Saccharum officinarum Bio-coagulant for Palm Oil Mill Effluent Treatment

doi:10.21203/rs.3.rs-3085142/v1

The coagulation process has become a common treatment method in water and wastewater treatment but not in palm oil mill effluent (POME) treatment. POME is known to have a high level of COD and turbidity and prior studies showed that the modified coagulation process using bio-coagulant may be feasible to remove primarily the COD and turbidity of the POME. In this study, an extract of Saccharum officinarum or bagasse is used as a source of bio-coagulant for the removal of COD and turbidity from POME at optimized coagulant dosage, pH, and contact time. The optimization was carried out via response surface analysis. This analysis stipulated that the application of Saccharum officinarum extract removed turbidity of more than 95%, COD of more than 65.0% with coagulation activity of 67.4% at optimum 50 mg/L coagulant dosage, pH 8, and slow mixing for 30 minutes. Henceforth, this study concludes the suitability of Saccharum officinarum as a bio-coagulant to primarily remove organic and solid pollutants during POME pre-treatment.

Saccharum officinarum

Bio-coagulant

Pre-treatment

Palm oil mill effluent (POME)

Palm oil mills in Malaysia currently use an open ponding system that runs on a biological treatment process. This method is preferable due to low initial capital expenditure and low operating costs (Lee et al. 2019). However, it requires a large amount of area, which results in a longer hydraulic retention time (Ng and Cheng 2015; Syamimi Zaidi et al. 2022). The implementation of the coagulation process as a pre-treatment can potentially reduce the drawbacks. Alum or evenly known as alumina sulphate is a common chemical coagulant that is used in the coagulation process. However, the use of chemical coagulants contributes to more sludge, which necessitates additional cost and space for sludge treatment and disposal (Lee et al. 2019; Zaidi et al. 2019a; Amran et al. 2021). The application of green waste in water and wastewater treatment is not an old technology (Loh et al. 2021, 2022). Natural coagulants can likely lessen the impact of chemical coagulants (Chaibakhsh et al. 2014). Previous research demonstrates that employing natural coagulants can effectively remove various contaminants, with removal rates surpassing 70%, including turbidity (Zaidi et al. 2019b), total suspended solids (TSS), colour (Jagaba et al. 2017), and COD (Zaidi et al. 2022).

In the coagulation process, the operating conditions such as coagulant dosage and pH determine its performance for water and wastewater treatment (Bahrodin et al. 2021). Existing studies on the application of bagasse as a natural coagulant for water and wastewater treatment mainly focus on the effect of optimum condition individually through the One-Factor-at-a-Time method (OFAT) (Chitra and Muruganandam 2019; Amran et al. 2022). However, this method is outdated and does not show any interaction effects between operating conditions (Shak and Wu 2014; Adesina et al. 2019). Currently, the application of Response Surface Methodology (RSM) which involves the design, analysis, and modelling of experiments manages to attract the attention of many researchers to be applied in various fields (Priyatharishini et al. 2019). Therefore, this study was conducted to identify the potential of Saccharum officinarum as a natural coagulant for palm oil mill effluent (POME) treatment by optimizing the coagulant dosage, pH, and contact time.

2.1 Preparation of Natural Coagulant

This study was carried out using the stem of Saccharum officinarum (bagasse) as a source of natural coagulant. Bagasse used in this study was collected from vendors around Taman Universiti, Skudai, Johor. Preparation of bagasse as a natural coagulant was done according to Bahrodin et al. (Bahrodin et al. 2022) involves the grinding, sieving, and base extraction process using sodium hydroxide (NaOH).

2.2 Collection and Characterization of POME

Palm oil mill effluent (POME) used in this study was collected from an actively operated palm oil mill located at Felda Taib Andak, Kulai, Johor. This palm oil mill uses an open ponding system for the treatment process. Upon collection, POME was stored in a cold room to ensure its freshness. The collected POME was characterized by its physical and chemical properties. Table 1 shows the initial characteristics of POME collected from the anaerobic pond.

