3.1 Characterization of Paint wastewater
The results obtained from the characterization of the paint wastewater sample are presented in Table 5. From Table 5, it can be observed that the paint wastewater has high content of TSS and TDS of 2685 mg/L and 1318 mg/L, respectively as against the NERS (National effluent regulatory standard) of 705 mg/L and 1200 mg/L[16]. The high TSS and TDS observed in paint wastewater indicate that the wastewater contains high particle load. Comparing the total solid content with NERS, it was observed that the paint wastewater sample contains 2098 mg/L in excess of the NERS. Hence it can be inferred that the paint wastewater is highly turbid and cannot be discharged to the environment without treatment.
Table 5: Wastewater Analysis
|
|
Paint wastewater quality NERS
|
TS (mg/L)
|
4003 1905
|
TSS (mg/L)
|
2685 705
|
TDS (mg/L)
|
1318 1200
|
pH
|
7.89 7-8
|
|
|
|
TS: Total solid, TDS: Total dissolved solid, TSS: Total suspended solid
3.2 Characterization of GSF and GSC
3.2.1 The Proximate characterization of GSF
Proximate analysis was carried out on the gastropod shell flour (GSF) to determine the proximate compositions such as crude protein, oil content, ash content, bulk density and moisture content using standard methods cited in Table 2. The result of the proximate analysis shows that GSF contains high quantity of crude protein (42 %). Based on the protein content observed, it could be inferred that GSF is an efficient precursor for the extraction of raw protein (conchiolin) that can be used for wastewater treatment. The oil content was found to be 7.4%. Oil content of this percentage (˂10%) would have negligible inhibitory effect on the deprotenization process [16]. The total yield of 86 % was obtained indicating 14 % weight loss which could be attributed to the volatile components present in the GSF sample. The bulk density of 0.33 g/mL indicates that GSF is extremely aeratable. In addition, the ash content of 10 % shows that the flour is rich in minerals while negligible moisture content of 8.6 % was obtained.
3.2.2 Elemental Analysis
Elemental analysis was carried out on both the GSF and the GSC to evaluate their qualitative and quantitative composition. The results obtained are reported in Table 6. From the results, it could be seen that GSF has high content of calcium. Calcium content of 70 % was recorded which supports the claim that GSF contains between 70-98 % calcium [30]. This high calcium content is justified since the animal uses it for body replenishment [30]. High content of oxygen (26.36) in GSF can be traced to the presence of protein in the shell [16]. The presence of carbon (7.47%) is attributed to the carbonaceous nature of GSF. From the elemental characterization of GSC (Table 6), it could be observed that more elements were present (Na, Mg, Al. P, Si, Cl, K) which were not in GSF. The additional elements and the observed 20.7% reduction in calcium content in GSC can be attributed to the effect of GSF reaction with the extraction solution. The oxygen content of both GSF and GSC were observed to be approximately the same (26.36 and 25.94%), indicating that the deproteinization process was effective.
Table 6: Chemical Characterization of GSF and GSC
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Elements
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GSF
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|
GSC
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|
C
|
7.47
|
6.53
|
Na
|
|
|
21.7
|
Mg
|
-
|
0.23
|
|
Al
|
|
|
0.24
|
|
P
|
-
|
|
|
Si
|
-
|
0.14
|
Cl
|
-
|
|
0.21
|
K
|
-
|
|
0.19
|
|
Ca
|
65.8
|
45.1
|
O
|
26.36
|
25.94
|
3.3 Instrumental analysis for GSC
3.3.1 FTIR Studies
The infrared spectra of GSF and GSC shown in Figures 4 and 5 revealed peaks representing different functional groups. It is observable that the spectra fall within the mid infrared region (4000-400 cm-1). Figures 4 and 5 were analyzed and compared with the existing FTIR data base (FDM NIST08 Mass Spectral Library) [16, 30]. From the regions of absorbance, some functional groups were observed. The FTIR spectrum pattern for GSF (Fig. 4) exhibits 20 discernable peaks at frequency of 4000 – 700 cm-1, threshold of 0.44; while in that of GSC, 16 discernable peaks were observed (Figure 5) between the frequencies of 4000 – 600 cm-1. The principal peaks in the spectrum were detected at 3648 cm-1, 3627 cm-1, 3580 cm-1, 3291 cm-1, 1457 cm-1,1082 cm-1, 1017 cm-1, 844 cm-1, 712 cm-1 and 700 cm-1(Figure 4). The highest peak at 1457 cm-1 was observed within the FTIR fingerprint region. The presence of aromatic group was exhibited by the broad bands in the regions above 3000 cm-1 (3281 cm-1, 3580 cm-1, 3627 cm-1 and 3648 cm-1). The peak at 2919 cm-1shows the presence of asymmetric methyl group, peaks at 1082 cm-1 and 1017 cm-1 depict the aliphatic C–N stretching while peaks at 844 cm-1, 712 cm-1 and 700 cm-1 show the presence of phosphorous compound of P–F stretching.
