Abstract�—The aim of process control has been to control heating of steel tubes in front of roll-mill. Equal temperatures along 17m length steel tubes have been required. The temperature of charge direct to measure was not possible in reheating furnace from objective reasons. The temperature of combustion products in reheating furnace has been direct measured. Simulation model of reheating furnaces was used for development of control system. First simulations shown that is necessary to change construction of furnaces and the localization of burners. The proposal of creating burner�’s zone was done by simulation likewise. This way is well-know as a cooperation of design construction conditions by automation equipments. The control system consists of two levels. First stabilization level is based on algorithm bang-bang control. Second level adapts required values for first level on principle direct measured temperatures of charge outside furnace. The idea of control system was verified by simulation.
Keywords�— project; simulation model; device; control; integral solution
I. INTRODUCTION Industrial and process furnaces experience provides a
comprehensive reference to all aspects of furnace operation and design, with coverage of key topics that plant and process engineers and operators need to understand, including the combustion process and its control, furnace fuels, efficiency, burner design and selection, aerodynamics, heat release profiles, furnace atmosphere, safety and emissions [1], [2]. It helps to understand complex heat and mass transfer and combustion problems. This access is classical and it does not help to solve problem with design of control system. The task of designing and analyzing industrial furnaces is complex and time consuming. During the proposal phase of furnace buying, the customer�’s needs are communicated to the furnace manufacturer, who, in turn, comes up with several design solutions to satisfy these requirements. This process usually involves several iterations of lengthy engineering calculations.
Considering complicated mutual interrelations of partial processes such as those in industrial furnaces, the designer does not dare to implement a radically new solution. The designer (project companies) based on experience from the initial
solution gradually innovates industrial devices.
New project
Ev
alu
ati
on
New project
Ev
alu
ati
on
New project
Ev
alu
ati
on
New project
Years
know-how
Figure 1. Classical evolutionary approach to design.
This approach can be characterized as evolutional (see Fig. 1), which has certain disadvantages , e.g. slow development of techniques, small growth of labor productivity, low rate of decreasing energy consumption, etc. Thanks to advances in computer technology, the barriers formerly posed by these challenges have been leveled. More and more engineers are now using software tools for accurate analysis and simulation of equipment and process. Moreover, the software provides a window on the process, enabling engineers to better understand what happens inside the parts as they are heat treated. Furnace manufactures can use the FurnXpert program to accurately and efficiently size furnaces for their customers, while heat treaties, process engineers, and plant operators can use design and analysis software to determine the best setup for any furnace/part combination [3]. Reference [3] focuses on computer software that functions as a furnace design and setup tool but it does not solve problem of design of the control system again. Reference [4] pointed out fact; PID controller is suitable only for linear systems with known mathematical models. But controlling of temperature of industrial furnace system is a non-linear, time delay and time varying. Hence, conventional PID controllers can't produce satisfactory results when it is used to control the temperature of the industrial furnace system. There is proposed furnace cascade control system. Conclusively, the performance of the proposed controller architecture is evaluated by finding the dynamic performance characteristics. The entire system is modeled by using MATLAB/Simulink, and the simulation results have shown that the proposed fuzzy logic controller has rapidity, good robustness and good dynamic performance [4]. This contribution focuses on developing two new variants of furnaces including control with different architecture as [4].
II. DESIGN OF FURNACE SYSTEM BY THE HELP SIMULATION
Unlike classical approach of projecting (Fig. 1) of new industrial devices by means of expert system it�’s possible to design, and by simulations to verify several variants of solutions. Finally, the best solution to choose of them is already easy. Even though exist several reasons for evolutional development, one of most important reasons is knowledge development of specialists �– experts, which obtain these knowledge step by step with every new project. In general, this knowledge can give us also simulation process. It requires existing of simulation model. Most easily approach of design by means of simulation model is shown in Fig. 2. By gradually changing input data and based on the evaluation of simulation results, the designer or the potential user gains knowledge on technical and economic parameters of the future device. Thereby the designer gradually models, from his viewpoint, the constructional, material, geometry dimensions and economic parameters of the future device. An advantage of simulation model usage for the design of new technological device is low cost and mainly the possibility to verify different variants, which is not possible to realize in the first approach (Fig. 1). It must be emphasized, that the area of utilization of simulation models is wider than that of the design of industrial devices. They can be used also for the reconstruction or repair of these devices. In metallurgy these devices are heat aggregates that work continuously. The wear of these devices has negative influence on the quality and quantity of production, therefore the determination of the measure of damage can be the task for simulation systems for the design of industrial devices. In this article, the furnace system is understood as cooperation between the furnace and control system. In others words have spoken, the furnace system consists from subsystems a furnace and a control system.
