chemical engineering & processing

Chemical Engineering and Processing Copyright 2004 Elsevier B.V. All rights reserved Volume 44, Issue 1, Pages 1-137 (January 2005) 1. A new method for producing anhydrous puffed borax Pages 1-6 mer ahin, Nasrettin Genli and Mustafa zdemir 2. The influence of temperature and inlet velocity on cyclone pressure drop: a CFD study Pages 7-12 Jolius Gimbun, T. G. Chuah, A. Fakhrul-Razi and Thomas S. Y. Choong 3. A pollution reduction methodology in reactor design Pages 13-21 Qishi Chen and Xiao Feng 4. Effects of hydraulic residence time on metal uptake by activated sludge Pages 23-32 Tlay A. zbelge, H. nder zbelge and Murat Tursun 5. Porous catalyst intraparticle status of parallel, equilibrium-restrained reactions Pages 33-39 Guangsuo Yu, Fuchen Wang, Zhenghua Dai and Zunhong Yu 6. PID controller tuning using mathematical programming Pages 41-49 George Syrcos and Ioannis K. Kookos 7. Separation of acetic acidwater mixtures through acrylonitrile grafted poly(vinyl alcohol) membranes by pervaporation Pages 51-58 N. Alghezawi, O. anli, L. Aras and G. Asman 8. Countercurrent flow distribution in structured packing via computed tomography Pages 59-69 Shaibal Roy, A. Kemoun, M. H. Al-Dahhan, M. P. Dudukovic, Thomas B. Skourlis and Frits M. Dautzenberg 9. Effect of cycling operations on an adsorbed natural gas storage Pages 71-79 O. Pupier, V. Goetz and R. Fiscal 10. Effect of internal on the hydrodynamics in external-loop airlift reactors Pages 81-87 Tongwang Zhang, Jinfu Wang, Tiefeng Wang, Jing Lin and Yong Jin 11. Multicriteria synthesis of flexible heat-exchanger networks with uncertain source-stream temperatures Pages 89-100 Cheng-Liang Chen and Ping-Sung Hung 12. On-line dynamic optimization and control strategy for improving the performance of batch reactors Pages 101-114 A. Arpornwichanop, P. Kittisupakorn and I. M. Mujtaba

13. Synthesis of nano-sized particles from metal carbonates by the method of reversed mycelles Pages 115-119 Christo Karagiozov and Dafina Momchilova 14. Mechanism of mass transfer from bubbles in dispersions: Part II: Mass transfer coefficients in stirred gasliquid reactor and bubble column Pages 121-130 V. Linek, M. Korda and T. Moucha 15. Separation of n-hexaneethyl acetate mixtures by azeotropic batch distillation with heterogeneous entrainers Pages 131-137 I. Rodriguez-Donis, J. Acosta-Esquijarosa, V. Gerbaud, E. Pardillo-Fondevila and X. Joulia 16. Inside front cover - Editorial Board EDITORIAL BOARD Pages CO2-CO2 Volume 44, Issue 2, Pages 139-334 (February 2005) Pneumatic Conveying and Handling of Particulate Solids Edited by Janos Gyenis and Avi Levy 1. Special issue on conveying and handling of particulate solids Pages 139-140 Janos Gyenis and Avi Levy 2. A review of the research work of Professor Predrag Marjanovi Pages 141-151 David Mills 3. Influence of the particle diameter and density in the gas velocity in jet spouted beds Pages 153-157 Mara J. San Jos, Sonia Alvarez, Alvaro Ortiz de Salazar, Martn Olazar and Javier Bilbao 4. Taking-off model of particles with a wide size distribution Pages 159-166 Isabelle Descamps, Jean-Luc Harion and Bernard Baudoin 5. Solids deposition in low-velocity slug flow pneumatic conveying Pages 167-173 J. Li, C. Webb, S. S. Pandiella, G. M. Campbell, T. Dyakowski, A. Cowell and D. McGlinchey 6. Identification of material specific attrition mechanisms for polymers in dilute phase pneumatic conveying Pages 175-185 Lars Frye and Wolfgang Peukert 7. Two-dimensional numerical simulations of the pneumatic drying in vertical pipes Pages 187-192 I. Skuratovsky, A. Levy and I. Borde 8. The formation of fine particles by salting-out precipitation Pages 193-200 Judit Tth, Andrea Kardos-Fodor and Susan Halsz-Pterfi 9. Micronized cocoa butter particles produced by a supercritical process Pages 201-207 J. -J. Letourneau, S. Vigneau, P. Gonus and J. Fages

10. Food powder handling and processing: Industry problems, knowledge barriers and research opportunities Pages 209-214 John J. Fitzpatrick and Lilia Ahrn 11. Microencapsulation of particles using supercritical carbon dioxide Pages 215-219 H. Krber and U. Teipel 12. Plasma spheroidization of ceramic particles Pages 221-224 Z. Kroly and J. Szpvlgyi 13. Treatment of particulate metallurgical wastes in thermal plasmas Pages 225-229 I. Mohai and J. Szpvlgyi 14. Defluidization modelling of pyrolysis of plastics in a conical spouted bed reactor Pages 231-235 Roberto Aguado, Rubn Prieto, Mara J. San Jos, Sonia Alvarez, Martn Olazar and Javier Bilbao 15. The energy of bed processing during drum granulation Pages 237-243 Tadeusz Gluba 16. Control of aggregation in production and handling of nanoparticles Pages 245-252 Wolfgang Peukert, Hans-Christoph Schwarzer and Frank Stenger 17. Effect of different types of impact surface on coal degradation Pages 253-261 R. K. Sahoo and D. Roach 18. Using statistical moments to describe grinding in a ball mill for industrial-scale process Pages 263-266 Andrzej Heim, Tomasz P. Olejnik and Agnieszka Pawlak 19. On possible instability of throughputs in complex milling circuits Pages 267-272 V. Mizonov, V. Zhukov, A. Korovkin and H. Berthiaux 20. Optimising design of continuous grinding mill-classifier systems Pages 273-277 P. B. Kis, Cs. Mihlyk and B. G. Lakatos 21. Air classification of solid particles: a review Pages 279-285 M. Shapiro and V. Galperin 22. Application of a vertical venturi separator for improved recycling of automotive tires Pages 287-291 W. McBride and S. Keys 23. Particle movement during granular intermingling in a pulsated bottom mixer Pages 293-296 Mikls Nemnyi and Attila J. Kovcs

24. Estimating the homogenization efficiency of mammoth silos by process dynamics and simulations: Comparing the results of process dynamics with the simulations Pages 297-302 D. L. Schott, L. A. van Wijk and W. J. Vlasblom 25. Assessing the homogeneity of powder mixtures by on-line electrical capacitance Pages 303-313 N. Ehrhardt, M. Montagne, H. Berthiaux, B. Dalloz-Dubrujeaud and C. Gatumel 26. Solid transport in a pyrolysis pilot-scale rotary kiln: preliminary resultsstationary and dynamic results Pages 315-321 N. Descoins, J. -L. Dirion and T. Howes 27. Controlling dust emissions and explosion hazards in powder handling plants Pages 323-326 Peter Wypych, Dave Cook and Paul Cooper 28. Photocatalytic degradation of trichloroethylene (TCE) over TiO2/silica gel in a circulating fluidized bed (CFB) photoreactor Pages 327-334 Tak Hyoung Lim and Sang Done Kim 29. Inside front cover - Editorial Board EDITORIAL BOARD Pages CO2-CO2

Chemical Engineering and Processing 44 (2005) 16

A new method for producing anhydrous puffed boraxmer Sahin, Nasrettin Genli, Mustafa zdemir

Department of Chemistry, Harran University, S. Urfa, TurkeyReceived 30 June 2003; received in revised form 29 July 2003; accepted 8 March 2004

Available online 24 April 2004

Abstract

This paper describes the production of anhydrous puffed borax from borax pentahydrate (BPH) in a batch calcinator. The calcination ofBPH is incomplete since agglomeration starts at 300 C. In order to avoid agglomeration at temperatures higher than 300 C, the surface ofBPH particles have been covered with a CaO layer in aqueous media having a higher melting point than pure BPH. To investigate the effectof this CaO layer on the calcination of BPH, the samples were fed into the batch calcinator. During the calcination process, quantities such asthe bulk density value, particle size distribution, sodium borate and calcium content of anhydrous borax have been determined as a functionof temperature. It was found that the anhydrous borax can be obtained by calcination of BPH particles covered with CaO in 50, 20 and 5 mintime intervals at a temperature range of 300, 400 and 500 C, respectively. As a result, puffed anhydrous borax of 99% purity with bulk densityof 0.082 g cm3 and containing 0.670% Ca2+ has been produced by this method in 5 min interval at 500 C. 2004 Elsevier B.V. All rights reserved.

Keywords: Borax pentahydrate; Anhydrous borax; Calcium oxide; Coating

1. Introduction

Borax pentahydrate (BPH) is one of the most importantcommercial boron compounds containing water of crystal-lization. The structure formula of BPH can be best repre-sented as Na2B4O5(OH)42.67H2O which means that thereare 2 mol of water in the molecular structure, remaining be-ing real crystal water [1]. BPH is used in many areas, such asperborate and boric acid production and detergent formula-tions. However, its water content is not appropriate in someapplications such as the manufacture of high quality glass,frit production ceramic and the refinement of precious met-als. Thus, BPH should be dehydrated to anhydrous borax(BA) state.

The production methods of BA from BPH or borax dec-ahydrate may be categorized into three main groups. Theseare: (a) azeotropic distillation, (b) melting and (c) dehydra-tion in fluidized bed.

The method of azeotropic distillation is not yet suitablefor industrial purposes because of low yield and high heatinput needed. In addition, BPH cannot be converted by BAusing this method [2].

Corresponding author. Fax: +90-414-315-1998.E-mail address: [email protected] (. Sahin).

With current technology, BA is produced from BPH orborax decahydrate (BDH) by the melting method which is atwo-stage process: first dehydration, and then fusion. BPHor BDH is fused at a temperature higher than the meltingpoint of BA, ranging from 1000 to 1400 C at different partsof firebox. The high temperature decreases the viscosity ofthe molten mass, in order to produce proper fluidizationconditions for flow out from the firebox. The molten boraxis highly corrosive which can only be prevented by forminga layer of solid calcined borax on the refractory material ofthe furnace, thus the melting of borax is the most criticalan the most expensive step, which cause some difficulties inoperation and results in somewhat contaminated products.Slow cooling gives crystal formation but immediate coolinggives an amorphous glass [3]. By using this process, highdensity of BA is produced, but there are many technicaldifficulties in the process, such as corrosion and handlingproblems. The other disadvantage of this method is that theproduct needs crushing, grinding and homogenization beforebeing used.

