Abstract

Performance of green treatment systems such as adsorption to treat textile effluents often suffers lack of longevity and efficiency due to the presence of complex compounds of varying reactivity. There is scope for improving the operational efficiency of such processes using real-time monitoring systems. The present study aimed to evaluate the performance of an activated biosorbent prepared from the leaves of Acalypha indica for treating textile industry effluent by simulating process control with real-time monitoring. Batch experiments were performed with synthetic and real-time dye effluents to identify the optimum conditions (pH = 3.0, dosage = 1.0 g/L; time = 1 h) for the highest adsorption capacity (6 mg g−1 and 2 mg g−1). The evaluation of physical parameters suggested best fit for Freundlich isotherm model and pseudo-second-order kinetic model. The LabVIEW-based simulation control system enabled close monitoring of pH and temperature during the process. Based on the inputs, an alteration of initial pH has resulted in substantial reduction in chemical oxygen demand (COD) (73.91%), turbidity (52.43%) and total dissolved solids (TDS) (19.43%). The average incremental increase was highest for COD (45.80 ± 0.06%) compared to TDS (10.13 ± 0.06%) and turbidity (−1.74 ± 0.03%) for varying dosage (3 g to 11 g). The proposed framework for incorporating a process-control-based monitoring system can help to achieve better performance.

HIGHLIGHTS

  • Activated carbon produced from the leaves of Acalypha indica as a biosorbent.

  • Adsorption isotherm provides best fit with Freundlich model.

  • Adsorption kinetics indicate that the textile effluent follows pseudo-second-order kinetics.

  • LabVIEW has been used for the measurement of pH and temperature of the effluent.

  • Adjustment of the initial pH led to substantial reduction in the COD, TDS and turbidity.

INTRODUCTION

Water pollution is a perilous issue threatening our very existence despite extraordinary advances in technological innovations. There is an open call for water conservation either by reducing the usage economically or by enhancing reuse/recycle to meet the growing demands (Zhang et al. 2016; Kausar et al. 2018). Among the water consuming industries, textile industries and pulp and paper industries are of major concern not just due to their enormous volumetric rate but more importantly due to their hazardous nature of their ingredients. Depending on the nature of dyeing operations, there are several categories of dyes such as direct, reactive, vat, azo and sulphide dyes which are regularly used in the textile industries (Saravanan et al. 2012a; Kausar et al. 2018). For example, direct dyes are water soluble while azo dyes consist of insoluble azo groups (Kanwal et al. 2017). Hence, a common strategy for the removal of dyes is still a major issue in the treatment of the textile effluents (Xu et al. 2017).

Being the second largest producer and a major consumer (63%) of textile products, the Indian textile industry is regulated by a few statutory laws such as Environment Act (1986), Environment Protection Rules (1986), Water Act (1974), Hazardous Waste Rules (2008), and the Prevention and Control of Pollution Act (1981). Upon realizing the limitations for these legislations in creating a positive impact on the environment, further guidelines were issued by the National Green Tribunal (2014) and the Central Pollution Control Board (2015) to implement zero liquid discharge. The target industries include pharmaceuticals, paper and pulp, tanneries, and refineries and dyeing and textiles. This has resulted in a never-before strive for the industries to achieve complete recycling of their effluents by installing additional treatments units such as adsorption, evaporation and incineration.

Having sufficiently exposed the problems with textile effluents, it is important to compare the available solution strategies for the elimination of dye compounds from water (Long et al. 2017). It is inevitable to accept the associated advantages and disadvantages of the available treatment techniques (physical methods such as adsorption and membrane separation, and chemical processes such as coagulation, chemical oxidation, froth-flotation, oxidation, ozonation and solvent extraction) as it is difficult to establish the absolute superiority of one process over other (Troster et al. 2002; Saravanan et al. 2012b; Ang et al. 2015; Crini & Lichtfouse 2018). Another important aspect is the number of stages in the downstream process line in textile dyeing before reaching the final product (such as desizing, bleaching, dyeing, printing and finishing) where the quantity and strength of the effluent vary considerably (Srebrenkoska et al. 2014). Hence, from a resource recovery point of view, hybrid solutions are suggested to achieve simultaneous separation of dyes and salts along with purification of water (Lin et al. 2015; Kumar & Saravanan 2017). However, the selective reactivity and huge economic requirements can pose sufficient hindrance to the stakeholders to rethink about modifying some of the existing sustainable solutions such as adsorption treatment.

