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Ind. Eng. Chem. Res. 2002, 41, 5034-5041
Factorial Experiments in the Optimization of Alkaline Wastewater Pretreatment M. Prisciandaro,† A. Del Borghi,‡ and F. Veglio` *,† Department of Chemistry, Chemical Engineering and Materials, University of L’Aquila, Monteluco di Roio, 67040 L’Aquila, Italy, and Department of Process and Chemical Engineering, University of Genoa, “G.B. Bonino”, via Opera Pia, 5, 16145 Genoa, Italy
Alkaline wastewaters (AWWs), such as those produced by chemical plants in the manufacture of several organic molecules of commercial interest including caprolactam, are characterized by very high alkalinity, salinity, and COD values (about 350 g/L), in addition to the presence of recalcitrant organic molecules; thus, they are very difficult to clean. A previously devised treatment process for such wastewaters consists of three main steps: an initial pretreatment to acidify the polluted stream, a successive extraction of the biorecalcitrant compound, and a final biological process. In particular, this study focuses on the first step, during which a black mud precipitate is formed. An optimization of this step is necessary to render such slurries more treatable for the usual solid-liquid separation units (i.e., filtration, centrifugation etc.), because of their serious fouling characteristics. In this work, CaCl2 was used to produce CaSO4 as a coprecipitator to improve the AWW filtration capacity, COD abatement, etc. The experimental analysis was carried out by factorial experiments to optimize this pretreatment process. Table 1. Composition of the Alkaline Wastewaters5
Introduction
parameter
range
Alkaline wastewaters (AWWs) produced by caprolactam plants polymerizing the fibers of nylon-6 are characterized by very high alkalinity, salinity, and COD values, in addition to the presence of recalcitrant organic molecules.1,2 These characteristics render AWWs very difficult to treat; therefore, the specification of the appropriate sequence of various pretreatments and treatments to be performed in a depuration process is of great interest.3,4 This issue is important because in the South of Italy, for example, about 100 000 tons of this waste has accumulated because of the lack of any suitable purification treatment and the impossibility of discharging the waste because of environmental limitations. Thus, AWW treatment represents a very serious problem for this category of industrial activities.5 Nevertheless, an extensive literature analysis, reported in detail elsewhere,4 has shown that few researchers have devoted their study to AWW treatment, thus rendering the problem more complex. In general, AWWs contain many organic compounds, as reported in Table 1, that are technically classified into three main groups: neutral compounds, acidic components, and inextractable compounds (see Table 15). Actually, the main organic constituents of AWWs produced by caprolactam plants contain carboxylic groups, namely, the Na salts of cyclohexanecarboxysulfonate isomers and derivatives and the Na salt of 2-aminocapronic acid, whose concentration in solution is referred to as the CGC (carboxylic group concentration).5 Among compounds containing carboxylic groups, cyclohexanecarboxysulfonic acid (abbreviated in the following as CHCS) is biorecalcitrant, whereas other
a Neutral components. b Inextractable components. c Acid components. d Excluding PCB, dioxanes, pesticides, and hydrocarbon aromatic polycycles. e Expressed as Na2CO3.
* Corresponding author. Tel.: [+39] (862) 434223. Fax: [+39] (862) 434203. E-mail:
[email protected]. † University of L’Aquila. ‡ University of Genoa.
