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Eraslan (Turkey) Ryoichi Chiba (Japan) P.K.Dutta (India) Kimon Antonopoulos (Greece) Josefa Mula (Spain) Amiya Ku.Rath (India) Fabio Leao (Brazil) Francisco Jesus Fernandez Morales (Spain) MultiCraft International Journal of Engineering, Science and Technology Vol. 1, No. 1, 2009, pp. i INTERNATIONAL JOURNAL OF ENGINEERING, SCIENCE AND TECHNOLOGY www.ijest-ng.com 2009 MultiCraft Limited. All rights reserved Editorial The Editor, on behalf of the Editorial Board and Reviewers, has great pleasure in presenting this first issue of the journal International Journal of Engineering, Science and Technology (IJEST), to the research community and the world at large, comprising those seeking to publish their works and those who wish to keep up with the latest findings in their areas of research. The journal provides opportunities for all the scholars around the globe to publish findings in their areas. IJEST is a response to the needs of researchers for a rapid publishing outlet for the best research, presenting new ideas, concepts and principles that will stimulate readers to superior investigations and significant research reporting. The journal strives to be a one-stop source of scientific ideas through detailed investigations and reporting by the authors. The review process is approached through a peer review system in which experts evaluate manuscripts in a blind manner such that they are unknown to the authors and vice-versa during the evaluation process. IJEST is obtainable in electronic form, which is available worldwide on the Internet and can be accessed at the following URL: http://www.ijest-ng.com Although numerous research findings have been made by researchers in academic institutions and research institutes, particularly in developing countries, unfortunately, dissemination of these information in print seems to be a challenge since some investigators are ignorant of the relevant publishing outlets, are often scared of high page charges, and are sometimes disturbed by excessive manuscript delays for review and possible publication. Fortunately, IJEST is endowed with prestigious, knowledgeable, experienced and highly committed specialist members of the editorial board and reviewers in the various fields of engineering, science and technology who provide timely, valuable constructive criticisms and information to authors for the improvement of their manuscripts and subsequent publication in IJEST. Thus, IJEST is enthusiastic in establishing a benchmark in engineering, science and technology research and in providing the widest possible dissemination of research findings. IJEST is deemed for success globally and will achieve this through the commitment of its stakeholders - Members of the Board, Reviewers, Authors, Editorial Assistants and the Readers of the journal. It is hoped that through the manuscripts received, reviewed, revised and finally published, potential authors will draw inspiration from the various issues of the journal and send their works to us for possible publication. A condition for a scientific journal to be included in ISI index relates to the achievement of a threshold level of number of citations in the international scientific literature. Our authors and others are therefore urged to cite articles published in IJEST whenever they publish in an ISI-indexed journal; we hope to be ISI-indexed in the near future. The Editor is grateful to all the Board Members and Reviewers that have invested a tremendous commitment of time and energy; without their assistance it would not be possible to publish a refereed journal such as IJEST. We have included the names of the Editorial Board Members and Reviewers in the List of Editorial Board Members and Reviewers. If we have omitted anyone as oversight, please accept our apologies. S.A. Oke Editor 21 July 2009 International Journal of Engineering, Science and Technology (IJEST) CONTENTS Page Editorial i Design characteristics of Curved Blade aerator w.r.t. aeration efficiency and overall oxygen transfer coefficient and comparison with CFD modeling L.B. Bhuyar, S.B. Thakre, N.W. Ingole 1 A variational analysis for large deflection of skew plates under uniformly distributed load through domain mapping technique Debabrata Das, Prasanta Sahoo and Kashinath Saha 16 Evaluation of the information servicing in a distributed learning environment by using monitoring and stochastic modeling R. P. Romansky, E. I. Parvanova 33 A computational approach to the design of a cryogenic turbine blade profile Subrata Kr. Ghosh, R. K. Sahoo, Sunil K. Sarangi 43 Single electron based binary multipliers with overflow detection Souvik Sarkar, Anup Kumar Biswas, Ankush Ghosh, Subir Kumar Sarkar 61 Transverse vibration of spinning disk with attached distributed patch and discrete point masses using finite element analysis Vinayak Ranjan and M.K.Ghosh 74 Comparative performance analysis of Thyristor and IGBT based induction motor soft starters Ahmed Riyaz, Atif Iqbal, Shaikh Moinoddin, SK. MoinAhmed, Haitham Abu-Rub 90 Pb(II), Cd(II) and Zn(II) adsorption on low grade manganese ore K. Rout, M. Mohapatra, B.K. Mohapatra, and S. Anand 106 Modelling and analysis of abrasive wear performance of composites using Taguchi approach S.S. Mahapatra and Vedansh Chaturvedi 123 Solving the K-of-N Lifetime Problem by PSO Tzung-Pei Hong and Guo-Neng Shiu 136 Design aids for fixed support reinforced concrete cylindrical shells under uniformly distributed loads Srinivasan Chandrasekaran, S.K.Gupta, Federico Carannante 148 Bioeconomic analysis of Marylands Chesapeake Bay oyster fishery with reference to the optimal utilization and management of the resource T. K. Kar and Kunal Chakraborty 172 A parametric study on the growth of yield front in rotating annular disks Shubhankar Bhowmick, Dipten Misra and Kashi Nath Saha 190 CONTENTS (contd) Special Issues (Announcements) 205 Generalized similarity method in unsteady two-dimensional MHD boundary layer on the body which temperature varies with time Dragisa Nikodijevic, Zoran Boricic, Dragica Milenkovic, Zivojin Stamenkovic 206 Response of multiphase magneto-electro-elastic sensors under harmonic mechanical loading B. Biju, N. Ganesan and K.Shankar 216 Propagation of shear waves in an irregular magnetoelastic monoclinic layer sandwiched between two isotropic half-spaces A. Chattopadhyay, S. Gupta, A.K. Singh and S.A. Sahu 228 Effects of superficial gas velocity and fluid property on the hydrodynamic performance of an airlift column with alcohol solution V. Sivasubramanian and B.S. Naveen Prasad 245 A combined approach of complex eigenvalue analysis and design of experiments (DOE) to study disc brake squeal M. Nouby, D. Mathivanan, K. Srinivasan 254 Cardinal priority ranking based decision making for economic-emission dispatch problem Lakhwinder Singh and J.S. Dhillon 272 New table look-up lossless compression method based on binary index archiving R. Rdescu 283 MultiCraft International Journal of Engineering, Science and Technology Vol. 1, No. 1, 2009, pp. 1-15 INTERNATIONAL JOURNAL OF ENGINEERING, SCIENCE AND TECHNOLOGY www.ijest-ng.com 2009 MultiCraft Limited. All rights reserved Design characteristics of Curved Blade Aerator w.r.t. aeration efficiency and overall oxygen transfer coefficient and comparison with CFD modeling L.B. Bhuyar1, S.B. Thakre1*, N.W. Ingole2 1*P.R.M. Institute of Technology & Research, Badnera, Amravati (M.S), India 444701 2 Principal, IBSS College of Engineering, Ghatkheda, Amravati (M.S), India 444605 *Corresponding Author: e-mail:sbthakre2007@rediffmail.com,sbthakre2007@gmail.com Abstract The main objective of this work is to design a high efficiency curved-blade-surface mechanical aerator for oxidation ditch, which is used to treat municipal and domestic sewage. Aeration experiments were conducted in oxidation ditch made up of mild steel sheets to study the design characteristics of curved blade surface mechanical aerator. The paper critically examines six different configurations of aerators, which were developed, fabricated and tested in the laboratory for its various dynamic parameters, such as diameter of aerators (D), speed (N) and immersion depth (h). Out of the different