IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 40, NO. 1, JANUARY/FEBRUARY 2004
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Optimization of Electrostatic Separation Processes Using Response Surface Modeling Lucian Dascalescu, Senior Member, IEEE, Amar Tilmatine, Florian Aman, and Michaela Mihailescu
Abstract—The efficiency of electrostatic separation processes depends on a multitude of factors, including the characteristics of the granular mixtures to be sorted, the feed rate, the configuration of the electrode system, the applied high voltage, and the environmental conditions. The possibility of optimizing the operation of industrial electrostatic separators using rather simple computed-assisted experimental design techniques has already been demonstrated. The aim of the present work is to analyze the peculiarities of application of a more sophisticated group of response surface experimental design techniques that make use of quadratic functions for modeling the electrostatic separation process. One unique contribution to this work is to consider the economic value of the process in addition to the technical result. The 11 electrostatic separation tests, corresponding to a central composite design, were carried out on samples of chopped electric wire wastes. The CARPCO laboratory roll-type electrostatic separator employed for this study enabled a rigorous control of two factors: the applied high-voltage level and the speed of the rotating roll electrode. The objective was to maximize the benefits from the recycling of both constituents of the binary copper–polyvinyl chloride granular mixture. The optimum operating conditions computed with the quadratic model derived from the experimental results were in good agreement with the data of pilot-plant tests. Thus, the highest extraction of useful materials was obtained at high voltage and low speed, while the optimum conditions for greatest economic value were found to be high voltage and high speed. The response surface methodology can be easily applied to most of the industrial applications of electrostatic separation technologies. Index Terms—Design of experiments, electrostatic separation, experimental modeling, response surface method.
I. INTRODUCTION
E
XTENSIVE laboratory and pilot-plant experimentation is needed for the development of a new electrostatic separation application (Fig. 1) [1]–[6]. The efficiency of the separation process depends on the characteristics of the granular mixtures to be sorted, the feed rate, the configuration of the electrode
Paper MSDAD-A 03–14, presented at the 2001 Industry Applications Society Annual Meeting, Chicago, IL, September 30–October 5, 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, 2001 and released for publication October 9, 2003. L. Dascalescu is with the Electronics and Electrostatics Research Unit, LAIIESIP, UPRES-EA 1219, University Institute of Technology, 16021 Angoulême Cedex, France (e-mail:
[email protected]). A. Tilmatine was with the Electronics and Electrostatics Research Unit, LAIIESIP, UPRES-EA 1219, University Institute of Technology, 16021 Angoulême Cedex, France. He is now with the Faculty of Electrical Engineering, University of Sidi-Bel-Abbes, 22000 Sidi-Bel-Abbes, Algeria. F. Aman is with HAMOS GmbH, 82377 Penzberg, Germany. M. Mihailescu is with the High-Intensity Electric Fields Laboratory, Technical University of Cluj-Napoca, 3400 Cluj-Napoca, Romania. Digital Object Identifier 10.1109/TIA.2003.821812
Fig. 1. Constituents of the granular mixture subjected to separation: (a) polyvinyl chloride and (b) copper.
system, the applied high voltage, and the environmental conditions [7], [8]. In some cases, mathematical models of the multifactorial electrostatic separation process [9], [10] can be used for estimating the values of the input variables that are likely to optimize the response. Nevertheless, the optimum of the process is usually obtained only after an impressive number of tests (up to several hundreds, in the case of the electrostatic separation of complex minerals [3]). In spite of the fact that the electrostatic separation of polyvinyl chloride (PVC) wire insulation from copper conductor (Fig. 1) is an already classical application [8], each new type of electric cable waste necessitates the tuning of process parameters. The experimental design techniques [12]–[15] enable the choosing of the proper number of tests and the conditions of accomplishing them, in order to attain a well-defined objective. They contribute to reducing the costs, as well as the time consumed for experiments. With such techniques, all the relevant factors of a process are varied simultaneously over a set of planned experiments. The results are connected by a mathematical model that is subsequently used for interpretation, prediction, and optimization. Thus, the investigators can identify the input variables that have a real
0093-9994/04$20.00 © 2004 IEEE
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contributing factors (the input variables quadratic dependency [12], [15]
,
) by a
(1) Assuming that the variation of the input is and the central , normalized centered values for value is the input variables can be defined as (2) With the new variables, the response is expressed as follows: (3)
Fig. 2. Laboratory electrostatic separator (CARPCO, Jacksonville, FL); 1: high-voltage supply; 2: control panel; 3: feed system; 4: high-voltage electrodes; 5: grounded rotating roll electrode; 6: collecting boxes.