constituents, such as -caprolactam and related polymers, 6-aminocaproic acid, and similar compounds, can be biologically removed.5
pH density (kg/m3) dry weight at 105 °C (%) water (%) cyclohexanecarboxylic salt (%)a N-hexahydrobenzoyl-5-aminocaproic acid sodium salt (%)a 2-aminocaproic acid sodium salt (%)b cyclohexanecarboxysulfonate (CHCS) bisodium salt (%)b caprolactamc benzoic acid sodium salt (%)a N-hexahydrobenzoic 5-aminovaleric acid sodium salt (%) adipic acid sodium salt (%)a 1-cyclomethylhexanecarboxylic acid sodium salt (%)a 5-ethyldihydro-2-(3H)-furanone (%)c 5-methylvalerolactone (%)c other organic productsd (%) sodium sulfate (%) sodium chloride (%) ammonia (%) total alkalinitye (%) total phenols (mg/L) monochlorophenol (mg/L) 2,4-dichlorophenol (mg/L) 2,4,6-trichlorophenol (mg/L) toluene (mg/L) benzene (mg/L) n-hexane (mg/L) nitrites (mg/L) nitrates (mg/L) COD (mg/L)
10.1021/ie0202827 CCC: $22.00 © 2002 American Chemical Society Published on Web 09/10/2002
10.0-12.5 1.1-1.2 22-34 66-78 0.2-2.0 0.1-1.8 0.3-4.0 8.0-12.5 0.02-1.60 0.01-0.5 0.01-0.3 0.01-0.25 0.03-0.25 0.01-0.25 0.01-0.25 0.005-0.020 0.7-1.5 2.0-4.5 0.02-0.15 2.5-9.0 60-150 1-20 0.01-2.0 0.01-1.0 0.05-10.0 0.01-0.50 0.01-0.50 50-100 150-350 325 000-350 000
Ind. Eng. Chem. Res., Vol. 41, No. 20, 2002 5035
Figure 1. Scheme of the complete AWW treatment process.
In fact, the direct biological treatment of this AWW, after some pretreatments such as dilutions, etc., has already been the focus of a study that highlighted how some organic compounds such as CHCS (classified as an inextractable compound) are not biologically degradable.5 Thus, it was concluded that CHCS could be extracted in an acid environment by liquid-liquid extraction using secondary amines.6,7 Preliminary data, not shown here, suggested that CHCS recovered by liquid-liquid extraction could potentially be reused for the conventional caprolactam process after a suitable thermal treatment. To realize this liquid-liquid extraction, an acidification step is necessary,6,7 during which the precipitation of a black mud takes place: this precipitate (which contains the majority of the neutral and acid compounds, e.g., benzoic acid) has very fouling characteristics and renders unusable the usual solid-liquid separation units. Therefore, the identification of proper coprecipitators for improving the filterability of the black mud, allowing a filterable precipitate to be obtained, so that excessive fouling of the experimental apparatus can be avoided and good COD removal can be achieved, represents a major aspect to be studied in the development of a suitable purification treatment. These considerations and the preliminary results reported elsewhere5 permitted the determination of a reliable treatment process consisting of three main steps: an initial pretreatment to acidify the polluted stream, a successive chemical treatment, and a final biological process. More specifically, the initial pretreatment has the important outcomes of rendering the wastewater suitable for the subsequent chemical treatment and precipitating black mud through solution acidification. The chemical treatment consists of the extraction of the biorecalcitrant compound (CHCS) by means of a solvent diluted resin. Two streams are generated from this process: a CHCS-saturated stream, sent to regeneration, and the process stream. This last stream, with previous neutralization by Ca(OH)2 to adjust the pH to a neutral value, is sent to the biological process, which consists of two steps: an anaerobic
