With the rapid rise of industry, the quantity of wastewater generated by production processes has increased dramatically. Effluents from such industries, particularly the paint industry, are usually rich in solids such as total dissolved solids (TDS) and contain a complex mixture of stabilizers, pigments, toxic ions, surfactants, and other persistent compounds that pose a serious risk to public health and the environment [1,2]. High TDS levels in wastewater result in large volumes of salt, limit light penetration in receiving water bodies, and adversely affect aquatic life. It also poses a risk of contaminating groundwater and agricultural soils [3]. Consequently, discharge regulations for industrial effluents in most urban cities have become increasingly stringent [3]. Treatment of paint wastewater (PWW) has generally been based on a chemical coagulation–flocculation process using standard jar-test methods to establish appropriate coagulant dosages and mixing conditions in accordance with the findings of [4,5]. The most widely used coagulants are inorganic coagulants because they are effective at destabilizing colloidal and suspended particles. The most common are aluminum- and iron-based salts [5]. These chemicals are effective but produce large amounts of sludge and may also cause residual metal ions in the treated wastewater, raising concerns over secondary contamination and increasing the expense and complexity of sludge management as reported in [1-3, 5]. These problems have driven increased research interest in more sustainable and biodegradable treatment alternatives.

Plant-based coagulants, also known as bio-coagulants, are gaining attention as environmentally friendly alternatives for wastewater treatment because of their biodegradability, availability, low cost, and smaller environmental footprints than traditional chemical coagulants [1-5]. The recent studies by Miyah et al. [6], Jabar et al. [7], and Zhang et al. [8] on green remediation materials stressed the increasing importance of sustainable adsorbents and bio-based treatment technologies in wastewater purification. The active components of the plant-seed-derived materials, avocado pear seed (PS) and moringa oleifera seed (MOS), promote both coagulation–flocculation and adsorption processes [9]. For example, studies reported in [9,10] indicate that PS consists primarily of polysaccharides and lignocellulosic matter that contribute to particle bridging and aggregation, whereas MOS includes cationic proteins and charged functional groups that facilitate electrostatic attraction and binding of dissolved substances. Moreover, these plant-based materials have substantial adsorption capacity for dissolved particles in addition to their coagulating ability, making them good candidates for reducing TDS in complex industrial effluents [10]. As stated by Obiora-Okafo et al. [11], functional groups are important because the performance of adsorption-based treatment systems depends on surface functional groups, electrostatic interactions, adsorption kinetics, and mass transfer processes that influence the contaminant removal efficiency and uptake.

Many studies [12-14] have demonstrated the effectiveness of bio-coagulants for the reduction of turbidity, color, and chemical oxygen demand in wastewater, but limited studies are available on TDS removal from paint wastewater by a synergistic adsorption-assisted coagulation–flocculation process. Moreover, most of the published research is focused on treatment efficiency, and there are limited detailed mechanistic explanation based on adsorption isotherms, kinetic modelling, diffusion analysis, and surface-charge characterization [13,14]. Understanding the adsorption kinetics is important, as it aids in the identification of rate-controlling steps, better prediction of system performance, comparison of different coagulants used, and optimization of process conditions for possible scale-up [15-17].

Studies [16,18] described the pseudo-first-order and pseudo-second-order kinetic models as the most commonly used for the description and deduction of adsorption behavior and governing mechanisms. Specifically, the pseudo-first-order model is related to physisorption, which includes weaker attraction forces such as van der Waals forces, hydrogen bonding, or electrostatic attraction, without significant electron exchange or bond formation [18]. The pseudo-second-order model is related to chemisorption, in which there are stronger interactions involving the sharing or exchange of electrons between functional groups on the surface of the adsorbent and the species of the adsorbate [19]. A previous study [20] showed that the Elovich model is useful for systems with heterogeneous surfaces and varying activation energies. These models enable the quantitative estimation of characteristics such as adsorption capacity and rate constants, which are needed for the comparison of processes and the design of full-scale systems [16, 18-20].

