Graphical Abstract

Highlights

• Nano silica (nSiO₂) was synthesized from indigenous waste rice husk ash and used to prepare chitosan-nSiO₂
• Adsorptive elimination of Methylene Blue dye from aqueous solution had been done well by the nanocomposite in fixed-bed continuous column mode.
• The effects of various process parameters, such as bed depths, flow rates, column diameters, and influent MB concentration, were examined.
• Seven kinetic models were tested with the experimental data.
• The maximum adsorption capacity of the adsorbent according to the best-fit Thomas kinetic model was reported and compared with the literature.
• The regeneration study was performed, and the scale-up design was also proposed.
• The potential application of multiple linear regression and genetic algorithm techniques to forecast the elimination of MB was also verified.

Over the last few decades, ground and surface water contamination due to various organic and inorganic pollutants has been one of the most significant environmental apprehensions. High concentrations of organic dyes like MB in wastewater due to excessive use in various industrial sectors such as textile, paper, leather, and pharmaceuticals have revealed severe environmental effects [2–4]. Many dyes are toxic and even carcinogenic. Again, the coloring attribute of the dyes restrains dissolved oxygen in water bodies. Due to the presence of aromatic rings, synthetic dyes often become nonbiodegradable and can generate secondary toxic products [4, 5]. Therefore, removing these dyes before discharge into the water bodies is an environmental necessity.

Among several conventional methods, viz. ion exchange [6], Nano filtration [7], photo catalytic oxidation [8], membrane separation [9], etc., used for organic dye removal, adsorption has been of impressive significance for its inexpensiveness and acceptability in third-world countries [1]. Chitosan (CS) is one of the major reported bio polymeric adsorbents. With the upgrading of nanotechnology, scientists are searching for new alternatives. However, the separation of nanoparticle adsorbents from wastewater is not viable at the industrial level. Therefore, in recent years a wide range of CS-metal oxide (MO) nano-biocomposites have been engineered with different MOs viz. SiO₂ [1], Fe₂O₃ [10], MnO₂ [11], Fe₃O₄ [12], TiO₂ [13], and ZnO [14], for dye removal.

Statistical applications are essential for understanding the relationship between several independent parameter(s) and the dependent parameter. One such method is the multiple linear regression (MLR). From the literature survey, it has become clear that MLR is becoming popular [15].

During the last two decades, many AI (artificial intelligence) applications have been applied for several engineering applications. This number increases with time, as evident from the literature review [16]. It is now clear that these applications of AI techniques are observed in a wide variety of engineering problems related to all fields [15,17]. Genetic algorithm (i.e., GA) has evolved from the concept associated with genetics (related to biological and medical science). The computer is used to modify various parameters (e.g., chromosome, mutation, etc.) for the formulation of GA. It is the most basic form of multi-object optimization or evolutionary computation. The hybridization of the GA and the neural network has been used as one optimization technique for the last few years [18].

Our previous study [1] showed that CSNC had a promising role in MB adsorption in batch mode. Now, it has been employed for MB removal in the fixed bed down flow continuous column under variable operating conditions. Whilst fixed-bed column technique has better efficiency over batch process to determine the adsorbent quantity required for the removal of pollutants from bulk amount of industrial wastewater [3], current literature review reveals that the use of chitosan-based Nano composites for sequestering of organic dyes by fixed bed technique is limited [19]. Hence, the present work focuses on filling out this space. Here, the indigenous waste, rice husk ash (RHA), was utilized to synthesize Nano silica (nSiO₂) [20]. The regeneration process of used adsorbent and scale-up design were also reported. The breakthrough adsorption curves were examined by seven kinetic models. Critical attention was also given to applying MLR and hybrid techniques to forecast the elimination of MB.

Materials

Analytical grade reagents, namely chitosan powder (low MW) extra pure, SRL Pvt. Ltd., India, de-acetylation degree: 90% (min.), and all other chemicals such as acetic acid, 25% glutaraldehyde solution, ethanol, MB, sodium hydroxide pellets, and hydrochloric acid were obtained from E. Merck India Ltd., and doubly distilled water was employed through the experiments. RHA was used to prepare nSiO₂ [20]. All the instrumental details are listed in Table S1.

Table S1: Details of the Instruments Used.

Methods

Preparation of Adsorbent (chitosan/nSiO₂ nanocomposite)

According to our previous study, CSNC adsorbent was prepared with chitosan: nSiO2 (W/W) = 2:1 (CSNC2-1) [15]. As this adsorbent was most potent for MB removal in the batch process [1], hence it was chosen in the fixed-bed continuous column study.

Characterization of Adsorbent

CSNC2-1 was characterized through SEM, FTIR, BET, TGA, and point of zero charge (pH_PZC) determination was done [1,15].

Adsorbate Solution Preparation

1000 mg of MB was dissolved in 1 L of double-distilled water to prepare an adsorbate stock solution. This stock solution was diluted to get experimental solutions of requisite concentrations.

