFORMS; RECOVERY OF PLASTICS OR OTHER CONSTITUENTS OF WASTE MATERIAL CONTAINING PLASTICS
- B29B13/00—Conditioning or physical treatment of the material to be shaped
- B29B13/06—Conditioning or physical treatment of the material to be shaped by drying
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F26—DRYING
- F26B—DRYING SOLID MATERIALS OR OBJECTS BY REMOVING LIQUID THEREFROM
- F26B25/00—Details of general application not covered by group F26B21/00 or F26B23/00
- F26B25/001—Handling, e.g. loading or unloading arrangements
- F26B25/002—Handling, e.g. loading or unloading arrangements for bulk goods
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F26—DRYING
- F26B—DRYING SOLID MATERIALS OR OBJECTS BY REMOVING LIQUID THEREFROM
- F26B25/00—Details of general application not covered by group F26B21/00 or F26B23/00
- F26B25/22—Controlling the drying process in dependence on liquid content of solid materials or objects
- F26B25/225—Controlling the drying process in dependence on liquid content of solid materials or objects by repeated or continuous weighing of the material or a sample thereof
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F26—DRYING
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- F26B3/00—Drying solid materials or objects by processes involving the application of heat
- F26B3/02—Drying solid materials or objects by processes involving the application of heat by convection, i.e. heat being conveyed from a heat source to the materials or objects to be dried by a gas or vapour, e.g. air
- F26B3/06—Drying solid materials or objects by processes involving the application of heat by convection, i.e. heat being conveyed from a heat source to the materials or objects to be dried by a gas or vapour, e.g. air the gas or vapour flowing through the materials or objects to be dried
- F26B3/08—Drying solid materials or objects by processes involving the application of heat by convection, i.e. heat being conveyed from a heat source to the materials or objects to be dried by a gas or vapour, e.g. air the gas or vapour flowing through the materials or objects to be dried so as to loosen them, e.g. to form a fluidised bed
- F26B3/092—Drying solid materials or objects by processes involving the application of heat by convection, i.e. heat being conveyed from a heat source to the materials or objects to be dried by a gas or vapour, e.g. air the gas or vapour flowing through the materials or objects to be dried so as to loosen them, e.g. to form a fluidised bed agitating the fluidised bed, e.g. by vibrating or pulsating
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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Abstract
An intelligent control system for a PVC drying fluidized bed device aims to solve the problems of multiple undetectable characteristics, complex process and long and delayed response in a control process of the fluidized bed device, thereby reducing steam consumption and improving product quality. The technical scheme is as follows: firstly, the actual feeding amount entering the system is determined by the material concentration soft measurement module and the current actual slurry flow, so that the preliminary estimation of the heat required by drying is achieved. Secondly, counting data of all estimated actual feeding amount, hot water, hot air, steam and other related heat sources in the time length of the stay heated section of the materials in the bed by analyzing the characteristics of the fluidized bed, carrying out real-time iterative calculation on the heat quantity and the heated quantity required by the materials staying in the bed to obtain the heat quantity required by the materials in the bed, and correcting the core bed temperature of the fluidized bed. And designing heat source proportion and device fluidization heat source limiting conditions through bed temperature and fluidized bed processes, distributing corresponding heat changes of all heat sources, and guiding a steam valve to adjust the steam consumption.
Description
Technical Field
The invention relates to the technical field of chlor-alkali chemical industry, in particular to an intelligent control system of a polyvinyl chloride drying optimization method based on coupling of various unmeasured factors and process coupling.