Table 1

Initial properties of the collected POME from the anaerobic pond
Composition	Unit	Value
pH	-	8.4 ± 0.1
Turbidity	NTU	1662 ± 65.5
TSS	mg/L	988 ± 41.5
COD	mg/L	2010 ± 169.2
NH₄-N	mg/L	250.7 ± 14
Nitrate	mg/L	30 ± 7.8
Phosphate	mg/L	555.7 ± 23.8

2.3 Jar Test

The coagulation process was carried out using a jar test. 200 mL of POME that had been thoroughly mixed was poured into a 250 mL beaker. Bagasse extract as a natural coagulant with various dosages was added into each of the beakers followed by a rapid mix for 1 minute at 200 rpm. After 1 minute, the mixing speed was reduced to 80 rpm with a varied contact time. After a certain time of slow mixing, the solution was left for the sedimentation process for 30 minutes. To prevent disturbing the settled flocs, water samples for turbidity removal, COD removal, and coagulation activity were taken from the surface and were measured immediately.

2.4 Coagulation Performance

Turbidity removal and coagulation activity were determined by identifying the before and after turbidity content using a turbidity meter (Milwaukee turbidimeter). COD content in the POME was analysed based on the USEPA Reactor Digestion Method (Method 8000). COD removal of the POME after the coagulation process can be determined using Eq. (2). For the coagulation activity, the turbidity content of POME without the addition of coagulant was determined and labelled as turbidity blank. The coagulation activity can be computed using Eq. (3).

Turbidity removal (%) = ((Turbidity_Before – Turbidity_After) / Turbidity_Before ) x 100 (1)

COD removal (%) = ((COD_Before – COD_After) / COD_Before ) x 100 (2)

Coagulation activity (%) = ((Turbidity_Blank – Turbidity_After) / Turbidity_Blank ) x 100 (3)

2.5 Experimental Design via Response Surface Methodology

The dosage of the coagulant, the solution's pH, and the contact time of slow mixing were optimized using the Response Surface Methodology (RSM) via Central Composite Design (CCD) in the Design Expert software. The dependent variables were predicted using Eq. (4).

The CCD analysis was made up of duplicates of 2³ factorial designs, supplemented with a duplicate of six (6) axial points and six (6) central points; 34 tests were performed at random. The high and low values were chosen after examining the literature and performing preliminary trials. Table 2 shows the levels of operating conditions in the CCD. Analysis of Variance (ANOVA) was used to evaluate the effect of coagulant dosage, pH, and contact time on turbidity removal, COD removal, and coagulation activity. The order of statistical significance, as well as the model's hierarchy criterion, were utilized to decide whether the terms would remain in the prediction models or not. Following the development of the prediction models, three-dimensional (3D) response surface plots were created to show and investigate the impact of the operating conditions and interactions.

Table 2

Levels of operating condition in the CCD
Operating Conditions	Level of Operating Conditions
Operating Conditions	- α	-1	0	+ 1	+α
Coagulant Dosage	26.3	50	125	200	223.7
pH	4.5	5	6.5	8	8.5
Contact time	20.5	30	60	90	99.5

Y = β_{o +} \(\sum _{i=1}^{n}{\beta }_{i}{X}_{i}\) + \(\sum _{i=1}^{n}{\beta }_{ii}{{x}^{2}}_{i}\)+ + \(\sum _{i=1}^{n}\sum _{j=i+1}^{n}{\beta }_{ij}{x}_{i}{x}_{j}\)+Ɛ (4)

where,

Y = Predicted responses (turbidity removal and coagulation activity)

β_o = Constant coefficient

\({\beta }_{i}\) = Linear coefficient

\({\beta }_{ii}\) = Quadratic coefficient

\({\beta }_{ij}\) = Interactive coefficient

\({x}_{i}{x}_{j}\) = Influenced factors

Ɛ = Random error

3.1 Statistical Analysis

RSM was carried out to optimize the selected operating conditions (coagulant dosage, pH, and contact time). 34 experimental runs from the central composite design with various coagulant dosage, pH, and contact time was carried out to identify its effect on turbidity removal, COD removal, and coagulation activity. All experimental runs were analysed by Analysis of variance (ANOVA) to identify the correlation between the operating factors by using the p-value. Results from the ANOVA analysis are shown in Table 3.