Recent studies have indicated that adsorption is still a preferred technique for the remediation of various compounds in the textile effluents due to their promising results in sustainability measurements (Vasudevan et al. 2016; Padmanabhan et al. 2017; Barakan et al. 2020; Linggi et al. 2020; Mohammadi et al. 2020). In view of the characteristic porosity contributing to a high surface area and notable adsorption capacity, activated carbon (AC) derived from natural biomass is found to be efficient in the removal of several kinds of dyes, heavy metals, and gases (Saravanan et al. 2012a; Kausar et al. 2018). Biosorbents prepared from natural materials and wasted agricultural products are found to be effective in removing water-soluble synthetic dyes such as Alcian Blue, Methylene Blue, Malachite Green, Violet 6 and Basic Red 18 (Khorramfar et al. 2010; Gupta et al. 2011; Mallampati et al. 2015; Jendia et al. 2020). Similarly, heavy metals are also removed from the aqueous solution by specific biosorbents under selected experimental conditions (Acharya et al. 2009; Boudrahem et al. 2011; Ranjith et al. 2011; Vasudevan et al. 2016; Padmanabhan et al. 2017; Natarajan & Al-Qasmi 2018; Shyla et al. 2018; Poonam & Kumar 2020; Ramos-Vargas et al. 2020).

The synthesis of low-cost biosorbents has assumed high importance owing to the economic benefit, while the treatment efficiency is on par with many advanced techniques (Peng et al. 2017; Cechinel et al. 2014). In this study, the biosorptive capacity of an indigenous medicinal plant, Acalypha indica was studied for treating textile industry effluents. Biosorbents prepared from the leaves of Acalypha indica were found to be effective in removal of Reactive Red 198 (Praveena et al. 2011), Methylene Blue (Ramathilagam & Ananthakumar 2016) and Methyl Orange (Janani et al. 2019) from aqueous solutions. Suneetha et al. (2015) used the cost-efficient AC obtained from the stems of Acalypha indica plant for the elimination of fluoride from the polluted water. Although this species has been used in several studies for the elimination of water-soluble dyes, the application of the same species for treatment of a real-time textile effluent is not well understood.

In many cases, it is not feasible to undergo a forensic investigation to characterize these pollutants in detail due to various economic, technical and institutional limitations. Nonetheless, basic physico-chemical analysis of textile effluents still proves to provide many practically useful insights to adopt conservative and sustainable remediation strategies. The compositional heterogeneity of the textile effluents makes it cumbersome to predict the variability in sorptive mechanisms, as the expected physico-chemical parameters undergo incoherent variations during the adsorption process. These problems can be quite severe during large-scale applications where even a slight variation in pH or temperature can cause significant reduction in the treatment efficiency, causing further economic liability. In this situation, a real-time process monitoring scheme can help the production engineers to intervene during the process and, in some cases, can make suitable modifications to improve the performance of the system (Schreiner et al. 2006).

The graphical programming environment called Laboratory Virtual Engineering Workbench (LabVIEW) is widely used to simulate the temporal variability of the selected environmental parameters by real-time monitoring and simultaneous recording of the data. In a previous study, Sivapragasam et al. (2017) used the LabVIEW platform for real-time estimation of the water footprint for hydropower generation. However, it is important to customize these process-control systems based on the real-time monitoring data inputs. Therefore, the objectives of the study were: (i) to prepare and characterize AC from Acalypha indica, (ii) to compare the removal efficiencies and performance mechanisms while using a real-time textile effluent instead of synthetic dye solution, and (iii) to evaluate the variations in pH and temperature as process-control parameters by real-time monitoring using the LabVIEW environment. This work is novel compared with other similar studies on adsorption in the sense that a conceptual design and implementation of LabVIEW has been proposed for the adsorption process control for the first time.

MATERIALS AND METHODS

Sampling and analysis of textile effluent

The effluent was collected from a textile industry in Rajapalayam Taluk of Tamil Nadu, and was processed water after bleaching prior to any preliminary or primary treatment in the industry. The physico-chemical characteristics of the real-time textile industry effluent (TIE) samples collected are given in Table 1. The industry had already attempted a few popular treatment methods like coagulation, precipitation and ion exchange processes for treating their effluent. However, considering the overall economic and environmental impacts, they are interested in designing an adsorption-based treatment system for the removal of toxic dyes from their effluents.

Table 1

Characteristics of textile industry effluent collected

ParameterConcentration
Biological oxygen demand (BOD) 11.2 mg l−1 
Chemical oxygen demand (COD) 2,000 mg l−1 
Dissolved oxygen (DO) 6.7 mg l−1 
pH 9.9 
Total dissolved solids (TDS) 1,330 mg l−1 
Turbidity 124.7 NTU 
ParameterConcentration
Biological oxygen demand (BOD) 11.2 mg l−1 
Chemical oxygen demand (COD) 2,000 mg l−1 
Dissolved oxygen (DO) 6.7 mg l−1 
pH 9.9 
Total dissolved solids (TDS) 1,330 mg l−1 
Turbidity 124.7 NTU 

Preparation and characterization of AC

In order to prepare the biosorbent, about 100 leaves of Acalypha indica were collected from local gardens in Rajapalayam Taluk of Tamil Nadu and washed with distilled water and subsequently dried at 70 °C for a time period of 48 h. The dried leaves were powdered and the particles were sieved using a British Standard Sieve mesh size 60 (250 μ) and stored in an airtight container. The dried powder was mixed with 1 M hydrochloric acid (HCl) at an impregnation ratio of 1:1 (w/v) for 24 hours for activation at room temperature, followed by oven drying at 70 °C for another 24 hours. This chemical activation was followed by thermal activation to promote carbonization by keeping the dried materials inside a stainless steel box and heating in a muffle furnace at a temperature of 250 °C for 20 minutes. After subsequent washing with distilled water and drying, the AC was made ready for use.