Production of BA without fusion is very attractive withrespect to energy consumption and corrosion. Thus, the at-tempts have mostly been in dehydration borax pentahydratein a fluidized bed via a stage-wise calcination [4,5]. Themain disadvantage of this process is the puffing of the par-

0255-2701/$ see front matter 2004 Elsevier B.V. All rights reserved.doi:10.1016/j.cep.2004.03.004

2 . Sahin et al. / Chemical Engineering and Processing 44 (2005) 16

ticles during removal of water. In a fluidized bed, the calci-nation of BPH cannot exceed 300 C at the earlier stage ofthe process. At this temperature, it turns into an amorphousform of BA and softens easily. Softening of the particlescause agglomeration, which disturbs the fluidization condi-tion in the fluidized bed calcinator [6].

BPH can be calcined to BA without puffing by at leasttwo-stage calcination which each needs at long time intervalin fluidized bed [7]. As a result, the calcination of BPH influidized bed have some problem such as stage-wise, puffing,agglomeration and needed long time.

Thus, a considerable number of attempts have been madeto develop a new method for production anhydrous boraxfrom BPH. As it is known the calcination of BPH cannotbe conducted above agglomeration temperature of 300 C influidized bed. At temperatures higher than 300 C, the amor-phous BA particles which soften readily adhere strongly toeach other since BPH dissolves in their water. This prob-lem has been solved in our system by covering the particleswith a material that has a higher melting point than the fi-nal agglomeration temperature. For this propose, the surfaceof BPH was covered with Ca(OH)2 layer in aqueous me-dia. The presence of Ca(OH)2 prevents the agglomeration ofBPH particles up to 500 C, since it controls the accelerationof water vapor on the surface of BPH particles. Using thismethod BPH can be converted to puffed BA containing 99%Na2B4O7 in a 5-min interval at the temperature of 500 C.The degree of puffing is very high, thus the calcination wascarried out in an oven.

The only disadvantage of this method is that the ob-tained product (BA) has a lower bulk density. Such a puffedproduct, with large quantities of liquids and gas, can beloaded onto the expanded borax. For example, organic suchas trichloroethylene, cyclohexanone and pentachlorophenolcan be loaded at very high percentages onto the puffed bo-rax, as can non-ionic and anionic surfactants, with the prod-uct retaining its free-flowing characteristics. This rendersthe puffed borax very useful in such diverse compositionsas dairy cleaners, fabric softeners and bath additives [8].

In addition, this puffed BA having large surface area canbe used for solidgas reactions. Recently, one popular appli-

Fig. 1. Schematic diagram of the experimental procedure.

cation of this BA reaction has been the production of sodiumborohydride. Sodium borohydride which is used as a hydro-gen storage medium can also be synthesized by the reactionof BA, with MgH2 through ball milling at room temperatureas shown the following reaction [9]:8MgH2+Na2B4O7+Na2CO3 4NaBH4 + 8MgO+ CO2The obtained anhydrous borax is very fragile, thus it can beeasily converted to powder form by grinding. This operationincreases the bulk density of BA.

2. Experiments

The calcination of BPH to BA was carried out in a batchcalcinator (thermolyne 6000 furnace) which is heated to apredetermined calcination temperature (Fig. 1). During con-stant temperature experiments, particles were fed into thecalcinator, which was in thermal equilibrium at the experi-mental temperature. At a predetermined time interval, sam-ples were withdrawn using a vacuum sampling tube. Titri-metric method was used to determine the Na2B4O7 contentof sample [10]. The Ca2+ content of samples was determinedby Jenway PFP 7 model flame photometer. Bulk density ofBA was measured by a standard method [11].

The particle size of BPH used in the experiments waschosen in the range 630 + 450m and bulk density wasdetermined as 0.91 g cm3. Technical grade BPH obtainedfrom ETIHOLDING (Kirka, Turkey) was used in the ex-periments. Original Na2B4O7 content of samples was deter-mined as 69.95% by titrimetric method [10].

Each series of experiments carried out with 10 g of BPHwashing 20 ml Ca(OH)2 solution with predetermined con-centration during 1 min. This washed BPH particles werethen dried at 65 C in an oven for 5 h. In another series ofexperiments, a mixture containing BPH and 2, 4, 5, 7, 10,17 and 34 wt.% CaO were fed into the calcinator at the tem-perature of 500 C for 10 min.

The other series of experiments, particles covering withdifferent Ca(OH)2 fed into the calcinator to investigate thethermal decomposition behavior in the calcinator at 300,

. Sahin et al. / Chemical Engineering and Processing 44 (2005) 16 3

400, 500 C. At each temperature samples was taken at 3,5, 10, 20, 45 and 60 min intervals to determine the degreeof calcination.

3. Results and discussion

The main aim of this study is that the calcination of BPHto BA is performed without agglomeration at higher temper-ature than 300 C at which dehydration and decompositionsteps of it take place very fast. Normally, particles of BPHdissolve their water and stick to each other to give agglom-eration.

In this study, agglomeration was prevented by both cov-ering the surface of BPH particles in Ca(OH)2 aqueous so-lution and BPHCaO mixture in solid state.

Table 1The effect of different BPHCaO mixture on the calcination of BPH particles at 500 C at the end of 10 min

Particle size (m) %CaO5 10 17

% fraction Ca2+ % (g cm3) % fraction Ca2+ % (g cm3) % fraction Ca2+ % (g cm3)+1250 40.517 0.51 0.0583 31.609 0.69 0.0612 39.931 1.40 0.0635+1000 30.747 0.35 30.172 0.69 16.319 0.38+800 6.897 0.12 10.632 0.35 6.597 0.12+630 1.724 0.03 2.010 0.06 +560 0.287 +450 +315 315 20.115 4.05 25.287 5.21 18.942 10.16

Fig. 2. TG curves for BPH both in pure and containing 2.46 wt.% Ca2+.

In the first group of experimental work, mixtures contain-ing BPH and 2, 4, 5, 7, 10, 17 and 34 wt.% CaO were fedinto the batch calcinator for 10 min at 500 C. At the endof this kind of calcination, the conversion of BPH to BA isfound as about 99.6%. But, the BPH samples containing 2and 4 wt.% CaO agglomerated slightly. However, agglomer-ation was not observed at the concentration of CaO higherthan 5 wt.%. Table 1 shows the values found at the end ofthis group. As can be seen from this table, the maximumcontent of Ca2+ in BA particle was found in the particle sizesmaller than 315m. The particle size of BA is expandedabout twice higher than used for BPH particles at the begin-ning of experiment by thermal shock. The result obtainedat the end of this step shows that the agglomeration can beprevented in presence of CaO. However, the obtained prod-uct (BA) at the end of this kind of operation contains high


70

75

80

85

90

95

100

0 10 20 30 40

time(min.)

%N

a 2B

4O7

50

300 C

400 C

500 C

Fig. 3. Changes of calcination time with temperature for the BPH samples containing about 2.5 wt.% Ca2+.

Table 2Properties of dehydrated BPH covering with 2.5 wt.% Ca2+ layer

Particle size (m) 300 C 400 C 500 C% fraction Ca2+ % (g cm3) % fraction Ca2+ % (g cm3) % fraction Ca2+ % (g cm3)

+1250 96.407 1.01 0.0552 90.998 1.00 0.0312 81.344 1.00 0.0589+1000 8.927+800 0.638+630 +560 +450 +315 315 3.593 1.88 9.002 1.361 9.009 1.50

values of CaO which is not suitable for industrial applica-tions.

In the second group of experiment, the surface of BPHparticle were covered with Ca(OH)2 in aqueous solution.TG analysis of the BPH both in pure state and covered with2.46 wt.% Ca2+ were performed under nitrogen atmosphereat 10 C/min heating rate. Fig. 2 shows the results of TGanalysis. As can be seen from Fig. 2, the covered BPH par-ticles with Ca(OH)2 dehydrate slowly with respect to purestate. This phenomena involves the simultaneous transfer ofheat to evaporate the liquid and transfer of vapor within thesolid and vapor from the surface into the hot carrier gas. Inthe case of pure BPH at temperature higher than 300 C, all

Table 3Properties of dehydrated BPH particles containing about 0.670 wt.% Ca2+

Particle size (m) 300 C 400 C 500 C% fraction Ca2+ % (g cm3) % fraction Ca2+ % (g cm3) % fraction Ca2+ % (g cm3)

+1250 33.463 0.201 0.0727 61.318 0.170 0.044 28.940 0.210 0.0824+1000 39.779 26.689 38.607+800 18.739 5.574 22.708+630 4.013 3.482+560 +450 +315 315 4.006 0.467 6.419 0.498 6.263 0.471

crystal water and some structure water dehydrated quicklycaused agglomeration. Thus, the mass transfer in this steplooked as the constant rate period where moisture movementwithin the solid is sufficiently rapid to maintain a saturatedcondition at the surface. In the case of dehydration of BPHparticles covered with Ca(OH)2 layer to increase the masstransfer resistance between the particles and hot carrier gas,the calcination is mainly controlled by the Ca(OH)2 layer.

