Visual feedback Control based on image information for the Swirling-flow Melting Furnace

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1 Visual feedback Control based on image information for the Swirling-flow Melting Furnace Paper Tomoyuki Maeda Makishi Nakayama Hirokazu Araya Hiroshi Miyamoto Member In this paper, a new visual feedback control for a swirling-flow melting furnace is proposed. This method control the furnace combustion by manipulating the burner fuel based on image data, which shows the melting situation inside the furnace. The feature of this control system is that, a visual-tracking property is realized by using an adaptive control algorithm which changes the control gain based on image evaluation as if the melting point varies. Through actual experiments, it is clear that its combustion can be controlled very precisely by this visual feedback control system. Keywords: visual feedback, image processing, process control 1. Introduction The swirling-flow melting furnace melts the sludge ash with the swirling-flow and added heavy oil. It is important to keep on setting a furnace temperature to a desirable value which represents the best combustion condition. Actually, the furnace combustion is controlled by manual operation with monitoring of the slag flow image which represents a melting condition, because it is difficult to measure the temperature directly and the melting point varies according to the ash characteristics. In order to achieve an automatic operation, a control system for the swirling-flow melting furnace based on image data of the furnace interior is proposed in this paper. This system is realized as a new visual feedback control system. The features are as follows: The image evaluation data of a melting material flow is used as controlled data The visual-tracking property is realized by using an adaptive control theory based on the above image evaluation value. We present the system configuration and the visual feedback control algorithm, and show application results in the actual operation.. Swirling-flow melting furnace A schematic diagram of the swirling-flow melting furnace is shown in Fig. 1. A swirling flow pattern is made by the primary air and the secondary air inside the furnace. The sludge ash which is carbonized at the combustion furnace is fed from the top of the furnace with the swirling flow, and the ash melts in a short time. The melting material collides with the wall due to centrifugal force and falls down. The falling down behavior of the melting material from the slag separating section is observed by using an industrial television. An example of the image data indicating this situation is shown in Fig.. Production Systems Reserach Laboratory, Kobe STEEL LTD , Takatsukadai, Nishi-ku, Kobe city, Hyogo Department of Mechanical Engineering, Fukui University of Technology 3-6-1, Gakuen, Fukui city, Fukui Waste Treatment Systems and Process Development Department, Kobelco Eco-Solutions Co., LTD , Wakinohama-cho, Chuo-ku, Kobe city, Hyogo Fig. 1. Swirling-flow melting furnace 174 IEEJ Trans. IA, Vol.15, No., 005

2 Visual feedback Control Systems Fig.. An example of the slag flow image data Fig. 4. Characteristics extraction Fig. 3. Visual feedback control system 3. Visual feedback System Usually, the combustion temperature of the swirling-flow melting furnace has been controlled by skilled operators to make the melting situation stable. The reasons are that the inside temperature cannot be measured directly and the control reference temperature, which is the ash melting point, varies according to the ash characteristics. So far the operator always observes the state of melted slag by the television and controls the burner fuel value according to his estimation of the melting situation. In order to solve these problems, a visual feed-back control is tried. This control system consists of two parts, one is characteristics extraction by using image processing and the other is an adaptive control algorithm for the burner fuel control. In the image processing part, we use the image data of the melted slag flow instead of skilled operators eyes and estimate the melting situation, and the controller is designed automatically and sequentially by using an adaptive control algorithm. The outline of this control system is shown in Fig Characteristics Extraction The slag situation mainly means the furnace combustion. For example, in a case where the inside temperature is lower than the melting point, the heat for melting the sludge ash is insufficient. Then much of the un-melted ash sticks on the inside wall of the furnace and the rest is melted down and becomes slag. The amount of the slag in the visual image is very small and it falls down slowly and wavy. On the other hand, if the inside temperature is higher, the slag on the inside wall is melted by the remaining heat and a lot of slag falls down quickly. The operator evaluates these characteristics of the slag, while in this system the slag characteristics can be extracted instead of the operator s evaluation. The features of this extraction are to estimate the slag volume by image processing and to evaluate the stability of the slag flows from continuous image data. We propose a slag volume estimation method. In focusing on the slag image data (shown in Fig. ), the slag is brighter than the background and steam, and it falls down nearly straightway. To extract the slag by the image data, the projection operation, which is one of the image processing operations is applied. The image data is a set of pixel values and it is defined as follows: { Pij :1 i N, 1 j M } (1) where N, M is the size of a matrix which is an image data pixels set. Then, the projection data is explained by equation (): M L i : L i = P ij, 1 i N () j=1 From the result of this calculation (shown in Fig. 4) we confirm that the slag part stands out in this data, but this part does not indicate an absolute evaluation value because the combustion situation varies by changing the background brightness. To measure the relative volume between the D