The mass-transfer processes are important to the effectiveness of the treatment process, in addition to the surface reaction kinetics. This is where the Weber–Morris intra-particle diffusion model is used. The model is mainly applied to determine whether the rate of adsorption is controlled by diffusion within the pores of an adsorbent [21]. In ref. [22] it is noted that adsorption rarely occurs in a single step but instead follows a sequence that begins with external mass transfer across the boundary layer (film diffusion), continues with intra-particle diffusion, and concludes with equilibrium adsorption. The non-zero intercepts of the linear plots of the Weber–Morris model are attributed to the combined effect of boundary-layer resistance and intra-particle diffusion. Then, the slope of the plot reveals the intra-particle diffusion rate constant, and the intercept indicates the magnitude of film diffusion effects [21,22]. Understanding these transport phases is critical for identifying rate limitations and improving the treatment efficiency of the process [2].

Isotherm analysis, apart from adsorption kinetics, provides essential information on adsorption capacity, surface properties, and the type of adsorbent-adsorbate interactions [15,23]. Commonly used models are the Langmuir isotherm, which assumes monolayer adsorption on a homogeneous surface with finite active sites [15], and the Freundlich isotherm, which explains adsorption on heterogeneous surfaces with variable adsorption energies [23]. The Temkin isotherm addresses adsorbent-adsorbate interactions and assumes that the heat of adsorption decreases linearly with increasing surface coverage [24]. Therefore, isotherm modelling can contribute to understanding the adsorption mechanisms and the feasibility of adsorbent materials for applications in wastewater treatment.

Unlike previous studies that primarily focus on one bio-coagulant or adsorbent for pollutant removal from different industrial effluents [9-10, 12-14], the present study is focused on the utilization and comparative evaluation of two readily available and low-cost plant-based bio-coagulants, PS and MOS, for the treatment of paint wastewater with a high concentration of TDS using an adsorption-assisted coagulation–flocculation technique. The paper also discusses the mechanisms involved in TDS removal under different operating settings, such as pH, coagulant dosage, and contact time, as well as the treatment performance. It should be noted that the experimental system investigated in the present study is the same as that reported in our previous publication, which focused on the optimization and predictive modeling of TDS removal using response surface methodology (RSM) and artificial neural network (ANN) [25]. In contrast, the objectives of this study are fundamentally different and are to: (i) assess the effectiveness of PS and MOS as sustainable bio-coagulants for TDS removal from paint wastewater; (ii) optimize the coagulation–flocculation process by investigating the effect of pH, bio-coagulant dosage and contact time on TDS removal; (iii) characterize the bio-coagulants and determine their surface properties through proximate analysis, Fourier transform infrared (FTIR) spectroscopy and point of zero charge (pHpzc); (iv) analyze the contribution of adsorption through adsorption capacity, isotherm, kinetic, and Weber––Morris intra-particle diffusion analyses; and (v) compare the performance of PS and MOS and suggest a mechanistic pathway for TDS removal from paint wastewater. The findings of this study could support the development of sustainable and low-cost treatment systems for industrial wastewater management, particularly in regions where conventional chemical coagulants are costly or less accessible.

2.1. Equipment, Reagents, and Bio-Coagulant Preparation

The experiments were conducted utilizing laboratory equipment such as mortar and pestle, graduated cylinders, beakers, electronic balance, magnetic stirrer, pH meter, oven, and Whatman No. 1 filter paper. The mixing and settling periods during the jar test procedures were recorded using a stopwatch. The primary materials used were avocado pear seeds (PS), moringa oleifera seeds (MOS), and paint wastewater (PWW). pH correction was also carried out using distilled water, sodium hydroxide, hydrochloric acid, ethylenediaminetetraacetic acid (EDTA), and appropriate buffer solutions. The seed materials were washed to remove impurities, then dried in an oven at 60 °C for 24 h. They were then milled into a powder and sieved to obtain a homogeneous particle size, as described in [26]. The resultant powders were employed as bio-coagulants and stored in sealed containers at room temperature until needed.

2.2. Sampling of Paint Wastewater

Paint wastewater (PWW) was obtained from an offline storage tank at Legacy Paints Limited, located in the Emene Industrial Layout, Enugu, Nigeria. After collection, the samples were kept at 4 °C and analyzed within 24 h to preserve their stability. The initial TDS concentration was determined before any treatment was applied.

