Sequestration of Methylene Blue Dye in a Fixed-Bed Column Using Activated Carbon-Infused Polyurethane Composite Adsorbent Derived from Coconut Oil

Abstract

In this research, a new method of treating wastewater is introduced using a highly recyclable and sustainable material derived from coconut oil. This material aims to address the issues commonly faced by conventional sorbents, such as limited performance and costly production. These challenges impede a sorbent material from unlocking its full utility in treating wastewater. An exceptional sorbent material was synthesized by incorporating coconut shell-based activated carbon (AC) into a coconut oil-based polyurethane matrix to produce an activated carbon-infused polyurethane (ACIP). The effective adsorption was elucidated by the synergistic interaction between the ACIP material and methylene blue (MB) through electrostatic attraction, π-π interactions, and hydrogen bonding. To provide an exhaustive analysis of the ACIP material, several analytical techniques were employed, including Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM) analysis, X-ray diffraction (XRD) analysis, and thermogravimetric analysis (TGA). A detailed assessment using a fixed-bed column setup investigated its adsorption behavior by encompassing various factors such as inlet concentration, adsorbent bed height, feed flow rate, and solution pH. Results revealed that the ACIP composite exhibited a maximum adsorption capacity of 28.25 mg g⁻¹. Empirical evidence with a high correlation coefficient (R² > 0.93) obtained from the Thomas and Yoon–Nelson model suggests the suitability of the composite material to operate efficiently under these diverse circumstances. Notably, after five consecutive adsorption–desorption cycles, ACIP demonstrated its remarkable reusability by maintaining 86% of its regeneration efficiency. Given its outstanding performance and potential for scalability, this innovative ACIP composite presents a more sustainable approach to addressing wastewater issues within industrial environments.

1. Introduction

Industrial innovation is critical in propelling economic prosperity. As the economy progresses, water consumption also intensifies due to the expanding population and the surge in industrial pursuits. Among the sectors, the textile industry stands out due to its water-intensive processes, which include water pretreatment, dyeing, printing, and finishing operations. These processes contribute significantly to water pollution by releasing toxic substances that are resistant to natural degradation into the water bodies due to their inherent heterogeneity, weak biodegradability, and toxic compositions [1,2]. The lack of compliance with regulations and insufficiencies of proper wastewater management from this industry, among others, have led to the continuous depletion of clean water resources. The textile industry utilizes over 100,000 types of dyes annually [3,4,5,6], with approximately 10 to 15% being lost during the industrial processes and released into the environment [7,8,9], constituting 17 to 20% of industrial wastewater [10,11]. The level of these dyes found in effluents typically ranges between 10 and 250 mg·L⁻¹ [12,13], which remains noticeable even at trace amounts (<1 mg·L⁻¹) and thus continues to raise serious environmental concerns because of its toxicity [5].

Methylene blue (MB), a positively charged compound frequently used for coloring paper, dyeing cotton, and wool, effortlessly dissolves in water at room temperature due to its high water solubility [14,15]. However, this solubility poses challenges when removing MB from water solutions. If not managed properly, the accumulation of high concentrations of MB may detrimentally affect aquatic life and human health. The presence of intensely colored dye effluents hampers sunlight penetration, inhibits photosynthesis, and directly harms living organisms [16,17]. In addition, human exposure to MB can result in health issues that include a rapid heartbeat, nausea, bodily shocks, skin discoloration, jaundice, and necrosis [18]. Traditionally, limits for colored water have been set based on esthetic considerations. However, because of its persistence, legislation from different countries has specified the allowable limits for wastewater pigmentation in the public water supply [19,20,21,22]. These varied regulations emphasize the importance of addressing the potential environmental and health risks associated with MB on a global scale.

The cleanup of dyes from wastewater generated in the textile industry has been intensively explored using several wastewater treatment processes. While techniques like flocculation, membrane filtration, advanced oxidation, ozonation, photocatalytic degradation, and biodegradation have shown effectiveness [23], they also come with limitations such as low efficiency, high energy consumption and operational costs, and the generation of secondary pollutants [24]. These drawbacks in wastewater treatment drive the need for more efficient and cost-effective methods to remove dyes from textile industrial wastewater. Thus, designing more effective and affordable methods for removing colors from industrial effluent is essential. Advanced techniques, particularly those centered around adsorption, have gained prominence due to their inexpensive and eco-friendly technology. This process removes dye molecules from the water and can be regenerated through desorption [25]. Some of the most common materials used in adsorption include activated carbon (AC), zeolites, graphene, fullerenes, and carbon nanotubes [26]. This method is valued for its simplicity, cost-effectiveness, and minimal generation of secondary pollutants.