In order to ascertain the recovery of used AC from the effluent after adsorption, an experiment was performed to prepare magnetized activated carbon (MAC) using chemical precipitation. For this, 3.9 grams of ferrous sulphate (FeSO4.7H2O) was mixed with 7.8 grams of ferric chloride (Fe2Cl3) and was heated at 40 °C in a hot air oven for 30 min. To this mixture, 3.3 grams of prepared carbon was added, followed by drop-wise addition of 100 ml of 5 M sodium hydroxide (NaOH) solution with continuous stirring. The resulting precipitate of ferric oxide (Fe2O3) was separated by filtration and taken for reuse after washing and drying at 60 °C (Altıntıg et al. 2017).

In order to characterize the surface features of the adsorbent, various instrumentation techniques were employed such as Brunauer–Emmett–Teller (BET) analysis for calculating the surface area, scanning electron microscope (SEM) image analysis (EVO18, M/s Carl Zeiss, Germany) and X-ray diffractometer analysis (D8 Advanced ECO, M/s Bruker, India) for determining the surface morphology and Fourier transform infrared (FTIR) spectroscope (RTracer-100, M/s Shimadzu, Japan) for identifying the presence of functional groups on the surface of the prepared AC adsorbent. The FTIR analysis was performed in the region from 4,000 cm−1 to 400 cm−1.

Batch adsorption studies

In order to understand the feasibility of adopting adsorption as an effective remediation technology for TIE, a few batch experiments were performed in the laboratory. As a reasonable assumption, Malachite Green (MG) was used as the representative compound for preparing a synthetic dye solution (SDS) with varying initial concentrations 250, 500, 750 and 1,000 mg l−1 in order to match with the physico-chemical characteristics of the collected sample (Table 1). For this, a standard graph was plotted between the selected range of initial concentrations and the corresponding absorbance values obtained with a UV-visible spectrophotometer (Model-1800, M/s Shimadzu, Japan) at a wavelength of 325 nm. The wavelength was adjusted to this maximum value in order to match the normal peak absorbance range reported for azodyes which are very commonly present in textile effluents. Based on the trial experiments, the initial concentrations for the TIE samples were varied from 30, 50, 70, 90 and 110 mg l−1 in order to estimate the suitable dosage conditions.

A series of batch experiments was conducted by varying the adsorbent dosage, contact time and pH of the solution to estimate the maximum adsorption capacity for both SDS and TIE samples. While testing with the SDS, the adsorbent dosage was varied in the range 0.05, 0.1, 0.15 and 0.2 g per 100 ml of prepared sample, whereas the dosage range for treating the TIE samples was kept higher (1, 3, 5, 7, 9 and 11 g) for the same volume. The pH of the sample was modified using either NaOH or HCl to make the trials for the range of 3.0, 5.0, 7.0 and 9.0. All the experiments were conducted in 200 ml Erlenmeyer flasks with continuous agitation by placement in a rotary shaker at 200 rpm to maintain a good contact between the adsorbent and the dye compounds. The equilibrium experiments were performed for the selected optimum time (1 h) by keeping the optimum dosage (as mentioned in the Results section) and varying the initial concentration from 250 mg l−1 to 1,000 mg l−1. For finding the temporal variation of the prevailing concentration in the agitated medium during the kinetic studies, 2 ml samples were collected at equal intervals of 10 minutes up to a period of 4 h. The collected samples were centrifuged at 10,000 rpm for 10 minutes and the supernatant was taken for measurement. In a similar way, the collected effluent samples were also treated with the prepared biosorbent by maintaining the optimum dosage and pH conditions for maximum adsorption.

The adsorption capacity of the prepared biosorbent was determined using the following equation (Cazettaa et al. 2011):
formula
(1)
where qe is the adsorption capacity (mg g−1), Co and Ct are the initial and equilibrium concentrations of the sample (mg g−1) respectively, V is volume of the sample (ml) and W is mass of adsorbent (g).

Monitoring of environmental parameters

As the nature and extent of physico-chemical modifications happening in the system during the adsorption process are practically very difficult to monitor merely by visual means, we used the LabVIEW platform to simulate the temporal variability of the selected environmental parameters. In the present study, a ds18b20 temperature sensor and Robocraze pH sensor were used to measure the temperature and pH of the effluent respectively as a function of time. The sensor was connected to the Data Acquisition (DAQ) unit of LabVIEW which has a triggering and data storing set up to acquire and store the sensor data in a very short period of time. The program was set to record the temperature and pH readings every 5 minutes. The DAQ was initialized by giving an input while the output data were stored in a specified file path. The measured output readings are in voltage which can be converted to temperature and pH readings using Equations (2) and (3):
formula
(2)
formula
(3)
where T is the temperature (°C) and V is the voltage reading (V).