2.3. Treatment Mechanism and Experimental Approach

In this work, the coagulation–flocculation process was examined as the main mechanism. We also evaluated adsorption as a supplementary mechanism contributing to TDS removal because PS and MOS bio-coagulants include surface functional groups capable of interacting with dissolved species. The bio-coagulants were characterized by FTIR and pHpzc investigations, and their adsorption behavior was evaluated by contact-time studies, kinetics, and equilibrium isotherm modeling to estimate their impact.

2.3.1. Coagulation–Flocculation Experiments

The influence of operating parameters on TDS removal from paint wastewater was studied by jar test experiments in 500 mL beakers under controlled laboratory settings using the approach described in [26]. The effect of three important operational factors (pH, bio-coagulant dosage, and contact time) was examined. The effluent was adjusted to pH 2, 4, 6, 8 and 10 prior to treatment. Bio-coagulant doses were 1, 2, 3, 4, and 5 g per 500 mL of wastewater with an initial TDS content of 200 mg/L. Contact time was assessed at 10, 20, 30, 40, and 50 min. The dose of bio-coagulant required for each experiment was added to 500 mL of wastewater in a beaker at room temperature. The coagulation–flocculation process consisted of rapid mixing (150 rpm, 2 min), moderate mixing (40 rpm, 20 min), and settling under quiescent conditions. Aliquots were taken at 10 min intervals up to 50 min after treatment to investigate the effect of contact time on TDS removal kinetics. The TDS concentration of each sample was determined, and the removal efficiency was computed using Equation (1) [25]:

where ${\mathrm{C}}_0$ is the initial TDS concentration (mg/L) and ${\mathrm{C}}_{\mathrm{t}}$ is the final TDS concentration (mg/L) after coagulation–flocculation treatment. The optimum operational settings derived from the experimental investigation were further validated by independent verification studies.

2.4. Optimum Operating Conditions and Validation

To validate the optimum operating parameters obtained from the experimental study, validation experiments were performed at the predicted optimum pH, dosage, and contact time. The experimental TDS removal efficiencies were compared with the corresponding predicted values, and the percentage deviation was calculated to evaluate the accuracy and reproducibility of the treatment procedure.

2.5. Bio-Coagulant and Wastewater Characterization

The physicochemical properties of the bio-coagulants and paint wastewater were characterized to provide information on their composition, surface chemistry, charge behavior, and treatment potential. Proximate composition analysis, FTIR, pHpzc determination, and wastewater characterization were carried out using standard analytical procedures.

2.5.1. Proximate Composition Analysis

Selected proximate composition parameters of PS and MOS, including protein, carbohydrate, moisture, ash content, surface area, and yield, were determined using the procedure of [27]. The investigation evaluated the compositional properties of the bio-coagulants and their potential influence on coagulation–flocculation and adsorption processes.

2.5.2. Fourier Transform Infrared (FTIR) Analysis

FTIR spectroscopy was used to identify the functional groups responsible for adsorption, charge neutralization, and pollutant binding during wastewater treatment. FTIR analysis was performed in the selected wavenumber range, and the obtained spectra confirmed the presence of functional groups.

2.5.3. Point of Zero Charge (pHpzc) Determination

The pH point of zero charge (pHpzc) of PS and MOS was found using the pH drift method. Solutions with different initial pH values were prepared and contacted with the bio-coagulants until equilibrium was attained. The pHpzc was obtained from the intersection of the ΔpH versus initial pH (pHi) plot at ΔpH = 0. The pHpzc was used to study the surface charge properties of bio-coagulants and their interaction with dissolved species at different pH settings.

2.5.4. Physicochemical Characterization of Paint Wastewater

The performance of the bio-coagulants was evaluated by characterizing the paint wastewater before and after treatment. The parameters, including total solids (TS), total suspended solids (TSS), total dissolved solids (TDS), chemical oxygen demand (COD), biological oxygen demand (BOD5), pH, and electrical conductivity, were measured according to conventional procedures [10, 12-13, 28].