degradation, in which microrganisms remove part of the COD and a biogas is produced, and an aerobic degradation, in which COD abatement is completed, until the required levels are reached. A sketch of the process flowsheet is presented in Figure 1. In this paper, the optimization of the pretreatment process is studied with calcium chloride being used as the coprecipitating agent during the acidification step of the AWW treatment. In particular, factorial experiments were carried out by adding CaCl2 to the solution, in addition to sulfuric acid, to study the changes in the characteristics of the black mud precipitates with the aim of reducing their fouling characteristics and thus optimizing their handling by conventional solid-liquid separation units. Experimetal Section Materials and Methods. AWW samples were taken from an industrial storage tank located in the south of Italy; their average chemical compositions and properties are reported in Table 1.5 The samples, usually less than 100 cm3, continuously stirred, were mixed with different amounts of sulfuric acid (from 16 to 136 kg/m3 of AWW) and calcium chloride (from 5 to 45 kg/m3 of AWW). Subsequently, these samples were centrifuged or filtered to isolate the precipitate. In particular, the centrifugal gravity applied was about 1100 g for 30 min. Filtration tests were carried out by using a filtration setup (filters of 0.8 µm diameter, Millipore) connected to a vacuum pump, with the precipitate weight calculated as the difference between the weight of the filter alone and its weight after filtration. Measurements were carried out on both wet precipitate (after filtration) and dry precipitate, i.e., after drying at 80 °C for 24 h. On the AWW samples, measurements of the CGC; sulfate, sodium, and calcium concentrations; COD; and pH were performed.5 In particular, the specific protocol devised to determine the CGC is reported in detail elsewher.5 The sulfate concentration was evaluated by turbidity measurements using a UV-visible Cary 1E/ Varian spectrophotomer at 410 nm. The sodium con-
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Ind. Eng. Chem. Res., Vol. 41, No. 20, 2002
Table 2. Factors and Levels Investigated in the Centered Factorial Experiments (First Series)9 A
B
C
D
H2SO4 (kg/m3 of AWW)
temp (°C)
time (h)
CaCl2 (kg/m3 of AWW)
16 25 33
30 50 70
1 3 5
0 5 -
centration was measured with an atomic absorption spectrophotometer (Perkin-Elmer 2380). Calcium analyses were carried out by ETDA titration, using murexide as the indicator. COD was measured as the absorbance at 620 nm of the clear centrifugation supernatants after reaction with K2Cr2O7, and the supernatant pH was obtained with a Crison/Micro TT 2050 pH meter. Data Analysis. The experiments were planned and carried out by two-level or three-level factorial designs.9 The first two-level factorial experiments were carried out preliminarily as screening tests, and then three-level factorial experiments were performed to better understand the dependence of the investigated responses of the process (e.g., final pH) on the independently varied factors (sulfuric acid and calcium chloride concentrations). The effect of a factor is the change in response produced by a change in the level of the factor. When the effect of a factor depends on the level of another factor, the two factors are said to interact. After each experimental design, the data analysis was carried out by an analysis of the variance (ANOVA), and the statistical significance of the factors on the selected responses of the process was determined by means of F-tests; major details of these procedures can be found elsewhere.8,9 For each treatment, the following responses were generally considered in this study: filtration rate (y1), pH (y2), wet (y3) and dry (y4) precipitates, Ca2+ (y5) and Na+ (y6) concentrations, and residual COD (y7) in the wastewater. Results and Discussion Factorial experiments were carried out to establish the more suitable conditions under which precipitation can take place, with the amounts of precipitates and their features monitored as output parameters. The first experimental series was aimed at the determination of combined effects of the temperature, reaction time, and sulfuric acid on amount of precipitate: this was the only response taken into consideration because these tests were used as preliminary screening experiments. Table 2 reports the factors and levels investigated, and Table 3 shows the process