Molecular modeling and rational design of flotation reagents
Pradip *,Beena Rai
Tata Research Development and Design Centre,54B,Hadapsar Industrial Estate,Pune 411013,India
Received 15January 2003;received in revised form 10March 2003;accepted 30June 2003
Abstract
A scientific design methodology based on molecular modeling tools available today is presented for arriving at the most suitable reagent combinations for a given flotation separation problem.The power and the utility of this approach are illustrated with examples taken mainly from our work on mineral flotation with alkyl hydroxamates.D 2003Elsevier B.V .All rights reserved.
Keywords:flotation reagents;atomistic simulation;hydroxamates;phosphonic acids;molecular modeling;force field
1.Introduction
The design and the selection of highly selective flotation reagents for specific applications remain a challenging task.Most commercially successful flo-tation reagents were discovered largely through a trial and error methodology based on educated guesswork and empirical rules of thumb.For the beneficiation of multicomponent,highly disseminated and difficult-to-treat ore deposits,the conventional approaches of reagent design and selection are likely to prove woefully inadequate.Considering the demands made of reagents,namely,cost and efficiency/selectivity,coupled with environmental constraints,the scientific challenge facing the mineral engineers today lies in developing highly selective reagents customized for a specific task (Pradip,1994).Moreover,there is now a perceptible shift in the mining chemicals industry to
design and market tailor-made performance chemi-cals customized for specific applications rather than offer conventional,generic,multipurpose commodity chemicals as flotation reagents (Cappuccitti,1994;Malhotra,1994;Nagaraj et al.,1999).
A critical review of the recent literature on this topic (Somasundaran and Nagaraj,1984;Nagaraj,1988;Pradip,1988,1991,1997,1998;Marabini et al.,1989;Fuerstenau and Herrera-Urbina,1989;Aplan,1994,Fuerstenau,1999a,b;Klimpel,1999;Ackermann et al.,2000;Pradip et al.,2002c)clearly reveals that there is an urgent need to develop a theoretically robust framework that can provide a rational basis for design and selection of novel reagent combinations for specific applications.Some of the important issues that need to be addressed in this context are discussed in the following paragraphs.It is not only intuitively obvious but also borne out by several examples in flotation practices that the selectivity of flotation reagents is determined to a large extent by the molecular recognition phenomena underlying their adsorption at mineral/water interfaces (Pradip,1994).Recognition at a molecular level is a
0301-7516/$-see front matter D 2003Elsevier B.V .All rights reserved.doi:10.1016/S0301-7516(03)00090-5
*Corresponding author.Tel.:+91-20-4042309;fax:+91-20-4042399.
E-mail address:pradip@pune.tcs.co.in (Pradip)https://www.doczj.com/doc/a014156097.html,/locate/ijminpro
Int.J.Miner.Process.72(2003)95–
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fundamental characteristic of several natural systems involving a wide variety of organic–inorganic inter-faces(Mann,1988;Weissbuch et al.,1991;Schneider, 1991;Lehn,1993;Abbott,2001).These molecular recognition mechanisms are essentially based on the structural/stereochemical compatibility between the molecular architecture of the adsorbing reagent and the specific surface structure(the nature and the spatial arrangement of adsorption sites on the surface) of the substrate it is interacting with.The strong templating effects of the surface have been noted and documented in several cases(Black et al.,1991; Davey et al.,1991;Grases et al.,1991;Heywood and Mann,1992;Arad et al.,1993;Bromely et al.,1994; Pradip,1994;Ravishankar et al.,1995;Manne and Gaub,1995;Coveney et al.,2000;Davey and Rebello,2001;Rai and Pradip,2002;Osman and Suter,2002).It is our contention that one can enhance selectivity of flotation reagents by orders of magni-tude if we are able to identify the specific molecular recognition mechanism and design the molecular architecture accordingly for a separation task which involves several competing surfaces for the same flotation reagent.It is therefore imperative to identify and catalogue different kinds of molecular recognition mechanisms controlling selectivity in flotation sepa-ration systems that are already known to be amenable to a high degree of separation efficiencies.This approach should help us in providing a scientific rationale as well as a methodology for designing more selective reagents.It is similar to designing a targeted drug delivery system guaranteeing the required quan-tity of specific drug molecule to reach the desired active site among so many other competing sites in biological systems.
Aforementioned strategy is,however,incomplete since there exists a whole host of molecular recogni-tion mechanisms that can be utilized to design several families of molecular architectures as possible candi-dates for the separation problem under investigation. Since it is not possible to synthesize all possible molecules and test each one of them to confirm their efficacy,there is a need for a sound,robust and,most importantly,a quantitative methodology to screen and shortlist the most promising ones from the list of possible candidates.It is in this context that we have proposed and validated a methodology based on molecular modeling computations.While there are several limitations in this approach and efforts are underway to overcome them,our results suggest that the essence of the underlying molecular recognition phenomena can certainly be captured in the interac-tion energy computations with the available tools.In the absence of any other quantitative methodology for selection,molecular modeling is,in our opinion,the best and most reliable option available at present.
We have made considerable progress in developing a robust and reliable methodology based on molecular modeling tools to arrive at the most promising mo-lecular structures for the separation problems which are considered not amenable to or too complex for a satisfactory solution(Pradip and Rai,2002;Pradip et al.,2002a,b,c).In this communication,we describe the most important components of this novel para-digm of reagent design with the help of our recent work on alkyl hydroxamates,a relatively new family of flotation reagents,commercialized recently in the mining industry.We demonstrate how molecular modeling tools available today can be gainfully employed for reagent design in this area.
Firstly,one can design and screen and thus pro-gressively narrow the focus on preferred molecular architecture for a given mineral surface which is based on the interaction energies computed through a com-pletely theoretical methodology of molecular model-ing.Since this search is based on completely theoretical computations,one can evaluate a much larger number of molecules than what is possible with conventional trial and error experimental approaches.
Secondly,based on similar computations,it is possible to select the most efficient(which may not be the most selective)reagent from several possible candidates for a given separation task involving more than one mineral surface.This selection is based on not the affinity of a reagent for a particular mineral alone,but a comparative assessment of its ability to discriminate among several competing surfaces.The reagent to which a particular mineral responds the best need not be the most preferred choice in case of multicomponent systems.
Several other research groups are currently en-gaged in studying surfactant–surface interactions using molecular modeling tools(Coveney and Humphries,1996;Frank et al.,1996;Goddard et al.,1997;de Leeuw et al.,1998;Hass et al.,1998; Numata et al.,1998;Shevchenko and Bailey,1998;
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de Leeuw and Parker,1999;Hirva and Tikka,2002; Tunega et al.,2002).We present in this paper the results of our molecular modeling computations on the interactions of alkyl hydroxamates with several minerals of interest.Alkyl hydroxamates have been found to be highly selective flotation collectors for a wide variety of minerals such as those containing iron,rare earths,tin and tungsten minerals,kaolin and,more recently,for separation among calcium minerals including flotation separation in apatite–dolomite system(Fuerstenau et al.,1967,1970, 1983;Fuerstenau and Petersen,1969;Gorlovskii et al.,1969;Bogdanov et al.,1973;Evrard and De Cuyper,1975;Fuerstenau and Pradip,1984;Pradip, 1987,1996;Ravishankar et al.,1988;Pradip and Fuerstenau,1989,1991;Pradip et al.,1991,1993, 1995,1997;Yoon et al.,1992;Pradip and Chaudhari, 1997;Lee et al.,1998;Miller et al.,2001a,b).As presented in the following sections,trends predicted on the basis of theoretically computed interaction energies correlate quite well with our own experi-mental microflotation results as well as with those available in the published literature.
2.Molecular modeling methodology
While we have utilized both the quantum chemical and force field(also known as atomistic simulation) computations in our work,for the sake of uniformity, all the results presented in this paper are confined to those obtained with one technique only,that is,the universal force field(UFF)(Casewit et al.,1992a,b, Rappe′et al.,1992,1993).The Cerius2program (Accelrys,USA)has been used to model the sur-face–reagent interactions by UFF.We have success-fully demonstrated through our earlier work that UFF can be used to model the mineral–reagent systems with reasonable accuracy.A detailed methodology of modeling the mineral–reagent interactions has been presented recently(Pradip and Rai,2002;Pradip et al.,2002a,b,c).A brief summary is presented in the following paragraphs.
2.1.Reagent molecule
The UFF-optimized atomic structure of octyl hydroxamic acid molecule along with partial charges on the constituent atoms is shown in Fig.1.The UFF-optimized structural parameters compare well with those reported in literature(Table1).
2.2.Mineral surface
A surface cell was created from the unit cell of the mineral at a given Miller plane(usually the cleavage plane)and then was optimized.The surface energy was then calculated for this optimized mineral surface. For a given mineral,the plane selected was either based on the literature data(experimentally observed cleavage plane)or the surface energy values(the one with lowest surface energy was considered as the cleavage plane).
To establish the validity of the UFF for modeling mineral surfaces,we optimized their crystal struc-tures and compare them with experimental measure-ments(Dana and Ford,1949).As summarized in Table2,the UFF-predicted values of lattice param-eters are in reasonable agreement with those exper-imentally reported.The computed surface energy for corresponding cleavage plane is also included in the
table.
