Application of Orchis mascula tuber starch as a natural coagulant for oily-saline wastewater treatment: Modeling and optimization by multivariate adaptive regression splines method and response surface methodology

Journal Pre-proof Application of Orchis mascula tuber starch as a natural coagulant for oily-saline wastewater treatment: Modeling and optimization by multivariate adaptive regression splines method and response surface methodologyHamidi D, Fard MB, Yetilmezsoy K, Alavi J, Zarei H, Application of Orchis mascula tuber starch as a natural coagulant for oily-saline wastewater treatment: Modeling and optimization by multivariate adaptive regression splines method and response surface methodology, Journal of Environmental Chemical Engineering, doi: 10.1016/j.jece.2020.104745–> Donya Hamidi (Conceptualization) (Methodology) (Software) (Formal analysis) (Investigation) (Resources) (Data curation) (Writing - original draft) (Visualization), Moein Besharati Fard (Supervision) (Project administration) (Conceptualization) (Methodology) (Software) (Formal analysis) (Investigation) (Resources) (Data curation) (Writing - original draft) (Visualization), Kaan Yetilmezsoy (Conceptualization) (Methodology) (Software) (Formal analysis) (Investigation) (Resources) (Data curation) (Writing - original draft) (Visualization), Javad Alavi (Software) (Writing - original draft) (Visualization), Hossein Zarei (Software) (Writing - original draft) (Visualization)

PII:

S2213-3437(20)31094-0

DOI:

https://doi.org/10.1016/j.jece.2020.104745

Reference:

JECE 104745

To appear in:

Journal of Environmental Chemical Engineering

Received Date:

24 July 2020

Revised Date:

7 October 2020

Accepted Date:

30 October 2020

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Application of Orchis mascula tuber starch as a natural coagulant for oilysaline wastewater treatment: Modeling and optimization by multivariate adaptive regression splines method and response surface methodology

Donya Hamidi1, Moein Besharati Fard2, *, Kaan Yetilmezsoy3, *, Javad Alavi4, Hossein Zarei2

Department of Chemical Engineering, University of Guilan, Rasht, Iran

2

Department of Civil Engineering, K. N. Toosi University of Technology, Tehran, Iran

3

Department of Environmental Engineering, Faculty of Civil Engineering, Yildiz Technical

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University, Davutpasa Campus, 34220, Esenler, Istanbul, Turkey

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1

Department of Applied Mathematics, School of Mathematical Sciences, University of

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Guilan, Rasht, Iran

E-mail addresses:

Donya Hamidi ([email protected])

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Moein Besharati Fard ([email protected])

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Kaan Yetilmezsoy ([email protected]; [email protected]) Javad Alavi ([email protected])

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Hossein Zarei ([email protected])

*Corresponding authors: E-mail addresses: [email protected] (Moein Besharatifard) [email protected]; [email protected] (Kaan Yetilmezsoy)

REVISED GRAPHICAL ABSTRACT (JECE-D-20-02408.R1)

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REVISED HIGHLIGHTS (JECE-D-20-02408.R1)

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 Orchis mascula (OM) was used as a new coagulant in synthesized bilge water treatment.  More than 90% of COD, TU, and O&G removals were achieved at pH 5.0 and 4 mg/L of OM.  Coagulation-flocculation (C-F) process with OM followed the second-order kinetics.

 C-F with OM was effectively described by FCCCD-RSM and MARS models for the first time.  Adsorption and inter-particle bridging mechanism were predominant for C-F with OM.  Cost-effectiveness and naturalness of OM showed its potential for industrial scale.

Abstract Applicability of coagulation-flocculation process by the Orchis mascula tuber starch as a novel natural coagulant was investigated for the first time in the treatment of oily-saline

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wastewater. Three inputs variables (pH, coagulant dose, and contact time) and two outputs of

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chemical oxygen demand (COD) and turbidity (TU) were studied for the proposed system.

Orchis mascula tuber starch showed a remarkable performance on treatment of bilge water at

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the optimum conditions (4 mg L-1 of coagulants dose, pH of 5.0, and contact time of 15 min), with COD and TU removal efficiencies of 92.21% and 90.63%, respectively. Also, this material could remove the surfactant and oil-grease up to 23% and 93%, respectively. The

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face-centered central composite design-response surface methodology (FCCCD-RSM) and multivariate adaptive regression splines (MARS), which were used comparatively for the first

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time in the quantitative evaluation of the studied coagulation-flocculation process, revealed satisfactory predictive performances (R2 > 0.97) for both COD and TU removals. The kinetic study concluded that the second-order model performance was superior to the first-order

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model. Moreover, the bonding between the particles was also observed from the Fourier-

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transform infrared spectroscopy (FTIR) analysis of the Orchis mascula tuber starch.

Keywords: Orchis mascula tuber starch; Oily-saline wastewater; Coagulation-flocculation;

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Response surface methodology; Multivariate adaptive regression splines

1. Introduction Wastewaters, such as ballast, black and grey waters, and bilge waters, generated on the ships are harmful for the marine environment. Among them, bilge waters due to presence of petroleum-derived products are particularly dangerous [1]. Bilge water is a mixture of seawater, various fuels, lubricating oils, cooling water, detergents, solid particles, and so forth [2]. In 1973, the International Convention on the Prevention of Pollution from Ship (MARPOL) was regulated and modified in 1978 and stated that any bilge water discharge

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cannot exceed 15 ppm amount of oil discharged at 22.2 km from nearest land [3]. Marine oily wastewater are salty, alkaline, indecomposable, and serious emulsification, and available in four states in the water, including floating oil (>100 µm), dispersed oil (10–100 µm),

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emulsified oil (0.1–10 µm), and dissolved oil (< 0.1 µm) [4]. Bilge water accounts for

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approximately 20% of the oily wastewater discharged into the oceans throughout the world [5]. It has been reported that the characteristics of actual bilge water vary greatly, and pH of

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actual bilge water ranges from 6.8 to 9.0, and also oil content ranges from 36 to 2953 ppm [2]. Although there are a host of methods for the treatment of oily-saline wastewater such

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as biological, chemical and physical [6-14] processes, the low cost and compactness of the chemical processes made the coagulation-flocculation popular. However, there are many

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inorganic coagulants used in water and wastewater treatment, resulting several drawbacks such as high cost and environmental impacts [15]. In order to decrease the environmental

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impacts of coagulation-flocculation process, use of natural coagulants has become more favored [16]. Natural coagulants, such as Moringa oleifera [17, 18], Plantago major L. [19], rice starch [20], chickpea (Cicer arietinum) [21], rice husk ash [22], chitosan [23], Ocimum basilicum L. [24], lateritic soil [25], Opuntia ficus-indica [26], Artocarpus heterophyllus seeds [27], Salvia hispanica [15] are biodegradable and reduce the undesired environmental impacts of treatment.

Polysaccharide-based bio-coagulants, including alginate, cellulose, chitosan and starch, and microbial-based raw materials, which are typically obtained from arthropods, seaweeds, and plants, increased the attention for application in industries such as water and wastewater treatment due to their high performance in turbidity, chemical oxygen demand (COD), solids, color, and dyes removal [28-30]. One of the most available natural polymers, starch, is an popular substance which can be used as a coagulant [31]. Due to renewability, biodegradability, non-toxicity, and low cost, biopolymers have attracted the attention for their

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industrial use in water and wastewater treatment [32]. The crude form of starch consists of a mixture of anhydroglucose units, amylase, and amylopectin [33]. Most of the available researches conducted on modification of starch in order to increase the efficiency of the

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coagulant in wastewater treatment which has some drawbacks such as formaldehyde, highly

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corrosive caustic soda, and high amount of solvent. Use of unmodified starch, if performed well in water and wastewater treatment, can be chosen as a preferable alternative which is

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able to reduce the harmfulness of modified starch [34].

Orchis mascula is widespread across Europe, northwest of Africa and Middle East

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countries (e.g. Lebanon, Syria, Iraq) up to Iran [35]. It has been used to make salep (also spelled sahlep or sahlab), which is prepared from dried and grounded tubers containing a

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nutritious and starchy polysaccharide (long chain polymeric carbohydrates composed of monosaccharide units) called glucomannan (a water-soluble polysaccharide that is considered

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a dietary fiber), and was popular in England before the introduction of coffee and tea. [36]. The pharmacological studies indicated that Orchis mascula can also be used for many diseases [37]. However, besides its different features mentioned above, there is no specific research in the relevant literature for the application of Orchis mascula as a natural coagulant for wastewater treatment.

The response surface methodology (RSM) is a combination of statistical and mathematical methods, which is applied extensively in industries for the optimization of different processes and used as a practical methodology to study the influence of multiple parameters or variables on various systems to reduce the number of experiments [38]. Among the RSM-based methods, central composite design (CCD) is used as a standard, reliable, and popular experimental design to predict the behavior of factors in the area of evaluation and to evaluate and optimize the effect of each operating independent variables on dependent

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variables (responses) [39, 40]. On the other hand, the multivariate adaptive regression splines (MARS) technique is applied as an adaptive non-parametric regression which is an accurate tool for the nonlinear conditions. The splines are used to solve various problems including

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heat transfer, advection-diffusion problems, motion planning, stiffness modeling, and so on

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[41-45]. Comparing with the other methods, it has some advantages like precision, fastness, and easy implementation [46].

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The water treatment with natural coagulants is an area well-explored in the relevant literature, however, any new attempt in this field is still an interesting topic and remains as an

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active research subject among scientists today, particularly for the sustainable environmental goals of developing countries. Nevertheless, to the best of the authors’ knowledge, no

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previous investigation has specifically reported the results of a systematic analysis (combination of experimental and modeling studies) of a novel natural coagulant (Orchis

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mascula tuber starch) for the coagulation-flocculation process of oily-saline wastewater (synthesized bilge water) treatment. Considering the scarcity of the literature in this area, the present study was also implemented as the first soft computation-based quantitative assessment for the studied process using face-centered central composite design-response surface methodology (FCCCD-RSM) and multivariate adaptive regression splines (MARS). Furthermore, the kinetic study (the first-order and second-order models) of the current system

was introduced for the first time to evaluate the industrial design of the present coagulationflocculation process consisting of three factors (pH, contact time, and coagulant dose) and two responses (COD and TU removals).