response to the treatments performed. Table 4 reports the results of the ANOVA performed by the Yates method9 that was used to analyze the data obtained. It can be observed that, as also indicated in Figure 2, of the parameters studied the sulfuric acid concentration is the one that mainly increases the precipitate weight; CaCl2 is also very effective, followed by the temperature and last by the reaction time, perhaps because of the precipitation of calcium sulfate. A similar conclusion can be obtained from the ANOVA results reported in Table 4 (see the significant effects larger than 95%).9 In this manner, it can be concluded that all four of the factors investigated increase the mud precipitation as their levels increase but that sulfuric acid and calcium chloride are the most important process parameters. Obviously, as mud precipitation
Figure 2. Significant effects in the 24 factorial experiments as illustrated by a Daniel diagram.9 Table 3. Treatments Investigated in the Centered 24 Factorial Experiment and Response of the Process A
B
C
D
H2SO4 temp time CaCl2 precipitate test (kg/m3 of AWW) (°C) (h) (kg/m3 of AWW) wt (kg/m3) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 I II III I II III
16 33 16 33 16 33 16 33 16 33 16 33 16 33 16 33 25 25 25 25 25 25
30 30 70 70 30 30 70 70 30 30 70 70 30 30 70 70 50 50 50 50 50 50
1 1 1 1 5 5 5 5 1 1 1 1 5 5 5 5 3 3 3 3 3 3
0 0 0 0 0 0 0 0 5 5 5 5 5 5 5 5 0 0 0 3 3 3
6.4 12.9 8.6 12.9 7.4 12.0 10.7 15.0 11.9 13.1 12.1 16.0 12.4 16.5 15.3 18.3 9.9 10.3 12.2 14.1 13.8 15.1
increases, a reduction of the COD in the AWW takes place (data not reported here); moreover, the precipitate presents good qualitative characteristics that render it more suitable for filtration processes. Considering the importance of these two factors in the pretreatment process, the second experimental series focused on the effects of sulfuric acid and calcium chloride, each considered at three levels, on different other factors in cluding the filtration rate (y1), pH (y2), wet (y3) and dry (y4) precipitates, Ca2+ (y5) and Na+ (y6) concentrations, and residual COD (y7) in the wastewater. The selected investigated levels are 110, 123, 136 and kg/m3 of AWW for H2SO4 and 15, 30, and 45 kg/m3 of AWW for CaCl2. Table 5 shows the process responses (yi with i )1-7) to the treatments performed. Note that the experimental runs were all replicated, as indicated by the notation I and II in Table 5, so that the experimental error variance could be estimated.9 The average values are also reported in Figures 3a,b and 4a,b as an example, where the wet and dry precipitate weights as a function of the sulfuric acid concentration for different calcium chloride concentrations (and vice versa) are reported.
Ind. Eng. Chem. Res., Vol. 41, No. 20, 2002 5037 Table 4. Results of the ANOVA by the Yates Method9 precipitate wt (kg/m3)
I
II
III
IV
6.4 12.9 8.6 12.9 7.4 12.0 10.7 15.0 11.9 13.1 12.1 16.0 12.4 16.5 15.3 18.3
19.3 21.5 19.4 25.7 25.0 28.1 28.9 33.6 6.5 4.3 4.6 4.3 1.2 3.9 4.1 3.0
40.8 45.1 53.1 62.5 10.8 8.9 5.1 7.1 2.2 6.3 3.1 4.7 -2.2 -0.3 2.7 -1.1
85.9 115.6 19.7 12.2 8.5 7.8 -2.5 1.6 4.3 9.4 -1.9 2.0 4.1 1.6 1.9 -3.8
201.5 31.9 16.3 -0.9 13.7 0.1 5.7 -1.9 29.7 -7.5 -0.7 4.1 5.1 3.9 -2.5 -5.7
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
A B AB C AC BC ABC D AD BD ABD CD ACD BCD ABCD
effect
MS
F
significance (%)
3.99 2.04 -0.11 1.71 0.01 0.71 -0.24 3.71 -0.94 -0.09 0.51 0.64 0.49 -0.31 -0.71
63.6 16.6 0.1 11.7 0.0 2.0 0.2 55.1 3.5 0.0 1.1 1.6 1.0 0.4 2.0
64.46 16.83 0.05 11.89 0.00 2.05 0.23 55.87 3.56 0.03 1.06 1.65 0.96 0.39 2.06
99.87 98.52 16.81 97.39 1.89 77.53 34.25 99.83 86.79 13.13 63.96 73.14 61.81 43.66 77.53
Table 5. Results of the Second Series of Experiments: 32 Factorial Design9 a A
B
H2SO4 (kg/m3 of AWW)
CaCl2 (kg/m3 of AWW)
I
II
I
II
I
II
I
II
I
II
I
II
I
II
110 123 136 110 123 136 110 123 136
15 15 15 30 30 30 45 45 45
0.80 0.64 0.52 0.75 0.75 0.66 1.12 0.90 0.57
0.89 0.67 0.51 0.77 0.91 1.07 1.19 0.86 0.62