Fig.1.UFF-optimized structure of HXMA-8with partial charges on the constituent atoms.
Pradip,B.Rai/Int.J.Miner.Process.72(2003)95–11097
As an illustration,we present in Fig.2a the opti-mized surface of bastnaesite mineral cleaved at {100}plane.Since the literature data on the cleavage plane for bastnaesite was not available,we computed surface energy values for different miller planes.The {100}plane was found to have the lowest surface energy and hence selected as the cleavage plane for bastnaesite.
The surface cell thus created was extended to a 2D
periodic superlattice of 26?32A
?.The atoms in top eight layers were allowed to relax,while the coordi-nates of those in the bottom eight layers were kept unchanged during optimization.The surface energy for this plane was computed to be 1.5J/m 2.2.3.Mineral–reagent complex
The optimized reagent molecule was then docked on the mineral surface.The initial geometry of sur-face–reagent complex was created physically on the screen with the help of molecular graphics tools,taking into consideration the possible interactions of reagent functional groups with surface atoms.The reagent molecule was then allowed to relax complete-ly on the surface.Several initial conformations (f 20)were assessed so as to locate the minimum energy conformation of the mineral–reagent complex.For illustration,the optimized complex of hydroxa-
Table 1
A comparison of UFF-optimized structural parameters of hydroxa-mic acid with literature values
Bond Length (A
?)UFF
(this work)
Ab initio
(Lipczynska-Kochany and Iwamura,1982)Experimental (Pakkanen et al.,1987)C M O 1.22 1.22 1.25C U N 1.37 1.34 1.30N U O 1.37 1.44
1.41C 1U C 2
1.51 1.51
Other C U C
1.53
Table 2
A comparison of UFF-optimized crystal structures with experimentally observed crystal structures of minerals
Mineral Lattice parameters (in j and A
)Plane
Surface UFF-predicted
Experimental (Dana and Ford,1949)energy (J/m 2)Alumina
a =
b =90,
c =120;a =b =4.32,c =11.64a =b =90,c =120;a =b =4.75,c =12.90{001} 1.22Barite
a =
b =
c =90;a =8.82,b =5.05,c =7.15a =b =c =90;a =8.88,b =5.45,c =7.15{001} 3.00Bastnaesite a =b =90,c =120;a =b =6.97,c =9.26a =b =90,c =120;a =b =7.16,c =9.78{100} 1.50Calcite a =b =90,c =120;a =b =4.92,c =15.5a =b =90,c =120;a =b =4.99,c =17.06{104} 1.50Cassiterite a =b =c =90;a =b =4.69,c =2.87
a =
b =
c =90;a =b =4.74,c =3.19
{110} 1.28Chalcocite a =c =90,b =116.47;a =15.4,b =12.4,c =13.2
a =c =90,
b =116.35;a =15.22,b =11.88,
c =13.5
{110}0.62Chalcopyrite a =b =c =90;a =b =5.93,c =11.52a =b =c =90;a =b =5.29,c =10.43{011}0.79Cerussite a =c =b =90;a =7.54,b =7.18,c =5.67a =c =b =90;a =6.15,b =8.44,c =5.19{110}0.06Covellite a =b =90,c =120;a =b =4.22,c =14.94a =b =90,c =120;a =b =3.79,c =16.34{001} 1.14Dolomite a =b =90,c =120;a =b =5.13,c =18.32a =b =90,c =120;a =b =4.81,c =16.01{101} 1.52Fluorite a =b =c =90;a =b =c =5.13
a =
b =
c =90;a =b =c =5.46
{111}0.80Fluorapatite a =b =90,c =120;a =b =9.22,c =6.52a =b =90,c =120;a =b =9.37,c =6.88{100} 1.20Hematite a =b =90,c =120;a =b =5.13,c =12.76a =b =90,c =120;a =b =5.03,c =13.75{001} 1.09Ilmenite a =b =90,c =120;a =b =5.20,c =14.11a =b =90,c =120;a =b =5.08,c =14.04{003} 3.11Malachite a =c =90,b =97.3;a =8.22,b =11.6,c =3.96
a =c =90,
b =98.8;a =9.50,b =11.97,
c =3.20
{001} 2.25Monazite a =c =90,b =102.6;a =6.79,b =7.01,c =6.30
a =c =90,
b =104.4;a =6.78,b =7.00,
c =6.45
{100}0.16Rutile a =b =c =90;a =b =4.39,c =3.02
a =
b =
c =90;a =b =4.59,c =2.96
{110}0.78Silica
a =
b =90,
c =120;a =b =5.25,c =5.80a =b =90,c =120;a =b =4.91,c =5.40{100} 1.10Smithsonite a =b =90,c =120;a =b =5.36,c =16.59
a =
b =90,
c =120;a =b =4.65,c =15.03{110}0.02Wolframite a =c =90,b =90.31;a =4.53,b =5.79,c =5.29a =c =90,b =90;a =4.83,b =5.74,c =4.99{010}0.54Zircon
a =
b =
c =90;a =b =7.13,c =6.35
a =
b =
c =90;a =b =6.60,c =5.98
{110}
1.53
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Pradip,B.Rai/Int.J.Miner.Process.72(2003)95–11099
Fig.2.(a)Top view of bastnaesite{100}surface.(b)Side view of the optimized complex of HXMA-8on bastnaesite{100}surface.
mate molecule on bastnaesite{100}surface is shown in Fig.2b.
2.4.Simulation details
The partial charges on the atoms were calculated using charge equilibration method(Rappe′and God-dard,1991).The intramolecular van der Waals inter-actions were calculated only between atoms which
were located at distances greater than fourth nearest neighbors.A modified Ewald summation method (Karasawa and Goddard,1989)was used for calcu-lating the nonbonded coulomb interactions,while for van der Waals interactions,a direct cutoff at a distance less than r/2(where r is the length of the simulation cell)was employed.Smart minimizer as implemented in Cerius2was used for geometry optimization.The optimization was considered to be converged when a gradient of0.0001kcal/mol was attained.
2.5.Interaction energy
The interaction energy was calculated for the most likely/favorable conformation using the following equation:
Interaction energyeD ET
?EecomplexTà?EereagentTtEesurfaceT
where E(complex),E(reagent)and E(surface)are the total energies of optimized surface–reagent complex,re-agent molecule and surface cluster,respectively.It is worth noting that the more negative the magnitude of interaction energy(D E),the more favorable is the interactions between the surface and the reagent. Accordingly,the interaction energy obtained is considered to be a quantitative measure of the intensity of interaction and correlated with experi-mentally measured macroscopic properties,as for example,flotation behavior of the system under investigation.
2.6.Effect of aqueous environment
In the current formulation,we cannot easily ac-count for the effect of aqueous environment on mineral–reagent interactions(Pradip et al.,2002a).It is possible,however,to compute separately the interaction energies for water molecules adsorbed on the surface.As the interaction energies for reagents adsorbed on the mineral surface are observed to be more negative than those computed for water,it indirectly supports the generally adopted mechanism of reagent replacing water on the surface.For exam-ple,the interaction energies for hydroxamates on bastnaesite and barite are compared with those for water(Table3).The interaction energies for the flotation reagent binding with the surface were com-puted to be consistently higher than those for water for all the systems reported in this paper.
3.Mineral flotation response and its correlation with theoretically computed interaction energies 3.1.Rare-earth ore minerals
Fuerstenau et al.(1983),Fuerstenau and Pradip (1984)and Pradip and Fuerstenau(1983,1985, 1989,1991)have reported the separation of bastnae-site from associated gangue minerals by flotation. They observed that selectivity of flotation separation of bastnaesite{(Ce,La)FCO3}from calcite(CaCO3) and barite(BaSO4)with hydroxamate collectors is higher than that observed with conventional fatty acids(oleate).It is interesting to note that the theoretically computed interaction energies accurate-ly predict the experimentally observed flotation trends(Fig.3).Bastnaesite responds to hydroxamate flotation more strongly as compared to barite and calcite.
Hydroxamates are known to be excellent flotation collectors for iron minerals.We compare the adsorp-tion data,that is,the equilibrium adsorption isotherms for the four minerals,namely,bastnaesite,hematite (Fe2O3),calcite and barite with corresponding com-Table3
UFF-computed interaction energies for water and hydroxamic acid molecules
Mineral surface Interaction energy(kcal/mol)
Hydroxamic acid Water Bastnaesite{100}à66.4à24.0 Barite{001}à33.0à22.4
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puted interaction energies in Fig.4.The interaction of these four minerals with alkyl hydroxamates is found to be in the following order
Bastnaesite >hematite >calcite >barite
It is precisely the same order as predicted by our molecular modeling computations.It is worth noting
that hydroxamates are even stronger flotation collec-tors for bastnaesite as compared to hematite,as borne out by experimental data also (Fig.4).3.2.Calcium minerals
Separation among calcium minerals is one of the most difficult problems in mineral processing (Finkel-stein,1989;Pradip,1994).We have earlier reported results on this system using different families of flota-tion reagents,namely,oleate,alkyl hydroxamates (HXMA-8),octylimino-bis-methylene diphosphonic acids (IMPA-8)and 1-hydroxy-octylidene-1,1diphos-phonic acid (Flotol-8).Except for oleate,the order of flotation response to the three minerals,namely,fluo-rite (CaF 2),calcite and fluorapatite {Ca 10(PO 4)6F 2},is experimentally observed to be (Pradip and Rai,2002;Pradip et al.,2002a,b):
Fluorite >Calcite >Fluorapatite In case of oleate,the order is:Fluorite >Fluorapatite >Calcite
The order of flotation response to different reagents as predicted on the basis of molecular modeling computations is exactly the same.In fact,even though the reasons for different behavior of oleate from other reagents are still not clear,it is remarkable that the theoretical molecular modeling computations do predict the observed trends correctly in all cases (Table 4).
