Computer simulation of an industrial wastewater treatment process

Water Res. Vol. 19. No. 6. pp. 719-72-t. 1985 Printed in Great Britain. All rights reserved

0043-1354/85 $3.00+0.00 Copyright ~ 1985Pergamon Press Ltd

COMPUTER SIMULATION OF AN INDUSTRIAL WASTEWATER TREATMENT PROCESS DENNIS R. JENKE* and FRANK E. DIEBOLD Department of Chemistry and Geochemistry, Montana College of Mineral Science and Technology, Butte, MT 59701, U.S.A. (Received October 1984)

Abstract--The computer program REDEQL.EPAK has been modified to allow for the prediction and simulation of the chemical effect of mixing two or more aqueous solutions and one or more solid phases. In this form the program is capable of modeling the lime neutralization treatment process for acid mine waters. In its present form, the program calculates the speciation of all influent solutions, evaluates the equilibrium composition of any mixed solution and provides the stoichiometry of the liquid and solid phases produced as a result of the mixing. The program is used to predict the optimum treatment effluent composition, to determine the amount of neutralizing agent (lime) required to produce this optimum composition and to provide information which defines the mechanism controlling the treatment process.

INTRODUCTION

Tens of millions of gallons of acid waste solutions are produced daily by the non-ferrous mining industry; primarily due to economic constraints, the state-ofthe-art in treatment technology involves neutralization of this material with lime/limestone. In order to be effective on an industrial scale, this methodology must be able to produce effluents whose chemical composition make them acceptable for reuse at a minimal cost in terms of operator control and reagent use. Given the chemical complexity of the acid solutions to be treated, the large magnitude of liquid which must be processed per unit time and the complicated mechanism by which the neutralization process proceeds, it becomes difficult to optimize this process in terms of realizing these two objectives. In their evaluation of the lime neutralization of acid solutions associated with the copper mining industry, Jenke et al. (1983) observe that for this system thermodynamic precipitation of stoichiometric hydroxides is the predominate mechanism for heavy metal removal from solution; the time over which the chemical interaction occurs is sufficiently short that transfer of O2 and CO,. from the atmosphere (triggering metal oxidation and precipitation of carbonates) does not occur to any large extent. This process can thus be envisioned as a sequence of chemical reactions which are thermodynamically driven towards equilibrium conditions; equilibrium thermodynamics can thus be applied to model and optimize the system. The application of such an approach to the consideration of the neutralization of coal mining waste solutions has been successful for prediction of solution pH (O'Brien et al., 1974) and the stoichiometry of the sludge produced during treatment (Morow and Wentz, 1974). *Present address: Travenol Laboratories, 6301 Lincoln Ave, Morton Grove, IL 60053, U.S.A.

Consideration of the large number of equilibria which can contribute to the neutralization process is facilitated by the use of computer techniques. The focus of this report, is a discussion of both the generation of a problem which can in effect model the neutralization process and its application to the process employed at a particular mining site. EXPERIMENTALMETHODS Site description and analysis The wastewater treatment process employed by the Anaconda Mining Company at its open pit copper mining facility in Butte, MT involved the mixing of water pumped from the underground mines, acid effluent from a galvanic copper recovery plant and a lime fortified sludge (30% by weight solids) produced during froth flotation benification of the low grade ore. Grab type samples of these influents and the effluents resulting from their mixing were obtained on each of sixteen different sampling occasions and were characterized for their cation content by inductivelycoupled plasma emission spectroscopy and for their anion content by ion chromatography. Solution temperature, pH and absolute volume flow rates were also determined. A more detailed description of the site and the analytical methodologies employed is presented in an earlier report (Jenke and Diebold, 1983). Computer simulation The mixed phase equilibrium program REDEQL.EPAK (Ingle et al., 1980) was modified to allow for the modeling of the mixing process and the addition of chemical reagents to the aqueous system. The input data included total chemical composition and flow rate data for each influent stream and estimates of the lime addition rates. Given the experimental uncertainty in characterizing the influent streams, it was possible that the input data may have described a solution which was electrically unbalanced and thermodynamically unstable. Electronentrality for these solutions was achieved by adjustment of the total sulfate concentration (since this was the dominant species in solution) while minor thermodynamic supersaturation was allowed to exist. For all the mixtures produced and all simulations performed, thermodynamic equilibrium was assumed and precipitation controlled accordingly. The lime

