Set Point Identification and Robustness Testing of ... - IEEE Xplore

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Set Point Identification and Robustness Testing of Electrostatic Separation Processes Karim Medles, Amar Tilmatine, Farid Miloua, Abdelber Bendaoud, Mohamed Younes, Mostéfa Rahli, Member, IEEE, and Lucian Dascalescu, Senior Member, IEEE

Abstract—Identification of the optimal operating conditions and evaluation of their robustness are critical issues for the industrial application of electrostatic separation techniques. In spite of extensive investigations performed in recent years, no standard procedure is available for guiding the research of the set point and for minimizing the process sensibility to changes in certain critical factors. The aim of this paper is to formulate a set of recommendations regarding the choice of high-voltage, roll-speed, and feed-rate values for an important class of electrostatic separation applications: the selective sorting of conductive and nonconductive constituents of granular industrial wastes. The experiments were carried out on a laboratory separator, built by one of the authors, with various samples of chopped wire wastes furnished by l’Entreprise des Industries des Câbles, Biskra, Algeria. Several one-factor-at-a-time experiments, followed by two factorial designs (one composite, the other fractional), were performed based on the following three-step strategy: 1) identifying the domain of variation of the controlled variables; 2) finding the best choice of the set point; and 3) assessing the robustness of the process, i.e., testing whether the performance of the system remains satisfactory even when the factors vary slightly around that point. The results presented in this paper are strictly valid only for a well-defined category of processed materials, but a similar approach could be adopted for a wider range of electrostatic separation applications. Index Terms—Design of experiments, electrostatic separation, experimental models, robustness testing.

I. I NTRODUCTION

E

LECTROSTATIC separation of granular solid mixtures is a mature technology [1]–[6]. In recent years, the industry demand for the development of new applications, especially in the mineral processing and recycling industry [7], [8], has Paper MSDAD-06-18, presented at the 2004 Industry Applications Society Annual Meeting, Seattle, WA, October 3–7, and approved for publication in the IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS by the Electrostatic Processes Committee of the IEEE Industry Applications Society. Manuscript submitted for review October 15, 2004 and released for publication November 22, 2006. K. Medles, A. Tilmatine, F. Miloua, A. Bendaoud, and M. Younes are with the Department of Electrical Engineering, University Djillali Liabes, Sidi Bel Abbes 22000, Algeria (e-mail: [email protected]; [email protected]; [email protected]; [email protected]; [email protected]). M. Rahli is with the Department of Electrical Engineering, University of Sciences and Technology, Oran 31000, Algeria (e-mail: [email protected]). L. Dascalescu is with the Electronics and Electrostatics Research Unit, Laboratory of Automatics and Industrial Informatics, Faculty of Engineering, University Institute of Technology, 16021 Angoulême Cedex, France and also with the Electrostatics of Dispersed Media Research Unit, Electro-hydrodynamics Group, Laboratory of Aerodynamic Studies, University of Poitiers, 86034 Poitiers Cedex, France (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TIA.2007.895683

Fig. 1. Variables of an electrostatic separation process: high-voltage level U ; roll-speed n; angular α1 and radial s1 position of the corona electrode; angular α2 and radial s2 position of the electrostatic electrode; and angular positions γ1 and γ2 of the splitters.

grown at a rapid pace. For each such application, identification of the optimal operating conditions (i.e., the set point) is a crucial issue [9]–[13]. The design of experiments methodology has proved to be an effective way to address it [14]. Robustness testing is also critical for the effective implementation of a new application, as it assesses to what extent the performance of the process remains satisfactory even when some influential factors are allowed to vary. The objective is to minimize the system’s sensibility to small difficult-to-avoid changes in certain control factors [15]. In spite of several studies published on these issues in recent years [16]–[18], no standard procedure is available for guiding the research of the set point and for identifying the factors that should be better controlled for the process to be claimed robust. The difficulty of the problem resides in the fact that electrostatic separation is a multifactorial process. In a rolltype corona-electrostatic separator (Fig. 1), for instance, the list of factors influencing the outcome of the process includes the feed rate, the granule size, the high-voltage level, the electrode configuration, and the roll speed [9]. The granular mixture to be separated is supplied at an adjustable feed rate on the surface of a grounded roll electrode, which rotates at a regulated speed. The electric field that performs the separation in these installations is generated between this roll and one or several electrodes connected to a high-voltage supply [10], [11]. The insulating particles are charged by ion bombardment in the corona field zone [19], [20] and are pinned to the surface