Even more significant is the result of our molecular modeling computations with respect to fluorapatite–dolomite {(Ca,Mg)CO 3}system.The interaction en-ergy calculations presented in Fig.5clearly indicate that alkyl hydroxamates are likely to be more selective reagents as compared to conventional oleate collec-tors.The gap is much higher for hydroxamate.While we ourselves have done considerable work to confirm this important finding,we have chosen to compare in Fig.5our theoretical predictions with recent experi-mental results reported by Miller et al.who have demonstrated that alkyl hydroxamates are more selec-tive collectors than fatty acids for the flotation sepa-ration of fluorapatite from dolomite (Miller et al.,2001a,b)
.
Fig.3.(a)Flotation recovery of bastnaesite,calcite and barite as function of molar concentration of HXMA-8.(b)UFF interaction energy of HXMA-8on bastnaesite {100},calcite {104}and barite {001}surfaces (experimental data taken from Pradip,1981).
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3.3.Rutile–hematite separation
We had earlier explored the utility of alkyl hydrox-amates for the flotation separation of rutile (TiO 2)from hematite,but our efforts were not successful (Fig.6).Subsequent molecular modeling computa-tions indicated that energies are comparable for both hematite and rutile interacting with alkyl hydroxa-mates as well as for the conventional oleate collector (Fig.6).It is therefore not surprising to find no selectivity in separation with these collectors.More significantly,our molecular modeling com-putations with another family of reagents,that is,octylimino-bis-methylene diphosphonic acids (IMPA-8),revealed that it is likely to be a more selective collector than oleate and hydroxamate for the flotation separation of hematite from its mixtures with rutile (Fig.6).While the interaction energies are similar in magnitude for oleate and hydroxamate interactions with these minerals,there is a substantial gap in interaction energies in case of IMPA-8.It is interesting therefore to cite the work of Collins et al.(1984)
who,
Fig.4.A comparison of equilibrium adsorption isotherms for octyl hydroxamate adsorption on bastnaesite,hematite,calcite and barite minerals with the corresponding interaction energy computed using UFF for HXMA-8adsorption on bastnaesite {100},hematite {001},calcite {104}and barite {001}surfaces (experimental data taken from Pradip,1987).
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102
working with Briquest 2N81-25S (same as IMPA-8),did observe this selectivity.Our predictions are con-sistent with their results,reproduced in Fig.9along with corresponding interaction energies computed by us.The shaded portion in the figures represents the region where flotation was observed by these authors.
Table 4
A comparison of experimentally observed flotation trends with those predicted on the basis of molecular modeling computations Reagent Computed UFF interaction energy (kcal/mol)Order of flotation
Fluorite (F)Calcite (C)Fluorapatite (A)response (experimental)IMPA-8à191.4à100.5à87.9F>C>A Flotol-8à198.2à128.5à120.7F>C>A HXMA-8à64.6à44.0à43.3F>C>A Oleate
à52.6
à40.2
à46.8
F>A>C
The number 8represents the alkyl chain length (8carbon)of the reagent.
Experimental data taken from Pradip et al.(2002a,b)for HXMA-8,Flotol-8and IMPA-8and Pugh and Stenius (1985)for
oleate.
Fig.5.Microflotation recovery of francolite (Florida apatite)and dolomite as a function of Aero 6493,a commercial hydroxamic acid from Cytec,USA (top left),FA/FO (commercial fatty acids/fuel oil reagent combination in 7:3ratio by volume)addition at natural pH 6.5,with distilled water (top right)and its corresponding UFF interaction energies for fluorapatite {100}and dolomite {110}surfaces interacting with octyl hydroxamic acid (bottom left)and oleate (bottom right),respectively (experimental data taken from Miller et al.,2001b ).
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103
3.4.Copper–lead–zinc ore minerals
Alkyl hydroxamates have been demonstrated to be excellent auxiliary collectors (in combination with conventional xanthates)for the beneficiation of mixed sulfide-oxide ore deposits.The work of Pradip et al.(1991,1995),including the final plant trials at Malanjkhand Copper Ore Beneficiation plant in India,clearly proved that xanthate–hydroxamate combina-tion is an excellent choice for this task.Copper recovery in the plant could be enhanced by as much as 10%in case of an oxidized ore reporting to the milling circuit.The ore contained significant amounts of secondary sulfides and oxide minerals of copper such as malachite {CuCO 3,Cu(OH)2},chalcocite (Cu 2S)and covellite (CuS)in addition to chalcopyrite {(Cu,Fe)S 2}.Minerals other than chalcopyrite are not
recovered with xanthate,but the addition of alkyl hydroxamates facilitates flotation of these minerals along with chalcopyrite.More recently,Lee et al.(1998)also reported their experiences with xan-thate–hydroxamate reagent combination in the bene-ficiation of similar ores.It was therefore of interest
to
Fig.6.Flotation recovery of rutile and hematite with oleate and hydroxamate collectors,respectively.Corresponding interaction energies for rutile {110}and hematite {001}surfaces with octyl hydroxamic acid and oleate are shown in the insert (experimental data taken from Pradip et al.,2003).
Table 5
Correlation of UFF-computed interaction energies on copper minerals with their experimental flotation response for hydroxamates Mineral
Interaction energy (kcal/mol)Dosage required for 100%flotation during Hallimond tube tests (g/l)(pH 9F 0.2)Chalcopyrite à54.5f 1Chalcocite à34.020Malachite
à34.0
34
Experimental data taken from Pradip et al.(1990)
.
Fig.7.Flotation recovery of malachite,cerussite and smithsonite with hydroxamate collector.Corresponding interaction energies for malachite {001},cerussite {110}and smithsonite {110}surfaces with octyl hydroxamic are shown in the insert (experimental data taken from Pradip et al.,1997).
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104
carry out molecular modeling computations on the interaction of hydroxamates with different copper minerals.The interaction energies are compared in Table5with corresponding microflotation results wherever available.
We have also studied the flotation of oxidized sulfide ores containing copper,lead and zinc minerals. We have reported earlier on the possibility of selective separation among malachite,cerussite(PbCO3)and smithsonite(ZnCO3)minerals using modified SALO compounds(Das et al.,1995)and alkyl hydroxamates (Pradip et al.,1997).Our experimental flotation results with potassium octyl hydroxamate indicated that malachite is significantly more amenable to hydroxamate flotation as compared to cerussite and smithsonite(Fig.7).The corresponding interaction energies computed for this system using UFF are also plotted in the same figure.There is a reasonable agreement between theoretically computed trends and experimental observations.
4.Customized flotation collectors for ore beneficiation:reagent interaction with other constituent minerals
The discussion in the preceding section demon-strates how molecular modeling computations can be utilized as a valuable quantitative measure to grade/ shortlist/screen various possible reagents(either designed or available off the shelf)for the flotation of a particular mineral.For instance,on the basis of above-mentioned computations,one can rule out certain family of reagents/certain molecular architec-tures for the flotation of a particular mineral and narrow down the choice.One can establish important structure–property relationships also based on these computations.
4.1.Fluorite ore
The next question to be addressed,in case of multicomponent ores,is whether the particular reagent is adequately selective against other gangue minerals present.One can prepare the comparative selective data for each reagent of interest for a particular set of mineral constituents of the ore based on molecular modeling computations without carrying out experi-ments.An illustrative set of nine minerals and three different reagents,for the beneficiation of fluorite ore, is shown in Fig.8as an example.Significant differ-ences are observed between the response of different minerals to the selected reagents,namely,oleate, hydroxamate and diphosphonic acids(IMPA-8).For example,based on the theoretical computational results presented in Fig.8,by virtue of having larger difference in the interaction energies for fluorite vis-a`-vis other minerals,one would prefer hydroxamate and IMPA-8for the flotation separation of fluorite from other minerals as compared to oleate.Even though it is selective against all minerals included in study,
IMPA-Fig.8.A comparison of UFF interaction energies computed for a set of nine minerals with three reagents,namely,oleate,hydroxamate and IMPA-8.
Pradip,B.Rai/Int.J.Miner.Process.72(2003)95–110105
8will not be the best choice in case ores contain barite. Barite is found to have interaction energies comparable to fluorite in case of IMPA-8.Hydroxamates,however, are likely to be more selective against barite.Our flotation results did confirm these findings.