2.6. Adsorption Capacity, Kinetic and Diffusion Models

2.6.1. Adsorption Capacity

Adsorption capacities were calculated from the experimental data to explore the role of adsorption in TDS removal during coagulation–flocculation treatment, and the data were fitted to kinetic and diffusion models. The adsorption capacity at time t ( $q_t$ ) and the equilibrium adsorption capacity ( $q_e$ ) of PS and MOS for TDS removal were calculated using Equations (2) and (3), respectively [22,29]:

where $C_e$ is the equilibrium TDS concentration (mg/L), $V$ is the solution volume (L), and m is the mass of bio-coagulant (g).

2.6.2. Kinetic Models

The experimental TDS removal data were fitted to kinetic models to understand the adsorption mechanism using pseudo-first-order (PFO), pseudo-second-order (PSO), Elovich, and Weber–Morris intra-particle diffusion models. Rate constants and model parameters for PFO, PSO and Elovich were obtained from linearized graphical plots according to Equation (4), Equation (5) and Equation (6), respectively [21,29]:

where t is the contact time (min), $K_1$ is the pseudo-first-order rate constant (min⁻¹), $K_2$ is the pseudo-second-order rate constant (g/mg·min), α is the initial adsorption rate (mg/g·min), and β is the desorption constant related to surface coverage (g/mg).

2.6.3. Diffusion Model

The intra-particle diffusion rate constant, $K_{id}$ (mg/g·min^0․5), was obtained from the Weber–Morris intra-particle diffusion model given by equation (7) by plotting $q_t$ vs $t^{{\displaystyle \frac{1}{2}}}$ [21,29]. The slope of the plot gives $K_{id}$ .

where $t^{{\displaystyle \frac{1}{2}}}$ is the square root of time (min) and $C$ is the intercept (mg/g) representing the thickness of the boundary layer.

2.7. Adsorption Isotherm Models

To further evaluate the nature of adsorbate-adsorbent interactions, the equilibrium data collected from coagulation–flocculation tests carried out at varied TDS concentrations were fitted to the adsorption isotherm models. The data were fitted to Langmuir, Freundlich and Temkin models to characterize the equilibrium relationship between TDS adsorption capacity and residual concentration in solution and to analyze the adsorption mechanism and surface features of PS and MOS. The model parameters for Langmuir, Freundlich and Temkin were calculated from the linear plots of the transformed data according to Equations (8), (9), and (10), respectively [23]:

where $C_e$ is the equilibrium concentration (mg/L), $q_{max}$ is the maximum monolayer adsorption capacity (mg/g), and $K_L$ is the Langmuir constant related to adsorption affinity (L/mg).

where $K_F$ is the Freundlich adsorption capacity constant and ${\displaystyle \frac{1}{n}}$ represents adsorption intensity or surface heterogeneity.

where A is the Temkin equilibrium binding constant (L/g), and B is related to the heat of adsorption and adsorbate–adsorbent interactions.

3.1. Characterization of Bio-Coagulants and Paint Wastewater

The bio-coagulants were characterized for proximate composition as shown in Table 1. A significant difference was observed in protein and carbohydrate content. MOS had more protein (37.16%) than PS (18.96%), but PS contained more carbohydrate (53.08%) than MOS (44.63%). These results suggest that the MOS coagulant increased electrostatic interactions and binding of dissolved species in the wastewater sample, while the PS coagulant increased adsorption [10,30]. This behavior can be explained by their protein and carbohydrate composition. This conclusion agrees with what has been reported in the literature on bio-coagulants.

From the FTIR spectra in Table 2, it can be observed that important functional groups such as hydroxyl (C-O stretch), carboxyl (C=O stretch), and amine (N-H bend and N-H stretch) groups were involved in the adsorption process. These functional groups are important for TDS removal, since they can interact with dissolved ions and organic matter commonly found in PWW [30].

The first assessment of the paint wastewater sample, as shown in Table 3, showed a significantly polluted effluent with a high TDS value of 2524.43 mg/L. Other associated characteristics such as pH, COD, BOD5, Turbidity and electrical conductivity additionally indicated the complexity of the effluent. TDS concentrations were reduced to 165.54 mg/L and 100.13 mg/L by PS and MOS treatment, respectively, representing around 93% and 96% elimination. This result shows the efficiency of both bio-coagulants for treating paint effluent.