1.5 1.0 0.6 1.7 0.8 0.5 1.7 0.8 0.6
1.7 1.0 0.7 1.9 0.8 0.5 1.7 0.8 0.6
100.2 110.5 106.5 175.8 184.7 181.7 216.5 234.0 211.8
98.2 105.5 107.0 176.2 189.0 189.0 206.0 222.0 201.3
61.7 65.8 66.2 101.3 102.8 102.7 126.5 130.2 124.7
60.2 64.3 64.3 99.7 104.3 104.0 122.8 128.2 123.5
0.319 0.275 0.267 0.260 0.251 0.270 0.320 0.315 0.447
0.319 0.260 0.320 0.275 0.251 0.315 0.267 0.270 0.447
61.9 50.9 59.0 54.0 55.7 52.9 52.3 60.6 47.1
61.9 54.0 52.3 50.9 55.7 60.6 59.0 52.9 47.1
233.4 226.4 224.8 243.2 207.8 219.8 238.4 223.2 214.4
198 209 200 215 208 200 210 201 199
1 2 3 4 5 6 7 8 9 a
y2
y1
y3
y4
y5
y6
y7
I and II indicate two replicated measures.
Table 6. Correlation Matrix of the Responses Considered (yi) y1 y2 y3 y4 y5 y6 y7
y1
y2
y3
y4
y5
y6
y7
1 0.488 595 0.488 805 0.471 443 -0.300 07 0.445 853 0.242 321
1 -0.112 61 -0.092 59 -0.261 73 0.252 095 0.888 886
1 0.993 282 0.203 143 -0.317 71 -0.1353
1 0.287 722 -0.363 73 -0.113 92
1 -0.501 84 -0.346 98
1 0.000 165
1
Before the ANOVA was performed, the correlation matrix8 was calculated from the data reported in Table 5, to verify the existence of possible relationships among the responses considered in this study (residual COD, etc.). The results of this analysis are reported in Table 6. From an analysis of the table it is evident that, in addition to the obvious correlation between wet and dry precipitates, a correlation does exist between the pH (y2) and the COD (y7): the higher the pH, the higher the COD, thus explaining why the largest amounts of organic compound precipitate at the lowest pH’s. The correlation identified in this way is more clearly shown in Figure 5, which displays the residual COD as a function of the solution pH. The data reported in Table 5 were elaborated by using the Yates method for 32 factorial experiments,9 to determine whether changes in the levels of the factors investigated (sulfuric acid and calcium chloride concentrations) generate statistically significant changes in the selected responses of the pretreatment process (e.g., y2 ) final pH after the treatment). Examples of ANOVA obtained by this method (the Yates method) are reported in Tables 7 and 8 for the pH (y2) and wet precipitates (y3) realized in the pretreatment process, respectively. From an analysis of these results, it is possible to draw the following conclusions:
(1) Figure 6a,b reports the filtration rate (response y1) as a function of calcium chloride and sulfuric acid consumption for the three levels investigated. The data seem to indicate qualitatively that, aside from experimental error, the filtration rate is slightly related to the H2SO4 and CaCl2 concentration. This was also found in an ANOVA in which the statistical significance of the H2SO4 and CaCl2 factors, as well as their two-order interaction, was very large (ANOVA not shown here). In particular, an average decrease of the filtration rate was observed with increasing sulfuric acid amount, whereas the behavior of CaCl2 was more complex because of the presence of a significant interaction between the two factors investigated. Nevertheless, the average filtration ratestill increases with increasing CaCl2 amount in the AWW. This was an expected result because calcium chloride was, in fact, added to increase the filtration rate. This phenomena can be explained by the coprecipitation of CaSO4 in the black mud (revealed by SEM analysis not reported here) that renders it more treatable with respect to the usual solid-liquid separation processes, thus avoiding the fouling of all related equipment (tanks, centrifugation units, filtration units, piping, etc.). (2) The sulfuric acid concentration exerts a significant effect on the final pH value (response y2) (linear and
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Ind. Eng. Chem. Res., Vol. 41, No. 20, 2002
Figure 3. Behavior of (a) wet and (b) dry precipitate weights as a function of sulfuric acid concentration for different calcium chloride levels: 0, CaCl2 ) 45 kg/m3; 3, CaCl2 ) 30 kg/m3; O, CaCl2 ) 15 kg/m3.
