In this study, natural coagulants obtained from banana peel and Moringa stenopetala seed were applied to remove total dissolved solids (TDS) and turbidity from river water. Central composite design (CCD) method was applied for the operating conditions of pH (3–10), coagulant dosage (0.3–1 g/L), stirring speed (30–90 rpm), and settling time (20–60 min). The optimum conditions obtained from the numerical optimization for pH, coagulant dosage, stirring speed, and settling time were 8.52, 1.000 g/L, 33.58 rpm, and 37.92 min, respectively, with a desirability value of 0.883 when banana peel powder was used as a natural coagulant. Under those optimum conditions, the experimental results for banana peel showed 81.32 and 93.09%, removal efficiency for TDS and turbidity, respectively. Similarly, the optimum conditions obtained from numerical optimization for pH, coagulant dosage, stirring speed, and settling time were 9.99, 0.999 g/L, 30.0 rpm, and 39.96 min, respectively, with a desirability value of 0.963. Under these optimum conditions for M. stenopetala seed powder, the experimental results showed 83.64 and 95.13%, removal efficiency for TDS and turbidity, respectively. Overall, M. stenopetala seed powder shows a higher potential for TDS and turbidity removal efficiency than banana peel powder.

  • Banana peel and Moringa stenopetala seed powder were used as a natural coagulants for the removal of TDS and turbidity.

  • R2 values (0.9606 and 0.9534) confirmed a high correlation between the experimental and predicted results for banana peel powder.

  • R2 values (0.9715 and 0.9531) confirmed a high correlation between the experimental and predicted results for Moringa stenopetala seed powder

The ability of water to natural purification and purifying itself through sedimentation and flocculation processes, which allow pollutants to settle out, is an existing trend (Kazi et al. 2013). However, this natural process is insufficient when harmful contaminants are present in excessive amounts (Debora et al. 2013). Such water is called non-potable water and is unsuitable for drinking due to being a cause of death for human in many countries (Choubey et al. 2012). To ensure the quality of drinking water, the World Health Organization (WHO) provides recommended values for physical, chemical, and biological parameters (Choubey et al. 2012). However, inadequate sanitation and polluted water are still responsible for up to 80% of all diseases worldwide (Janna 2016). Despite two-thirds of the Earth being covered with water (Rajasulochana 2016), the availability of clean water remains uncertain due to pollution and inadequate treatment (Kazi et al. 2013).

Total dissolved solids (TDS) are an important indicator of water quality as they measure the amount of dissolved inorganic and organic substances present in water. These substances can include minerals, salts, and other compounds that can impact the taste, odor, and overall quality of water (Town et al. 2013; U.S. EPA 2019). High TDS levels can also cause scaling and corrosion in pipes and equipment, leading to increased maintenance costs and decreased efficiency (U.S. EPA 2019). To address TDS levels in water, various treatment processes such as reverse osmosis, distillation, or ion exchange are commonly used. However, these methods can be costly and energy-intensive. Natural coagulants have been studied as a potential low-cost and environmentally friendly solution for TDS removal (Balamurugan & Shunmugapriya 2019). Turbidity is a crucial factor in determining the quality of water for human consumption. It measures the clarity of water and indicates the presence of suspended particles like sediment, algae, and other organic and inorganic matter (Kazi et al. 2013). High levels of turbidity can negatively impact the safety and quality of drinking water by reducing the effectiveness of disinfection and providing a breeding ground for microorganisms that can cause diseases (U.S. EPA 2019). The high turbidity of water is due to the presence of colloidal materials, which can absorb harmful chemicals and cause unpleasant tastes and odors (Kazi et al. 2013).

The coagulation–flocculation process is a widely used method for producing potable water and treating wastewater (Debora et al. 2013). Various factors, including pH, coagulant dosage, stirring speed, and settling time, can influence the efficiency of this process. Coagulants can be either natural or chemical in nature (Janna 2016). Chemical coagulants, such as aluminum and ferric salts, have been used for water treatment for centuries (Kazi et al. 2013; Ghernaout 2020). However, these chemical coagulants can release harmful substances into the environment, negatively impacting human health (Choubey et al. 2012; Diver et al. 2023). They are also expensive, produce large amounts of sludge, significantly affect the pH of treated water, and can cause diseases like Alzheimer's (Thakur & Choubey 2014). Moreover, advanced oxidation processes (AOPs) are a viable solution to address environmental concerns caused by organic materials that cannot be treated conventionally (Liu et al. 2019; Gallo-cordova et al. 2021). Different natural coagulants are believed to provide a sustainable and cost-effective solution for water treatment in rural areas, where access to clean water is often limited (Koul et al. 2022). Natural coagulants derived from plant sources are a more sustainable alternative (Saritha et al. 2017). While the effectiveness of these natural coagulants in treating river water has been investigated, there is still a significant gap in exploring the operating conditions to optimize the river water treatment process, the interaction effects of the factors, and the understanding of the morphological and chemical composition of the coagulants themselves. According to Liu et al. (2019), four-factor, five-level central composite design (CCD) was chosen to optimize degradation conditions using ozone-based AOP.

So, it is necessary to develop inexpensive and efficient water treatment accesses that are derived from natural coagulant materials that are readily available in large quantities and are economically feasible. In that context, there is a possibility of using natural coagulants derived from banana peel and Moringa stenopetala seed, which are abundantly available in the southern part of Ethiopia, at a very low cost.

This study aimed to investigate the coagulation capacity of two natural coagulants, namely banana peel powder and M. stenopetala seed powder, in the removal of TDS and turbidity of river water.

Preparations of coagulants and reagents

Except for the raw banana peel and M. stenopetala seed, all the reagents used in this analysis were analytical grade.

The overall flowchart for the experimental investigation of this study is shown in Figure 1.
Figure 1

The general framework of the study.

Figure 1

The general framework of the study.

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Preparation of banana peel powder

The raw banana peels were washed with distilled water to remove any external dirt, then sun-dried for 1 day and placed in the oven to dry for 24 h at 105 °C (Maurya & Daverey 2018). The dried banana peels, which come from oven drying, were grinded and sieved to a size of 600 μm. Finally, the powder form was kept inside the desiccator until further experimentation as shown in Figure 2.
Figure 2

Coagulant preparation (a) raw banana peel, (b) after oven dry, and (c) powder form.

Figure 2

Coagulant preparation (a) raw banana peel, (b) after oven dry, and (c) powder form.

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Preparation of M. stenopetala seed powder

Also, the preparation of the M. stenopetala seed powder was done according to the method described by Dalvand et al. (2016). The seeds were de-shelled by hand, and the kernels were washed with distilled water to remove any dirt, followed by drying for 2 h in a hot air oven at 80 °C. The dried seeds were then ground finely to a powder by using an electronic grinder and sieved to a size of 600 μm. Finally, the powder form of M. stenopetala seed was kept inside the desiccator until further experimentation as shown in Figure 3.
Figure 3

Coagulant preparation (a) raw seed, (b) after oven dry, and (c) powder form.