Fig. 3 illustrates the Na2B4O7 content of the samplescontaining about 2.50 wt.% Ca2+ taken from the batch cal-cinator at different stages during isothermal decompositionconducted at various temperatures. The total Na2B4O7 con-tent of samples increased with increasing calcination tem-

. Sahin et al. / Chemical Engineering and Processing 44 (2005) 16 5

peratures. The BA contains some CaO, hence the conver-sion percentages of BA never attained 100% as seen fromFig. 3. The conversion of BPH to BA takes place in twostages namely dehydration and calcination [7]. But dehy-dration stage cannot be observed in Fig. 3 because of rapiddehydration of BPH. Fig. 3 illustrates that the calcinationof BPH to BA at 300 C completed in 50 min, whereas thesame degree of calcination realized in 5 min at the temper-ature of 500 C. The Ca2+ content, bulk density and sieveanalysis of BA obtained at the end of this group of exper-iments are illustrated in Table 2. As can be seen from thistable, the Ca2+ content of calcination realized at 300, 400and 500 C is about 2.5 Ca2+ wt.%. The chosen particle sizerange of BPH before calcination was 630 + 450m andthe particle size of almost nine to ten part of calcines wasincreased to +1210m shown in Table 2. Thus, it can besaid that puffing is more effective in cause size incrementwith respect to shrinking and fragmentation of puffed parti-cles. The operation of covering BPH particles with Ca(OH)2can not be attained at the temperature higher than 500 Cbecause of agglomeration. It is interesting to see that thebulk density increased from 0.0317 to 0.0599 g cm3 withincreasing the temperature from 400 to 500 C. This behav-ior of BPH can be easily explained by increasing sinteringeffect which depends on the increasing temperature. In thelight of above results, it can also be concluded that betteroperation could be possible from the second group of ex-periment by decreasing the Ca2+ layer on the surface ofBPH particles. Thus, a third group of experiments were car-ried out using very low Ca(OH)2 aqueous solution to coverthe BPH particles surface. The changes of Na2B4O7 con-tent of samples containing about 0.60 wt.% Ca2+ with timein this group are given in Fig. 4 at various constant temper-atures. The Na2B4O7 content of the samples increases withincreasing calcination temperature. The calcination rate ofthis group is higher than the previous group given in Fig. 3.This behavior may be attributed to the fact that increas-ing the percentages of additive caused the formation of a

70

75

80

85

90

95

100

0 10 20 30 40

time (min.)

%N

a 2B

4O7

50

300 C

400 C

500 C

Fig. 4. Effect of temperature on the calcination of BPH samples containingabout 0.67 wt.% Ca2+.

thick and homogenous cover on the surface of the anhy-drous borax. The thickness of CaO layer controls the trans-fer rate of liberated water from borax pentahydrate to air.The sieve analysis, bulk density and Ca2+ content of BAobtained at the end of operation having different tempera-

Fig. 5. Microscopic photographs of borax pentahydrate: (a) pure, (b)containing 0.67 wt.% Ca2+ and (c) containing 2.5 wt.% Ca2+.


tures are given in Table 3. As can be seen in this table, parti-cle size shows a distribution which is attributed to the Ca2+content and temperature. In addition, the particles smallerthan 315m contain twice higher Ca2+ than all other parti-cle sizes contained. Microscopic photographs of borax pen-tahydrate in pure, containing 0.67 wt. and 2.5 wt.% Ca2+are given in Fig. 5, respectively. In pure state, the surfaceof BPH particles are smooth whereas the quality of par-ticles are decreased with increasing Ca2+ quantity on thesurface.

4. Conclusion

In order to obtain anhydrous borax with low bulk den-sity by calcination in batch calcinator, particular attention ispaid to cover BPH surface with Ca(OH)2 layer. The follow-ing conclusion can be withdrawn from the results obtainedduring the calcination of covered BPH particles in a batchcalcinator:

(a) The calcination of BPH with single stage cannot becarried out at the temperature higher than 300 C sinceagglomeration is started. The calcination of BPH to BAalso takes longer at around 300 C.

(b) BPH can be transformed to BA without agglomerationby mixing CaO with BPH in solid state at temperaturerange 300500 C. In this case, the content of CaO inBA must be higher than 7%.

(c) Covering the BPH particles with Ca(OH)2 aqueoussolution also prevented the agglomeration in tempera-ture range 300500 C. In this kind of operation, theBPH particle surface was covered with a thin layer of

Ca(OH)2 to control the transport rate of liberated waterfrom inside BPH particle to air. By this method, thecontent of BPH can be decreased up to about 0.68 wt.%Ca2+.

(d) Calcination of BPH covered with Ca(OH)2 to BA re-alized in 50, 20 and 5 min intervals at 300, 400 and500 C, respectively. This result shows that at temper-atures higher than 400 C, the puffed anhydrous boraxcan be obtained by continuous operation. Also, the highbulk density may be obtained by crashing puffed borax.

(e) The obtained low bulk density puffed borax is preferredpriority in some chemical process such as solid statereaction, adsorption and high temperature applicationof BA.

References

[1] R.P. Douglas, F.G. Donald, Refinement of the structure of tincal-conite, Acta Cryst. C47 (1991) 22792282.

[2] G. Nencetti, A. Pennacchi, Chim. Ind. Milan 46 (5) (1968) 518525.[3] Kirk-Othmer, Enc. Chem. Thech., 4 ed., vol. 4, 1992, p. 388.[4] O. Sahin, A.N. Bulutcu, Turk. J. Chem. 26 (2002) 8996.[5] S. Kocakusak, K. Akcay, T. Ayok, H.J. Koroglu, O.T. Savasc, R.

Tolun, Ind. Eng. Chem. Res. 35 (1996) 14241428.[6] O. Sahin, U.G. Beker, A.N. Bulutucu, Int. J. Storing Handl. Process.

Powder 7 (1995) 165167.[7] O. Sahin, A.N. Bulutucu, Chem. Eng. Process. 41 (2002) 135141.[8] T.E. Raymond, US Patent 4 412 978 (1983).[9] Z.P. Li, N. Morigazaki, B.H. Liu, S. Suda, J. Alloys Compd. 349

(2003) 232236.[10] D.F. Snell, C.L. Hilton, Encyclopedia of Industrial Chemical Anal-

ysis, vol. 7, Wiley, New York, 1968, pp. 373384.[11] ISO 3424, Sodium perborate for industrial use-determination of bulk

density.


The influence of temperature and inlet velocity on cyclone pressure drop:a CFD study

Jolius Gimbun, T.G. Chuah, A. Fakhrul-Razi, Thomas S.Y. ChoongDepartment of Chemical and Environmental Engineering, Faculty of Engineering, Universiti Putra Malaysia 43400 UPM Serdang, Selangor D. E., Malaysia

Received 16 February 2004; received in revised form 22 March 2004; accepted 22 March 2004Available online 18 May 2004

Abstract

This work presents a computational fluid dynamics (CFD) calculation to predict and to evaluate the effects of temperature and inlet velocityon the pressure drop of gas cyclones. The numerical solutions were carried out using spreadsheet and commercial CFD code Fluent 6.1. Thispaper also reviews four empirical models for the prediction of cyclone pressure drop, namely [Air pollution control: a design approach, in: C.David Cooper, F.C. Alley (Eds.), Cyclones, second ed., Woveland Press Inc., Illinois, 1939, p. 127139] [Chem. Eng. (1983) 99] [DoctoralThesis, Havarad University, USA, 1988], and [Chem. Eng. Progress (1993) 51]. All the predictions proved to be satisfactory when comparedwith the presented experimental data. The CFD simulations predict excellently the cyclone pressure drop under different temperature and inletvelocity with a maximum deviation of 3% from the experimental data. Specifically, results obtained from the computer modelling exercisehave demonstrated that CFD is a best method of modelling the cyclones operating pressure drop. 2004 Elsevier B.V. All rights reserved.

Keywords: Cyclone; CFD; Pressure drop; Temperature; Inlet velocity

1. Introduction

Cyclones are devices that employ a centrifugal forcegenerated by a spinning gas stream to separate particlesfrom the carrier gas. Their simple design, low capital costand nearly maintenance-free operation make them ideal foruse as pre-cleaners for more expensive final control devicessuch as baghouses or electrostatic precipitators. Cyclonesare particularly well suited for high temperature and pres-sure conditions because of their rugged design and flexiblecomponents materials. Cyclone collection efficiencies canreach 99% for particles bigger than 5m [12], and can beoperated at very high dust loading. Cyclones are used forthe removal of large particles for both air pollution controland process use. Application in extreme condition includesthe removing of coal dust in power plant, and the use as aspray dryer or gasification reactor.

Engineers are generally interested in two parameters inorder to carry out an assessment of the design and perfor-mance of a cyclone. These parameters are the collection

Corresponding author. Tel.: +60-19-248-9101; fax: +60-38946-7120.E-mail address: [email protected] (J. Gimbun).

efficiency of particle and pressure drop through the cy-clone. An accurate prediction of cyclone pressure drop isvery important because it relates directly to operating costs.Higher inlet velocities give higher collection efficiencies fora given cyclone, but this also increases the pressure dropacross the cyclone. Therefore, a trade off must be madebetween higher collection efficiency and low pressure dropacross the cyclone. Computational fluid dynamics (CFD)has a great potential to predict the flow field characteristicsand particle trajectories inside the cyclone as well as thepressure drop [8]. The complicated swirling turbulent flowin a cyclone places great demands on the numerical tech-niques and the turbulence models employed in the CFDcodes when modelling the cyclone pressure drop.

In this study, pressure drop calculations are performed us-ing CFD and compared with four empirical model of Shep-herd and Lapple [11], Casal and Martinez [3], Dirgo [5],and Coker [4]. These four empirical models and CFD pre-diction are compared with the experimental data presentedin the literature. In this study, the CFD calculations arecarried out using commercial finite volume code Fluent 6.1and the empirical models are performed in Microsoft Excelspreadsheet.


8 J. Gimbun et al. / Chemical Engineering and Processing 44 (2005) 712

D

b De

a Sh

H

B ELEVATION PLAN

Fig. 1. Tangential cyclone configuration.

2. Cyclone design

There are a number of different forms of cyclone butthe reverse flow cyclone represented in Fig. 1 is the mostcommon design used in the industry. The cyclone consists offour main parts: the inlet, the separation chamber, the dustchamber and the vortex finder. Tangential inlets are preferredfor the separation of solid particles from gases [1]. In thisstudy, the numerical simulation deals with the standard caseof reverse flow cyclone with a tangential rectangular inlet.Cyclone dimension used in this simulation are as shown inTable 1.

3. Computational fluid dynamics approach

Fluent is a commercially available CFD code whichutilises the finite volume formulation to carry out coupledor segregated calculations (with reference to the conserva-tion of mass, momentum and energy equations). It is ideallysuited for incompressible to mildly compressible flows.The conservation of mass, momentum and energy in fluidflows are expressed in terms of non-linear partial differen-tial equations which defy solution by analytical means. Thesolution of these equations has been made possible by theadvent of powerful workstations, opening avenues towardsthe calculation of complicated flow fields with relative ease.