2. Material and method 2.1. Bilge water characteristics Over a two-month sampling period, a sufficient amount (about 20 L) of bilge water was taken

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from the ships in the Caspian Sea for multiple times to identify the actual properties of the effluent and thus to obtain the most accurate results. In accordance with this, the pollutants existing in the bilge water were identified. The main pollutants in bilge water were oil and

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surfactant. According to the determined characteristics, the synthesized samples were

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prepared for the current study. Samples were collected and maintained according to Standard Methods for the Examination of Water and Wastewater [47]. Pumping, shipboard vibrations,

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and sea states cause the emulsion generation. Also, the micelles formed at surfactant concentrations greater than the critical micellization concentration can cause self-

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emulsification which is called micellization self-assembly [2, 48]. In this research, the synthesized bilge water was prepared by mixing 1 L of seawater and 0.6 mL waste oil

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prepared from mechanical equipment of ships. In order to emulsify the oil with seawater, 0.05 g C12H25O4S.Na (Merck Chemical Corp., dodecyl sulfate sodium salt for synthesis, product

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code: 8.22050.0025) was added as a surfactant. Although the surfactants help to stabilize the emulsion, mixing is also used to make the emulsion more stabilized by influencing the size of oil droplets [2]. The main characteristics of synthesized bilge water are presented in Table 1. All experiments were carried out in triplicates and repeated at least three times. The results were presented as the mean of the independent replicated experiments along with the corresponding standard deviations.

[Table 1, here] The solvent extraction was used to extract the petroleum hydrocarbons. After the completion of the extraction, the extract was injected to the gas chromatography/mass spectrometry (GC/MS) instrument (Agilent 7890A GC, CA, USA). The GC/MS analysis took place to obtain the most certain organic compounds and the characteristics of bilge water before and after coagulation-flocculation process. Fig. 1 shows the chromatogram of total petroleum hydrocarbons (TPHs) and polycyclic aromatic hydrocarbons (PAHs) of the

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synthesized bilge water. Results of GC/MS analysis indicated that the influent sample

contained several organic compounds such as alkanes and aromatics. Fig. 1a demonstrates

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that the alkanes (C10-C36) are the major constituents in the bilge water, where the most intense peaks are assigned to C10 and C19. The chromatogram of PAHs (Fig. 1b) was

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represented negligible constituents of aromatic compounds.

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[Fig. 1, here]

2.2. Preparation of Orchis mascula tuber starch

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The Orchis mascula tuber was provided from a local spicery in Shiraz, Fars province (Iran). It

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was soaked with water (100 °C) for 1 h, and then it was partially dried for about 10 min at 100 °C. Afterward, it was powdered by an electric spice mill (Moulinex Grinder, Modele Depose Type 320, 230-240V, 700 W, Italy). Thereafter, it was dried in an oven (Memmert

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100-800) at 100 °C for 1 h. Thereafter, 1 g of Orchis mascula tuber powder was added to 100 mL of 0.9 % NaCl solution and stirred for 45 min at 60 °C. The high viscous polysaccharide was extracted from the Orchis mascula tuber and used as the coagulant.

2.3. Characterization of coagulant and its sludge

Fourier-transform infrared spectroscopy (FTIR), scanning electron microscope (SEM), and energy dispersive X-ray spectroscopy (EDS or EDX) analyses were conducted for analyzing the elemental, physical, and chemical characteristics of the samples. FTIR spectrometer (Thermo AVATAR, U.S) was used for the determination of chemical characteristics by identifying the variety of functional groups of the samples. The spectra were displayed by the detector in the region of 400–4000 cm-1. The sample surface characteristics and morphological analysis were conducted by using a SEM (TESCAN MIRA 3, The Czech

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Republic) that could produce images of a sample by scanning the surface with a focused beam of electrons. The EDS was used to analyze elements. It was determined by analyzing the X-

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rays emitted to the sample by an energy-dispersive spectrometer.

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2.4. Experimental procedure

The experiments were carried out in a jar-test apparatus (Zag Chemie Co.), and the time and

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speed were monitored by an automatic controller. Each beaker was filled with 250 mL of bilge water and pH adjustment (5.0–9.0) was done using either 1 M H2SO4 (Merck Chemical

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Corp., Sulfuric Acid 95–97% Gr for Analysis Iso, product code: 1.00731.2500) and 3 M NaOH (Merck Chemical Corp., Sodium Hydroxide Pellets Gr for Analysis Iso, product code:

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1.06498.5000). The samples were simultaneously stirred at the same speed with six flat paddle impellors while appropriate dosage of coagulant (4–320 mg L-1) was injected. The

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beakers were rapidly mixed at 120 rpm for 5 min, and then slowly mixed at 40 rpm for various time intervals (15–45 min). After that, the beakers were kept stagnated for 90 min to allow settling. Thereafter, the supernatant was filtered through a Whatman® filter paper with the pore size of 20–25 m.

2.5. Experimental design and model development

Using the traditional method to screen and optimize the coagulation-flocculation process by changing one operating parameter at a time is not only a waste of time, but also is a hard way to determine the relationship and interaction between operating parameters. Therefore, the design of experiment (DOE) methodology has been carried out for screening and optimization of various processes to overcome the disadvantages of the conventional approach [20, 49-51]. In order to investigate the interactions of independent variables on responses, the RSM has been used to optimize the process by the formation of a low-order polynomial equation [20,

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49, 52]. The selection of appropriate design of experiment among of various models, which can be used to optimize and study the interaction effects of the variables on the coagulation-

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flocculation of bilge water by Orchis mascula tuber starch, is highly influence on the building

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of the response surface and the prediction accuracy [51]. Among the most commonly used RSM techniques, the central composite design (CCD) has a number of advantages that justify

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its widespread preference: (a) it is capable to run sequentially into two subsets of points which means that the linear and two factor interaction effects and the curvature effects are estimated

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in the primary and secondary subsets, respectively; (b) it is widely used due to efficiency and flexibility, since it provides a useful information on the effects of variables and a reasonable

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amount of data for testing lack-of-fit with a minimum number of experiments [39, 53, 54]; and (c) it has also several varieties such as Spherical Central Composite Design (SCCD),

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Rotatable Central Composite Designs (RCCD), Orthogonal Central Composite Design (OCCD), and Face-Centered Central Composite Design (FCCCD), which enables the usage under different experimental regions [55, 56]. In the present study, face-centered central composite design (FCCCD) in the form of 23 full factorial design falling under the RSM was applied for the quantitative assessment of the studied coagulation-flocculation process by the Orchis mascula tuber starch. For this aim, three selected significant operating parameters,

such as pH (X1), coagulant dosage (X2), and contact time (X3), were considered while the responses were selected as COD and TU removals. Table 2 presents the arrangement of FCCCD-RSM design in such a way that allows the development of the appropriate empirical second-order polynomial multiple regression equations and corroboration of the connection between the responses (COD and TU removals) and the optimum conditions of the process. The second-order response surface models can be expressed in the following equation form:

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𝑌 = 𝛽0 + ∑3𝑖=1 𝛽𝑖 𝑋𝑖 + ∑3𝑖=1 𝛽𝑖𝑖 𝑋𝑖2 + ∑ ∑3𝑖<𝑗 𝛽𝑖𝑗 𝑋𝑖 𝑋𝑗 + 𝜀

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(1)

where Y is the desired response predicted by the model, and the X1, X2, and X3 show the

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independent variables. Moreover, β0 is the constant coefficient, and βi, βij, βii, and 𝜀 are the

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coefficients of linear, interaction, quadratic terms, and statistical error, respectively.

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[Table 2, here]

2.6. Multivariate adaptive regression splines method (MARS)

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Multivariate adaptive regression splines (MARS), one of the regression techniques applied for wide variety of engineering problems, was firstly explained by Jerome Harold Friedman in

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1991 [57]. MARS is a non-parametric regression method that is able to produce nonlinear models and model the interactions between variables [58]. Since its advent in 1991 [57], it has been widely used in different areas such as hydrology [59], energy performance [60],

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ergonomics [61], transportation [62], geotechnical engineering [63, 64], building engineering [65], biological networks [66], and so forth. The functional relationship between the dependent and the independent variables can

be defined by MARS. The spline, a continuous piecewise-defined polynomials, is the core of this method [61, 64]. The MARS model includes two phases [58] such as training phase and testing phase. In the forward stage of MARS, the basis functions are added repeatedly which

selected from the observed data automatically and produce the largest model with many basis functions. However, this model may be overfit, so the backward stage is used to decrease the complexity by basis functions removal which causes a slight increase in the residual squared error [66]. The MARS model is described as follows [64]: 𝑓 (𝑥) = ∑𝑁 𝑖=1 𝑐𝑖 𝐵𝑖 (𝑥) (2)

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where x is an independent parameter, 𝐵𝑖 (𝑥) is the basis function, N is number of terms, and ci

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is the least square method estimation coefficient.

𝑥 𝑖𝑓 𝑥 ≥ 0 0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

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𝐵𝑖 (𝑥) = {

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The basis functions (𝐵𝑖 (𝑥)) are expressed as follows [65, 66]:

(3)

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The generalized cross validation (GCV) is applied in order to determine which basis functions involved in the model [61]. The GCV is computationally less expensive than the

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other techniques. By the division of mean squared residual error over a penalty, the GCV

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equation is expressed as follows [63, 67]: 1

𝐺𝐶𝑉 =

∑𝑁 [ ]2 𝑖=1 𝑦𝑖 −𝑓(𝑥 𝑖)

𝑀+𝑑×(𝑀−1)/2 2 ] 𝑁

[1−

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(4)

𝑁

where M is the number of basis functions, N is the number of data points, d is the penalizing parameter, (M-1)/2 is the number of knots, and f(xi) is the predicted value. In this study, the ARESLab (adaptive regression splines toolbox), an open-source MATLAB® toolbox was applied within the framework of MATLAB® R2019a software

(V9.6.0.1072779, 64-bit (win64), License Number: 968398, MathWorks Inc., Natick, MA) running on a Sony VAIO Laptop (Intel® Core™ i5-2410M Processor 2.30 GHz with Turbo Boost up to 2.90 GHz, 8 GB of RAM, 64-bit) PC [68]. In the computational analysis, 20 runs of FCCCD-RSM were used to establish the model. Out of 20 runs, 18 data sets were used for the construction of the model, and remaining 2 data sets were evaluated for the testing purpose. The flowchart of the implemented MARS method on the present coagulationflocculation process is depicted in Fig. 2.