Figure 3

Coagulant preparation (a) raw seed, (b) after oven dry, and (c) powder form.

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Characterization of coagulants

The functional groups involved in the coagulation process were identified by Fourier transform infrared (FTIR) analysis (spectrum 65 FTIR, Perkin Elmer) in the range 4,000–400 cm−1 using KBr pellets. Origin Pro 2023 software (Version 10.0.5) was used to plot and smooth the graphs of absorbance against wavelength for every sample.

An FTIR chart (Nandiyanto et al. 2019) was used to determine the functional groups present in the respective samples through the identification of the IR absorption peaks at different wavelengths on the graphs. The surface morphology of banana peel powder and M. stenopetala seed powder (before and after coagulation) was investigated using scanning electron microscopy (SEM) images with different magnifications obtained from a Quanta 200 scanning electron microscope (FEI Company, USA).

Determination of physicochemical water quality

In this study, portable pH meter (model pH3310), digital turbidity meter (LA-34) Digital Nephelometer, Digital TDS Meter (SI-187), jar test apparatus (LCD display digital flocculator), stirrers and 1,000-ml beakers, oven, and mass balance were used. The samples that were gathered were moved to the laboratory while adhering to the usual operating procedures using the APHA standard (APHA 1998).

Determination of pH

The pH of raw water samples and coagulated water was determined using the portable pH meter (model pH3310). Calibration of the pH meter probes was performed using standard solutions. The probes were then carefully inserted into the samples, ensuring complete submersion and contact with the sensing edge. The pH values were recorded once the meter's display showed stable readings. This procedure allowed for the accurate measurement of pH levels in the water samples.

Determination of turbidity

In order to assess the turbidity of both raw water samples and coagulated water after the coagulation experiments, a digital turbidity meter (LA-34) Digital Nephelometer was employed. The meter was calibrated using distilled water and a formazine standard solution to ensure accurate readings. The raw water samples and coagulated water were then measured for turbidity using the calibrated meter. The measurements were taken when the display on the meter reached a stable state, ensuring reliable and consistent results.

Determination of TDS

TDS measurements were conducted using a digital TDS meter, specifically the Digital TDS Meter (SI-187). The meter was calibrated using a standard solution. Both raw water samples and coagulated water after the coagulation experiments were then measured for TDS using the calibrated meter. The TDS values were recorded once the meter's display became stable.

Coagulation experiment

The coagulation experiment for both coagulants was done with the jar test apparatus according to the order of treatment combinations set based on factors involved in this study: dosage, pH, stirring speed, and settling time. In this study, a jar test apparatus (LCD display digital flocculator) was used. This apparatus normally consists of six stirrers and six 1,000-ml beakers, but for this study, three 1,000-ml beakers were used by considering the time and quantity of sample water required. Each of the beakers was filled with a river water sample, after which varying dosages of 0.3, 0.65, and 1 g/L were added. The mixture of sample water and dosed coagulant in the jar (Figure 4) was stirred rapidly at 150 rpm for 2 min to mix uniformly, which is common for all experiments, and then slowly mixed with varying speeds of 30, 60, and 90 rpm for a constant mixing time of 20 min. The pH of sample water was adjusted within the range of 3, 6.5, and 10 using 0.1 M sodium hydroxide or 0.1 M hydrochloric acid before adding the coagulants. Subsequently, the samples were allowed to settle without disturbance for 20, 40, and 60 min in the beaker. Then, overlying clear water was conveyed by a syringe and measured for TDS and turbidity. In this study, all experiments were done three times, and the average was taken. The percentage of coagulant TDS and turbidity removal efficiency was calculated using Equations (1) and (2) adopted from Ramavandi (2014) and Gali Aba Lulesa et al. (2022).
formula
(1)
where To is turbidity of water before coagulation (NTU) and Tf is turbidity of water after coagulation (NTU).
formula
(2)
where TDSo is TDS of water before coagulation and TDSf is TDS of water after coagulation .
Figure 4

Jar test experimental setup.

Figure 4

Jar test experimental setup.

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Experimental design and statistical data analysis

The experimental design for this study was implemented using Design Expert software version 13.0.5.0, which utilizes statistical and mathematical methods to model the relationship between the input variables and the output variables, which were considered factors and responses, respectively. To assess and optimize the interactions among the independent variables (pH, coagulant dosage, stirring speed, and settling time) and their effects on the dependent variables (response) TDS and turbidity removals, a response surface methodology (RSM)-based face-centered CCD was employed. The RSM approach allowed for obtaining maximum information with the minimum number of experiments, as it reduced the required number of runs (Nor & Wan 2020). The levels of the experimental factors for the independent variables are presented in Table 1.

Table 1

Experimental factor levels for independent variables

Variables (Factors)SymbolReal values of coded levels
Low level (−1)High level (+1)
M. stenopetala 
Coagulant dose (g/L) A 0.3 
Settling time (min) B 20 60 
Stirring speed (rpm) C 30 90 
pH D 10 
Banana peel 
pH A 10 
Coagulant dosage (g/L) B 0.3 
Stirring speed (rpm) C 30 90 
Settling time (min) D 20 60 
Variables (Factors)SymbolReal values of coded levels
Low level (−1)High level (+1)
M. stenopetala 
Coagulant dose (g/L) A 0.3 
Settling time (min) B 20 60 
Stirring speed (rpm) C 30 90 
pH D 10 
Banana peel 
pH A 10 
Coagulant dosage (g/L) B 0.3 
Stirring speed (rpm) C 30 90 
Settling time (min) D 20 60 

Mathematically, Equation (3) adopted from Bayuo et al. (2020) was used to determine the total number of runs (N) performed.
formula
(3)

Here, k represents the number of factors, and n represents the number of center points. In this study, there were four independent variables, so a 24 full factorial CCD was utilized. It consisted of 16 factorial points, 8 axial points, and 6 replicates at the center points, resulting in a total of 30 experiments.

In addition, the face-centered CCD is well-suited for fitting quadratic response surfaces and optimizing response processes, as highlighted by Dawood et al. (2013). The experimental CCD data obtained from the response surface were utilized to establish the relationship between the independent variable factors (A, B, C, and D) and the dependent variables response (Y) using a generalized form of second-order multiple regression, as shown in Equation (4) adopted from Adelodun et al. (2019).
formula
(4)
where Y represents the predicted response model for percentage turbidity and TDS removal. βo denotes the constant coefficient, βi represents the coefficient of the linear term, βii corresponds to the quadratic term, and βij represents the interaction effect term. The variable k represents the number of independent variables, while xi and xj represent the coded values of the independent variables.