One can thus prepare a comparative chart for all the mineral constituents of an ore and for each reagent under consideration.This information can be used to select/design the preferred reagent combination for best separation.4.2.Wolframite ore
Beneficiation of tungsten ores typically involves selective flotation of wolframite[(Fe,Mn)WO4]from its associated gangue minerals such as silicates,fluo-rite,hematite,barite,calcite,apatite and rutile(Pradip, 1996).One thus needs to design a powerful collector capable of not only floating wolframite effectively, but also being sufficiently selective against other minerals present in the ore.We present in the
follow-
ing paragraph our molecular modeling prediction/ results for a set of reagents selected for this study and a comparison of the predicted selectivity trends with experimental results.
The microflotation tests indicated that wolframite exhibited comparable flotation response with all three common family of reagents,namely,oleate,hydrox-amate and IMPA(Pradip and Chaudhari,1997).This finding is consistent with the magnitude of the inter-action energies computed for wolframite for these reagents(Fig.8).The choice of the reagent for wolframite flotation for a given ore,however,will depend on the selectivity of that reagent with respect to other constituent minerals.We illustrate the meth-odology with the help of experimental data available with respect to IMPA-8.
Collins et al.(1984)reported on the flotation of different minerals using IMPA reagents.Fig.9shows a correlation of our computed interaction energies with experimental flotation results of Collins et al. The shaded regions define the flotation limits in terms of dosage and pH for various minerals as reported by the authors.The closely shaded regions represent excellent flotation and the less densely shaded signifies relatively less flotation.Our interac-tion energy values are in general agreement with the experimentally observed trends.For example,in the absence of experimental data,we would have still predicted the flotation response to be in the order barite f fluorite>hematite f calcite>apatite>wolf-ramite H rutile>silica.
Based on such computations,therefore,one can a priori rule out IMPA-8for wolframite flotation for an ore containing fluorite,barite and hematite as constit-uent minerals.IMPA-8on the other hand will prove to be an excellent collector for ores containing wolfram-ite in association with rutile and silica.One can thus play around with the molecular architecture as well as the nature of different functional groups to shortlist the preferred molecules suitable for separation of wolframite from other constituent minerals present in the ore under investigation.The same exercise if carried out through conventional approach will be quite costly both with respect to resources as well as time and thus may not be even attempted.With the proposed methodology,however,we can very conve-niently simulate the anticipated flotation separation selectivity with a wide variety of reagents.We need to synthesize/test only those which meet the desired criteria.
5.Concluding remarks
As demonstrated through several examples in this paper,the proposed molecular modeling-based meth-odology provides a convenient and efficient means of screening the most promising molecules from a set of large number of possible candidates for a given application.Since this approach does not involve any experimentation except for validation,the search/design space can thus be enlarged immensely. One can conceive of several new families of molec-ular architectures for the separation problem under investigation and ultimately shortlist a few for detailed study and experimentation.The same technique can be extended to optimization of the molecular structure till we arrive at an acceptable level of efficiency and/ or cost criteria.Considering the usual constraints of time and resources,the proposed approach should certainly reduce the time to develop new tailor-made products for targeted applications in the mineral industry.
With rapid advances being made in the field of molecular modeling in terms of being able to con-duct molecular dynamics simulation of realistic sys-tems consisting of hundreds of molecules and perhaps thousands of atoms,in the very near future, it would be possible to model,simulate and thus predict much more subtle effects of molecular archi-tecture on the self-assembly at interfaces.This would certainly facilitate the design of highly selective reagents.The whole field of reagents design is thus on the threshold of a major breakthrough.A para-digm shift from conventional trial and error method-ology to a more rational design of reagents based on molecular modeling tools appears to be on the horizon.