The additive and multiplicative terms in Equations (2) and (3) represent the calibration constants, indicating the possible range of correct data with offsets for the measured values. The LabVIEW platform developed for the temperature and pH measurements is shown below (Figure 1). A conceptual design framework of real-time process-control systems using LabVIEW is also presented in this study in a later section.

Figure 1

The LabVIEW configuration environment for the continuous monitoring of (a) pH and (b) temperature.

Figure 1

The LabVIEW configuration environment for the continuous monitoring of (a) pH and (b) temperature.

RESULTS AND DISCUSSION

Adsorbent characterization studies

Surface morphological features

The firsthand evidence of adsorptive removal of MG using AC was observed by comparing the SEM images before and after adsorption (Figure 2). On a peripheral screening approach, the presence of bright spots on the AC grains (Figure 2(b)) indicated the active mass transfer of MG molecules from the aqueous phase to the solid (sorbed) phase. Unlike a uniform surface coating, the discontinuous surface-filling phenomenon provided evidence for the prevailing non-linear mass transfer mechanism which will be further elucidated using adsorption kinetic models. This inference is in corroboration with the conclusions reported by Arami et al. (2005) who have reported a pseudo-second-order (PSO) kinetic model to represent the rapid adsorption in a continuous flow/mixing process. The BET surface area analysis showed that the AC is essentially micro-porous and possesses larger surface area (in the order of 1,500 m2 g−1) when compared to the plain leaves without activation (in the order of 70 m2 g−1).

Figure 2

SEM images showing the change in surface roughness of the prepared AC (a) before and (b) after adsorption.

Figure 2

SEM images showing the change in surface roughness of the prepared AC (a) before and (b) after adsorption.

Significance of pre-treatment in adsorbent preparation

As the surface functional groups play a major role in the adsorption process, activation of adsorbents generally follow some selective pre-treatment processes to enhance their sorptive capacity. Literature shows that compared to granular activated carbon (GAC), fine-sized MAC is more hydrophilic in nature due to the interaction between separated bond spins and conduction electron spins (Nakayama et al. 1993). Generally, magnetization is shown to improve the presence of oxygen on the adsorbent surface, thus leading to the formation of more C–O and M–O bonds as observed in FTIR spectra (Rekos et al. 2016; Anyika et al. 2017). Presence of more hydroxyl groups can also impact higher adsorption due to the abundance of hydrogen bonds (Moosavi et al. 2020). However, MAC is usually preferred for removal of heaving metals form aqueous solution due to their easiness in separation after adsorption (Adibmehr & Faghihian 2018). In addition, it also showed improved adsorption capacity under highly acidic conditions (pH 1.8–2.4) (Shirun et al. 1997). Yang et al. (2008) observed high adsorption capacity for Methylene Blue by rice husk-based AC after magnetization with 23% (w/w) Fe3O4.

In order to elucidate the specific reactive combination for the adsorbent and the dye compounds, a preliminary comparison was made between the MAC and normal AC in terms of their adsorptive capacities. Figure 3(a) depicts the comparison of the observed adsorption capacity for MAC and normal AC. The normal AC is found to be more effective in dye removal (in terms of qeq) when compared to the magnetized dye for the given set of adsorption conditions (1 g adsorbent for 30 mg l−1 synthetic dye solution). It was observed that ferrous sulphate used in the magnetization process had added additional colour to the solution due to the formation of magnetic iron oxides thereby reducing the preference of the target dye compounds to occupy the adsorbent surface. It can also be inferred that the magnetized carbon may not be preferred for the treatment of mixed dye effluent due to the formation of additional free radicals that can impart more reactivity to the toxic dyes in aqueous environment (Praveena et al. 2011; Suneetha et al. 2015). In addition, the reduced activity of GAC may also be attributed to the non-metallic nature of the pollutant (MG dye) and the irregular pore structure formed during the activation (Moosavi et al. 2020). Due to the reduced adsorption capacity, it may lead to additional expense for treatment for removal of coloured dyes. The temporal variations in the adsorptive removal efficiency showed promising results for the TIE samples when the initial concentration was varied from 30 mg l−1 to 110 mg l−1. Figure 3(b) showed that higher adoption capacity can be obtained for higher initial concentrations indicating a responsive time delay in reaching the equilibrium at higher concentrations. Hence it can be understood that adsorption mechanism is not limited by the surface features of the adsorbent within the selected range of initial concentrations.

Figure 3

Comparison of the variation in adsorption capacity in response to the variations in (a) adsorbent pre-treatment and (b) initial dye concentration.

Figure 3

Comparison of the variation in adsorption capacity in response to the variations in (a) adsorbent pre-treatment and (b) initial dye concentration.

A detailed analysis of this result was further carried out with a two-factor ANOVA without repetition to understand the significance of the variations in some of the experimental conditions on the expected outcome. The analysis considered contact time and initial concentration as independent variables and maximum adsorption capacity as the dependent variable for both SDS and TIE samples (Table 2). The sum of squares (SS) values indicated the amount of variation caused by the systemic difference within the selected range of independent parameters (i.e. contact time and initial concentration) separately (Maxwell et al. 2017). While considering both samples, the SS values for contact time (1,175.73 and 3,377.76) were lower compared to the values for initial concentration (3,271.34 and 25,007.03). This shows that the relative change in adsorption capacity with incremental increase in contact time is less significant. In other words, the adsorption process followed a fairly uniform increasing trend for the increase in contact time. However, the change in initial concentration had drastically changed the adsorption capacity at every time step due to the increased physico-chemical interactions, resulting in higher values of internal variance.