Table 3

Analysis of variance (ANOVA) for the response surface quadratic model
Term	Turbidity removal		COD removal		Coagulation activity
Term	p-value	F-value	p-value	F-value	p-value	F-value
Model	< 0.0001	19.19	< 0.0001	14.97	< 0.0001	22.49
A: Coagulant dosage	0.1929	1.82	< 0.0001	28.86	0.5943	0.2930
B: pH	0.0039	10.68	< 0.0001	46.22	0.0006	16.54
C: Contact time	0.1126	2.75	0.2337	1.49	0.9613	0.0024
A x B	< 0.0001	30.75	0.0002	19.05	0.0003	19.56
A x C	0.0002	21.66	0.0425	4.59	< 0.0001	34.60
B x C	0.0017	13.19	0.3316	0.9820	0.0008	15.38
A x A	0.4856	0.50	0.0482	4.34	0.1919	1.82
B x B	< 0.0001	33.85	0.0006	15.58	< 0.0001	65.79
C x C	< 0.0001	55.23	0.0028	11.10	< 0.0001	33.97
A x B x C	0.1929	1.82	-		0.0441	4.62
A x A x B	0.1155	2.71			< 0.0001	74.32
A x A x C	0.0001	23.38			0.2153	1.64
A x B x B	0.0689	3.70			0.9365	0.0065
Lack of fit (LOF)	0.0003	19.58	0.3145	1.07	0.0048	10.19
R² value	0.93		0.89		0.94
Adjusted R²	0.88		0.82		0.89
Predicted R²	0.77		0.74		0.82

Identification of the suitability of the model generated can be seen through the F-value of the response, lack of fit (LOF) value, and R² value of the operating conditions. Based on Table 3, the model F-value of the response is big enough that there is only a 0.01% chance that it could occur due to noise. Lack of fit (LOF) F-value of COD removal is not significant relative to the pure error. There is a 31.45% chance that a LOF value this large could occur due to noise. Meanwhile, for turbidity removal and coagulation activity, the LOF F-value is significant. There is only a 0.03% and 0.48% chance for the F-value of turbidity removal and coagulation activity this large could occur due to noise. A significant lack of fit is not preferable as it indicates that the model is not fit. However, there are a few studies such as Cheraghipour and Pakshir (Cheraghipour and Pakshir 2021) and Tamoradi et al. (Tamoradi et al. 2021) that used the generated model despite the significant LOF F-value, and it still shows good findings. Therefore, the generated model is still usable.

In a model, R-Squared (R² or the coefficient of determination) is a statistical metric that measures how well the data fit the model. Based on Table 3, the R² value of the turbidity removal model, COD removal model, and coagulation activity model are close to 1.0 which indicates they fit the model. Normal probability plots as shown in Fig. 1 further shows the plots distribute along the normal distribution line. Based on this value, it indicates that 7.42%, 11.03%, and 6.4% of the variability in the turbidity removal, COD removal, and coagulation activity cannot be explained by the model which is very low thus, it shows that the data did fit the model. Besides that, the predicted R² of turbidity removal, COD removal, and coagulation activity is in reasonable agreement with the adjusted R² as the difference between both is less than 0.2.

The generated model can be further justified its adequacy through the plots of predicted versus actual values as shown in Fig. 2. Based on the plots, shows that the actual values are distributed fairly close along the y = x line and have linear behaviour. This result illustrates the strong performance of the generated model and demonstrates satisfactory agreement between actual data and data from the created models (Banch et al. 2019; Chua et al. 2019). Therefore, the effect of coagulant dosage, pH, and contact time on turbidity removal, COD removal, and coagulation activity was modelled using a second-order model as given in Eq. (5)–(7).

