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CHARACTERIZATION AND COLOUR REMOVAL FROM AN AQUEOUS SOLUTION USING BIO-COAGULANTS: RESPONSE SURFACE METHODOLOGICAL APPROACH

Okechukwu Dominic Onukwuli1, Ifeoma Amaoge Obiora-Okafo2, Monday Omotioma2

ABSTRACT

The improvement of the performance of natural coagulants for color removal of Crystal Ponceau 6R dye is studied. The proximate analysis, the structure and the surface morphology of the coagulant are investigated using Fourier-transform infrared and scanning electron microscopy. A polymeric make-up of the coagulant enhancing the coagulation potential of the biomass is observed. The face-centred central composite design optimizes four variables including pH, the coagulant dosage, the dye concentration, and the time. The values of pH and time show the highest effect on the color removal. The latter efficiency obtained by optimization analysis is found equal to 99.20 % in case of pH of 2, a coagulant dosage of 247.40 mg/l, a dye concentration of 34.32 mg/l and time of 540 min. The verifica-tion experiments of a standard error of 0.09 % agree with the predicted values. The overlay contour plot finds the optimum area where the predicted response variable is in an acceptable range (≥ 70 %) in respect to the optimum conditions. The face-centred central composite design approach is appropriate for optimizing the process providing higher removal efficiency when compared to that determined on the ground of the main effect plots. In conclusion, high-efficiency color removal from a dye containing wastewater could be feasible if the appropriate coagulant is selected for an effective destabilization process. It can enhance flocs growth leading to reasonable settling.

Keywords: coagulation-flocculation, vegetable coagulants (bio-coagulants), Telfairia occidentalis, Acid Red 66, Response surface methodology, Face-centred central composite design.

Received 23 November 2017Accepted 31 May 2018

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1 Department of Chemical Engineering, Faculty of Engineering Nnamdi Azikiwe University, Awka, Nigeria E-mail: onukwuliod@yahoo.com 2 Department of Chemical Engineering, Faculty of Engineering and Technology Madonna University, Elele, Nigeria

INTRODUCTION

Wastewater discharge is one of the major causes of the public health problem occurring globally. Dyes and pigments are among the main contaminants present in wastewaters [1]. Presently about 10,000 commercial dyes and pigments are used and over 7.11x107 kg/y are produced worldwide [2]. Dye production together with textile, rubber, pulp, paper, plastic, cosmetics, food, pharmaceutical, leather tanning, printing industries generate wastewater characteristically high in color as well as organic and inorganic contents. These dye wastewaters are toxic and carcinogenic. They slow down the streams self-purification by decreasing the light penetration, retarding the photosynthetic activity and inhibiting the growth of biota [3]. Therefore, dye waste-

waters generated from industrial applications have to be treated following corresponding discharging limits [4].

The dyes are classified as anionic (direct, acid and reactive dyes), cationic (basic dyes), and non-ionic (dis-perse dyes). The anionic dyes are the largest class of dyes used in the world [5]. In an aqueous solution, the anionic dyes carry a net negative charge due to the presence of sulphonate (SO3

-) groups. The acid dyes are anionic compounds containing mainly azo- and anthraquinone groups and hence are characterized by the presence of an azo bond (R-N=N-R2) and an amino group.

The removal of the dye wastewaters contaminants is often difficult because of their highly soluble and semi-soluble nature [6]. Their complex aromatic structures as well as their synthetic origin hamper their biodeg-radability and treatment [4]. Biebrich scarlet or acid

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red 66 (AR 66) presents a typical example. It is a dark, brownish powdered azo dye. It is used in dyeing wool, silk, polyurethane fibres, and nylon [7].

The methods used for removal of dye wastewater contaminants can be mainly divided into physical, chemical, and biological. The physical treatments as precipitation, ion exchange, membrane filtration, ir-radiation, ozonation, and adsorption are widely used. Coagulation-flocculation, precipitation, photo-catalysis, oxidation, and chemical sludge oxidation refer to the physico-chemical treatment methods. The biological treatment techniques usually include aerobic degrada-tion, anaerobic degradation, and living/dead microbial biomass variation [8]. Coagulation-flocculation is an established technique for contaminant removal due to its vast application to treatment of waters of a biochemical oxygen demand (BOD) [9], color [10], dissolved organic carbon (DOC) [11], turbidity [12], chemical oxygen demand (COD) [13], grease and oil [14], suspended solids, TSS [9], heavy metals [15, 16]. It is considered a chemical treatment because of the addition of a coagu-lant. The typical agents used refer to inorganic salts as Al(SO4)3 or FeCl3, as well as synthetic organic polymers [17, 18]. Although these chemicals are rather effective in removing dyes and suspended matter from the aqueous solution, several disadvantages have recently arisen, such as their impact on human health as for example the Alzheimer’s disease caused by inorganic salts and a neurotoxin attributed to an acrylic amide [19].