influence on responses and analyze the interactions between the factors. They can also predict the responses for given values of the input factors or find the best settings of the factors to achieve optimal conditions for process operation. In the case of electrostatic separation, the objective could be to increase the efficiency of the process and/or the purity of the products. Computer-assisted experimental design techniques have already proven their usefulness in the optimization of electrostatic separation process. Thus, in a previous work [16], the authors have employed full and fractional design techniques to identify the effects of three factors (high voltage, roll speed, and corona electrode angular position), as well as their interactions. Then, they determined the optimum operating conditions using a gradient method. The aim of the present work is to analyze the peculiarities of application of response surface modeling (RSM) and compare it with other experimental design techniques.
II. THEORETICAL ASPECTS In a roll-type corona-electrostatic separator, the granular mixture to be separated is fed with a certain speed on the surface of a rotating roll electrode, connected to the ground (Fig. 2). A high-intensity electric field is generated between this roll and one or several electrodes connected to a high-voltage supply [9]. The insulating particles are charged by ion bombardment in the corona field zone and are pinned to the surface of the rotating roll electrode by the electric image force. The conducting particles are not affected by the corona field; they charge by electrostatic induction in contact with the grounded roll and are attracted to the high-voltage electrode [10]. Consequently, the list of factors influencing the electrostatic separation process should include the high-voltage level, the electrode configuration, the feed rate, the granule size, and the roll speed [7]. The RSM techniques employed for the present work assume that the target function (the output variable ) is related to the
The dispersion of the measurements can be evaluated with Cochran’s criterion [17], which enable the estimation of the experimental noise. The coefficients of can be evaluated by multiple regression [18]; Student’s or Fischer’s criteria [19] can be then used for probing the validity of the model. Indeed, the interpretation of the model necessitates the evaluation of the relative weight of each coefficient, and—whenever possible )—the adequacy of the quadratic dependency (i.e., established between the factors and the response. In the present paper, the regression analysis was based on the and [18]. The former is evaluation of two parameters: called the goodness of fit, and is a measure of how well the regression model can be made to fit the raw data; it varies between 0 and 1, where 1 indicates a perfect model and 0 no model at all. The latter is called goodness of prediction, and estimates the pre, has the upper bound dictive power of the model. Like 1, but its lower limit is minus infinity. For a model to pass the diagnostic test, both parameters should be high, and preferably not separated by more than 0.2–0 .3. When designing an RSM experiment, it is important to choose the factors that are relevant for the process under study, and to define the appropriate central value and variation interval for each of them. The next sections of the paper try to address these problems and demonstrate the efficiency of computer-assisted RSM techniques for a specific electrostatic separation application. III. MATERIAL AND METHOD The experiments were carried out on 200 g samples of a synthetic granular mixture of 20% copper and 80% PVC. The material originated from chopped electric wire and cable wastes granule size 5 mm, and was provided by ALCATEL, France. The tests were performed on a laboratory console-type electrostatic separator (Figs. 2 and 3) at the University Institute of Technology, Angoulême, France. Of the various factors that influence the efficiency of an electrostatic separation process, the following two were selected as input variables for the RSM exkV; the roll speed, periments: the applied high voltage, r/min. Besides being easily adjustable under industrial operating conditions, they are known to have a significant effect on the outcome of electrostatic separation processes [16].