Background
Polyvinyl chloride (PVC), is a polymer obtained by polymerizing Vinyl Chloride Monomer (VCM) with initiators such as peroxides and azo compounds or under the action of light and heat according to a radical polymerization mechanism. PVC has been the most widely used plastic in the world and is used in a very wide range of applications. Polyvinyl chloride in China mainly has two major consumer markets: hard articles and soft articles. The hard products are mainly various profiles, pipes, plates, hard sheets, blow molding products and the like, and the soft products are mainly films, wires and cables, artificial leather, fabric coatings, various hoses, gloves, toys, floor paving materials, plastic shoes, special coatings, sealants and the like. The PVC production device designed by China in 1956 is in trial production in Liaoning Jinxi chemical plant, and 3000t devices are in formal industrial production in 1958, and the national polyvinyl chloride production capacity reaches 2568 million tons as of 2020. As the raw material of the polyvinyl chloride is mostly (about 57 percent of mass) chlorine which is the inevitable byproduct of the alkali manufacturing industry, the raw material source is rich, and the polyvinyl chloride is one of important products for developing the chlor-alkali industry and balancing the chlorine.
The drying of polyvinyl chloride is the final decisive link in the preparation of polyvinyl chloride products. The quality of the finished polyvinyl chloride product has great influence on the manufacturing continuity of downstream hard products and soft products and the quality of the finished product. The national standard superior product volatile index of the polyvinyl chloride finished product is 0.4 percent. However, since the chlor-alkali chemical industry started in China in 1956, because volatile matters within 4% of a polyvinyl chloride finished product are bound water in the material, the drying difficulty is high, and the volatile matter index of the polyvinyl chloride finished product is always controlled below 0.15% for ensuring the high-grade product rate of the finished product by enterprises. In actual production, the finished product volatile index is too low and can lead the polyvinyl chloride finished product to generate a large amount of static electricity, and the material can be adsorbed on the device and the vibrating screen, so that the blanking of the system is not smooth, and even the system can be stopped due to serious blockage. Many domestic process designs can even increase the steam humidifier in order to overcome the too big static, material unloading compensation problem, and material drying process control is actually inaccurate, can only be passive in order to guarantee that the finished product rate of merit spends more steam and dries the material excessively, goes to increase material moisture, fails to reach the optimal control target on the contrary under the condition of consuming more energy. And secondly, excessive drying of the materials easily leads to the overproof of black and yellow point impurities of the polyvinyl chloride finished product, and the finished pipe and soft products processed by downstream have obvious flaws, so that the quality of the finished product is reduced.
The material drying device of present whole trade is in pure manual control state basically, influences the dry main influence factor that can't put into automation of material and has: firstly, the concentration of the material slurry can not be measured on line; secondly, physical properties such as porosity and fineness of the material cannot be measured on line; the drying device has different processes, the material drying time is too long, and the whole drying process is difficult to control; fourthly, the heat sources are more in types and are seriously coupled with each other. For a control system with complex process, serious coupling, long whole control process and various unmeasurable key influence factors, the control difficulty is extremely high, and the problem can not be effectively solved for more than 50 years after the industry starts to produce megaton-level industrial products every year.
Disclosure of Invention
The invention aims to solve the problems that: the invention relates to an intelligent control system for a fluidized bed device, which solves the problems of multiple unmeasurable characteristics, complex process, long control process and high process control response difficulty of the device, realizes the intelligent control of the device, greatly improves the stability and uniformity of the product quality, and achieves the aims of reducing steam consumption, improving the product quality and stabilizing the long-period operation of the device.
In order to solve the problem of the characteristic that the concentration of the material slurry cannot be measured, the invention adopts the operating parameters of the centrifuge to develop a soft measurement method to represent the concentration of the material, and overcomes the problem that the feeding quantity of a drying system cannot be measured due to the change of the concentration of the material slurry.
In order to solve the problem that physical properties such as porosity and fineness of materials cannot be measured on line, the invention combines a moisture content prediction module with device load factors to overcome the defects that the moisture content cannot be measured at a heating terminal and the characteristics cannot be measured on line according to the change of the physical properties of the materials to make response in time.
Aiming at solving the problem that the drying process of the materials in the bed is difficult to grasp due to the overlong drying time in the drying device process, the invention provides a rolling time domain generalized prediction algorithm for a black box device staying for a long time and solves the problem by carrying out real-time iterative computation on the heating process of the materials in the bed.