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of the rotating roll electrode by the electric image force [21]. The conducting particles, which are not affected by the corona field, get charged by electrostatic induction in contact with the grounded roll and are attracted to the high-voltage electrode. The high-voltage level and the interelectrode spacing determine the intensity of the electric field and, hence, the magnitude of the electric forces exerted on the particles. The aim of this paper is to formulate a set of recommendations regarding the procedure to follow for the choice of highvoltage, roll-speed, and feed-rate values for an important class of electrostatic separation applications: the selective sorting of conductive and nonconductive constituents of granular industrial wastes. Several one-factor-at-a-time experiments, followed by two factorial designs (one composite, the other fractional), were performed based on the following three-step strategy: 1) identifying the domain of variation of the controlled variables; 2) finding the best choice of the set point; and 3) assessing the robustness of the process.

Taking advantage of the conclusions of a previous paper [16], the response surface method (RSM) was employed for set point identification in this paper. The central composite face-centered (CCF) design [15] is most commonly used with the RSM, as it supports quadratic polynomial models. With such models, the response y of the process is expressed as a function of e factors ui (i = 1, . . . , e) as

II. D ESIGN OF E LECTROSTATIC S EPARATION E XPERIMENTS

y = f (xi) = a0 + Σai xi + Σai,j xi xj + Σaii x2i

Regardless of the domain of application, design of experiments is useful for three objectives: 1) screening; 2) optimization; and 3) robustness testing [22], [23]. Initial screening experimental designs pave the way for optimization work, whereas robustness assessment is usually carried out only in the final stage of development of a new product or technology, as a last test to ensure quality [15]. Within this framework, many different experimental strategies can be imagined, as an answer to the specific requirements of each application. In this paper, the experiments simulate the operation of a typical industrial electrostatic separator, which disposes of only three control variables (Fig. 1): 1) high-voltage level U (in kilovolts); 2) roll speed n (in revolutions per minute); 3) feed rate m (in kilograms per hour). Employed at the beginning of the investigation of a new application, screening experiments are designed either to explore many factors, in order to reveal whether they have an influence on the responses, or to identify their appropriate ranges. Taguchi’s methodology [24], [25], which was thoroughly analyzed in a previous paper [17], is particularly suitable for screening a large number of factors. To solve the problem formulated in the first paragraph of this section, the screening experiments should be designed with a different purpose in mind: defining the domain of variation of the three factors that can be adjusted from the control panel of an industrial electrostatic separator. In the case of a relatively well-known process such as the insulation/metal electrostatic separation, classical “one-factor-at-a-time” experiments are expected to be more effective than other designs. The optimization stage of an experimental procedure should enable the identification of the “set point,” i.e., the values of the control factors for which the response of the process is a maximum, is a minimum, or approaches a target [26]. For electrostatic separation processes, the minimization of the middling fraction could be the chosen criterion of evaluation.

y = f (ui ) = c0 + Σci ui + Σci,j ui uj + Σcii u2i .

(1)

A normalized centered value can be defined for each factor as follows: xi = (ui − uic )/∆ui = u∗i

(2)

where uic = (ui max + ui min )/2

∆ui = (ui max − ui min )/2.