configurations tested, the curved blade rotor (CBR) emerged as a potential aerator with blade tip angle of 47. The overall oxygen transfer co-efficient (KLa) was observed to be as high as 10.33 h-1 and the optimum aerator efficiency (AE) was found to be 2.269 kgO2/kWh. The standard aeration efficiency (SAE) of CBR was observed to be higher as compared to other aerators used for oxidation ditch process. Dimensional analysis was used to develop equations that describe the aerators behavior. Further, a CFD model is also developed for better understanding of the process that takes place inside the ditch. To prepare it 3D and steady flow, k-e turbulence model of flow was used and the simulation runs were carried out for one phase model to generate the data so as to compare it with experimentally observed values. Keywords: Oxidation ditch, dissolved oxygen, aerator, overall oxygen transfer coefficient, aeration efficiency, CFD 1. Introduction Oxygen transfer, the process by which oxygen is transferred from the gaseous to liquid phase, is a vital part of the waste-water treatment process (Metcalf and Eddy, 2001). Due to low solubility of oxygen and consequent low rate of oxygen transfer, sufficient oxygen to meet the requirement of aerobic waste does not enter through normal surface air water interface. To transfer the large quantities of oxygen that are needed, additional interfaces are created by employing aeration process. The creation of additional interfaces enhances the rate of oxygen transfer so that the dissolved oxygen level gets raised to allow aerobic bacteria to remove biochemical oxygen demand of the effluent. To provide the required amount of oxygen, an aeration system is always needed. Aeration is usually the single largest cost in a waste water treatment system comprising as much as 50-90% of the total energy requirements of a secondary waste-water treatment plan (Wasner et al, 1977). Dissolved oxygen (DO) concentration is one of the most important water quality parameters affecting the quality of waste-water. Various types of aeration systems have been developed over the years to maintain the desired level of DO concentration in the waste water as an effort to improve the energy efficiency of the oxygen mass transfer process. The three basic categories of aeration methods are (Thakre et al, 2008 a): - 1) Surface or mechanical aeration method, which increases interfacial area by spraying water droplet into the air. 2) Diffused aeration method, which release air bubbles beneath the surface of water. 3) Combined and turbine aeration methods, which introduced large air bubble into water and reduce their sizes mechanically. Bhuyar et al./ International Journal of Engineering, Science and Technology, Vol. 1, No. 1, 2009, pp. 1-15 2 Out of these three, the mechanical surface aerators are widely used because they offer better efficiency as well as convenience in operation and maintenance (Rao and Kumar. 2007). Further, oxygen transfer rate from gas to liquid phase is dependent on various factors for given method of aeration such as dynamic variables like speed, mixing intensity and turbulence, geometrical parameters like size and number of blades, depth of flow etc and physicochemical properties of the liquid. Even though the designer or operator can fix or control some of these parameters, successful design requires the knowledge of the effect of all such parameters on reaeration rate. A wide variation in performance of aerators in terms of standard aeration efficiency was found, viz. Taiwanese aerator (1.17 kgO2/kWh), Japanese aerator (1.03 kgO2/kWh) and Auburn university design (2.25 kgO2/kWh) (Boyd and Watten, 1989; Boyd, 1998). For proper aeration, i.e., proper mixing of DO throughout the water volume, American public health association (APHA, 1985) mentioned that the power-to-water volume ratio should lie within 0.01-0.04 kW/m3. However, the water volume used in the aeration studies conducted so far has not been quantified properly. In the field of wastewater treatment, many investigators have successfully made use of the theory of dimensional analysis and obtained optimal geometric similarity of horizontal rotor aerators under different conditions (Eckenfelder, 1956; Horvath, 1984; Ognean, 1993; Zolkarnik, 1976; Schmidtke and Horvath 1977) presented the occurrence of scale effects due to the Reynolds and the Froudes laws of the aeration performance under similar geometric conditions. The criterion of power per unit volume was found to be very useful in geometrically similar systems for scale-up of horizontal rotor aerators (Rao, 1999). Application of the aeration phenomena in waste-water treatment and aquaculture has been different. The needs of the aquaculture industry, however, are different than those of the waste-water treatment industry. In aquaculture applications, the dissolved oxygen concentration must be much higher than that in waste-water treatment (Cansino et al., 2004). Boyd and Watten (1989) summarized the importance of the dissolved oxygen concentration in aquaculture. They have shown that most warm water species can tolerate concentrations as low as 2-3 mg/l of dissolve oxygen, and many cold-water species can tolerate 4 or 5 mg/l. The problem is that aquatic organisms eat and grow better and are healthier when the dissolved oxygen concentration is at or near saturation. But in case of waste-water treatment the dissolved oxygen concentration of 2 mg/l is sufficient to carry out the treatment process. Several aerators for oxidation ditches have already been developed. It is necessary to investigate the efficiency of aerator that has been used from the point of view of energy consumption. Questions such as best number of blades used in the design, the necessary number of blades, the shape of the blades, the relevant geometric parameters involved in the mass transfer, which are interesting to study in order to determine the optimum aeration efficiency have yet not been determined. To design high efficiency surface aerator it is necessary to identify the parameters that are relevant to the oxygen transfer phenomena and to investigate the best way to optimize the aeration efficiency value. This means that the value of overall oxygen transfer coefficient (KLa) must be increased and the power consumption must be kept at the same level or diminished. Therefore, the objective of the present research work is to design a high efficiency curved blade surface mechanical aerator for oxidation ditch and to determine optimum aeration efficiency, which could be used to treat municipal and domestic sewage with minimum power consumption. Secondly, to generate a model based on computational fluid dynamic (CFD) so as to match the results generated by physical model with that of CFD model. The experimental apparatus of the oxidation ditch with different aerators was designed, fabricated and installed in the research laboratory of the institute. 2. Materials and Methods A simplified schematic sketch of experimental setup used for present study is shown in Figure 1. The aeration experiments were conducted in Oxidation Ditch (OD) of dimension 2.5 m 0.35 m 0.2 m. The oxidation ditch used for experimentation was made up of mild steel sheet and was situated above the ground with the supports provided at front, rear and middle of the ditch. Arrangements were made during fabrication of ditch, for varying the depth of immersion of the rotor. Gears and sprocket were mounted on rotor shaft and d.c. motor, respectively. Chain drive was preferred over belt drive to avoid slip during the power transmission. The motor was connected to variable speed controller to obtain variation in the speed of rotation. The experimental setup mainly consists of an Oxidation Ditch, D.C. motor (0.25 H.P, 1 Amp) with variable speed controller, digital wattmeter (range 0-200W), dissolve oxygen meter, thermometer, and digital tachometer. 