In order to solve the problems of more heat sources and serious mutual coupling of the fluidized beds, the proportion of dry heat sources is designed according to the process, the lower limit of the heat source required by fluidized bed material fluidization is combined, the coordinated control of all the heat sources is realized by adopting a heat source load balancing algorithm, and the control coupling is reduced.
The technical scheme adopted by the invention for solving the technical problems is as follows: firstly, the actual feeding amount entering the system is determined by the material concentration soft measurement module and the current actual slurry flow, so that the preliminary estimation of the heat required by drying is achieved. Secondly, counting data of all estimated actual feeding amount, hot water, hot air, steam and other related heat sources in the time length of the stay heated section of the materials in the bed by analyzing the characteristics of the fluidized bed, carrying out real-time iterative calculation on the heat quantity required by all the materials staying in the bed and the heated quantity of all the materials in the current bed to obtain the heat quantity required by the materials in the bed, and correcting the core bed temperature of the fluidized bed. And designing heat source proportion conditions and device fluidization heat source limiting conditions through bed temperature and a fluidized bed process, distributing corresponding heat changes of all heat sources, and guiding a steam valve to adjust, wherein a specific control scheme schematic diagram is shown in an attached figure 2.
In the real-time control stage, according to the final real-time moisture content detection result, the bed temperature index of the core of the fluidized bed is corrected in real time in a secondary mode by combining the load change of the device, and the adverse effects of the mixing characteristics of the fluidized bed, the secondary lag of the material cooling section and the non-online measurable characteristics of the physical properties of the materials on the quality control of finished products are reduced.
The invention is also suitable for devices with some other processes being lengthy and having non-measurable characteristics.
The technical scheme comprises the following aspects:
a PART 1 material concentration soft measurement model is mainly characterized in that a centrifuge torque or power measurement device is additionally arranged on the basis of a normal slurry flow meter. Firstly, data preprocessing analysis needs to be carried out on complex centrifuge torque and power signals to obtain accurate numerical values capable of well representing the working conditions of equipment (see attached figure 1, a dark color part is signals before processing, and a middle light color part is signals after processing). Secondly, by analyzing the feeding working characteristics and the self-equipment characteristics of the centrifuge equipment, the corresponding matching relation is made between the thrust of the separated material after the centrifuge is dried or the centrifuge power NI 'and the actual slurry flow FT', and the soft measurement result of the current material slurry concentration is obtained
；
The basic principle formula of the material concentration soft measurement model is as follows:
；
wherein:
is the soft measurement result of the concentration of the current material slurry; NI' is the thrust of the separated material or centrifuge power; FT' is the actual slurry flow; c, soft measurement engineering experience parameters.
Firstly, the basic principle of establishing a fluidized bed process parameter model is as follows: because the fluidized bed is in the whole drying process, the materials are in the bedThe internal residence time is at least 1 hour. It is assumed here that the residence time of the material in the fluidized bed is h hours and the instantaneous material feed is Wt. Due to the non-interruptible nature of the process industry, then from T0T after h hours from the time start1Until time, therefore T1The material storage W in the bed at any momentT1The method comprises the following steps:
；
wherein: wT1The total weight of the material in the current bed of the fluidized bed in a dry state is (T)0，T1Total weight of materials entering the bed for h hours in a time period; wtIs the total weight of the material entering the fluidized bed at the present moment.
Secondly, the heat source in the bed is at least divided into fluidized hot air and hot water (or steam), so the heating quantity Q of the material received by the feeding quantity per unit masstIs T0All the entering beds (fluidized hot air and hot water) carry heat to multiply the heat exchange efficiency during the period from the moment to the moment t,
wherein: qA TThe total heat brought into the system by the hot air at the moment t; etaA t: at the moment t, the heat exchange efficiency between the staying position of the material in the bed and hot air, the heat exchange efficiency is different due to the designed heat exchange area of the bed type, and the heat exchange temperature difference is a time-varying function; qB TThe total heat brought into the system by the hot water (or steam) at the moment t; etaB t: and (3) at the time t, the heat exchange efficiency between the staying position of the material in the bed and the hot water (or steam) is improved.