Table 2

Results of ANOVA study for various contact time and initial concentrations for synthetic dye solution (SDS) and real textile industry effluent (TIE)

SampleSource of variationSum of squares (SS)Degree of freedom (df)Mean square (MS)F-valuep-valueF-crit
SDS sample Contact time 1,175.73 195.95 2.57 0.0457 2.51 
Initial concentration 3,271.34 817.83 10.72 3.94e-05 2.77 
TIE sample Contact time 3,377.76 562.96 1.09 0.3897 2.42 
Initial concentration 25,007.03 5,001.41 9.70 1.37e-05 2.53 
SampleSource of variationSum of squares (SS)Degree of freedom (df)Mean square (MS)F-valuep-valueF-crit
SDS sample Contact time 1,175.73 195.95 2.57 0.0457 2.51 
Initial concentration 3,271.34 817.83 10.72 3.94e-05 2.77 
TIE sample Contact time 3,377.76 562.96 1.09 0.3897 2.42 
Initial concentration 25,007.03 5,001.41 9.70 1.37e-05 2.53 

The degree of freedom (df) and mean square (MS) are the consequent parameters based on the variance, in order to compute the F-values separately (Maxwell et al. 2017). As the F-values for the initial concentrations are higher compared to their F-critical values, we may infer the significant influence of variation in initial concentration on adsorption compared to the variation in contact time (Mandel 1961; Alin & Kurt 2006). This can also be inferred using the p-values, as the higher p-values (great than 0.05) for the time interval indicated that the variation in time interval had no significant influence on the adsorption capacity compared to the initial concentrations. It was also observed that the variation in MS of initial concentrations was high for the results with TIE samples compared to SDS samples, indicating the possibility of progressive adsorption due to the low range of initial concentrations used in the experiments. This analysis provides the statistical evidence for the cause of difference in errors while comparing the differences in adsorption capacities.

Influence of pH and adsorbent dosage on adsorption capacity

The performance of the adsorbent was further evaluated for its suitability with the environment in terms of optimum conditions of pH and adsorbent dosage. The adsorbent was found to have strong surface reactivity in an acidic environment as observed from the variation in adsorption capacity (qe) with respect to pH (Figure 4(a)). The adsorption capacity of the prepared biosorbent was found to be at maximum when the pH of the solution was 3.05 owing to the strong electrostatic attraction between anionic dye molecules and the cationic adsorbent. It is also important to note that the formation of non-polar functional groups such as the methyl group (R–CH3) may prevail at higher pH values that can reduce the adsorption capacity (Aljeboree et al. 2014). Hence from the results obtained, a pH of 3.0 was selected as the optimum for the rest of the experiments. Figure 4(b) represents the variation of qe when the adsorbent dosage was varied from 0.05 to 11 g. The uptake was found to be maximum (6.6 mg g−1) at an adsorbent dosage of 5 g and thereafter decreased. The steep escalation in the adsorption capacity during the initial increase in the dosage can be attributed to presence of a significant number of active sites owing to the large surface area of the adsorbent. However, the later decrease in the adsorption capacity may be due to the inefficient filling of active sites, and possibly due to the agglomeration of biomass.

Figure 4

Impact of (a) solution pH and (b) adsorption dosage on adsorption capacity of synthetic dye solution (SDS) samples.

Figure 4

Impact of (a) solution pH and (b) adsorption dosage on adsorption capacity of synthetic dye solution (SDS) samples.

The optimum conditions obtained for SDS samples were further compared with those for TIE samples having a different range of initial concentrations. The optimum pH for TIE samples was found to be at a low value of 2.0 compared to SDS samples showing the higher preference of acidic pH for the adsorbent (Figure 5(a)). It is also inferred from the results that the process produced a significant shift towards acidic nature during the process of adsorption and the change in pH was found to produce a direct linear response with the adsorbent dosage. This is because the increase in adsorbent dosage causes aggregation of adsorbent and consequently, a decrease in the available adsorption sites and hence, the adsorption intensity (Ramathilagam & Ananthakumar 2016). It was also revealed from the studies that the adsorption was efficient at an optimal dosage of 5 g per 100 ml of the TIE samples (Figure 5(b)). Hence it is quite appropriate to anticipate that a relatively small quantity of adsorbent would be sufficient for effectively removing the colour of the textile effluent within the equilibrium time of 60 minutes.

Figure 5

Impact of (a) solution pH and (b) adsorption dosage on adsorption capacity of real textile industry effluent (TIE) samples.

Figure 5

Impact of (a) solution pH and (b) adsorption dosage on adsorption capacity of real textile industry effluent (TIE) samples.