2.1Fabrication of aerator rotor: CBR was fabricated using impeller fans made up of fiber and are used in centrifugal pump, which are available in the market in various sizes. A 23 cm in diameter fan disc with a 12 small fins of size 5 cm 3 cm mounted on it was selected to configure an aerator so that the effective diameter of the disc becomes 26 cm. A fiber pipe of 1.5 inch, 2 inch, 3 inch of appropriate length was cut into pieces such that 24 equal strips of length 14 cm and 4 cm width were yielded from each pipe. The different diameter pipes were used so as to obtain the change in blade tip angles. A pair of fan disc was taken and the strips thus fabricated were screwed on projected fins of the fan discs as shown in Figure 2. Figure 2 represents the side view of the aerator representing the fin dimensions for a fiber pipe of 2 inch diameter, which yielded the blade tip angle of 47 degrees. 1.1 cm indicates the projected length of the fin thus obtained after fabrication. Since optimum oxygen transfer was observed with 47 degree blade tip angle, therefore this configuration of aerator is represented in the figure. Bhuyar et al./ International Journal of Engineering, Science and Technology, Vol. 1, No. 1, 2009, pp. 1-15 3 Figure 1. Experimental setup for oxidation ditch, aerators and driving mechanism Figure 2. Side view of the curved blade aerator (All dimensions in cm) The fiber pipe of 1.5 inch and 3 inch diameter were used to get different blade tip angles. By using above diameter pipes, blade tip angle of 27 degrees and 60 degrees were obtained. Since these configurations did not prove to be the best for the optimum aeration efficiency, therefore the figures pertaining to these configurations are not provided. The strips were screwed in such a fashion that the projected length of the strip over the disc fins was 1.5 cm. Therefore the effective diameter of rotor thus fabricated amounts to 29 cm with 12 blades (fiber strips) mounted on each aerator rotor. The overall dimensions are shown in Figure 3.This assembly of aerator was fastened tightly to the shaft and then fixed in the bearings provided on the collars of the oxidation ditch. The depth of immersion (DOI) was varied from 4.8 cm to 7.2 cm and the speed was varied from 36 rpm to 60 rpm. The performance was evaluated for each kind of configuration of aerator rotor by changing various parameters such as, aerator blade tip angles, speeds, and depth of immersion with respect to power consumption. R O T O RM O T O RD . C .W A T T M E T E R V A R I A B L E S P E E D C O N T R O L L ERM A I NSC H A IN D R IV EP U L L EYS P R O C K E T S P R O C K E TP U L LE YB E L T D R IV EE X P E R I M E N T A L S E T U PBhuyar et al./ International Journal of Engineering, Science and Technology, Vol. 1, No. 1, 2009, pp. 1-15 4 Figure 3. Front View of curved blade aerator (All dimensions in cm) 3. Aeration Test The deoxygenating-oxygenation procedure used was the non-steady-state reaeration test (Moulick, et al 2005; Thakre et al, 2009). The test water was deoxygenated with 10 mg/L of sodium sulphite, cobalt chloride was not used during test since it is considered hazardous to human health (Cancino, 2004). After maintaining DO between 0.0 0.1 mg/L for about 5 minutes, both the aerator were put in operation at the same moment and at the same rotational speed and immersion depth. Increase in DO concentration was measured by DO probe at the surface of water and at the half depth from the surface. The readings were taken at equal time intervals until DO increased from 0% saturation to at least 90% saturation. The dissolved oxygen saturation concentration (Cs) used for calculating the KLa was estimated using the highest dissolved oxygen concentration from each test. 4. Results and Discussion Out of the various factors which may affect aeration or dissolve oxygen level, time of aeration, depth of immersion, speed of aerator, and blade tip angle mounted on the aerator, are mainly considered for performance evaluation of an aerator. For every set of observation, overall oxygen transfer coefficient KLa, which is a measure of aeration, is computed and its behavior is studied with respect to other variables, keeping other variables constant at that time. KLa, the overall oxygen transfer coefficient is the rate of oxygen transfer for a unit saturation deficit and it is constant for particular system of aeration. Assessment of overall oxygen transfer coefficient of an aeration system is one of the most important factors. Underestimating the oxygen transfer rate results in over designed system, which may be energy intensive and expensive to operate. On the other hand, overestimating the oxygen transfer rate results in inadequate oxygen supply, which in turn, reduces efficiency. 4.1 Mathematical model for measurement of overall oxygen transfer co-efficient ( KLa) For treatment of domestic and industrial waste, mathematical modeling is done. The engineers wish to fit an analytical function based on observed data or to evaluate the parameters of some prescribed functional representation. The eight types of equations are used and out of these equations, linear fit equation is adopted for mathematical modeling in the present study. Aeration is transfer of air or oxygen in the water (Kumar, 1991; Thakre et al. 2008b). The oxygen transfer through water is governed by Ficks Law of diffusion and is a liquid film controlled process. In aeration, the rate of oxygen transfer was expressed earlier as, )tCs.a.(CLK = dt dc (1) Bhuyar et al./ International Journal of Engineering, Science and Technology, Vol. 1, No. 1, 2009, pp. 1-15 5 where, KLa is the rate of oxygen transfer for saturation deficit. Therefore, this is referred as measurement of aeration and it forms a good basis of studying the behavior under various variables. This is employed in the study in the unit of min-1 or hr-1.The rate of oxygen transfer equation was converted into ).a.hLK1 (.a.hLK.) ( +=+esC etCh tC (2) This is comparable to the equation of straight line of the form: Y= m.X +A (3) Thus, if a plot is obtained in between ) ( h tC+andtC , it would yield a straight line and slope of this line (m) represent the value of .a.h KLe from which .aLK can be calculated. Similarly, the intercept on Y-axis (A) represents the term ).a.hLK1 ( esC from which the value of sC can be known. Thus it clearly indicates that in order to determine the value of .aLK , it must be carried out at a uniform interval of time. In the present study the curves have been drawn between ) ( h tC+against tC and time interval h is taken as 15 minutes. The slope of this line is known and the value of overall oxygen transfer coefficient .aLK is calculated. This method is used for calculation of performance of different aerators. The plot between C (t+h) and Ct is obtained for different configuration of rotor aerator at different speeds of 36, 42, 48 and 60 rpm. The data generated from different rotors followed different curves and indicated different values of KLa and aeration efficiency. 4.2 Variation of DO with Time for various depth of immersion We refer to figure 4, which illustrates that the increase in DO concentration is very high in first 15 minutes, and then it gradually attains a saturation value pertaining to the performance of the respective aerator. It is observed that the maximum increase in DO concentration, that is, from 0.0 mg/L to 8.2 mg/L is attained by CBR aerator when the blade tip angle is 47 degrees and the depth of immersion is 5.5 cm. All the curves were plotted at 48 rpm aerator speed. The literature cited by the author reveals that the optimum performance is generally obtained at blade angle of 45 degrees. Therefore the above blade angle is chosen. 