Turbidity removal (%) = 131.46–0.31A – 11.52B – 0.14C + 0.05AB + 0.0016AC – 0.025BC + 0.001A² + 1.06B² + 0.0018C² (5)

COD removal (%) = 56.36 + 0.2A – 3.45B + 0.078C – 0.059AB – 0.00088AC – 0.049BC + 0.00029A² + 0.85B² + 0.0002C² (6)

Coagulation activity (%) = − 64.22 + 4.80A – 20.09B + 0.43C – 0.73AB – 0.0045AC – 0.229BC – 0.026A² + 5.76B² + 0.01C² (7)

3.2 Response Surface: Effect of Saccharum officinarum on Coagulation Performance

The 3D surface graph plots created from the models were used to further evaluate and understand the influence and interactions between coagulant dosage, pH, and contact time towards turbidity removal, COD removal, and coagulation activity. The plots were created using a function of two operating conditions at a time while maintaining the other factor. The 3D surface graph plots for turbidity removal, COD removal, and coagulation activity are shown in Fig. 3, Fig. 4, and Fig. 5 respectively.

3.2.1 Effect of Coagulant Dosage

Coagulant dosage is one of the most important operating conditions in the coagulation process to optimize the removal of pollutants and is most studied by researchers worldwide. To study the effect of coagulant dosage on the removal of turbidity and COD and coagulation activity, the coagulant dosage was varied from 50 mg/L to 200 mg/L. Based on Fig. 3(a, b), coagulant dosage is a significant operating condition that could increase turbidity removal. The plot shows that 50 mg/L is the optimum coagulant dosage for turbidity removal and increasing the coagulant dosage will only worsen the turbidity removal. The finding from this study is aligned with Tong et al. (Tong et al. 2021) which used Moringa oleifera for fish farm wastewater. Both studies show that a lower coagulant dosage is preferable and as the dosage increases, it will reduce turbidity removal. Coagulant dosage less than the optimum dosage is known as underdosing where the coagulant is insufficient to cater to the pollutants. Meanwhile, increasing the coagulant dosage beyond the optimum coagulant dosage will only cause overdosage. During overdosage, too many coagulants but insufficient pollutants could react with the coagulant. Eventually, both underdosing and overdosage will only cause the pollutants to repel each other instead of attracting each other, thus worsening the turbidity removal (Adesina et al. 2019; Nouj et al. 2022).

Based on Fig. 4(a, b), increasing the coagulant dosage from 50 mg/L to 200 mg/L reduces the removal of COD. This finding is in line with the findings by Shan et al.(Shan et al. 2017) and Fard et al. (Besharati Fard et al. 2021). Shan et al.(Shan et al. 2017) used Moringa oleifera for the treatment of wastewater from industrial estates and landfill. Meanwhile, Fard et al.(Besharati Fard et al. 2021) used Lallemantia mucilage for the treatment of saline oily wastewater. Both studies show the higher the coagulant dosage used in the coagulation process, the lower the COD removal. However, the study by Khader et al.(Khader et al. 2018) shows otherwise as increasing coagulant dosage improves the COD removal. The study by Shan et al.(Shan et al. 2017) and Fard et al.(Besharati Fard et al. 2021) extracts the natural coagulant prior to application using either alkali or ethanol while the study by Khader et al.(Khader et al. 2018) did not extract the coagulant. According to Shan et al. (Shan et al. 2017), the solvent used could probably reduce COD removal. Anyhow, sources of natural coagulants such as bagasse, Moringa oleifera, and mucilage contain their organic compound (Simin et al. 2022). The extraction process that was carried out not only extract the active coagulant agent but also manage to extract the organic compound as well which contribute to increasing COD content as a higher coagulant dosage was used. The advantage from this is that very low coagulant dosage is sufficient for optimum removal of COD unlike others that require higher coagulant dosage.