AC is a globally used material that eliminates organic and inorganic contaminants from effluents. Its highly porous and well-developed structure makes it an ideal adsorption material. However, its low adsorption capacity often requires physicochemical modification to improve its affinity for targeted pollutants. The restriction arises predominantly from several factors, including a small specific surface area, high ash content, insufficient adsorption selection capabilities, constraints related to surface functional groups, and shortcomings in electrochemical properties [23,27,28]. Moreover, AC faces challenges related to its reusability [29], as it exhibits a suspended dispersive property in water and requires a high-cost operation method, making it non-viable for high flux environments [30]. Therefore, in light of its limitations, recently published works pivoted towards innovative solutions to reduce material deterioration by devising more resilient and sustainable alternatives.

One effective solution involves immobilizing AC within a polymer matrix to create an adsorbent with three-dimensional (3D) networks, an elevated surface area, and multiple porosities. The polymer matrix seamlessly facilitates unhindered interactions between the AC adsorbents and cationic pollutants [31], allowing for easy reuse and recovery from water. Among polymeric materials, polyurethane (PU) stands out due to its excellent abrasion resistance, structural stability in water, highly porous nature, and ease of production. Research shows that PU works well as a matrix to immobilize a variety of adsorbents [28,32,33], making it an ideal sorbent material for dye adsorption [16,34], particularly for methylene blue. Moreover, incorporating AC into the PU matrix has the potential to possess the higher sorption capacity of the resulting sorbent material, owing to the high sorption capacity of AC [35], compared to the very low sorption capacity of unmodified PU [16]. Despite this, the conventional production of PU still relies heavily on finite fossil feedstock. Given the escalating oil prices and their overall environmental impact, there is a growing interest in substituting petroleum-based polyols with bio-renewable raw materials to promote sustainable technological development [36,37].

In this research, an investigation was carried out to develop a bio-based polyol derived from coconut oil to produce an activated carbon-infused polyurethane (ACIP) composite using AC from coconut shells for efficient methylene blue (MB) removal. The developed ACIP composite is designed to uphold high recyclability, cost-effectiveness, and stability in thermal and chemical environments [38,39]. The suitability of the developed ACIP composite to remove MB was assessed in a dynamic setup through a fixed-bed column where factors such as the initial MB dye concentration, feed flow rate, solution pH, and the height of the column bed were evaluated to determine its impact on the adsorption process. By employing models like Adams–Bohart, Thomas, and Yoon–Nelson, the study evaluated the ACIP composite efficiency in removing MB from wastewater.

2. Materials and Methods

2.1. Materials

The Philippine–Japan Active Carbon Corporation in Davao City, Philippines, provided the activated carbon (AC) utilized in this study. The coconut shell AC was crushed and passed through a 50-grade mesh screen to attain uniform size distribution. The MB compound (C16H18ClN3S, 319.85 g·mol⁻¹) was of analytical grade generously provided by Caraga State University (CSU) in Butuan City, Philippines. The catalyst (Polycat^® 8) and surfactant (INV^® 690) were acquired from Sigma-Aldrich Chemicals, Philippines. Chemrez Technologies, Inc. (Quezon City, Philippines) supplied additional components such as the stabilizer and dispersant silicon oil, petroleum-based polyether polyol (Voranol^® 4701), and polymeric methylene diphenyl diisocyanate (MDI) (PAPI 135 SH). The abaca fiber, used for cellular reinforcement, was purchased locally in Iligan City, Philippines.

2.2. Characterization

A variety of analytical tools and techniques for characterization were employed to analyze the synthesized ACIP. A Fourier transform infrared spectrometer with an attenuated total reflectance (FTIR-ATR) accessory (Shimadzu IRTracer-100, Kyoto, Japan) was used to examine the presence of the different functional groups at the 500–4000 cm⁻¹ range, with a 4 cm⁻¹ resolution at 20 scans per run. A scanning electron microscope (SEM) (JEOL JSM-6510LA, Tokyo, Japan) was used to study the surface morphology of the ACIP. The optical characterization of the MB samples was performed using a UV-Vis spectrometer (Thermo Fisher Scientific, Genesys 10s, Waltham, MA, USA). The pH measurements of the solutions were obtained using an automatic potentiometric titrator (KEM AT-710, Kyoto, Japan). The thermal degradation of the ACIP was studied using a thermogravimetric analysis (TGA) (Shimadzu DTG 60H, Kyoto, Japan) performed under a nitrogen ambient atmosphere. Using a copper Kα radiation source operating at 40 kV and 30 mA, X-ray diffraction (XRD) (Shimadzu, XRD Maxima 7000, Kyoto, Japan) patterns were obtained at a 3−60° 2θ range and a rate of 0.02° 2θ/0.60 s. The ASTM 2856 test method was employed to determine the open cell content, utilizing a pycnometer (Quantachrome ULTRAPYC 1200e, Boynton Beach, FL, USA), and was reported as a percentage. The open cell content percentage was calculated accordingly using the following equation:

$$OP\left(\%\right)={\displaystyle \frac{V_g-V_p}{V_g}}\times 100\%$$

(1)

where OP is the percent opened cell content, V_g is the geometric volume, and V_p is the pycnometric volume.

