Statistical modelling of endocrine disrupting compounds adsorption onto activated carbon prepared from wood using CCD-RSM and DE hybrid evolutionary optimization framework: Comparison of linear vs non-linear isotherm and kinetic parameters
Graphical abstract
Introduction
Bisphenol A (BPA) is one of the phenol derivatives and the most important organic pollutants found in industrial wastewater. The IUPAC name for BPA is 2,2-Bis(4-hydroxyphenyl) propane [1]. It is an important industrial chemical and is applied in the production of polycarbonate plastic and epoxy resins used in baby bottles, food container lids, and also used as stabilisation factors in agriculture and consumer goods [2]. During manufacturing processes, BPA enter inadvertently into the environment, polluting rivers and groundwater. BPA enters into groundwater through the leakage of agricultural and industrial wells [3]. It inhibits the activity of natural hormones and is carcinogenic [4,5], and presents many dangers for living organisms and the environment [6]. Due to the many health risks associated with BPA, it is necessary to be removed from the aqueous solutions. There are various physical, chemical and biological methods like adsorption, ultra-filtration, reverse osmosis, advanced oxidation, ion exchange and biological reduction for the treatment of this type of wastewater [4,[7], [8], [9], [10]]. Most of these methods have disadvantages such as high cost, the need for additional treatment, low efficiency, sludge formation and therefore they are applicable only for lower pollutant concentration. However, among the purification methods, the efficacy of biological methods is unsatisfactory, due to toxicity to aerobic and anaerobic bacteria from BPA [11]. A chemical deposition is a less considered treatment method due to the high volume of sludge production and relatively low efficiency [12]. Other methods based on heterogeneous photocatalyst (dependent on the production of hydroxyl radicals for environmental regeneration) are used to purify BPA from wastewater and have satisfactory results. There are various other methods for the degradation of BPA, mostly including various preliminary filtration methods such as hydrolysis, Fenton oxidation, peroxidation, ultrasound and ozonisation [3,13,14]. Electrochemical methods are also used in the treatment of wastewater containing BPA [15,16].
One of the effective treatment processes for the removal of organic compounds is adsorption [13]. The most common method for BPA removal is surface adsorption and the transfer of pollutants from one phase to another [17,18]. Large scale or industrial-scale applications depends on the amount of adsorbents, its availability and cost. For the selection of low-cost adsorbents, several factors should be considered. These adsorbents should be available anywhere needed, inexpensive and non-hazardous in nature [19,20]. In addition, for good results, the high carbon and oxygen adsorption of the adsorbent is necessary. Other features include high abrasion resistance, high thermal resistance and small pores. The activated carbon contains a wide range of carbonaceous materials that have a high degree of porosity and a large surface area, and as a result, the large surface area increases adsorption capacity. Basically, commercial activated carbon is prepared from wood [21,22]. Activated carbon is a highly amorphous solid with very high porosity and is retrievable [23]. These properties make active carbon widely used in water purification processes. Given its salient features, it is considered as the best adsorbent in wastewater treatment [24,25]. It plays a recognised role in the elimination of most microbeads; whose disposal is usually difficult by other treatment methods.
Therefore, the main objective of this research is to investigate the efficacy of activated carbon (obtained from wood) for the removal of BPA from the aqueous environment. Also, the form of activated carbon, in terms of powdered and granular forms on the performance is also investigated. A data-driven model is identified for prediction of BPA adsorption efficiency. Owing to the fact that the influence of process variables on the adsorption efficiency, the optimal values of these variables are estimated using the response surface methodology (RSM) and popular genetic algorithm (GA).
Section snippets
BPA standard curve preparation
BPA from Merck, Germany was obtained in solid form. The physicochemical characteristics of BPA are shown in Table 1. Commercial activated carbons (PAC and GAC) used in this study were purchased from Mahab Zist Co., Iran. In order to determine the appropriate concentrations for the removal, the linear relationship between adsorbance and an adsorption range of 0.5–1.5 was drawn according to the standard curve method by plotting adsorption to various concentrations of BPA. To prepare the standard
Statistical analysis and model fitting based on the RSM
The actual and predicted adsorption efficiencies of BPA by both PAC and GAC are shown in Table 3. Quadratic model obtained based on ANOVA, gives an F-value of 1345.26 and 459.11 for PAC and GAC adsorbents. The sum of squares, p-value for both quadratic models are summarised in Table 4. The regression coefficient (R2) values of the models for PAC and GAC adsorbents were 0.9992 and 0.9997 respectively. Also, the predicted R2 is in reasonable agreement with the adjusted R2, which confirms that the
Conclusions
In this research, the adsorption percentage of two adsorbents (powdered and granular activated carbon) was investigated for BPA removal. Overall the BPA adsorption process follows the isotherms in the order Langmuir > Sips > Redlich-Peterson > Toth > Temkin > Freundlich for GAC; whereas for PAC, they are Freundlich > Redlich-Peterson > Sips > Toth > Temkin > Langmuir. From the kinetic study, it was observed that the R2 > 0.999 for the pseudo-second-order kinetic model. The R2 values of the
CRediT authorship contribution statement
Mohammad Hadi Dehghani: Conceptualization, Methodology, Software, Supervision. Rama Rao Karri: Writing - review & editing. Zeinab Tafaroji Yeganeh: Data curation, Writing - original draft. Amir Hossein Mahvi: Visualization, Investigation. Heshmatollah Nourmoradi: Software, Validation. Mehdi Salari: Software, Validation. Ahmad Zarei: Visualization, Investigation. Mika Sillanpää: Writing - review & editing.
Acknowledgements
This research has been supported by the Tehran University of Medical Sciences.
Declaration of competing interest
The authors of this article declare that they have no conflict of interests.
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