719

?"I)

D[-!",NIS P-.. JE~,KE and FR~XK E DIFBOLD INPUT

OPERATOR PROGRAM

OUTPUT

LIME ADDITION L ~ H

SPECIATION FOR EACH INFLUENT (ION BALANCE AND NO PRECIPITATION ALLOWED)

REDEQL'EPAK TOTAL CONCENTRATION U

/

AND FLOW RATES OF ,NF OENTS /

/

DILUTION MODEL

__ TOTAL CONCENTRATION OF METALS AND LIGANDS FOR EACH MIXTURE - (INCLUDING TH% TOH-)

=

'-RE DEQL'EPAK

SPECIATION OF THE AQUEOUS EFFLUENT, SOLID MASS BALANCE

YES

END

Fig. 1. Flow chart for computer program. addition was simulated by assigning this material a stoichiometry of Ca(OH),.. The computer output consisted of the identification of solid phases formed, the distribution of mass between the solid and aqueous phases, the speciation of the solution phase and solution pH. RESULTS AND DISCUSSION

Computer program In general, the REDEQL.EPAK program assumes that thermodynamic equilibrium exists in a given solution and solves simultaneous stability constant expressions by Newton-Raphson iteration to determine the solution's chemical species distribution. The specific algorithm employed is described in detail by McDuff and Morel (1973). Modification of the program for application in this research involved essentially the updating of the thermodynamic data file and generation of subroutines which (1) mathematically performed the mixing and addition process, (2) guide the program through the reuse of its basic calculational algorithm and (3) allows for the inclusion of the additional data required in the abovementioned additions. A general outline of the mathematical approach employed is shown in Fig. 1. From the input of the bulk description of the influent solutions, the program calculates and prints as output, their chemical speciatlon based on the assumption that minor deviations from thermodynamic equi-

librium (i.e. supersaturation) are acceptable and charged balance is maintained by adjusting the total sulfate concentration. Since the predicted pH of the resultant mixture is determined by charge balance, it is critical that the influent compositions input into the dilution model represent electrically neutral solutions. However. since the input data represents a survey of analytical determinations, each of which having an experimental error associated with it, it is unlikely that neutrality is achieved in a real sense. The excellent precision of both the plasma emission and chromatographic methods employed (typical coefficients o f variation of _+ 1.5% RSD) minimizes the magnitude of the charge discrepancy in the input data; it is highly unusual that the sulfate concentration of the influents needs to be adjusted by more than + 5%. Given the balanced composition of the influents and their relative flow rates, a dilution calculation is performed and the equilibrium composition of the resulting mixture determined. The stoiehiometry and rate of addition of any neutralizing agent is then entered as input; the program converts this material into the aqueous equivalent of its solid form and re-evaluates the solution phase equilibrium composition. For lime addition, the addition can be simulated by means of its equivalent concentration of Ca 2+ since charge balance is maintained in the dilution calculation by O H - (H÷). Given the slow rate

Computer simulation of an industrial v,astewater treatment process

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Table I. Average composition of the system's influents (mgl -~) Concentrator influents Species