3 slag and the others, we apply the polynomial approximation method based on the least square mean method in which the polynomial coefficients are calculated. The performance index in this method is shown in equation (3): N J 0 = (L i F(i)) (3) F(i) = i=1 k a m i m + a 0 (4) m=1 and k is an order of polynomial function (in actual system fourth oder function is used). The error between the polynomial function value and the data are explained by equation (5): Err(i) = L i k â m i m â 0 (5) m=1 where â m, â 0 are estimated parameters. The error indicates the slag (shown in Fig. 4), so we evaluate the area of this part to measure its volume. In order to calculate the area, we define P 0 that is the point on which Err(i) is maximal. Next we chose the nearest zero crossing points by P 0, and denote smaller one P l and the other P u. By using them the area of the gray part is explained by equation (6) P u S = Err(i) (6) i=p l E 1 is defined as the average value of S for continuous image data during some period. Next, we propose a stability evaluation method for the slag flow which is measured by the position change of the flow. In order to find the change, we search for the point on which the projection data value is maximum and do this search using continuous image data for some period and calculate the standard deviation of the sampled data. Finally we define the characteristics of the image data weighted combination of above two values as follows: y = w 1 E 1 + w E (7) where E 1 is an average of the estimated slag volume, E is a standard deviation of the slag s position data and w 1, w are weighting coefficients, which are obtained based on operators evaluation for actual slag image. The result, which is a step response of the image evaluation value when the burner fuel is changed, is shown in Fig. 5. It shows that the evaluation value varies according to the change of the burner fuel. 5. Visual Feedback Control System The transfer function between the burner fuel value u and the image evaluation value y is nonlinear and time-variant because the furnace process is nonlinear and the melting process is time-variant. Therefore the model of the transfer function is not led easily so we think the function is led from the step response. From the step response data (shown in Fig. 5), we recognize that the overshoot exists and the transfer function between the burner fuel value (u) and the image evaluation value (y) can be approximated by the following second order system: ω G(s) = s + ηω s + ω (8) The step response shows that this controlled system is a time-variant system. So we apply the adaptive control theory (3) to design a controller. We try the simplest order controller for the tracking problem (4) (6) that the image evaluation value (y) track to a desirable evaluation value (r). It is described as follows: C(s) = K p + K I /s (9) where K p is a proportional gain and K I is an integral gain. In order to estimate two gain parameters K p and K I, the optimization technique of modern control theory are applied. The closed-loop transfer function from a desirable evaluation value to image evaluation value is as follows: K p ω s + K I ω G ry (s) = (10) s 3 + ηω s + ω (K p + 1)s + K I ω This transfer function means that the closed-loop is third order system with three integration factors and state feedback gains. By transferring equation (10), the equivalent state space equation is described as follows: ẋ = Ax + bu y = cx u = fx where, x 1 0 A = 0 0 1, x = x, b = 0 (11) x 3 1 c = [ K I ω K p ω 0 ] f = [ ] f 1 f f 3 f 1 = K 1 ω, f = ω (K p + 1), f 3 = ηω Fig. 5. Step response of the swirling-flow melting furnace Since this system is controllable and observable, the optimum regulator theory is applied to obtain the optimum gains. Next we show that the state feedback gain f is a one of these optimum gains if K p and K I are properly selected. In the optimum regulator theory, the state feedback gains k 176 IEEJ Trans. IA, Vol.15, No., 005

4 Visual feedback Control Systems are decided as minimizing the following performance index: ( J = x T Qx + u T Ru ) dt 0 where (1) Q 0, R > 0 The solution of this problem, is described by equation (13): u = kx = R 1 b T Px (13) Here, P is the solution matrix of the following Riccati equation: PA + A T P PbR 1 b T P + Q = 0 (14) Therefore, the optimum gains of the state space equation (11) are described by equation (15): u = 3 ( ) R 1 p i xi (15) i=1 Here, p i (i = 1,, 3) are the third row elements of the solution matrix P and satisfy the condition equations (18) if the weighted matrix of performance index are selected as follows: Q = diag(1, 1, 0) (16) R = 1 ηω 1 + η ω 1)} (17) ( )} p 1 /{ηω = η ω 1 p = p 1p ηω 1 + η ω 1)} (18) p 3 = p )} ηω 1 + η ω 1 difference equation with sampling time t as follows: y t = d 1 y t 1 + d y t + gu t () where + ηω t d 1 = 1 + ηω t + ω t 1 d = (3) 1 + ηω t + ω t ω t g = 1 + ηω t + ω t We define ˆd i (i = 1, ) as the estimated parameters of d i (i = 1, ). If we do calculate ˆd i (i = 1, ) from time series data that are the burner fuel value u and the image evaluation value y, by usingan identification methodlike a leastsquares estimation method, ω, η are obtained by the following equations using these ˆd i, and by them the gains are calculated to the optimum values in equation (0) and (1). ω = 1 ˆd 1 + ˆd 1 (4) t ˆd ˆd 1 ˆd η = ( ) (5) ˆd ˆd 1 + ˆd 1 The flowchart of this adaptive control algorithm is shown in Fig. 6. By solving these equations (18), the state feedback gains are obtained as follows: R 1 p 1 = ηω ( 1 + η ω 1 ) R 1 p = η ω (19) R 1 p 3 = ηω Therefore, it is clearly shown that the gain f in equation (11) is equivalent to the optimum gain if the two gain parameters K p and K I are designed as follows: K p = η 1 (0) K I = η ( 1 + ω 1 )/ ω (1) This means that a servo compensator with optimum gains based on the optimum regulator theory is automatically designed by using two parameters of the controlled system. The parameters (ω, η) of the transfer function (10) represent the combustion dynamics, which changes gradually. To apply the above optimum technique, these parameters must be continually decided every by on-line parameter estimation. They are estimated by using the serial least mean square estimation method. By using backward divided difference method, the transfer function (10) can be transferred to the Fig. 6. Flowchart of the control system D