Acknowledgements
Authors are grateful to Prof.Mathai Jospeh for his constant support and encouragement during the course of this work and to Prof.P.C.Kapur for critically reviewing the manuscript.
Pradip,B.Rai/Int.J.Miner.Process.72(2003)95–110107
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DTSD106-M100三相电子式电能表 用户使用手册山东力创科技股份有限公司
1.概述 DTSD106-M100三相电子式电能表是山东力创科技股份有限公司集多年的电能计量产品设计经验，所推出的新一代导轨式安装的微型电能表。 该电能表采用LCD显示，可显示三相电压、三相电流、总有功功率、总功率因数及总有功电能，并具有电能脉冲输出功能；可用RS485通讯接口与上位机实现数据交换，极大的方便用电自动化管理。 该电能表具有体积小、安装方便等优点，且具有极高的精度和良好的EMC性能，符合国标 GB/T17215中电子式电能表的相关技术要求。 2.产品规格. 表1：模块产品规格 型号类型等级电压输入电流输入脉冲常数（imp/kWh）DTSD106-M100 直接接入0.5级3*220/380 1.5(6) 4800 3.技术参数 表2：模块技术参数及指标 性能参数 输入测量显示 电 压 测量范围0.7Un～1.2Un 精度RMS精度:0.2级 电 流 量程5A 精度RMS测量（电流精度:0.2级） 频率三相交流50/60Hz电压、电流；输入频率：45～75Hz 功率有功精度: 0.5级；无功精度：1级 功率因数功率因数精度: 0.5级； 电供电电源取自相电压，120～264Vac（50/60Hz）,无需外供电
源 功耗 <2W 输 出 可 编 程 通 讯 输出接口 RS-485接口，二线制，±15kV ESD 保护 通讯规约 标准MODBUS-RTU 通讯规约，DLT645规约 数据格式 可设置；10位，1位起始位0，8位数据位，1位停止位1；或11位，为奇、偶或无校验可软件设置； 通讯速率 BPS1200、2400、4800、9600、19.2k ，可设置 显示 LCD 段码显示 有功电能脉冲输出 脉冲宽度80ms ±20ms ，光隔离 环 境 工作环境 工作温度：-20～70℃ 存储环境 存储温度：-30～80℃ 相对湿度 相对湿度≤90%不结露 安 全 耐压 输入和输出>1kV 输出和输出>1kV 绝缘 输出、输入和电源对外壳>5M 4. 安装与接线 4.1 外形尺寸：单位（mm ） 正视图 侧视图 图1 DTSD106-M100三相电子式电能表外形图 4.2 安装方式 DTSD106-M100三相电子式电能表采用35mm 标准导轨式安装方式，如下图
DES算法实验报告 姓名：学号：班级： 一、实验环境 1．硬件配置：处理器（英特尔Pentium双核E5400 @ 2.70GHZ 内存：2G） 2．使用软件： ⑴操作系统：Windows XP 专业版32位SP3(DirectX 9.0C) ⑵软件工具：Microsoft Visual C++ 6.0 二、实验涉及的相关概念或基本原理 1、加密原理 DES 使用一个 56 位的密钥以及附加的 8 位奇偶校验位，产生最大 64 位的分组大小。这是一个迭代的分组密码，使用称为 Feistel 的技术，其中将加密的文本块分成两半。使用子密钥对其中一半应用循环功能，然后将输出与另一半进行“异或”运算；接着交换这两半，这一过程会继续下去，但最后一个循环不交换。DES 使用 16 个循环，使用异或，置换，代换，移位操作四种基本运算。 三、实验内容 1、关键代码 ⑴子密钥产生
⑵F函数以及加密16轮迭代 2、DES加密算法的描述及流程图 ⑴子密钥产生 在DES算法中，每一轮迭代都要使用一个子密钥，子密钥是从用户输入的初始密钥产生的。K是长度为64位的比特串，其中56位是密钥，8位是奇偶校验位，分布在8，16，24，32，40，48，56，64比特位上，可在8位中检查单个错误。在密钥编排计算中只用56位，不包括这8位。子密钥生成大致分为：置换选择1（PC-1）、循环左移、置换选择2（PC-2）等变换，分别产生16个子密钥。 DES解密算法与加密算法是相同的，只是子密钥的使用次序相反。 ⑵DES加密算法 DES密码算法采用Feistel密码的S-P网络结构，其特点是：加密和解密使用同一算法、
DTSD106-M100三相电子式电能表 用户使用手册山东力创科技股份有限公司
1.概述 DTSD106-M100三相电子式电能表是山东力创科技股份有限公司集多年的电能计量产品设计经验，所推出的新一代导轨式安装的微型电能表。 该电能表采用LCD显示，可显示三相电压、三相电流、总有功功率、总功率因数及总有功电能，并具有电能脉冲输出功能；可用RS485通讯接口与上位机实现数据交换，极大的方便用电自动化管理。 该电能表具有体积小、安装方便等优点，且具有极高的精度和良好的EMC性能，符合国标 GB/T17215中电子式电能表的相关技术要求。 2.产品规格. 型号类型等级电压输入电流输入脉冲常数（imp/kWh）DTSD106-M100 直接接入0.5级3*220/380 1.5(6) 4800 3.技术参数 性能参数 输入测量显示 电 压 测量范围0.7Un～1.2Un 精度RMS精度:0.2级 电 流 量程5A 精度RMS测量（电流精度:0.2级） 频率三相交流50/60Hz电压、电流；输入频率：45～75Hz 功率有功精度: 0.5级；无功精度：1级 功率因数功率因数精度: 0.5级； 电源供电电源取自相电压，120～264Vac（50/60Hz）,无需外供电功耗<2W 输出可编程 通 讯 输出接口RS-485接口，二线制，±15kV ESD保护 通讯规约标准MODBUS-RTU通讯规约，DLT645规约 数据格式可设置；10位，1位起始位0，8位数据位，1位停止 位1；或11位，为奇、偶或无校验可软件设置； 通讯速率BPS1200、2400、4800、9600、19.2k ，可设置 显示LCD段码显示 有功电能脉冲输出脉冲宽度80ms±20ms，光隔离 环境工作环境工作温度：-20～70℃存储环境存储温度：-30～80℃相对湿度相对湿度≤90%不结露 安全耐压输入和输出>1kV 输出和输出>1kV 绝缘输出、输入和电源对外壳>5M
成都信息工程学院课程设计报告 AES加密解密软件的实现 课程名称：应用密码算法程序设计 学生姓名：樊培 学生学号：2010121058 专业班级：信息对抗技术101 任课教师：陈俊 2012 年6月7日
课程设计成绩评价表
目录 1、选题背景 (4) 2、设计的目标 (4) 2.1基本目标： (4) 2.2较高目标： (5) 3、功能需求分析 (5) 4、模块划分 (6) 4.1、密钥调度 (6) 4.2、加密 (8) 4.2.1、字节代替（SubBytes） (8) 4.2.2、行移位（ShiftRows） (10) 4.2.3、列混合（MixColumn） (11) 4.2.4、轮密钥加（AddRoundKey） (13) 4.2.5、加密主函数 (14) 4.3、解密 (16) 4.3.1、逆字节替代（InvSubBytes） (16) 4.3.2、逆行移位（InvShiftRows） (17) 4.3.3、逆列混合（InvMixCloumns） (17) 4.3.4、轮密钥加（AddRoundKey） (18) 4.3.5、解密主函数 (18) 5.测试报告 (20) 5.1主界面 (20) 5.2测试键盘输入明文和密钥加密 (20) 5.3测试键盘输入密文和密钥加密 (21) 5.3测试文件输入明文和密钥加密 (22) 5.4测试文件输入密文和密钥加密 (22) 5.5软件说明 (23) 6.课程设计报告总结 (23) 7.参考文献 (24)
1、选题背景 高级加密标准（Advanced Encryption Standard，AES），在密码学中又称Rijndael加密法，是美国联邦政府采用的一种区块加密标准。这个标准用来替代原先的DES，已经被多方分析且广为全世界所使用。经过五年的甄选流程，高级加密标准由美国国家标准与技术研究院（NIST）于2001年11月26日发布于FIPS PUB 197，并在2002年5月26日成为有效的标准。2006年，高级加密标准已然成为对称密钥加密中最流行的算法之一。该算法为比利时密码学家Joan Daemen和Vincent Rijmen所设计，结合两位作者的名字，以Rijndael 之命名之，投稿高级加密标准的甄选流程。（Rijndael的发音近于 "Rhine doll"） 严格地说，AES和Rijndael加密法并不完全一样（虽然在实际应用中二者可以互换），因为Rijndael加密法可以支援更大范围的区块和密钥长度：AES的区块长度固定为128 位元，密钥长度则可以是128，192或256位元；而Rijndael使用的密钥和区块长度可以是32位元的整数倍，以128位元为下限，256位元为上限。加密过程中使用的密钥是由Rijndael 密钥生成方案产生。大多数AES计算是在一个特别的有限域完成的。 截至2006年，针对AES唯一的成功攻击是旁道攻击 旁道攻击不攻击密码本身，而是攻击那些实作于不安全系统（会在不经意间泄漏资讯）上的加密系统。2005年4月，D.J. Bernstein公布了一种缓存时序攻击法，他以此破解了一个装载OpenSSL AES加密系统的客户服务器[6]。为了设计使该服务器公布所有的时序资讯，攻击算法使用了2亿多条筛选过的明码。有人认为[谁？]，对于需要多个跳跃的国际互联网而言，这样的攻击方法并不实用[7]。 Bruce Schneier称此攻击为“好的时序攻击法”[8]。2005年10月，Eran Tromer和另外两个研究员发表了一篇论文，展示了数种针对AES的缓存时序攻击法。其中一种攻击法只需要800个写入动作，费时65毫秒，就能得到一把完整的AES密钥。但攻击者必须在执行加密的系统上拥有执行程式的权限，方能以此法破解该密码系统。 虽然高级加密标准也有不足的一面，但是，它仍是一个相对新的协议。因此，安全研究人员还没有那么多的时间对这种加密方法进行破解试验。我们可能会随时发现一种全新的攻击手段会攻破这种高级加密标准。至少在理论上存在这种可能性。 2、设计的目标 2.1基本目标： （1）在深入理解AES加密/解密算法理论的基础上，能够设计一个AES加密/解密软件系统，采用控制台模式，使用VS2010进行开发，所用语言为C语言进行编程，实现加密解密； （2）能够完成只有一个明文分组的加解密，明文和密钥是ASCII码，长度都为16个字符(也就是固定明文和密钥为128比特)，输入明文和密钥，输出密文，进行加密后，能够进