The abundance, the multifunctionality and the low price of the natural polymer coagulants (plant-based) determine the growing research interest to them. Besides, they are environmentally friendly and biodegradable in water. Fluted Pumpkin (Telfairia occidentalis) is a per-ennial plant of a great economic importance in Nigeria. Telfairia occidentalis (TO) seed is shown in Fig. 1. It is

worth noting that the seeds are rich in essential nutrients. They have a high protein content of about 28.09% which provides their application as coagulants in wastewater treatment [20].

This communication reports a study of Telfairia occidentalis application to decolourization of AR 66 dye (Biebrich scarlet) in an aqueous solution. A novel approach to the active coagulant agent extraction is adopted. The choice of the coagulant type and the dye ionic nature is of importance for high-efficiency jar test performance. The response surface methodology (RSM) is used to determine the optimum operating conditions and best region satisfying the operating specifications.

EXPERIMENTALPreparation of coagulants

Matured pods containing TO seeds were purchased from the local market of Enugu city. The seeds were removed from the pod and dried under the sun at 35oC for days. Then the external shells were removed. The matured seeds showing no signs of discoloration, sof-tening or extreme desiccation were selected for further treatment. The seeds were ground to a fine powder (63 µm - 600 µm) using an ordinary food processor aiming to achieve complete solubilization of the active ingredients.

Extraction of the active componentThe active component from the coagulant was ex-

tracted by adding distilled water to the fine powder to achieve 2 % suspension (2 g of the powdered sample in 98 ml water). The suspension was stirred at room temperature for 20 min at 120 rpm using a magnetic stirrer. This provided the extraction and the enhance-ment of the cationic agent. A local sieve cloth was used to filter the suspension to enable the presence of nano, micro, and macro-particles in the filtrate for an enhanced adsorption-flocculation. The resultant filtrate was used as a coagulant. The latter was prepared daily and kept refrigerated to prevent ageing effects (such as a change of pH, viscosity and coagulation activity). The solutions were shaken vigorously prior to the experiments and used immediately after that.

Characterization of the coagulantThe yield, the bulk density, as well as the content

of moisture, ash, proteins, fats, carbohydrates and fib-Fig. 1. a) Pod containing Fluted Pumpkin seeds;

b) Fluted Pumpkin seeds.

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ers of the seed powder were determined by the standard analytical methods of A.O.A.C [21]. Fourier-transform infrared (FTIR) spectrometer supplied by IR Affinity-1, Shimadzu Kyoto, Japan, was used to study the chemi-cal structure and the functional groups present in the sample. The spectra were measured in the mid-infrared range (4000 cm-1 - 400 cm-1). The surface structure, the morphology, and the fiber-metric/pore measurements of the seed powder were carried out using a scanning electron microscope (SEM) supplied by Phenom Prox., world Eindhoven, Netherlands.Buffered solution preparation

All assays were done in a pH-stable medium. Buff-ered solutions (pH of 2, 4, 6, 7, 8 and 10) were prepared by the standards of the National Bureau of standards (NBS) and were standardized using a digital pH meter. All reagents used were of an analytical purity grade.

Dye preparation and spectrophotometric decolouri-zation procedures

The water soluble dye Acid Red 66 (AR 66) was provided by May & Baker, England (Its molecular struc-ture is shown in Fig. 2, while its physical characteristics are summarized in Table 1). The absorption spectrum of the dye was obtained by dissolving 1000mg/l of AR 66 in distilled water. A sample of the solution was scanned against a blank sample of distilled water in the range of 250 nm - 850 nm using Shimadzu UV-Vis spectropho-tometer (Model UV-1800). A dye stock solution of 1000 mg/l was prepared. The desirable experimental concen-trations of 10 mg/l - 50 mg/l were prepared by diluting the stock solution. The wavelength of maximum absorb-ance (λmax) and calibration curve at λmax was determined.