Precipitation plant effluent

Kelly mine v,ater

AI Ca Cu Fe K Mg Na Mn SiO., - H:O Zn SO",-

575 155 125 1240 l 590 21 200 48 660 9975

125 215 29 815 -..~ 225 72 90 22 155 4750

pH

2.3

Flow, gpm

2.5

4690

Solution chemistry The average compositions of the solution's influent to the treatment system are contained in Table I. Both the acidic mine and precipitation plant solutions are dominated by iron(ll) and sulfate but contain large quantities of other base metals. The chemical composition of the acid solutions is remarkably consistent and seldom differs by more than + 10% from the documental average. The alkaline concentrator influents are dominated by both calcium and

w o Lime

0.2 845 0. I 0 I 47 0. I 42 0.1 I 0. I 1760

0.1 517 0. I 0.1 39 0. I 38 0.1 1.7 0. I 1750

1 t.6

4470

of Oz transport into the solution during mixing, all iron and manganese is assumed to exist in their divalent form; additionally, the ability of the REDEQL.EPAK program to estimate metal removal directly related to surface adsorption is not used due to the uncertainty in both characterizing the nature of the materials upon which the adsorption would occur and estimating the magnitude of the effective stability constants for the metal adsorption reaction.

With lime

l 1.4

11250

9940

sulfate and differ, in terms of solution chemistry, due to the addition of lime to one of the streams prior to mixing. The total dissolved solids content of each influent is high, resulting in a large solution ionic strength and a solution speciation which is characterized by the presence of significant quantities of ion pairs. As shown in Table 2, the dominant species in solution exist in approximately a 60/40 mixture of charged free ion and uncharged 1 to 1 ion pair. Thus sulfate can complex a large quantity of the heavy metals in the acid solutions and of the calcium in the alkaline slurries. The minor amounts of dissolved metals in the alkaline solutions exist primarily as the multi-hydroxyl complex. While the speciation of these solutions is complex and can potentially effect the reactivity of the solutions, its significance to the treatment process is somewhat limited. By reducing the free metal concentration, the formation of metal-sulfate ion pairs could increase the pH at which metal hydroxide precipitation is initiated.

Table 2. Typical speciation of the treatment solutions* Concentrator Precipitation plant

Mine water

Ion

Species

Ca

Ca" + C,'iSO~(aq.) Mg:+ MgSO~(aq) MgOH + K* KSOf Na + NaSO,Fe-'* FeSO,(aq) Fe(OH)-, Fe(OH~" Mn "+ MnSO~(aq.) MnOH Cu"" CuSO4(aq.) Cu(OH):. C u ( O H ) f Zn z+ ZnSO4(aq.) Zn(OH ).,, Zn(OH)~ AI J÷ AISO~', AI(SO~ ),.AI(OH)f

57. I 42.9 62.6 37.4

59.9 40. I 65.3 34.7

90.5 9.5 93.8 6.2 62.6 37.4

92.4 7.6 95.1 4.9 65.3 34.7

57.1 42.9

59.9 40.1

51.4 48.6

54.3 45.7

51.4 48.6

54.3 45.7

27.8 72.0

29.0 70.8

so~,-

52.1

As metal complex

47.8 2.3

52.0 48.0 2.5

Mg

K Na Fe

Mn

Cu

Zn AI

so~pH

Expressed as % of total dissolved ion.

w Lime

w/o Lime

Mixbox effluent

65.0 34.5 65.0 27.5 7.5 95.1 4.9 96.9 3.1 8.7 3.7 87.6 44.1 23.5 32.4

65.4 34.4 67.8 28.3 4.0 93.3 4.7 98.0 3.0 16.2 6.8 72.9 52.8 27.7 27.7

65.2 34.6 65. I 32.1 3.0 93.5 4.5 96.9 3.1 20.7 8.5 70.3 63.0 34.1 2.8

100

100

100

100

100

100

100 66.0 32.9 11.6

100 65.1 33.7 11.4

100 65.1 33.7 10.1

-22

DE:'-xts R. JENKE and FRANK E. DIE~)LI)