(3)

With these notations, the response function becomes (4)

where xi can take any value between −1 (for the minimum input value ui min ) and +1 (for the maximum input value ui max ). For the three factors considered in this paper, i.e., x1 = U ∗ , x2 = n∗ , x3 = m∗ , the quadratic model is y = a0 + a1 U ∗ + a2 n∗ + a3 m∗ + a1,2 U ∗ n∗ + a1,3 U ∗ m∗ + a2,3 n∗ m∗ + a1 U ∗2 + a2 n∗2 + a3 m∗2 . (5) If y is the quantity of middling collected after separation, then the optimization means the minimization of (5). The MODDE 5.0 program (Umetrics, Sweden) performs both model regression from experimental data, using either the multilinear or the partial least squares algorithm, and optimum research, using the so-called “simplex” methodology [15]. Robustness testing is usually the last experiment to be carried out before the industrial release of a new process. Its aim is to ascertain that the response is not sensitive to small changes in the factors around the set point. In case that nonrobustness is detected, the experiment should indicate how to regulate the factors (alter their bonds) for the outcome to remain within given specifications. As each factor is explored within a narrow range, a linear model is likely to be the most appropriate choice for robustness testing. Such a model would provide an adequate answer to the following question: Which factors should be better controlled so that process robustness may be claimed? Fractional factorial designs are recommended for robustness testing, as they fit linear models, in the present case y = a0 + a1 U ∗ + a2 n∗ + a3 m∗ .

(6)

III. M ATERIALS AND M ETHODS A roll-type corona-electrostatic separator built by one of the authors at the University of Sidi Bel Abbès was employed for the experimental study (Fig. 2). The angular (α1 = 30◦ ) and radial (s1 = 30 mm) position of the corona electrode, the angular (α2 = 70◦ ) and radial (s2 = 70 mm) position of the

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TABLE I CCF EXPERIMENTAL DESIGN FOR RESPONSE-SURFACE MODELING AND PROCESS OPTIMIZATION

Fig. 2. SELMAG laboratory electrostatic separator. (1) Control panel. (2) Roll-speed control. (3) Feed control. (4) Feed hopper. (5) Vibratory feeder. (6) Corona electrode. (7) Electrostatic electrode. (8) Grounded roll electrode. (9) Brush. (10) Collector splitter. (11) Electric motor.

electrostatic electrode with respect to the roll electrode, and the angular positions γ1 = 45◦ and γ 2 = 0◦ of the splitters were kept constant in all the experiments to simulate the conditions in an industrial equipment. The tests were carried out on a synthetic material, which was obtained from genuine chopped electric wire wastes processed in the recycling industry (25% copper, 75% insulating materials, particle size > 1 mm and < 2 mm). The mass of each sample was 200 g. The products were collected in three bins: 1) conductor; 2) nonconductor; and 3) middling. Each fraction was weighted on an electronic balance (resolution: 0.1 g). All the tests were carried out on the same sample, at stable environmental conditions: 18–20 ◦ C, 56–60 relative humidity (RH: %). A three-step experimental procedure was adopted. Step 1) Define the domain of variation of the control factors (Umin , Umax ), (nmin , nmax ), and (mmin , mmax ) by three “one-factor-at-a-time” experiments. Experiment 1.1. Variable voltage U (5–50 kV), at constant n = 70 r/min and m = 6 kg/h. Experiment 1.2. Variable roll speed n (20–140 r/min), at constant U = 25 kV and m = 6 kg/h. Experiment 1.3. Variable feed rate m (1.5–45 kg/h), at constant U = 25 kV and n = 70 r/min. Step 2) Identify the set point (Uo , no , mo ) by using a central CCF design (Table I). The two levels −1 and +1 are the limits established in Step 1) for each of the three control variables (Umin , Umax ), (nmin , nmax ), and (mmin , mmax ), with the central point (Uc , nc , mc ) calculated as follows: Uc = (Umin + Umax )/2 nc = (nmin + nmax )/2

(7) (8)

mc = (mmin + mmax )/2.

(9)

TABLE II FRACTIONAL FACTORIAL FACE EXPERIMENTAL DESIGN FOR ROBUSTNESS TESTING

TABLE III RESULTS OF EXPERIMENT 1.1 (n = 70 r/min AND m = 6 kg/h)

Step 3) Test the robustness of the process by a fractional factorial experimental design (Table II). The levels −1 and +1 were chosen by considering the central

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TABLE IV RESULTS OF EXPERIMENT 1.2 (n = 70 r/min AND m = 6 kg/h)

Fig. 3. PVC recovery, purity of the conductor product, and amount of middling as functions of the applied high voltage for n = 70 r/min and m = 6 kg/h.