单相导轨式电能表 安装使用说明书V2.0 上海燕赵电子科技有限公司 目录 一、概述 (2) 二、产品规格 (2) 三、技术参数 (2) 四、安装与接线 (3) 4.1、安装外形图及接线图 (3) 4.1.1、外形及尺寸 (3)
4.1.2、安装图 (4) 4.1.3、接线图 (4) 4.2 安装注意事项及方法 (4) 5 .................................................................................................................................. . 五、使用说明5.1、面板格式.. (5) 5.2、功能说明 (5) 5.3 显示说明： (6) 5.3.1 经济型表 (6) 5.3.2 标配型表 (7) 5.3.3 高配型电能表 (9) 六、电能脉冲 (10) 七、典型应用 (11) 订货规范 (12) 一、概述 导轨式电能表是本公司集多年的电表设计经验，所推出的新一代微型电能表。配备经济型、标准型、高配型三种型号以便于用户在不同场合下使用。 该电能表采用LCD显示，可进行时钟、费率时段等参数设置，并具有电能脉冲输出功能；可用RS485通讯接口与上位机实现数据交换，极大地方便了用电自动化管理。 该电能表具有体积小、精度高、可靠性好、安装方便等优点，性能指标符合国标GB/T17215、GB/T17883和电力行业DL/T614对电能表的各项技术要求。 二、产品规格 产品系列型号精度等级额定电压电流规格脉冲常数 12800imp/kW-h 1.5(6)A 经济型3200imp/kW-h 5(20)A 单相导轨220V 1.0 1600imp/kW-h 电能表10(40)A 标配型800imp/kW-h 20(80)A 高配型 三、技术参数 技术指标项目经济型标配型高配型级2.0级，无功：1.0有功：精度等级． 220V 额定电压1.5(6)A、5(20)A、10(40)A、20(80)A 电流规格 正常工作电压范围：0.9～1.1Un 工作电压极限工作电压范围：0.7～1.2Un 50Hz 或参比频率60Hz 0.004Ib 直接接入起动电0.002In 流CT接入经≤5VA/电压线路相功耗<4VA/ 电流线路相脉冲宽度：80ms±20ms；光耦隔离，集电极开路输出电能脉冲输出
package des; /** * 加密过程： * 1.初始置换IP：将明文顺序打乱重新排列，置换输出为64位。 * 2.将置换输出的64位明文分成左右凉拌，左一半为L0，右一半称为R0，各32位。 * 3。计算函数的16轮迭代。 * a）第一轮加密迭代：左半边输入L0，右半边输入R0：由轮函数f实现子密钥K1对R0的加密， * 结果为32位数据组f(R0,K1), * b)第二轮加密迭代：左半边输入L1=R0，右半边输入R1=L0⊕f(R0,K1),由轮函数f实现子密钥 * K2对R1的加密，结果为32位数据组f(R1，K2)，f(R1,K2)与L1模2相加，得到一个32为数据组L1⊕f(R1,K2). * c)第3到16轮迭代分别用密钥K3，K4……K16进行。4.再经过逆初始置换IP-1，将数据打乱重排，生成64位密文。 * * 子密钥生成过程： * 1.将64位的密钥经过PC-1置换生成56位密钥。 * 2.将56位的密钥分成左右两部分，分别进行移位操作（一共进行16轮），产生16个56位长度的子密钥。 * 3.将16个56位的子密钥分别进行PC-2置换生成16个48位的子密钥。 * * 轮函数f的工作过程： * 1.在第i次加密迭代过程中，扩展置换E对32位的Ri-1的各位通过置换表置换为48位的输出。 * 2.将该48位的输出与子密钥Ki进行异或操作，运算结果经过S盒代换运算，得到一个32位比特的输出。 * 3。该32位比特输出再经过P置换表进行P运算，将其各位打乱重排，生成32位的输出。 * * author Ouyang * */ public class Des { int[] byteKey; public Des(int[] byteKey) { this.byteKey = byteKey; } private static final int[] IP = { 58, 50, 42, 34, 26, 18, 10, 2, 60, 52, 44, 36, 28, 20, 12, 4, 62, 54, 46, 38, 30, 22, 14, 6, 64, 56, 48,
DIN导轨式单相电子式电能表使用说明书 1．用途及适用范围 DIN导轨式单相电子式电能表系我公司采用微电子技术计量电能，采用进口专用大规模集成电路，应用数字采用处理技术及SMT工艺等先进技术研制开发的新型单相全电子式电能表。该电表完全符合GB/T17215.321-2008国家标准及IEC62053国际标准中1级或2级单相电能表的相关技术要求，可直接精确地测量电能计量中正向有功电能，由7+1位LCD显示总用电量；具有可靠性高、体积小、重量轻、外表美观、工艺先进、35mmDIN标准导轨方式安装等特点；并具有良好的抗电磁干扰、低工耗节电、高精度、 高过载、高稳定性、防窃电。长寿命。-由乐清市黔兴电气有限公司提供 该表适用于计量额定频率为50Hz或60Hz的单相交流有功电能、供固定安装在室内使用，适用于环境温度不超过-25℃～+55℃，相对湿度不大于95%，且空气中不含有腐蚀气体及避免尘沙、霉菌、盐雾、凝露、昆虫等影响。 2．主要规格及技术参; 2.1．电能表规格： 规格 型号 准确度额定电压 Ub 额定电流（A） 1级220V 5(20)A,5(30)A,5(32)A 2级 注：额定电流栏中，括号前的数值为额定电流值Ib，括号内的数值为额定最大电流值Imax。 2.2.技术参数： 2.2.1 误差限 电流值功率因数 （COSΦ）百分数误差限（%） 直接接入经互感器接入1级2级 0.05Ib 0.02Ib 1 ±1.5 ±2.5 0.1Ib 0.05Ib 0.5L ±1.5 ±2.5 0.8C ±1.5 --- 0.1Ib～Imax 0.05Ib～Imax 1 ±1.0 ±2.0 0.2Ib～Imax 0.1Ib～Imax 0.5L ±1.0 ±2.0 0.8C ±1.0 --- 2.2.2 电动 在额定电压、额定频率及COSΦ=1的条件下，当电能表负载电流为下规定值时，电能表起动并连续计量电能。 仪表类型准确度1级准确度2级 直接接入式0.004Ib 0.005Ib 2.2.3 潜动 当电能表的电流线路中无电流，而加于电压线路的电压为额定值的115%时，电能表的测试输出不应产生多于一个的脉冲。 2.2.4 绝缘性能 电能表的所有线路对外壳间能经受波形为1.2/50μS，峰值为6KV的冲击电压，在相同极性下试验10次，不出现电弧放电或击穿现象。 电能表的所有线路对地绝缘能经受频率为50Hz的实际正弧形的交流电压2KV，历时一分钟试验不击
DT(S)S三相电子式有功电能表导轨表产品安装使用说明书
浙江天普胜电气有限公司 1.概述 DT（S）S型三相电子式电能表，使用在供电部门、工厂、企业、商业、农业的动力、照明设备的有功电能计量，该表采用进口专用大规模集成电路，16位A/D转换、数字乘法器、数字采样处理技术及SMT工艺，根据工业用户实际用电状况所设计、制造的新型电能仪表。用于计量频率为50Hz的交流三相有功电能,并电量采用液晶显示，读数直观便于抄表,本仪表可扩展RS485通讯功能，为三相电能测量提供先进、可靠的计量工具。 本仪表采用的计量模块能精确地测量三相正反双向有功电能。计量准确、稳定、可靠、具有良好的抗电磁骚扰、低功耗、高准确度、防窃电、宽量程、长寿命等特点。 本仪表符合标准DL/T 645-1997《多功能表通讯规约》和GB/T 17215-2002《１级和２级静止式交流有功电能表》以及IEC 61036:2000。 2. 工作原理
本电能表采用三相供电方式，并在任一单相供电情况下都能正常工作。 电能表工作时，电压、电流经取样电路分别取样后，送至大规模专用集成电路缓冲放大，再由16位A/D转换器转换成数字信号，经数字乘法器运算后，转换成和电能大小成正比的脉冲，驱动计度器进行电能累加和驱动脉冲指示灯，并输出脉冲，供计量。 3. 规格型号及技术指标 3.1 规格型号 3.2基本误差（平衡负载基本误差极限）
注：(Ib为基本电流In为额定电流Imax为最大电流) 不平衡负载各电流点的百分数误差极限为1级表±2.0，2级表±3.0。 3.3 起动 在参比电压、参比频率及功率因数为1.0的条件下，负载电流为0.004Ib(1级)、0.005Ib(2级)仪表应能起动，并连续计量电能。 3.4 潜动 电压回路加1.15倍的参比电压，电流线路中无电流时，仪表的测试输出不应产生多于一个的脉冲。 3.5 工作电压范围 正常工作电压：0.9Un～1.1Un 极限工作电压：0.0Un～1.15Un 3.6 功耗 电压线路功耗：≤2W(10VA) 电流线路功耗：≤4.0VA 3.7 环境条件 正常工作温度：-10℃～+45℃, 工作极限温度：-25℃～+55℃, 存贮和运输温度：-25℃～+70℃ 相对湿度：年平均≤75% 3.8 外形尺寸：228mm×145mm×72mm； 3.9 重量：约1.5kg； 3.10安全性能：产品符合GB/T 17215-2002规定的各项安全指标和要求。 3.11 电量显示采用7位液晶显示,6位整数,1位小数。
DES加解密算法的实现 一、DES算法的概述 DES（Data Encryption Standard）是由美国IBM公司于20世纪70年代中期的一个密码算（LUCIFER）发展而来，在1977年1月15日，美国国家标准局正式公布实施，并得到了ISO的认可，在过去的20多年时间里，DES被广泛应用于美国联邦和各种商业信息的保密工作中，经受住了各种密码分析和攻击，有很好的安全性。然而，目前DES算法已经被更为安全的Rijndael算法取代，但是DES 加密算法还没有被彻底的破解掉，仍是目前使用最为普遍的对称密码算法。所以对DES的研究还有很大价值，在国内DES算法在POS、ATM、磁卡及智能卡（IC卡）、加油站、高速公路收费站等领域被广泛应用，以此来实现关键的数据保密，如信用卡持卡人的PIN码加密传输，IC卡与POS机之间的双向认证、金融交易数据包的MAC 校验等，均用到DES算法。 DES算法是一种采用传统的代替和置换操作加密的分组密码，明文以64比特为分组，密钥长度为64比特，有效密钥长度是56比特，其中加密密钥有8比特是奇偶校验，DES的加密和解密用的是同一算法，它的安全性依赖于所用的密钥。它首先把需要加密的明文划分为每64比特的二进制的数据块，用56比特有效密钥对64比特二进制数据块进行加密，每次加密可对64比特的明文输入进行16 轮的替换和移位后，输出完全不同的64比特密文数据。由于DES 算法仅使用最大为64比特的标准算法和逻辑运算，运算速度快，密