Column Adsorption Experimentation

The adsorption of MB from aqueous media was carried out within a laboratory-scale fixed bed column under varying experimental conditions using three glass columns 33 cm long. A stainless steel sieve plate was employed as base support, and some glass wool was put over it. The columns were filled with the required amount of adsorbent to obtain 3 cm, 5 cm, and 7 cm bed depths. The influent MB solution was passed through the columns using a peristaltic pump in a downflow manner at optimum pH 10 and room temperature. The residual dye solution was collected at specific intervals to analyze the concentration through a UV spectrophotometer at 668 nm. Effects of different experimental factors, viz. bed depth, an inner diameter of the column, influent MB concentration, and influent flow rate, were also examined.

Percentage removal efficiency was calculated using Eqs. (1)-(3) [21].

Where

And

Characterization

SEM micrographs (Figure S1 a, b) showed the uneven and rough surface of CSNC2-1 with pores of numerous shapes and sizes, attributable to potential adsorption. A relatively compact, even, and flat surface was observed after adsorption.

Figure S1: SEM Micrograph of (a) CSNC2-1 before (b) CSNC2-1 after Adsorption.

FTIR spectra of the adsorbent were represented in Figure S2, and Table S2 showed the major peaks.

Figure S2: FTIR Spectra of CSNC2-1 before and after Adsorption Respectively. [The Letters A And B in Figure S2 Stand for after and before Adsorption Respectively].

Table S2: Wave Numbers (Cm⁻¹) Of Dominant Peaks from FTIR for CSNC2-1 before and after Adsorption of MB.

The particle size of CSNC2-1 was 250-350 μm, achieved by sieving (-44+52 mesh size, BSS, 1962).

The specific surface area was 166 m²g^-1.

TGA thermogram (Figure S3) indicated weight loss below 200°C due to the loss of physisorbed water molecules and between 200°C - 400°C due to the breakdown of the organic backbone of the adsorbent. The breakdown process ended at ~ 540°C with a residuary mass of ~3% attributable to nSiO₂ [15]. pH_PZC was determined as 7.27. 

Figure S3: TGA Thermogram of CSNC2-1.

Influences of Operational Parameters on Fixed-Bed Adsorption

Influence of Variation of Bed Depth

The adsorption experiments were conducted at various bed heights (3 cm, 5 cm, and 7 cm) with a flow rate of 10 cm³min^-1 and influent MB concentration of 10 mgL^-1, and the breakthrough curves were illustrated in Fig. 1. Removal of MB, as well as breakthrough period and exhaustion period, increased with bed depth due to greater availability of adsorptive sites and increased bed residence time at higher bed depth [21].

Again, a broadened mass transfer zone at higher bed height indicated more resistance to mass transfer and prolonged adsorption kinetics. The repulsive forces between the adsorbed MB molecules on the CSNC2-1's surface and the MB molecules present in the aqueous solution were the major cause of mass transfer resistance [23]. Yet again, diffusion prevailed over the axial dispersion at higher bed heights [23,24].

Figure 1: Effect of Variation of Bed Depth.

Influence of Variation of Influent Flow Rate

The adsorption experimentations were conducted at different influent flow rates (10-30 cm³min^-1) with a bed height of 3 cm and influent MB concentration of 10 mgL^-1 and the breakthrough curves were illustrated in Fig. 2. Removal of MB, as well as breakthrough period and exhaustion period, decreased with flow rate due to decreased contact time and decreased film diffusion resistance of adsorbate on the adsorbent at increased flow rates as a result of insufficient time for the passage of the aqueous solution to the adsorbent surface [18]. On the other hand, intraparticle diffusion prevailed at lower flow rates [23].

Figure 2: Effect of Variation of Influent Flow Rate.

Influence of Variation of Influent Dye Concentration

The adsorption experiments were conducted with different influent dye concentrations (10-30 mgL^-1) with a bed height of 5 cm and a flow rate of 10 cm³min^-1 and the breakthrough curves were shown in Fig. 3. Removal of MB as well as breakthrough period and exhaustion period decreased with dye concentration due to increased rate of diffusional mass transfer and quicker rate of saturation of binding sites at increased dye concentration [18,21].

Figure 3: Effect of Variation of Initial MB Concentration.

Influence of Variation of the Inner Diameter of the Column

The adsorption experiments were conducted with different columns having different inner diameters (1.5 cm and 1.8 cm) at a bed height of 3 cm, a flow rate of 10 cm³min^-1 and an influent MB concentration of 10 mgL^-1 and the breakthrough curves were illustrated in Fig. 4. Removal of MB, as well as the breakthrough period and exhaustion period, increased with column diameter due to greater availability of adsorptive sites and increased bed residence time at higher bed depth.

Figure 4: Effect of Variation of Inner Diameter of Column.

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