For the turbulent flow in cyclones, the key to the successof CFD lies with the accurate description of the turbulentbehaviour of the flow [8]. To model the swirling turbulent

Table 1Cyclone geometry used in this simulations

Geometry a/D b/D De/D S/D h/D H/D B/D Da

Stairmand high efficiency 0.5 0.2 0.5 0.5 1.5 4 0.375 0.305Bohnet [2] 0.533 0.133 0.333 0.733 0.693 2.58 0.333 0.15

a Unit in meters.

flow in a cyclone separator, there are a number of turbulencemodels available in Fluent. These range from the standardk model to the more complicated Reynolds stress model(RSM). The k model involves the solution of transportequations for the kinetic energy of turbulence and its dis-sipation rate and the calculation of a turbulent contributionto the viscosity at each computational cell. The standardk, RNG k and realizable k model was not optimizedfor strongly swirling flows found for example in cyclones[10,6]. Turbulence may be stabilised or destabilised in theparts of flow domain where strong streamline curvature ispresence. However to reduce the computational effort theRNG k model can be used with about 12% deviation onexperimental data [8]. The numerical studies carried out byFredriksson [7] reveal that the RNG k model under pre-dicts the variation of the axial velocity profile across theradial direction and also over predicts the magnitude of thetangential velocity and the cyclone pressure drop.

The Reynolds stress model requires the solution of trans-port equations for each of the Reynolds stress componentsas well as for dissipation transport without the necessity tocalculate an isotropic turbulent viscosity field. The Reynoldsstress turbulence model yield an accurate prediction on swirlflow pattern, axial velocity, tangential velocity and pressuredrop on cyclone simulation [7,6,13,10].

The finite volume methods have been used to discretisedthe partial differential equations of the model using the Sim-ple method for pressurevelocity coupling and the secondorder upwind scheme to interpolate the variables on the sur-face of the control volume. The segregated solution algo-rithm was selected. The Reynolds stress turbulence modelwas used in this model due to the anisotropic nature ofthe turbulence in cyclones. Standard fluent wall functionswere applied and high order discretisation schemes werealso used.

Under the RSM second order upwind for discretisationthere is a difficulty to reach the convergence in simulation.The residuals may exhibit cyclic tendencies which meanthat the transient pattern occurs. In this instance, the solvermust be changed to a transient solver and makes the timestep something in the region of 0.025 s or a tiny fraction ofthe residence time of the cyclone. The simulation is thensolved with a coupling of unsteady and steady state solverin Fluent. For the simulation using RNG k model thesteady state solver is sufficient to reach the convergence. TheCFD simulation was performed with a Pentium IV 2.8 GHzHP workstation XW8000 with 512 cache-memory, 1 GBRAM-memory, and 110 GB hard disc memory.

J. Gimbun et al. / Chemical Engineering and Processing 44 (2005) 712 9

4. Pressure drop empirical models

The pressure drop across the cyclone is an important pa-rameter in the evaluation of cyclone performance. It is ameasure of the amount of work that is required to operate thecyclone at given conditions, which is important for opera-tional and economical reasons. The total pressure drop overa cyclone consists of losses at the inlet, outlet and withinthe cyclone body. The main part of the pressure drop, i.e.about 80%, is considered to be pressure losses inside the cy-clone due to the energy dissipation by the viscous stress ofthe turbulent rotational flow [9]. The remaining 20% of thepressure drop are caused by the contraction of the fluid flowat the outlet, expansion at the inlet and by fluid friction onthe cyclone wall surface.

In this study, four empirical models in the literature havebeen chosen to predict the pressure drop over a cyclone,namely Shepherd and Lapple [11], Casal and Martinez [3],Dirgo [5], and Coker [4]. In these four models, the totalpressure drop in cyclone is either assumed equal to the staticpressure drop or as a function of cyclone dimension andpressure drop coefficient. Generally cyclone pressure dropis proportional to the velocity head and can be written in theform of

P = gv2i

2(1)

In the Shepherd and Lapple [11] model, is obtained byassuming static pressure drop given as

= 16 abD2e

(2)

In Casal and Martinez [3], is derived from the statisticalanalysis on experimental data given as

= 11.3(

abD2e

)2+ 3.33 (3)

In Dirgo [5] model, is a function of cyclone dimensiongiven as

= 20(

abD2e

)[S/D

(H/D)(h/D)(B/D)

]1/3(4)

In Coker [4], is given as

= 9.47 abD2e

(5)

5. Result and discussion

5.1. Pressure drop prediction under different inlet velocity

Measurement of the cyclone pressure drop was carried outfor inlet velocity ranging from 4.62 to 14.16 m/s by Bohnet[2], and from 5.1 to 25 m/s by Griffiths and Boysan [8].The numerical calculation was made with a fine numerical

Fig. 2. CFD surface mesh for (A) Stairmand high efficiency, and (B)Bohnet [2] cyclone.

grid as shown in Fig. 2. Several empirical correlation fromliterature, Shepherd and Lapple [11], Casal and Martinez [3],Dirgo [5] and Coker [4], were also considered to comparedexperimental data and numerical solution from Fluent code.Figs. 3 and 5 present the comparison. The three-dimensionmap of static pressure of Bohnet and Stairmand cyclones isshown in Figs. 4 and 6, respectively.

5.2. Pressure drop prediction under differentoperating temperature

Measurement of the cyclone pressure drop of different op-erating temperature was carried out for temperature rangingfrom 293 to 1123 K by Bohnet [2]. The comparison betweenthe Bohnet experiment, empirical model and CFD predictionis shown in Figs. 7 and 8. Fig. 9 shows the three-dimensionmap of static pressure for operating temperature of 950 K.

The calculated static pressure drop of cyclone betweeninlet and outlet for the different numerical model is shownin Figs. 3, 5, 7 and 8. It is shown that good agreement of

0

500

1000

1500

2000

2500

4 6 8 10 12 14Inlet gasvelocity (m/s)

Pres

sure

Dro

p (P

a)

16

Dirgo

Shepherd & Lapple

Coker

Casal & Martinez

CFD RSM

CFD RNGk-

Fig. 3. Evolution of pressure drop with inlet velocity. Comparison betweendata presented by Bohnet [2], the predictions of CFD and four empiricalmodels (P = 1 bar, T = 293 K, D = 150 mm, geometry Bohnet [2]).


Fig. 4. Evolution of pressure drop with inlet velocity. Comparison betweendata presented by Graffiths and Boysan [8], the predictions of CFD andfour empirical models (P = 1 bar, T = 293 K, D = 0.305 m, geometryStairmand high efficiency.

0

500

1000

1500

2000

2500

5 10 15 20Velocity (m/s)

Pres

sure

Dro

p (P

a)

25

Shepherd & Lapple

DirgoCoker

Casal & Martinez

CFD RSM

CFD RNG k-

Fig. 5. 2D and 3D map of static pressure of Bohnet [2] cyclone for inletvelocity of 4.62 m/s and temperature 293 K.

Fig. 6. 3D map of static pressure of Stairmand cyclone for inlet velocityof 20 m/s and temperature 293 K.

0

500

1000

1500

2000

2500

0 200 400 600 800 1000 1200

Temperature (K)

Pres

sure

Dro

p (P

a)

Dirgo

Coker

Casal & Martinez

CFD RSM

Shepherd & LappleCFD RNGk-

Fig. 7. Evolution of pressure drop with operating temperature. Compar-ison between data presented by Bohnet [2], the predictions of CFD andfour empirical models (Q = 100 m3/h, T = 2931123 K, D = 150 mm,geometry Bohnet [2].

0

200

400

600

800

1000

1200

1400

1600

0 200 400 600 800 1000 1200Temperature (K)

Pres

sure

Dro

p (P

a)Dirgo

Coker

Casal & Martinez

CFD RSM

Shepherd & LappleCFD RNGk-

Fig. 8. Evolution of pressure drop with operating temperature. Compar-ison between data presented by Bohnet [2], the predictions of CFD andfour empirical models (Q = 80 m3/h, T = 2931123 K, D = 150 mm,geometry Bohnet [2].

Fig. 9. 3D Map of static pressure of Bohnet [2] cyclone for inlet velocityof 11.48 m/s and temperature 850 K.

J. Gimbun et al. / Chemical Engineering and Processing 44 (2005) 712 11

the CFD numerical calculation when compared with experi-mental data, and predictions from empirical correlation. Theresults show that the CFD prediction by using the Fluentcode can be used for pressure drop evaluation in cyclonedesign. This low-pressure centre can be responsible for theflow reversion and deviation of the axial velocity peak tothe wall of the vortex finder pipe as showed in Figs. 4, 6and 9.

The Fluent code with the RSM turbulence model, predictvery well the pressure drop in cyclones and can be used incyclone design for any operational conditions (Figs. 3, 5,7 and 8). In the CFD numerical calculations a very smallpressure drop deviation were observed, with less than 3%of deviation at different inlet velocity which probably inthe same magnitude of the experimental error. The CFDsimulations with RNG k turbulence model still yield areasonably good prediction (Figs. 3, 5, 7 and 8) with thedeviation about 1420% of an experimental data. It consid-erably tolerable since the RNG k model is much less oncomputational time required compared to the complicatedRSM turbulence model. In all cases of the simulation theRNG k model considerably underestimates the cyclonepressure drop as revealed by Griffiths and Boysan [8].However under extreme temperature (>850 K) there is nosignificant difference between RNG k and RSM modelprediction.

The cyclone pressure drop can be rewritten as a func-tion of inlet velocity head. The empirical model used forthe prediction of pressure drop is much depends on the cy-clone operating condition. Shepheard and Lapple [11] andDirgo (1990) model show a good prediction on cyclone pres-sure drop under different operational inlet velocity (Figs. 3and 4), the prediction within 620% of the measured value.However, Dirgos model does not take into account temper-ature in its model: its predictions are, therefore, not reliableunder different operating temperature (Figs. 7 and 8). Underhigh temperature Dirgos model considerably overestimatesthe cyclone pressure drop with relative error of more than90%.

The pressure drop decreases significantly with risingtemperature. This effect is mainly due to the decrease of thedensity and the increase of the viscosity of the gas. Accord-ing to Figs. 7 and 8, the models of Shepheard and Lapplegive quite a good approximation of the pressure drop withan error in the prediction of about 37%. The model of Casaland Martinez, and Coker were under predicts the cyclonepressure drop under different operating temperature withrelative error of 72 and 52%, respectively. Since Casal andMartinez, and Coker models consistently underestimate thecyclone pressure drop in all the conditions studied, they aretherefore not particularly useful for design purposes. It isalways more practical to design for a larger pressure dropthan for a smaller one. In overall, the cyclone pressure dropis somewhat closer to Coker model for the operating tem-perature more than 500 K and close to Shepherd and Lapplemodel for the operating temperature lower than 500 K.