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[Fig. 2, here] 2.7. Model verification

One of the most important steps of modeling is validation which shows the reliability and

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accuracy of a procedure [69, 70]. The coefficient of determination (R2 ) was determined from

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the predicted and experimental values of the model in order to evaluate the performance of

()

2

R =1−

𝑖 ∗ ∑𝑛 𝑖=1(𝑦𝑖 −𝑦𝑝 ) ∗ ̅)2 ∑𝑛 𝑖=1(𝑦𝑖 −𝑦

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multivariate adaptive regression splines method and FCCCD-RSM [71, 72]:

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(5)

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where 𝑦̅ is the average of 𝑦 over 𝑛 data, the 𝑖th target and predicted responses are 𝑦𝑖∗ and y(𝑖) p ,

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respectively.

2.8 Kinetic study

In this study, first-order and second-order kinetic models were applied to analyze the kinetic behavior of the studied coagulation-flocculation process. The Brownian motion of small particles leads to collision and coagulation of them [73]. The coagulation kinetics can be described as following equation:

𝑑𝐶 𝑑𝑡

= −𝑘𝐶 𝑛

(6) where 𝐶 is the total mass per liter, 𝑡 is time, 𝑛 is the order of the coagulation process, and 𝑘 is the 𝑛th order rate constant [74, 75]. The first-order (𝑛 = 1 ) kinetics of coagulation is described by the following equation: 𝑑𝐶 𝑑𝑡

= −𝑘1 𝐶

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(7) After integration, the linearized form of Eq. (7) is obtained as follows:

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𝑙𝑛 𝐶 = −𝑘1 𝑡 + 𝑙𝑛 𝐶0

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(8)

where 𝑘1 is the first-order reaction rate constant (min-1), 𝐶0 and C are the initial concentration

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and the concentration at time 𝑡 (mg L-1), respectively.

𝑑𝐶 𝑑𝑡

= −𝑘 2 𝐶 2

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(9)

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For the second-order kinetics (𝑛 = 2) of coagulation, we have:

Taking the integral of Eq. (9) gives: 1

=

1 𝐶0

+ 𝑘2𝑡

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𝐶

(10)

The second-order rate constant (k 2, L mg-1 min-1) is obtained by multiplying the collision efficiency E by the Smoluchowski rate constant (KRc, min-1) for the rapid coagulation [75, 76]:

𝑘 = 𝐸𝐾𝑅𝑐 (11) The Smoluchowski rapid coagulation rate constant is expressed as follows: 𝐾𝑅𝑐 =

4𝑘 𝐵𝑇 3𝜇

(12) where 𝑘 𝐵 is the Boltzmann constant (m2 kg s-2 K-1)), 𝑇 is the absolute temperature (K), and 𝜇

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is the viscosity of the bilge water (kg m-1 s-1).

The Brownian diffusion coefficient (D, kg2 m-1 s-1) is described in the following

𝑘 𝐵𝑇 𝛽

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𝐷=

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equation [75]:

(13)

constant is defined as follows [77]:

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𝛽 = 2𝑘

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where 𝛽 is friction factor (m3 kg-1 s-1)) and its relation to the nth order coagulation rate

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(14)

Another important time-dependent characteristics for the coagulation is the half-life (min) that is the duration it takes to reduce the concentration to the half of the initial

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concentration [75, 78]. For the first-order and the second-order models, it is expressed as follows [75, 79]:

(First − order) : 𝑡1⁄ = 2

(15)

𝑙𝑛2 𝑘1

(Second − order) : 𝑡1⁄ = 2

1 𝑘 2𝐶0

(16) 2.9. Analytic methods Chemical oxygen demand (COD), biological oxygen demand (BOD5), ammonium nitrogen (NH4+-N), phosphate phosphorus (PO43--P), surfactant, chloride (Cl-), total dissolved solids (TDS), turbidity (TU), oil-grease (O&G), and pH were analyzed according to the standard

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APHA methods [80]. The ammonia and phosphate concentrations were analyzed by

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spectrophotometer (UNICO 2100Vis) and photometer (AL450- Aqualytic®), respectively.

Due to the high concentration of chloride, the COD of the samples was determined by using

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the Freire and Sant’Anna method [81]. A thermo-reactor (AL125-Aqualytic®) was used to heat COD samples for 2 h, and the analysis of COD was carried out by using a COD meter.

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Turbidity is a reduction in the intensity of a beam of light passing through a

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suspension and determined for many reasons including control of flocculation processes [82]. In this study, a portable turbidity meter (TU-2016-Lutron) was used to determine the turbidity of the solutions. Coagulation pH is one of the factors that may have a considerable influence

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on the process according to the properties of coagulants [83]. For measuring the pH of the

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samples, a glass electrode pH meter (AL15-Aqua Lytic-Germany) was employed in the present analysis. In order to analyze the surfactant concentration, a spectrophotometer (Jenway) was used. TDS and conductivity were analyzed by CLEAN CON500

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conductivity/TDS/salinity meter (Shanghai ZhenMai Instruments Co., Ltd, Shanghai, China). The GC/MS test was done by the injection of 2 µL of the samples which were extracted

by CH2Cl2 (Merck Chemical Corp., methylene chloride or dichloromethane (DCM) hypergrade for organic trace analysis, product code: 1.06454.1000) as solvent after agitating in a separatory funnel for 2 min with a 10:1 water/solvent ratio and concentrated with dry nitrogen to final volume of 10 mL [84, 85]. The GC/MS was equipped with a HP-5MS

capillary column. The running condition was 60 ºC initial temperature, 1 min isothermal, 6 ºC min-1 ramp to 100 ºC, 10 ºC min-1 second rate to a final temperature of 285 ºC with a 15 min isothermal, for total run time of 35 min for each sample.

3. Results and discussion 3.1. FCCCD-based experimental design The face-centered central composite design (FCCCD) was used to define the mathematical

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relationship between the independent variables and the process responses (COD and TU removal percentages). In order to investigate the best-fitted RSM-based statistical model to the experimental results or actual values of COD and TU removals (Table 3), various model

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structures, including linear, two factorial, quadratic, and cubic formulations, were

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experimentally derived by the RSM using Design-Expert® software (Stat-Ease Inc., MN, USA). Comparing the analysis of variance (ANOVA) of these models that resulted from

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Fisher’s statistical test, it was found that the quadratic models were the most suitable option since they yielded very small p-values, the highest R2 and Fisher’s F values [86, 87]. The

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derived quadratic models are presented as follows:

ur n

COD removal (%) = 132.09213 - 8.65264 X1 - 5.13405 X2 - 0.65363 X3 + 0.16843 X1 X2 + 0.018018 X1 X3 + 7.71950E-003 X2 X3 + 0.43092 X12 + 0.16910 X22 + 6.55201E-003 X32

Jo

(17)

TU removal (%) = 118.87510 - 8.40964 X1 - 6.83056 X2 + 0.35225 X3 + 0.20175 X1 X2 + 0.024177 X1 X3 - 0.031479 X2 X3 + 0.38039 X12 + 0.36509 X22 - 9.69910E-003 X32 (18) where X1, X2, and X3 are the actual factors for pH, coagulant dose, and contact time, respectively.

[Table 3, here] The ANOVA results of the quadratic summary statistics are given in Table 4. Table 5 presents the ANOVA results of the quadratic models for COD and TU removals. Validation of the models was verified by p-value, F-value, and the lack of fit (LOF). The variety of data around the appropriate models are specified by the lack of fit and also models would be significant when the p-values are less than 0.05, Fisher’s F statistics are large and the p-values for lack of fit test are greater than 0.05 [88-90]. According to the Tables 4 and 5, the large p-

oo

f

values of the lack of fit for the derived quadratic models indicate that the outputs of the

models satisfactorily fit to the observed data, and the respective F-values and the p-values

pr

also corroborate that the proposed models are significant within the confidence level of 95%

e-

[15, 91]. [Tables 4 and 5, here]

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The performance of the designed models are investigated by the coefficient of determination (𝑅2 ), adjusted 𝑅 2 , and the coefficient of variation (CV) [92]. The value of 𝑅2 is

al

in the range of 0 to 1, indicating the proportion of the total variation in the response predicted

ur n

by the models. Additionally, it stands for the ratio of total sum of squares due to regression (SSR) to the total sum of squares (SST) [89, 90, 93]. For the proposed quadratic models of COD and TU reduction, the R2 values were obtained as 0.9972 and 0.9981, respectively. The

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results represented a good agreement between the predicted and the experimental values and showed lower error in the developed models [88, 94]. Also, it revealed that only 0.28% and 0.19% of the total variations could not be explained by COD and TU models, respectively [53, 87, 88]. The coefficient of variance (CV) is other determining parameter that indicates the reproducibility of the model. The CV is the ratio of estimate to the mean value of the observed response, and if it is not higher than 10%, the model can be considered reproducible

[93, 94]. For the present models, the CV values revealed that the process responses in terms of COD and TU removals could be precisely estimated by the derived quadratic models. In order to compare the range of the predicted values to the average prediction error at design point the adequate precision (AP) was used. AP indicates the signal-to-noise ratio (often abbreviated as SNR or S/N) of the model [19]. The AP values were calculated as 72.628 and 84.297 for COD and TU removals, respectively. Since the AP values were higher than the desired value (4), the models could be utilized for the navigation of the design space

oo

f

[90, 92, 93]. Based on the ANOVA results of the quadratic models for both COD and TU removals, the results indicated that the first-order main effects of pH (X1, A: p < 0.0001), coagulant dose

pr

(X2, B: p < 0.0001), and contact time (X3, C: p < 0.0001) were found to be more significant

e-

than some of their interaction effects (AC: p = 0.0202 and BC: p = 0.0417 for COD removal, and AC: p = 0.0202 for TU removal) and some of their quadratic effects (A2: p = 0.0004 and

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C2: p = 0.0013 for COD removal, and A2: p = 0.0030 and C2: p = 0.0002 for TU removal) (Table 5). These values revealed that pH, coagulant dose, and contact time showed a direct

al

impact on both COD and TU removals.