4.3 Variation of KLa and power for various blade angles and speed From Figure 5, it can be observed that the variation of KLa and power for various blade angles and speed, with respect to CBR aerator and considering the depth of immersion 5.5 cm is an optimum value. It is very clear from figure that as the power and speed increases the corresponding values of KLa also increases. It is also evident from the figure that at a speed of 48 rpm the power requirement is observed to be in the medium range that is 73.8 W, whereas there is a considerable increase in value of KLa in the tune of 10.33 h-1. As the power consumption decreases below 73.8 W, the value of KLa also decreases accordingly, which further decreases the aeration efficiency, which is not acceptable as per the values quoted by the researchers. When the power consumption increases beyond 73.8 W, there is a marginal increase in KLa value, but aeration efficiency drops sharply from 2.269 kgO2/kWh to1.37 kgO2/kWh. As the speed of CBR aerator is increased from 48 rpm to 60 rpm, it is observed that splashing of wastewater outside the ditch takes place, which deteriorates the surrounding atmosphere and thereby causing problems for working people present at the plant. This may also create problems in driving mechanism as the motor is mounted below the ditch. Bhuyar et al./ International Journal of Engineering, Science and Technology, Vol. 1, No. 1, 2009, pp. 1-15 6 01234567890 15 30 45 60 75 90 105 120Time Interval minDO Concentration mg/lDOI 4.8CM DOI 5.5CM DOI 6.3CM DOI 7.2CM Figure 4.Variation of DO with Time for different depth of immersion and blade tip angle of 47 deg. 0102030405060708090100110120130140150KLa Power KLa Power KLa Power< 27 Degree < 47 Degree < 60 DegreeSpeed 42 RPMSpeed 48 RPMSpeed 60RPM Figure 5. Variation of KLa and Power for CBR for various Blade angles and Speeds, depth of immersion 5.5 cm 4.4 Variation in DO for various blade tip angle with Time The variation in DO for various blade tip angle with time is shown in Figure 6. The figure 6 represents the curves plotted at 42 rpm and 5.5 cm depth of immersion. Three types of blade tip angles were tested, which suggest that maximum DO concentration is achieved when the blade tip angle is at 47 degrees. With this configuration, DO level of 8.2 mg/L is attained in 45 minutes and thereafter the DO concentration curve remains constant which indicates that the saturation deficit of oxygen is met. Bhuyar et al./ International Journal of Engineering, Science and Technology, Vol. 1, No. 1, 2009, pp. 1-15 7 01234567890 15 30 45 60 75 90 105 120Time Interval minDO Concentration mg/lDO 27 deg DO 47 deg DO 60 deg Figure 6. Variation of DO Vs Time for various angles of CBR, speed 42 rpm and DOI- 5.5 cm Figure 7 and Figure 8 represent the same plot as above follow the similar trend as that of Figure 6. Combined analysis of all the three figures reveals that the optimum performance is recorded at blade tip angle of 47 degrees. 01234567890 15 30 45 60 75 90 105 120Time Interval minDO Concentration mg/lDO 27 deg DO 47 deg DO 60 deg Figure 7. Variation of DO Vs Time for various angles of CBR, speed 48 rpm and DOI- 5.5 cm 4.5 Plot between C (t+h) and Ct for determination of KLa As stated earlier, the plot between C (t + h) and Ct will yield a straight line. The slope of the line is known and the value of KLa is calculated. The time interval h is maintained as 15 minutes (Figure 9). The data obtained for different depth of immersion produced different curves. It is quite evident from the figure that the curve obtained for 5.5 cm depth of immersion, 48 rpm speed, and 47 degree blade tip angle yielded maximum value of KLa. Bhuyar et al./ International Journal of Engineering, Science and Technology, Vol. 1, No. 1, 2009, pp. 1-15 8 01234567890 15 30 45 60 75 90 105 120Time Interval minDO Concentration mg/lDO 27 deg DO 47 deg DO 60 deg Figure 8. Variation of DO Vs Time for various angles of CBR, speed 60 rpm and DOI-5.5 cm y = 0.0754x + 7.4954R2 = 0.994401234567890 2 4 6 8 10DO Level (mg/l)at any time (t),CtDO Level (mg/l)at time (t+h),C(t+h)DOI=4.8 cm DOI=5.5cm DOI=6.3cm DOI=7.2cm Figure 9. DO Level at time (t+h) Vs DO Level at time t for Determination of KLa The variation in KLa and power required are presented in Table1. It also suggests that depth of immersion is directly proportional to the power consumption. On the other hand the increase in KLa is not that significant. As a golden mean, the value of KLa is finally taken as 10.33 h-1 when the power required is 73.8 W and it is observed that the value of AE is the highest for these values, which is discussed later on. Table 1.Variation of KLa and Power with Depth of Immersion for 48 rpm Speed S.No. Depth of Immersion (cm) KLa (h-1) Power (W) 1 4.8 8.414 69.9 2 5.5 10.33 73.8 3 6.2 10.58 93.5 4 7.2 10.93 136 Bhuyar et al./ International Journal of Engineering, Science and Technology, Vol. 1, No. 1, 2009, pp. 1-15 9 4.6 Variation in Aeration efficiency and KLa with respect to power Figure 10 describes the variations of KLa and AE for various blades tip angles and speeds. 6.849.726.7110.3311.317.438.21.4681.6832.2691.4341.2391.1711.5211.41.3651.5491.370123456789101112Kla AE Kla AE Kla AEKLa (h-1) & Aeration efficiency (kgO2/kWh)KLa, < ===max12max1max minmin12min1;1; 0;1Gi GiNgiGi GikGi Gi GiGi GiNgiGi GikPGiP P P PrP P PP P P PrU ) (( )> < ===max12max1max minmin12min1;1; 0;1Gi GiNgiGi GikGi Gi GiGi GiNgiGi GikQGiQ Q Q QrQ Q QQ Q Q QrU p , q are lagrangian multipliers, kr1is penalty factor. The Newton-Raphson method is applied to obtain the non-inferior solutions for simulated weight combinations, to achieve the necessary conditions. 4. Cardinal priority ranking The fuzzy sets are defined by equations called membership functions, which represent the goals of each objective function. The membership function represents the degree of achievement of the original objective function as a value between 0 and 1 with ( )iF =1 as completely satisfactory and ( )iF = 0 as unsatisfactory. Such a linear membership function represents the decision makers fuzzy goal of achievement, and at the same time scales the original objective functions with different physical units into measure of 0-1. By taking account of the minimum and maximum values of each objective function together with the rate of increase of membership satisfaction, the decision maker must determine the membership function ( )iF in a subjective manner. ( ) F F ; 0F F F ; F FF FF F ; 1F maxi imaxi iminiminimaxiimaximini ii = (14) where miniF and maxiF are minimum and maximum values of ith objective function in which the solution is expected. The value of the membership function indicates how much (in the scale from 0 to 1) a noninferior solution has satisfied the iF objective. The sum of the membership function values ( ( )iF ; i = 1, 2, , L) for all the objectives can be computed in order to measure the accomplishment of each solution in satisfying the objectives. The accomplishment of each non-dominated solution can be rated with respect to all the M non-dominated solutions by normalizing its accomplishment over the sum of the accomplishment of the M non-dominated solutions as follows: = ===MkLikiLikikDFF1 11) () ( (15) Singh and Dhillon / International Journal of Engineering, Science and Technology, Vol. 1, No. 1, 2009, pp. 272-282 276 where kikiF F ) ( 1 ) ( = The function kD can be treated as an unsatisfied membership function for non-dominated solutions in a fuzzy set and represented as a fuzzy cardinal priority ranking of the non-dominated solutions. The smaller the unsatisfied cardinal priority, the better is the solution. The function kS is defined as: } { ) (min max minS S S SkDk + = (16) where minS and maxS are minimum and maximum values of scaling factor to map the function, kS in the required range to adjust the cardinal priority. Regression analysis is performed between cardinal priority ranking and simulated weights, iw ; i = 1, 2, , L to achieve maximum satisfaction. 5. Flow chart The economic emission dispatch problem is solved by various steps. The step wise procedure is depicted in flow chart given in Figure 1. 6. Test system and results The validity of the proposed method is illustrated on 11-bus, 17-lines IEEE system, comprising of three generators (Singh et al., 2006). Minimum and maximum values of the objectives are obtained by performing minimum economic and emission dispatch respectively. Minimum and maximum values of the objectives are shown in Table 1. Table 1: Minimum and maximum values of objectives =min1F 4584.7830 $/h max1F = 4742.0610 $/h =min2F 619.1288 kg/h =max2F 953.5742 kg/h =min3F 2848.7130 kg/h max3F = 6246.4340 kg/h =min4F 5.887253 kg/h max4F = 15.05706 kg/h To obtain the solution of EED problem, three different cases are considered in which weights are simulated with different step sizes so that their sum remains equal to one and are as: Case-I: Weights are simulated by giving variation in step of 0.05 Case-II: Weights are simulated by giving variation in step of 0.02 Case-III: Weights are simulated by giving variation in step of 0.01 Non-inferior solutions, corresponding membership functions for all the objectives along with the cardinal priority ranking for the simulated weight combinations are shown in Table 2, Table 3 and Table 4 for all the above three cases. Singh and Dhillon / International Journal of Engineering, Science and Technology, Vol. 1, No. 1, 2009, pp. 272-282 277 No No NoStartYesRead data Compute initial values of P and GiP when 0 =q, 0 =lossP, 0 =lossQ and also calculate initial fuel cost, prevsCostF Run load flow, Compute loss coefficientsCalculate real and reactive power losses using Eq.