2.3. Synthesis of Bio-Based Polyol

The coconut-based polyol was synthesized following a previously outlined procedure [40] employing a successive two-step method. The first step involved the reaction between coconut oil with a calculated amount of glycerol and CaO. Through glycerolysis, the triglycerides in the coconut oil were broken down into their monoglyceride counterpart in a closed Parr reactor under constant stirring at 180 °C for 3 h. The resulting monoglycerides were further processed in the second step through a polycondensation process at 200 °C for 3 h to produce the bio-based polyol. After the synthesis, the polyol mixture was allowed to cool down to room temperature and stored in a sealed container.

2.4. Preparation of MB Solution

A stock solution of 1000 ppm was meticulously prepared by dissolving 1 g of MB powder in a liter of distilled water. To meet the specific requirements for the continuous setup, the desired concentrations of 10 mg·L⁻¹, 15 mg·L⁻¹, and 20 mg L⁻¹ were accurately achieved through the systematic dilution of the stock solution. This approach was performed to ensure uniformity and reliability in the experimental setup.

2.5. Preparation of ACIP Composite Foam

The synthesis of ACIP involved blending the A-side (MDI component) and the B-side (polyol and formulation components) through a free-rise method. Initially, a calculated amount of the polyol blend composed of the polyol, surfactant, catalyst, AC, and abaca fiber was continuously agitated at 1000 rpm for 60 s. Then, a measured amount of MDI was added to the blend and stirred for another 15 s at 1000 rpm for thorough mixing. Afterward, the mixture was left undisturbed to facilitate the vertical expansion of the ACIP and was set aside for an hour. After the expansion, the resulting foam was cured in an oven set at 60 °C for 2 h. Subsequently, all samples were allowed to cure for 72 h at room temperature for further curing before being cut into 0.125 cm⁻³ dimensions for testing and analysis.

2.6. The Fixed-Bed Column Experiment

Figure 1 presents a schematic depiction of the fixed-bed column employed in this study. A laboratory scale of the dynamic setup features an acrylic tube with an inner diameter of 20 mm and a height of 200 mm, enclosed with a 3D-printed funnel on both ends. Additionally, the bottom funnel was secured with a 3D-printed mesh at the opening to ensure the proper stacking of the ACIP. A peristaltic pump was used to propel the MB-containing solution into the dynamic system. Then, samples from specified time intervals were collected from the column outlet and were assessed spectrophotometrically using a UV-Vis spectrophotometer.

To assess the influence of the different variables on the ACIP’s adsorption capacity against MB, a specified volume of the adsorbent material was stacked into the column at various bed heights (50 mm, 100 mm, and 150 mm), initial concentrations (10 mg·L⁻¹, 15 mg·L⁻¹, and 20 mg·L⁻¹), pH levels (4, 6, and 8), and flow rates (6 mL·min⁻¹, 8 mL·min⁻¹, and 10 mL·min⁻¹). All experiments were performed at room temperature and persisted until saturation point. Then, from the data collected, breakthrough curves were established as a fundamental aspect in determining the dynamic response of the fixed-bed column.

2.7. Column Studies

Interpreting the breakthrough curves from the data collected plays a substantial role in understanding the behavior of an adsorbent in a fixed-bed column. These curves visually represent the effective dye load removal from an effluent solution within a fixed-bed column by depicting the final concentration to the initial concentration ratio, C_t/C₀, against time t [41]. The effluent volume, v_eff (mL), can be obtained using Equation (2) as follows:

$$v_{eff}=Q{t}_{total}$$

(2)

where Q is the volumetric flow rate (mL·min⁻¹) and t_total is the total flow time (min).

The total amount of MB adsorbed is equivalent to the area under the breakthrough curve, as measured by Equation (3) below:

$$q_{total}={\displaystyle \frac{QA}{1000}}={\displaystyle \frac{Q}{1000}}{{\displaystyle \int }}_0^{t_{total}}{C}_{ad} dt$$

(3)

where C_ad is the concentration difference in the inlet, C_o, and outlet dye, C_t, in mg·L⁻¹.

Equation (4) defines the total amount of adsorbed ions, q_eq (mg·g⁻¹), by the total amount of sorbed contaminant, q_total (mg), per gram of sorbent (w).

$$q_{eq}={\displaystyle \frac{q_{total}}{w}}$$

(4)

2.8. Dynamic Models

Several adsorption models were employed to forecast the adsorption behavior of ACIP over time. These models depict the response of the adsorbent material at varying conditions and give valuable information in optimizing the fixed-bed column when scaling up. Specifically, the study uses three widely recognized mathematical models: the Adams–Bohart, Thomas, and Yoon–Nelson models.