Drying a unit mass material from an initial state to an ideal state mainly influences the drying time T, the drying contact area S and the ambient humidity RHaInitial material humidity RH0And the material blank characteristic X. Thus the heat quantity Q required for drying the material per unit massMThe expression is as follows:
。
through the design and process analysis of the fluidized bed type, the heat Q required by drying the unit mass material can be obtainedMLinearly simplified by engineering experience to the following equation:
the theoretical heating quantity Q of the unit mass material in the drying bed at the later time after the t moment can be known through an energy demand matching modelNeed toThe equation is as follows:
the above equation can obtain the instantaneous heat demand Q of all the materials in the bedRThe equation of (1) is as follows:
。
the fluidized bed model has general problems in engineering, namely the calculation and analysis are carried out based on an ideal state, the calculation process is complex, the data volume is overlarge, and the prediction is invalid after long-term operation in order to avoid data overflow and explosion during prediction, so that an MHE algorithm is introduced. Firstly, a rolling time domain is adopted, the minimum data size required by prediction is kept, the data analysis intensity is reduced, and the real-time prediction efficiency is improved. The method needs to analyze the minimum analysis time length h according to the fluidized bed process, and then when the core data of the fluidized bed is screened, if the current time is T, the core data records are all data in the (T-h, T) time period. Thus (T) mentioned hereinbefore0,T1) Time period, should be with respect to T0According to the following function T1=T0+h。
Secondly, due to the strong continuity of the process industry, in the model, secondary discretization processing analysis is carried out on a large amount of continuous data. The actual operation trend of the device is obtained mainly by preprocessing the data, and the data discretization processing time interval is determined by analyzing the fluctuation characteristics in the data operation trend. The data processing result obtained by data discrete processing still has to be capable of representing the operation trend of the device. Therefore, the authenticity of the prediction data can be ensured, and the calculation amount of the prediction algorithm can be reduced again.
By rolling time domain statistics of model data, the real-time correction of the rolling data of the field working conditions in the whole long-period control process is realized, the timeliness and the prediction accuracy of model processing of the predictive control are improved, a good foundation is provided for optimizing the secondary rolling correction of the predictive control, and the dynamic controllability of a control system is greatly improved.
In the fluidized bed design, the temperature of the dried material is too high, which is not favorable for the heat sensitivity of the material and the subsequent packaging of finished products. Therefore, the tail part of the fluidized bed drying process is designed with a cold air drying stage or a weak heating drying capacity stage. Because a baffle plate type material flowing mode is adopted in a part of bed types, an online moisture result can not be directly obtained through a moisture meter under the condition that the moisture of a finished product is basically determined after the drying of a main drying section is finished, and the problem of feedback result lag in a cold air drying stage is caused. In order to solve the phenomenon, the invention specially develops a moisture content prediction module.
The module mainly determines that the bed temperature TE of the fluidized bed represents the optimal heat quantity index required by the current load material, namely the optimal bed temperature TE = ƒ (Q) according to the process analysis of the fluidized bedM) TE is related to the moisture content AT, and the correlation is also determined by the material distribution characteristics of the apparatus and the load W of the drying bedtAnd physical properties of the material. The correlation model of the bed temperature and the moisture can be simplified into the following equations by engineering empirical analysis:
wherein, at: is the coefficient of physical property of the material, i.e. the material body characteristic XFunction, at=ƒ（X)；PVAT: the actual measurement value of the current moisture meter is obtained; b: is a fluidized bed drying load engineering coefficient; wt-tx: is a fluidized bed at (t-t)x) The load of drying at any moment, because of the long-period drying of the fluidized bed, the load influencing the bed temperature can generate time offset and needs engineering correction; c: and adjusting engineering parameters for the correlation between the bed temperature TE of the fluidized bed and the moisture content AT.