Figure 5 (a) and Fig. 5 (b) show the effect of coagulant dosage on coagulation activity. Based on these figures, coagulant dosage is significant for coagulation activity. The purpose of carrying out coagulation activity (%) is to identify the removal of pollutants in POME treatment is either due to the coagulation process or not. Lower coagulant dosage will show high coagulation activity. These plots show that the removal of COD is due to the coagulation process.

3.2.2 Effect of pH

Besides coagulant dosage, pH also plays an important role in improving the coagulation process as it could significantly affect the responsible coagulation mechanism. To identify the effect of pH by using bagasse as a natural coagulant for the treatment of POME, the pH was varied from pH 5 to pH 8. Based on Fig. 3(a, c), Fig. 4(a, c), and Fig. 5(a, c), pH is a significant operating condition upon improving all three responses, turbidity removal, COD removal, and coagulation activity. Compared to coagulant dosage, pH is a more significant operating condition. All plots show that turbidity removal, COD removal, and coagulation activity is highest at pH 8. From pH 5, the higher the pH, the higher the turbidity removal, COD removal, and coagulation activity. This finding is similar to findings by Rifi et al.(Rifi et al. 2022) that use Moringa oleifera for the treatment of olive oil mill wastewater as well as Desta and Bote(Desta and Bote 2021) that also used Moringa oleifera seeds but for the treatment of domestic wastewater. Both studies showed that pH 8 as optimum pH for most effective pollutant’s removal.

The coagulation mechanism of natural coagulants can be either interparticle bridging or charge neutralization. According to Bahrodin et al. (Bahrodin et al. 2022), the coagulation mechanism of Saccharum officinarum is interparticle bridging and not charge neutralization. Interparticle bridging relies on the polymeric chain that extends and attaches to pollutants or colloids and forms a complex structure of coagulant-colloid-coagulant. Bahrodin et al. (Bahrodin et al. 2022) have also identified the active coagulant agent of Saccharum officinarum is an extremely high polysaccharide content and according to the FT-IR analysis, the carboxyl group is part of the functional group. Guo et al. (Guo et al. 2017) state that the carboxyl group (-COOH) is the functional group that makes the polysaccharide acidic. At lower temperatures, the pollutants will have similar pH as the natural coagulant thus, both will repel each other. At higher pH, removal will also increase probably due to the availability of more adsorption sites from the polymeric chain (Shak and Wu 2014; Choy et al. 2015). This is because, at slightly alkali conditions, ionization of -COOH can occur and produce -COO- group and -OH group. As a result, the -OH group will increase thus, causing intramolecular electrostatic repulsion which will enhance the uncoiling and extension of the polymeric chain (Lürling et al. 2020; Aziz et al. 2021). This will result in more adsorption sites available and a more complex structure can be formed for the removal of pollutants. However, further, increasing the pH will only extensively uncoil and stretch the polymeric chain which will wrap the pollutants instead of only attaching them. Similarly charged colloids will be produced and will only repel each other. The optimum pH of pH 8 agrees with the statement by Yin (Yin 2010) and Bratby (Bratby 2015) that identifying the natural coagulant based on the plant is best to be used at a pH range of 7 to 10. It is also worth noting that at pH 8, the coagulation activity is also high indicating that the removal of turbidity and COD is due to the coagulation and not from other reactions.

3.2.3 Effect of Mixing Contact Time

Besides coagulant dosage and pH, contact time during slow mixing can also affect the coagulation process but the least study focuses on this operating condition. To study the effect of contact time on turbidity removal, COD removal, and coagulation activity, the contact time was varied from 30 minutes to 90 minutes. Based on Fig. 3(b,c), Fig. 4(b,c), and Fig. 5(b,c), contact time during slow mixing is a significant operating condition but not as significant as pH as some of the condition is insignificant. However, based on these plots, a shorter contact time is preferable and results in higher turbidity removal, COD removal, and coagulation activity compared to a longer contact time. The finding from this study is similar to the findings by Choong Lek et al. (Choong Lek et al. 2018) that used Cicer arietinum as a natural coagulant for the treatment of POME as well as Kusuma et al. (Kusuma et al. 2021) used Ipomoea batatas for water treatment. All studies show shorter contact time shows better coagulation performance.