(9) and Eq.(10) Apply Newton-Raphson Method to solve Eq.(13) Modify values ofnewP , newqandnewGiP YesSimulate weight combinationsNo More non-inferior solutionsStopYes Compute DP and DQ Err P P PlossNbiDiNgiGi 1 1Err Q Q QlossNbiDiNgiGi 1 1ITI=ITI+1 YesError F Fprevstnewt cos cosIT=IT+1 Compute membership function of objectives using Eq.(14) Calculate cardinal priority ranking and function kSusing Eq.(15) and Eq. (16), resp. Best weights are evaluatedWrite optimal values of GiP, GiQand real & reactive losses Figure 1: Flow chart for economic emission dispatch problem Singh and Dhillon / International Journal of Engineering, Science and Technology, Vol. 1, No. 1, 2009, pp. 272-282 278 Table 2: Non-inferior solutions, membership functions and cardinal priority ranking for case-I Weights Objectives and membership functions Objectives 1F $/h 2F kg/h 3F kg/h 4F kg/h 1w 2w 3w 4w Membership 1( ) F 2( ) F 3( ) F ) (4F kS Objectives 4701.1930 863.6878 2899.6690 9.214497 0.25 0.25 0.25 0.25 Membership 0.740153 0.731237 0.014997 0.362848 0.7900239 Objectives 4713.5030 885.0974 2874.9760 9.344164 0.20 0.20 0.30 0.30 Membership 0.818423 0.795253 0.007730 0.376988 0.8537466 Objectives 4685.3470 837.9921 2945.2890 9.039403 0.30 0.30 0.20 0.20 Membership 0.639403 0.654407 0.028424 0.343753 0.7117372 Objectives 4711.3310 866.9720 2892.6060 9.467503 0.20 0.30 0.30 0.20 Membership 0.804613 0.741057 0.012918 0.390439 0.8326569 Objectives 4689.8210 858.0472 2913.2680 8.939130 0.30 0.20 0.20 0.30 Membership 0.667847 0.714372 0.019000 0.332818 0.7408092 Objectives 4723.0240 903.7136 2861.3140 9.432228 0.15 0.15 0.35 0.35 Membership 0.878959 0.850916 0.003709 0.386592 0.9057738 Objectives 4706.1980 752.3051 3217.0410 10.69450 0.35 0.35 0.15 0.15 Membership 0.771978 0.398200 0.108404 0.524247 0.7701989Objectives 4720.8640 868.8564 2889.2360 9.709036 0.15 0.35 0.35 0.15 Membership 0.865224 0.746692 0.011927 0.416779 0.8717871 Objectives 4676.0400 848.1931 2940.1950 8.621339 0.35 0.15 0.15 0.35 Membership 0.580227 0.684908 0.026925 0.298162 0.6793687 Objectives 4730.3710 920.6932 2853.7960 9.483382 0.10 0.10 0.40 0.40 Membership 0.925673 0.901685 0.001496 0.392171 0.9488586 Objectives 4730.1370 869.8763 2888.1770 9.945444 0.10 0.40 0.40 0.10 Membership 0.924186 0.749741 0.011615 0.442560 0.9091604 Objectives 4736.1320 937.0949 2849.9040 9.502926 0.05 0.05 0.45 0.45 Membership 0.962301 0.950726 0.000351 0.394302 0.9858791 By performing linear regression analysis, the obtained best values of weights and the corresponding values of fuel cost, NOx emission, SOx emission, COx emission for all the three cases are compared and are shown in Table 5. It has been observed from Case-I, Case-II and Case-III that the minimum value of the membership functions of the objectives is improved by decreasing the step size of the weights. The membership function increases from 0.5165955 to 0.5241862 with the decrease in step size from 0.05 to 0.02 and further 0.5241862 to 0.5430062 with the decrease in step size from 0.02 to 0.01. The results obtained in the proposed method are also compared with the results of Brar et al. (2002). It has been observed that, the solutions achieved in proposed method have more membership satisfaction as compared to the results presented in Brar et al. (2002). It is clear from all the three cases that by reducing the step size of the weights, better membership satisfaction is achieved and when the overall outcome is less than some of the non-inferior solutions try with the reduced step size which will improve the overall outcome. The best power generation schedule for all the three cases of proposed method is given in Table 6. Singh and Dhillon / International Journal of Engineering, Science and Technology, Vol. 1, No. 1, 2009, pp. 272-282 279 Table 3: Non-inferior solutions, membership functions and cardinal priority ranking for case-II Weights Objectives and membership functions Objectives 1F $/h 2F kg/h 3F kg/h 4F kg/h 1w 2w 3w 4w Membership 1( ) F 2( ) F 3( ) F ) (4F kS Objectives 4703.9120 868.2581 2893.4810 9.243966 0.24 0.24 0.26 0.26 Membership 0.757442 0.744903 0.013176 0.366062 0.08304875 Objectives 4698.3330 858.9479 2906.6590 9.183201 0.26 0.26 0.24 0.24 Membership 0.721970 0.717065 0.017054 0.359435 0.08013305 Objectives 4703.2930 864.4938 2897.8570 9.266447 0.24 0.26 0.26 0.24 Membership 0.753506 0.733647 0.014464 0.368513 0.08254325 Objectives 4699.0450 862.7900 2901.7310 9.161678 0.26 0.24 0.24 0.26 Membership 0.726496 0.728553 0.015604 0.357088 0.08067229 Objectives 4708.9530 876.9422 2883.1320 9.297540 0.22 0.22 0.28 0.28 Membership 0.789494 0.770868 0.010130 0.371904 0.08573294 Objectives 4707.3750 865.8672 2894.8760 9.368151 0.22 0.28 0.28 0.22 Membership 0.779460 0.737754 0.013586 0.379604 0.08432090 Objectives 4692.1590 848.9004 2923.5310 9.115059 0.28 0.28 0.22 0.22 Membership 0.682715 0.687023 0.022020 0.352004 0.07696564 Objectives 4713.5030 885.0973 2874.9760 9.344166 0.20 0.20 0.30 0.30 Membership 0.818423 0.795252 0.007730 0.376989 0.08820449 Objectives 4711.3300 866.9719 2892.6060 9.467499 0.20 0.30 0.30 0.20 Membership 0.804607 0.741057 0.012918 0.390438 0.08602533 Objectives 4717.6100 892.8093 2868.5340 9.384130 0.18 0.18 0.32 0.32 Membership 0.844535 0.818312 0.005834 0.381347 0.09048350 Objectives 4684.6950 854.7670 2921.8040 8.818712 0.32 0.18 0.18 0.32 Membership 0.635256 0.704564 0.021512 0.319686 0.07419626 Objectives 4715.1920 867.8572 2890.9120 9.565063 0.18 0.32 0.32 0.18 Membership 0.829161 0.743704 0.012420 0.401078 0.08767353 Singh and Dhillon / International Journal of Engineering, Science and Technology, Vol. 1, No. 1, 2009, pp. 272-282 280 Table 4: Non-inferior solutions, membership functions and cardinal priority ranking for case-III Weights Objectives and membership functions Objectives 1F $/h 2F kg/h 3F kg/h 4F kg/h 1w 2w 3w 4w Membership 1( ) F 2( ) F 3( ) F ) (4F kS Objectives 4701.1930 863.6878 2899.6690 9.214497 0.25 0.25 0.25 0.25 Membership 0.740153 0.731237 0.014997 0.362848 0.08131214 Objectives 4698.3330 858.9479 2906.6590 9.183201 0.26 0.26 0.24 0.24 Membership 0.721970 0.717065 0.017054 0.359435 0.07982981 Objectives 4703.2930 864.4938 2897.8570 9.266446 0.24 0.26 0.26 0.24 Membership 0.753506 0.733647 0.014464 0.368513 0.08223089 Objectives 4706.4970 872.6721 2887.9970 9.271636 0.23 0.23 0.27 0.27 Membership 0.773878 0.758101 0.011562 0.369079 0.08409920 Objectives 4705.3530 865.2177 2896.2670 9.317632 0.23 0.27 0.27 0.23 Membership 0.766604 0.735812 0.013996 0.374095 0.08312687 Objectives 4695.3240 854.0240 2914.5690 9.150059 0.27 0.27 0.23 0.23 Membership 0.702839 0.702342 0.019382 0.355821 0.07828473 Objectives 4708.9530 876.9423 2883.1330 9.297540 0.22 0.22 0.28 0.28 Membership 0.789494 0.770869 0.010130 0.371904 0.08540853 Objectives 4692.1590 848.9002 2923.5310 9.115069 0.28 0.28 0.22 0.22 Membership 0.682715 0.687022 0.022020 0.352005 0.07667442 Objectives 4711.2870 881.0804 2878.8130 9.321701 0.21 0.21 0.29 0.29 Membership 0.804334 0.783242 0.008859 0.374539 0.08666504 Objectives 4711.3310 866.9720 2892.6060 9.467502 0.20 0.30 0.30 0.20 Membership 0.804613 0.741057 0.012918 0.390439 0.08570008 Objectives 4713.2720 867.4395 2891.6940 9.516477 0.19 0.31 0.31 0.19 Membership 0.816954 0.742455 0.012650 0.395780 0.08652723 Objectives 4717.6100 892.8094 2868.5340 9.384129 0.18 0.18 0.32 0.32 Membership 0.844535 0.818312 0.005834 0.381347 0.09014110 Table 5: Comparison of results Cost ($\h) NOx emission (kg/h) SOx emission (kg/h) COx emission (kg/h) Weights 0.1355 0.4247 0.0753 0.3645 Objectives 4660.8120 744.4364 3278.2300 9.624076 Case-I Membership 