Coagulation studiesA conventional jar test apparatus supplied by Phipps

and Bird, VA, USA, equipped with six beakers of 1000ml capacity and six paddle stirrers was used to perform the coagulation-flocculation experiment. The latter was conducted to evaluate the coagulation performance of the coagulants agent extracted and to establish the best oper-ating condition for the coagulation-flocculation process. The procedure involved 4 min of rapid mixing at 100 rpm. The mixing speed was reduced to 40 rpm for another 25 min. The study was conducted by varying pH, coagulants dosages, dye concentrations and time. The solution pH was adjusted to the desired value using 0.1M HCl and 0.1M NaOH. All suspensions were left for settling at a varying time between 60 min - 540 min. Supernatant samples were withdrawn after settling for absorbance analysis. The color concentration (mg/l) measurement was carried out using a calibration curve. The color re-moval efficiency was determined using Eq. (1).

color removal (%) = ( ) x 100 (1)

where Co and C were the initial and the final color con-centration (mg/l) of the dye solutions prior to and after the coagulation-flocculation treatment, respectively.

Experimental design and data analysisIn this study, RSM was used to develop a mathemati-

cal correlation between the operating variables affecting the dye removal. Central composite design (CCD) is a very efficient design tool for fitting second-order models Fig. 2. Structure of Biebrich scarlet dye (Acid red 66).

Property Data Chemical Name Sodium 6- (2-hydroxyna

phthylazo) – 3, 4’ – azodibenzenesulfonate.

Chemical formula

C22H14N4Na2O7S2

Molecular Weight

556.48

CAS number 4196 – 99 - 0 ECC number 224 – 084 - 5 Melting point 181 - 1880C. UV /visible Absorbance

Max (water): 505+ 6nm.

C.I number 26905 Class Azo C.I name Acid red 66 Common name Biebrich scarlet.

Table 1. Physical characteristics of Biebrich scarlet dye.

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through fitting quadratic surfaces. It usually works well for process optimization [22]. Face-centred central com-posite design (FCCD) was implemented in the CCD. A CCD was made face-centred by the choice of α = 1 [23]. The star points of FCCD were positioned at the face of the design cube portion [24]. This provided that the axial runs were not more extreme than the factorial portion. The solution pH (A), the coagulant dosage (B), the dye concentration (C), and the time (D) were the independent variables selected for this study. A 24 two-level factorial for four independent variables consisting of 16 factorial points coded to the usual ± notation, 8 axial points and 6 replicates at the centre point were conducted, given a total of 30 experiments. The experimental design table showing the face-centered design array is presented in Table 2. Eq. (2) was used to determine the total number of the runs performed, N:

N = 2k + 2k + n (2)

where k was the number of factors, while n stood for the centre points.

Design-expert software (9. 0 State Ease, Min-neapolis, USA) was used to analyze the experimental data fitted to a second-order polynomial model aiming the optimization of the variables in the coagulation-flocculation process. The variance analysis (ANOVA) was also applied. The response was used to develop an empirical model which correlated the response to the dye coagulation-flocculation variables using a second-degree polynomial equation as given by Eq. (3): Y = b0 + � 𝑏𝑏𝑖𝑖𝑋𝑋𝑖𝑖

𝑛𝑛

𝑖𝑖=1 +

∑ 𝑏𝑏𝑖𝑖𝑖𝑖𝑛𝑛𝑖𝑖=1 𝑋𝑋𝑖𝑖2 + ∑ ∑ 𝑏𝑏𝑖𝑖𝑖𝑖 𝑋𝑋𝑖𝑖𝑛𝑛

𝑖𝑖=𝑖𝑖+1 𝑋𝑋𝑖𝑖𝑛𝑛−1𝑖𝑖=1 + ε (3)

where Y was the predicted response, b0 was the constant coefficient, bi was the linear coefficients, bii was the quadratic coefficients, bij was the interaction coefficient, Xi , Xj were the coded values of the variables, n was the

number of independent test variables, while ε was the random error.

The adequacy of the proposed model was obtained using the diagnostic tool provided by ANOVA. The quality of the model fit was expressed by the coefficient of determination (R2) showing the extent of variability in the interaction between the response and the factors. The analysis was done by Fisher’s ‘F’ test and P-value (probability). The model terms were evaluated by the P-value using 95 % confidence level [24]. The optimum operating conditions of the color removal were obtained by analyzing the main effects plots, the contour and the overlaid contour plots using Minitab 16 software.