DASCALESCU et al.: OPTIMIZATION OF ELECTROSTATIC SEPARATION PROCESSES USING RSM
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TABLE I AVERAGE NUMBER OF PVC PARTICLES IN 100 PARTICLES COLLECTED IN THE BOXES #3 AND #4 (CONDUCTING PRODUCT)
Fig. 3. Main variables and parameters of the electrode system of the electrostatic separator.
The separator was provided with only one wire-type corona , electrode, the position of which was fixed at mm (Fig. 3), based on the available data on previous experiments with similar materials [8]. The position parameters of the , mm, . In tubular electrode were: all the tests, the feed rate was correlated with the roll speed, to ensure a uniform monolayer of granules on the surface of the rotating electrode. The products of the electrostatic separation were recovered in four collecting boxes. The material in each box was weighted separately. The left-hand-side box included the nonconducting (NC), metal-free fraction. The conducting (C) fraction was collected in the two right-hand-side boxes; it contained less than three granules of PVC for 100 granules of metal. The remaining of the four boxes collected the middling product, the composition of which was roughly similar to that of the initial granular mixture (20% Cu and 80% PVC). The choice of the central values and of the variation intervals of the input variables was guided by two preliminary experiments. In the first preliminary experiment, the roll speed was fixed r/min, and the applied voltage was progressively at increased at a rate of 1 kV/s, until a current of 0.1 mA was indicated by the ammeter of the high-voltage supply. The corresponding value read on the voltmeter was considered to be the corona onset voltage. The applied voltage was then progressively increased at the same rate of 1 kV/s, until the corona discharge turned into sparks. The value indicated by the voltmeter was considered to represent the spark onset voltage. The second preliminary experiment was carried out at a voltage right below spark onset. The roll speed was succes90, 100, 110, and 120 r/min. For each test, sively fixed at the PVC and Cu particles in the two right-hand-side boxes were distinctly counted, to evaluate the purity of the C product. The program MODDE 5.0 (Umetrics AB, Umea, Sweden) is a Windows program for the generation and evaluation of experimental design [20]. A design wizard guides the user from the start of the investigation to the generation of a worksheet: definition of the factors and responses, selection of the objective (screening or RSM), choice of the model (linear, quadratic), and design. When the worksheet is complete, the program fits the model by multiple regression. It assists the investigator in the
interpretation of the data and prediction of the responses. Thus, MODDE 5.0 computes the coefficients of the model, displays the response surface, and identifies the best settings of the factors to achieve optimal performance of the process. The response of the separation process was defined as the financial result of selling the C and NC products
(4) where and are the selling prices for 1 kg of C and NC and are the corresponding products, respectively, and quantities recovered by electrostatic separation. As the study and , a simpler target function was carried out for fixed could be considered (5) with . In the present work, . The feed rate (mass/unit of time) at which the granular mixture is supplied to the active zone of the separation is proportional to , so that to ensure a uniform monolayer of particles on the surface of the carrier electrode, corresponding to optimum charging conditions. and of respectively C Consequently, the masses and NC products collected in the unit of time depend linearly , . The above of the roll speed: observation suggested the definition of a derived response as (6) A more accurate estimation of the financial result of an electrostatic separation process could be obtained by taking into account the cost of the disposal/recycling of each kilogram of middling product (7) where
. IV. RESULTS AND DISCUSSION
A. Preliminary Experiments The first preliminary experiment enabled the measurement of kV and the corona onset and spark onset voltages, kV, respectively, for the given electrode arrangement. From a theoretical point of view, corona charging processes and hence the electrostatic separation should be more effective at higher values of the applied voltage. The maximum of the response function is expected to be obtained for voltages closer to spark
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TABLE II RESULTS OF THE RSM EXPERIMENT
onset. Therefore, kV was chosen as the central value of kV, kV) the RSM experimental design ( The results of the second preliminary experiment are presented in Table I. They show that the roll speed cannot be increased beyond 100 r/min, as the conducting product would contain too many PVC impurities. Consequently, r/min was chosen as the central value for the roll speed r/min, r/min). ( B. RSM Experiment The conclusions of the preliminary experiments served for establishing the conditions of a central composite experimental design. The results obtained for this experiment are given in Table II. With the responses measured in the four experimental points kV, r/min; of a simple 2 factorial design (i.e., kV, r/min; kV, r/min; kV, r/min), MODDE 5.0 was employed for computing the coefficients of the first-order model (8) where is the economic function defined at (5), and , are, respectively, the scaled centered values of and . The corresponding contour plot can be examined in Fig. 4. The contour plot of the quadratic model of , obtained with MODDE 5.0 (9) is given in Fig. 5, which points out a quite significant curvature in the response, especially at higher voltage and lower roll speed.
