Advertisement

SN Applied Sciences

, 1:1590 | Cite as

Introducing an applied reactor for treatment of wastewater containing propylene glycol

  • Mahdi GhaderiEmail author
  • Amin Tamadoni
  • Asieh Mahdizadeh
Research Article
  • 117 Downloads
Part of the following topical collections:
  1. Engineering: Nanofluids in Applied Sciences

Abstract

Propylene glycol (PG), classed as alcohol, has been used in many industrial additives. Leaching PG in aquatic environments would cause a significant decrease in dissolved oxygen, and this is the main reason for treatment of runoffs and wastewater contaminated with this pollutant. Previous researches on PG removal from wastewater indicate that biological methods are more economically suitable. Therefore, a new biological reactor was introduced and used for the treatment of wastewater containing PG. Two main approaches for increasing removal efficiency in presented research are utilizing two serial lab-scale Sequencing Batch Reactors and feed backward connection between these reactors. This novel reactor was named Feed Backward Serial Sequencing Batch Reactor. Moreover, response surface method was used for modeling of PG treatment and investigation of interactions and simultaneous effects of independent parameters. Retention time, influent COD, and flow recirculation percentage were considered as independent variables, where COD removal efficiency was the dependent variable. According to the results, the best COD removal efficiency was 47%, and it was achieved in 3.52 h retention time, 1667.76 mg/L influent COD, and 23.33% flow recirculation percentage. Based on the results of the presented research, PG treatment with the presented reactor is feasible.

Keywords

Biological reactors FBSSBR Propylene glycol Sequencing batch reactor 

1 Introduction

In the contemporary world, different environmental problems have been caused as a result of constant development of additives and substances, most of which were supposed to enhance the quality of life [2, 4, 54]. Propylene glycol (PG) is one of these chemicals which is used in various industries including cosmetics, detergents, pesticides, foods, pharmaceutical, and most importantly aircraft deicing fluid, in tremendous amounts [53]. PG can affect organisms and the environment through penetrating groundwater [3] and leakage in surface water. PG significantly impacts marine life by reducing the dissolved oxygen [51]. Also it threatens human health with renal inadequacy and hepatic debilitation [55]. Based on these risks, PG treatment in wastewater seems to be necessary.

There are series of ways for industrial wastewater treatment such as physical, chemical and biological processes [6, 12, 13, 19, 24, 25, 41, 42]. Studying about nano materials and using nano fluids is one of new research interests [33, 34, 35, 36, 37, 38, 39, 40, 43, 44, 45]. Many ways are being used for PG contaminated wastewater treatment. Take, for instance, physical and chemical processes. The conclusion of these studies was that high cost is associated with chemical processes, and they produce secondary pollutants [30, 5]. Also, physical treatment methods showed low efficiency, and it only changed the ambiance of contamination [10, 14]. PG degradation with the Fenton process and FeSO4, H2O2 [51], and propylene glycol phenyl ether adsorption by activated carbon [52] are some physical and chemical procedures.

On the other hand, biological methods are immensely effective at a lower cost. Research on biological PG treatment has shown promising results. For instance, biological treatment of PG wastewater with methanogen bacteria in a semi-continuous reactor at a 35 °C temperature reached 95% of COD removal (Sezgin and Tonuk [31]). In another research, activated sludge achieved 85% PG removal [48]. Ethylene glycol treatment from paint industries wastewater in a continuous biological system showed 97% removal efficiency [18].

However, bio-treatment is not flawless either. In the past century, activated sludge has been widely used in all kinds of wastewater treatment; this method requires a great deal of energy, biomass byproduct, operation cost, and sludge management cost. Many alternatives have been recommended because of technological achievements in the past decades; one such alternative is the sequence batch reactor (SBR) system [20, 46].

Several studies on complicated compound degradation have underscored the importance of ambiance conditions in SBR systems. These studies include research on hydraulic retention time (HRT) study on synthetic petroleum wastewater [32], and olive oil wastewater treatment with SBR [11].

Prediction is crucial in environmental engineering and was studied in previous researches [7, 8, 21, 26, 27, 28, 29, 47]. In past studies, modeling, multivariate optimization, simultaneous effects of independent parameters, and synergistic and antagonistic effects of variables in biological treatment of contaminations were not considered. In this research, SBR was upgraded, and a new biological reactor called Feed Backward Serial Sequencing Batch Reactor (FBSSBR) was introduced. Application of FBSSBR in PG wastewater treatment was evaluated, for the first time. Modeling, multivariate optimization, simultaneous effects of independent parameters, and synergistic and antagonistic effects of variables in biological treatment of PG wastewater were done based on response surface methodology (RSM).