RESULTS AND DISCUSSIONCoagulant characterization results Proximate composition

The proximate analysis of the coagulant precursor is summarized in Table 3. The moisture content values show the water absorption ability of the coagulants. The high crude protein value of 55.09 % recorded in the pre-cursor indicates the presence of protein is in agreement

Variables Factors Unit Range and levels

Lowest -α

Low -1

Centre 0

High +1

Highest +α

pH A - 2 2 6 10 10 Dosage B mg /l 200 200 600 1000 1000

Dye concentration C mg/l 10 10 30 50 50 Time D min 60 60 300 540 540

Table 2. Levels and range of the variables tested in the CCD design.

S/No Parameters Values

1. Yield 38.40

2 Bulk density (g/ml) 0.354

3. Moisture Content (%) 12.58

4. Ash content (%) 1.52

5. Protein content (%) 55.09

6. Fat content (%) 17.17

7. Fibre content (%) 0.87

8. Carbohydrate (%) 12.77

Table 3. Proximate compositions of Telfaria occidentalis seed powder.

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with the literature data according to which the precursor contains cationic poly-peptides (long chains of amino acids held together by peptide bonds) [20]. The fiber content established shows that the precursor is an organic polymer. The latter contains repeating small molecules that could extend as tails and loops when dispersed in water [25]. These characteristics lead to a new insight of the adsorption mechanism studied.

FTIR spectrometry resultsThe FTIR peaks of Telfairia occidentalis (TO) coag-

ulant prior to and following the coagulation-flocculation are illustrated in Fig. 3 and 4, respectively. Fig. 3 shows that there is an absorption peak at 3656.92 cm-1 which is attributed to OH-groups stretching vibration and to those of the water absorbed, or the complexes present in the coagulant [26]. The free hydroxyl groups found in the spectrum confirm the presence of carboxylic acids, alcohols, and phenols in the coagulant. The band consid-

ered also corresponds also to OH-stretching vibrations of cellulose, pectin, and lignin [27]. The FTIR spectrum reveals also the characteristic absorption peaks of an ali-phatic primary amine (N-H stretch) at 3656.92 cm-1 and of a primary amine at 2545.96 cm-1 - 2588.40 cm-1. The presence of N-H stretching signal shows the presence of amino compounds which confirms the protein contents of the coagulant pointed out in Table 3. The peaks in the range of 2545.96 cm-1 - 2588.40 cm-1 (2545.96 cm−1 prior to coagulation and 2588.40 cm-1 after coagulation) are attributed to the bending vibration of water molecule OH-groups, namely the H–O–H angle distortion fre-quency, indicating that the coagulant contains structural and adsorbed water. A major band present in the broad region of 2036.77 cm-1 - 1836.18 cm-1 usually indicates the presence of a C=O group (a carbonyl compound). The FTIR spectrum of TO and the proximate analysis suggest the presence of moisture, proteins, and esters, which in turn justifies TO performance as a coagulant.

Fig. 3. FTIR spectrum of TOC before coagulation-flocculation studies.

Fig. 4. FTIR spectrum of TOC after coagulation-flocculation studies.

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SEM resultsThe surface morphologies of TO in 1000x mag-

nifications prior to and following the coagulation are shown in Fig. 5. Fig. 5a reveals pores of different shapes and sizes. The presence of pores (micro-, macro- and mesopores) is a property which is unique for an organic polymer. The rough surface observed shows that the coagulant is coarse fibrous substance largely composed of cellulose and lignin which confirms its polymeric characteristics. Particles could be attached to these polymer chains through adsorption, intraparticle bridg-ing or electrostatic contacts. The morphology shows also a compact-net structure which is more favorable for particle coagulation-flocculation due to adsorption and bridge formation among flocs as compared to a branched structure [28]. Fig. 5b shows large clusters due to particles flocculation. There is also adsorption on the polymer surface resulting in pores number decrease [29].

In combination with Phenom™ desktop scanning electron microscope, the fibre-metric application pro-vides accurate size information referring to micro and nano-fibre samples. The pores size measurement results are presented in the histogram shown in Fig. 6. The minimum/maximum and average fibre size are displayed below the histogram. Fig. 7 shows the predefined pore measurement areas of the fibre-metric image and the pore distribution. It reveals an automated pore measure-ment of a polymer membrane sample.