5 polynomial approximation method and the other is the adaptive control based on the modern control theory and the parameter estimation. Through experimental results, it is clear that the combustion of the furnace can be controlled stably by the proposed system. (Manuscript received Oct. 1, 003, revised June 3, 004) Fig. 7. Block diagram of the visual feedback control system References ( 1 ) T. Suzuki, et al.: The sewage sludge melting technology, KOBELCO RE- VIEW, Vol.36, No. (1988) ( ) T. Suzuki, et al.: Proceeding of the 11th Conference of the Resources Academy, p.19 (199) ( 3 ) K.J. Astron and B. Wittenmark: Adaptive Control, Addison Wesley (1989) ( 4 ) A. Kitamura, et al.: Optimization of a hydraulic screw-down AGC system for a plate rolling mill, Journal A, Vol.9, No.1 (1988) ( 5 ) E.J. Davison: The Robust Control of a Servome-chanism Problem for Linear Time-Invariant Multivariable Systems, IEEE Trans. Automatic Control, AC-1-1, pp.5 34 (1976) ( 6 ) M. Ikeda: Modern control theory seminar senior course, SICE, pp.13 4 (1990) ( 7 ) W.H. Press: Numerical Recipes in C, Cambridge University Press (1988) Tomoyuki Maeda () was born in Osaka, Japan, on December 1, He received M.S. degrees in Systems Engineering from Kobe University in 199. He joined Kobe Steel, Ltd. in 199, and is presently a researcher at Production Systems Research Laboratory in Kobe Steel, Ltd. His research area is system development based on control engineering and image processing. Fig Experimental Results Experimental results This system consists of two computers, an image processing computer and adaptive controller one as shown in Fig. 3. The image processing computer extracts the characteristics of the image data, and in the others the parameters of transfer function are estimated and the optimum control gains are calculated and tuned adaptively. Then the calculated control signals are transferred to the local PID controller (Shown in Fig. 7). We apply the adaptive control method to the furnace combustion control under the condition that the disturbances have been added on, after the parameter estimation has been finished. As shown in Fig. 8, the image evaluation value can be controlled to the reference value at 0 (min.), and its deviation value decreases gradually. Thus it is clear that the slag flow can be controlled stably by applying this visual feedback control system. 7. Conclusions We proposed a visual feed back control system for the swirling-flow melting furnace. It contains two main parts, one is the image characteristic extraction by using the Makishi Nakayama (Member) was born in Osaka, Japan, on July 3, He received M.S. and Ph.D. degrees in Control Engineering from Osaka University in 1985 and 1999, respectively. He joined Kobe Steel, Ltd. in 1985, and is presently a senior researcher at Production Systems Research Laboratory in Kobe Steel, Ltd. His research area is system development based on control engineering and image processing. He is a Member of ISCIE and SICE. Hirokazu Araya () was born in Hyogo, Japan, on May 3, He received a Doctor of Engineering s degree from Osaka University in 003, and is presently a professor at Fukui University of Technology. He has worked on automation of construction machinery. Japan Society of Mechanical Engineers, Institute of Systems, Control and Information Engineers Member. Hiroshi Miyamoto () was born in Okayama, Japan, on September 6, He received a master degree in Mechanical Engineering from Kyoto University in 1995, and is presently an engineer at Kobelco Eco- Solutions Co., Ltd. He has worked on sewage sludge incineration and melting system. The Japan Society of Mechanical Engineers Member. 178 IEEJ Trans. IA, Vol.15, No., 005