organism to the hea,,y metal stress. Such scener~o is predicted by the c o m p u t e r model. Tables 4 and 5 d o c u m e n t the predicted chemical changes that occur in both the solid and liquid phases as the influent streams are mixed and various a m o u n t s of lime added. The average compositions and flow rates shown in Table 1 were used in this simulation. As can be seen in the first column o f these Tables. the c o n c e n t r a t o r solutions have only a small neutralization capacity. At the resultant pH o f 3.6 the solution has insufficient total hydroxide to produce a precipitate which contains a significant a m o u n t of the heavy metals: only a minor quantity o f an AI salt is obtained. It is interesting to note that the stoichiometry of this first formed precipitate is an aluminosilicate as o p p o s e d to the pure hydroxide. The large quantities of sulfate in the acid solutions and calcium in the alkaline influents results in a mixed solution which is thermodynamically supersaturated with respect solid CaSO4. However, since the formation of solid CaSO4 is relatively slow, its complete precipitation is not immediate. As additional hydroxide is added to the system by the dissolution of lime, a variety of changes in both solution and solid phase chemistry is observed. The continued precipitation o f C a S Q results in a decrease in the dissolved S O l - concentration; as this occurs the concentration o f Ca '-+ in soluton begins to increase slightly. Very rapidly the aluminosilicate becomes thermodynamically unstable and is replaced by the hydroxide as the solid sink for AI 3+. The released silicate ion reacts with an equivalent concen-

Table 3 pH or" saturation (or heavy metals m precipitatton plant effluent Species From total metal From free metal AIIOH); 38 4.0 Cu(OH): 5.8 5.9 Fe{OH), 7.8 7.9 Mg{OH), 96 97 Mn(OH): 8.9 90 Zn(OH): 6.3 6.4

However, the formation o f these ion pairs, which are uncharged, also serves to reduce the solution's ionic strength which in this case will tend to increase the chemical activity o f the remaining free metal ions. As shown in Table 3, there is only a m i n o r difference between the pH at which precipitation should occur when calculated from the total metal content and when calculated considering the solution speciation. Table 2 also contains the speciation o f a typical treatment process (mixbox) effluent; in this case the dominance o f hydroxide complexes in the heavy metal speciation is o f vital importance; Heavy metal poisoning by these effluents is o f primary concern in their potential reuse. It has been established in many cases [e.g. heavy metal toxicity to fish (Pagenkopf, 1983)] that the effect o f the heavy metals is related not directly to their absolute concentration (although this quantity defines the magnitude o f any potential interaction) but by their chemical form. If the free ion form o f a given heavy metal is the form responsible for an environmental or reuse stress, decreasing its concentration by the formation o f multi-hydroxyl ion pairs at high p H can increase the tolerance o f a given

Table 4. Stoichiometryof the predicted mixbox solution as a function of rate of lime addition" Amount of limed added (Ib min -~) .

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Species 0 2 4 6 8 I0 AI 88 45 Ca 539 542 547 556 568 580 Ca 30 30 27 Fe 358 358 358 261 K 33 33 33 33 33 33 Mg 114 114 114 ll4 114 114 Mn 43 43 43 43 43 43 Na 38 38 38 38 38 38 SiO,. 2H,O 15 0.4 0.4 0.4 0.4 0.4 SO]3250 30t0 2775 2550 2330 2000 Zn 112 112 112 88 75 19 pH 3.6 4.2 6.3 7.9 8.0 8.3 *As mg 1-~, empty spaces refer to concentrations less than 0. ling 1-~.

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12 0.7 594

14 1.2 613

16 15 659

20 165 812 0.6

33 89 5 38 0.4 1800 3 9.9

33 29 2 38 0.4 1695 0.3 10.1

33 2.2 0.4 38 0.4 1555 5 11.2

33

Table 5. Stoichiometryof solid phase produced during treatment" Rate of lime addition (Ib rain -t ) 0 2 4 6 8 10 12 14 pH 3.6 4.2 6.3 7.9 8.0 8.3 9.9 10.1 CaSO., 400 740 1080 1395 1710 2015 2315 2605 AIzSi,O~(OH)., 28 50 50 AI(OHh I1 240 270 270 270 265 265 Cu(OH)~ 5 46 46 46 46 46 Fe(OH), 155 370 530 580 580 ZnSiO~ 60 60 60 60 60 Zn(OH): 29 105 130 130 Mg(OH), 60 205 Mn(OH), 60 65 • As mg I-' in treated solution, blanks mean less than I rag I-~ as the solid,