Slow mixing is an essential step in the coagulation process to ensure the coagulant can react with pollutants and flocs can be formed. However, overmixing will cause more collisions between the flocs formed. The frequent collision between flocs will cause excessive mechanical shearing that can lead to floc breakage and lead to restabilization of the pollutants which will increase the concentration of the pollutants (Ramphal and Sibiya 2014; Ernest et al. 2017; Karam et al. 2021). Besides that, overmixing can also disturb the coagulation mechanism. The coagulation mechanism of Saccharum officinarum is interparticle bridging that relies on the polymeric chain for the formation of complex structures. According to Owodunni and Ismail (Owodunni and Ismail 2021), overmixing can break the polymeric chain and will hinder the formation of complex structures (flocs) that can settle via gravity. It is good that a shorter contact time is sufficient for the removal of turbidity and COD because extending the contact time not only can cause floc breakage but also require more energy which eventually will increase the total operating cost of the coagulation process (Choong Lek et al. 2018).

3.3 Identification of Optimum Operating Conditions and Validation of the Models

Based on the plots in Fig. 6, Fig. 7, and Fig. 8, optimum removal of turbidity and COD with high coagulation activity was obtained by using 50 mg/L coagulant dosage, slow mixed for 30 minutes with pH of wastewater maintained at pH 8. Optimum removal obtained from models was compared with the experimental response with the same operating condition to identify errors between the predicted and experimental response. Table 4 shows the comparison between experimental and predicted values with the same optimum operating conditions.

Table 4

Value comparison between experimental and predicted responses
Response	Experimental Response	Predicted Response	% Error
Turbidity removal (%)	95.7	95.0	0.75
COD removal (%)	62.9	66.8	3.86
Coagulation activity (%)	55.0	58.3	3.28

Based on Table 4, the error between the predicted response with the experimental response is between 0.7–4%. According to Priyatharishini and Mokhtar (Priyatharishini et al. 2019), the error should be less than 10% to validate that the experimental and predicted response is in good agreement. Error obtained from this finding which is less than 10% indicates that there are good agreements between the value obtained from experimental and predicted. Joaquin et al. (Joaquin et al. 2022) state that an error of less than 10% is generally acceptable due to the nature of the experiment involving several fluctuating variables.

Coagulation-flocculation technique was used to demonstrate the efficiency of Saccharum officinarum (bagasse) as a possible natural coagulant for turbidity and COD removal from palm oil mill effluent (POME). Bagasse has been proven to offer promising treatment of wastewater with high COD content. A central composite design (CCD) was used to describe the treatment process using a second-order model. The operating condition that could affect the process such as coagulant dosage, pH and contact time during slow mixing was regulated to obtain optimum removal of turbidity and COD with high coagulation activity. The model validation report proved that the proposed model matched the experimental data with an R² value exceeding 85%. To obtain optimum turbidity removal and COD removal with high coagulation activity, the dosage of bagasse as a natural coagulant is 50 mg/L, pH of 8, and slow-mixed for 30 minutes. Findings from this study demonstrate that bagasse as a natural coagulant can be used for pre-treatment in POME to reduce both turbidity and COD content.

Acknowledgment

The work was funded by the Ministry of Higher Education (MOHE) Malaysia under Fundamental Research Grant Scheme (FRGS/1/2022/TK08/UTM/01/1)

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No competing interests reported.

Saccharum officinarum Bio-coagulant for Palm Oil Mill Effluent Treatment

Status:

Version 1

Abstract

Figures

1. Introduction

2. Materials and Methods