According to the sum of squares (SS) of each individual factor obtained from the ANOVA

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of the fitted quadratic models (Table 5), the percentage of contributions (PC) were computed for the first-order (A, B, C), interaction (AB, AC, BC), and quadratic (A2, B2, C2) terms. The

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results demonstrated that the coagulant dose (X2: B) showed the highest level of significance for COD and TU removals with contributions of 84.92% and 84.35%, respectively. This was followed by the contribution of the pH (X1: A) with PC values of 7.67% and 4.43%, respectively, for COD and TU removals. In the present computational analysis, the total PC values (TPC: total percentage of contributions) were calculated for the first-order, interaction, and quadratic terms, respectively, from the following equations [95-97]:

𝑇𝑃𝐶𝑖(%) = ∑𝑛

∑𝑛 𝑖=1 𝑆𝑆𝑖

𝑛 𝑖=1 ∑𝑗=1 𝑆𝑆𝑖 +𝑆𝑆𝑖𝑗 +𝑆𝑆𝑖𝑖

× 100

(19)

𝑇𝑃𝐶𝑖𝑗 (%) = ∑𝑛

𝑛 ∑𝑛 𝑖=1 ∑𝑗=1 𝑆𝑆𝑖𝑗

𝑛 𝑖=1 ∑𝑗=1 𝑆𝑆𝑖 +𝑆𝑆𝑖𝑗 +𝑆𝑆𝑖𝑖

× 100

(20)

𝑇𝑃𝐶𝑖𝑖(%) = ∑𝑛

∑𝑛 𝑖=1 𝑆𝑆𝑖𝑖

× 100

oo

f

𝑛 𝑖=1 ∑𝑗=1 𝑆𝑆𝑖 +𝑆𝑆𝑖𝑗 +𝑆𝑆𝑖𝑖

(21)

pr

where TPCi, TPCii, and TPCij indicate the total percentage contributions (TPC) of the firstorder, interaction, and quadratic and terms, respectively. Similarly, SS i, SSij, and SSii, are the

e-

computed sum of squares for the first-order, interaction, and quadratic terms in the respective

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order.

A visual representation of the TPC values of each individual term is illustrated in Figs. 3 and 4. As seen from these schematics, the first-order terms (A, B, C) showed the highest

al

level of impact for COD and TU removals with TPCi values of 94.94% and 92.49%,

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respectively, compared to TPC values obtained for other factors. This was followed by the TPC values of the quadratic terms (A2, B2, C2) for COD and TU removals with TPCii values of 3.27% and 5.07%, respectively. According to the computed TPC values, the interaction terms

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(AB, AC, BC) showed the lowest level of significance for COD and TU removals with TPCij values of 1.79% and 2.44% in the respective order. These results indicated that the interaction terms did not contribute to COD and TU removals as much as others for the coagulationflocculation process by the Orchis mascula tuber starch in treatment of the synthesized bilge water. On the other hand, the computational results suggested that the simultaneous contribution of the first-order independent factors provided the highest magnitude of the

significance on the present process, indicating that the coagulant dose (X2: B) played a pivot role on the performance of the studied system. [Figs. 3 and 4, here] Finally, the diagnostic plots are shown in Fig. 5. These plots present an overview of the comparison between the predicted versus actual values to find out the satisfactoriness of the models [90]. According to Fig. 5 that shows the correlations between the experimental

oo

designed models are acceptable, reliable, and have a good performance.

f

data and quadratic models’ outputs for COD and TU removals, it can be seen that the

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[Fig. 5, here]

3.2. Effects of process-related parameters on COD and TU removals

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3.2.1. Interaction between pH and coagulant dose

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3D surface responses and counter plots were analyzed to study the interaction between coagulant dose, pH, and contact time as the effective variables and COD and TU removals as responses (Fig. 6). Fig. 6 (a1 and b1) was plotted for the interaction between pH and coagulant

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dose with COD and TU removals at a constant condition of 15-min contact time. The

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maximum COD and TU removals at pH 5.0 and with coagulant dosage of 4 mg L-1 were 92.21% and 90.63%, respectively. As can be seen from Fig. 6 that the COD and TU removals increased as the pH value approached the acidic range. Many researches showed that by the

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increase of the pH, COD [22, 98], and TU [20, 23, 98, 99] removal efficiencies decreased. This is due to the cationic form of the coagulant, which should be higher at acidic pH level and helps to form hydrophobic interactions in the bridging and bonding mechanism [18]. The high concentration of H+ at the surface of the natural coagulant in the lower pH region promote the surface attachment between the coagulant cationic chains and negatively charged the active centers in the contaminant molecules due to a higher adsorption through the

reduction of electrostatic repulsion between particles. As a result, more particles are linked and bridged together to form flocs [18, 20, 22, 23, 99, 100], leading to the maximum reduction of COD and TU. Furthermore, the excessive H+ ions prevent neutralization of negative charged of bilge water and coagulation by organic ligands and active compounds of Orchis mascula tuber starch due to van der Waals forces which remove the repulsive forces [21, 31]. The hydrophilic and hydrophobic equilibrium of the system affect the protein solubility of Orchis mascula tuber starch which increases after pH 4.0. As the pH of solution

oo

f

increase to more than 4.0, the removal efficiency will increase due to increase of soluble proteins and active compounds [21].

The results indicated that by raising the pH of the solution from 5.0 to 9.0, TU

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removal decreased from 90.63% to 79.82% at a constant amount of coagulant dose (4 mg L-1)

e-

and 15 min of contact time (Fig. 6b1). The result was compliant with the COD removal. At a constant coagulant dose of 4 mg L-1, the COD removal was decreased from 92.21% to

Pr

82.88% by increasing the pH of the solution from 5.0 to 9.0 (Fig. 6a1). The result indicated that COD and TU removals were highly influenced by the concentration of Orchis mascula

al

tuber starch. The higher coagulant dosage caused the saturation of the bridge sites which led to re-stabilization of the destabilized particles and reduction of TU [101] and COD [22]

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removal efficiencies. The results also showed that when the pH of the solution was maintained at 5.0 and the dosage increased from 4 mg L-1 to 320 mg L-1, COD and TU

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removals decreased from 92.21% to 70.04% and from 90.63% to 64.27%, respectively. [Fig. 6, here]

3.2.2. Interaction between pH and contact time Fig. 6 (a2 and b2) illustrates the interaction of pH and contact time for COD and TU removals. Fig. 6a2 shows that the optimum points are at pH 5.0 and contact time of 15 min for the COD removal. The results indicated that COD and TU removals in acidic pH were maximized, but

with increasing pH to more than 7.0, both COD and TU removals showed a decreasing trend. Under the alkaline condition, the excessive OH- ions compete with the negative charges of bilge water colloids which reduce the active compounds of Orchis mascula tuber starch [21, 31]. On the other hand, the negative charge of carboxyl groups (-C(=O)OH or -COOH) which causes repulsion among wastewater components and polymer chain, reduces the efficiency of adsorption and inter-particle bridging mechanism [21]. When the pH of solution was fixed at 5.0 and the contact time increased from 15 min to 45 min at a constant amount of coagulant

oo

f

dose (4 mg L-1), COD removal decreased from 92.21% to 87.12%. Moreover, Fig. 6b2 shows that TU removal decreases from 90.63% to 87.27% by increasing the contact time from 15

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min to 45 min at pH 5.0 and 4 mg L-1 of coagulant dose.

e-

3.2.3. Interaction between contact time and coagulant dosage

Fig. 6 (a3 and b3) illustrates the interaction of contact time and coagulant dosage for COD and

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TU removals. According to the obtained results, the COD removal decreased from 92.21% to 70.04% by enhancing the coagulant dose up to 320 mg L-1 at contact time of 15 min, and it

al

decreased to 87.12% with 4 mg L-1 of coagulant dose at contact time of 45 min (Fig. 6a3). The maximum reduction of TU was obtained at contact time of 15 min and 4 mg L-1 of natural

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coagulant. This can be ascribed to the fact that above the optimum dose, coagulant may restabilize the colloids due to the charge reversal causing re-dispersion and disturbance of

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particle settling through precipitation or charge neutralization [19, 32, 102, 103]. The TU removal efficiency increased with the reduction of contact time from 45 to 15 min, when coagulant dose was between 4 mg L-1 and 320 mg L-1 (Fig. 6b3).

3.3. Coagulation-flocculation mechanism

The possible coagulation mechanism was identified according to the results obtained from FTIR and EDX analyses and comparing active compositions of Orchis mascula tuber starch with other natural coagulants in the previous studies. Aggregation of colloidal particles in the solution can occur via four main coagulation-flocculation mechanisms including (i) double layer compression, (ii) sweep flocculation, (iii) adsorption and charge neutralization, and (iv) adsorption and inter-particle bridging [104]. Among them, the adsorption and inter-particle bridging mechanism are the predominant mechanisms in most of the plant-based organic

oo

f

coagulants [105]. The long-chained structures of natural polymers, such as polysaccharides and proteins (especially polymers with high molecular weight), which are able to extend into the solution and bind multiple colloids together by increasing the number of unoccupied

pr

adsorption sites, could induce coagulation process via bridging mechanism [104, 106]. The

e-

bridging mechanism occurs due to van der Waals force, static, hydrogen bonds or even chemical reaction between some radical groups of the natural polymers and the colloidal

Pr

particles in the solution [107]. It is possible to consider that the coagulation-flocculation mechanism of Orchis mascula tuber starch, rice starch [20, 34, 108], and red lentil extract

al

[92] are the same, as they have similar characteristics and active agents. After dispersion and stretching of the starch in the bilge water, particle destabilization occurs through the

ur n

adsorption of the larger number of colloidal impurities onto the long chains of polysaccharides of starch through dipole-dipole interactions and hydrogen bonding. The