To evaluate the adequacy of the model and the effects of the input parameters on the response variable, an analysis of variance (ANOVA) was conducted. A statistical evaluation of the p-value and F-value of the regression coefficient at a 95% confidence interval was performed. Additionally, the coefficient of determination (R2), adjusted coefficient of determination (R2 adj), adequate precision (AP), and coefficient of variation (CV) were used to assess the quality of fit of the developed model. Furthermore, 3D response surface plots were generated to visualize the interaction between the independent factors and their respective effects on the response variable.

Characterization of coagulants

Surface morphology

The surface morphology of banana peel and M. stenopetala seed powder before and after the coagulation process was examined using scanning electron microscopy (SEM) images, as illustrated in Figure 5. Prior to coagulation, the coagulants exhibited a rough texture and noticeable pores of various sizes and shapes, as shown in Figure 5(a) and 5(c). However, after the coagulation process, morphology of the coagulants appeared different, as illustrated in Figure 5(b) and 5(d). The pores were observed to be filled, resulting in a much smoother surface. This indicates that the coagulation process facilitated the trapping and adsorption of suspended particles.
Figure 5

SEM images of banana peel and M. stenopetala seed powder before coagulation (a and c) and after coagulation (b and d), respectively.

Figure 5

SEM images of banana peel and M. stenopetala seed powder before coagulation (a and c) and after coagulation (b and d), respectively.

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Functional group determination

To investigate the potential functional groups involved in the coagulation process, the coagulant derived from banana peel and M. stenopetala seed powder were examined. The coagulation test was accompanied by before and after FTIR analyses. As shown in Figure 6, the FT-IR spectra of banana peel powder reveal a prominent peak at 3,443 cm−1, which corresponds to the stretching of hydroxyl groups (O–H). Other peaks were observed at wave numbers 2,931, 1,653, and 1,057 cm−1, signifying the stretching of aliphatic C = H groups, amide I stretching of C = O, and C–O stretching, respectively (Lalung et al. 2022). These functional groups suggest the presence of polymeric substances like carbohydrates and proteins, which may contribute to the coagulation activity (Daverey et al. 2019). After coagulation, the peaks exhibited slight shifts, and new peaks emerged at wave numbers 3,438, 2,928, 1,643, and 1,046 cm−1, respectively. This shift and the appearance of new peaks indicate the involvement of functional groups during the coagulation process.
Figure 6

FTIR spectrum 4,000–400 cm−1 banana peel powder before and after coagulation.

Figure 6

FTIR spectrum 4,000–400 cm−1 banana peel powder before and after coagulation.

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As shown in Figure 7, the FT-IR spectra of M. stenopetala seed powder, the maximum peak was observed at 3,451 cm−1 due to O–H stretching vibration. The peak at wave numbers 2,927, 1,748, and 1,170 cm−1 associated with aliphatic C = H group stretching, C = O is due to the stretching of carboxylic acid, and the C–N bond of aliphatic amines, respectively (Kebede et al. 2019). Those are the main parts of protein powder, which are active and able to bind with colloids. After coagulation, the peaks exhibited slight shifts, and new peaks emerged at wave numbers 3,449, 2,926, 1,745, and 1,168 cm−1, respectively. The appearance and shifting of peaks to new values also indicate the involvement of functional groups during the coagulation process.
Figure 7

FTIR spectrum 4,000–400 cm−1M. stenopetala seed powder before and after coagulation.

Figure 7

FTIR spectrum 4,000–400 cm−1M. stenopetala seed powder before and after coagulation.

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Effect of various parameters on coagulation of TDS and turbidity

The TDS and turbidity removal efficiency of natural coagulants can be affected by several factors. Some coagulants choose specific pH conditions, while others work in an extensive pH range. This kind of challenge is also very common with stirring speed, settling time, and coagulant doses for different coagulant types. Therefore, it is imperative to assess the optimum conditions for the TDS and turbidity removal efficiency of banana peel powder and M. stenopetala seed powder coagulant. In this regard, different dose levels 0.3, 0.65, and 1 g/L; different pH levels such as 3, 6.5, and 10; different settling times 20, 40, and 60 min; and stirring speeds including 30, 60, and 90 rpm are used for optimization purposes.

Effect of coagulant dosage on removal efficiency

Coagulant dosage is one of the most significant factors that have been considered to determine the optimum condition for coagulants performance in the coagulation and flocculation processes. Essentially, insufficient amounts or overdosing would result in reduced performance in flocculation. Therefore, it is significant to determine the optimum dosage to minimize the dosing cost and sludge formation and also to obtain maximum performance in the treatment process (Patel & Vashi 2013). As shown in Figures 8 and 9, an experiment was carried out where 0.3, 0.65, and 1 g/L of each coagulant were dosed simultaneously based on the sequence of the treatment combinations to find the optimum coagulant dosage.
Figure 8

Effect of banana peel powder dosage on the removal of TDS and turbidity.

Figure 8

Effect of banana peel powder dosage on the removal of TDS and turbidity.

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Figure 9

Effect of M. stenopetala powder on removal of TDS and turbidity.

Figure 9

Effect of M. stenopetala powder on removal of TDS and turbidity.

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As shown in Figure 8, banana peel coagulant shows a continuous removal of TDS and turbidity with increases in coagulant doses up to 0.65 g/L. At this dosage, a maximum TDS and turbidity removal efficacy of 66.8 and 82.06%, respectively, were found. This increment in removal of TDS and turbidity is due to an increase in the active site of the banana peel powder, and also negatively charged colloidal particles are adsorbed onto positively charged functional groups of the natural polymer, which causes particles in the river water to be destabilized and flocculated. Then, the removal efficiency of TDS and turbidity decreased further, increasing from 0.65 to 1 g/L, respectively, which might be attributed to the over dosage of the flocculants in the river water sample that led to electrostatic repulsive forces and poor removal efficiency.

The study on M. stenopetala seed powder found that as the dosage of the coagulant increased, the clarity of the water also increased up to 0.65 g/L. This improvement in clarity may be due to the poly-cationic nature of the coagulant, which induces a charge neutralization mechanism, causing destabilization and flocculation of the negatively charged colloidal particles. At a dosage of 0.65 g/L, the study found a maximum TDS and turbidity removal efficiency of 69.72 and 93.42%, respectively (Figure 9).

From the experimental results of this study, the increment from a lower dose to a higher dose increased the TDS and turbidity removal efficiency of both coagulants up to the 0.6 g/L dosage. This is due to the increase in the coagulant's active site (Mohammed & Shakir 2018). But further dosage increments show a decrease in water quality. This may be due to the fact that when over-dosage occurs, the water has the color of Moringa powder and turbid.

Effect of pH on the removal efficiency

The pH of the sample water was 5.8, but for this experimental study, it was adjusted to three ranges: 3, 6.5, and 10 in order to check the coagulant effectiveness in acidic, near-neutral, and basic conditions. So as the experimental result shows, when treating river water using banana peel coagulant, coagulant turbidity and TDS removal increased up to the near-neutral state and decreased at the basic condition.