According to the correspondence between the bed temperature and the moisture content, after the delay time H of the cold air drying stage, the moisture value fed back to the moisture meter can be used for predicting the moisture value in the subsequent time period by adopting the historical bed temperature, so that the delay of the measurement result caused by the cold air drying stage is reduced. Then the predicted water content PV is obtained according to the formulaATpThe results are as follows:
wherein PVATp: is a predicted moisture content value; TEt-H: the bed temperature of the drying bed at the time t-H; wt-tx-H: is t-txMoment H affects the load of the bed temperature of the drying bed.
Because the moisture measured value is greatly influenced by the mixing characteristics of the fluidized bed, the predicted moisture value refers to the bed temperature and the load change from the bed temperature at the corresponding moment to the time e after the influence of the mixing characteristics, and therefore the predicted moisture formula is corrected as follows:
wherein the content of the first and second substances,
the drying bed temperature rolling average value of the fluidized bed in the (t-H, t-H + e) region time is shown;
similarly, the time average of the area of the load affecting the bed temperature of the desiccant bed.
Because the moisture content AT of the fluidized bed is a core control target, the manipulated variable is corrected by adopting the control variable after the secondary time lag, and the influence of the early-stage prediction correction variable on the current correction inevitably exists, the influence of the predicted corrected variable Delta AT on the current result must be added into the moisture prediction module,
wherein, the delta AT is the correction deviation of the bed temperature by the moisture optimization control module.
For a heat source load balancing algorithm, namely under the constraint condition of a fluidized bed, searching a load interval allowed by production operation, and then carrying out corresponding coordination distribution according to the heat load proportion in process design, thereby achieving the optimal coordination of the load linkage of each heating medium;
wherein: qA is provided withThe total heat brought into the system by hot air in the design of the fluidized bed; etaA is provided with: the heat exchange efficiency of the material and hot air in the fluidized bed is improved; qB is provided withThe total heat brought into the system by hot water (or steam) in the fluidized bed design; etaB is provided with: the fluidized bed design has the heat exchange efficiency of the material staying in the bed and hot water (or steam).
Meanwhile, Q is used for ensuring the normal fluidization state of the materials under different production loadsA is provided withAnd QB is provided withThe control constraints of the system need to be met;
QA min≤ Qa is provided with≤QA max
QB min≤ QB is provided with≤QB max 。
Therefore, Q under constraint can be derivedA tAnd QB tThe system of equations is as follows:
the invention has the advantages that:
(1) the material concentration soft measurement model can effectively convert the non-measurable variable of the material concentration into a dimensionless parameter which influences the actual feeding quantity of the system, and the heat demand of the fluidized bed model is controlled, so that the accurate matching of a heat energy supply end is improved, and the control precision of a final finished product is ensured.
(2) The rolling time domain generalized prediction algorithm can effectively provide the optimal real-time correction of the rolling data of the field working condition of the whole long-period control process for the generalized prediction algorithm through the rolling time domain model, the processing timeliness and the prediction accuracy of the prediction control model are improved, a good foundation is provided for the optimization of the secondary rolling correction of the prediction control, and the dynamic controllability of a control system is greatly improved. The self-adaptive correction function of the generalized prediction algorithm can better correct the operation condition of the fluidized bed according to the final finished product moisture value, and the optimal control process optimization of the long-period control process is achieved.
(3) The moisture prediction module carries out optimization correction on the core temperature of the fluidized bed in real time by referring to the influences of the mixing characteristics of the device, the load of the drying bed and the physical properties of materials, so that the trouble that moisture at the tail part of the heated section cannot be measured is greatly reduced, and the pure hysteresis influence of a real-time feedback optimization scheme adopted in the stage is reduced.