Fig. 4. Contour plot of the first-order model of the response v .
The first-order model fits quite well the measured data , but has a rather low predictive power . As expected, the experimental responses are better fitted by the , which also has a higher predicquadratic model . It should be noted that these results tive power are obtained with only two replications for the central experimental point. Both first order and quadratic models lead to similar qualitative conclusions: the extraction of C and NC increases with the increase of the voltage and the decrease of the roll speed. The RSM (quadratic) model can be employed for finding the best settings for minimizing the response . Thus, is obtained for kV and r/min. The response was analyzed in a similar way. The contour plots of the corresponding first order and quadratic models of
DASCALESCU et al.: OPTIMIZATION OF ELECTROSTATIC SEPARATION PROCESSES USING RSM
Fig. 5. Contour plot of the quadratic model of the response v .
Fig. 6.
Contour plot of the first-order model of the response y .
can be examined in Figs. 6 and 7, respectively. The maximum kV and r/min. In spite of corresponds to of the fact that the extraction of useful materials from the input granular mixture diminishes with the increasing of , as clearly evidenced by the model of the response , the response is higher at elevated speeds. Indeed, larger quantities of materials are processed in a given unit of time, and the corresponding amounts of C and NC products recovered in a unit of time are higher. kV and This does not necessarily mean that r/min represent the optimum operating conditions of the electrostatic separation process. The best settings of the factors should take into account the cost of middling product disposal/recycling. This could be done by analyzing the response under various hypotheses: (the contour plot is represented in Fig. 8) and . The same electrostatic separation installation was employed for two pilot-plant tests that were carried out on 10 kg of chopped electric wire wastes, containing 20% of copper and kV and 80% of PVC. The high voltage was set at the roll speed was successively adjusted at r/min, and r/min (Fig. 9 and Table III). The measured response g is significantly for the second test g . This can lower than predicted by MODDE 5.0
Fig. 7.
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Contour plot of the quadratic model of the response y .
Fig. 8. Contour plot of the quadratic model of the response y .
Fig. 9. Mass distribution of PVC and copper, expressed as % of feed, in the NC (nonconducting), M (middling), and C (conducting) products of two pilot-plant tests of electrostatic separation, carried out at U = 31 kV, and n = 90 r/min (test 1) and n = 100 r/min (test 2). S1: PVC-test 1; S2: copper-test 1; S3: PVC-test 2; S4: copper-test 2.
be easily explained by the diminution of the PVC amount collected in box #1, due to the increased centrifugal force. It should also be noted the poor purity of the C product: 96.8% of copper.
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TABLE III RESULTS OF TWO PILOT-PLANT TESTS
3) The procedure presented in this paper can be easily adapted to other electrostatic processes.