Formulationof aims
Creating ofmathematical andsimulation model
Definition of inputsfor simulation model Simulation
Verification ofmodel
modifications of model
Evaluation ofresults fromsimulation
Newproject
Figure 2. The proposal of project by simulation.
A. Mathematical model One of key aspects of furnace sizing and design is the need
to understand the heat transfer among furnace walls, furnace atmosphere, and the parts being heated treated. It�’s well-known that heat flows from higher temperatures to lower temperatures, and heat transfer takes place by radiation, convection, and heat flux. However, determination of each of these heat transfer
components involves complex calculation. For example, at high temperatures, heat transfer takes place by radiation. Unfortunately, it is not linear function. Moreover, thermo physical properties change with temperature. Engineers and designers use rules of thumb and manual calculation to solve these complex problems. Some may even use a computer spreadsheet program. The fact is that meaningful solutions to these problems cannot be obtained by manual or simple spreadsheet means. The same argument applies to furnace users. Process engineers and heat treaters are always faced with the challenges of operating their furnaces at the highest efficiency without sacrificing product quality. Therefore, the mathematical model of preheating furnace consists from following relevant processes.
where: c �– specific heat capacity (J.kg-1.K-1), $ & density (kg.m-3), x �– coordinate (m), t �– temperature (°C), # �– thermal conductivity (W.m-1K-1), % & time(s).
where: is the Stefan-Boltzmann�’s constant (( = 5.6703.10&8 [W.m-2K-4]), Qr �– radiation heat flow (W), T �– temperature (K), TAij, TAji - total heat exchange surfaces between i-th and j-th zone (m2).
Heat exchange by streaming gasses between k-th and n-th volume zone:
2013 14th International Carpathian Control Conference (ICCC)162
nkck
ck
ck
sn ptcVQ ,"""= , (5)
where: Q �– heat flow [W], V - volume flow [m3.s-1], c �– specific heat capacity [J.kg-1.K-1], t �– temperature [°C],
pk,n �– part of volume flow, which flows from k-th zone to n-th zone, k ,n �– index of volume zones [-].
Of course, the mathematical model preheating furnace was more complicated as it has been shown by (1), (5). Equation�’s system from (1) to (5) was solved including energy balance equation for each volume zone every simulation step s which was defined by the condition of stability for solution (2). An integrated multidisciplinary approach to furnace design that considers the interdependence between furnace elements and other furnace systems, and control, is necessary to achieve minimum consumption of fuel and heating quality which is given by regulation deviation. By regarding of control system, there were considered following control algorithms.
Algorithm by dead zone:
( )iiii esignuuu ")+=+1 , (6)
consti
i
uuunless
thenyi
)=)
=))+**)& 0uwwf i
Algorithm of PS controller:
( ) %)+&+= &+ iIiiPii eKeeKuu 11 , (7)
Algorithm of PSD controller:
( ))
&&+)+&+= &&&+ %
% 2111
2 iiiDiIiiPii
eeeKeKeeKuu , (8)
Algorithm of bang- bang control:
wy if imax <= uui , (9a)
wy if imin += uui , (9b)
where: u �– the control variable, y �– the controlled variable e �– regulation deviation, w �– required variable, �– allowable deviation from w, u �– the gain control, KP �– the proportional constant, KI �– the integration constant, KD �– the derivative time constant, i �– the index of control period, % �– time of control period.
Control variables are volume fuel flows of burners in furnace. On base mathematical description for furnace (1)-(5) and control (6)-(9) has been created simulation model of furnace system. Of course, during one simulation case is used only one type of the control algorithm from (6) to (9). During simulation permanently is valid condition (10).
%% )<) s . (10)
The entire system is modeled by using programmed language FORTRAN.