As shown in Figure 10, pH affects the banana peel coagulant removal efficiency of TDS and turbidity. Hence, as the pH of the sample water increases, the coagulant effectiveness increases up to 6.5 and then decreases. At pH 6.5, the study found a maximum TDS and turbidity removal efficiency of 92.37 and 69.25%, respectively. The best result occurred at the near-neutral condition (State et al. 2017).
Figure 10

Effect of pH on TDS and turbidity removal using banana peel powder.

Figure 10

Effect of pH on TDS and turbidity removal using banana peel powder.

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In the case of treating a river water sample using M. stenopetala seed coagulant, the experimental results, as shown in Figure 11, showed that the pH increased from 3 to 10, the removal efficiency increased for turbidity removal, with a maximum removal of 93.23% obtained at pH 10, and also in TDS removal, a maximum removal efficiency of 72.47% was obtained at pH 6.5 in the near-neutral state. So this coagulant removes pollutants in a wider pH range, specifically in a neutral and basic condition, than in an acidic condition, and the same result was obtained by Abd El-Hack et al. (2018). These findings suggest that Moringa-based coagulants are more effective at neutral or slightly basic pH conditions.
Figure 11

Effect of pH on TDS and turbidity removal using M. stenopetala powder.

Figure 11

Effect of pH on TDS and turbidity removal using M. stenopetala powder.

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Effect of stirring speed on removal efficiency

As per the experimental details of this study, stirring speed plays a very important role in the coagulation and flocculation processes of the river water treatment. So since this study tries to check the effect of stirring speed on the coagulation process at 30, 60, and 90 rpm, at 30 rpm some suspended small flocs were dispersed on the full surface of the sample water in the beaker and took time for sedimentation at this slow stirring speed. But at 60 rpm, the stirrer was highly rotated, and suspended small flocs were collected at the center of the sample water surface and showed the formation of an agglomeration of those small flocs, which quickly and easily settled.

As shown in Figures 12 and 13, as the stirring speed increases from 40 to 60 rpm, the removal efficiency also increases, but when the stirring speed increases further from 60 to 90 rpm, it results in rapid decreases in removal efficiency (State et al. 2017). This may be due to the breakdown of the flocs and the redispersion of colloidal particles at high speed.
Figure 12

Effect of stirring speed on the removal of TDS and turbidity using banana peel powder.

Figure 12

Effect of stirring speed on the removal of TDS and turbidity using banana peel powder.

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Figure 13

Effect of stirring speed on the removal of TDS and turbidity using M. stenopetala seed powder.

Figure 13

Effect of stirring speed on the removal of TDS and turbidity using M. stenopetala seed powder.

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Effect of settling time on removal efficiency

As per the experimental details of this study, as shown in Figures 14 and 15, settling time plays a vital role in the coagulation and flocculation processes of both coagulants. So since this study tries to check the effect of settling time on the coagulation process at 20, 40, and 60 min, as the settling time increases from 20 to 60 min, the removal efficiency also increases, and then after the 60 min, they remain constant. These findings are consistent with Alnawajha et al. (2022) statement that further increases in settling times do not have a significant impact on the removal of contaminants.
Figure 14

Effect of settling time on the removal of TDS and turbidity using banana peel powder.

Figure 14

Effect of settling time on the removal of TDS and turbidity using banana peel powder.

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Figure 15

Effect of settling time on the removal of TDS and turbidity using M. stenopetala seed powder.

Figure 15

Effect of settling time on the removal of TDS and turbidity using M. stenopetala seed powder.

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Response surface method for TDS and turbidity removal

Response surface methodology was employed to determine the association between the dependent variable and experimental factors. In this model, 30 experiments were carried out as shown in the Supplementary Appendices. The quadratic model was suggested to associate the response variable with the four independent variables. Because there is a close agreement between adjusted R2 and predicted R2 values, it has the highest coefficient of determination (R2) approaching 1, and it also has the lowest standard deviation and p-value (Dawood et al. 2013).

Development of regression model equation and model analysis for M. stenopetala seed powder

The resulting quadratic model equation, which expresses the relationship between the interactions of the factors and the responses, is shown in regression model Equations (5) and (6) in terms of coded values without the involvement of non-significant terms as stated (Nor & Wan 2020).
formula
(5)
formula
(6)
where A, B, C, and D are coagulant dose, settling time, stirring speed, and pH in coded units, respectively.

As shownin Tables 2 and 3, the Model F-value of 36.50 and 21.79 implies the model is significant. There is only a 0.01% chance that an F-value this large could occur due to noise. The lack of fit F-values of 3.02 and 0.78, respectively, for TDS and turbidity removal imply that the lack of fit is not significant relative to the pure error. There is an 11.71 and 65.31% chance that a lack of fit F-value this large could occur due to noise. A non-significant lack of fit is good.

Table 2

ANOVA results of the quadratic model for TDS removal

SourceSum of squaresdfMean squareF-valuep-value
Model 5,283.87 14 377.42 36.50 <0.0001 Significant 
A – Coagulant dose 236.89 236.89 22.91 0.0002  
B – Settling time 187.53 187.53 18.14 0.0007  
C – Stirring speed 168.67 168.67 16.31 0.0011  
D – pH 732.17 732.17 70.81 <0.0001  
AB 264.88 264.88 25.62 0.0001  
AC 135.14 135.14 13.07 0.0025  
AD 383.18 383.18 37.06 <0.0001  
BC 611.33 611.33 59.12 <0.0001  
BD 0.2256 0.2256 0.0218 0.8845  
CD 1,598.00 1,598.00 154.55 <0.0001  
A² 59.19 59.19 5.72 0.0303  
B² 87.77 87.77 8.49 0.0107  
C² 154.42 154.42 14.93 0.0015  
D² 22.86 22.86 2.21 0.1578  
Residual 155.10 15 10.34    
Lack of fit 133.07 10 13.31 3.02 0.1171  Not significant 
Pure error 22.03 4.41    
Cor total 5,438.97 29     
SourceSum of squaresdfMean squareF-valuep-value
Model 5,283.87 14 377.42 36.50 <0.0001 Significant 
A – Coagulant dose 236.89 236.89 22.91 0.0002  
B – Settling time 187.53 187.53 18.14 0.0007  
C – Stirring speed 168.67 168.67 16.31 0.0011  
D – pH 732.17 732.17 70.81 <0.0001  
AB 264.88 264.88 25.62 0.0001  
AC 135.14 135.14 13.07 0.0025  
AD 383.18 383.18 37.06 <0.0001  
BC 611.33 611.33 59.12 <0.0001  
BD 0.2256 0.2256 0.0218 0.8845  
CD 1,598.00 1,598.00 154.55 <0.0001  
A² 59.19 59.19 5.72 0.0303  
B² 87.77 87.77 8.49 0.0107  
C² 154.42 154.42 14.93 0.0015  
D² 22.86 22.86 2.21 0.1578  
Residual 155.10 15 10.34    
Lack of fit 133.07 10 13.31 3.02 0.1171  Not significant 
Pure error 22.03 4.41    
Cor total 5,438.97 29     
Table 3