ACKNOWLEDGMENT Fruitful discussions with Dr. R. Köhnlechner and Eng. S. Billaud on the industrial application of the response surface method and on the plan of this paper are acknowledged with gratitude. Thanks are also expressed to D.-L. and L.-M. Dascalescu, who carried out part of the preliminary experiments that prepared this work. Thus, the pilot-plant test confirmed the best settings recomkV and mended by the laboratory experiment: r/min. The results reported in this paper were obtained for a well-defined electrode configuration. The use of other types of electrodes or a change of position would alter the optimum settings of the applied high voltage and of the roll speed. The result of the RSM experiment would not be the same, if the conditions in which the products are conditioned were changed. Including other factors in a RSM optimization procedure implies an increase of the experimental effort. Thus, a three-factor central composite design would require 17 tests (14 3 tests in the central point of the experimental domain). RSM can be considered at the laboratory stage of the development of a new technology, but is too labor demanding for being systematically proposed for the optimization of industrial processes. Therefore, it should be reserved for those situations where the value of the results justifies the number of experimental runs.
V. CONCLUSION RSM has proven to be an effective tool for the research and development of a new electrostatic separation application. The assistance of a versatile computer program (MODDE 5.0) has considerably facilitated all the steps of an investigation: the experimental design, the statistical analysis of the measured data, the interpretation of the model, the prediction of the responses, and the finding of the best settings for the factors. Several conclusions can be derived from the example analyzed in the present paper. 1) Preliminary experiments are needed, in order to facilitate the choice of the central values and the intervals of variation of the input variables (applied high voltage, ). Taking into account the nonlinearity roll speed, of the involved physical mechanisms, these intervals should not usually exceed 10% of the central value. 2) User-friendly computer programs make possible the analysis of a multitude of response functions, by exploiting one and the same set of experimental data. In this way, various hypotheses can be examined within a short lap of time before taking a decision on the best settings of the factors for a given application.
REFERENCES [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,” Particulate Sci. Technol., vol. 16, pp. 77–90, 1998. [6] I. I. Inculet, G. S. P. Castle, and J. D. Brown, “Electrostatic separation of plastics for recycling,” Particulate Sci. Technol., vol. 16, pp. 77–90, 1998. [7] R. Morar, A. Iuga, L. Dascalescu, and A. Samuila, “Factors which influence the insulation-metal electroseparation,” J. Electrostat., vol. 30, pp. 403–412, 1993. [8] A. Iuga, R. Morar, A. Samuila, and L. Dascalescu, “Electrostatic separation of metals and plastics from granular industrial wastes,” in Proc. IEE—Sci. Meas. Technol., vol. 148, 2001, pp. 47–54. [9] L. Dascalescu, R. Morar, A. Iuga, A. Samuila, and V. Neamtu, “Electrostatic separation of insulating and conductive particles from granular mixes,” Particulate Sci. Technol., vol. 16, pp. 25–42, 1998. [10] L. Dascalescu, A. Mizuno, R. Tonazeon, P. Atten, R. Morar, A. Iuga, M. Mihailescu, and A. Samuila, “Charges and forces on conductive particles in roll-type corona-electrostatic separators,” IEEE Trans Ind. Applicat., vol. 31, pp. 947–956, Sept./Oct. 1995. [11] H. Schenck Jr., Theories of Engineering Experimentation. New York: McGraw-Hill, 1972. [12] G. E. P. Box and N. R. Draper, Empirical Model-Building and Response Surface. New York: Wiley, 1987. [13] N. L. Frigon and D. Mathews, Practical Guide to Experimental Design. New York: Wiley, 1996. [14] C. R. Hicks and K. V. Turner Jr., Fundamental Concepts in the Design of Experiments. Oxford, U.K.: Oxford Univ. Press, 1999. [15] J. Goupy, Plans d’Expériences pour Surfaces de Réponse. Paris, France: Dunod, 1999. [16] 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. Applicat., vol. 38, pp. 1174–1181, Sept./Oct. 2002. [17] W. G. Cochran and G. M. Cox, Experimental Design. New York: Wiley, 1950. [18] L. Eriksson, E. Johansson, N. Kettaneh-Wold, C. Wikström, and S. Wold, Design of Experiments. Principles and Applications. Stockholm, Sweden: Learnways AB, 2000. [19] R. A. Fisher, Experimental Design and Scientific Inference. Oxford, U.K.: Oxford Univ. Press, 1990. [20] MODDE 5.0. User Guide and Tutorial, Umetrics AB, Umea, Sweden, 1999.