B. The Analysis of Simulation Casses The preheating furnace serves for heating steel blocks in
front of tube rolling mill. Required temperature pushed steel blocks from furnace is 950 oC. There are considered two different furnace systems. Therefore, the first question is very important. Which type of industrial furnace from two variants will be better? The first variant is schematically shown in Fig. 3 and the second variant is shown in Fig 4. The steel charge inputs from right side and gradually step by step is shifted along furnace length in both cases. Finally, the heating charge is pushed on left side from furnace. The crown construction both furnaces are different. The second variant has decreased crown. It is cased by using other burner�’s type. Classical burners enable fluently to change volume fuel flow. Regenerative burners work with higher efficiency in comparing with classical burners. But there are not possibilities for fluent change of volume flow of fuel because they work in regime on �– off switch. From standpoint of control, algorithms (6), (7), (8) are possible to use for control classical burners and algorithm (9) for regenerative burners. The difference between the first and second variant is not only in constructional solution, but there are also differences in the manner of combustion product exhaust.
Figure 3. Scheme of furnace with classical burners (first variant).
Figure 4. Scheme of furnace with regenerative burners (second variant).
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In the first variant classical burners were used while in the second variant regenerative burners were used. Suitable algorithm for control was analyzed in principle which is shown in Fig.5.
Inputs
SIMULATION MODELOF CONTROL
SIMULATION MODELOF DEVICE
yOutputs
x uControl
Figure 5. Simulation scheme of industrial object (furnace) includes its control.
That is next positive factor of this approach. Let us emphasize that this created the simulation model of all technological devices including the control system. Usually the industrial project is designed by a design agency and the control system for this object is designed by another team of designers. A disadvantage of this approach is a one-sided relation. Another drawback is that there is no space and time for a revision of the project, if it is possible to meet the aims of the project more effectively. With the presented integrated solution, according to Fig.5, a synergetic effect will apply interactively. In other words, what is better in terms of meeting the aims will be realized by constructional changes or by innovations in control system? The course of controlled temperature of steel charge by using algorithm (6) is shown in Fig.6
Figure 6. The course of charge temperature in furnace for first variant.
The control according algorithms (6), (7) was studied for first variant (see Fig.3). But PS algorithm obtained worse results in comparing with algorithm (5). PS, PSD controllers are suitable only for linear systems with known mathematical models. But controlling of temperature of industrial furnace is a non-linear, time delay and time varying. Hence, conventional PS and PSD controllers can't produce satisfactory results when it is used to control the temperature of the industrial furnace.
Algorithm of bang- bang control was used for second construction variant of furnace. The course of controlled temperature of steel charge by using algorithm (9) is shown in Fig.7. In both pictures the behavior of charge temperature is simulated from its input to its output from furnace.
The charge going out from the furnace, according to the first variant, has smaller temperature deviation. The charge after heating in second variant has higher temperature skips toward the end. This is caused by regenerative burners. Regenerative burners switch every 10 seconds and that is the reason of greater temperature skips in the second variant since the temperature of fresh combustion products is higher than the temperature of exhausted combustion products. Consequently the final solution will depend on the fulfillment of requirements formulated in the aims for the future device. If the regulation deviation ( t=46°C) in the second case is not suitable, then a new project will be realized according to the first variant of reheating the furnace. Quality of heating and an economy for both variants are shown in Tab. I.
Figure 7. The Course of charge temperature in furnace for second variant.
Moving of charge (steps)
Temperature (oC)
Temperature (°C)
Moving of charge (steps)
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TABLE I. COMPARING QUALITY AND AN ECONOMY OF CHARGE HEATING
Type of furnace
Parameters
Furnace capacity (th-1) Regulation deviation (oC)
Specific consumpti
on of energy (GJt-1)
Classical burners
58.02 9.8 0.67
Regene-rative burners
58.02 46 0.41
Second variant furnace with regenerative burners has higher
efficiency than first variant but worse quality of steel charge heating which is given by regulation deviation (see Tab. I.). What is reason? There is needed to explain a way of measuring controlled temperature of steel charge. Directly to measure charge temperature in furnace by thermocouple is impossible because the charge is moving. The measuring of this temperature by optical instruments is same problem because optical properties of furnace atmosphere are strongly changed. Usually, temperature of combustion products is controlled variable and its required value is defined as constant in furnace system. The measuring temperature of combustion products is realized without any problems in furnace practice. Therefore, bang- bang control considered with temperature of combustion products and required temperature was defined as constant for all burners. It means there are not relationships on charge temperatures. It is reason a higher charge regulation deviation in second variant.