TABLE V RESULTS OF EXPERIMENT 1.3 (U = 25 kV AND n = 70 r/min)

Fig. 4. PVC recovery, purity of the conductor product, and amount of middling as functions of the roll speed for U = 25 kV and m = 6 kg/h.

point given by (Uo , no , mo ) determined in Step 2) and the variations ±∆Uo = 1 kV, ±∆no = 2 r/min, ±∆mo = 0.5 kg/h that can occur in the set values of the control factors during normal operation of an industrial installation. IV. R ESULTS A. Domain of Variation of the Control Factors The results of Experiments 1.1–1.3 are given, respectively, in Tables III–V. Two tests were realized for each value of the control factor.

Fig. 5. PVC recovery, purity of the conductor product, and amount of middling as functions of the feed rate for U = 25 kV and n = 70 r/min.

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TABLE VI RESULTS OF EXPERIMENT 2 (SET POINT IDENTIFICATION)

The amount of middling, the recovery of polyvinyl chloride (PVC), and the purity of the conductor product (copper) were considered as significant for the evaluation of the process and represented as functions of the three control factors in Figs. 3–5. Together with the representation of copper recovery as a function of roll speed (Fig. 4), these data served to the definition of the domain of variation of U , n, and m, in view of Step 2) of the experimental procedure. Thus, the graph in Fig. 3 points out that in the conditions of Experiment 1.1, the mass of middling represents more than 7.5% of the feed for U < 20 kV and U ≥ 35 kV. As the recovery of PVC is poor (< 90%) and the purity of the conductor product is less than 100% for U < 20 kV, while the recovery of copper degrades for U ≥ 35 kV, Umin = 20 kV and Umax = 30 kV were retained as the limit values for the voltage. In the conditions of Experiment 1.2 (Fig. 4), for n ≥ 90 r/min, the mass of middling exceeds 10% of the feed, the recovery of PVC decreases below 90%, and the conductor product is unpurified with nonconducting particles. On the other hand, the copper recovery at n ≤ 50 r/min represents less than 90% of the quantity existing in the feed. Consequently, the domain of variation of the speed was defined as: nmin = 60 r/min and nmax = 80 r/min. Although the lower limit of the feed rate mmin = 5 kg/h represents the minimum hourly quantity of material that can be advantageously treated, from an economic point of view, the upper limit mmax = 15 kg/h was established after the examination of the experimental data. Thus, in the conditions of Experiment 1.3 (Fig. 5), for feed rates of 20 kg/h and higher, the mass of middling exceeded 5% of the feed, the recovery of PVC was less than 95%, and the conductor product has less than 100% copper.

B. Set Point Identification The conditions of Experiment 2 were established by taking into account the results obtained in Step 1), for assigning the limit values to each variable, and (7)–(9), for calculating the respective central values as follows: Uc = (20 + 30)/2 = 25 kV nc = (60 + 80)/2 = 70 r/min

(10) (11)

mc = (5 + 15)/2 = 10 kg/h.

(12)

The results of the experiment are given in Table VI. The regression model of the response considered for optimization (i.e., the mass of middling) was obtained with MODDE 5.0 (Fig. 6) as follows: Log y = 0.925 + 0.116 U ∗ + 0.108 n∗ + 0.114 m∗ + 0.014 U ∗ n∗ + 0.042 U ∗ m∗ + 0.058 n∗ m∗ + 0.19 U ∗ 2 + 0.046 n∗ 2 + 0.06 m∗ 2. (13) According to this model, the optimum of the process (i.e., the smallest amount of middling) should be obtained for U = 27 kV, n = 68 r/min, and m = 5 kg/h. By imposing a higher feed rate m = 10 kg/h, in order to process larger quantities of materials, the set point recommended by the optimization routine of MODDE 5.0 is: U = 27 kV and n = 69 r/min. C. Robustness Testing The central point for the experiment was chosen as: Uo = 27 kV, no = 69 r/min, and mo = 10 kg/h. The environmental conditions were slightly different: 19–21 ◦ C, 55–58 RH%.