6. Conclusions

The CFD code FLUENT with the RSM turbulence model,predict very well the pressure drop in cyclones and can beused in cyclone design for any operating conditions. In theCFD numerical calculations a very small pressure drop de-viation were observed, with about 3% of deviation, probablyin the same magnitude of the experimental error. Howeverbehind the accuracy of the complicated RSM model it doesrequire much expensive computational effort compared tothe RNG k model. CFD with RNG k turbulence modelstill yield a reasonably good prediction on cyclone pressuredrop with deviation of 1418% on measured value.

The cyclone pressure drop can be rewritten as a functionof inlet velocity head. The model used for the prediction ofpressure drop depends on the cyclone operating condition.Both Shepherd and Lapple, and Dirgo models show a goodprediction on cyclone pressure drop under different opera-tional inlet velocity. However, Dirgos model is unable topredict accurately the pressure drop under different oper-ating temperature. For the various temperature conditions,Shepherd and Lapples pressure drop model prediction isthe best. We therefore, conclude that the Shepherd and Lap-ple model should be used for estimation of pressure dropin cyclone design.

Acknowledgements

The authors would like to thank Dr. Tom Fraser, FluentIndia and Fluent Europe UK for their guidance and support.The authors are grateful to the referees for their useful com-ments.

Appendix A. Nomenclature

a cyclone inlet height (m)b cyclone inlet width (m)B cyclone dust outlet diameter (m)D cyclone body diameter (m)De cyclone gas outlet diameter (m)h cyclone cylinder height (m)H cyclone height (m)P cyclone pressure drop (Pa)S cyclone gas outlet duct length (m)vi inlet velocity (m/s)

Greek letters velocity head, pressure drop coefficient (m)g gas density (kg/m3)

References

[1] S. Altmeyer, V. Mathieu, S. Jullemier, P. Contal, N. Midoux, S. Rode,J.-P. Leclerc, Comparison of different models of cyclone prediction


performance for various operating conditions using a general soft-ware, Chem. Eng. Process. 43 (2004) 511522.

[2] M. Bohnet, Influence of the gas temperature on the separation effi-ciency of aerocyclones, Chem. Eng. Process. 34 (1995) 151156.

[3] J. Casal, J.M. Martinez, A better way to calculate cyclone pressuredrop, Chem. Eng. 90 (1983) 99.

[4] A.K. Coker, Understand cyclone design, Chem. Eng. Progr. 28 (1993)5155.

[5] J. Dirgo, Relationships between cyclone dimensions and performance.Doctoral Thesis, Havarad University, USA, 1988.

[6] T. Fraser, personal communication, 2003. [email protected], http://www.cfd-online.com.

[7] C. Fredriksson, Exploratory experimental and theoretical studies ofcyclone gasification of wood powder. Doctoral Thesis, Lulea Uni-versity of Technology, Sweden, 2003.

[8] W.D. Griffiths, F. Boysan, Computational fluid dynamics (CFD) andempirical modelling of the performance of a number of cyclonesamplers, J. Aerosol Sci. 27 (1996) 281304.

[9] A. Ogawa, Separation of particles from air and gasses, vols. 1 and2, CRC Press, Boca Raton, Florida, USA, 1984.

[10] M. Reddy, Fluent India, personal communication, 2003, [email protected].

[11] C.B. Shepherd, C.E. Lapple, Air pollution control: a design approach.In: C.D Cooper, F.C. Alley (Eds.), Cyclones, second ed., WovelandPress Inc., Illinois, 1939, pp. 127139.

[12] P.D. Silva, C. Briens, A. Bernis, Development of a new rapid methodto measure erosion rates in laboratory and pilot plant cyclones,Powder Technol. 131 (2003) 111119.

[13] M. Slack, Cyclonic separator, QNETCFD application Challenge,http://www.qnet-cfd.net, Accessed on 8th August 2003.


A pollution reduction methodology in reactor designQishi Chen, Xiao Feng

Department of Chemical Engineering, State Key Laboratory of Multi-Phase Flow in Power Engineering, Xian Jiaotong University, Xian 710049, ChinaReceived 20 January 2003; received in revised form 18 March 2004; accepted 26 March 2004

Available online 18 May 2004

Abstract

An algorithm for waste and pollutants reduction in reactor design is presented in this paper. This algorithm use potential environmentalimpact balance (PEI) and PEI rate-law expression to track the generated PEI throughout reaction process within a reactor; and study howreaction conditions (temperature, pressure, concentration, etc.) and various engineering factors (heat and mass transfer, and back-mixing andso on) affect process environmental performance. The form of PEI rate-law expression should be consistent with the method for calculatingthe overall PEI of mixtures of chemicals inside reactors. The algorithm can be used as a tool to aid in designing chemical reactors withenvironmentally friendliness processes. The use of the methodology is illustrated with the reaction system of allyl chloride production. 2004 Elsevier B.V. All rights reserved.

Keywords: Reactor design; Potential environmental impact; Chemical reaction engineering; Impact analysis

1. Introduction

There is currently a great deal of interest in the devel-opment of methods that can be used to prevent or at leastreduce the generation of pollution, because it may result inlower operating costs due to better utilization of raw mate-rials and energy and reduced waste treatment and disposalcosts. Researches have shown that the most opportunitiesof environmental impact minimization exist in the processresearch and development and design stage of chemical pro-cesses [1,2]. Waste and pollutant generation in many chemi-cal manufacturing process can be traced back to the reactionsystem, where reaction pathways that produce high yieldsmay also generate toxic byproducts or use environmentallyunfriendly raw materials, solvents, and catalystswhichmust be dealt with downstream at a significant cost [3];and the size, configuration, and composition of the reactorare very important factors controlling the greenness of aprocess [4]. Therefore, the design of new reactor is a veryimportant element in preventing pollution at the source.

Abbreviations: CSTR, continuous stirred-tank reactor; EIM, environ-mental impact minimization; NP, non-products; PEI, potential environ-mental impact; PFR, plug-flow reactor

Corresponding author. Tel.: +86-29-82668980;fax: +86-29-83237910.

E-mail address: [email protected] (X. Feng).

To address the ideal including environmental impact con-siderations into process design, Cabezas et al. [5] introduceda potential environmental impact (PEI) balance as an amend-ment of the Waste Reduction Algorithm [6]. However, thisalgorithm is simply a tool to be used to aid in evaluating theenvironmental friendliness of a process [7].

Making clear the effects of reaction conditions suchas temperature, pressure and concentration as well as en-gineering factors involving back-mixing, heat and masstransfer and so forth, on the performances of reaction pro-cess is one of critical bases for benign reactor design. Inthis paper, potential environmental impact balance and PEIrate-law expression are proposed as a tool to be used toeffectively discover these effects on environmental perfor-mance of a reaction process. With this methodology, thePEI of waste and pollutants could be tracked throughoutreaction process within reactors, and appropriate reactortype and reaction conditions, which could lead to envi-ronmentally benign reaction processes, could be selectedexpediently.

2. The potential environmental impact of achemical process

In the algorithm [59], the potential environmental im-pact of a certain quantity of a certain material and energy


14 Q. Chen, X. Feng / Chemical Engineering and Processing 44 (2005) 1321

is defined as the effect that this material and energy wouldhave on the environment if they were to be emitted into theenvironment. For a steady-state process, the balance equa-tion is as follows:

Iin Iout + Igen = 0 (1)where Iin and Iout are the input and output rates of PEI,respectively, and Igen denotes the PEI generation rate insidethe processes.

Iin =j

m(in)j

k

xkjk + (2)

Iout =j

m(out)j

k

xkjk + (3)

where m(in)j is the mass flow rate of input stream j, m(out)jthe mass flow rate of output stream j, xkj the mass fractionof chemical k in stream j, and k the overall potential envi-ronmental impact (PEI) of chemical k.k =

l

lskl (4)

where skl is the specific potential environmental impact ofchemical k for environmental impact category l, in units ofPEI/kg of chemical k. Details of the method for calculat-ing skl appeared in reference [7]. The impact categories in-volved in this theory fall into two general area concerningwith four categories in each area:

(1) The global atmospheric categories: Global warming potential (GWP). Ozone depletion potential (ODP). Acidification and acid-rain potential (AP). Photochemical oxidation or smog formation potential

(PCOP).(2) The local toxicological impact categories:

Human toxicity potential by ingestion (HTPI). Human toxicity potential by either inhalation or der-

mal exposure (HTPE). Aquatic toxicity potential (ATP). Terrestrial toxicity potential (TTP).

In Eq. (4), l is a relative weighting factor for impact typel independent of chemical k, and represent the value thatsociety places on particular types of environmental impact[8]. This factor allows Eq. (4) to be customized to specificor local conditions. The suggested procedure is to initiallyset all l to a value of one, and allow users to vary indi-vidual l from 1 to 10 according to local needs and polices[5,8].

3. The algorithm

In order to perform waste reduction and pollution pre-vention in reactor design, studying the effects of reaction

conditions and engineering factors and the like on the gen-eration of waste or pollutant is very important. However,studying each of wastes and pollutants within reactor istime-consuming and cost-consuming and the results may beconflicting or competing, that is to say that reducing one pol-lutant or waste may increasing the others. In addition, thisresearch method does not address the impact of the pollu-tion generated within a reactor. For example, reactor A mayproduce 50 kg/h of pollutants while reactor B may produce100 kg/h. However, the pollutants generated within reactorA may be much more environmentally unfriendly than thosegenerated within reactor B. Thus, the strategies for reduc-ing pollutants and wastes generated from reaction processshould be studied through their PEI rather than their massor concentrate, the results would be, then, scientific and be-yond conflicting.

Traditionally, the foundation of reactor design is the equa-tions such as reaction rate equation, mass, energy and mo-mentum balances as well as the model describing the fluidflow within reactors. With these equations, reactor designerscan track compositions of chemicals, pressure and tempera-ture throughout reaction process and discover the effects ofreaction conditions as well as engineering factors on theseparameters, and then, an appropriate reactor with profitableprocesses can be designed. Nevertheless, this kind of designmethod does not consider environmental perspective of pro-cesses effectively.

If the PEI generated within reactors could be trackedthroughout the process, the influence of reaction conditionsand engineering factors on the generated PEI could alsobe clarified, and then reactors that generate the minimumamount of PEI would be easily obtained. The above thinkingcan be implemented by using the PEI balance and the PEIrate-law expression in reactor design. In this way, the designmethod is similar to the traditional reactor design methodbased on balance equation and reaction rate equation.