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natural electrolytes of starch could effectively enhance the polymeric bridges via particlepolysaccharide-particle complexes formation and the growth of large rapid-settling flocs [99, 105]. It is highly possible that the presence of galacturonic acid, predominantly in polymeric form, in the structure of starch provides a bridge for adsorption of colloids [106, 109]. Furthermore, the EDX results indicate the high coagulation capability of Orchis mascula tuber starch is attributed to the presence of divalent ions such as Ca +2 and Mg+2 that have a

synergistic effect on the removal efficiency of coagulation-flocculation process [105]. Fig. 7 illustrates the scheme of adsorption and inter-particle bridging mechanism in coagulation and flocculation process with Orchis mascula tuber starch. [Fig. 7, here] 3.4. Optimization of coagulation-flocculation process Using the RSM approach, a numerical optimization depending on desirability functions was

f

used to determine the optimum conditions in order to reach the highest COD and TU

oo

removals. All variables were defined to be within the experimental domain, while COD and TU removals were targeted to be maximized. The optimum conditions under specified

pr

constraints are presented in Table 6. An additional experiment was carried out at the optimum

e-

conditions to confirm and validate the accuracy of the predictions of the derived quadratic models. The experimental results compared with the predicted values from the statistical

Pr

models are presented in Table 6. The standard deviations indicated good agreements between the experimental results and the predicted values. In order to explore the capability of Orchis

al

mascula tuber starch in terms of surfactant and oil-grease removal, the process was repeated at the optimum condition. The results demostrated that the removal efficiencies of Orchis

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mascula tuber starch for surfactant and oil-grease were 23% and 93% with influent characteristics of 55 ± 0.4 mg L-1 and 592.3 ± 9.5 mg L-1, respectively. Fig. 8 shows the total

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petroleum hydrocarbons (TPHs) chromatogram of effluent at optimum conditions. The chromatogram indicates that the peaks of many hydrocarbons are disappeared, revealing that the Orchis mascula tuber starch as natural coagulant is able to remove TPHs very well. [Table 6, here] [Fig. 8, here] 3.5. Multivariate adaptive regression spline

Fig. 9 indicates the three-dimensional response surface plots that were produced based on the multivariate adaptive regression spline method. The effects of each parameter were investigated by holding one of the parameters constant for COD and TU removal at the optimum conditions. Fig. 9 (a1 and b1) shows that the increase of coagulant dosage at the constant pH of 5.0 causes a noticeable reduction for both COD and TU removals. Getting far from the optimum dose of the coagulant caused the re-stabilization of colloids, resulting in a decrease in the removal efficiency. Fig. 9 (a2 and b2) indicates that the removal efficiency

oo

f

decreases with the increase of pH from 5.0 to 9.0 at constant optimum contact time and coagulant dose, implying that the performance of Orchis mascula is better in acidic ranges than the basic medium. Fig. 9 (a3 and b3) shows that the contact time has a slight effect on the

pr

removal efficiency. The increase of contact time more than 15 min decreased the removal

e-

efficiency, and the more contact time re-stabilized the flocs.

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[Fig. 9, here]

Fig. 10 depicts the regression plots of the experimental values and the predictions of

al

FCCCD-RSM and MARS models for COD and TU removals. According to their determination coefficient (R2) values, although the FCCCD-RSM approach exhibited a better

ur n

performance than the MARS model, the regression results showed that their approximations were approximately the same, also revealing a significant fit of the MARS model (p < 0.0001

Jo

for both COD and TU removals, respectively) which was used for the first time in bilge water treatment effluent estimation. [Fig. 10, here]

3.6. Kinetic study The first-order and second-order kinetic models were applied to explore the kinetics of the present coagulation-flocculation system. In order to investigate the process-related kinetics,

the COD removal was studied at the optimum experimental conditions of pH = 5.0, dosage = 4 mg L-1, and contact time = 15 min. During the slow mixing period, the samples were collected every three minutes. Fig. 11 demonstrates the kinetic diagrams of the studied coagulation-flocculation process. According to the determination coefficient (R2) values, the first-order model had a higher correlation than the second-order model. However, it should be noted that the R2 only shows the extent of linearity of the plots and does not reveal the best performance of models for the process. Although the R2 values of both kinetic models were in

oo

f

satisfactory agreement with the experimental data, the second-order model showed competitive lower error values in terms of various fundamental statistics (results are presented for the first-order and the second-order, respectively), such as mean absolute error (MAE =

pr

0.0184 and 1.98 × 10-4), root mean squared error (RMSE = 0.0198 and 2.33 × 10-4), mean

e-

squared error (MSE = 3.91 × 10-4 and 5.43 × 10-8), proportion of systematic error (PSE = 0.053 and 0.016), and standard error of the estimate (SEE = 0.0256 and 0.0003), indicating

Pr

the superior performance of the second-order model than the first-order model. Moreover, to find out which model best express the behavior of the present coagulation-flocculation

al

system, some functional parameters like E can be evaluated [110]. The rate constant (k 2) and collision efficiency (E) of the second-order model were determined to be 0.0004 L/mg/min

ur n

and 6.346 × 1013, respectively, and the interception of y-axis was 0.0031 (L mg-1). From the mentioned constants in Table 7, it is obvious that the process followed the second-order

Jo

kinetics.

[Table 7, here] [Fig. 11, here]

3.7. Characterization of coagulant and the sludge Fig. 12 delineates results of the EDS, SEM, and FTIR analyses of Orchis mascula tuber starch. As seen from Fig. 12a, the EDS spectrum approves the carbon, oxygen, sodium, magnesium, phosphorus, sulfur, chlorine, potassium, and calcium, with weight percentages

(wt. %) of 27.48, 18.47, 23.73, 0.64, 0.70, 0.63, 27.24, 0.49, and 0.61%, respectively. The high composition of carbon in Orchis mascula tuber starch as a binding agent is highly desirable due to formation of micro-flocs which improve the removal efficiency of coagulation-flocculation process [21]. The presence of some divalent ions, such as Ca +2 and Mg+2 in starch, might contribute to the removal efficiency of the coagulation and flocculation process [92]. The dried powdered of Orchis mascula tuber starch morphology was explored by

oo

f

SEM. Fig. 12b shows the irregular-shape flakes of the micro sheets in two magnifications of 5 KX and 10 KX. For the identification of active compounds and functional groups, which provide adsorption sites leading to the Orchis mascula tuber starch inter-particle bridging

pr

effect, the natural coagulant was characterized by FTIR spectroscopy (Fig. 12c). The strong

e-

peak observed at 3388.32 cm-1 indicated the O-H stretching in the structure of carboxylic acid of the polymeric compound, and acid pyranose ring in polysaccharide chain indicated the

Pr

presence of protein in the starch [22, 92, 111, 112]. The peaks observed at about 2927.20 cm-1 was due to the presence of C-H stretching of aliphatic structures and indicated the formation

al

of fatty acids and lipids [22, 34, 98]. Both O–H and C–H linkages could be related to the protein content found in the starch which could lead the high interaction of colloidal particles

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of bilge water and starch [100]. The peaks observed at 1730.33 cm-1 represented the carbonyl linkages (C=O) of aliphatic esters in pectins [34]. From the FTIR spectra, it can be seen that

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the presence of C=O and C–OH linkages can be detected at peaks of 1645.77 and 1420.86 cm1,

respectively. The peak observed in 1645.77 cm-1 is related to the carboxylic -COO- double

bond of deprotonated carboxylate (conjugate base of a carboxylic acid) in uranic acid (a class of sugar acids with both carbonyl and carboxylic acid functional groups) which aids the bridging and adsorption mechanism [102]. The peak observed in 1420.86 cm-1 assigned to the presence of uranic acid in the polysaccharide structure of starch. The peak at 1149.74 cm-1

and 1067.62 cm-1 are endorsed to C − O in C − OH bands of carbohydrates. The absorption peaks around 950–1200 cm-1 are correspond to C − O linkage in C − OH stretching vibration of aromatic compounds of galactose, rhamnose, galacturonic acid, and −OH of polysaccharide [102]. The spectrum was showed the presence of different functional groups in Orchis mascula tuber starch such as hydroxyl and carboxyl functional groups which aided to adsorb a wide range of pollutant [92].

f

[Fig. 12, here]

oo

The EDS, SEM, and FTIR analyses of the sludge are shown in Fig. 13. As depicted in Fig. 13a, the composition of the sludge at optimum conditions in the EDS spectra was as

pr

follows: carbon, oxygen, sodium, magnesium, phosphorus, sulfur, chlorine, potassium, and

e-

calcium, with weight percentage (wt. %) of 20.78, 25.90, 15.90, 8.79, 0.59, 7.57, 18.32, 0.93, and 1.23% respectively. In order to study the possibility of using Orchis mascula tuber starch

Pr

as a natural coagulant to remove COD and TU from bilge water, the morphology of flocs formed after the jar test experiment was observed through the SEM analysis (Fig. 13b). As

al

shown in Fig. 13b, formed flocs had large particle size that could be due to adsorption and inter-particle bridging mechanism [113]. The FTIR spectrum was conducted on the sludge

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produced at the optimum condition of process, and results were compared with the known signature of the identified components in the FTIR library (Fig. 13c). The vibration of

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hydroxyl groups (O-H) was confirmed the presence of broad bands at the region of 3244.00– 3403.93 cm-1 which were related to hydroxyl groups in protein and fatty acid structure [26, 114]. The peak in the region of 2856.19–2925.01 cm-1 and 600.25–663.86 cm-1 were endorsed to C-H and C-Cl linkages, respectively. Besides, the presence of C=C in the flocs were recognized by the stretches occurring at 1639.36 cm-1. The intensity of the peak at 1459.66 cm-1, which was related to the presence of uranic acid in sludge, was lower than that in spectra of starch. This demonstrated binding of starch with colloidal particles of bilge water and also

justified the responsibility of uranic acid in the polysaccharide structure for adsorption and bridging mechanism, as obtained in the literature [102].

[Fig. 13, here] 3.8. Cost analysis After determining the performance of the coagulant in terms of the pollutant removal, the financial aspect of that has been studied to evaluate and compare the economics of the process

f

in terms of the cost of treatment per cubic meter of wastewater. As a rule of thumb, the total

oo

production costs of the product is approximately 40% more than the cost of raw substance

[115]. According to the cost of raw Orchis mascula tuber (49 $ kg-1), the total production cost

pr

is 122.5 $ kg-1 including industrial applications such as crushing of Orchis mascula tuber and

e-

releasing of mucilage in hot water, and so forth. Thus, the cost of process will be $0.49 to treat each cubic meter of bilge water without considering energy and other costs (coagulant

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dose = 4 mg L-1). According to the optimum dosage of coagulants and their price, Table 8 indicates the comparison of the estimated costs of various coagulants per cubic meter of

al

wastewaters. As can be seen from Table 8, the cost of treatment for different wastewaters varies highly and depends on several criteria such as characteristics of wastewaters and types

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of coagulants. Although many researches showed that the inorganic coagulants like alum [116] has a lower cost, several issues (e.g. production of toxic sludge (metal hydroxide),

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presence of metal concentration in the treated water [117], and so forth) associated with the consequence of their usage increases the popularity of organic coagulants in different processes. From this perspective, Orchis mascula as a natural coagulant has lower environmental risks compared to inorganic and commercial coagulants. Therefore, considering its environmentally friendly nature, investigation of more detailed technoeconomic aspects of the implemented process will provide new insights into the applicability of Orchis mascula tuber starch for the coagulation-flocculation process in industrial scales.