Figure 5 shows distinct peaks for GSC. The reduction in number of peaks when compared with Figure 4 shows that some functional groups were removed during the extraction process. A shift in peaks orientation can also be observed from the X-H stretching region to fingerprint region. In Figure 4, 14 discernable peaks were found within the X-H stretching region, while 2 peaks were observed within the same region in Figure 5. The shift can be associated with longitudinal acoustical modes (accordion modes) resulting from molecular distortion and bond breaking during the extraction [30,41]. The stunted broad band between 4000 – 3000 cm-1 in Figure 4 was replaced with very broad strong band (in Figure 5) at 3645 cm-1and 3356 cm-1 indicating Si–OH stretching which can be confused with those of O-H frequencies. The highest peak on Figure 5 was observed at 1456 cm-1, with threshold frequency of 1.08. The sharp distinct peak at 1456 cm-1 can be connected to methylene scissoring in alkane group. Also, discernable peaks were recorded at the upper wave number end, the peaks at 699 cm-1, and 648 cm-1 are linked to C–H bending of alkyne group. The peak observed at 1082 cm-1 is an indication of C-O stretching band (Ethers) due to the C–O–C linkage. C-O stretching band can be observed near 1150 cm-1 (1154 cm-1), indicating the presence of Anhydrides. A broad N–H wagging band also appears at 750 – 650 cm−1 indicating the presence of secondary amine which can be linked to the protein constituent of GSC.
3.3.2 X-Ray diffraction analysis of GSF and GSC
The X-ray diffraction spectrum of GSF and GSC are shown in Figures 6 and 7. It can be observed that Figure 6 clearly shows well recognized intense peaks. This spectrum is an X-Y plot of 2θ vs X-ray count (intensity). Fourteen clear peaks assigned due to their different reflections and planes were observed at scattering angles of 2θ = 26.5o, 27.8o, 31.5o, 33o, 36o, 37o, 38o, 42o, 43o, 46o, 48o, 51o, 52.5o, 53o. From the nature of these peaks in Fig. 6, a symmetric organized crystalline structure can be inferred. The spectrum for GSC presented in Figure. 7 shows a less coherent arrangement of fourteen distinct peaks when compared with Figure. 5. The peaks can be observed at scattering angles of 2θ = 31.5o, 34o, 35o, 36.5o, 38o, 42o, 43o, 45o, 46o, 48o, 51o (Fig. 7). The asymmetric peaks arrangement in Figure.7 indicates a semi-crystalline molecular arrangement. This type of molecular arrangement infers that GSC is an isotropic amorphous compound [42]. Comparison between Figures 6 and 7 based on the nature of peaks show that GSF is more structurally stable than GSC.
3.3.3 DSC /TGA Analysis for GSF and GSC
The DSC and TGA representation of GSF and GSC obtained are shown below in Figure 8 (a, b, c and d), respectively. The Figure 8 (a and b) (the DSC profiles of GSF and GSC) represent application of DSC for the characterization of the phase transition that occurred in GSF and GSC over the temperature ranges of 38 – 298oC and 45 – 300 oC, respectively. The transition enthalpies of 23.091 kJ/mol and 11.620 kJ/mol, respectively were obtained. The thermal activation energy (ΔE) was evaluated through TGA to be 25.86 kJ/mol and 45.928 kJ/mol for GSF and GSC, respectively using method described by Menkiti and Ejimofor, [30].
GSF produced sharp transition in the temperature range of 62.5 – 81 oC, while GSC produced its sharp transition between 49-52 oC. These behaviors could be linked to spontaneous densification during thermal treatment of the samples. The densification of the aggregated mass took place at temperatures of 100 – 150 oC for GSF (Figure 8a) and 115 – 175 oC for GSC (Figure 8b). The glass transition temperatures were observed between 37.5 - 42 oC for GSF and 48-52 oC for GSC. Furthermore, it was observed that GSC has higher glass transition temperature than GSF; this implies that GSC can withstand more operational increase in temperature than GSF without being denatured [30,32]. Within the glass transition stage, the onset, midpoint and offset transition points can be observed at the temperatures of 37.5 oC, 39.3 oC and 42 oC for GSF, and 48 oC, 50 oC and 52 oC for GSC, respectively. A clear observation of the DSC graphs demonstrated a situation in which the heat flow discs indicate exothermic nature for both GSF and GSC.