Absorption spectra of the dye and calibration curve analysis

Fig. 8 presents the absorption spectra of 1000 mg/l stock solution of AR 66 in the wavelength range of 250 nm - 800 nm with maximum wavelength (λmax) of 536 nm. The calibrated curve obtained on the ground of dye concentrations in the range of 10 mg/l - 50 mg/l indicates that Beer-Lambert’s law is obeyed. It is evident from the straight line plot shown in Fig. 9 [30].

Development of the regression modelThe experimental design matrix showing the coded

and uncoded factor combinations together with the experimental (exp) and predicted (pre) decolourization efficiencies are shown in Table 4.

Fig. 5. SEM micrograph of TOC before (a) and after (b) coagulation experiment.

Fig. 6. Pore size histogram measurement.

Fig. 7. Fibre metric image measurement and pore distribution. Fig. 8. Spectrum peak report for AR 66.

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The adequacy of the model is justified by ANOVA as shown in Table 5. The quadratic regression analysis shows that the model is significant at 95 % confidence level by the Fisher’s testing. This is confirmed having obtained the F-value of 59.50. The P-value result of less than 0.05 (P-values of regression ≤ 0.05) shows a statistically significant model. It exhibits an insignificant lack-of-fit.

The coefficient of determination (R2) measures the model’s overall performance. A difference greater than 0.2 A between the predicted R2 and the adjusted R2 val-ues indicates that a non-significant term is included in the model [23]. A high R2 value, close to 1, is desirable

and ensures a satisfactory adjustment of the quadratic model to the experimental data. The R2 value of 98.23% indicates that the model total variation is 1.77 % which is insignificant. The difference between the predicted R2 and the adjusted R2 values is 0.041, which is less than 0.2. It indicates that the model is accurate. Table 6 shows that the coefficient terms (A, B and D), the interaction term (AD) and the square terms (A2, B2 and C2) are significant for the model response whereas other non-specified terms are insignificant.

Eqs. 4 and 5 show the final empirical equation in terms of coded and actual factors, respectively, after excluding the insignificant terms. The positive signs in front of the terms indicate an interactive effect among the factors. In conclusion, the quadratic model for the response measured is significant and adequate.

Ytoc = +40.3386 + -24.9667*A + 12.75*D + -2.4375*AD + 22.3228*A2 + -9.72719*B2 + -9.62719*C2 (4)

Ytoc = +71.73183 -22.23143*pH +0.040361*Time + -2.53906E-003*pH*Time +1.39518* pH^2 -6.07950E-005* Dosage^2 -0.024068* Dye Concentration^2 (5)

Fig. 9. Calibration curve for AR 66 at a wave length of 536 nm.

Std Order

Run Order

A: pH B: TOC Dosage (mg/l)

C: Concentration (mg/ l)

D: Time (min) Ytoc Colour removal (%)

Coded Uncoded Coded Uncoded Coded Uncoded Coded Uncoded Experimental (exp) Predicted (pre) 17 1 -1 2 0 600 0 30 0 300 99.2 87.63 28 2 0 6 0 600 0 30 0 300 39.2 40.34 20 3 0 6 1 1000 0 30 0 300 28.7 28.45 9 4 -1 2 -1 200 -1 10 1 540 86 88.39 8 5 1 10 1 1000 1 50 -1 60 10.6 8.72 16 6 1 10 1 1000 1 50 1 540 30.6 29.39 2 7 1 10 -1 200 -1 10 -1 60 14.7 12.96 7 8 -1 2 1 1000 1 50 -1 60 52.7 53.72 22 9 0 6 0 600 1 50 0 300 32 30.75 1 10 -1 2 -1 200 -1 10 -1 60 56.8 58.07 24 11 0 6 0 600 0 30 1 540 59.8 55.76 5 12 -1 2 -1 200 1 50 -1 60 57.1 58.17 12 13 1 10 1 1000 -1 10 1 540 29.9 29.34 4 14 1 10 1 1000 -1 10 -1 60 9.5 8.06 27 15 0 6 0 600 0 30 0 300 39.2 40.34 25 16 0 6 0 600 0 30 0 300 39.2 40.34 26 17 0 6 0 600 0 30 0 300 39.2 40.34 19 18 0 6 -1 200 0 30 0 300 34.8 32.77 18 19 1 10 0 600 0 30 0 300 28.4 37.69 14 20 1 10 -1 200 1 50 1 540 33.5 33.04 3 21 -1 2 1 1000 -1 10 -1 60 52.1 53.07 11 22 -1 2 1 1000 -1 10 1 540 83 84.10 10 23 1 10 -1 200 -1 10 1 540 34.5 33.54 15 24 -1 2 1 1000 1 50 1 540 81.9 84.15 13 25 -1 2 -1 200 1 50 1 540 86.4 87.90 30 26 0 6 0 600 0 30 0 300 39.2 40.34 29 27 0 6 0 600 0 30 0 300 39.2 40.34 23 28 0 6 0 600 0 30 -1 60 28.5 30.26 21 29 0 6 0 600 -1 10 0 300 31.7 30.67 6 30 1 10 -1 200 1 50 -1 60 14.1 13.06