The significance of variation among the pH and adsorbent dosage on the adsorption capacity was further evaluated based on the two-way ANOVA test without repetition (Table 3). The results indicated that the p-value was higher than the assigned level of significance (0.05) for the variations in pH and dosage, showing their lesser impact compared to the variation in type of sample. This was also confirmed with the comparison of F-values with respect to the F-critical values (Maxwell et al. 2017). Furthermore, the sum of squares (SS) was found to be highest for type of sample (75.95 and 100.41) having only 2 degrees of freedom (df) compared to the dosage (41.27) and pH (9.65) with degrees of freedom of 5 and 4 respectively. The higher degrees of freedom correspond to the availability of more number of trials (or the number of samples) as in the case of varying pH and dosage. This shows that the removal efficiency of the prepared adsorbent is not at the same level for SDS and TIE samples owing to the different range of chemicals present, as well as the different range of initial concentrations attempted. This was in accordance with the previous results that the impact of variation of environmental conditions (pH and dosage) varies with the effluent being treated (Kanwal et al. 2017).

Table 3

Results of ANOVA study for the significance of variations in pH and adsorbent dosage for synthetic dye solution (SDS) and textile industry effluent (TIE) samples

Source of variationSum of squares (SS)Degree of freedom (df)Mean square (MS)F-valuep-valueF-crit
pH 9.65 2.41 0.61 0.666 3.84 
Type of sample 75.95 37.97 9.63 0.007 4.46 
Dosage 41.27 8.25 1.53 0.264 3.32 
Type of sample 100.41 50.21 9.31 0.005 4.10 
Source of variationSum of squares (SS)Degree of freedom (df)Mean square (MS)F-valuep-valueF-crit
pH 9.65 2.41 0.61 0.666 3.84 
Type of sample 75.95 37.97 9.63 0.007 4.46 
Dosage 41.27 8.25 1.53 0.264 3.32 
Type of sample 100.41 50.21 9.31 0.005 4.10 

Mechanistic modeling of biosorption for different samples

The basic purpose of biosorption is to encourage direct attachment of dissolved organic/metallic compounds onto the surface of the solid phase material (biosorbent) where they can passively concentrate and be separated out from the system at a later time. The physical force of attraction holds the molecules on the adsorbent surface as the pore-filling process primarily depends on the size, shape and surface affinity of the compounds and the reduction in adsorption energy. Although many mechanistic models are available to explain various physico-chemical interactions happening at the pore scale, the exact mechanism of non-metabolic biosorption is still not fully understood. The results from the batch experiments are further evaluated to fit with the best isotherm and kinetic models in order to ascertain more inference about the prevailing mechanism of contaminant removal.

Isotherm models

Mechanistic models were used to compare the adsorption behavior of the prepared AC for the selected range of concentrations of SDS and TIE samples. A plot of Ce vs Ce/qe was drawn for the Langmuir isotherm and ln(qe) for the Freundlich isotherm. The values of correlation coefficients and regression factors indicated that the results fitted well with the Freundlich model (R2 = 0.9998 and 0.9677) and the Langmuir model (R2 = 0.9821 and 0.803) for the SDS sample and for the TIE samples respectively (Supplementary Files S1 & S2). This has a direct implication on the adsorption mechanism as the Freundlich model suggests an increasing rate of adsorption due to the presence of multi-layers in the adsorbent surface (Iqbal & Ashiq 2007). The details of the isotherm constants are provided in Table 4.

Table 4

Adsorption parameters obtained from isotherm studies

Type of sampleLangmuir model
Freundlich model
SDS Q0 = 0.1905 b = 8.85 R2 = 0.9821 K = 9.5162 n = 3.285 R2 = 0.9998 
TIE Q0 = 196.08 b = 0.01 R2 = 0.803 K = 5.026 n = 1.506 R2 = 0.9677 
Type of sampleLangmuir model
Freundlich model
SDS Q0 = 0.1905 b = 8.85 R2 = 0.9821 K = 9.5162 n = 3.285 R2 = 0.9998 
TIE Q0 = 196.08 b = 0.01 R2 = 0.803 K = 5.026 n = 1.506 R2 = 0.9677 

Kinetic models

A comparison of the adsorption rate is essential to differentiate the performance of samples with varying concentrations and chemical constituents. The basic comparison between the pseudo-first-order (PFO) and PSO models indicated that the present results fitted more with PSO both for SDS and TIE samples (Supplementary Files S3 & S4). Higher values of qe (8.643 mg g−1) for PSO indicated faster adsorption to the multiple layers as evident with the better fit for the Freundlich isotherm (Table 5). The higher value for TIE samples may be attributed to the varying characteristics of adsorbing chemicals present in the real-time effluent (Lin et al. 2015).