0.5165955 0.6253272 0.8735867 0.5924862 Weights 0.1737 0.3660 0.0854 0.3748 Objectives 4659.6180 765.2352 3177.3690 9.243322 Case-II Membership 0.5241862 0.5631382 0.9032716 0.6340087 Weights 0.1872 0.4139 0.0861 0.3128 Objectives 4656.6580 756.0088 3223.5530 9.296052 Case-III Membership 0.5430062 0.5907254 0.8896790 0.6282583 Weights 0.5400 0.2070 0.1270 0.1260 Objectives 4650.108 810.5679 3052.2760 8.243926 Brar et al. (2002) Membership 0.5846541 0.4275925 0.9400884 0.7429965 Singh and Dhillon / International Journal of Engineering, Science and Technology, Vol. 1, No. 1, 2009, pp. 272-282 281 Table 6: Generation schedule corresponding to optimal solutions Optimal values Case-I Case-II Case-III Brar et al. (2002) 1 GP (p.u.) 2.4461 2.4235 2.4155 2.2664 2 GP (p.u.) 0.6624 0.5897 0.6256 0.4888 3 GP (p.u.) 1.1012 1.1973 1.1654 1.4428 1 GQ (p.u.) 0.7217 0.7200 0.7171 0.6988 2 GQ (p.u.) 0.7248 0.7351 0.7250 0.7390 3 GQ (p.u.) 0.3942 0.3946 0.3916 0.3808 lossP 0.3635 0.3647 0.3605 0.3618 lossQ 0.5777 0.5872 0.5710 0.5762 7. Conclusions The solution set of the problem is non-inferior due to conflicting nature of the objectives and has been obtained through weighting method. The novel formulation as economic emission dispatch problem has made it possible to quantitatively grasp trade-off relations among conflicting objectives. The trade-off approach is effective only for two objectives, as the number of objectives increases the selection of best solution becomes cumbersome. Exploiting fuzzy set theory an interactive cardinal priority ranking method has been applied to identify the best compromise solution for EED problem, when conflicting objectives are more than two. The major characteristics and advantages of the cardinal priority ranking method are that the cardinal priority ranking functions, which relate the decision makers preference to the non-inferior, solutions though the trade-off functions, are constructed in the functional space and only then are transformed in to the decision space. The proposed method provides interface between the decision maker and the mathematical model through cardinal priority ranking. It also allows explicit trade-off between fuel cost of units with NOx emission, SOx emission and COx emission levels, respectively. Results of the proposed method are compared with Brar et al. (2002). The proposed method gives better results in terms of overall membership satisfaction and real and reactive power losses. Study can be extended by adopting -constraint method or shifted min-max method to generate the non-inferior solution surface. Generally, the weights are either simulated or searched in the non-inferior domain. Evolutionary search technique may be implemented to search the preferred weightage pattern in the non-inferior domain, which may correspond to the best compromised solution. References Abido M.A., 2003. Environmental/economic power dispatch using multiobjective evolutionary algorithms, IEEE Trans on Power Systems, Vol. 18, No. 4, pp. 1529-1537. Basu M., 2002. Fuel constrained economic emission load dispatch using Hopfield neural networks, Electric Power Systems Research, Vol. 63, pp. 51-57. Bath S.K, Dhillon J.S. and Kothari D.P., 2004. Fuzzy satisfying stochastic multi-objective generation scheduling by weightage pattern search methods, Electric Power Systems Research, Vol. 69, pp. 311-320. Brar Y.S., Dhillon J.S., and Kothari D.P., 2002. Multi-objective load dispatch by fuzzy logic searching weightage pattern, Electric Power Systems Research, Vol. 63, pp. 149-160. Chaaban F.B., Mezher T. and Ouwayjan M., 2004. Options for emissions reduction from power plants: an economic evaluation, International Journal of Electrical Power & Energy Systems, Vol. 26, No. 1, pp. 57-63. Chen S.D., and Chen J.F., 2003. A direct NewtonRaphson economic emission dispatch, International Journal of Electrical Power & Energy Systems, Vol. 25, pp. 411-417. El-Keib A.A., Ma H. and Hart J.L., 1994. Economic dispatch in view of clean air Act of 1990, IEEE Trans on Power Systems, Vol. 9, No. 3, pp. 972-978. Hota P.K., Chakrabarti R., and Chattopadhyay D.P.K., 2000. Economic emission load dispatch through an interactive fuzzy satisfying method, Electric Power Systems Research, Vol. 54, pp.151-157. Palanichamy C. and Babu N.S., 2008. Analytical solution for combined economic and emissions dispatch, Electric Power Systems Research, Vol. 78, No. 7, pp. 1129-1137. Ramanathan R., 1994. Emission constrained economic dispatch, IEEE Trans on Power Systems, Vol. 9, No. 4, pp. 1994-2000. Singh and Dhillon / International Journal of Engineering, Science and Technology, Vol. 1, No. 1, 2009, pp. 272-282 282 Talaq J.H., El-Hawary F., and El-Hawary M.E., 1994. A summary of environmental/ economic dispatch algorithms, IEEE Trans on Power Systems, Vol. 9, No. 3, pp.1508-1516. Singh L., Dhillon J.S., and Chauhan R.C., 2006. Evaluation of best weight pattern for multiple criteria load dispatch, Electric Power Components and Systems, Vol. 34, No.1, pp. 21-35. Singh L. and Dhillon J.S., 2008. Secure multiobjective real and reactive power allocation of thermal power units, International Journal of Electrical Power & Energy Systems, Vol. 30, No. 10, pp. 594-602. Tsay M.T., 2003. Applying the multi-objective approach for operation strategy of cogeneration systems under environment constraints, International Journal of Electrical Power & Energy Systems, Vol. 25, pp. 219-226. Biographical notes Lakhwinder Singh is Associate Professor & Head, Department of Electrical Engineering, Baba Banda Singh Bahadur Engineering College, Fatehgarh Sahib. He obtained his B.E. (Electrical) (1990) from Guru Nanak Dev Engineering College, Ludhiana and M.E. (Power Systems) (1995) from Punjab University, Chandigarh. He is currently pursuing Ph.D. from Punjab Technical University, Jallandhar. He is a life member of the Indian Society for Technical Education, and a member of the Institution of Engineers (India). He has published/presented 29 papers in national and international conferences/ journals. His research interests include power system operation, optimization, neural networks and fuzzy logic. J.S.Dhillon is Professor, Electrical and Instrumentation Engineering Department, Sant Longowal Institute of Engineering and Technology, Longowal, India (Deemed University). He received his B.E. (Electrical) (1983) from Guru Nanak Dev Engineering College, Ludhiana, M.E. (Systems) (1987), Punjab Agricultural University, Ludhiana, Ph.D., (1996) Thapar Institute of Engineering & Technology, Patiala. His research activities include Microprocessor applications, Multi-objective thermal dispatch, hydrothermal scheduling, Optimization, Neural Networks, Fuzzy theory and soft computing applications in Power system. A member of The Institution of Engineers (India), Life Member: ISTE, Life Member: SSI, has supervised 02 Ph.D, 13 M.E. scholars and supervising 06 Ph.D. Scholars. He has contributed in these areas as evidenced by 87 research papers published in various national and international Journals/ proceedings. He has co-authored 02 books "Power System Optimization", PHI, Publication, "Principles of Electrical & Electronics Engineering, Kalyani Publication. Received December 2009 Accepted December 2009 Final acceptance in revised form December 2009 MultiCraft International Journal of Engineering, Science and Technology Vol. 1, No. 1, 2009, pp. 283-290 INTERNATIONAL JOURNAL OF ENGINEERING, SCIENCE AND TECHNOLOGY www.ijest-ng.com 2009 MultiCraft Limited. All rights reserved Technical note New table look-up lossless compression method based on binary index archiving R. Rdescu Polytechnic University of Bucharest, 1-3, Iuliu Maniu Blvd, Sector 6, Bucharest, Romania. E-mail: rradescu@atm.neuro.pub.ro, phone: +40-21-402-48-73; fax: +40-21-402-48-21 Abstract This paper intends to present a common use archiver, made up following the dictionary technique and using the index archiving method as a simple and original procedure. The original contribution of the paper consists in the structure of the archived file and in the transformation of the dictionary codes into archived characters. This archiver is useful in order to accomplish the lossless compression for any file types. The application can offer important conclusions regarding the compression performances and the influence of the chosen dictionary over the parameters. Keywords: Archive, data compression, dictionary codes, lossless algorithms 1. Introduction The archivers, using dictionary techniques (Murgan, 1998; Rdescu, 2003a,b), can be very efficient, especially when using some files that have different words which are very often repeated. This happens because of the fact that the archivers generate their dictionaries during the archiving process, this way the program learns new words. Almost all the archiving programs, such as Zip, PKZip, LHArc, ARJ, GZIP, RAR etc., make use of the LZ77 and LZSS algorithms or their variants (Cover, 1991; Storer, 1998; Rao and Yip, 2001; Salomon, 2007, 2008; van Lint, 1992; Sayood, 2005; Pu, 2006; Hankerson et al., 2003; Wayner, 1999, Nelson, 1991) after the files are merged together in a reversible fashion. Because the application can make an archive that contains more files, the archive has to be very well configured, so that during the unpacking of the files it can be separated with lossless information. 