Table 4. CCD in coded and uncoded form and response result for colour removal from AR 66.

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A reliable model is expected to provide a good agreement between the actual removal efficiency (%) and the predicted removal efficiency (%). In fact this is the case as illustrated in Fig. 10. The actual values are distributed relatively near the straight line verifying the good model prediction.

Evaluation of the operational parametersThe plot illustrating the main factors effect on the

colour removal is shown in Fig. 11. It is evident that the solution pH has the highest effect which amounts to 74.1

%. It is explained his with the charge of the hydrolysis products and the precipitation of polymeric hydroxides which are both controlled by pH variation [31]. As the functional groups of the acid dyes are anionic, the hy-drolysis products of the organic biopolymer can neutralize the negative charges of the dye molecules. Generally, the adsorption of organic contaminants (OC) on the polymer hydroxide precipitate proceeding at high pH is limited. It is so because OC becomes more negatively charged with pH increase, while the polymer hydrolysis species become less positively charged which in fact result in lower floc-

Fig. 10. Equality plot for the actual and predicted colour removal (%).

a) b)

c) d)

Fig. 11. Main effects plot for the colour removal (%): a) pH; b) TOC Dosage; c) Dye Concentration; d) Settling Time.

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culation tendency. Therefore, coagulation-flocculation of OC in wastewater is recommended to be performed at low pH values along with the presence of soluble cationic polymer hydrolysis species. In conclusion, high removal efficiency at low pH values is predominant in OC removal from acid dyes. Similar results are reported by Beltran and Sanchez [32] and Moghaddan [22].

The plot shown in Fig. 11(b) indicates the effect of TOC dosage on the color removal. The highest color removal is achieved at TOC of 200 mg/l. Charge-neutralization and adsorption are found to predominate due to the high efficiency at a low dosage. The use of a cationic polymer for coagulation-flocculation of neg-atively-charged color particles is required to provide a good adsorption affinity and neutralization of the particle

charges [33]. The dye concentration is an important driv-ing force overcoming the mass transfer resistance of the dye between the aqueous and the solid phase [34]. This effect is shown in Fig. 11(c). It is obvious that the dye concentration has a little effect on the removal process.

The particles growth involves their interaction of the coagulant hydroxide precipitate obtained in the course of the hydrolysis reaction [35]. The coagulation-flocculation performance is mostly evaluated through the time-dependent decrease of the particle concentration and consequently, coincides with the growth of aggre-gates. Fig. 11(d) shows that the highest concentration decrease is observed at 540 min. The plot shows that the time has a significant effect on the removal process. The long flocculation time (60 min – 540 min) indicates

a) b)

c) d)

e) f)

Fig. 12. 3D Surface plots for colour removal as a function of: a) dye concentration and time; b) dosage and time; c) dosage and dye concentration; d) pH and time; e) pH and dye concentration; f) pH and dosage.

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adsorption proceeding. 3D surface plots referring to the optimal conditions

are presented in Fig. 12. They show that the maximum removal efficiency is obtained in the pH range of 2 - 4 at TO dosage of 200 mg/l - 400 mg/l. The dye concentration has to vary from 40 mg/l - 50 mg/l, while the settling time is expected to be in the interval of 350 min - 540 min.

Optimization AnalysisMinitab 16.0 is used to optimize the color removal

efficiency. The process optimization searches for a com-bination of factor levels that simultaneously satisfy the criteria placed on each response and factor. Numerical optimization is employed and the desired maximum goal is set for each factor and response. These goals are combined into an overall desirability function for

effective maximization. The optimal conditions and the optimization results are shown in Table 6.