A fundamental equation based on surface reaction theory developed by Bohart and Adams described the relationship between C_t/C₀ and t in a continuous system [42]. This model describes the initial section of the breakthrough curve and assumes that equilibrium does not occur instantaneously. The linear expression can be expressed as follows:

$$\ln \left({\displaystyle \frac{C_t}{C_0}}\right)=k_{AB}{C}_{0}t-k_{AB}{N}_0\left({\displaystyle \frac{Z}{U_0}}\right)$$

(5)

where C₀ and C_t (mg L⁻¹) denote the input and outflow dye concentrations, respectively. The kinetic constant is denoted as k_AB (L·mg⁻¹ min⁻¹), while U₀ (cm min⁻¹) stands for the linear velocity obtained by dividing the flow rate by the column section area (cm²). Z (cm) refers to the bed column depth, and N₀ (mg L⁻¹) is the concentration saturation. The values of k_AB and N₀ were obtained by using the intercept and slope of the linear plot of ln(C_t/C₀) versus t.

The Thomas model uses the Langmuir isotherm, which incorporates second-order reversible reaction kinetics and functions on the assumption of plug flow behavior inside the bed [43]. This widely recognized and utilized model explains the theory behind sorption processes within fixed-bed columns. This model is designed following the adsorption processes where neither external nor internal diffusion is the constraining factor, as expressed in the following linearized form:

$$\ln \left({\displaystyle \frac{C_0}{C_t}}-1\right)={\displaystyle \frac{k_{Th}{q}_{Th}w}{Q}}-k_{Th}{C}_{0}t$$

(6)

Here, C₀ (mg L⁻¹) represents the inlet MB concentration and C_t (mg L⁻¹) represents the outlet concentration at time t. The k_Th (mL∙min⁻¹∙mg⁻¹) denotes the Thomas rate constant, which can be calculated based on the slope, and the equilibrium MB uptake per gram of the adsorbent is given as q_Th (mg∙g⁻¹), which can be calculated from the intercept of a linear plot of ln((C_t/C₀) − 1) against time t. The mass of the adsorbent is represented by w (g), while Q (mL∙min⁻¹) is the flow rate and t (min) is the saturation time.

The Yoon and Nelson model [44] operates on the premise that the pace at which the probable rate of adsorbate molecule adsorption decreases is proportional to both the probability of adsorbate adsorption and its breakthrough on the adsorbent [41,45]. For a system with a single component, the linearized form of the Yoon–Nelson model is expressed as follows:

$$\ln \left({\displaystyle \frac{C_t}{C_0-C_t}}\right)=k_{YN}t-\tau k_{YN}$$

(7)

where C₀ (mg L⁻¹) represents the inlet MB concentration and C_t (mg L⁻¹) represents the outlet concentration. τ is the time (min) required to reduce the initial concentration to 50%, and t is the sorbate breakthrough time. The velocity rate constant is given by k_YN (min⁻¹), obtained from the intercept and slope using a linear plot of ln(C_t/C₀ − C_t) against t.

2.9. Desorption of ACIP

The desorption experiments involved treating the MB-saturated column with a desorbing agent, a 0.1 M HCl solution. The desorbing solution was pumped through the column for 30 min at a flow rate of 3 mL·min⁻¹. Following this, the column underwent flushing for 30 min using deionized water to prepare for the subsequent cycle of MB adsorption. The adsorption–desorption cycle was repeated five (5) times to evaluate the ACIP’s recyclability. After the experiment, the regeneration efficiency was calculated by the given equation:

$$Regenerationefficiency\left(\%\right)={\displaystyle \frac{{\left(q_e\right)}_r}{{\left(q_e\right)}_0}}\times 100$$

(8)

The adsorption capacity of the reactivated column is denoted by (q_e)_r (mg·g⁻¹), while (q_e)₀ stands for the initial capacity of the unused adsorbent (mg·g⁻¹).