[Table 8, here] 4. Conclusions This study was conducted to introduce a novel natural coagulant (Orchis mascula tuber starch) in treatment of bilge water for the first time and to make a comprehensive quantitative evaluation of the studied coagulation-flocculation process based on face-centered central composite design-response surface methodology (FCCCD-RSM), multivariate adaptive regression splines (MARS), and kinetic modeling. According to the experimental and

oo

f

numerical findings, the primary conclusions obtained within the framework of this study were drawn as follows:

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(1) The coagulation-flocculation process by the Orchis mascula tuber starch showed a conspicuous treatment performance at the optimum conditions (coagulant dose = 4 mg L-1,

e-

contact time = 15 min, and pH = 5.0) with more than 90% of COD, TU, and O&G removals

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from the synthesized bilge water.

(2) FCCCD-RSM (in the form of 23 full factorial design) and MARS models were implemented for the first time in the quantitative assessment of the present coagulation-

al

flocculation process and demonstrated remarkable predictions for both COD and TU

ur n

removals with R2 values greater than 0.97.

(3) Kinetic behavior of the Orchis mascula tuber starch-based coagulationflocculation of the oily-saline wastewater followed the second-order kinetics according to a

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series of statistical performance indicators such as MAE, RMSE, MSE, PSE, and SEE. (4) Fourier-transform infrared spectroscopy (FTIR) and energy dispersive X-ray

spectroscopy (EDX) analyses of the Orchis mascula tuber starch demonstrated that the predominant mechanisms for the present system were adsorption and inter-particle bridging phenomenon. Scanning electron microscope (SEM) results corroborated that the dried

powdered of Orchis mascula tuber starch exhibited a morphology of irregular-shape flakes of the micro sheets. (5) Cost evaluation results indicated that the Orchis mascula tuber starch with cost of 0.49 $ m-3 can be a base of further research for industrial applications. Considering cost effectiveness, naturalness, and environmental friendliness of Orchis mascula tuber starch used as a new coagulant in treatment of bilge water, a detailed techno-economic feasibility analysis is encouraged to provide information regarding the applicability of the relevant process for

oo

f

industrial scale with more certainty.

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CRediT authorship contribution statement

Donya Hamidi: Conceptualization, Methodology, Software, Formal analysis, Investigation,

e-

Resources, Data Curation, Writing - Original Draft, Visualization. Moein Besharati Fard: Supervision, Project administration, Conceptualization, Methodology, Software, Formal

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analysis, Investigation, Resources, Data Curation, Writing - Original Draft, Visualization. Kaan Yetilmezsoy: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data Curation, Writing - Original Draft, Visualization. Javad

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Alavi: Software, Writing - Original Draft, Visualization. Hossein Zarei: Software, Writing -

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Original Draft, Visualization.

Conflicts of interest

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The authors declare that there are no conflicts of interest including any financial, personal, or other relationships with other people or organizations.

Acknowledgements The authors would like to acknowledge University of Guilan for its cooperation for providing laboratory facilities.

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[94] S.S. Kumar, V. Kumar, R. Kumar, S.K. Malyan, N.R. Bishnoi, Ferrous sulfate as an in-situ anodic coagulant for enhanced bioelectricity generation and COD removal from landfill leachate, Energy, 176 (2019) 570-581. https://doi.org/10.1016/j.energy.2019.04.014 [95] H. Meng, X. Hu, A. Neville, A systematic erosion–corrosion study of two stainless steels in marine conditions via experimental design, Wear, 263 (2007) 355-362. https://doi.org/10.1016/j.wear.2006.12.007

[96] K. Yetilmezsoy, S. Demirel, R.J. Vanderbei, Response surface modeling of Pb(II) removal from aqueous solution by Pistacia vera L., Box–Behnken experimental design, Journal of Hazardous Materials, 171 (2009) 551-562. https://doi.org/10.1016/j.jhazmat.2009.06.035 [97] M. Foroughi, S. Chavoshi, M. Bagheri, K. Yetilmezsoy, M.T. Samadi, Alum-based sludge (AbS) recycling for turbidity removal in drinking water treatment: an insight into statistical, technical, and health-related standpoints, Journal of Material Cycles and Waste Management, 20 (2018) 1999-2017. https://doi.org/10.1007/s10163-018-0746-1 [98] B. Kakoi, J.W. Kaluli, P. Ndiba, G. Thiong'o, Optimization of Maerua Decumbent bio-coagulant

oo

f

in paint industry wastewater treatment with response surface methodology, Journal of Cleaner Production, 164 (2017) 1124-1134. https://doi.org/10.1016/j.jclepro.2017.06.240

pr

[99] J. Dotto, M.R. Fagundes-Klen, M.T. Veit, S.M. Palácio, R. Bergamasco, Performance of different coagulants in the coagulation/flocculation process of textile wastewater, Journal of Cleaner

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Production, 208 (2019) 656-665. https://doi.org/10.1016/j.jclepro.2018.10.112

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Pr

Preparation of new biocoagulants by shrimp waste and its application in coagulation-flocculation processes, Journal of Cleaner Production, (2020) 122397. https://doi.org/10.1016/j.jclepro.2020.122397

al

[101] F.M. Omar, N.N.N.A. Rahman, A. Ahmad, COD reduction in semiconductor wastewater by

ur n

natural and commercialized coagulants using response surface methodology, Water, Air, and Soil Pollution, 195 (2008) 345-352. https://doi.org/10.1007/s11270-008-9751-7 [102] S.M. Mirbahoush, N. Chaibakhsh, Z. Moradi-Shoeili, Highly efficient removal of surfactant

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from industrial effluents using flaxseed mucilage in coagulation/photo-Fenton oxidation process, Chemosphere, 231 (2019) 51-59. https://doi.org/10.1016/j.chemosphere.2019.05.118 [103] H. Harfouchi, D. Hank, A. Hellal, Response surface methodology for the elimination of humic substances from water by coagulation using powdered Saddled sea bream scale as coagulant-aid, Process Safety and Environmental Protection, 99 (2016) 216-226. https://doi.org/10.1016/j.psep.2015.10.019

[104] S. Choy, K. Prasad, T. Wu, R. Ramanan, A review on common vegetables and legumes as promising plant-based natural coagulants in water clarification, International Journal of Environmental Science and Technology, 12 (2015) 367-390. https://doi.org/10.1007/s13762-013-0446-2 [105] M. Choudhary, M.B. Ray, S. Neogi, Evaluation of the potential application of cactus (Opuntia ficus-indica) as a bio-coagulant for pre-treatment of oil sands process-affected water, Separation and Purification Technology, 209 (2019) 714-724. https://doi.org/10.1016/j.seppur.2018.09.033 [106] T. Freitas, V. Oliveira, M. De Souza, H. Geraldino, V. Almeida, S. Fávaro, J. Garcia, Optimization of coagulation-flocculation process for treatment of industrial textile wastewater using

oo

f

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pr

[107] H. Salehizadeh, N. Yan, R. Farnood, Recent advances in polysaccharide bio-based flocculants, Biotechnology Advances, 36 (2018) 92-119. https://doi.org/10.1016/j.biotechadv.2017.10.002

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Pr

352-364. https://doi.org/10.1016/j.ecoleng.2016.05.082

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al

[110] N. Nnaji, J. Ani, L. Aneke, O. Onukwuli, U. Okoro, J. Ume, Modelling the coag-flocculation

ur n

kinetics of cashew nut testa tannins in an industrial effluent, Journal of Industrial and Engineering Chemistry, 20 (2014) 1930-1935. https://doi.org/10.1016/j.jiec.2013.09.013 [111] S.M. Abdo, R.H. Mahmoud, M. Youssef, M.E. El-Naggar, Cationic starch and polyaluminum

Jo

chloride as coagulants for River Nile water treatment, Groundwater for Sustainable Development, 10 (2020) 100331. https://doi.org/10.1016/j.gsd.2020.100331 [112] A. Daverey, N. Tiwari, K. Dutta, Utilization of extracts of Musa paradisica (banana) peels and Dolichos lablab (Indian bean) seeds as low-cost natural coagulants for turbidity removal from water, Environmental Science and Pollution Research, 26 (2019) 34177-34183. https://doi.org/10.1007/s11356-018-3850-9

[113] A.R. Pendashteh, A. Fakhru’l-Razi, N. Chaibakhsh, L.C. Abdullah, S.S. Madaeni, Z.Z. Abidin, Modeling of membrane bioreactor treating hypersaline oily wastewater by artificial neural network, Journal of Hazardous Materials, 192 (2011) 568-575. https://doi.org/10.1016/j.jhazmat.2011.05.052 [114] W. Subramonian, T.Y. Wu, S.-P. Chai, An application of response surface methodology for optimizing coagulation process of raw industrial effluent using Cassia obtusifolia seed gum together with alum, Industrial Crops and Products, 70 (2015) 107-115. https://doi.org/10.1016/j.indcrop.2015.02.026 [115] H. Wilting, A. Hanemaaijer, Share of raw material costs in total production costs, PBL

oo

f

Publication Number 1506, The Hague, PBL Netherlands Environmental Assessment Agency (2014). [116] S. Haydar, J.A. Aziz, Coagulation–flocculation studies of tannery wastewater using combination

pr

of alum with cationic and anionic polymers, Journal of Hazardous Materials, 168 (2009) 1035-1040. https://doi.org/10.1016/j.jhazmat.2009.02.140

e-

[117] C.S. Lee, J. Robinson, M.F. Chong, A review on application of flocculants in wastewater treatment, Process Safety and Environmental Protection, 92 (2014) 489-508.