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models for Cu recovery is significant, it appears possible to relax the factor tolerances and still maintain a robust electrostatic separation process with respect to this response. V. D ISCUSSION The first step of the proposed procedure consists of several “one-factor-at-a-time” experiments, carried out with the objective of establishing the domain of variations of the considered control factors. The limits of this domain are determined with respect to a number of constraints, which are imposed to a selected set of process responses (here, PVC recovery > 95%, insulator product purity > 99.5%, conductor product purity > 99%, . . .). These can differ from one application to another and might be revised after the second step of the experimental procedure. This was not the case of the application described above, as the set point suggested by the optimizer program was within the domain of variation imposed to the control factors. In case that the set point should simultaneously optimize several process responses, its identification might be less straightforward than for the application analyzed in this paper. MODDE 5.0 contains an optimization routine that is capable of simultaneously processing several responses, affected by different weighting coefficients. For instance, it is possible to establish the optimum factor settings that minimize the amount of middling, maximize the recovery of copper, and the purity of the insulator product, with the respective weighting coefficients being 0.4, 1, and 0.2 (Fig. 7). This optimum differs slightly from the one established before: The optimum roll speed is higher, as the choice of the weighting coefficients put forward the recovery of copper. However, if only one response should be taken into account in the optimization process, it should be the amount of middling, as it reflects better than any other the overall performance of the process. Robustness testing carried out in Step 3) of the proposed experimental procedure evaluates the sensitivity of the process to slight variations in the controlled factors. This kind of study could be completed with an experiment capable of indicating how the variation in noncontrollable factors (humidity, temperature, and composition of the processed material) influences the outcome of the process. This issue was the object of a previous paper [18] and can be easily integrated in the proposed experimental procedure as “Step 2 1/2.” Fig. 6.

Response contour plots for middling.

The results of the corresponding fractional factorial experimental design are contained in Table VII. The outcome is within specifications. The results obtained in robustness testing for all the responses (PVC recovery, Cu purity, and middling mass) show clearly that the electrostatic separation process for this type of material is robust. There are no great differences between the responses when the three factors vary slightly around the set point. The models for middling and PVC recovery are nonsignificant, which is the ideal outcome of a robustness test. As the

VI. C ONCLUSION A three-step strategy is recommended for the development of new electrostatic separation processes. Step 1) Define the domain of variation of the control factors by using the results of several “one-factor-at-a-time” experiments. Step 2) Identify the set point by employing the RSM for process optimization based on a composite factorial experimental design. Step 3) Assess the robustness by performing a fractional factorial experiment.

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TABLE VII RESULTS OF EXPERIMENT 3 (ROBUSTNESS TESTING)

Fig. 7. Results of the optimization routine of MODDE 5.0 for the simultaneous minimization of middling, maximization of copper recovery, and insulator product purity.

For roll-type corona-electrostatic separators, the factors under control are typically the high voltage applied to the electrodes, the speed of the rotating roll electrode that carry the material through the electric field zone, and the feed rate. The results presented in this paper are strictly valid only for a welldefined category of processed materials (chopped electric cable wastes), but a similar approach could be adopted for a wider range of electrostatic separation applications. ACKNOWLEDGMENT The authors would like to thank Prof. A. Iuga, Prof. A. Samuila, Dr. R. Köhnlechner, and Dr. F. Aman for pertinent comments on the factors that influence the electrostatic separation efficiency. Fruitful discussions with Dr. K. Robinson, Dr. M. Mihailescu, and S. Billaud on the industrial application of design of experiments methods are acknowledged. R EFERENCES [1] O. C. Ralston, Electrostatic Separation of Mixed Granular Solids. Amsterdam, The Netherlands: Elsevier, 1961. [2] J. E. Lawver and W. P. Dyrenforth, “Electrostatic separation,” in Electrostatics and Its Applications, A. D. Moore, Ed. New York: Wiley, 1973, pp. 221–249.