quadratic effects shown in Table 7, denoted Al and Aq, respectively). This result is quite obvious because the lowering of the pH after the pretreatment process depends on the H+ concentration in solution, and in any case, the absence of AWW buffering characteristics was demonstrated. (3) The amount of black mud precipitate (response y3) is mainly related to the CaCl2 concentration. Table 8 shows that the linear and quadratic components of factor B (the CaCl2 factor, Bl and Bq, respectively) are very significant with respect to the experimental error.6 Moreover, there is a slight effect of the sulfuric acid amount, but its significance is only about 80% (see Table 8). The same conclusion can be qualitatively obtained just observing the results reported in Figures 3a,b and 4a,b. In particular, Figure 3a,b shows the behavior of wet and dry precipitate weight (the latter is response y4) as a function of the sulfuric acid concentration for different calcium chloride concentrations added to solution. Correspondingly, Figure 4a,b describes the same runs but reported as wet and dry precipitate weights as a function of calcium chloride concentration for three sulfuric acid levels. It is quite clear that, whereas the
Figure 4. Behavior of (a) wet and (b) dry precipitate weights as a function of calcium chloride concentration for different sulfuric acid levels: 3, H2SO4 ) 123 kg/m3; O, H2SO4 ) 110 kg/m3; 0, H2SO4 ) 136 kg/m3.
Figure 5. Correlation between residual COD and pH.
amount of H2SO4 added to the solution has a minor impact on the precipitate weight (Figure 3a,b), the addition of CaCl2 (Figure 4a,b) has a strong influence on both the wet and dry precipitates, whose weights increase. As reported for the filtration rate, this is due
Ind. Eng. Chem. Res., Vol. 41, No. 20, 2002 5039
Figure 6. Filtration rate as a function of the (a) calcium chloride and (b) sulfuric acid concentrations. Table 7. Example of ANOVA of the 32 Factorial Experiment by the Yates Method with pH as the Response (y2)9
1 2 3 4 5 6 7 8 9
A
B
H2SO4 (kg/m3 of AWW)
CaCl2 (kg/m3 of AWW)
variance
y2 average
I
II
110 123 136 110 123 136 110 123 136
15 15 15 30 30 30 45 45 45
0.020 0 0.005 0.020 0 0 0 0 0
1.60 1.00 0.65 1.80 0.80 0.50 1.70 0.80 0.60
3.25 3.10 3.10 -0.95 -1.30 -1.10 0.25 0.70 0.70
9.45 -3.35 1.65 -0.15 -0.15 0.45 0.15 0.55 -0.45
Al Aq Bl ABll AB ql Bq ABlq ABqq
divisor
MS
F
significance (%)
12 36 12 8 24 36 24 72
0.935 0.076 0.002 0.003 0.008 0.001 0.013 0.003
187.0 15.1 0.4 0.6 1.7 0.1 2.5 0.6
100.0 99.6 44.5 52.8 77.4 26.8 85.3 52.8
Table 8. Example of ANOVA of the 32 Factorial Experiment by the Yates Method with y3 as the Response9
1 2 3 4 5 6 7 8 9
A
B
H2SO4 (kg/m3 of AWW)
CaCl2 (kg/m3 of AWW)