3.2. Effects of Process Parameters

3.2.1. Effects of pH on TDS Removal

As shown in Figure 1, both PS and MOS exhibited the highest TDS removal at pH 4, and removal effectiveness declined gradually as pH increased. MOS performed somewhat better than PS in the examined pH range. To further understand the effect of pH on the surface functional groups of PS and MOS, the pHpzc values were examined. This explains the better elimination of pollutants under acidic conditions.

3.2.2. pH Point of Zero Charge (pHpzc)

The pHpzc of PS and MOS was calculated using the pH drift method to be 7.60 and 8.25, respectively (Table 4 and Figure 2). These values suggest that both of the bio-coagulants were positively charged at the optimum pH due to protonation of the functional groups on the bio-coagulant surfaces. This agrees with the results of Ali et al. [31] who found that the adsorption of anions is favored when pH < pHpzc, whereas the adsorption of cations is preferred at pH > pHpzc. Thus, in this investigation with pH < pHpzc, the pHpzc value of MOS (8.25) shows that MOS has a stronger positive surface charge at a wider pH range, resulting in a greater adsorption contribution to TDS removal than PS.

3.2.3. Effects of Dosage on TDS Removal

As shown in Figure 3, both PS and MOS achieved the highest TDS elimination at a dose of 4 g across the 1–5 g range, after which TDS removal decreased. PS consistently performed slightly worse than MOS at all dosages. The first observed increase is due to the presence of active sites and charge-neutralizing species, which facilitate particle destabilization, bridging, and the development of denser flocs, thereby boosting removal efficiency, according to Al-Hanaktah et al. [32].

3.2.4. Effect of Contact Time on TDS Removal

Figure 4 shows that TDS removal increased sharply from 10 to 40 min and then reached a near-equilibrium state for both PS and MOS. The rapid initial increase was due to the availability of abundant active sites and the strong concentration gradient between the wastewater and the bio-coagulant surface, similar to reports by Pushpalatha et al. [33]. Similar to previous results, MOS demonstrated slightly higher removal efficiencies than PS during the contact period, confirming the increased availability of active functional groups for interaction with pollutants [32,33].

3.2.5. Validation of Optimum Operating Conditions

Table 5 shows the validation results for optimum dosage, optimum pH, and optimum contact time, and the predicted and experimental TDS removal efficiencies for PS and MOS, together with their percentage deviations. Experimental and predicted TDS removal efficiencies agree well, as the percentage deviations for each bio-coagulant are less than 1% [34]. This confirms the adequacy, reliability, and reproducibility of the optimization model, as stated by Obinna et al. [34].

3.3. Effect of Contact Time on Adsorption Capacity

The effect of contact time on the adsorption capacity (qₜ) for PS and MOS coagulants is shown in Figure 5. In both cases, a quick uptake is observed in the first 20–30 min, followed by a gradual attainment of equilibrium between 40 and 50 min. The results suggest that the adsorption was a two-step process. The rapid initial rise in qt is consistent with findings reported in [5,22] and was attributed to the occupation of available active sites on the bio-coagulant surfaces. This may be related to the presence of key functional groups (–OH, –COOH, and –NH) and surface charge effects that promote attraction of dissolved ions [5,30]. After 40 min, the adsorption rate decreased and qt approached equilibrium. This suggests that most of the readily available binding sites were occupied and intra-particle diffusion became the rate-limiting phase. The difference in the maximum adsorption capacity measured for MOS (95.01 mg/g) and PS (93.01 mg/g) is related to their protein content and the density of cationic sites, which increase interactions with negatively charged species in the wastewater sample [22,30]. This behavior shows the combined effect of surface adsorption and intra-particle diffusion mechanisms on the efficient TDS removal.

3.4. Adsorption Kinetics and Diffusion Analysis

The adsorption kinetics of TDS removal by PS and MOS were evaluated using pseudo-first-order, pseudo-second-order, Elovich, and Weber–Morris intra-particle diffusion models, and the resulting parameters are summarized in Table 6. The combined kinetic and diffusion results suggest that TDS removal by both bio-coagulants occurred through a multi-step adsorption process involving surface interactions and mass-transfer effects, with the pseudo-second-order model providing the most suitable description of the adsorption behavior.