3.1. PEI rate-law expression

The PEI transformation rate of chemical k can be definedas:

rPEI,k = dIkV dt

(5)

where Ik represents PEI of chemical k, Ik = mkk, mkis the mass of substance k, k the overall PEI per unitmass of chemical k in units of PEI/kg, and V the reactionvolume. According to Eq. (2), the PEI transformation rateof a reaction system rPEI, in units of PEI/(L s), is:

rPEI =k

rPEI,k =k

rkkMk 103 (6)

where Mk denotes molecular weight of chemical k in unitsof g/mol, and rk the rate of appearance of chemical k in unitsof mol/(L s).

Q. Chen, X. Feng / Chemical Engineering and Processing 44 (2005) 1321 15

Such an expression, which shows how the rate depends onthe concentrations of reactants, is called PEI rate-law expres-sion. Note, PEI rate-law expression should be consistent withthe method for calculating the overall PEI in Eqs. (2) and(3), which ignore the combinational impacts that could beassociate with mixtures of chemicals which accounts for theadditional terms not included into those equations. It shouldbe realized that if the method for calculating the overall PEIof mixtures in reactors improve or adopt other method, theform of the PEI rate-law expression should be adjusted ac-cordingly.

Obviously, rPEI can be expressed in terms of reaction tem-perature and concentration or partial pressure of chemicalsand so on. Therefore, the PEI rate-law expression can behelpful to analyze the effect of concentrations and temper-ature on the PEI transformation rate, rPEI, and to study theinfluence of back-mixing on process environmental perfor-mance. At least, the PEI rate-law expression can reveal thefactors that control the transformation rate of PEI, so as toprovide guidance for the selection of reactor type and opera-tion conditions, and the inner structure of the reactor, whichproduce desired products while creating minimum undesiredpotential environmental impact.

3.2. PEI balance

Since desired products are the substance we desire and theobjective of this paper is to present a methodology of wastereduction, that is the primary concern is reducing the impactand the amount of the non-products, the k of the desiredproducts is not considered in PEI balance. This insures thatthe user or producer is not directly penalized for producinga chemical that has a high PEI value. Auxiliary materials(catalyst, solvent and so on) used in reaction processes mustleave the production process completely as waste or emis-sion [10], so the PEI of the auxiliary materials should beconsidered. Raw materials that if possible shall be entirelyprocessed into the desired product are not as a rule com-pletely converted into the product. The losses that arise arethe cause of generated waste and emission [10], thus theirpotential environmental impact should be taken into account.To sum up, the PEI balance of reaction system under steadystate is:INPgen = INPout INPin (7)where the superscript NP represents materials except de-sired products, that is, non-products. INPout and INPin are inputand output rates of PEI caused by non-products, and INPgenthe rate at which impact is generated in a reactor.

For a differential flow reactor, dV, we write a PEI balanceequation over the reactor:dIgen = rPEI dV (8)With PEI balance Eq. (7), combining with other equations,designers can reveal how PEI change with reaction time oraxial and radial direction inside reactors, i.e. can track the

changing of PEI throughout reactors; using Eq. (8) combin-ing with other rate equations, the influence of operation pa-rameters such as temperature, concentration, pressure andreaction time, etc. on environmental metrics of a chemicalprocess could be analyzed facilely, and then, reactors gen-erating a minimum of PEI could be designed easily.

4. Case study

In this case study, we use the environmental impact indicesbelow, which can be derived from Eq. (1), to characterizeenvironmental performance of reaction processes [7,8]:(1) Potential environmental impact generation rate by non-

products INPgen.(2) Specific potential environmental impact due to non-

products PEI.

PEI =INPgenp pp

(9)

where pp is the mass flow rate of product p, and PEI hasunits of potential environmental impact per mass of prod-ucts. Obviously, the smaller the values of INPgen and PEI,the more environmental friendly the process, and all othersfactors such as economics being equal, the more desirable.INPgen would be useful in comparing different designs on anabsolute basis, while PEI would be useful in comparingdifferent designs independently of plant size [8].

These indices characterize some aspects of the genera-tion of PEI within a reaction process. They are very usefulin addressing questions related to the ability of the reactorto produce desired products while creating a minimum ofundesired potential environmental impact.

To illustrate the use of the algorithm, a case of allyl chlo-ride manufacturing was used. Allyl chloride is manufacturedby means of non-catalytic chlorination of propylene. Con-sider the following reaction system.

The principle reaction is:

Cl2 + CH2=CHCH3 CH2=CHCH2Cl + HCl (10)The main secondary reaction is further chlorination of allylchloride to 1,3-dichloropropene:Cl2 +CH2=CHCH2ClClCH=CHCH2Cl+HCl (11)Another important side-reaction is the additive chlorinationof propene to 1,2-dichloropropane:Cl2 + CH2=CHCH3 CH2ClCHClCH3 (12)This reaction system is a kind of complex van de vussereaction, a typical reaction process involving consecutiveand parallel reactions. It is, therefore, sufficiently complexto illustrate the algorithm. Due to the limit to the space ofthe article, we only select temperature, concentration andback-mixing to be the object of research to illustrate the useof the algorithm.


The following calculations and curves drawing can be ac-complished by mathematical software such as Mathcad2000or Matlab.

4.1. Preconditions

The kinetics of the reaction system [11] are:r1 = k1CACB (13)r2 = k2CACC (14)r3 = k3CACB (15)where r1, r2, and r3 represent the rates of the reactionsin reaction Eqs. (10)(12), respectively, expressed interms of mol/(L s), and k1 = 1.5 106 e66271/RT, k2 =4.4 108 e99410/RT and k3 = 100 e33140/RT. Letters AFrepresent Cl2, propylene, allyl chloride, hydrogen chloride,1,3-dichloropropene and 1,2-dichloropropane, respectively.

Since the purpose of this case study is only to illustratethe use of PEI balance and PEI rate-law expression, nota practical reactor design, we might as well suppose thereaction process is isovolumetric.

For all cases in this case study, the inlet concentrationsof all chemicals are CA,0 = CB,0 = 0.024 mol/L, CC,0 =CD,0 = CE,0 = CF,0 = 0 mol/L. The volumetric flow rateof the reaction feed at the reactor inlet is v0 = 325 L/s.

The specific potential environmental impact values foreach impact category for the chemicals involved in the pro-cess are shown in Table 1. Details of the method for calcu-lating these data appear in reference [7]. Using these data,the overall PEI of each chemical can be obtained accordingto Eq. (4).

4.2. Mathematical models of ideal reactors

4.2.1. Plug-flow reactors (PFR)The differential mass balances of PFR reactors are:

v0 dCA = rA dV (16)v0 dCB = rB dV (17)v0 dCC = rC dV (18)where rA = r1+ r2+ r3, rB = r1+ r3, and rC = r1 r2.rA, rB and rC are the rate of appearance of A, B and C,

Table 1The specific potential environmental impact values for each category for the chemicals used in this case study [9]Compound HTPI HTPE ATP TTP GWP PCOP AP

A 0 5.4 104 22 0 0 0 0B 0 0 3.1 102 0 0 2.1 0C 0.51 5.4 104 0.10 0.51 0 0 0D 0.78 2.3 104 4.6 104 0.78 0 0 0.86E 1.1 0 0.014 1.1 0 0 0F 0.61 0 0.0059 0.61 0 0 0

Values are in PEI/kg.

respectively.The differential PEI balance of PFR reactorsis:

dIgen = rPEI dV (19)Dividing Eq. (18) by Eq. (17), then integrate, we obtain:

(CC)PFR = ba 1 (CB C

1aB,0 C

aB),

a = k2k1 + k3 , b =

k1k1 + k3 (20)

Dividing Eq. (19) by Eq. (17), and then integrate this, weobtain:

(Igen)PFR = v0 CBCB,0

rPEI

rBdCB (21)

4.2.2. Continuous stirred-tank reactors (CSTR)For CSTR reactors, the mass and PEI balances are:

v0CC = rCV (22)v0(CB,0 CB) = rBV (23)Igen = rPEIV (24)From Eqs. (23) and (24), we obtain:Igen = rPEI

rB(CB CB,0)v0 (25)

From Eqs. (22) and (23), we obtain:(rCrB

)CSTR

= CCCB CB,0 = a

(CCCB

) b

Solving this equation, we can get:

(CC)CSTR = bCB(CB,0 CB)a(CB,0 CB)+ CB (26)

4.3. The effect of back-mixing on environmentalperformance

It is well known that PFR represents reactors withoutback-mixing, CSTR represents reactors with the maximumback-mixing, we can therefore study effect of back-mixingon process environmental aspect by studying these two kindsof reactor.


4.3.1. The effect on Igen

4.3.1.1. l = 1. When l = 1, namely, the importance ofthe above eight environmental impact categories is deemedthe same, and the PEI of each non-product is considered,the PEI rate-law expression due to non-products is:

rPEI = 1.6k1CACB 1.5k3CACB 1.2k2CACC (27)

rPEI has units of PEI/(L s). The value of rPEI is negative be-cause the overall PEI of the reactant A is far greater than thatof each reaction products, in addition, the PEI of product Cis not considered. Suppose reaction temperature T = 530 K,and at the outlet of reactor, CB,out = 0.007 mol/L. Substi-tute Eqs. (20) and (26) into rPEI/rB, respectively, and thengraph them in Fig. 1, respectively.

rPEI

rB= 1.6k1 + 1.5k3 + 1.2k2(CC/CB)

k1 + k3 (28)

According to Eq. (25), Igen of CSTR is:

Igen = [AABCD + 1.5(0.024 0.007)]v0where AABCD is the area of rectangle ABCD in Fig. 1. Ac-cording to Eq. (21), Igen of PFR is:

Igen = [AABCD + 1.5(0.024 0.007)

]v0

where AABCD is the area of curved trapezoid ABCD (theshaded region) in Fig. 1. Obviously, when the outlet concen-tration CB,out is identical for both reactors, AABCD is greaterthan AABCD , thus, lower Igen will be achieved by usingCSTR. The calculation results show that, when CB,out =0.007 mol/L, and v0 = 325 L/s, Igen of CSTR is 3.7 104 PEI/h, and Igen of PFR is 3.3104 PEI/h. Apparently,Igen of CSTR is about 12% lower than that of PFR.