2. Structure of the archived file The archived file is composed from header, followed by archived data. The header is formed from general header, followed by n archived file headers, where n is the number of files in the archive. For the beginning, the structure of the general header is presented (an example could be given bit by bit): 3 bytes to store 3 letters (CBA). These letters are used as the identification of the archive. It is very important to verify these characters in order not to let the archiver to try unpacking a file that is not a CBA archive. 2 bytes to store the maximum length of the dictionary. 2 bytes to store the minimum length of the dictionary. 1 byte to store the settings. This byte is used to store 3 binary validation variables: o 1 bit if file has path; o 1 bit if we keep the unpacked size of the file; o 1 bit if file has password. Rdescu / International Journal of Engineering, Science and Technology, Vol. 1, No. 1, 2009, pp. 283-290 284 1 byte to store the length of the password (optional). n bytes to store the password, where n is the length of the password (optional). 2 bytes to store the number of files. After describing the general header, the archiver repeats the following sequence for every new added archive file (archived file header): 2 bytes to store the length of the string that contains the path of the file (optional). s bytes for the s characters of the string that contains the path of the file (optional). 1 byte for the length of the file name. f bytes for the f characters of the file name. 4 bytes to store the unpacked size of the file (optional). 1 byte for the archiver type. Some files can only be copied in the archive, because their archiving would cause the increasing of the file size. 4 bytes for the size of the packed file. nr bytes for the nr characters of the packed file. 3. Packing and unpacking of the files The process of packing and unpacking of files has 2 stages: the transformation of the initial characters into dictionary codes; the transformation of the dictionary codes into archive characters. Transformation of the initial characters into dictionary codes This stage is accomplished using the Lempel-Ziv-Welch (LZW) dictionary compression (Murgan, 1998; Rdescu, 2003a,b,c; Rdescu and Olteanu, 2005; Rdescu and Ene, 2005). Initially, it begins with a 257-word dictionary, i.e., the 256 ASCII characters and a special word that indicates the end of the file. The application allows a dictionary limitation. Therefore, all the values that exceed the minimum size of the dictionary will be deleted every time the maximum size of the dictionary is obtained. The user can set both the maximum and minimum values. According to the chosen values, the number of the packed files and the compression time change. The choice of a too large maximum value of the dictionary results in a very long waiting time, getting a too small dimension improvement. The optimal values for the two limitation dictionary variables are different from one file to another. Transformation of the dictionary codes into archive characters This method relies on tackling from two different perspectives of two strings of numbers, having the same basic table. Dictionary codes greater than 256 elements cannot be written in the archive using only one byte. Therefore, it is necessary to have a 2 bytes space. This space is too large comparing it to the necessary one, especially in the initial phases, where the dictionary has not a large size. From the first steps, the dictionary has a maximum of 512 elements, and the dictionary code can be written on 9 bits from the 16 available bits. Hence, 7 out of the 16 available bits remain unused, meaning almost half of the overall space. Grouping 8 codewords, 8 9 bits = 72 bits are needed. It can be written on 72 bits / 8 bits = 9 bytes, comparing to the 8 codewords 2 bytes (9 bits) = 16 bytes usually needed. Even for dictionary larger than 512 elements, this method will reduce the necessary code to store dictionary codes. Table 1 refers to the transformation of codewords (the dictionary indexes) into archive characters. Initially, it works with a 257-word dictionary (256 characters + 1 end of file control character). Table 1 is, in fact, an example in which the dictionary has a number of words 512. On the first column (423, 137, 481, 45, ) there are the codewords (dictionary indexes), which have values up to 512 that can be written on 9 bits. In order to have the certainty to obtain archive characters (8 bits), 8 codewords are used each time. The codewords (423, 137, 481, 45, ) are binary written on the rows. This means that it will be 1byte (8bits) on each column of the table (the archive characters): (8 codewords) (9 bits) = (9 archive characters) (8 bits) (1) The first row (161, 231, 44, 182, ) contains the character words obtained by transforming every column from binary to the 10th base. For example, 161 = 127+026+125+024+023+022+021+120 (2) It works similarly for the other values: 231, 44, 182, Rdescu / International Journal of Engineering, Science and Technology, Vol. 1, No. 1, 2009, pp. 283-290 285 If the dictionary has between 512 and 1024 words, the procedure is similar, Table I having 8 rows, but one extra column, because every dictionary word needs 10 bits (10 columns): (8 codewords) (10 bits) = (10 archive characters) (8 bits) (3) Table 1. Transformation of dictionary codes into archive characters ARCHIVE CHARACTERS CODEWORDS 161 231 44 182 14 93 152 137 241 423 1 1 0 1 0 0 1 1 1 137 0 1 0 0 0 1 0 0 1 481 1 1 1 1 0 0 0 0 1 45 0 0 0 1 0 1 1 0 1 94 0 0 1 0 1 1 1 1 0 248 0 1 1 1 1 1 0 0 0 176 0 1 0 1 1 0 0 0 0 395 1 1 0 0 0 1 0 1 1 4. Experimental results In order to test the application, different file types are used, so that one can remark the behavior of the archive (Rdescu and Balasan, 2004; Rdescu and Popa, 2004; Rdescu and Harbatovschi, 2006; Rdescu and Balanescu, 2006; Rdescu and Bontas, 2008; Rdescu, 2009). The characteristics of the test files are presented in Table 2. Table 2 Experimental files File type File no. Min. size [KB] Max. size [KB] Total size [B] Average size [B] XLS 6 1.08 84.5 224420 37403 DOC 3 44 77.5 199680 66560 PPS 2 111 179 296960 148480 PAS 6 0.53 1.81 6247 1041 EXE 6 11.4 83.8 195050 32508 RAR 3 16.7 100 163185 54395 BMP 5 1.24 47.5 120148 24030 WAV 4 1.16 78.9 97286 24322 DLL 6 7 69 147968 24661 MID 3 21.5 39.1 86425 28808 Next, the results of the compression are shown according to the maximum size of the dictionary. For the maximum size of 512 words and the minimum size of 256 words, the compression ratio and the packing time are shown in Table 3. Rdescu / International Journal of Engineering, Science and Technology, Vol. 1, No. 1, 2009, pp. 283-290 286 Table 3 Compression ratio and compression time for the parameters (256, 512) File type Size [B] Compression ratio [%] Compression time [s] Compression speed [KB/s] XLS 111924 49.87 42 5.22 DOC 82044 41.09 33 5.91 PPS 257445 86.69 70 4.14 PAS 3717 59.50 2 3.05 EXE 153063 78.47 45 4.23 RAR 163185 100.00 44 3.62 BMP 70209 58.44 24 4.89 WAV 96723 99.42 26 3.65 DLL 108927 73.62 