[3] I. I. Inculet, Electrostatic Mineral Separation. New York: Wiley, 1986. [4] K. Haga, “Applications of the electrostatic separation technique,” in Handbook of Electrostatic Processes, J. S. Chang, A. J. Kelly, and J. M. Crowley, Eds. New York: Marcel Dekker, 1995, pp. 365–386. [5] Y. Higashiyama and K. Asano, “Recent progress in electrostatic separation technology,” Part. Sci. Technol., vol. 16, no. 1, pp. 77–90, 1998. [6] I. I. Inculet, G. S. P. Castle, and J. D. Brown, “Electrostatic separation of plastics for recycling,” Part. Sci. Technol., vol. 16, no. 1, pp. 77–90, 1998. [7] A. Iuga, R. Morar, A. Samuila, and L. Dascalescu, “Electrostatic separation of metals and plastics from granular industrial wastes,” Proc. Inst. Electr. Eng.—Sci. Meas. Technol., vol. 148, no. 2, pp. 47–54, Mar. 2001. [8] A. D. Dance, T. Kojovic, and R. D. Morrison, “Development of electrostatic separation models for the mineral sands industry,” in Proc. Extractive Metallurgy, Perth, Australia, 1991, pp. 13–18. [9] R. Morar, A. Iuga, L. Dascalescu, and A. Samuila, “Factors which influence the insulation–metal electroseparation,” J. Electrost., vol. 30, pp. 403–412, 1993. [10] A. Iuga, V. Neamtu, I. Suarasan, R. Morar, and L. Dascalescu, “Highvoltage supplies for corona-electrostatic separators,” IEEE Trans. Ind. Appl., vol. 34, no. 2, pp. 286–293, 1998. [11] L. Dascalescu, R. Morar, A. Iuga, A. Samuila, and V. Neamtu, “Electrostatic separation of insulating and conductive particles from granular mixes,” Part. Sci. Technol., vol. 16, no. 1, pp. 25–42, 1998. [12] A. Iuga, R. Morar, A. Samuila, I. Cuglesan, M. Mihailescu, and L. Dascalescu, “Electrostatic separation of brass from industrial wastes,” IEEE Trans. Ind. Appl., vol. 35, no. 3, pp. 537–542, May/Jun. 1999. [13] A. Iuga, I. Cuglesan, A. Samuila, M. Blajan, D. Vadan, and L. Dascalescu, “Electrostatic separation of muscovite mica from feldspathic pegmatite,” IEEE Trans. Ind. Appl., vol. 40, no. 2, pp. 422–429, Mar./Apr. 2004.