3.4.1. Pseudo-First-Order Model

The pseudo-first-order model shown in Figure 6 provided relatively high-rate constants (k₁ = 0.5055 ${\mathrm{m}\mathrm{i}\mathrm{n}}^{-1}$ for PS and 0.6630 ${\mathrm{m}\mathrm{i}\mathrm{n}}^{-1}$ for MOS) but low correlation coefficients (R2 = 0.7160 for PS and R2 = 0.7968 for MOS), indicating that it did not sufficiently describe the adsorption process [5,19].

3.4.2. Pseudo-Second-Order Model

The pseudo-second-order model (Figure 7) was found to be the best fit (R² = 0.9899 for PS and 0.9925 for MOS) with equilibrium adsorption capacities of 123.46 and 117.65 mg/g and rate constants of 0.000549 and 0.000847 g mg⁻¹ min⁻¹ for PS and MOS, respectively, indicating that surface adsorption was the dominant mechanism for TDS removal [19-20, 35].

3.4.3. Elovich Model

The Elovich model (Figure 8) parameters further revealed appreciable initial adsorption rates of α = 15.84 and 24.31 mg g⁻¹ min⁻¹ for PS and MOS, respectively, with a better fit (R²) than the pseudo-first-order model and the Weber–Morris intra-particle diffusion model. This result confirmed the presence of energetically heterogeneous adsorption sites [19,20].

3.4.4. Weber–Morris Intra-Particle Diffusion Model

The constants of the Weber–Morris diffusion model (Figure 9) revealed a slightly higher intra-particle diffusion rate for PS (k_id = 11.961 mg g⁻¹ min^−0.5) than for MOS (10.348 mg g⁻¹ min^−0.5), while the higher intercept value for MOS (C = 28.039 mg/g) indicated a greater effect of the boundary-layer diffusion [19,21].

3.5. Adsorption Isotherm Analysis

The adsorption equilibrium data for TDS removal by PS and MOS were fitted to Langmuir, Freundlich, and Temkin isotherm models, and the resultant model parameters are reported in Table 7. The better fit of the Langmuir model, along with the positive values of the Freundlich and Temkin parameters, shows that TDS removal by PS and MOS mostly occurred via monolayer adsorption with heterogeneous surface contacts and incremental variations in the energy of adsorption [15,23].

3.5.1. Langmuir Model

The Langmuir model (Figure 10) produced higher correlation coefficients (R² = 0.9935 for PS and 0.9921 for MOS) compared to other isotherm models. From the derived model parameters in Table 7, the Langmuir maximum adsorption capacities of 55.56 mg/g for PS and 53.19 mg/g for MOS demonstrate the strong affinity of both bio-coagulants for dissolved solids [23,24].

3.5.2. Freundlich Model

The Freundlich model (Figure 11) shows favorable parameters, with R² = 0.9758 for PS and R² = 0.9588 for MOS. The adsorption intensity parameter, n > 1 for both bio-coagulants, indicates favorable adsorption and suggests the presence of heterogeneous adsorption sites [15,24].

3.5.3. Temkin Model

The Temkin model proposed that the adsorption process is affected by adsorbent-adsorbate interactions and that the heat of adsorption decreases gradually with increasing surface coverage [23,24]. As shown in Figure 12, both PS and MOS exhibited good linear relationships (R² = 0.9775 and 0.9537, respectively), indicating that the Temkin model adequately describes the adsorption behavior of the bio-coagulants.