C. Proposal of control system The positive feature of second construction variant with
regenerative burners is lower specific consumption of energy. In opposite, the negative fact of this solution is the higher regulation deviation. In previous subchapter, this reason was explained. Therefore, there is presented the control of charge temperature through the temperature of combustion products for second variant. The principle and structure of control system is shown in Fig. 8. The control of charge heating was based on the control in two levels. The first level was the level of stabilization and the second level presents adaptation of the required value of charge temperature. The stabilization level presents the bang - bang control of combustion products temperature. Adaptation methods are also different. That were analyzed more ideas for adaption required value of combustion products in depending upon measured charge temperature outside of furnace. On base analyzed simulation cases, that might be chosen the best method. It showed that it is best to adapt required values of the temperature from the output of the furnace. However, the output of furnace does not change very rapidly in real conditions. Therefore, an adaptive algorithm was designed by simulations (12). This algorithm responds for the change of required charge temperature. For real conditions it showed, that for the first burner�’s wall it is enough to define the required temperature of combustion products as constant. This constant and also the adaptation constant were determined by simulations. Of course in real conditions, charge temperatures will be measuring by pyrometer at place pushing out of charge
echmeasuredT arg
productcmeasuredT .
echrequiredT arg
productcmeasuredT .
Adaptation of required value
Bang-bang control of combustionproducts temperature
Controlled burner's zonePyrometer
FURNACE
Cha
rge
outp
ut
Cha
rge
inpu
t
Fuel
1. levelAdaptation of required value
2. levelStabilization of combustion
products temperature in controlzone
Figure 8. Architecture of control system for preheating furnace.
from furnace and the temperatures of combustion products by thermocouples. Finally, the algorithm for bang �– bang control is in following form:
If productscmeasuredT . < productsc
requiredT . , then Fuel=MAX. (Switch on) (11a)
If productscmeasuredT . + productsc
requiredT . , then Fuel=0. (Switch off) (11b)
Adaptation of required value of combustion products temperature was made according to algorithm: For front burner�’s wall:
productscrequiredT . = 850°C.
For back burner�’s wall:
1.
+kproductsc
requiredT = ( )echmeasured
echrequiredk
productscrequired TTaT argarg. &"+ k , (12)
where: a �– adaptation coefficient (a = 2.5), k �– time step,
productscrequiredT . , ech
requiredT arg , echmeasuredT arg are required temperatures for
combustion products, steel charge respectively and measured charge temperature by pyrometer.
The control (11), (12) was verified by simulation. That was founded out that regulation deviation had been decreased at 17.2 oC but specific energy consumption had been raised about 15 % in comparing with data in Tab. I.
III. CONCLUSIONS Simulation models and simulation cases are modern and
effective tools for projecting new industrial devices including its controlling. The article pointed out efficiency of integral solution furnace and control system because there are mutual relationships and the integral access enables to find better solution in comparing with individual solution of two projecting teams (furnace + control). From the control standpoint, the conventional PS controller could not be used for the control of non-linear processes like temperature. If is preferable efficiency in preheating furnace, regenerative burners are recommended for using. It is further argument for using other type controllers. So, the proposed bang �– bang algorithm based controller design can be a preferable choice to achieve this to be used for controlling non-linear processes like
2013 14th International Carpathian Control Conference (ICCC) 165
temperature. Design of reconstruction preheating furnace and control system has been applied in Steel work Podbrezova.
ACKNOWLEDGMENT This work was supported by grants VEGA No. 1/0095/11,
1/0265/13.
REFERENCES
[1] P. Mullinger, and B. Jenkins, Industrial and Process Furnaces: Principles, Design and Operation. Butterworth-Heinemann, 2008.
[2] N. Voermann et al., �“Furnace Cooling Design for Modern, High-Intensity Pyrometallurgical Processes,�” 4th Int. Conf. Copper 99�—Cobre 99. Vol. V: Smelting Operations and Advances, pp. 573�–582, May 1999.
[3] H. K. Nandi, M. C. Thomason and M. R. Delhunty, �“Furnace design and operation,�” Heat Treating Progress, ASM International, Ohio, pp. 329�–335, November 2002.
[4] B. V. Murthy, Y. V. P. Kumar, U. V. R. Kumaril, �“Fuzzy logic intelligent controlling concepts in industrial furnace temperature process control ,�” IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT), pp. 353-358, 2012.
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