(4) The heat source load balancing algorithm fully considers the coordination between the process characteristic requirement limitation of the fluidized characteristic of the fluidized bed and the actual fluidized bed design heat proportion, effectively avoids the response deficiency or multiple corresponding superposition coupling phenomena caused by partial adjustment reaching the upper limit due to insufficient coordination of various heat sources, and achieves the aim of stable coordination of the heat sources.
Drawings
FIG. 1: and (4) intelligent signal preprocessing effect diagrams.
FIG. 2 is a drawing: a fluidized bed control system schematic diagram.
FIG. 3: and (3) a rolling time domain generalized prediction model schematic diagram.
FIG. 4 is a drawing: schematic diagram of the heat of fluidized bed material.
FIG. 5: the relationship between the material heating and the heat demand of the fluidized bed.
FIG. 6: the instantaneous heat demand and the best response heat of the fluidized bed material are shown schematically.
Detailed Description
1. Soft measurement model of material concentration:
firstly, collecting material feeding flow FT and torque/power value NI, secondly, carrying out noise reduction analysis on the current actual feeding flow FT 'and NI' through an online signal preprocessing module, and finally, obtaining a reference value C through equipment operation condition analysis, wherein the obtained actual material feeding flow dimensionless parameter alpha is as follows:
thus the total weight of the material entering the fluidized bed at the present moment
。
2. Rolling time domain generalized prediction model algorithm:
firstly, the actual drying retention time h of the fluidized bed and the reasonable data discrete processing time phi are analyzed according to the fluidized bed process and the actual control process, and then the core data rolling processing array with the corresponding time can be obtained through the rolling time domain:
{TE1(T0)，TE1(T0+1)，TE1(T0+2)，…，TE1(T0+n)}；
{TE2(T0)，TE2(T0+1)，TE2(T0+2)，…，TE2(T0+n)}；
{TE3(T0)，TE3(T0+1)，TE3(T0+2)，…，TE3(T0+n)}；
{TE4(T0)，TE4(T0+1)，TE4(T0+2)，…，TE4(T0+n)}；
{WT(T0)，WT(T0+1)，WT(T0+2)，…，WT(T0+n)}；
{AT(T0)，AT(T0+1)，AT(T0+2)，…，AT(T0+n)}；
…………。
the following heat source heat exchange efficiency constant arrays of hot air, hot water and the like in the fluidized bed are confirmed through the distribution and the structure of the heating tubes or the heat exchange plate groups in the fluidized bed:
{ηA(T0)，ηA(T0+1)，ηA(T0+2)，…，ηA(T0+n)}；
{ηB(T0)，ηB(T0+1)，ηB(T0+2)，…，ηB(T0+n)}；
…………。
inputting the above array data into fluidized bed model, and calculating current material heat quantity array (see figure 4)
{Qt(T0)，Qt(T0+1)，Qt(T0+2)，…，Qt(T0+n)}。
Then according to the heat demand model QM==Kƒ（RH0Array of X) + Δ C and actual feed rate of material
{WT(T0)，WT(T0+1)，WT(T0+2)，…，WT(T0+n)}。
To obtain the final actual heat demand array corresponding to the time period (see the bar chart in figure 5)
{QNeed to(T0)，QNeed to(T0+1)，QNeed to(T0+2)，…，QNeed to(T0+n)}。
By the actual heat demand array and the remaining drying time length array of each position
{h-T0，h-T0-1，h-T0-2，…，h-T0-n}。
A heat demand balance array for a corresponding time period can be obtained (see figure 6)
{QNeed toˊ(T0)，QNeed toˊ(T0+1)，QNeed toˊ(T0+2)，…，QNeed toˊ(T0+n)}。
Obtaining the optimal heating quantity Q required in the current bed through a heat demand balance modelRequire avg。
At the moment, the rolling prediction model is combined, the characteristic parameters in the bed are iteratively updated in real time, and the optimal heating quantity Q is continuously subjected to online recursion through the generalized prediction model of the fluidized bedRequire avgAnd performing real-time rolling iterative correction. By optimal heating of QRequire avgCalculating the required total heat quantity QRAfter the heat carried by various heat sources is distributed according to a heat source load balancing algorithm, corresponding steam valves are guided to act, so that the optimal drying effect in the bed is achieved.