Pr

https://doi.org/10.1016/j.psep.2014.04.010

[118] A. Mohamed, A. Siggins, M.G. Healy, D. Ó hUallacháin, O. Fenton, P. Tuohy, Appraisal and ranking of poly-aluminium chloride, ferric chloride and alum for the treatment of dairy soiled water,

al

Journal of Environmental Management, 267 (2020) 110567.

ur n

https://doi.org/10.1016/j.jenvman.2020.110567

[119] M. Taheriyoun, A. Memaripour, M. Nazari-Sharabian, Using recycled chemical sludge as a coagulant aid in chemical wastewater treatment in Mobarakeh Steel Complex, Journal of Material

Jo

Cycles and Waste Management, 22 (2020) 745-756. https://doi.org/10.1007/s10163-019-00966-7 [120] G. Dotto, G. Rosa, M. Moraes, R. Weska, L. Pinto, Treatment of chitin effluents by coagulation–flocculation with chitin and aluminum sulfate, Journal of Environmental Chemical Engineering, 1 (2013) 50-55. https://doi.org/10.1016/j.jece.2013.03.006

REVISED FIGURE CAPTIONS (JECE-D-20-02408.R1) Fig. 1. Synthesized bilge water chromatograms: (a) TPHs and (b) PAHs

e-

pr

oo

f

a

Jo

ur n

al

Pr

b

Fig. 2. Flowchart of the implemented multivariate adaptive regression splines (MARS) method

e-

pr

oo

f

1. Number of basis functions

ur n

al

Pr

Check R2 closer to 1

Jo

Fig. 3. Total percentage contributions of model variables on the COD removal

TPCij 2,0

PC (%)

TPCii 1,88

2,0

1,39

1,0 0,5

1,5 1,0

0,23

0,16

AC

BC

0,0

0,80

0,59

AB

0,5 0,0 A-sqr

B-sqr

Total interaction = 1.79%

C-sqr

100 80 60 40 20 0

84,92

7,67 A

B

Pr

e-

COD

pr

Total first-order = 94.94%

For COD removal model

al

Total percentage of contributions (TPC) = TPCi + TPCij + TPCii = 100%

ur n

Fig. 4. Total percentage contributions of model variables on the TU removal

Jo

2,35

oo

PC (%)

Total quadratic = 10.04%

TPCi

f

PC (%)

2,5

1,5

C

TPCij 2,0

4,16

4,0 3,0

1,0

1,30 0,95

0,5

0,20

0,0

2,0 1,0

1,5

AB

0,0 A-sqr

AC

BC

0,61

0,30 B-sqr

Total interaction = 1.79%

C-sqr

100 80 60 40 20 0

84,35

4,43

3,71

oo

PC (%)

Total quadratic = 10.04%

TPCi

f

PC (%)

5,0

PC (%)

TPCii

A

B

C

e-

Pr

TU

pr

Total first-order = 94.94%

For TU removal model

al

Total percentage of contributions (TPC) = TPCi + TPCij + TPCii = 100%

ur n

Fig. 5. (a) Actual COD removal versus predicted value, (b) normal probability of residuals, (c) actual TU removal percentages versus predicted value, and (d) normal probability of

Jo

residuals

Design-Expert® Software

n-Expert® Software %

Normal Plot of Residuals

COD% Predicted vs. Actual

points by value of %: 8503

100

Color points by value of COD%: 91.8503

a

b

99 63.4096

4096

95

Normal % Probability

Predicted

90

80

70

90 80 70 50 30 20 10 5 1

60

70

80

90

-3.00

100

-2.00

-1.00

0.00

1.00

2.00

3.00

Externally Studentized Residuals

Actual

n-Expert® Software

Design-Expert® Software

Predicted vs. TU%Actual 100

Color points by value of TU%: 90.6255

c

6255

Normal Plot of Residuals 99

7709

51.7709

90 80

e-

Normal % Probability

95

80

70

70 50 30 20

Pr

Predicted

90

d

pr

points by value of

oo

f

60

10

5

60

1

50

60

70

al

50

80

ur n

Actual

90

100

-3.00

-2.00

-1.00

0.00

1.00

Externally Studentized Residuals

Fig. 6. Response surface plots of FCCCD-RSM: (a) COD and (b) TU removals

Jo

2.00

3.00

al

ur n

Jo a2

a3

f

oo

pr

e-

Pr

a1

b1

Pr

e-

pr

oo

f

b2

Jo

ur n

al

b3

Fig. 6. (continued)

Fig. 7. Scheme of adsorption and inter-particle bridging mechanism in coagulation and flocculation process with Orchis mascula tuber starch

f oo pr ePr al ur n Jo

Fig. 8. Chromatogram of coagulation-flocculation effluent (TPHs)

f oo pr

Jo

ur n

al

Pr

e-

Fig. 9. Response surface plots of MARS: (a) COD (b) TU removals

al

ur n

Jo a2

a3

f

oo

pr

e-

Pr

a1

f

b1

Pr

e-

pr

oo

b2

Jo

ur n

al

b3

Fig. 9. (continued)

Fig. 10. Regression plots of actual and predicted values of (a) COD and (b) TU removals for FCCCD-RSM and (c) COD and (d) TU removals for MARS

100

90 85 80

a

75 70

y  0.9972 x  0.2044 2 R 2  0.9972; Radj  0.9971

65

SEE  0.4135; F  6480.9713; p  0.0001 60

Data point (Experimental) Fitted line (FCCCD-RSM model)

90

80

b 70

y  0.9980 x  0.1324

60

2 R 2  0.9981; Radj  0.9980

SEE  0.4825; F  9394.7374; p  0.0001 50

65

70

75

80

85

90

95

50

60

Actual COD removal efficiency (%)

75

y  0.9825 x  1.2494 SEE  1.0885; F  907.7445; p  0.0001

60

90

80

d

e-

c

2 R 2  0.9806; Radj  0.9795

100

Fitted line (MARS model)

pr

80

Predicted TU removal efficiency (%)

85

65

90

Data point (Experimental)

70

y  0.9733x  1.8306

60

Pr

Predicted COD removal efficiency (%)

100 Data point (Experimental) Fitted line (MARS model)

70

80

Actual TU removal efficiency (%)

95 90

70

oo

60

f

Data point (Experimental) Fitted line (FCCCD-RSM model)

Predicted TU removal efficiency (%)

Predicted COD removal efficiency (%)

95

2 R 2  0.9734; Radj  0.9719

SEE  1.7763; F  659.1660; p  0.0001

50

60

65

70

75

80

85

90

95

al

Actual COD removal efficiency (%)

50

60

70

80

90

100

Actual TU removal efficiency (%)

Fig. 11. Kinetic diagrams of Orchis mascula at optimum conditions: (a) the first-order model

Jo

ur n

and (b) the second-order model

5,8 Experimental data (Orchis mascula) First-order model (Orchis mascula)

5,6

ln(C)

5,4

a

5,2 5,0 ln C  k1  t  ln C0 y  0.0636 x  5.5649

4,8

2 R 2  0.9946; Radj  0.9929

2

4

6

8

10

12

14

0,012 Experimental data (Orchis mascula)

16

18

pr

Time (min)

oo

0

f

SEE  0.0256; F  557.3591; p  0.0002

4,6

e-

Second-order model (Orchis mascula)

Pr

0,008

0,006

b

1 1  k2  t  C C0

al

1/C (L/mg)

0,010

y  0.0004 x  0.0031

ur n

0,004

2 R 2  0.9844; Radj  0.9792

SEE  0.0003; F  189.4671; p  0.0008

0,002

Jo

0

2

4

6

8

10

12

14

16

18

Time (min)

Fig. 12. Characterization of Orchis mascula tuber mucilage: (a) EDS, (b) SEM, and (c) FTIR spectra

ClK 12000

NaK

11000 10000 9000 8000

a

7000 6000

4000

CaL OK CK MgK

3000

ClK SK SK CaK PK KK PK K K CaK

2000 1000 0

keV

0

10.00

O

Na

Mg

P

S

Cl

Intensity

306.8

405.4

1329.0

32.8

51.3

49.4

2018.1

W (%)

27.48

18.47

23.73

0.64

0.70

0.63

27.24

A (%)

42.85

21.62

19.33

0.50

0.42

0.37

14.39

K

Ca

33.4

42.6

0.49

0.61

0.24

0.29

oo

C

b

Jo

ur n

al

Pr

e-

pr

Element

f

5000

Fig. 13. Characterization of the sludge: (a) EDS, (b) SEM, and (c) FTIR spectra

c

ClK 5000

NaK OK 4000

a

3000

SK

MgK 2000

CaL CK PK

1000

ClK SK PK

CaK KK KK CaK

keV

0

10.00

C

O

Na

Mg

P

S

Cl

K

Ca

oo

Element

f

0

Intensity 160.4 428.2 579.3 326.0 28.9

392.2 875.9 42.7

58.0

W (%)

20.78 25.90 15.90 8.79

0.59

7.57

18.32 0.93

1.23

A (%)

33.08 30.97 13.23 6.92

0.37

4.52

9.88

0.59

b

Jo

ur n

al

Pr

e-

pr

0.45

c

al

ur n

Jo

f

oo

pr

e-

Pr

REVISED TABLES (JECE-D-20-02408.R1) Table 1 Characteristics of synthesized bilge water Range

Mean ± SD

APHA methodology

Chemical oxygen demand, COD (mg L-1)

1190–1215

1202.5 ± 8.5

5220 D

Biological oxygen demand, BOD 5 (mg L-1)

590–625

610.8 ± 12.4

5210 B

Oil and grease, O&G (mg L-1)

576.6–604.6

592.3 ± 9.5

5520 B

Turbidity (NTU)

131–134

132.2 ± 1

2130 B

pH

6.95–7.20

7.08 ± 0.10

4500-H+ B

Total dissolved solids, TDS (mg L-1)

14920–15200

1533.33 ± 120

2540 C

Conductivity (mS/cm)

2.95–3.15

3.05 ± 0.08

2510 B

NH4+-N (mg L-1)

0.65–1

0.825 ± 0.1

4500-NH3 C

PO43--P (mg L-1)

2.9–4.5

3.8 ± 0.7

4500-P C

Surfactants (mg L-1)

54.49–55.48

55 ± 0.4

5540 C

-

0.9759

-

1285–1297

1291.33 ± 4.6

4500-Cl- B

Pr

e-

pr

oo

f

Constituent

Density (g/cm3)

al

Cl- (mg L-1)

Jo

ur n

SD: Standard Deviation; NTU: Nephelometric Turbidity Unit.