38 0.4 1425 12 11.8

Experimental mixbox 0.7 650 38 5.4 0.5 55 0.5 1750 0.2 10.1

16 11.2 2800

20 11.8 2990

225 46 580 60 125 270 70

80 46 580 60 115 270 70

C o m p u t e r s i m u l a t i o n of an industrial w a s t e w a t e r t r e a t m e n t process

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Table 6. Observed and predicted aqueous chemistry of mixbox effluent solution composition crag 1-~) Tdal

l

Species

Observed

AI Ca Cu Fe(II) K Mg Mn Na SiO:. H:O Zn SOjpH Rate of lime addition Ibmin -t)

0.t 7.80 0.1 23 52 139 67 63 5.0 19.0 2690 5.6

[I Predicted 0.1 45 I05 50 t37 40 60 5.0 45.0 2640 5.6 3.5

Ill

Observed

Predicted

Observed

0.5 600 0.02 0.29 43 31.5 1.3 49 5.5 0.13 20,10 7.9

0.l 575

0.1 785 0.12 0.36 51 10 0.05 65 3.5 0.7 2135 9.2

44 80 10 51 5.5 10 1780 7.9 5.5

IV Predicted 755 49 25 1.0 70 2.3 1.0 1840 9.2 II

Observed 2.60 817 0.21 0.42 72 0.43 0.05 80 2.5 1.3 2060 I 1.2

Predicted 14 800 0.19 0.05 70 0.50 0.03 8I 1.5 60 1925 11.2 17

tratton of Zn 2+ to form solid ZnSiO3 which remains equilibrium with respect to the precipitation of thermodynamically stable as the pH continues to rise CaSO4. The computer prediction of a higher concenin response to increasing lime dose. Hydroxide pre- tration of dissolved Mg 2÷ than is observed in the cipitation of the heavy metals (AP +, Cu 2÷, Fe z+, natural system indicates that for this species there Mg -'~, Mn -'*) is initiated at a pH of approx. 4.0, 6.0, exists a removal mechanism other than hydroxide 7.5, 8.0, 9.5 and 9.6 respectively. The alkali earths precipitation. Viable candidates for this relatively Na ÷ and K ~ are unaffected by the lime addition. minor effect include co-precipitation and/or adAs the rate of lime addition increases past sorption. Finally, one observes that the actual solu14 Ibmin -~, the remobilization of the heavy metals tion contains more Na ÷ than the combined mixed begins as their solid hydroxides become unstable with solution. It is suggested that this small excess of Na + respect to their multi-hydroxyl ion pairs. Thus at a could be obtained by leaching of the Na-containing loading of 161bmin -~ the AI(OH)3 solid begins to aluminosilicates which make up a large fraction of dissolve and the AI appears in solution as the the solid load contained in the concentrator slurry. AI(OH)j'. The significance of this behavior can be With this minor exception, it would appear that this discussed by considering two concepts; the way this material remains chemically inert during the treatdissolution changes the ability of the solution to ment process. initiate an unfavorable response in a potential reuse Table 6 documents the results of the computer situation and the way it changes the magnitude of the simulation of the mixbox system on four sampling unfavorable response. If it is truly the free ion form occasions during which the collected mixbox effluent of a species which contributes to an unfavorable reuse had a greatly different pH. For the solutions with a response, then the subsequent dissolution to form the pH less than 9, the deficiencies in the model with multi-hydroxyl ion pair represents no additional respect to the prediction of the iron, manganese, stress to the system. However, should such a stress be magnesium and zinc composition are readily apparinitiated, the increased total metal carrying capacity ent. While the magnitude of the discrepancy between resulting from the dissolution would increase the observed and predicted behavior for the iron, manmagnitude of the stress. It is suggested that increasing ganese and zinc species is consistent with the explathe magnitude of any potential stress is an un- nations given previously, it is unlikely that the favorable stragegy and therefore that the computer difference in magnesium behavior can completely be program has been useful in determining that the attributed to non-precipitation process. It is more addition of 14 Ib rain -~ of lime represents the opti- likely that at intermediate pH magnesium loss from mum treatment strategy. solution occurs via precipitation of a mixed hydroxAddition of this amount of lime to the mixed ide solid which becomes thermodynamically unstable solutions produces an effuent whose pH (10.1) is at higher pH. At the higher solution pH, the model equivalent to that observed for the effluent collected tends to overestimate the concentration of species during one of the sampling trips. If the computer (AI, Zn) which can become remobilized as their program is an adequate model for the system, the multi-hydroxyl complex. While the magnitude of this concentrations of the other component which define error is not large, it is a further indication that the the bulk composition of these solutions should be system is subject to kinetic constraints in terms of similar. For Cu, AI, K, Mn, SiO_,.H.,O and Zn, the precipitation/dissolution which cannot be predicted similarity between the two solutions is good. The with the model. The unilateral descrepancy between mixbox effluent contains more Ca "+ and SO~- than the observed and predicted behavior of calcium and does the computer-predicted solution; it is believed sulfate, representing slow precipitation kinetics, is that this difference reflects a slow approach to kinetic another reflection of this weakness in the model.