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[14] M. Mihailescu, A. Samuila, A. Urs, R. Morar, A. Iuga, and L. Dascalescu, “Computer-assisted experimental design for optimization of electrostatic separation processes,” IEEE Trans. Ind. Appl., vol. 38, no. 5, pp. 1174– 1181, Sep./Oct. 2002. [15] L. Eriksson, E. Johansson, N. Kettaneh-Wold, C. Wikstöm, and S. Wold, Design of Experiments, Principles and Applications. Umeaa, Sweden: Umetrics, 2000. [16] L. Dascalescu, S. Billaud, A. Tilmatine, R. Köhnlechner, and M. Mihailescu, “Optimisation of electrostatic separation processes using response surface modeling,” IEEE Trans. Ind. Appl., vol. 40, no. 1, pp. 53–59, Jan./Feb. 2004. [17] L. Dascalescu, A. Tilmatine, M. Mihailescu, A. Mihalcioiu, and A. Samuila, “A linear-interaction model for electrostatic separation processes,” presented at the Proc. IEEE/IAS Annu. Meeting Conf. Rec., Pittsburgh, PA, 2002, Paper 36-5. [18] L. Dascalescu, A. Samuila, A. Mihalcioiu, S. Bente, and A. Tilmatine, “Robust control of electrostatic separation processes,” in Proc. ESA-IEEE Joint Meeting Electrostat Conf. Rec., Little Rock, AR, pp. 131–140. [19] L. Dascalescu, R. Morar, A. Iuga, A. Samuila, V. Neamtu, and I. Suarasan, “Charging of particulates in the corona field of roll-type electroseparators,” J. Phys. D, Appl. Phys, vol. 27, no. 6, pp. 1242–1251, Jun. 1994. [20] L. Dascalescu, A. Samuila, D. Rafiroiu, A. Iuga, and R. Morar, “Multipleneedle corona electrodes for electrostatic processes application,” IEEE Trans. Ind. Appl., vol. 35, no. 3, pp. 543–548, May/Jun. 1999. [21] L. Dascalescu, A. Mizuno, R. Tobazéon, A. Iuga, R. Morar, M. Mihailescu, and A. Samuila, “Charges and forces on conductive particles in roll-type corona-electrostatic separators,” IEEE Trans. Ind. Appl., vol. 31, no. 5, pp. 947–956, Sep./Oct. 1995. [22] N. L. Frigon and D. Mathews, Practical Guide to Experimental Design. New York: Wiley, 1996. [23] C. R. Hicks and K. V. Turner, Jr., Fundamental Concepts in the Design of Experiments. Oxford, U.K.: Oxford Univ. Press, 1999. [24] G. S. Peace, Taguchi Methods. New York: Addison-Wesley, 1992. [25] R. K. Roy, Design of the Experiments Using Taguchi Approach. 16 Steps to Product and Process Improvement. New York: Wiley, 2001. [26] Y. Wu and W. H. Moore, Quality Engineering Product and Process Optimization. Dearborn, MI: Amer. Supplier Inst., 1986.

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Amar Tilmatine received the M.S. degree in electrical engineering and the Magister (Dr. Eng.) degree from the University of Science and Technology, Oran, Algeria, in 1988 and 1991, respectively. Since 1991, he has been teaching electric field theory and high-voltage techniques at the Department of Electrical Engineering, University Djillali Liabes, Sidi Bel Abbes, Algeria. He was the Chairman of the Scientific Committee of this institute from November 2002 to November 2005. He is the Head of the Electrostatics and High-Voltage Engineering Research Unit, IRECOM Laboratory. From 2001 to 2006, he visited the Electronics and Electrostatics Research Unit, University Institute of Technology, Angoulême, France, at least once a year as an Invited Scientist to work on a joint research project on new electrostatic separation technologies. His other fields of interest are high-voltage insulation and gas discharges.

Farid Miloua was born on September 18, 1974. He received the M.S. degree in electrical engineering and the Magister (Dr. Eng.) degree from the University Djillali Liabes, Sidi Bel Abbes, Algeria, in 1998 and 2004, respectively. Since 2004, he has been teaching electric field theory and high-voltage techniques at the Department of Electrical Engineering, University of Bechar, Bechar, Algeria. He is a part-time Ph.D. student at the Electrostatics and High-Voltage Engineering Research Unit, IRECOM Laboratory, University Djillali Liabes. His principal field of research concerns electrostatic applications, particularly electrostatic separation and precipitation. His other fields of interest are high-voltage insulation and gas discharges.

Abdelber Bendaoud was born in Oujda, Morocco, in 1957. He received the engineering degree in electrical engineering from the University of Sciences and Technology, Oran Algeria, in 1982, and the Magister (Dr. Eng.) degree in 1999 and the Ph.D. degree in 2004 from the University Djillali Liabes, Sidi Bel Abbes, Algeria. Since 1994, he has been a Professor of electric machines in the Department of Electrical Engineering, University Djillali Liabes. He is a member of the Intelligent Control Electrical Power System Laboratory (ICEPS). His current research interests include electrostatic separation technologies, high-voltage insulation and gas discharges, electric and magnetic fields, and electromagnetic compatibility.