Table 2 Experimental levels of independent variables considered in the face-centered central composite design (FCCCD) Levels Operating parameters

Unit

Symbol -1

0

1

-

X1

5.0

7.0

9.0

Coagulant dosage

mg L-1

X2

4

162

320

Contact time

min

X3

15

30

45

Jo

ur n

al

Pr

e-

pr

oo

f

pH

Table 3 FCCCD-based experimental design matrix and actual values of COD and TU removals Independent variables (Xi)

Dependent variables (Yi) CODa

TU

removal (%)

removal (%)

X2

X3

1

9

4

15

83.04

79.84

2

7

162

30

70.23

65.34

3

7

162

15

73.72

66.61

4

5

162

30

75.05

71.36

5

5

4

45

87.36

86.90

6

5

320

15

70.06

7

7

4

30

82.20

8

9

162

30

68.73

9

7

162

30

10

7

162

30

11

7

162

45

69.56

61.18

12

7

162

30

70.89

66.09

13

5

320

45

66.74

53.28

14

7

162

30

71.23

66.09

15

9

320

15

65.90

60.06

16

7

320

30

63.41

58.55

17

9

320

45

65.41

51.77

18

5

4

15

91.85

90.63

9

4

45

80.04

79.65

7

162

30

70.06

66.09

Jo

20

a

Samples filtered through Whatman filter (20–25 µm).

oo

pr

63.83 84.99 63.83

70.06

64.58

70.89

65.34

e-

al

ur n

19

f

X1

Pr

Test no

Table 4 ANOVA results of the quadratic summary statistics Response

Adjusted

Predicted

R2

R2

0.3566 0.9972

0.9948

0.4136 0.9981

0.9964

p-value

PLOF

<0.0001

<0.0001

R2

CV

AP

SD

0.9845

72.628

0.55

0.76

17.23

0.9905

84.297

0.65

0.95

20.86

(%)

PRESS

COD removal (%) TU removal

oo

f

(%)

p-values < 0.05 are significant; PLOF: Proportion due to Lack of Fit; R2: Coefficient of

pr

Determination; AP: Adequate Precision; SD: Standard Deviation; CV: Coefficient of

Jo

ur n

al

Pr

e-

Variation (%); PRESS: Predicted Residual Sum of Squares.

Table 5 ANOVA results of the quadratic models for COD and TU removals

Jo

TU removal (%)

F-value

p-value

Remark

1110.97 78.07 864.40

123.44 78.07 864.40

401.89 254.19 2814.22

<0.0001 <0.0001 <0.0001

Significant Significant Significant

23.93

1

23.93

77.89

<0.0001

Significant

14.16 2.34 1.67 8.17 19.14 5.98 3.07 1.80 1.27 1114.04

1 1 1 1 1 1 10 5 5 19

14.16 2.34 1.67 8.17 19.14 5.98 0.31 0.36 0.25

46.11 7.61 5.45 26.60 62.33 19.46

<0.0001 0.0202 0.0417 0.0004 <0.0001 0.0013

Significant Significant Significant Significant Significant Significant

2191.45 95.09 1809.40

9 1 1

pr

oo

f

Mean square

0.3566

Not significant

Pr

e-

1.41

243.49 95.09 1809.40

577.39 225.48 4290.57

<0.0001 <0.0001 <0.0001

Significant Significant Significant

79.48

1

79.48

188.46

<0.0001

Significant

20.32 4.21 27.83 6.37 89.23 13.10 4.22 2.32 1.89 2195.67

1 1 1 1 1 1 10 5 5 19

20.32 4.21 27.83 6.37 89.23 13.10 0.42 0.46 0.38

48.19 9.98 65.99 15.10 211.60 31.06

<0.0001 0.0102 <0.0001 0.0030 <0.0001 0.0002

Significant Significant Significant Significant Significant Significant

1.23

0.4136

Not significant

ur n

COD removal (%)

Model A-pH B-Coagulant dose C-Contact time AB AC BC A2 B2 C2 Residual Lack of Fit Pure Error Corrected Total Model A-pH B-Coagulant dose C-Contact time AB AC BC A2 B2 C2 Residual Lack of Fit Pure Error Corrected Total

Degrees of freedom 9 1 1

Sum of squares

al

Response Source

of

Table 6

ro

Optimum operating parameters and experimental verification Responses

Optimum conditions

(mg L-1)

(min)

4

15

Observed

Predicted

92.207

92.438

Deviation

re

time

lP

dose

rn a

5.0

Contact

Jo u

pH

Coagulant

TU removal (%)

-p

COD removal (%)

0.231

Desirability

Observed

Predicted

Deviation

90.626

90.329

0.297

100%

Table 7 Kinetic constants of the first-order and the second-order models Wastew

Initial

ic

ater

concentra ion

ant

(k 1

tion

types

or

mode

Condit

Coagul

R2

Kinet

k

D

E

𝑡1⁄

2

Refere nce

First-

Bilge

COD =

pH =

Orchis

0.99

order

water

1202.5

5.0

mascul 46

mg L-1

Dose = a

k1 =

0.12

3.2

1.0

10.

This

0.06

72

36

09

90

study

36

-p

4 mg

×

×

10-

101

20

6

re

L-1

of

k 2)

ro

l

Β

Time

lP

= 15 min Bilge

nd-

water

pH =

Orchis

k2 =

0.00

5.1

6.3

2.0

This

0.00

08

46

46

8

study

×

×

4 mg

10-

101

L-1

18

3

5.1

7.5

20.

[75]

1202.5

5.0

mg L-1

Dose = a

Jo

order

COD =

ur na

Seco

0.98

mascul 44

04

Time = 15 min

Seco

Tannery TU =

pH =

Morin

0.91

69

k2 =

0.00

nd-

industry

121.9

7.0

ga

order

effluent

mg L-1

Dose = oleifer 10 mL

37

0.00

08

04

a (1)

Time

96

06

×

×

10-

101

18

3

51

= 30 min Tannery TU =

pH =

Morin

0.89

k2 =

0.00

6.9

5.6

27.

nd-

industry

121.9

7.0

ga

81

0.00

06

28

29

34

order

effluent

mg L-1

Dose = oleifer

ro

a (2)

-p

10 mL

03

×

×

10-

101

18

3

re

Time

[75]

of

Seco

= 30

lP

min

Table 8

ur na

Moringa oleifera (1): Extracted with NaCl; Moringa oleifera (2): Extracted with KCl

Estimated costs of coagulants per cubic meter of wastewaters Wastewat er

Jo

Coagula nt

Orchis mascula

Bilge water

Red lentil extract Opuntia ficus-

T urbid water Oil sands process-

Effluent characteristi cs

T urbidit y removal (%)

T SS remov al (%)

COD remov al (%)

Optimu m pH

COD: 1202.5 ± 8.5 mg L-1 T urbidity: 132.2 ± 1 NT U T urbidity: 800 NT U pH: 6.8 COD: 162 mg L -1

90.626

-

92.207

5

Coagula nt optimu m dosage (mg L -1) 4

Coagula nt cost

T otal cost for wastewat er treatment

Referen ce

122.5 $

0.49 $ m -3

T his study

0.00789 $ m -3

[92]

0.24 $ m -3

[105]

-1

kg

98.68

-

-

4

26.3

0.30 $ kg-1

97

-

-

7

1500

0.16 $ kg-1

70

T urbid water T urbid water Alum

T annery wastewat er

Chitin effluents

T annery wastewat er

5.5–6

470

520 € m -3

1.05 € m -3

[118]

99

-

-

-

-

-

0.31 $ m -3

[119]

COD: 10.410 mg L-1 T urbidity: 6550 NT U T urbidity: 800 NT U pH: 6.8 T urbidity: 800 NT U pH: 6.8 COD: 2400 mg L -1 T SS: 1508 mg L -1 T urbidity: 1370 NT U T SS: 630 mg L -1 T S: 2540 mg L -1 pH: 12 T urbidity: 188 NT U COD: 2700 mg L -1 T SS: 1078 mg L -1 T urbidity: 1184 NT U

98

-

81

5.5–6

168

245 € m -3

0.75 € m -3

[118]

98.37

-

-

6.8

28.5

0.43 $

COD: 2480 mg L -1 T SS: 1104 mg L -1 T urbidity: 1302 NT U

99.4

COD: 10.410 mg L-1 T urbidity: 6550 NT U

99

-1

kg 95.42

-

T annery wastewat er

PACl

Dairy soiled water

Jo

Alum + Anionic polymer s

-

4

26

0.43 $ kg-1

99.7

86.3

96.4

90.2

97

93.5

ur na

Alum + Cationic polymer s

84

of

Chemical wastewat er of steel industry Dairy soiled water

-

ro

Ferric chloride (FeCl3)

99

53.3

96.3

-

-

-

6

36.2

48.3

83

240

-p

Dairy soiled water

T urbidity: 500 NT U pH: 8.1 COD: 10.410 mg L-1 T urbidity: 6550 NT U COD: 283 mg L -1 T urbidity: 92 NT U

re

affected water

lP

indica

-

-

6–6.5

300

0.43 $ -1

kg

4–30 $ -1

kg

5 (cationic polymer s) 100 (alum)

5 (anionic polymer s) 160 (alum) 250

Cationic polymer s: 4.98 $

0.00123 $ m -3

[92]

0.00112 $ m -3

[92]

0.18 $ m -3

[116]

1.2–9 $ m -3

[120]

0.07 $ m -3

[116]

0.08 $ m -3

[116]

1.36 € m -3

[118]

kg-1 Alum: 0.43 $ kg-1 Anionic polymer s: 3.33 $ kg-1 Alum: 0.43 $ kg-1 675 € m -3

COD: Chemical Oxygen Demand; NTU: Nephelometric Turbidity Unit; PACl: Poly Aluminium Chloride; TSS: Total Suspended Solids

71

72

of

ro

-p

re

lP

ur na

Jo