ANOVA results of the quadratic model for turbidity removal

SourceSum of squares df Mean squareF-valuep-value
Model 933.97 14 66.71 21.79 <0.0001 Significant 
A – Coagulant dose 121.16 121.16 39.58 <0.0001  
B – Settling time 72.40 72.40 23.65 0.0002  
C – Stirring speed 242.73 242.73 79.29 <0.0001  
D – pH 214.94 214.94 70.21 <0.0001  
AB 2.89 2.89 0.9440 0.3467  
AC 1.44 1.44 0.4704 0.5033  
AD 0.0225 0.0225 0.0073 0.9328  
BC 29.16 29.16 9.52 0.0075  
BD 6.50 6.50 2.12 0.1656  
CD 15.60 15.60 5.10 0.0393  
A² 2.69 2.69 0.8793 0.3633  
B² 62.70 62.70 20.48 0.0004  
C² 0.0846 0.0846 0.0276 0.8702  
D² 0.0124 0.0124 0.0041 0.9500  
Residual 45.92 15 3.06    
Lack of fit 28.05 10 2.80 0.7845 0.6531 Not significant 
Pure error 17.88 3.58    
Cor total 979.89 29     
SourceSum of squares df Mean squareF-valuep-value
Model 933.97 14 66.71 21.79 <0.0001 Significant 
A – Coagulant dose 121.16 121.16 39.58 <0.0001  
B – Settling time 72.40 72.40 23.65 0.0002  
C – Stirring speed 242.73 242.73 79.29 <0.0001  
D – pH 214.94 214.94 70.21 <0.0001  
AB 2.89 2.89 0.9440 0.3467  
AC 1.44 1.44 0.4704 0.5033  
AD 0.0225 0.0225 0.0073 0.9328  
BC 29.16 29.16 9.52 0.0075  
BD 6.50 6.50 2.12 0.1656  
CD 15.60 15.60 5.10 0.0393  
A² 2.69 2.69 0.8793 0.3633  
B² 62.70 62.70 20.48 0.0004  
C² 0.0846 0.0846 0.0276 0.8702  
D² 0.0124 0.0124 0.0041 0.9500  
Residual 45.92 15 3.06    
Lack of fit 28.05 10 2.80 0.7845 0.6531 Not significant 
Pure error 17.88 3.58    
Cor total 979.89 29     

The significance of model terms is determined by their p-values. p-values less than 0.05 indicate that the model terms are significant, while values greater than 0.1 indicate that they are not significant. In the context of TDS and turbidity removal, the significant model terms include A, B, C, D, AB, AC, AD, BC, CD, A², B², and C² for TDS removal and A, B, C, D, BC, CD, and B² for turbidity removal. If there are many insignificant model terms, reducing the model may improve its effectiveness.

In the case of M. stenopetata seed coagulants, the R2 values for TDS and turbidity removal were 0.9715 and 0.9531, respectively, indicating a high correlation between the response's actual and predicted values and a good fit of the model (Sharma et al. 2009). The summary of fit statistics shown in Table 4 demonstrated that the adjusted R2 values, which are 0.9449 and 0.9094, are closer to the predicted R2, and the difference is less than 0.2, which indicates that the experimental data is considered satisfactory (Sharma et al. 2009).

Table 4

Fit statistics for M. stenopetala seed powder

ResponsesCoefficient of determination (R2)Adjusted R2Predicted R2Adequate precision (AP)Coefficient of variance (CV)
TDS 0.9715 0.9449 0.8308 23.4022 4.85 
Turbidity 0.9531 0.9094 0.8176 19.5762 1.93 
ResponsesCoefficient of determination (R2)Adjusted R2Predicted R2Adequate precision (AP)Coefficient of variance (CV)
TDS 0.9715 0.9449 0.8308 23.4022 4.85 
Turbidity 0.9531 0.9094 0.8176 19.5762 1.93 

Furthermore, the values of the CV that measure the reproducibility of the model are 4.85 and 1.93% for TDS and turbidity removals, respectively. A CV value of less than 10% is considered appropriate for the reproducibility of any model (Salehi et al. 2010). The signal-to-noise ratio of the adequate precision measures for the response models was 23.4022 and 19.5762 for TDS and turbidity removals, respectively. The adequate precision value higher than 4 is appropriate and shows that the regression model equation can be employed within the range of factors in the design space (Dawood et al. 2013). These two indices, CV and adequate precision, suggested that the models are reproducible and have a high degree of reliability and accuracy in the experiments.

As indicated in Figure 16, the predicted versus actual value plots were close to each other; therefore, the plots ratify the suitability of the model.
Figure 16

Correlation of actual and predicted values for (a) removal of TDS and (b) removal of turbidity by M. stenopetala seed powder.

Figure 16

Correlation of actual and predicted values for (a) removal of TDS and (b) removal of turbidity by M. stenopetala seed powder.

Close modal

Interaction effect of experimental factors on the response in the case of M. stenopetala seed powder

Experimental factors like coagulant dosage, pH, stirring speed, and settling time are essential in the process of removing TDS and turbidity from water (Wang et al. 2018). The statistical significance of model terms and their interactions was evaluated using the ANOVA. The significance of the model coefficients for each of the two responses was assessed based on their corresponding F- and p-values at a 5% confidence level, as shown in Tables 3 and 4. The ANOVA results revealed the high significance of the quadratic models, as evident from the large model F-values of 36.50 and 21.79 for TDS and turbidity removals, respectively. Similarly, A, B, C, and D are the common significant model terms with a p-value of less than 0.05 for TDS and turbidity removals. Other significant model terms are listed in Figures 1720 for the TDS and turbidity removal, respectively.
Figure 17

3D surface plots of the interaction effects of (a) settling time and dose and (b) stirring speed and dose on TDS removal.

Figure 17

3D surface plots of the interaction effects of (a) settling time and dose and (b) stirring speed and dose on TDS removal.

Close modal
Figure 18

3D surface plots of the interaction effects of (c) pH and dose and (d) stirring speed and settling time on TDS removal.

Figure 18

3D surface plots of the interaction effects of (c) pH and dose and (d) stirring speed and settling time on TDS removal.

Close modal
Figure 19

3D surface plots of the interaction effects of (e) pH and stirring speed on TDS removal.

Figure 19

3D surface plots of the interaction effects of (e) pH and stirring speed on TDS removal.

Close modal
Figure 20

3D surface plots of the interaction effects of (a) stirring speed and settling time and (b) pH and stirring speed on turbidity removal.

Figure 20

3D surface plots of the interaction effects of (a) stirring speed and settling time and (b) pH and stirring speed on turbidity removal.