Model validation and confirmation experimentsThe optimum predicted values are further validated

by carrying out an experiment at the optimal predicted conditions. The results obtained are also shown in Table 6. It is seen that there is a good agreements with FCCD results, which indicates in turn that FCCD approach is appropriate for the coagulation-flocculation process op-timization. The standard error (%) between the predicted and the experimental value of 0.09 % shows clearly that the developed model is suitable and can predict the re-sponse using the operation parameters studied [36]. The adequacy of the model is once again effectively verified by the experimental data validation.

Fig. 13. Overlaid contour plots for colour removal (%): a) time and pH; b) time and dosage.

Table 5. ANOVA response results. Ytoc Source Sum of

Squares df Mean Square

F Value

p-value Prob > F R- Squared

Model 15877.45 14 1134.10 59.50 < 0.0001 significant A-pH 11220.02 1 11220.02 588.66 < 0.0001 B-Dosage 84.07 1 84.07 4.41 0.0430 C-Dye Concentration 0.027 1 0.027 1.428E-003 0.9704 D-Time 2926.12 1 2926.12 153.52 < 0.0001 AB 0.010 1 0.010 5.247E-004 0.9820 AC 1.819E-012 1 1.819E-012 9.543E-014 1.0000 AD 95.06 1 95.06 4.99 0.0412 BC 0.30 1 0.30 0.016 0.9014 BD 0.49 1 0.49 0.026 0.8748 CD 0.36 1 0.36 0.019 0.8925 A2 1291.07 1 1291.07 67.74 < 0.0001 B2 245.15 1 245.15 12.86 0.0027 C2 240.13 1 240.13 12.60 0.0029 D2 18.51 1 18.51 0.97 0.3400 Residual 285.90 15 19.06 Lack of Fit 285.90 10 28.59 Pure Error 0.000 5 0.000 Cor Total 16163.35 29 R - Squared 0.9823 Adjusted R - Squared 0.9658 Pred R - Squared 0.9250

a) b)

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Overlaid contour plotsThe overlaid contour plot (OCP) is used to present

visually the area of the acceptable predicted response variables. A compromise among the optimum conditions is desirable. The desirability function is used together with graphical optimization to achieve this goal [24]. By defining the desired limits, the optimum condition can be visualized graphically by superimposing the factors in an OCP, as shown in Fig. 13. The white shades in Fig. 13 refer to the optimum predicted spot. It shows the area satisfying the response goal of greater than or equal to 80 % color removal for the factor interactions at the optimum hold values. Areas that do not fit the optimization criteria are shaded grey. OCP is mostly applied when there is an emergency because it decreases the preparation time and the experimental cost. It shows clearly the high efficient region of the contour.

CONCLUSIONS

The coagulation ability of Telfairia occidentalis was tested following the effect of solution pH, the coagulant dosage, the dye concentration and the time. The proximate analysis results showed that it had the characteristics of a potential coagulant. The FTIR analy-sis indicated that some chemical bonds such as O-H,

N-H, C=H were present in the coagulant precursor. The SEM image revealed rough surfaces, different pores sizes, and a compact net structure. The characteriza-tion results showed the coagulant ability to destabilize the contaminant particles, to enhance the flocs growth because of its polymer properties and to adsorb the dye particles on its surface due to its rough surface and pores presence. The latter effect is well outlined leading to the conclusion that the coagulation-flocculation process is adsorption controlled.

The experimental results were statistically analysed by RSM technique using FCCD. It was found that the solution pH and time were the most influencing fac-tors. The optimal conditions from the main effects plots referred to pH of 2, a coagulant dosage of 200 mg/l, a dye concentration of 50 mg/l and time of 540 min. The optimum predicted color removal efficiency obtained by the optimization analysis amounted to 99.20 % at process conditions of pH of 2, a coagulant dosage of 247.40 mg/l, a dye concentration of 34.32 mg/l and time of 540 min. The further confirmation experiments demonstrated a good agreement with the predicted values indicating that FCCD was an effective optimization tool for the coagulation-flocculation process studied. The areas of OCP optimum variables agreed with the main effect and 3D surface plots. The removal efficiency obtained by using the FCCD model was higher than that found on the ground of the main effects plot.

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Table 6. Confirmation analyses of the model predicted using optimum values for colour removal from the dye.

Variables Units Optimum

Values

pH - 2

TOC Dosage mg/l 247.40

Dye Concentration mg/l 34.32

Time min 540

Colour removal

efficiency

(predicted)

% 99.20

Colour removal

efficiency

(experimental)

% 99.11

Standard Error % 0.09

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