3.6. Mechanistic Interpretation of TDS Removal by PS and MOS Bio-Coagulants

Figure 13 shows the adsorption-assisted coagulation–flocculation mechanism of PS and MOS for TDS removal. The treatment procedure starts with adding bio-coagulants to paint wastewater containing dissolved ions and colloidal species. FTIR and proximate analyses indicated the presence of functional groups, proteins, and carbohydrates, which can interact with dissolved contaminants and are responsible for charge neutralization. The higher pHpzc values of both bio-coagulants relative to the optimum treatment pH are consistent with protonation of surface functional groups and enhanced electrostatic attraction to negatively charged dissolved species. The protonated functional groups on PS and MOS favoured charge neutralization of dissolved species, leading to the development of larger aggregates through coagulation–flocculation and particle-bridging mechanisms. However, coagulation–flocculation alone did not fully account for the total removal of dissolved particles. Residual dissolved species, after floc formation, were transported from the bulk solution to the bio-coagulant surface by boundary-layer and intra-particle diffusion processes, as described by the Weber–Morris model. This diffusion stage allowed contaminants to access the available active sites, thereby enabling further adsorption. Residual dissolved ions and molecules were retained on the bio-coagulant surface during the adsorption stage, as shown by the best fits of the pseudo-second-order kinetic and the Langmuir isotherm models. This reduces particle concentration in the aqueous phase and contributes to the overall reduction of TDS in the treated wastewater.

3.7. The Superior Performance of MOS over PS

Table 8 shows why MOS outperformed PS even though PS exhibited faster diffusion, a slightly higher theoretical adsorption capacity, and stronger adsorption–model correlations. MOS achieved higher TDS removal because its protein-rich composition provided more cationic active sites. MOS’s higher pHpzc resulted in a stronger positive surface charge at optimum pH, which enhanced charge neutralization, destabilization of dissolved species, and floc formation through coagulation–flocculation and particle bridging before the adsorption became significant. Therefore, we affirm that adsorption favored PS slightly, whereas coagulation–flocculation favored MOS more strongly. Since the treatment process was primarily coagulation–flocculation-based, MOS achieved better overall performance. By contrast, if the treatment process had been based solely on adsorption, PS would likely have demonstrated superior overall TDS removal efficiency.

3.8. Comparison with Literature

The results of the present investigation are comparable with other plant-based coagulants used in industrial wastewater treatment as depicted in Table 9 [28, 36-40]. In addition to their high removal performance, PS and MOS offer advantages such as low cost, biodegradability, and local availability. These characteristics support their potential application as sustainable alternatives for industrial wastewater treatment. The study provides a deeper mechanistic understanding of the adsorption-assisted coagulation–flocculation process for TDS removal. However, the present study was limited to batch-scale research and did not include advanced surface characterization, thermodynamic evaluation, and bio-coagulant regeneration tests.

In this study, both bio-coagulants achieved high treatment efficiencies, with MOS exhibiting superior overall performance to PS. The integration of FTIR, pHpzc, kinetic, diffusion, and isotherm analyses enabled the development of a mechanistic pathway in which charge neutralization and flocculation were the primary removal processes, while diffusion and adsorption contributed to the further removal of dissolved solids remaining after coagulation–flocculation. The findings highlight the potential of PS and MOS as low-cost, biodegradable, and environmentally sustainable alternatives to conventional chemical coagulants for industrial wastewater treatment. Future studies should investigate the regeneration and reuse of the bio-coagulants, evaluate their performance under continuous-flow and pilot-scale conditions, and assess sludge characteristics and treatment economics. Additional surface characterization techniques such as SEM, BET, thermodynamic, and zeta potential analyses may further improve the understanding of the adsorption-assisted coagulation–flocculation mechanism and support large-scale implementation.

Conceptualization, methodology, validation, and formal analysis: C.O.A. and O.S.N.; Software, investigation, data curation, writing-original draft preparation, writing-review and editing, visualization: C.O.A.; Resources and supervision: O.S.N. and O.C.N.; Project administration: O.S.N. All authors have read and agreed to the published version of the manuscript.

The data presented in this study are available on request from the corresponding author.

The authors declare no conflicts of interest.

The study did not receive any external funding and was conducted using only institutional resources.

The authors acknowledge the Department of Chemical Engineering, Enugu State University of Science and Technology (ESUT), Enugu, Nigeria, for providing laboratory support for this study. The authors also acknowledge Nigerian Breweries Plc., Enugu State, Nigeria, for granting access to the brewery wastewater samples used in this research.

The authors confirm that no AI tools were used to generate any part of the manuscript text, including the research design, data collection, data analysis, interpretation of the findings, and manuscript preparation and revision. The authors take full responsibility for the accuracy, originality, integrity, and scientific content of the manuscript.