724

DE~,~IS R. JENKEand FRANK E. DILBOL~) CONCLUSIONS

Given that the treatment of acid waste solutions (e.g. metal removal) by lime neutralization is primarily a process controlled by so[idiliquid equilibrium thermodynamics, the described computer program is an effective means of modeling the chemical effects of this process. As such it is a useful means of predicting and optimizing the amount of neutralizing agent required to produce a desired effluent chemistry. The utility of the program is compromised when the methodology to be modeled relies on metal removal processes that either do not involve direct precipitation or are kinetically slow. Examples of these problems which occur in the documented application include the redox behavior of the ferrous and manganous ion, the contribution of surface adsorption to metal removal and the removal of sulfate by gypsum precipitation. It is noted that the first two problems are not inherent in the program since it contains algorithms capable of considering both redox and adsorption phenomena: however, both processes are of minor importance to the industrial process considered and thus are not used herein. It is also felt that the thermodynamic and analytical data required to use these capabilities are not sufficiently accurate to allow for the effective modeling of these processes, Acknowledgements--The authors wish to thank the Anaconda Mining Company for access to their facility and to

both Sara Ingle and .",lik~ OLear? ~\~r the', assistaf~ce m the generation of the computer program This research v,as funded in part under Grant No. J02~50-'2 from the United States Bureau of Mines

REFERENCES

Ingle S. E., Keniston J. A. and Schuldts D. W. (1980) REDEQL.EPAK--aqueous chemical equilibrium computer program. EPA Report 600~'3-80-044. U.S. Environmental Protection Agency, Washington. DC. Jenke D. R. and Diebold F. E. (1983) Recovery of valuable metals from acid mine drainage b? selective titration. Water Res. t7, 1585-[590 Jenke D. R., Pagenkopf G. K. and Diebold F. E. (1983) Chemical changes in concentrated, acidic: metal bearing wastewaters when treated with lime. Enrir. Sci. Technol. 17, 217-223. McDuff R. and Morel F. (1973) Description and use of the chemical equilibrium program REDEQL-2. Technical Report EQ-83-02. California Institute of Technology. Pasadena. CA. Morrow R. E. and Wentz D. A. (t974) tn Water Resources Problems Related to Mining (Edited b,~ Hadley R. F, and Snow D. T.), pp. 54-65. American Water Works Asso, clarion, Minneapolis, MN. O'Brien W. S., Gatli AI F. and Wen C. Y. (1974) Proceedings of the FiJ~h Symposium Coal Mine Drainage Research, pp. 192-204. National Coal Association, Washington, DC. Pagenkopf G. K. (1983) Gill surface interaction model for trace metal toxicity to fishes: role of comptcxation, pH and water hardness. Envir. Sci. Technol. 17, 342-347.