Table 5

Adsorption parameters obtained from kinetic studies

Synthetic dye solution (SDS) (C = 250 mg l−1)
Textile industry effluent (TIE) (C = 110 mg l−1)
PFO
PSO
PFO
PSO
k1qeR2k2qeR2k1qeR2k2qeR2
0.0214 1.7939 0.4217 0.6660 8.6430 0.9998 0.027 91.16 0.9491 0.0063 105.26 0.9923 
Synthetic dye solution (SDS) (C = 250 mg l−1)
Textile industry effluent (TIE) (C = 110 mg l−1)
PFO
PSO
PFO
PSO
k1qeR2k2qeR2k1qeR2k2qeR2
0.0214 1.7939 0.4217 0.6660 8.6430 0.9998 0.027 91.16 0.9491 0.0063 105.26 0.9923 

Decontamination efficiency on real-time effluent

The specific treatment efficiencies of various physico-chemical parameters are obtained from the experiments with varying adsorbent dosage. It was observed that the removal efficiency of COD, TDS and turbidity increased with increase in adsorbent dosage (Figure 6). This was attributed to the increased surface area of the adsorbent promoting intensive mass transfer. However, it is to be noted that the optimum performance of the adsorbent may not be achieved for the selected environmental conditions at a higher dosage. This could be verified based on the expected mass transfer kinetics as per the above results. In fact, this inference may serve as a feasible solution strategy based on the techno-economic conditions as well. In case of TDS and turbidity removal, the performance was quite apparent about 50–60% even at a higher dosage. As most of the chemical ingredients are possibly unknown in the real-time effluents, the COD removal efficiency has a significant role in the overall selection of the adsorbent. Similarly, the actual kinetics of adsorption may be significantly different for different dye compounds; hence, it was not possible to assess the real-time removal efficiency over a pre-defined period of time.

Figure 6

Removal efficiencies of COD, TDS and turbidity from the TIE sample using the prepared AC adsorbent.

Figure 6

Removal efficiencies of COD, TDS and turbidity from the TIE sample using the prepared AC adsorbent.

Real-time process monitoring

Sensitivity studies

The uncertainty in understanding the actual rate of adsorption and the details of chemical interactions during the process can be addressed to some extent by incorporating a real-time monitoring platform. In the present study, the real-time monitoring results for temperature and pH showed that there was an interesting shift in the mass transfer during the adoption. When the pH of the solution was initially fixed at 3 (based on the previous results for optimum conditions), the LabVIEW-empowered simulator could detect an increasing temporal variation in pH to a value of 6.0 after a period of 1 h (Figure 7(a)). This shows the specific affinity of the prepared AC adsorbent within the acidic range of pH. The variation in temperature of the solution showed minute but alternative variations during the adsorption process (Figure 7(b)). This variation was quite significant to understand the thermodynamics of adsorption as temperature serves as the direct indication of energy exchange during the mass transfer. However, in order to further elucidate about the adsorption mechanism, it is important to incorporate the temperature variations in the classical adsorption thermodynamic models.

Figure 7

The sensitivity of (a) pH and (b) temperature during the real-time monitoring using the LabVIEW environment.

Figure 7

The sensitivity of (a) pH and (b) temperature during the real-time monitoring using the LabVIEW environment.

It is quite possible that the simulator can be trained towards an expected range of performance while operating under varying concentrations of sample solutions and adsorbent dosages. Therefore, the performance of the present simulator for the TIE sample was compared for two different scenarios, (i) initial pH as it is and (ii) initial pH fixed at 3.0. The simulation results showed that alteration of initial pH had a significant effect on the removal efficiencies of COD, compared to turbidity and TDS (Table 6). This is a clear indication of the sensitivity of the system to express the real-time chemical changes happening during the adsorption. The smaller variations in the removal efficiencies of turbidity and TDS may be attributed to the net occurrence of finely adsorbed media present in the solution causing some residual impact on the colour and visibility. As explained earlier, the improved performance at higher adsorbent dosages shall be considered only as an indication of the increased mass transfer rather than deciding on the optimum adsorption conditions.

Table 6

Variation in removal efficiencies of turbidity, TDS and COD as observed before and after alteration of initial pH

Dosage (g)Turbidity
TDS
COD
Before alteration of pHAfter alteration of pHBefore alteration of pHAfter alteration of pHBefore alteration of pHAfter alteration of pH
21.93% 14.94% 0% 0% 5.77% 60.87% 
33.06% 32.24% 0% 17.14% 14.85% 63.04% 
49.80% 48.57% 6.86% 17.43% 25.00% 69.57% 
50.58% 51.93% 7.81% 19.43% 33.33% 73.91% 
11 53.43% 52.43% 8.13% 19.43% 33.33% 73.91% 
Dosage (g)Turbidity
TDS
COD
Before alteration of pHAfter alteration of pHBefore alteration of pHAfter alteration of pHBefore alteration of pHAfter alteration of pH
21.93% 14.94% 0% 0% 5.77% 60.87% 
33.06% 32.24% 0% 17.14% 14.85% 63.04% 
49.80% 48.57% 6.86% 17.43% 25.00% 69.57% 
50.58% 51.93% 7.81% 19.43% 33.33% 73.91% 
11 53.43% 52.43% 8.13% 19.43% 33.33% 73.91% 

Conceptual framework

The conceptual framework of the proposed LabVIEW control system is illustrated here with the help of a simple flowchart (Figure 8). This framework ensures that automatic control actions will adjust and maintain the parameters at the set point so that it will not interfere with the efficacy of the process. The optimized values for the parameters will be fed in the setup and the time interval can be set at which this program should take the readings. For example, in case of pH monitoring, this will enable addition of a base or an acid depending on the changes to the effluent until the pH reaches the set point. One specific outstanding feature of such a system is that the process control can also be achieved through a remotely operated mechanism by which the physical presence of the operator may be minimized. Signals or alarms will be produced if the value of the parameters exceeds the prescribed limits. This enables the operator to detect the unusual behavior in the system that needs to be addressed.