variance
y3 average
I
II
110 123 136 110 123 136 110 123 136
15 15 15 30 30 30 45 45 45
2 12.5 0.125 0.08 9.245 26.645 55.125 72 55.125
99.2 108 106.75 176 186.85 185.35 211.25 228 206.55
313.90 548.20 645.80 7.55 9.35 -4.70 -10.05 -12.35 -38.20
1508 12.2 -60.6 331.9 -12.25 -28.15 -136.7 -15.85 -23.55
to the coprecipitation of CaSO4, which renders the black mud more treatable by conventional filtration equipment. (4) Figure 7 reports calcium ion concentrations (y5) after the treatment and filtration steps as a function of CaCl2 and H2SO4 consumption. The ANOVA results (not shown here) highlight the significant effects of both investigated factors (sulfuric acid and calcium chloride concentrations). From an analysis of these results, it is possible to observe a minimum Ca2+ concentration for intermediate values of the investigated factors. No particular relation has been observed with respect the other investigated responses (see Table 5). (5) From the ANOVA carried out on the Na+ concentration (y6) (not reported here), there is no evidence of
Al Aq Bl ABll ABql Bq ABlq ABqq
divisor
MS
F
significance (%)
12 36 12 8 24 36 24 72
12.4 102.0 9177.0 18.8 33.0 518.7 10.5 7.7
0.48 3.94 354.71 0.73 1.28 20.05 0.40 0.30
49.4 92.2 100.0 58.3 71.2 99.8 45.9 40.1
a statistically significant effect of the investigated factors (sulfuric acid and calcium chloride amounts) on this response. This response was considered because, after the solid-liquid separation occurs, a crystallization of NaHSO4 takes place in the liquid phase. Thus, the Na+ concentration was evaluated as if this phenomenon starts during the pretreatment process. It should be remembered that the fact that a factor is not significant means that there are no observable changes in the response under study for the range of experimental conditions investigated and not that this factor can be neglected in the experimental procedure.9 (6) Figure 8a,b reports the value of the residual COD (y7) in the wastewater after the pretreatment and filtration steps (see Figure 1) as a function of sulfuric
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Ind. Eng. Chem. Res., Vol. 41, No. 20, 2002 Table 9. Empirical Model for the Evaluation of Precipitate Weight8 a,b coefficients standard error below 95% above 95% intercept X1 X2 X22 b
-38.22 0.156 9.76 -0.101
38.23 0.260 1.58 0.026
-136.5 -0.51 5.698 -0.168
60.1 0.826 13.822 -0.034
a X ) H SO (kg/m3 of AWW), X ) CaCl (kg/m3 of AWW). 1 2 4 2 2 R2 ) 0.991.
cated in Table 7. According to the regression results, the developed equation is
y3 ) -38.22 + 0.156X1 + 9.76X2 - 0.101X22 (1)
Figure 7. Ca2+ concentration as a function of the sulfuric acid and calcium chloride concentrations.