Close modal

However, the p-values (0.8845) in Table 3 and (0.3467, 0.5033, 0.9328, and 0.1656) in Table 4 indicate that the interaction effect between settling time and pH on TDS removal, dose and settling time, dose and stirring speed, dose and pH, as well as pH and settling time, has an insignificant impact on the turbidity removal efficiency of M. stenopetala seed powder. The p-value of lack of fit exceeding 0.05 signifies an insignificant p-value and the ability of the model to fit the experimental data accurately (Kalsido et al. 2021). The 3D plot showing the interaction effect of the operating parameters is indicated in Figures 1720.

Development of regression model equation and model analysis for banana peel powder

The resulting quadratic model equation, which expresses the relationship between the interactions of the factors and the responses, is shown in regression model Equations (7) and (8) in terms of coded values.
formula
(7)
formula
(8)
where A, B, C, and D are pH, coagulant dose, stirring speed, and settling time in coded units, respectively.

As shown in Tables 5 and 6, the Model F-value of 26.14 and 21.91 implies the model is significant. There is only a 0.01% chance that an F-value this large could occur due to noise. The lack of fit F-values of 2.75 and 0.80, respectively, for TDS and turbidity removal implies that the lack of fit is not significant relative to the pure error. There is a 13.78 and 64.21% chance that a lack of fit F-value this large could occur due to noise. A non-significant lack of fit is good.

Table 5

ANOVA results of the quadratic model for TDS removal

SourceSum of squaresdfMean squareF-valuep-value
Model 3,260.61 14 232.90 26.14 <0.0001 Significant 
A – pH 137.23 137.23 15.40 0.0014  
B – Coagulant dose 133.93 133.93 15.03 0.0015  
C – Stirring speed 125.35 125.35 14.07 0.0019  
D – Settling time 49.00 49.00 5.50 0.0332  
AB 239.48 239.48 26.88 0.0001  
AC 137.48 137.48 15.43 0.0013  
AD 901.50 901.50 101.18 <0.0001  
BC 0.3906 0.3906 0.0438 0.8370  
BD 5.41 5.41 0.6067 0.4482  
CD 93.61 93.61 10.51 0.0055  
A² 6.47 6.47 0.7258 0.4077  
B² 452.82 452.82 50.82 <0.0001  
C² 295.52 295.52 33.17 <0.0001  
D² 347.42 347.42 38.99 <0.0001  
Residual 133.65 15 8.91    
Lack of fit 113.10 10 11.31 2.75 0.1378 Not significant 
Pure error 20.56 4.11    
Cor total 3,394.26 29     
SourceSum of squaresdfMean squareF-valuep-value
Model 3,260.61 14 232.90 26.14 <0.0001 Significant 
A – pH 137.23 137.23 15.40 0.0014  
B – Coagulant dose 133.93 133.93 15.03 0.0015  
C – Stirring speed 125.35 125.35 14.07 0.0019  
D – Settling time 49.00 49.00 5.50 0.0332  
AB 239.48 239.48 26.88 0.0001  
AC 137.48 137.48 15.43 0.0013  
AD 901.50 901.50 101.18 <0.0001  
BC 0.3906 0.3906 0.0438 0.8370  
BD 5.41 5.41 0.6067 0.4482  
CD 93.61 93.61 10.51 0.0055  
A² 6.47 6.47 0.7258 0.4077  
B² 452.82 452.82 50.82 <0.0001  
C² 295.52 295.52 33.17 <0.0001  
D² 347.42 347.42 38.99 <0.0001  
Residual 133.65 15 8.91    
Lack of fit 113.10 10 11.31 2.75 0.1378 Not significant 
Pure error 20.56 4.11    
Cor total 3,394.26 29     
Table 6

ANOVA results of the quadratic model for turbidity removal

SourceSum of squaresdfMean squareF-valuep-value
Model 1,222.29 14 87.31 21.91 <0.0001 Significant 
A – pH 200.00 200.00 50.20 <0.0001  
B – Coagulant dose 104.64 104.64 26.26 0.0001  
C – Stirring speed 456.02 456.02 114.45 <0.0001  
D – Settling time 256.13 256.13 64.28 <0.0001  
AB 2.72 2.72 0.6833 0.4214  
AC 7.02 7.02 1.76 0.2042  
AD 4.20 4.20 1.05 0.3207  
BC 18.06 18.06 4.53 0.0492  
BD 21.62 21.62 5.43 0.0342  
CD 11.22 11.22 2.82 0.1140  
A² 32.80 32.80 8.23 0.0117  
B² 7.12 7.12 1.79 0.2012  
C² 9.77 9.77 2.45 0.1382  
D² 3.18 3.18 0.7981 0.3858  
Residual 59.77 15 3.98    
Lack of fit 36.83 10 3.68 0.8031 0.6421 Not significant 
Pure error 22.93 4.59    
Cor total 1,282.06 29     
SourceSum of squaresdfMean squareF-valuep-value
Model 1,222.29 14 87.31 21.91 <0.0001 Significant 
A – pH 200.00 200.00 50.20 <0.0001  
B – Coagulant dose 104.64 104.64 26.26 0.0001  
C – Stirring speed 456.02 456.02 114.45 <0.0001  
D – Settling time 256.13 256.13 64.28 <0.0001  
AB 2.72 2.72 0.6833 0.4214  
AC 7.02 7.02 1.76 0.2042  
AD 4.20 4.20 1.05 0.3207  
BC 18.06 18.06 4.53 0.0492  
BD 21.62 21.62 5.43 0.0342  
CD 11.22 11.22 2.82 0.1140  
A² 32.80 32.80 8.23 0.0117  
B² 7.12 7.12 1.79 0.2012  
C² 9.77 9.77 2.45 0.1382  
D² 3.18 3.18 0.7981 0.3858  
Residual 59.77 15 3.98    
Lack of fit 36.83 10 3.68 0.8031 0.6421 Not significant 
Pure error 22.93 4.59    
Cor total 1,282.06 29     

The significance of model terms is determined by their p-values. p-values less than 0.05 indicate that model terms are significant, while values greater than 0.1 indicate that they are not significant. In the context of TDS and turbidity removal, the significant model terms include A, B, C, D, AB, AC, AD, CD, B², C², and D² for TDS removal and A, B, C, D, BD, and A² for turbidity removal. If there are many insignificant model terms, reducing the model may improve its effectiveness.

In the case of banana peel coagulants, the R2 values for TDS and turbidity removal were 0.9606 and 0.9534, respectively, indicating a high correlation between the response's actual and predicted values and a good fit of the model (Sharma et al. 2009). The summary of fit statistics shown in Table 7 demonstrated that the adjusted R2 value, which is 0.9239 and 0.9099, is closer to the predicted R2, and the difference is less than 0.2, which implies that the experimental data is considered satisfactory (Wantala et al. 2012).