Figure 8

Process flow sheet depicting the LabVIEW program.

Figure 8

Process flow sheet depicting the LabVIEW program.

Scope and challenges in real-time applications

The results of biosorption using Acalypha indica for treating a mixture of complex dye compounds suggest the possibility of developing a low-cost technology for industrial effluents before their final disposal to the environment. There is enormous scope in optimizing the entire process flow to accommodate the intervention at any stage of textile processing to withdraw, treat and reuse the effluents in a continuous manner. The suggested process-control framework can be elaborated by incorporating many other parameters (both systemic and environmental) of critical influence. While considering the complex interactions between the financial risk and process reliability of large-scale adsorption systems (as seen in a packed tower or agitated media), there are certainly advantages on the real-time applications of biosorption using low-cost natural materials.

The process-control method presented in this study is simple and convenient for scaling up; however, it can be improved with an additional optimization module. There should be an in-built protocol for selecting additional parameters which may vary in their number and preference from case to case. One critical factor to consider is the regeneration capacity of the biosorption system under regular feed of heterogeneous dye chemicals. When the pH and dosage are not regularly monitored, there may be chances of adsorbent becoming under-utilized due to the agglomeration of partially coagulated particles, which may even influence the hydraulic mixing regime in real-time adsorption towers. Therefore, the real-time monitoring can help the system maintain conditions specifically suitable for adsorption without causing unfavorable coagulation behavior. The accuracy and service life of the sensors and other major equipments, controlling the feed, mixing and separation are also to be carefully evaluated. The separation of the treated effluent and the used adsorbent (for further reuse) can further deploy necessary mechanisms for the recovery of valuable dyes and associated salts. It has to be further developed as a viable option towards achieving circular economy-based sustainable treatment of textile effluents.

As our results propose, the industry-scale application of biosorption using Acalypha indica could eventually result in mass-scale cultivation of the crop which is a traditional medicinal herb and listed in the Pharmacopoeia of India as an expectorant to treat asthma and pneumonia. As we understand from the literature, the current regulatory policy does not, however, hinder the gross cultivation or alternate industrial application for the herbs unless it violates the existing rules such as Biological Diversity Act (2002) and The Drugs and Cosmetics Act (1940, amended in 2005). The current regulations concerning the cultivation and marketing of medical plants, especially from forest area are governed by the Scheduled Tribes and Other Traditional Forest Dwellers (Recognition of Forest Rights) Act (2006) and a notification regarding the submission of consumption data of Raw Material used by ASU Drugs Manufactures (Link 1; Link 2). As far as the authors understand, there are no regulatory restrictions in promoting the industrial usage of Acalypha indica in India (Sahoo & Manchikanti 2013; Katoch et al. 2017).

In order to encourage investments and production in the technology sector, the Government of India has announced attractive booster plans in the latest financial budget 2020. According to the referred sources, there is a proposal to withdraw the dividend distribution tax (DDT) to prevent the cascading effect on taxation (Link 3). Furthermore, many start-up initiatives are promoted with attractive early-life funding and seed funding along with income tax benefits. These policies are also applicable to the textile industries operating with common effluent treatment plants in India. Considering the suitability of agro-climatic conditions, the parent plant of the adsorbent (Acalypha indica) can be cultivated in industrial scale in most of the South-East Asian countries. Based on the current economic conditions and strategic industrial policies, most of these countries are on a par with India in managing large numbers of isolated industries utilizing the dye chemicals. Hence it is envisaged that the results of the present study will support the futuristic industrial operation trends in those countries.

CONCLUSION

Real-time monitoring and process-control-based biosorption system for treating real-time textile effluents has been studied using LabVIEW. The selected biosorbent prepared from Acalypha indica indicated the satisfactory removal of colour and concentration of dyes from the original textile effluent within a time period of 60 minutes, under an optimal adsorbent dosage of 0.1 gram per 100 ml of diluted sample. The primary investigation on the adsorption mechanism (Freundlich isotherm and PSO kinetics) suggested the possibility for enhanced capacity upon surface modification and controlled interactions. The conceptual framework for a real-time monitoring–controlling system was demonstrated by measuring pH and temperature for varying initial conditions of chemical concentration and adsorbent dosage to result in substantial reduction in COD (73.91%), TDS (19.43%) and turbidity (52.43%). The study also discusses the techno-economic avenues for scaling up the process for real-time industrial applications.

DATA AVAILABILITY STATEMENT

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

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