acid and calcium chloride concentrations. An analysis of these two figures reveals that a certain (about 25%) COD abatement is realized, which increases as more H2SO4 is added. Moreover, the ANOVA shows that the sulfuric acid concentration has a slightly significant effect (probability of 76%) on the COD (y7) reduction. With increasing sulfuric acid concentration in the investigated range of experimental conditions, the average COD decreases from 225 to 209 g/L. As described above, this is due to the precipitation of neutral (caprolactam and related polymers) and some acidic extractable compounds. Considering the results discussed above, it was considered important to have an empirical equation describing the precipitate amount as a function of the investigated significant factors, so the y3 relation with respect to the investigated factors was further analyzed by regression analysis.5 Table 9 reports the results of the regression analysis in which the dependent variable y3 (wet precipitates) was related to the independent variables X1 (H2SO4) and X2 (CaCl2) amounts as indi-
In this manner an empirical relationship for estimating the wet mud precipitate is available for process analysis and simulation purposes. Similar results were obtained for the other responses in which the selected factors were significant from the statistical point of view after the ANOVA. Conclusions In the present paper, an optimization of the AWW pretreatment step, consisting of an acidification step by using sulfuric acid with the concomitant precipitation of black slurries in the presence of CaCl2, has been carried out. This stage is necessary to render such slurries more treatable for the usual solid-liquid separation units (i.e., filtration, centrifugation, etc.), because of their serious fouling characteristics. The experimental analysis was carried out by factorial experiments to optimize this pretreatment process. The results obtained show that the process devised is suitable for acidifying the waste and at the same time improving the precipitation of black mud, rendering it filterable and lowering the COD of the AWW. As a result, the stream can be easily filtered, and the solution can go to the CHCS extraction as indicated in the process flow sheet depicted in Figure 1. In particular, the following conclusions can be drawn from the analysis: (1) Sulfuric acid and
Figure 8. Residual COD as a function of the (a) calcium chloride and (b) sulfuric acid concentrations.
Ind. Eng. Chem. Res., Vol. 41, No. 20, 2002 5041
calcium chloride are, as expected, the most effective process parameters in terms of their influence on the filtration rate. In particular, the best filtration rates are achieved with the highest CaCl2 content (45 kg/m3 of AWW) and the lowest H2SO4 content (110 kg/m3 of AWW) (2) CaCl2 has a strong effect on both the wet and dry precipitates, increasing their weights. (3) A correlation does exist between pH and COD; specifically, the higher pH, the higher the residual COD. Finally, an empirical equation was devised that relates the wet precipitate weight to the H2SO4 and CaCl2 concentrations, which provides a useful tool for process analysis and simulation. Acknowledgment The authors are grateful to Mr. Marcello Centofanti and Mrs. Lia Mosca for their important assistance during the execution of the experimental work. Literature Cited (1) Donati, G.; Sioli, M.; Taverna, M. Caprolattame da toluene: il processo SNIA-Viscosa. Chim. Ind. (Milan) 1968, 50, 9971001. (2) Tempesti, L.; Giuffre`, G.; Buzzi, F.; Serena, E.; Monomeri. An investigation in to the kinetics of reaction between cyclohex-
ane-carboxylic acid and oleum. Chim. Ind. (Milan) 1976, 58, 247251. (3) Hashim, J.; Kulundai, R. S.; Hassan. Biodegradability of branched alkylbenzene sulphonates. J. Chem. Technol. Biotechnol. 1992, 54, 201-214. (4) Fantauzzi, E.; Baccella, S.; Chiarini, M.; Veglio`, F.; Toro, L.; Cerichelli, G.; Lepidi, A. Development of a chemical and biological treatment of alkaline industrial waste waters: A preliminary study. Fresenius Environ. Bull. 1998, 7, 934-950. (5) Baccella, S.; Fantauzzi, E.; Cerichelli, G.; Chiarini, M.; Ercole, C.; Lepidi, A.; Toro, L.; Veglio`, F. Biological treatment of alkaline industrial waste waters. Proc. Biochem. 2000, 35, 595602. (6) Brandani, S.; Brandani, V.; Veglio`, F. On the purification of β-naphthalensulfonic acid from dilute acqueous solutions containing sulfuric acid. Ind. Eng. Chem. Res. 1998, 37, 4528-4530. (7) Brandani, S.; Brandani, V.; Veglio`, F. Extraction of anions from aqueous solutions using secondary amines. Ind. Eng. Chem. Res. 1998, 37, 292-295. (8) Himmelblau, D. M. Process Analysis by Statistical Methods; John Wiley & Sons: New York, 1978. (9) Montgomery, D. C. Design and Analysis of Experiments, 3rd ed.; John Wiley & Sons: New York, 1991.
Received for review April 15, 2002 Revised manuscript received July 9, 2002 Accepted July 10, 2002 IE0202827