3. Results and Discussion

3.1. Adsorbent Characterization

3.1.1. Fourier Transform Infrared Analysis

An FTIR analysis was employed in the 500 to 4000 cm⁻¹ wavelength range to provide a comprehensive understanding of the chemical properties of the ACIP. Figure 2 showcases the specific peaks in the spectra, corresponding to the functional groups present in the composite material. The peak at 3302 cm⁻¹ indicates the N-H stretching associated with urethane linkages, which can be observed to be more prominent in the ACIP than that of the unmodified PU (UNPU). This distinction highlights the existence of excess OH groups, which could also be attributed to the inherent O-H groups found in the AC. At 1733 cm⁻¹, the discernible peak highlights the existence of carbonyl groups, C=O, which are associated with the polyol structure used in the foam synthesis. A peak observed at 1598 cm⁻¹ denotes an aromatic ring featuring a particular C=C bond directly associated with the structure of MDI and the bio-based polyol. Further examination reveals a peak at 1222 cm⁻¹, which signifies the presence of C-C ethers resulting from polyether polyol. The spectral peaks at 3302, 1733, and 1535 cm⁻¹ correspond to hydroxyl, carboxylic, and N-H bending from the amide groups. These functional groups act as proton donors, a key mechanism facilitating efficient MB adsorption by coordinating with positively charged MB ions through deprotonated hydroxyl and carboxyl groups, thereby playing an essential role in the process [46]. Furthermore, Van der Waals dispersion forces were crucial in the physical mechanisms underlying MB-ACIP contact. The presence of slightly reduced peaks during ACIP desorption provides evidence for the successful regeneration of the composite.

3.1.2. Scanning Electron Microscope and Pycnometer Analysis

The morphological differences between the blank and the ACIP composite were established in Figure 3 through SEM analysis. It can be observed in Figure 3b that the UNPU displays a thin PU film that led to the occasional tear observed in the PU structure. Furthermore, the UNPU manifests a smooth surface with a broad range of cell sizes. On the contrary, as can be observed in Figure 3e, the assimilation of AC into the matrix resulted in a more uniform PU structure with thicker cell walls. Additionally, this resulted in an increased roughness of surface texture and a more well-defined open porous structure, facilitating the increased penetration of MB into the composite material.

Furthermore, pycnometer analysis revealed a substantial increase in the open-cell content, from 75% in the UNPU to 83% in the ACIP composite. This highlights the composite material’s high porosity. Consistent with previous research, these results affirm the connection between higher open-cell content and improved adsorption capacity, owing to the increased surface area that facilitates effective adsorption [46,47].

3.1.3. X-Ray Diffraction Analysis

Figure 4 presents the comprehensive analysis performed on the ACIP composite, using an XRD to reveal its intricate crystalline structure. The analysis revealed two prominent peaks, where broad and gentle diffraction peaks appeared around 19° and 43° 2θ angles, indicating an amorphous carbon skeleton. These peaks signify a disordered carbon arrangement, aligning with the crystallographic planes of (0 0 2) and (1 0 0) [48]. The subdued intensity of these peaks indicates limited graphitization, suggesting a carbonaceous structure with relatively low crystallinity [49]. The existence of amorphous carbon within the ACIP structure suggests the likelihood of improved adsorption abilities owing to its broad surface area and porous nature with a highly disordered structure. During the sorption process, the broad surface area of the material tends to equate to more active sites for interaction, while the highly disordered structure facilitates molecular diffusion owing to a lower degree of entanglement or obstruction [50]. These characteristics make ACIP a promising material for water treatment and environmental remediation applications.

3.1.4. Thermogravimetric Analysis

Figure 5 illustrates the TGA analysis of the ACIP composite, revealing its thermal stability to evaluate its appropriateness in wastewater treatment applications. It can be observed that the ACIP exhibits a complex thermal behavior represented by multiple peaks at around 187, 290, 342, and 453 °C. The observed peak at 187 °C represents the breaking down of the primary and secondary alcohols of the urethane segments and continues to degrade at around 290 °C [51]. At 342 °C, the degradation peak can be associated with the decomposition of the fatty acids of the bio-based polyol component of the matrix [48], and the peak at 453 °C can be attributed to the pyrolysis as AC components [52] present in the ACIP formulation. From these observations, it was apparent that the incorporation of the bio-based polyol did not have a negative effect on the thermal stability of the ACIP, as highlighted by the analysis of DTG curves.

3.2. The Column Analysis

Accurately forecasting breakthrough curves under specific operational factors is essential for effectively planning and managing a fixed-bed adsorption process.

Table 1. Summary of operational column data parameters for the removal of methylene blue (MB) using activated carbon-infused polyurethane (ACIP).

Inlet Concentration (mg·L−1)	ACIP Bed Height (mm)	Flow Rate (mL·min−1)	pH	qtotal (mg)	qeq (mg·g−1)	veff (mL)	ttotal (min)
10	100	8	6	54.89	10.98	11,040	1380
15	100	8	6	59.78	11.96	9600	1200
20	100	8	6	63.87	12.77	7200	900
15	50	8	6	28.82	11.53	5280	660
15	150	8	6	96.47	12.86	12,000	1500
15	100	6	6	77.09	15.42	9360	1560
15	100	10	6	52.31	10.46	9000	900
15	100	10	4	20.25	4.05	5400	540
15	100	10	8	141.27	28.25	20,400	2040

summarizes the performance of the ACIP for the effective adsorption of MB in a fixed-bed column at varying operational conditions.