33 4.38 MID 70956 82.10 20 4.22 For the maximum size of 640 words and the minimum size of 256 words, the compression ratio and time are shown in Table 4. Table 4 Compression ratio and compression time for the parameters (256, 640) File type Size [B] Compression ratio [%] Compression time [s] Compression speed [KB/s] XLS 103614 46.17 42 5.22 DOC 79726 39.93 35 5.57 PPS 264199 88.97 73 3.97 PAS 3458 55.35 2 3.05 EXE 152520 78.20 45 4.23 RAR 163185 100.00 45 3.54 BMP 68126 56.70 24 4.89 WAV 96506 99.20 27 3.52 DLL 107743 72.82 34 4.25 MID 69431 80.34 20 4.22 For the maximum size of 768 words and the minimum size of 256 words, the compression ratio and compression time are shown in Table 6. Table 5 Compression ratio and compression time for the parameters (256, 768) File type Size [B] Compression ratio [%] Compression time [s] Compression speed [KB/s] XLS 97888 43.62 42 5.22 DOC 77860 38.99 36 5.42 PPS 266541 89.76 78 3.72 PAS 3414 54.65 1 6.10 EXE 151155 77.50 46 4.14 RAR 163185 100.00 48 3.32 BMP 66000 54.93 25 4.69 WAV 96292 98.98 28 3.39 DLL 106453 71.94 36 4.01 MID 67374 77.96 22 3.84 Rdescu / International Journal of Engineering, Science and Technology, Vol. 1, No. 1, 2009, pp. 283-290 287 For the maximum size of 1024 words and the minimum size of 256 words, the compression ratio and compression time are shown in Table 6. Table 6 Compression ratio and compression time for the parameters (256, 1024) File type Size [B] Compression ratio [%] Compression time [s] Compression speed [KB/s] XLS 90852 40.48 45 4.87 DOC 76123 38.12 39 5.00 PPS 268006 90.25 87 3.33 PAS 3396 54.36 3 2.03 EXE 150764 77.30 48 3.97 RAR 163185 100.00 53 3.01 BMP 64273 53.49 27 4.35 WAV 96056 98.74 30 3.17 DLL 15846 10.71 39 3.71 MID 66594 77.05 23 3.67 The diagrams shown in Figures 14 are obtained from Tables 26. Figure 1. Compression speed [KB/s] for different file types [extensions]. Rdescu / International Journal of Engineering, Science and Technology, Vol. 1, No. 1, 2009, pp. 283-290 288 Figure 2. Compression ratio [%] for different file types [extensions]. Figure 3. Compression time [s] according to the maximum size of the dictionary [KB]. Rdescu / International Journal of Engineering, Science and Technology, Vol. 1, No. 1, 2009, pp. 283-290 289 Figure 4. Compression ratio [%] according to the maximum size of the dictionary [KB]. 5. Conclusion The application described in this paper represents a good example of the way the archive performance and the waiting time are determined in the case of the alternation of the dictionary, making thus easier to understand the dictionary-based lossless compression. At the same time, the indexes archiving method can be very efficiently used not only by specialized archivers (Grupo RAR, 2009), but also in programs that manage information. This method is also recommended for storing the information for long time, where it is necessary only to check periodically the information, because of the good archiving speed. References Cover T.M., Thomas J.A., 1991. Elements of information theory, Wiley, New York. Hankerson D., Harris G.A., Johnson P.D., Jr., 2003. Introduction to information theory and data compression, 2nd Ed., Chapman&Hall/CRC. Nelson M., 1991. The Data compression book, IDG Books Worldwide, Inc. Foster City, CA, USA. Murgan A.T., 1998. Principles of information theory in information engineering and communication engineering, Romanian Academy Press, Bucharest, (in Romanian). Pu I. M., 2006. Fundamental data compression, Elsevier. Rdescu R., 2003. Digital information transmission practical works, Polytechnic Press, Bucharest, (in Romanian). Rdescu R., 2003. Lossless compression methods and applications, Matrix Rom Press, Bucharest, (in Romanian). Rdescu R., 2003. Integrated study system of lossless data compression, Symposium of Educational Technologies on Electronic Platforms in Engineering Higher Education, Technical University of Civil Engineering of Bucharest, 9-10 May 2003, pp. 415-422, Conspress Bucharest, (in Romanian). Rdescu R., Blan I., 2004. Recent results in lossless text compression using the burrows-wheeler transform (BWT), Proceedings of IEEE International Conference on Communications 2004 (COMM04), pp. 105-110, Bucharest, Romania, 3-5 June. Rdescu / International Journal of Engineering, Science and Technology, Vol. 1, No. 1, 2009, pp. 283-290 290 Rdescu R., Popa R., 2004. On the performances of symbol ranking text compression method, Scientific Bulletin of the Politehnica University of Timioara, Romania, Transactions on Electronics and Communications, special issue dedicated to the Electronics and Telecommunications Symposium ETC 2004, 22-23 October 2004, Vol. 49 (63), No. 2, pp. 25-27. Rdescu R., t. Olteanu, 2005. Text and image compression using derived LZW algorithms, EEA Revue of Electro-technique, Electronics and Automatics, Vol. 53, No. 4, pp. 7-10, October-December (in Romanian). Rdescu R., Ene Al., 2005. Interactive learning of lossless compression methods, Proceedings of the Symposium Educational Technologies on Electronic Platforms in Engineering Higher Education (TEPE 2005), Technical University of Civil Engineering of Bucharest, 27-28 May, pp. 211-218, CONSPRESS Publishing House. Rdescu R., Harbatovschi C., 2006. Compression methods using prediction by partial matching, Proceedings of the 6th International Conference Communications 2006 (COMM2006), pp. 65-68, Bucharest, Romania, 8-10 June. Rdescu R., Blnescu C., 2006. Lossless text compression using the star (*) transform, Proceedings of the 6th International Conference Communications 2006 (COMM2006), pp. 69-71, Bucharest, Romania, 8-10 June. Rdescu R., C. Bonta, 2008. Design and implementation of a dictionary-based archiver, Scientific Bulletin, Electrical Engineering Series C, University Politehnica of Bucharest, Vol. 70, Nr. 3, pp. 21-28. Rdescu R., 2009. Transform methods used in lossless compression of text files, Romanian Journal of Information Science and Technology (ROMJIST), Publishing House of the Romanian Academy, Bucharest, Vol. 12, Nr. 1, pp. 101-115. Rao K.R., P.C. Yip (editors), 2001. The transform and data compression handbook, Boca Raton, CRC Press LLC. Salomon D., 2007. Data Compression: The complete reference, 3rd Ed., Springer, Berlin-New York. Salomon D., 2008. Concise Introduction to data compression, Springer. Sayood K. (editor), 2005. Introduction to data compression. 3rd Edition, Morgan Kaufmann Series in Multimedia Information and Systems. Storer J.A., 1998. Data compression: Methods and theory, Computer Science Press. van Lint J.H., 1992. Introduction to coding theory, Springer, Berlin-New York. Wayner P., 1999. Data compression for real programmers, Elsevier. www.rar.com, Grupo RAR (accessed December 2009) Biographical notes Radu Rdescu is an Associate Professor at the Faculty of Electronics, Telecommunications and Information Technology from the Polytechnic University of Bucharest, Romania. In 1992, he registered as a member of the IEEE Information Theory Society. He became PhD in Electronics in 1998. He made traineeships at technical universities in Darmstadt, Germany (1997) and Lyon, France (1999-2000 and 2001-2002, specializing in e-learning). He prepared 15 books and manuals, 14 guides for practical works, 7 new disciplines of study, and over 110 scientific papers. He worked on 24 national and international research projects. He participated in cooperation programs with the Federal Polytechnic School in Lausanne, Switzerland, Sdertrns University of Stockholm, Sweden, and Politecnico di Torino, Italy. In 2006, he was awarded the Creativity Prize at the National Conference on Virtual Learning in Bucharest. His major scientific contributions are in the fields of information theory, signal processing, e-learning systems, computer architecture, peripherals, and multimedia. Received September 2009 Accepted October 2009 Final acceptance in revised form December 2009 International Journal of Engineering, Science and Technology (IJEST) 2009 MultiCraft Limited. All rights reserved No part of this journal may be reproduced by any means, nor transmitted, nor translated into any language without permission of the publisher Published in Nigeria by MultiCraft Limited 14, Olajide Street, Iyana Ipaja, Lagos.