The cooling section designed by the fluidized bed process causes the lag of the actual feedback moisture value, the process lag time H is analyzed according to the fluidized bed process, all bed temperature parameters within the (T-H, T) time are obtained if the current time is T, and then the theoretical bed temperature mean value at the current time is obtained according to the material mixing characteristics in the bed.
The theoretical bed temperature mean value AT the current moment is brought into a moisture prediction model, and the material property coefficient a can be reversely deduced through the moisture deviation delta ATtA change has occurred. Expressing the expression coefficient a of the material property XtSubstituted into the heat demand equation
QM=Kƒ（RH0，X)+CT+CS+CRHa=Kƒ（RH0，X)+ΔC
The current equation K value can be corrected in real time, and the total heat demand of all materials in the bed can be directly corrected. Iterative updating is carried out through a rolling time domain generalized prediction model to realize QRequire avgAnd correcting the deviation by the model again so as to respond to the unmeasured characteristic change of the physical property of the material.
Suppose current QRequire avgThe value is 10000MJ, and the original process design brings in the heat of hot water and hot air of the systemThe quantity ratio is 8: and 2, analyzing the adjustable interval of the process limit of each heat source of the fluidized bed to obtain a hot water adjustable interval (2000 MJ, 10000 MJ) and a hot air adjustable interval (500 MJ, 2000 MJ). From the above, the lower limit of the basic heat of the hot air and hot water is 2000MJ +500MJ =2500MJ, and only 7500MJ is needed to perform actual load distribution. According to the hot water load interval 8000MJ and the hot air load interval 1500MJ, the following equation can be obtained assuming that the two loads are theoretically distributed as A and B:
2000+A=4（B+500）
A+B=7500
a =6000MJ and B =1500MJ were obtained.
The actual hot water load rate is 6000/8000= 75%; the hot air coincidence rate is 1500/1500=100%, and the heat provided by the hot air reaches the upper limit, so that the hot air cannot be regulated any more, so the following equation should be referred to
A/（10000-2000）=B/(2000-500）
A+B=7500
A =6320MJ, B =1180MJ is obtained.
And averaging the current state to obtain A =6150MJ and B =1350 MJ.
And converting the finally calculated heating quantity value into the hot water/hot air temperature as a real-time manipulated variable. And then the control signal is sent to an actuating mechanism through a steam adjusting controller, and the opening of the valve is changed to adjust the real-time tracking calculation value of the steam flow.
Claims (4)
1. The utility model provides an intelligent control system for dry fluidized bed of PVC which characterized in that: (1) a soft measurement method for representing material concentration by using centrifuge operation parameters; (2) aiming at a rolling time domain generalized prediction algorithm of the black box device staying for a long time, carrying out real-time iterative computation on the heating process of the materials in the bed; (3) predicting the moisture content by using the device load factor; (4) and (3) according to the process design, the proportion of a drying heat source is combined with the lower limit of a heat source required by fluidized bed material fluidization, and a heat source load balancing algorithm is adopted to realize the coordination control of all heat sources.
2. An intelligent control system applied to a PVC drying fluidized bed according to the claim 1, characterized in that the material concentration soft measurement method of the characteristic (1):
making a corresponding matching relation between the thrust of the separated material after the centrifugal machine is dried or the centrifuge power NI 'and the actual slurry flow FT', and obtaining a soft measurement result of the current material slurry concentration
，
The basic principle formula of a material concentration soft measurement model is as follows:
，
wherein the content of the first and second substances,
: the current material slurry concentration soft measurement result; NI' is the thrust of the separated material or centrifuge power; FT' is the actual slurry flow; c, soft measurement engineering experience parameters.