Table 7

Fit statistics for banana peel powder

ResponsesCoefficient of determination (R2)Adjusted R2Predicted R2Adequate precision (AP)Coefficient of variance (CV)
TDS 0.9606 0.9239 0.7631 22.5021 4.64 
Turbidity 0.9534 0.9099 0.8212 21.0647 2.22 
ResponsesCoefficient of determination (R2)Adjusted R2Predicted R2Adequate precision (AP)Coefficient of variance (CV)
TDS 0.9606 0.9239 0.7631 22.5021 4.64 
Turbidity 0.9534 0.9099 0.8212 21.0647 2.22 

Additionally, the values of the CV that measure the reproducibility of the model are 4.64 and 2.22% for TDS and turbidity removals, respectively. A CV value of less than 10% is considered appropriate for the reproducibility of any model (Salehi et al. 2010). The signal-to-noise ratio of the adequate precision measures for the response models was 22.5021 and 21.0647 for TDS and turbidity removals, respectively. The adequate precision value higher than 4 is appropriate and shows that the regression model equation can be employed within the range of factors in the design space (Dawood et al. 2013).

As indicated in Figure 21, the predicted versus actual value plots were close to each other; therefore, the plots ratify the suitability of the model.
Figure 21

Correlation of actual and predicted values for (a) removal of TDS and (b) removal of turbidity by banana peel powder.

Figure 21

Correlation of actual and predicted values for (a) removal of TDS and (b) removal of turbidity by banana peel powder.

Close modal

Interaction effect of experimental factors on the response in the case of banana peel powder

Experimental factors like coagulant dosage, pH, stirring speed, and settling time are essential in the process of removing TDS and turbidity from water (Wang et al. 2018). The statistical significance of model terms and their interactions was evaluated using the ANOVA. The significance of the model coefficients for each of the two responses was assessed based on their corresponding F- and p-values at a 5% confidence level, as shown in Tables 5 and 6. The ANOVA results revealed the high significance of the quadratic models, as evident from the large model F-values of 26.14 and 21.91 for TDS and turbidity removals, respectively. Similarly, A, B, C, and D are the common significant model terms with a p-value of less than 0.05 for TDS and turbidity removals. Other significant model terms are listed in Figures 2224 for the TDS and turbidity removal models.
Figure 22

3D surface plots of the interaction effects of (a) stirring speed and pH and (b) dose and pH on TDS removal.

Figure 22

3D surface plots of the interaction effects of (a) stirring speed and pH and (b) dose and pH on TDS removal.

Close modal
Figure 23

3D surface plots of the interaction effects of (c) settling time and pH and (d) settling time and stirring speed on TDS removal.

Figure 23

3D surface plots of the interaction effects of (c) settling time and pH and (d) settling time and stirring speed on TDS removal.

Close modal
Figure 24

3D surface plots of the interaction effects of (a) settling time and dose and (b) stirring speed and settling time on turbidity removal.

Figure 24

3D surface plots of the interaction effects of (a) settling time and dose and (b) stirring speed and settling time on turbidity removal.

Close modal

However, the p-values (0.8370 and 0.4482) in Table 5 and (0.4214, 0.2042, 0.3207, and 0.1140) in Table 6 indicate that the interaction effect between dose and stirring speed and also dose and settling time on TDS removal, pH and dose, pH and stirring speed, pH and settling time, as well as dose and stirring speed, has an insignificant impact on the turbidity removal efficiency of banana peel powder. The probability value of lack of fit exceeding 0.05 signifies an insignificant p-value and the ability of the model to fit the experimental data accurately (Kalsido et al. 2021).

Optimization of response using desirability function

Numerical optimization allows the selection of a desirable value in the form of a range, target, minimum, or maximum value for each input variable factor and response. A desirability function is a technical approach in a statistical design that simultaneously measures the optimal settings of input parameters to produce optimum performance levels of one or more output variables (Mourabet et al. 2017). In this study, the input variables were given specific ranged values, whereas the output variables were designed to achieve a maximum, as shown in Tables 8 and 9.

Table 8

Constraints for optimization condition of all factors in banana peel powder

NameGoalLower limitUpper limitLower weightUpper weightImportance
A – pH is in range 10 
B – Coagulant dose is in range 0.3 
C – Stirring speed is in range 30 90 
D – Settling time is in range 20 60 
TDS removal Maximize 42 88.2 
Turbidity removal Maximize 68 99 
NameGoalLower limitUpper limitLower weightUpper weightImportance
A – pH is in range 10 
B – Coagulant dose is in range 0.3 
C – Stirring speed is in range 30 90 
D – Settling time is in range 20 60 
TDS removal Maximize 42 88.2 
Turbidity removal Maximize 68 99 
Table 9

Constraints for optimization condition of all factors in M. stenopetala seed powder

NameGoalLower limitUpper limitLower weightUpper weightImportance
A – Coagulant dose is in range 0.3 
B – Settling time is in range 20 60 
C – Stirring speed is in range 30 90 
D – pH is in range 10 
TDS removal Maximize 30.2 87.7 
Turbidity removal Maximize 74 97.9 
NameGoalLower limitUpper limitLower weightUpper weightImportance
A – Coagulant dose is in range 0.3 
B – Settling time is in range 20 60 
C – Stirring speed is in range 30 90 
D – pH is in range 10 
TDS removal Maximize 30.2 87.7 
Turbidity removal Maximize 74 97.9 

Validation of experimental optimization

In order to verify the optimization results, an experiment was performed under the predicted conditions of the developed model. The response surface method (RSM) in its optimum condition was used in an experiment (Liu et al. 2019).

To validate the optimization results, an experiment was conducted under the optimum conditions predicted by the developed model. The results of the experiment in the case of banana peel powder coagulant showed that the TDS and turbidity removal were 81.32 and 93.09%, respectively, as presented in Table 10. Figure 25 illustrates that the predicted TDS and turbidity removal were 83.28 and 95.06%, respectively, at pH 8.52, coagulant dose of 1 g/L, stirring speed of 33.58 rpm, and settling time of 37.92 min. These conditions had the highest desirability value of 0.883.
Table 10

Predicted vs. experimental value for banana peel powder

ResponsePredictedObserved95% PI
low
95% PI
high
Percentage error
TDS removal 83.28 81.32 75.3999 91.1568 1.958 
Turbidity removal 95.060 93.09 89.7922 100.329 1.970 
ResponsePredictedObserved95% PI
low
95% PI
high
Percentage error
TDS removal 83.28 81.32 75.3999 91.1568 1.958 
Turbidity removal 95.060 93.09 89.7922 100.329 1.970 
Figure 25

Optimization result with desirability ramp of banana peel powder.

Figure 25

Optimization result with desirability ramp of banana peel powder.

Close modal

For verification of the optimization results, an experiment was performed under the predicted conditions of the developed model, which resulted in the TDS and turbidity removal (83.64 and 95.13%), respectively, as shown in Table 11.