Fig. 2. Curves of rPEI/rB vs. CB of PFR ignoring the overall PEI of A. rPEI /rB in units of PEI/mol. The numbers 17 denote the reaction temperaturesof 450, 470, 490, 500, 510, 520 and 530 K, respectively.

Fig. 1. Curves of rPEI/rB vs. CB, rPEI/rB in units of PEI/mol. The solidcurve is for PFR, the dashed for CSTR.

4.3.1.2. Not considering PEI of A. To illustrate the use ofthe algorithm effectively, suppose A = 0 PEI/kg, and theweighting factor for each impact type is likewise 1, and thePEI of each non-product except Cl2 is also considered. Notethat this case is probably impractical in real manufacturingprocesses. The PEI rate-law expression, rPEI, in units ofPEI/(L s), is:

rPEI =1.1 103k1CACB + 0.049k3CACB+ 0.33k2CACC (29)

rPEI

rB= 1.1 10

3k1 0.049k3 0.33k2(CC/CB)k1 + k3 (30)

Using the same method as mentioned previously, we canobtain the curves describing the changing of rPEI/rB ofPFR and CSTR with CB at various reaction temperature, asshown in Figs. 2 and 3, respectively.

According to Eq. (21), if the inlet and outlet concentrationof B are 0.024 and 0.007 mol/L, respectively, Igen of PFRequals the area of the region below the curves in the rangeof CB = 0.0070.024 mol/L in Fig. 2 multiplied by v0. Forexample, when reaction temperature T = 450 K, Igen of


Fig. 3. Curves of rPEI/rB vs. CB of CSTR ignoring the overall PEI of A. rPEI/rB in units of PEI/mol. The numbers 17 denote the reaction temperaturesof 450, 470, 490, 500, 510, 520 and 530 K, respectively.

PFR equals the area of the shaded region multiplied by v0.From Eq. (25), Igen of CSTR is the product of v0 and thearea of the corresponding rectangle in Fig. 3. For example,when reaction temperature is 450 K, Igen of CSTR equalsthe area of the rectangle in Fig. 3 multiplied by v0. In suchcase, the value of Igen is positive number. Comparing Fig. 2with Fig. 3, it can be found that lower Igen will be obtained ifwe adopt PFR and lower reaction temperature. For example,when reaction temperature is 500 K, and v0 is 325 L/s, Igenof PFR is 4.8 101 PEI/h, while for CSTR, Igen = 1.1 103 PEI/h, which is 2.3 times that of PFR.

4.3.2. The effect on PEIFrom Eqs. (9), (21) and (25), we obtain the formulas for

calculating PEI. Suppose v0 is 325 L/s, then for PFR:

PEI =Igen

v0CC,outMC 103 =13.0CC,out

CBCB,0

rPEI

rBdCB (31)

for CSTR:

PEI =Igen

v0CC,outMC 103 =13.0CC,out

rPEI

rB(CB CB,0)

(32)where CC,out is the outlet concentration of C.According to Eqs. (31) and (32), using the curves about

rPEI/rB versus CB and corresponding area, the superior-ity in environmental index PEI about PFR and CSTRcan be discovered. When l = 1, the outlet concentrationCC,out = 0.011 mol/L and reaction temperature T = 500 K,using CSTR will obtain lower PEI than using PFR. Inthis case, for CSTR, PEI = 31 PEI/kg; for PFR, PEI =26 PEI/kg. Nevertheless, if not considering A, for thesame CC,out and T, using PFR will obtain lower PEIthan using CSTR, for PFR, PEI = 0.30 PEI/kg, while forCSTR, PEI = 0.80 PEI/kg, which is 2.6 times that of PFR.

4.4. The effect of temperature on environmentalperformance

4.4.1. The effect on IgenWhen l = 1, from Eq. (27), it can be seen easily that the

value of rPEI decreases (its absolute value increase) as thereaction temperature rises, that is, Igen can be lowered byincreasing reaction temperature. For example, when reactiontemperature increases from 500 to 600 K, and the outletconcentration CB,out = 0.007 mol/K, and v0 is also 325 L/s,Igen of PFR and CSTR would decrease from 3.2104 and3.5104 to3.7104 and4.2104 PEI/h, respectively.The effect of reaction temperature on Igen can also be foundfrom Figs. 2 and 3.

When not considering the PEI of Cl2, from the PEIrate-law expression, expressed in Eq. (29), we can find thatin the initial stage of the reaction, the determining partin the right side of Eq. (29) is 1.1 103k1CACB +0.049k3CACB, due to the concentration of C is very lowwhile the concentrations of A and B are relatively high.Comparing the magnitudes of the two activation energiesin the two terms, we conclude that the value of this partdecreases as the reaction temperature increases. Thus, inthe early stage of this reaction process, to lower the valueof rPEI, the reactor should operate at relatively high tem-perature. As the reaction proceeds, the concentration of Cis increasing while CA and CB is decreasing, so that theinfluences of the third term, 0.33k2CACC, is becoming sig-nificant. Since the magnitude of activation energy in thisterm is the maximum, operating at relatively low temper-ature would result in a relatively low value of rPEI in thisstage. Accordingly, for the reaction system in this case, aPFR operating at higher temperature first and then lowertemperature must generate lower value of Igen than that sim-ply operating at a corresponding uniform temperature. The


curves in different temperature in Fig. 2 intersect each other,this fact also indicate this result. For example, when the inletand outlet concentrations of B are 0.024 and 0.007 mol/L,respectively, and v0 is 325 L/s, for PFR operating at reactiontemperature of 450K, Igen = 3.9 101 PEI/h, and for PFRat 530 K, Igen = 6.7 101 PEI/h. While for a two-stagePFR, in which the first stage operates at 530 K, and thesecond stage operates at 450 K, the inlet concentration of Bof the second stage is 0.018 mol/L, Igen = 3.4 101 PEI/h.Obviously, it is 13% lower than that of the PFR simplyoperating at reaction temperature of 450 K, and 49% lowerthan that of the PFR simply operating at 530 K.

4.4.2. The effect on PEIKnown from Eqs. (31) and (32), rPEI/rB can be used to

study the effect of the reaction temperature. When l = 1,it is not difficult to find that increasing reaction temperaturewould decrease the values ofrPEI/rB, accordingly the valueof PEI also decreases. For example, for CSTR operating at500 K, if its outlet concentration CC,out = 0.011 mol/L, andv0 is 325 L/s, the value of PEI is 31 PEI/kg, while forCSTR operating at 530 K and with the same CC,out, the valueof PEI is 36 PEI/kg, that is, this CSTR reduced the valueof PEI by 16%. Similar to this example, for the case of notconsidering the potential environmental impact of Cl2, onecan also conclude the effect of temperature on PEI.

4.5. The effect of concentration on environmentalperformance

We here only study the effect of concentration on PEI.Similar to previous method in Section 4.4.2, one can also userPEI/rB to study the effect of concentration. From Eqs. (31)and (32), it can be found that for a particular outlet concen-tration of C the value of PEI would be decreased by in-creasing the value of rPEI/rB. When l = 1, from Eq. (28),the value of rPEI/rB can be increased by augmenting CC anddecreasing CB within a reactor. Since C is a product whereasB is a reactant, using CSTR will lead to higher CC and lowerCB within a reactor than using PFR. Therefore, in this case,using CSTR would result in lower value of PEI than usingPFR operating at the same reaction conditions. When notconsidering the potential environmental impact of Cl2, fromEq. (30), decreasing CC and increasing CB within a reactorwill increase the value of rPEI/rB, accordingly, using PFRwould result in lower PEI than using CSTR. These resultsare also concluded in Section 4.3.2.

5. Discussion

In the case study, the indexes involved are Igen and PEI.However, these two indexes only express the situation ofPEI generation within processes. The environmental perfor-mance of a process also relates to its emission of PEI. There-fore, in the WAR algorithm, the output rate of PEI, Iout, and

the total PEI per mass of product streams leaving the system,Iout, are another category of indices to characterize someaspects of emission of PEI from a manufacturing process.

According to Eq. (1):Iout = Iin + Igen (33)Iout is obtained by dividing Iout by the output of products togive:

Iout = Ioutp p

= Iin + Igenp p

(34)

Substituting Eq. (9) into Eq. (34), we obtain:

Iout = Iinp p

+PEI (35)

For the case study, it is not difficult to obtain that Iin =4.6 104 PEI/h, i.e. Iin is a constant. Therefore, when thefeed condition of a reaction process is fixed, back-mixingand reaction temperature affect Igen and Iout in the sameway. Since

p p = v0CC,outMC 103, it is not difficult

to find that, when the feed condition and CC,out are spec-ified, back-mixing and reaction temperature influence Ioutand PEI in the same way, too.

Thus, in Section 4.3, when l = 1 and the reaction pro-cess is operated in the specified conditions, the results thatIgen and PEI of CSTR are less than those of PFR indicatethat using CSTR can obtain lower Iout and Iout than us-ing PFR. That means at the given conditions, back-mixingmake the emission of potential environmental impact de-crease. By contrast, when not considering the PEI of A, theresults are converse, i.e. using PFR would gain lower PEIgeneration rate than using CSTR; and the value of PEI ofCSTR that operate at reaction temperature 500 K and out-let concentration CC,out = 0.011 mol/L is 2.6 times that ofPFR. The results indicate that at the given reaction condi-tions, back-mixing will increase the emission of potentialenvironmental impact of the reaction processes, accordingly,reactor designers should try to avoid it in such case.

In Section 4.4, when l = 1, the results show that a higherreaction temperature is favorable for the decrease of Igenand PEI in the giving conditions. Therefore, increasing thereaction temperature will lead to the decrease of Iout andIout. When not considering the PEI of Cl2, PFR operatingat a high temperature in the first section and low tempera-ture in the remainder section can result in lower generationand emission of PEI than that operating at a correspondinguniform temperature.

To study the effects of concentration on environmentalperformance of reaction processes, two analyses are intro-duced in Section 4.5. The results show that when l = 1,operating at relatively low concentration of B and highconcentration of C is favorable for decreasing the poten-tial environmental impact created in manufacturing a unitmass of the product C. This also is true for total PEI leav-ing the system per mass of product streams according to


the relations about these two categories of indexes. Bycontrast, when neglecting the PEI of Cl2, the results areconverse.