3.2.1. Effect of Initial MB Concentration

Figure 6 demonstrates the intricate relationship between the initial MB concentration and its consequential effect on the breakthrough curve. The study varied the initial concentration of MB from 10 to 20 mg·L⁻¹ while maintaining the adsorbent column height (100 mm), feed flow rate (8 mL·min⁻¹), and pH level (6) constant. It can be observed from the figure how an increase in MB concentration speeds up the saturation of the adsorption process, resulting in a shorter breakthrough time [53,54]. Conversely, more spread-out breakthrough curves and slower breakthrough times can be observed at lower initial MB concentrations. Moreover, as influent concentration increases, the MB loading rate rises, simultaneously reducing the length of the adsorption zone due to intensified mass transfer driving forces [45,55,56]. The trend of these findings aligns with similar research on fixed-bed adsorption systems [25,41,57].

3.2.2. Effect of ACIP Composite Bed Height

To investigate the effect of ACIP bed height on the breakthrough curve for MB adsorption, the adsorption process was studied at different bed heights of 50, 100, and 150 mm while maintaining a consistent flow rate (8 mL·min⁻¹), initial MB concentration (15 mg·L⁻¹), and pH level (6). As illustrated in Figure 7, when the column height was increased, the time required for saturation and the volume of the effluent, Veff, also increased. This phenomenon can be attributed to a more significant contaminant quantity requirement to saturate a higher ACIP bed, facilitating a prolonged interaction between MB and ACIP and enhancing removal efficiency [41,45]. The increase in bed height corresponded to a reduced breakthrough curve slope, indicating a broader mass transfer zone [58], thus resulting in a more efficient reduction in the solute concentration due to the availability of more adsorption sites [59,60].

3.2.3. Effect of Solution MB Flow Rate

To evaluate the influence of flow rate in the breakthrough curve, the flow rate was varied from 6 to 10 mL·min⁻¹ while maintaining a consistent bed height (100 mm), initial MB concentration, and pH level (6). Figure 8 demonstrates that higher flow rates resulted in decreased breakthrough times. In contrast, lower flow rates allow for increased interaction time between MB and ACIP, resulting in enhanced MB ion removal and higher breakthrough times. These differences in breakthrough curves can be attributed to mass transfer principles where higher flow rates accelerate mass transfer, leading to quicker saturation [58]. Notably, an increased flow rate compromises removal efficiency, as it does not provide sufficient residence time for the solute to interact with the sorbent in the column [61]. These observations align with findings reported by other researchers [25,41,57].

3.2.4. Effect of the Initial MB Solution pH

The correlation between the solution pH and the breakthrough curve is illustrated in Figure 9. Here, the pH of the inlet solution varied from 4 to 8 while keeping the concentration (15 mg·L⁻¹), flow rate (10 mL·min⁻¹), and bed height (100 mm) constant. It can be inferred that lower pH levels produced a more rapid breakthrough curve than higher pH levels. According to the results, adsorption quantities and capacities exhibited an upward trend at higher pH values. This trend is attributed to the dissociated state of MB dye as cationic dye ions in aqueous solutions at higher pH. Since the pKa of MB is 3.8, the solution predominantly contains cationic MB species at pH values above this threshold. At lower pH levels, additional H⁺ ions in the solution compete with MB ions for active adsorption sites, leading to reduced uptake and faster saturation [62]. Conversely, as the solution adopts an alkaline state, it leads to a proportional increase in the negatively charged surface of ACIP, amplifying MB uptake through heightened Van der Waals interactions between the dye and the composite. These findings align with a similar study showing that higher pH levels required more time to reach saturation [24,63].

3.3. The Model Analysis

Establishing breakthrough curves is essential to estimate the effectiveness of the composite material in a column design. However, to better illustrate the suitability of its performance in the laboratory for future industrial applications, three of the most commonly applied mathematical models have been employed to predict the dynamic behavior of the ACIP in the column.

3.3.1. The Adams–Bohart Model

Based on the data collected from the relationship relating to ln(C_t/C₀) against t, the N₀ and k_AB values were obtained, as detailed in

Table 2. Parameters of the Adams–Bohart model analysis across varied operational factors.

Feed Concentration (mg·L−1)	ACIP Bed Height (mm)	Inlet Flow Rate (mL·min−1)	pH	kAB (L-mg−1 min−1) × 10−4	N0 (mg L−1)	R2
10	100	8	6	3.2612	694.1377	0.8140
15	100	8	6	2.4456	857.9272	0.7974
20	100	8	6	2.4417	900.3202	0.8376
15	50	8	6	3.5092	970.7570	0.6899
15	150	8	6	2.1195	827.9164	0.9146
15	100	6	6	2.1181	894.1829	0.9089
15	100	10	6	2.9093	830.5218	0.7970
15	100	10	4	3.1910	463.9978	0.6470
15	100	10	8	1.5781	1893.7349	0.854

. It can be observed that higher bed heights and concentrations led to a reduction in the rate constant, k_AB. This indicates that the system kinetics during column adsorption phases largely depended on external mass transfer [14,64,65]. With R² values falling below the 0.90 threshold, this suggests that the Adams–Bohart model does not adequately capture the complexities of the adsorption dynamics. Despite being widely applied in depicting continuous experiments, its effectiveness is only limited in the initial segment of the breakthrough curve and does not account for the diffusion of the adsorbate into the pores of the adsorbent [41,61,66].