Figure 8 (c and d), shows the thermal-gravimetric analysis (TGA) profiles of the two samples (GSF and GSC). Graphically, the Figures represent variation in weight with respect to temperature. The final residual masses for GSF and
GSC estimated based on weight loss with respect to temperature are 5.72218 mg and 1.974 mg, representing 89.2% and 74.9% of the original weights of GSF (6.415mg) and GSC (2.634mg) sample, respectively. The initial weight loss observed in Figure 8 (c and d) could be linked to internal moisture content and gaseous loss from the matrix molecules [30]. The second phase weight loss may be as a result of decomposition in the samples. The results conclusively suggested thermal operational stability of the GSF and GSC as indicated by significant final residue percentage of 89.2% and 74.9% for GSF and GSC, respectively.
3.3.4 SEM characterization of GSC
The analysis of the external morphology (texture) of GSC was obtained via scanning electron microscopic evaluation (SEM). The SEM image obtained is presented in Figure 9. A compact structure with tiny pores and small stick littered external morphology is observable at 100um. It shows a good characteristic for an effective coagulant. Coagulants with reduced particle sizes and increased surface porosity would provide a better platform for adsorption of fine suspended particles.
3.4 Effect of process variables via OFAT
Conventional application of one factor at a time (OFAT) method evaluates the impact of one variable within a process by holding other variables at constant level [43].
3.4.1 Effect of pH on coagulation efficiency
The effect of pH on the coagulation system under consideration is illustrated in Figure 10. The pH of the solution is a critical parameter in most treatment processes [40,41]. It affects the surface charge of the coagulant [44]. From Figure 10, at constant GSC dosage of 5g and 30min settling time, two regions are notable, a region where the removal efficiency was at maximum and a region where it was at minimum. The removal efficiency was highest at pH of 4. A similar result was reported by Zhao et al.[45] on the effect of pH on coagulation, using ferric based coagulant in yellow river water treatment. Also, Sun et al [46] reported best removal efficiency within the same acidic region. The high removal efficiency within 2-4 could be attributed to progressive protonation of the coagulation system as GSC releases positive charges which progressively conjugated with the available negative species towards equilibrium. The region between the pH of 5-8 represents a region of progressive decline in particle neutralization and floc formation. This decline can be attributed to decline in GSC solubility within the system as a result of change in pH from strong acidity to alkalinity. At pH of 8, minimum particle removal efficiency ( 12%) was observed. This pH of minimum particle removal may be referred to as the point of zero charge for GSC. At this point (pH of 8), the surface charges of the coagulant available for charge neutralization are negligible. After the point of zero charge of GSC, the coagulation environment becomes more alkaline. At this stage, GSC is less soluble. However, the surface charge is not completely zero, which resulted in slight increase in removal efficiency at pH of 10. This result (subsection 3.4.1) suggests that GSC is more effective in acidic environment.
3.4.2 Effect of coagulant dosage on coagulation efficiency
Variation in turbidity removal efficiency with GSC dosage at constant time (30min) and best pH (4) adopted from section 3.31 is presented in Figure 10. Turbidity removal efficiency was found to increase from minimum to the maximum with increase in GSC dosage before a sharp decline (Figure 11). This trend (progressive increase of removal efficiency to maximum) could be as a result of increase on the availability of positively charged particles provided by the GSC for destabilization of the negatively charged suspended particles. A sharp decline in particles removal efficiency from 4 g/L could be attributed to excess coagulant concentration, which may have resulted in re-turbidization of the effluent [16]. Re-turbidization results from charge reversal due to increased net concentration of positively charged coagulant particles [16,17]. The curved profile of Figure 11 shows the effect of individual coagulant dosage on the particles decontamination process. The minimum particles decontamination efficiency (18%) was observed at the coagulant dosage of 0.5g/L, while the highest particles removal efficiency (98.1%) was observed at 4g/L of GSC.
3.4.3. Effect of settling time on coagulation efficiency at different temperatures
The variations in removal efficiency with time at different temperatures are shown in Figure 12. Figure 12 shows that removal efficiency increased with increase in settling time till equilibrium was attained. At equilibrium stage, 93.1%, 93.9%, and 98.7% reduction in turbidity was achieved at 25 oC, 35 oC and 45 oC, respectively. The equilibrium stages were observed at 20 min after which there was no more significant reduction in turbidity of the treated paint wastewater samples. Hence, most of the flocs settled between 0-20 minutes. 20 minutes equilibrium time is desirable for peri-kinetic coagulation. In addition, it was observed that increase in temperature increased the coagulation efficiency as well. This increase in efficiency resulted from increased particle excitation due to increase in particles kinetic energy [30]. Also increase in temperature lowers the viscosity of PW. Hence, the particles movement becomes more rapid leading to more effective particle collision and floc formation. Highest efficiency of 93.16% was observed at 25 oC. Slight increase was observed (from 93.16% to 95.433%) as the temperature increased to 35 oC. Furthermore, the removal efficiency increased to 98.224% as the temperature was increased to 45 oC.