Karim Medles was born in Tipaza, Algeria, in 1972. He received the M.S. and Magister (Dr. Eng.) degrees in electrical engineering from the University Djillali Liabes, Sidi Bel Abbes, Algeria, in 1994 and 1999, respectively, and the Ph.D. degree in 2006, with a thesis that he partly prepared at the University Institute of Technology, Angoulême, France, with an 18-month research scholarship awarded by the French Government. In 1999, he joined the Department of Electrical Engineering, University of Djillali Liabes, as a Senior Lecturer, where he is currently an Assistant Professor. He is a member of the Electrostatics and High-Voltage Engineering Research Unit, IRECOM Laboratory, University Djillali Liabes. He has published several scientific papers in international and national journals, as well as in conference proceedings. He has been a Visiting Scientist in France. His current research interests include power systems, high-voltage engineering, and electrostatics.

Mohamed Younes was born in Mostagnem, Algeria, in 1965. He received the engineering degree in electrical engineering from the University of Sciences and Technology, Oran, Algeria, in 1989, and the Magister (Dr. Eng.) degree from the University Djillali Liabes, Sidi Bel Abbes, Algeria, in 1998. He is currently a Senior Lecturer of electrical engineering at the University of Djillali Liabes. He is a member of the Electrostatics and High-Voltage Engineering Research Unit, IRECOM Laboratory, University Djillali Liabes. His current research interests include high-voltage engineering, computational electrostatics, and fuzzy logic.

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Mostéfa Rahli (M’04) was born on October 24, 1949, in Mocta-Douz, Algeria. He received the B.S., Magister (Dr. Eng.), and Ph.D. degrees in electrical engineering from the University of Sciences and Technology, Oran, Algeria, in 1979, 1985, and 1996, respectively. From 1987 to 1991, he was a Visiting Professor at Montefiore’s Electrical Institute, University of Liege, Liege, Belgium, where he worked on power systems analysis. He is currently a Professor of electrical engineering with the University of Sciences and Technology, Oran. His research interests include operation and planning of electric energy systems, as well as optimization theory and its applications.

Lucian Dascalescu (M’93–SM’95) received the Bachelor’s degree (with first class honors) from the Technical University of Cluj-Napoca, Cluj-Napoca, Romania, in 1978, the Dr. Eng. degree in electrotechnical materials from the Polytechnic Institute of Bucharest, Bucharest, Romania, and the Dr. Sci. degree and then the “Habilitation à Diriger de Recherches” diploma in physics from the University “Joseph Fourier,” Grenoble, France. His professional carrier began at CUG (Heavy Equipment Works), Cluj-Napoca. In 1983, he moved to the Technical University of Cluj-Napoca as an Assistant Professor, later becoming an Associate Professor of electrical engineering. From October 1991 to June 1992, he received a Research Fellowship from the Laboratory of Electrostatics and Dielectric Materials (LEMD), Grenoble, where he returned in January 1994, after one year as an Invited Research Associate and Lecturer at Toyohashi University of Technology, Toyohashi, Japan, and three months as a Visiting Scientist at the University of Poitiers, Poitiers, France. For four years, he taught a course on the electromechanical conversion of energy at the University Institute of Technology, Grenoble, France. In September 1997, he was appointed as a Professor of electrical engineering and automated systems and the Head of the Electronics and Electrostatics Research Unit, University Institute of Technology, Angoulême, France. Since 1999, he has also been the Head of the Department of Management and Engineering of Manufacturing Systems. He is currently the Head of the Electrostatics of the Dispersed Media Research Unit, which is part of the Electro-hydro-dynamics Group, Laboratory of Aerodynamic Studies, University of Poitiers. He is the author of several textbooks in the field of electrical engineering and ionized gases. He is the holder of 14 patents, has written more than 70 papers, and was invited to lecture on the electrostatics of granular materials at various universities and international conferences in China (1988), Poland (1990), U.S. (1990, 1997, and 1999), Japan (1993), France (1993), U.K. (1998), Romania (1999, 2004, and 2006), Canada (2001), Belgium (2002), and Algeria (2005 and 2006). Prof. Dascalescu is a Senior Member of the IEEE Industry Applications Society and the Chair of its Electrostatics Processes Committee. He is a member of the Electrostatics Society of America, Electrostatics Society of Romania, Société des Electriciens et Electroniciens (SEE), and Club Electrotechnique, Electronique, Automatique (EEA) of France.