3.3.2. The Thomas Model

The analysis of the column data employed the Thomas model to calculate the rate constant, k_Th, and equilibrium uptake, q_Th, as presented in

Table 3. Parameters of the Thomas model analysis across varied operational factors.

Feed Concentration (mg·L−1)	ACIP Bed Height (mm)	Inlet Flow Rate (mL·min−1)	pH	kTh (L-mg−1 min−1) × 10−4	qTh (mg L−1)	R2
10	100	8	6	5.6381	11.3896	0.9706
15	100	8	6	4.4688	13.1442	0.9711
20	100	8	6	4.6365	13.5278	0.9879
15	50	8	6	7.3921	12.8581	0.9383
15	150	8	6	3.6353	13.8249	0.9893
15	100	6	6	3.2857	15.9385	0.9843
15	100	10	6	5.8508	11.6545	0.9771
15	100	10	4	8.6588	4.6352	0.9417
15	100	10	8	3.0400	29.2860	0.9941

. In particular, the Thomas model displayed a better fit. The Thomas model applies to the adsorption process, which implies that neither external nor internal influences place restrictions on the situation [45]. The results indicated that the k_Th decreases with the increase in bed height, and q_Th is observed to increase with the increase in initial concentration. This means that the higher the MB concentration, the more it will be attracted to the ACIP composite, leading to greater adsorption. Moreover, the results show that smaller flows and deeper bed depths heightened the adsorption capacity of MB on the ACIP column. This aligns with similar observations in other materials and strengthens the understanding of the underlying dynamics of the adsorption process [14,25,65].

3.3.3. The Yoon–Nelson Model

Table 4. Parameters of the Yoon–Nelson model analysis across varied operational factors.

Feed Concentration (mg·L−1)	ACIP Bed Height (mm)	Inlet Flow Rate (mL·min−1)	pH	KYN (min−1) × 10−3	τ (min)	R2
10	100	8	6	5.6381	711.8503	0.9706
15	100	8	6	6.7032	547.6747	0.9711
20	100	8	6	9.2729	422.7436	0.9879
15	50	8	6	11.0880	267.8778	0.9383
15	150	8	6	5.4529	813.2294	0.9893
15	100	6	6	4.9285	885.4724	0.9843
15	100	10	6	8.7761	388.4836	0.9771
15	100	10	4	12.9880	154.5078	0.9417
15	100	10	8	4.5600	976.1989	0.9941

outlines the statistical parameters obtained from the Yoon–Nelson model. According to the model’s findings, the 50% breakthrough curve demonstrates an increasing trend with higher bed height but declines with increased concentrations and flow rates. This trend occurs because higher flow rates, higher concentrations, and lower bed heights prompt the faster saturation of the ACIP within the column. Additionally, the calculated τ values closely match the time taken for a 50% breakthrough of the ACIP composite, as observed in the previous breakthrough figures. The R² values exceeding 0.94 strongly affirm the model’s suitability for the current system. Based on this, it can be inferred that the dynamics of MB adsorption in a fixed-bed column can be predicted well using the Thomas and Yoon–Nelson models.

3.3.4. Desorption of MB

Desorption experiments were conducted on ACIP samples saturated with MB to demonstrate the recyclability of the ACIP. The desorption process entailed the use of a 0.1 M HCl solution as the desorbing agent [67]. Under an acidic setting, the active sites in the adsorbent material gained protons, leading to a decrease in the MB molecules’ electrostatic attraction to the active centers of the ACIP. This reduction in attraction facilitated the movement of MB molecules out of the adsorbent material, enabling their diffusion [68]. The desorption was accomplished by pumping the desorbing agent through the column at a rate of 3 mL·min⁻¹ for 30 min. Subsequently, the column underwent a 30 min flush with deionized water to prepare for the next cycle of MB adsorption [69].

Figure 10 clearly illustrates that the removal efficiency of the ACIP in adsorbing MB gradually decreases after multiple reuse cycles, consistent with findings from previous studies [14,69]. Following the completion of five cycles, the removal efficiency decreased from 95% to 86%, primarily due to the saturation of the ACIP’s surface and the subsequent blockage of its active binding sites, resulting in reduced removal efficiency [70]. Additionally, the breakthrough curves exhibited a leftward shift, indicating a decrease in the amount of dye removed in successive adsorption cycles. The results highlight the exceptional performance of ACIP even after five consecutive runs, showcasing remarkable efficiency [71,72]. Hence, ACIP emerges as a potential and environmentally sustainable alternative adsorbent for effectively removing MB dye from polluted water.