钥容易产生，适合于在大多数计算机上用软件快速实现，同样也适合于在专用芯片上实现。 二、DES算法描述 DES算法的加密过程首先对明文分组进行操作，需要加密的明文分组固定为64比特的块。图2-1是DES加密算法的加密流程。图2-2是密钥扩展处理过程。
DDSF8001D含义 DDSF8001D、DTSF8001、DDS8001、DDS8001D为4P导轨表DTS8003D、DTS8003为7P导轨表 以下用4P、7P简称说明其功能 永诺电气有限公司
目录 一、概述 (1) 二、产品规格 (2) 三、技术参数 (3) 四、安装与接线 (5) 4.1安装外形图及接线图 (5) 4.2安装注意事项及方法 (11) 五、使用说明 (12) 5.1面板格式 (12) 5.2功能说明 (16) 5.3显示说明 (18) 5.3.1编程设置 (28) 5.4通信说明 (31) 5.5MODBUS-RTU通讯地址信息表 (38) 5.6注意事项 (54) 六、典型应用 (55)
一、概述 多功能电力仪表是本公司集多年的电表设计经验，所推出的新一代微型电能表。 该仪表采用LCD显示，可进行时钟、费率时段等参数设置，并具有电能脉冲输出功能；可用RS485通讯接口与上位机实现数据交换，极大地方便了用电自动化管理。 该仪表具有体积小、精度高、可靠性好、安装方便等优点，性能指标符合国际GB/T17215、GB/T17883和电力行业DL/T614对仪表的各项技术要求。 二、产品规格 三、技术参数 项目技术指标 4P 7P-1 7P-0.5
精度等级有功：1.0级有功：0.5级 无功：2.0级 额定电压220V 3X220/380V 电流规格 1.5（6）A、5(20)A、10（40）A、20（80）A 工作电压正常工作电压范围：0.9-1.1Un 极限工作电压范围：0.7-1.2Un 参比频率50Hz或60Hz 启动电流直接接入0.004Ib 经CT接入0.002In 功耗电压线路≤5VA/相 电流线路＜4VA/相 脉冲输出脉冲宽度：80ms±20ms;光耦隔离，集电极开路输出 数字通讯RS485，MODBUS-RTU(其他协议可定制） 时钟误差≤0.5s/d 温度范围正常工作温度：-10℃-+45℃ 极限工作温度：-20℃-+55℃；存储温度：-40℃-+70℃相对湿度≤95%（无凝露） 76×91×74（mm) 126×91×74（mm) 外形尺寸 （长×宽×高） 平均无故障工作时间≥50000 四、安装与接线 4.1、安装外形图及接线图 4.1.1、外形尺寸图 （1）4P 正视图侧视图 图 1 4P尺寸 图 （2） 7P-1/7P-0.5 正视图侧视图
D E S加密解密课程设计报 告 Prepared on 22 November 2020
成都信息工程学院课程设计报告 DES算法加密与解密的设计与实现课程名称：密码算法程序设计 学生姓名： 学生学号： 专业班级： 任课教师： XX年 XX 月 XX 日
目录
1背景 DES算法概述 DES（Data Encryption Standard）是由美国IBM公司于20世纪70年代中期的一个密码算（LUCIFER）发展而来，在1977年1月15日，美国国家标准局正式公布实施，并得到了ISO的认可，在过去的20多年时间里，DES被广泛应用于美国联邦和各种商业信息的保密工作中，经受住了各种密码分析和攻击，有很好的安全性。然而，目前DES算法已经被更为安全的Rijndael算法取代，但是DES加密算法还没有被彻底的破解掉，仍是目前使用最为普遍的对称密码算法。所以对DES的研究还有很大价值，在国内DES算法在POS、ATM、磁卡及智能卡（IC卡）、加油站、高速公路收费站等领域被广泛应用，以此来实现关键的数据保密，如信用卡持卡人的PIN 码加密传输，IC卡与POS机之间的双向认证、金融交易数据包的MAC校验等，均用到DES算法。 DES算法是一种采用传统的代替和置换操作加密的分组密码，明文以64比特为分组，密钥长度为64比特，有效密钥长度是56比特，其中加密密钥有8比特是奇偶校验，DES的加密和解密用的是同一算法，它的安全性依赖于所用的密钥。它首先把需要加密的明文划分为每64比特的二进制的数据块，用56比特有效密钥对64比特二进制数据块进行加密，每次加密可对64比特的明文输入进行16轮的替换和移位后，输出完全不同的64比特密文数据。由于DES算法仅使用最大为64比特的标准算法和逻辑运算，运算速
DIN 导轨式安装单相电子式有功电能表 (液晶红外型) 概述 DDS986型DIN 导轨式安装单相电子式有功电能表系我公司采用微电子技术与专用大规模集成电路，应用数字采样处理技术及SMT 工艺等先进技术全新研制开发的单相两线有功电能表。该表技术性能完全符合IEC 62053-21国际标准中1级单相有功电能表的相关技术要求，能直接精确地测量额定频率为50Hz 或60Hz 三相交流电网中负荷的有功电能的消耗。该表由7位LCD 显示器显示有功用电量，具有可靠性好、体积小、重量轻、外形美观、安装方便等特点。应用广泛与设配套便捷. 功 能 特 点 ● 35mm DIN 标准导轨安装，符合DIN EN 50022标准，或者板前式安装(安装孔中心距63 mm)，两种安装方式可由用户任意选择。 ● 7极宽度(模数17.5mm)，符合DIN 43880标准。 ● 7位LCD 显示器，标准配置6+1位(999999.1kWh)显示，可选择5+2位显示. ● 2个LED 分别电能脉冲信号(红色)与绿色为红外通讯指示，另外两个为红外发射头和红外接收头。 ● 标准配置含检测负荷电流潮流方向，自动检测负荷电流潮流方向，并由一个单独的LED 指示。 ● 单方向测量单相两线有功电能消耗，与负荷电流潮流方向无关，符合IEC 62053-21标准。 ● 标准配置S 型接线(底端进线，顶端出线)，直接接入式使用，可选择CT 接入式使用和PT & CT 接入式使用. ● 标准配置短的接线端子盖，可选择延长型接线端子盖，保护用电安全。 ● 具有红外通讯功能,可通过红外对电表进行抄表和设置. 标准配置通讯协议符合DL/T645-1997标准 技术参数及规格 名 称 型 号 精 度 参比电压 电流规格 单相导轨安装电子式 电能表 (LCD 显示、红外) DDS866 1.0级 220/380V 230/400V 240/415V 1.5(6)A 5(30)A 10(60)A 20(80)A 30(90)A 2.0级 外型及安装尺寸 接线图:
DES算法及其程序实现 一．D ES算法概述 ①DES算法为密码体制中的对称密码体制，又被成为美国数据加密标准，是1972年美国IBM公司研制的对称密码体制加密算法。明文按64位进行分组，密钥长64位，密钥事实上是56位参与DES运算（第8、16、24、32、40、48、56、64位是校验位，使得每个密钥都有奇数个1）分组后的明文组和56位的密钥按位替代或交换的方法形成密文组的加密方法。 ②DES算法的特点：分组比较短、密钥太短、密码生命周期短、运算速度较慢。 ③DES算法把64位的明文输入块变为64位的密文输出块，它所使用的密钥也是64位，整个算法的主流程图如下： 二．D ES算法的编程实现 #include
const static char ip[] = { //IP置换 58, 50, 42, 34, 26, 18, 10, 2, 60, 52, 44, 36, 28, 20, 12, 4, 62, 54, 46, 38, 30, 22, 14, 6, 64, 56, 48, 40, 32, 24, 16, 8, 57, 49, 41, 33, 25, 17, 9, 1, 59, 51, 43, 35, 27, 19, 11, 3, 61, 53, 45, 37, 29, 21, 13, 5, 63, 55, 47, 39, 31, 23, 15, 7 }; const static char fp[] = { //最终置换 40, 8, 48, 16, 56, 24, 64, 32, 39, 7, 47, 15, 55, 23, 63, 31, 38, 6, 46, 14, 54, 22, 62, 30, 37, 5, 45, 13, 53, 21, 61, 29, 36, 4, 44, 12, 52, 20, 60, 28, 35, 3, 43, 11, 51, 19, 59, 27, 34, 2, 42, 10, 50, 18, 58, 26, 33, 1, 41, 9, 49, 17, 57, 25 }; const static char sbox[8][64] = { //s_box /* S1 */ 14, 4, 13, 1, 2, 15, 11, 8, 3, 10, 6, 12, 5, 9, 0, 7, 0, 15, 7, 4, 14, 2, 13, 1, 10, 6, 12, 11, 9, 5, 3, 8, 4, 1, 14, 8, 13, 6, 2, 11, 15, 12, 9, 7, 3, 10, 5, 0, 15, 12, 8, 2, 4, 9, 1, 7, 5, 11, 3, 14, 10, 0, 6, 13, /* S2 */ 15, 1, 8, 14, 6, 11, 3, 4, 9, 7, 2, 13, 12, 0, 5, 10, 3, 13, 4, 7, 15, 2, 8, 14, 12, 0, 1, 10, 6, 9, 11, 5, 0, 14, 7, 11, 10, 4, 13, 1, 5, 8, 12, 6, 9, 3, 2, 15, 13, 8, 10, 1, 3, 15, 4, 2, 11, 6, 7, 12, 0, 5, 14, 9, /* S3 */ 10, 0, 9, 14, 6, 3, 15, 5, 1, 13, 12, 7, 11, 4, 2, 8, 13, 7, 0, 9, 3, 4, 6, 10, 2, 8, 5, 14, 12, 11, 15, 1, 13, 6, 4, 9, 8, 15, 3, 0, 11, 1, 2, 12, 5, 10, 14, 7, 1, 10, 13, 0, 6, 9, 8, 7, 4, 15, 14, 3, 11, 5, 2, 12, /* S4 */ 7, 13, 14, 3, 0, 6, 9, 10, 1, 2, 8, 5, 11, 12, 4, 15, 13, 8, 11, 5, 6, 15, 0, 3, 4, 7, 2, 12, 1, 10, 14, 9, 10, 6, 9, 0, 12, 11, 7, 13, 15, 1, 3, 14, 5, 2, 8, 4, 3, 15, 0, 6, 10, 1, 13, 8, 9, 4, 5, 11, 12, 7, 2, 14,