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Lucian Dascalescu (M’93–SM’95) graduated with first class honors from the Faculty of Electrical Engineering, Technical University of Cluj-Napoca, Cluj-Napoca, Romania, in 1978, received the Dr.Eng. degree in electrotechnical materials from the Polytechnic Institute of Bucharest, Bucharest, Romania, and the Dr.Sci. degree and the “Habilitation à Diriger de Recherches” diploma in physics from the University “Joseph Fourier,” Grenoble, France. His professional career began at CUG (Heavy Equipment Works), Cluj-Napoca, Romania. 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 at the Laboratory of Electrostatics and Dielectric Materials (LEMD), Grenoble, France, where he returned in January 1994, after one year as an Invited Research Associate and Lecturer at Toyohashi University of Technology, Japan, and three months as a Visiting Scientist at the Laboratory of Physics and Mechanics of Fluids, Poitiers, France. For four years, he taught a course in electromechanical conversion of energy at the University Institute of Technology, Grenoble, France. In September 1997, he was appointed Professor of Electrical Engineering and Automated Systems and Head of the Laboratory of Advanced Electric and Electronic Technologies at the University Institute of Technology, Angoulême, France. Since 1999, he has also been Head of the Department of Management and Engineering of Manufacturing Systems. He is the author of several textbooks in the field of electrical engineering and ionized gases. He is the holder of 14 patents, has authored more than 50 papers, and was invited to lecture on the electrostatics of granular materials at various universities and international conferences in China (1988), Poland (1990), the U.S. (1990, 1997, and 1999), Japan (1992, 1993), France (1991 and 1993), the U.K. (1998), Romania (1999),Canada (2001), and Belgium (2002). Prof. Dascalescu is a Senior Member of the IEEE Industry Applications Society and Vice-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) France.
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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 Institute of Electrical Engineering , University of Sidi-Bel-Abbes, Sidi-Bel-Abbes, Algeria. He has been Chairman of the Scientific Committee of this institute since November 2002 and is also Head of the research group “Electrostatics and High-Voltage Engineering.” He visited the Electronics and Electrostatics Research Unit of the University Institute of Technology, Angoulême, France, in 2001 and 2003, 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.
Florian Aman received the M.S. degree in mechanical engineering from the Polytechnical Institute of Timisoara, Timisoara, Romania, in 1967, the M.S. degree in economics from the University of Timisoara, Timisoara, Romania, in 1979, and the Ph.D. degree in electrical engineering from the Technical University of Cluj-Napoca, Cluj-Napoca, Romania, in 2001. He spent most of his professional career as a Design Engineer, Project Manager, and then Head of the marketing division of UMT, one of the major manufacturers of technological equipment in Romania. In 1998, he joined Hamos GmbH, Penzberg, Germany, as Project Manager. He is actively involved in the R&D of new electrostatic separation equipment for the recycling industry, and has coauthored several technical papers in this field.
Michaela Mihailescu received the M.S. degree in electrical engineering and the Dr.Eng. degree from the Technical University of Cluj-Napoca, Cluj-Napoca, Romania, in 1980 and 2000, respectively. She was with the Institute of Research and Development for Automation, Cluj-Napoca, Romania, for 12 years. For several years, she was the Head of the CAD Division, in charge of various projects concerning automatic testing of PCBs. At present, she is the Technical Manager of EGH, Cluj-Napoca, Romania, a company specialized in computerized cartography, and an Associate Lecturer in the Department of Electrical Engineering, Technical University of Cluj-Napoca. Her present interests include the design of the experiments and the prototyping of an expert system for electrostatic separation processes.