In the cases study, we have only studied the effects ofback-mixing on the environmental performance. Whereasthe phenomena of back-mixing is only one of engineeringfactors, which still involve pre-mixing, heat and mass trans-fer, thermostability and so on. However, the effects of thoseengineering factors not involved in the case study on envi-ronmental performance can also be simulated and thereforewell studied through a combination of reactor mathematicalmodel, which relates to corresponding engineering factors,with PEI balances. As for the effects of other reaction con-ditions not involved in the case study, such as space time,pressure, compositions of feeds and so on, which may af-fect the environmental performance of reaction processessignificantly in some cases, can also be studied with thehelp of corresponding PEI balances and the PEI rate-lawexpressions, because we can ultimately simulate the changeof the environmental indexes with these reaction conditionsthrough PEI balance and PEI rate-law expression.

The reaction system involved in the case studied is a kindof relatively complex van de vusse reaction, nevertheless thereaction system in real manufacturing process may involvemore reaction types and, therefore, is more complex thanthat one. One can, however, simulate the change of envi-ronmental indexes within a reactor by combining traditionalreactor mathematical model with the PEI balance, and mayalso discover the effects of reaction conditions and engineer-ing factors on environmental performance by PEI rate-lawexpression and/or combinations it with other reaction rateequations as well as other related equations in reactor math-ematical models.

Note that assuming l = 1 and not considering the PEI ofCl2 are just for illustrating the algorithm, these assumptionsmay be inappropriate for a real reactor design. Other users ofthis methodology may choose to weight effects differentlyto reflect local needs and polices where the plants locate [8].

In the case study, the quantitative environmental impactassessment and the PEI indexes are cited from the WAR al-gorithm, it is worth noting that, however, quantitative impactassessment and environmental indexes are likely to improvewith time, and that these improvements can be incorporate

Appendix A. Nomenclature

AF chlorine, propylene, allyl chloride, hydrogen chloride, 1,3-dichloropropene and1,2-dichloropropane, respectively

CA, CB, etc. concentration of chemicals A, B, etc. (mol/L)CA,0, CB,0, etc. initial concentration of chemicals A, B, etc. (mol/L)CB,out, CC,out, etc. outlet concentration of chemicals B and C, etc. (mol/L)dV, dC, dIgen differential V, C and IgenIgen potential environmental impact generation rate (PEI/h)INPgen potential environmental impact generation rate of non-products (PEI/h)

into this methodology as they become available. It is suggestthat either the WAR algorithms assessment method and thePEI indexes, another similar method and indexes, or even amore sophisticated and comprehensive method and indexesbe used to this pollution reduction methodology.

In the case study, the calculations of the impact indexesshould be accurate to no more than one significant figure[12]. But two significant figures are used in order to allowreaders to reproduce the calculations if necessary. It is alsoworth noting that, even being accurate to one significantfigure, one can still discern significant differences in theindex between different cases.

6. Conclusions

To perform pollution prevention in the process of reac-tor design, studying the effects of reaction conditions suchas temperature, pressure and concentration as well as vari-ous engineering factors on environmental performance of areaction process is very important. In this paper, potentialenvironmental impact balance and PEI rate-law expressionare proposed as a tool to be used to carry out this work. Theform of PEI rate-law expression should be consistent withthe method for calculating the overall PEI of mixtures ofchemicals inside reactors. The effectiveness of the methodhas been demonstrated through the reaction of allyl chlo-ride production. From the processes of the analyses in thecase study, analyzing with PEI balance and PEI rate-law ex-pression combining with other corresponding equations andsome mathematic knowledge can give important insightsinto how reaction conditions as well as engineering factorsaffect the environmental performance of reaction processes.This kind of study is one of the bases for developing chem-ical reactors with environmental benign processes.

Acknowledgements

Financial support provided by the National Natural Sci-ence Foundation of China under Grant No. 20176405 andthe Major State Basic Research Development Program un-der Grant No. G2000026307 is gratefully acknowledged.


Iin potential environmental impact input rate (PEI/h)INPin potential environmental impact input rate of non-products (PEI/h)Ik potential environmental impact of chemical k (PEI)Iout potential environmental impact output rate (PEI/h)INPout potential environmental impact output rate of non-products (PEI/h)m(in)j mass flow rate of stream j into a process (kg/h)

m(out)j mass flow rate of stream j out of a process (kg/h)

mk mass of chemical kmk,0 mass of chemical k at initial state of reactionmk, out mass of chemical k at reactor exit or at the moment of reaction terminationmp mass flow rate of desired product p (kg/h)Mk molecular weight of chemical k (g/mol)rA, rB, etc. the rate of appearance of chemicals A, B, etc. (mol/(L s))rPEI total PEI transformation rate (PEI/(L s))rPEI,k PEI transformation rate of chemical k (PEI/(L s))S area of a certain figureT reaction temperature (K)v0 volume flow rate entering a reactor (L/s)xkj mass fraction of chemical k in stream j

Greek lettersl weighting factor for impact category lPEI generated PEI per unit mass of all desired products (PEI/kg)skl the specific PEI of chemical k for environmental impact category l (PEI/kg of chemical k)k overall PEI of chemical k (PEI/kg of chemical k)

References

[1] H.T. Kohlbrand, From waste treatment to pollution prevention andbeyond: opportunities for the next 20 years, AIChE Symp. Ser.94 (320) (1998) 117121.

[2] A.P. Rossister, Waste Minimization Through Design, McGraw-Hill,New York, 1995.

[3] J.A. Dyer, K.L. Mulholland, Prevent pollution via better reactordesign and operation, Chem. Eng. Prog. 94 (2) (1998) 6166.

[4] A.D. Curzons, D.J.C. Constable, D.N. Mortimer, So you think yourprocess is green, how do you known? Using principles of sustain-ability to determine what is greena corporate perspective, GreenChem. 3 (2001) 16.

[5] H. Cabezas, J.C. Bare, S.K. Mallick, Pollution prevention with chem-ical process simulator: the generalized waste reduction (WAR) algo-rithm, Comput. Chem. Eng. 21 (1997) S305S310.

[6] A.K. Hilaly, S.K. Sikdar, Pollution balance: a new methodology forminimizing waste production in manufacturing process, J. Air WasteManage. Assoc. 44 (1994) 13031308.

[7] D.M. Young, H. Cabezas, Designing sustainable process with simu-lation: the waste reduction (WAR) algorithm, Comput. Chem. Eng.23 (1999) 14771491.

[8] H. Cabezas, J.C. Bare, S.K. Mallick, Pollution prevention withchemical process simulator: the generalized waste reduction (WAR)algorithmfull version, Comput. Chem. Eng. 23 (1999) 623634.

[9] D.M. Young, R. Scharp, H. Cabezas, The waste reduction (WAR)algorithm: environmental impacts, energy consumption, and engi-neering economics, Waste Manage. 20 (2000) 605615.

[10] W. Schramm, New findings on the generation of waste and emissions,and a modified cleaner production assessment approachillustratedby leather production, J. Cleaner Prod. 5 (4) (1997) 291300.

[11] B. Pahor, Z. Kravanja, N.I. Bedenik, Synthesis of reactor networksin overall process flowsheets within the multilevel MINLP approach,Comput. Chem. Eng. 25 (2001) 765774.

[12] S. Mallick, H. Cabezas, J.C. Bare, et al., A pollution reductionmethodology for chemical process simulator, Ind. Eng. Chem. Res.35 (11) (1996) 41284138.


Effects of hydraulic residence time on metal uptake by activated sludgeTlay A. zbelge, H. nder zbelge, Murat Tursun

Chemical Engineering Department, Middle East Technical University, 06531 Ankara, Turkey

Received 28 April 2003; received in revised form 12 April 2004; accepted 13 April 2004Available online 17 June 2004

Abstract

The combined uptake of Cu2+ and Zn2+ by activated sludge (biomass) was investigated at steady state in an activated sludge process (ASP)without recycle (namely, once-thro ASP), for different values of influent metal concentrations and hydraulic residence time in the range of2.540 h. The experiments were performed at a constant pH of 7 and temperature of 25 C. The results showed that the percentage removal ofboth copper and zinc by activated sludge increased with the increasing residence time; moreover, the percent metal uptakes were higher forboth metals at the low level of influent metal concentrations [(Cu2+) = 1.5 mg/L and (Zn2+) = 9 mg/L], than those at the higher level ofconcentrations [(Cu2+) = 4.5 mg/L and (Zn2+) = 27 mg/L]. The least represented forms of both Cu2+ and Zn2+ in activated sludge weretheir sulfides, and the dominant metal forms were found to be organically bound for both of the metals, Zn2+ and Cu2+. 2004 Elsevier B.V. All rights reserved.

Keywords: Biosorption; Activated sludge process; Removal of heavy metals; Sequential extraction of metals; Metal uptake

1. Introduction

Industrial discharges containing heavy metals (HMs) frommining, metal refining and other industries are hazardous forpublic health unless the HMs are removed at the site of thewaste production by appropriate methods [1]. One of thesetechniques for removing the HMs from wastewaters is touse the well-known activated sludge process (ASP) [2]. Thestudies on the treatment of these wastes biologically are notcomplete yet, since some metals are toxic to microorganisms(MOs) even at very low concentrations and their inhibitioneffects are not exactly known.

In giving a literature survey on this subject, the followingare the important points to be noted: (i) the form of HM inASP affects the degree of its toxicity, the soluble forms be-ing the most toxic [2]; (ii) different oxidation states of thesame metal can have different binding mechanisms to thesludge [3]; and (iii) it is known that the dissolved oxygenconcentration in the medium has an important effect on theoxidation states of the metals present in the system [4]. Theother factors affecting the forms of HMs are the solution pHand the presence of complexing agencies in the medium [5].

Corresponding author. Tel.: +90 312 210 2621;fax: +90 312 210 1264.

E-mail address: [email protected] (T.A. zbelge).

It was observed that the toxic effects of HMs decreased asthe concentrations of complexing agencies increased [5,6],which was explained with the decrease in free metal ionsdue to their reactions with complexing agents. Toxicity char-acteristics of a certain metal can be greatly modified bythe other HMs because they compete for available organicligands in sludge and wastewater [7]. On the other hand,non-interactive effects among some metals may also occurin ASP [8]. The diversity of MOs in ASPs may vary fromplant to plant. Individual species of MOs present may evenchange within the same system from time to time. There-fore, responses of different systems, or even the same sys-tem at different times and operating conditions, to HMs areunlikely to be identical, and the response mainly dependson the composition of activated sludge [4].

Complexa

chemical engineering & processing

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