实验名称DES算法实验报告实验（实习）日期________得分 ______ 指导教师沈剑 计算机系专业软件工程年级 11 班次3 __________姓名张渊学号 931 1、实验目的 理解对称加解密算法的原理和特点 理解DES算法的加解密原理 2、D ES算法详述 DES算法把64位的明文输入块变为64位的密文输出块，它所使用的密钥也是64位,其功能是把输入的64位数据块按位重新组合，并把输出分为L0、R0两部分，每部分各长32 位，其置换规则见下表： 58,50,12,34,26,18,10,2,60,52,44,36,28,20,12,4, 62,54,46,38,30,22,14,6,64,56,48,40,32,24,16,8, 57,49,41,33,25,17, 9,1,59,51,43,35,27,19,11,3, 61,53,45,37,29,21,13,5,63,55,47,39,31,23,15,7, 即将输入的第58位换到第一位，第50位换到第2位，……，依此类推，最后一位是原来的第7位。L0、R0则是换位输出后的两部分，L0是输出的左32位，R0是右32位， 例：设置换前的输入值为D1D2D3? D64,则经过初始置换后的结果为：L0=D550 (8) R0=D57D49 (7) 经过26次迭代运算后，得到L16、R16,将此作为输入，进行逆置换，即得到密文输出。逆置换正好是初始置的逆运算，例如，第1位经过初始置换后，处于第40位，而通过逆置 换，又将第40位换回到第1位，其逆置换规则如下表所示： 40,8,48,16,56,24,64,32,39,7,47,15,55,23,63,31, 38,6,46,14,54,22,62,30,37,5,45,13,53,21,61,29, 36,4,44,12,52,20,60,28,35,3,43,11,51,19,59,27, 34,2,42,10,50,18,58 26,33,1,41, 9,49,17,57,25, 放大换位表 32,1,2, 3, 4, 5, 4, 5, 6, 7, 8, 9, 8, 9, 10,11, 12,13,12,13,14,15,16,17,16,17,18,19,20,21,20,21, 22,23,24,25,24,25,26,27,28,29,28,29,30,31,32, 1, 单纯换位表 16,7,20,21,29,12,28,17, 1,15,23,26, 5,18,31,10,
终端电能计量表计及系统选型手册 一、概述 Acrel-3000系列电能管理系统是紧密把握电力系统用户的需求，遵循电力系统的标准规范而开发的一套具有专业性强、自动化程度高、易使用、高性能、高可靠等特点的适用于低压配电系统的电能管理系统。通过遥测和遥控可以合理调配负荷，实现优化运行，有效节约电能，并有高峰与低谷用电记录，从而为用电的合理管理提供了数据依据。--终端电能计量表计及系统选型手册 二、参照标准 GB50052-2009供配电系统设计规范 GB50054-2011低压配电设计规范 GB/T17215.321-2008交流电测量设备特殊要求第21部分：静止式有功电能表(1级和2级) GB/T17215.322-2008交流电测量设备特殊要求第22部分：静止式有功电能表(0.2S级和0.5S级) GB/T17215.301-2007多功能电能表特殊要求 DL/T448-2000《电能计量装置技术管理规程》 DL/T698电能信息采集与管理系统 DL/T814-2002配电自动化系统功能规范 DL/T5137-2001《电测量及电能计量装置设计技术规程》 三、系统结构 电能管理系统可对低压设备消耗的电能进行分项计量，该系统由站控管理层、网络通讯层、现场设备层三部分组成。 现场设备层采用安科瑞低压智能计量箱AZX J，内部安装预付费电能表以及卡轨式电能表。通过低压智能计量箱配合电能管理监控系统，利用计算机、后台监控管理软件和网络通讯技术，将采集到的用电设备的能耗数据上传到统一的监测管理平台，实现对用电系统的监控管理，对高能耗用电设备的合理控制，最终使整套用电系统达到节能效果。电能管理系统网拓扑图见下图。--终端电能计量表计及系统选型手册
导轨式三相费控智能电能表 安装使用说明书 （2016．03.Ver 1.0）
目录 一、概述 (1) 二、主要功能 (1) 三、技术参数 (1) 四、安装与接线 (2) 4.1外形尺寸 (2) 4.2 安装图 (2) 4.3 接线图 (2) 五、使用与操作 (2) 5.1插卡方法 (2) 5.2用户购电 (3) 5.3电能计量 (3) 5.4 电量报警 (3) 5.5 跳闸与合闸 (3) 5.6 显示 (3) 5.6.1插卡显示 (4) 5.6.2按键翻页 (5) 六、恶性负载控制 (7) 七、过负荷控制 (7) 八、电能脉冲输出 (7) 九、数字通讯 (7) MODBUS-RTU通讯地址信息表 (8) 典型应用接线图 (9)
导轨式三相费控电能表 一、概述 导轨式三相费控电能表主要用于频率在45~65Hz范围内的三相四线网络的电能管理领域。可测量电网中的电量信息，通过加密的IC卡或485通讯与上位机进行数据交互。仪表内置大功率继电器可实现本地跳闸、合闸操作，从而实现预付费功能；表内配备精确的时钟源，配合完善的时间切换机制实现分时计费功能。用户可根据现场实际情况设置电能表内部参数，使用方便、操作简单、精确度高；广泛用于各类住宅、智能建筑、集贸商场及集体宿舍、学校等领域。 一次电流规格在100A以内为直接输入型，无需外配断路器，通过仪表内置的继电器实现通断操作；一次电流规格大于100A时，需外配电流互感器和断路器，通过仪表输出的干接点信号控制断路器实现通断操作。 产品符合GB/T17215、GB/T17883相关标准,是改革传统用电体制,提高用电管理水平的理想电表。 二、主要功能 三、技术参数
网络安全课程设计 课题名称DES算法程序实现 院系电子与信息工程学院计算机系 班级网络工程 学号 姓名 指导老师 起止周次2017-2018学年第二学期第10-18周 一、课题设计目标 提高学生在网络安全方面综合运用理论知识解决实际问题的能力。使学生得到一次科学研究工作的初步训练，懂得网络加密/解密的方法和实现、网络相关安全工具的使用方法与网络攻击的防范 二、课题设计任务 a、对称密码算法的理论学习 b、利用VC/C/C++/Java（任意一种）实现DES算法，要求，有界面，输入明文，点击加密，可实现加密，把明文转变为密文；点击解密，可实现解密，即把密文转变为明文。 三、课题设计硬件环境 带有Windows操作系统的计算机。 四、课题设计软件环境 VS2010编译环境 五、设计步骤及代码 1．算法描述 DES算法的流程如图1-1和1-2所示。首先把明文分成若干个64bit的分组，算法一个分组作为输入，通过一个初始置换（IP）将明文分组分成左半部分（L0）和右半部分（R0）各位32比特。然后进行16轮完全相同的运算，这些运算称为函数f，在运算过程中数据与密钥相结合。经过16轮运算后，左、右两部分合在一起经过一个末置换（初始转换的逆置
换IP-1），输出一个64bit的密文分组。 2.程序源代码 #include
58, 50, 42, 34, 26, 18, 10, 2, 60, 52, 44, 36, 28, 20, 12, 4, 62, 54, 46, 38, 30, 22, 14, 6, 64, 56, 48, 40, 32, 24, 16, 8, 57, 49, 41, 33, 25, 17, 9, 1, 59, 51, 43, 35, 27, 19, 11, 3, 61, 53, 45, 37, 29, 21, 13, 5, 63, 55, 47, 39, 31, 23, 15, 7 }; const static char fp[] = { //最终置换 40, 8, 48, 16, 56, 24, 64, 32, 39, 7, 47, 15, 55, 23, 63, 31, 38, 6, 46, 14, 54, 22, 62, 30, 37, 5, 45, 13, 53, 21, 61, 29, 36, 4, 44, 12, 52, 20, 60, 28, 35, 3, 43, 11, 51, 19, 59, 27, 34, 2, 42, 10, 50, 18, 58, 26, 33, 1, 41, 9, 49, 17, 57, 25 }; const static char sbox[8][64] = { //s_box /* S1 */ 14, 4, 13, 1, 2, 15, 11, 8, 3, 10, 6, 12, 5, 9, 0, 7, 0, 15, 7, 4, 14, 2, 13, 1, 10, 6, 12, 11, 9, 5, 3, 8, 4, 1, 14, 8, 13, 6, 2, 11, 15, 12, 9, 7, 3, 10, 5, 0, 15, 12, 8, 2, 4, 9, 1, 7, 5, 11, 3, 14, 10, 0, 6, 13, /* S2 */ 15, 1, 8, 14, 6, 11, 3, 4, 9, 7, 2, 13, 12, 0, 5, 10, 3, 13, 4, 7, 15, 2, 8, 14, 12, 0, 1, 10, 6, 9, 11, 5, 0, 14, 7, 11, 10, 4, 13, 1, 5, 8, 12, 6, 9, 3, 2, 15, 13, 8, 10, 1, 3, 15, 4, 2, 11, 6, 7, 12, 0, 5, 14, 9, /* S3 */ 10, 0, 9, 14, 6, 3, 15, 5, 1, 13, 12, 7, 11, 4, 2, 8, 13, 7, 0, 9, 3, 4, 6, 10, 2, 8, 5, 14, 12, 11, 15, 1, 13, 6, 4, 9, 8, 15, 3, 0, 11, 1, 2, 12, 5, 10, 14, 7, 1, 10, 13, 0, 6, 9, 8, 7, 4, 15, 14, 3, 11, 5, 2, 12, /* S4 */ 7, 13, 14, 3, 0, 6, 9, 10, 1, 2, 8, 5, 11, 12, 4, 15, 13, 8, 11, 5, 6, 15, 0, 3, 4, 7, 2, 12, 1, 10, 14, 9, 10, 6, 9, 0, 12, 11, 7, 13, 15, 1, 3, 14, 5, 2, 8, 4, 3, 15, 0, 6, 10, 1, 13, 8, 9, 4, 5, 11, 12, 7, 2, 14,