Table 11

Predicted vs. experimental value for M. stenopetala seed powder

ResponsePredictedObserved95% PI
low
95% PI
high
Percentage error
TDS removal 85.594 83.64 76.4888 94.7072 1.95 
Turbidity removal 97.010 95.13 92.0525 101.966 1.88 
ResponsePredictedObserved95% PI
low
95% PI
high
Percentage error
TDS removal 85.594 83.64 76.4888 94.7072 1.95 
Turbidity removal 97.010 95.13 92.0525 101.966 1.88 

Figure 26 illustrates that the predicted TDS and turbidity removal were 85.59 and 97.01%, respectively, at pH 9.99, coagulant dose of 0.999 g/L, stirring speed of 30 rpm, and settling time of 39.96 min, respectively, with the highest desirability value of 0.963 for M. stenopetala seed powder coagulant.
Figure 26

Optimization result with desirability ramp of M. stenopetala seed powder.

Figure 26

Optimization result with desirability ramp of M. stenopetala seed powder.

Close modal

As illustrated in Figures 25 and 26 and Tables 10 and 11, respectively, the TDS and turbidity removal using banana peel powder and M. stenopetala seed powder as coagulants demonstrate that the predicted and experimental values agreed well with a small deviation. This suggests that for the expected value, the model is thought to be accurate and dependable (Vera et al. 2014).

Comparison of turbidity and TDS removal of some natural coagulants

Turbidity removal efficiency of some natural coagulants

In various regions around the world, locally available natural coagulants are utilized to reduce water turbidity. According to research conducted by Beyene et al. (2016), a dosage of 3.5 g/L of cactus powder was found to eliminate 54.80% of turbidity from an initial turbidity level of 41.38 NTU. Another study by Asrafuzzaman et al. (2011) revealed that a dosage of 0.1 g/L of Moringa oleifera successfully removed 94.1% of the turbidity from a water sample containing 100 NTU. Additionally, Birhanu & Leta (2021) study discovered that 5 g/L of odarcha soil eliminated 88.46% of turbidity from an initial turbidity level of 800 NTU.

Generally, when we compare the turbidity removal efficiency of banana peel and M. stenopetala seed coagulants with other natural coagulants mentioned in Table 12, both coagulants show good efficiency.

Table 12

Comparison of various natural coagulants turbidity removal

NoCoagulantsDose (g/L)Initial turbidity (NTU)Removal efficiency (%)References
Cactus powder 3.5 41.38 54.80 Beyene et al. (2016)  
Odaracha soil 800 88.46 Birhanu & Leta (2021)  
Moringa oleifera 0.1 100 94.1 Asrafuzzaman et al. (2011)  
Banana peel powder 1 81.6 93.09 This study 
M. stenopetalaseed powder 0.99 81.6 95.13 This study 
NoCoagulantsDose (g/L)Initial turbidity (NTU)Removal efficiency (%)References
Cactus powder 3.5 41.38 54.80 Beyene et al. (2016)  
Odaracha soil 800 88.46 Birhanu & Leta (2021)  
Moringa oleifera 0.1 100 94.1 Asrafuzzaman et al. (2011)  
Banana peel powder 1 81.6 93.09 This study 
M. stenopetalaseed powder 0.99 81.6 95.13 This study 

TDS removal efficiency of some natural coagulants

According to research conducted by Jeje (2021), a dosage of 0.5 g/L of cactus powder was found to eliminate 45.1% of TDS from an initial TDS level of 255 mg/L. Another study by Gali Aba Lulesa et al. (2022) revealed that a dosage of 0.5 g/L of M. oleifera successfully removed 84% of the TDS from a water sample containing 70.1 mg/L. Additionally, Jacob's (2023) study discovered that 0.02 g/L of orange peel powder eliminated 20% of TDS from an initial TDS level of 1,500 mg/L.

Generally, when we compare the TDS removal efficiency of banana peel and M. stenopetala seed coagulants with other natural coagulants mentioned in Table 13, both coagulants show good efficiency next to M. oleifera.

Table 13

Comparison of various natural coagulants TDS removal

NoNatural coagulantsDose (g/L)Initial TDS (mg/L)Removal efficiency (%)References
Moringa olifera 0.5 70.1 84.5 Gali Aba Lulesa et al. (2022)  
Cactus powder 0.5 255 45.1 Jeje (2021)  
Orange peel powder 0.02 1,500 20 Jacob (2023)  
Banana peel powder 1 48 81.32 This study 
M. stenopetalaseed powder 0.99 48 83.64 This study 
NoNatural coagulantsDose (g/L)Initial TDS (mg/L)Removal efficiency (%)References
Moringa olifera 0.5 70.1 84.5 Gali Aba Lulesa et al. (2022)  
Cactus powder 0.5 255 45.1 Jeje (2021)  
Orange peel powder 0.02 1,500 20 Jacob (2023)  
Banana peel powder 1 48 81.32 This study 
M. stenopetalaseed powder 0.99 48 83.64 This study 

This study investigated the effectiveness of banana peel powder and M. stenopetala seed powder in removing TDS and turbidity from Batena river water. The coagulation capacity of these coagulants was confirmed through Fourier transform infrared and SEM characterization analysis, revealing the presence of pores, void spaces, and polymeric substances like carbohydrates and proteins. The coagulation and flocculation capacity of both coagulants were found to be influenced by pH, coagulant dosage, stirring speed, and settling time.

The optimum conditions for both coagulants were determined using numerical optimization-based desirability function and compared with predicted values from the second-order quadratic model of CCD. The results showed that banana peel powder had the highest removal efficiency for TDS and turbidity under these optimum conditions, while M. stenopetala seed powder had the highest removal efficiency for TDS and turbidity under these conditions.

The analysis of variance evaluation of the p-value and F-value of the regression coefficient at a 95% confidence level showed that all individual factors and interaction effects had a significant effect on the removal efficiency of banana peel powder coagulant. In contrast, M. stenopetala seed powder coagulant had the highest removal potential for TDS and turbidity.

These findings suggest that natural coagulants, derived from readily available materials, can effectively reduce TDS and turbidity in water treatment processes, particularly M. stenopetala seed powder. This finding highlights the potential of natural coagulants as an alternative to chemical coagulants in water treatment, providing affordable and efficient water treatment solutions for communities.

The authors are grateful to the Hydraulics and Water Resources Engineering department for allowing us to use laboratory equipment.

The authors appreciate Wachemo University post-graduate schools for allowing us to work on this research at laboratories.

This research work complies with the research project's ethical standards.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

Abd El-Hack
M. E.
,
Alagawany
M.
,
Elrys
A. S.
,
Desoky
E. S. M.
,
Tolba
H. M. N.
,
Elnahal
A. S. M.
,
Elnesr
S. S.
&
Swelum
A. A.
2018
Effect of forage Moringa oleifera L. (moringa) on animal health and nutrition and its beneficial applications in soil, plants and water purification
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