Table 5. Specifics of the acidic eluent utilized in the desorption study.

Adsorbent	Cycles	Eluent	Removal Efficiency	Reference
Pyrolytic tire char	3	0.1 M HNO3	64%	[14]
Monolithic starch cryogel	3	Ethanol	62%	[71]
Alginate–water hyacinth beads	3	0.1 M HNO3	81%	[73]
Polyaniline/Tectona grandis sawdust	4	0.1 M HCl	<50%	[67]
Chitosan–clay composite	5	Distilled water	>50%	[74]
ACIP	5	0.1 M HCl	86%	This study

lists more desorption studies of various other chemical compounds utilizing eluents and other techniques.

3.3.5. Other Adsorbents

Table 6. Comparative adsorption performance of previously documented adsorbents for MB ion sequestration.

Adsorbents	qeq	Reference
Banana peel bioadsorbent	22.11	[75]
Pyrolytic tire char	3.85	[14]
Zeolite	4.36	[61]
Coal-based commercial activated carbon	12.06	[72]
Poly(acrylonitrile-co-acrylic acid)-modified thiourea	28.51	[69]
Unmodified PUF	4.00	[16]
ACIP	25.25	This study

outlines the comparison between different adsorbents for the removal of cationic dyes. By comparing ACIP’s adsorption capacity for MB removal with findings in the existing literature, it becomes evident that AC generally displayed higher or similar adsorption capabilities. Meanwhile, other adsorbent materials showcased similar uptake capacities. It is important to note that the polymer matrix within ACIP creates a porous adsorbent with multiple porosities that securely hold the AC in place, enhancing its physical and chemical durability. The straightforward production process of ACIP, along with the widespread availability of coconut products that serve as an alternative bio-based polyurethane solution, positions ACIP as a game-changing solution for effectively removing MB dye in fixed-bed columns.

3.3.6. Proposed Adsorption Synergism Between ACIP and MB

Figure 11 presents the different types of interaction between the surface of ACIP and MB for its synergistic adsorption. The adsorption process of MB can be described as the collective effect of different noncovalent interactions involving its functional groups with the corresponding functional groups in ACIP. First is the electrostatic interaction due to the cationic nature of MB, which facilitates attraction toward the negatively charged surface of the ACIP [76]. Additionally, in its protonated state, the presence of the urethane, hydroxyl, and carboxyl groups in ACIP contain hydrogen atoms bound to electronegative oxygen atoms, making these hydrogens partially positive, which imparts a partial positive charge on the surface of ACIP, enabling it to act as a hydrogen bond donor or electron acceptor. This promotes hydrogen bonding (H-bonding) with the electronegative groups of MB, such as the amino group with lone electron pairs and the aromatic rings, which serve as hydrogen bond acceptors or electron donors [77]. Correspondingly, the prevalence of aromatic and heterocyclic groups in both MB and ACIP signify the feasible occurrence of a particular type of van der Waals force, known as π-π interactions. This attractive force augments the stability of adsorbed MB due to the association between its rings and those on the surface of ACIP through a stacked alignment [78]. These synergistic interactions enhance the overall adsorption efficiency of MB onto the ACIP [76,79,80].

4. Conclusions

Herein, this study explored the development of a highly effective and reusable composite material by infusing activated carbon into a coconut oil-based polyurethane matrix. Experimental results revealed that the ACIP composite is effective in adsorbing MB in an aqueous media, as evidenced by the fixed-bed column experiments. The morphological analysis of the composite material revealed a more uniform and well-defined porous structure that facilitated a better MB adsorption mechanism. Furthermore, the results also indicate that the adsorption efficiency was dependent on several factors such as the inlet concentration, pH, flow rate, and bed height, where higher flow rates lead to reduced removal capacity contrary to the increased bed height. The Thomas and Yoon–Nelson models provided a more precise representation of the breakthrough curves for the adsorption processes across different fixed-bed conditions among the models employed in the analysis of experimental data. The study also showcased the capacity of the ACIP to be recycled up to five times and not lose a significant degradation in its adsorption capacity, highlighting its excellent reusability. The adsorption of MB onto the surface of ACIP exhibits synergistic effects through electrostatic attractions, hydrogen bonding, and π-π interactions, portraying the dynamics governing the interplay between MB and ACIP. In conclusion, the study uncovers a straightforward method for creating a cost-effective, eco-friendly, and highly porous composite. These findings not only propose a practical solution but also open avenues for the broader utilization of the ACIP to combat cationic dye pollution in aquatic environments. This work lays the groundwork for further research and real-world applications aimed at addressing critical challenges related to dye pollution in our water systems.