3.4.4 Characteristics of the treated water after coagulation
After the coagulation experiment, the treat wastewater had TS of 791±0.03mg/l, TSS of 275±0.024, TDS of 516±0.016 and pH of 7.2 as against the national discharge limit of 1905mg/l, 705mg/l, 1200 and pH 7-8. Hence, it can be inferred that the treated wastewater can be discharged into the environment without adverse effect.
3.5 Response prediction and optimization using ANN and GA
Figure 13 shows the artificial neural network system histogram. ANN system histogram represents the error that approaches the network mean square error (MSE). It shows that approximately 12 instances were used for training, 5 for testing while the remaining was used for validation. The zero error line reveals -6.202 errors in 20 bins. The error shows the extent of correlation between the target and prediction. Hence, the negative sign show that predictions are higher than target. However, error of values < 10 indicates relatively good correlation between the target and the predicted responses. The network progress was monitored based on validation error
Network training was terminated at increase in validation error. The root mean squared error (RMSE) network performance curve shown in Figure 14 illustrates the plot of the observed RMSE against the epochs. In Figure 14, three different curves were built for training, testing and validation. The dotted line shows the best possible network conditions for training, testing and network validation based on the root mean squared error. The best validation performance was obtained at RMSE of 7.7084 at epoch 5. The validation performance suggests that after the fifth iterations (epoch 5), the network attained its best learning stage at the lowest possible RMSE (7.7084). Also, the RMSE measures the correlation between the target and ANN predicted responses. The best RMSE < 10 infers relative high correlation between the output (ANN predicted turbidity removal) and the target (Experimental turbidity removal). In addition, the network capacity to predict significant output was illustrated by the training state plot (Figure 15).
The network gradient of 1.9506-11 (loss function) was computed to illustrate the error contribution of each neuron at 5 epochs (Figure 15). Lower error is better [16]. The gradient was very much less than unity (1.9506e-11), it indicates that the error contribution of each neuron within the 5 epochs is minimal. Momentum gain (Mu) is the training gains and its value is expected to be less than one [16] “Mu” closer to zero shows high capacity of the trained network in making significant response prediction. In support of the RMSE and the training output result in network accuracy, the training, testing, validation and the overall network correlation coefficient (R2) of 0.97, 0.99, 0.97 and 0.93 were obtained from the performance plot of training, testing, validation and All (overall network performance) as shown in Figure 16.
Based on the R2-values, (0.97, 0.99, 0.97 and 0.93), it is inferred that the network has significant fitting performances in all the stages (training, testing and validation). The overall network performance equation (Equation 2) shows the overall relationship between the target and the output. The network output (predicted Turbidity removal efficiency (%)) obtained from software iteration within the hidden layers (Table not shown) was compared with the target (experimental removal efficiency) based on the correlation coefficient.
Output = 0.73*target+24 (2)
A strong correlation is always inferred for R2 value ≥ 0.7 [30].Correlation coefficient of 0.92 and mean average percentage error (MAV.PE) of 0.36 were obtained (Low average percentage error (close to zero) is desired because it indicates insignificant error between the experimental and predicted data). From the present comparison very strong correlation between the two data sets (output and target) can be inferred. In support of this judgment based on R2 values and the MAV.PE, Figure 17 shows the plot of output and target with respect to experimental run order. Visual assessment of Figure 17 gave credence to the result obtained in Figure 14. The slight deviations observed at run 14 and 20 (Figure 17) may be attributed to data approximation during generation of hidden hyper-parameters for response prediction.
The optimization of turbidity removal efficiency based on ANN predicted overall model equation (Equation 2) was carried out through genetic algorithm (GA). This metaheuristic AI approach provides an approximate optimal by evaluating sets of chromosomes created via GA operational environment. The target of the response optimization (set at 99.999%) is subject to the total weight such that the optimal response is as higher as possible and not exceeding the maximum weight [16,30] which is 100% for the present study. The weight of the data set was scaled into a continuous interval using fuzzy C-means. Population data in Table 4 applied as the initial population to Equation 2 (transfer function relating the weight to target) passed through reoccurring data crossover and mutation before termination. The outcome of the GA operation based on the convergence of optimal solutions show that the optimal turbidity removal of 98% can be accomplished at GSC dosage, time and temperature of 4g, 20min and 45oC, respectively. This optimal response was selected from a queue of 20 local optima conditions (not shown) returned at termination of GA operation.