3. The intelligent control system applied to the PVC drying fluidized bed as claimed in claim 1, wherein the rolling time domain generalized prediction algorithm for the black box device with long residence time in the feature (2) performs real-time iterative computation on the heating process of the materials in the bed, and comprises the following steps:
1) determining the instantaneous material feeding amount entering the fluidized bed at the current moment as W by combining the current actual slurry flow through the material concentration soft measurement moduleT= α FT' from T0Time to T1The material storage W in the bed at any momentT1The method comprises the following steps:
；
2) calculating the data of all heat sources in the bed, wherein the heat sources in the bed are at least divided into fluidized hot air and hot water (or steam), and the heating quantity Q of the materials suffered by the feeding quantity per unit masstIs T0Is at the momentAll the entering beds (fluidizing hot air and hot water) carry heat during the time t to multiply the heat exchange efficiency,
；
wherein: qA TThe total heat brought into the system by the hot air at the moment t; etaA t: at the moment t, the heat exchange efficiency between the staying position of the material in the bed and hot air, the heat exchange efficiency is different due to the designed heat exchange area of the bed type, and the heat exchange temperature difference is a time-varying function; qB TThe total heat brought into the system by the hot water (or steam) at the moment t; etaB t: at the moment t, the heat exchange efficiency between the staying position of the material in the bed and hot water (or steam) is improved;
3) drying the unit mass material from the initial state to the desired state with the required heat QMLinearly simplified by engineering experience to the following equation: qM=Kƒ（RH0，X)+CT+CS+CRHa=Kƒ（RH0，X)+ΔC；
4) The theoretical heating quantity Q of the unit mass material in the drying bed at the later time after the t moment can be known through an energy demand matching modelNeed toThe equation is as follows:
Qneed to=
=
；
5) The above equation can obtain the instantaneous heat demand Q of all the materials in the bedRThe equation of (1) is as follows:
；
6) and performing real-time iterative computation on the model data and performing rolling time domain statistics.
4. An intelligent control system for PVC drying fluid bed, according to claim 1, characterized by the fact that the prediction of the moisture content in the said feature (3):
1) according to the analysis of the fluidized bed process, the determination of the bed temperature TE of the fluidized bed represents the optimal heat index required by the current load material, namely the optimal bed temperature TE = ƒ (Q)M) TE is related to the moisture content AT, and the correlation is also determined by the material distribution characteristics of the apparatus and the load W of the drying bedtThe physical properties of the materials; the correlation model of the bed temperature and the moisture can be simplified into the following equations by engineering empirical analysis: TE = atPVAT+bWt-tx+c；
Wherein, at: is a function of the physical property coefficient of the material, namely the material body characteristic X, at=ƒ（X)；PVAT: the actual measurement value of the current moisture meter is obtained; b: is a fluidized bed drying load engineering coefficient; wt-tx: is a fluidized bed at (t-t)x) The load of drying at any moment, because of the long-period drying of the fluidized bed, the load influencing the bed temperature can generate time offset and needs engineering correction; c: adjusting engineering parameters for the correlation between the bed temperature TE of the fluidized bed and the moisture content AT;
2) the method comprises the following steps of carrying out optimization correction on the core temperature of the fluidized bed in real time by referring to the influences of the mixing characteristics of a device, the load of a drying bed and the physical properties of materials, wherein the corrected final moisture content value calculation formula is as follows:
wherein PVATp: in order to be a predicted value of the moisture content,
the drying bed temperature rolling average value of the fluidized bed in the (t-H, t-H + e) region time is shown;
similarly, to influence the drying bedThe time average value of the area of the load quantity of the bed temperature, delta AT, is the correction deviation of the bed temperature by the moisture optimization control module.
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| CN115157481A (en) * | 2022-06-20 | 2022-10-11 | 沈阳华控科技发展有限公司 | Heat control method of PVC drying device |
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