2 Materials and methods

2.1 Reactor design

According to Figs. 1 and 2, the FBSSBR reactor system had two similar reactors with 10 cm diameter, a height of 35 cm, and volume of 2.75 L each. Furthermore, the indicator was placed 30 cm from the bottom. Accordingly, the effective height was 30 cm, and effective volume was 2.335 L. The connection between two reactors for having an FBSSBR was with regular PVC tubes.
Fig. 1

3D scheme of feed backward serial sequencing batch reactor (A: reactor 1, B: reactor 2, C: feed container, D: aeration pump, E&F: cycling pump, H&G: sampling valve)

Fig. 2

2D scheme of feed backward serial sequencing batch reactor

Each time the initial concentration of PG in wastewater was changed, the following steps were followed:
  • At first, both reactors were full of wastewater, and then aeration units were turned on until the end of the retention time. Then aeration was stopped, and each reactor had 30 min time for settling time. Afterward, reactor two was emptied and used for concentration measurement, and reactor two was filled with the content of reactor 1. Finally, reactor one was filled with raw wastewater from the feed container.

  • Secondly, the two reactors were again aerated during their retention time, and in this stage, the wastewater was in circulation between two reactors through the connection pipes. So that, during the retention time, a certain percentage of each reactor volume was transferred to the other (the noted volume percentage in the adaptation phase was 40% and in the main experiment were as “flow recycling percent” column in Table 1). Then, as at the end of Step 1, reactor 2’s content was discharged after the retention time and used for testing. Reactor 1’s wastewater was pumped to reactor two and then, reactor one was filled with raw wastewater from the feed container.
    Table 1

    RSM main experiments details

    Run

    Retention time (h)

    Influent COD (mg/L)

    Flow recirculation percentage (%)

    1

    5.00

    1500.00

    27.50

    2

    3.00

    900.00

    20.00

    3

    5.00

    1500.00

    27.50

    4

    5.00

    1500.00

    40.11

    5

    3.50

    2100.00

    20.00

    6

    5.00

    1500.00

    27.50

    7

    7.52

    1500.00

    27.50

    8

    3.50

    900.00

    35.00

    9

    2.48

    1500.00

    27.50

    10

    6.50

    2100.00

    35.00

    11

    5.00

    1500.00

    14.89

    12

    3.50

    2100.00

    35.00

    13

    5.00

    1500.00

    27.50

    14

    6.50

    900.00

    35.00

    15

    6.50

    900.00

    20.00

    16

    5.00

    1500.00

    27.50

    17

    6.50

    2100.00

    20.00

    18

    5.00

    1500.00

    27.50

    19

    5.00

    490.92

    27.50

    20

    5.00

    2509.08

    27.50

  • Finally, for each concentration of PG, step 2 was repeated until reaching steady-state conditions (less than 2% difference in treatment efficiency). The removal efficiency in steady-state conditions was reported in this paper.

2.2 Dependent and independent parameters and RSM

In this research, a completely new reactor (FBSSBR) was introduced for the biological treatment of propylene glycol. The primary purposes of this research were statistical analysis, modeling, and determination of optimum conditions. For these objectives, the experiments were designed with the response surface methodology.

Response surface methodology has been used in environmental studies, previously [1, 17, 22, 23, 49, 50]. The foundation of RSM is mathematical and statistical techniques. In RSM, linear or square polynomial functions are used for modeling, optimization, and system study [9].

RSM modeling requires effective factors selection. The next step in modeling is determining each factor’s maximum and minimum for experimental matrix design. This process shows the number of main experiment tests. The main goal of the experimental design is to obtain statistically reliable results. The code levels (+ 1) and (− 1) indicate the maximum and minimum limits of each parameter that these two levels should determine based on the main idea of the study. Finally, there is a third level or central (0) between the maximum and minimum.

Design-Expert 7.0.0 software was used for experimental design and analysis of the results. Based on the conditions mentioned in Table 2, three independent variables (retention time, influent COD, and flow recirculation percentage) and a dependent variable of COD removal were selected, and RSM model suggested twenty experiments for the main experiments.
Table 2

RSM design, experiments condition summary

Factor

Units

Type

Low actual

High actual

Low coded

High coded

Mean

Retention time

h

Numeric

3.50

6.50

− 1.000

1.000

5.00

Influent COD

mg/L

Numeric

900.00

2100.00

− 1.000

1.000

1500.00

Flow recirculation percentage

%

Numeric

20.00

35.00

− 1.000

1.000

27.50

2.3 Microorganisms preparing and adaptation; nutrient compound details

Microorganisms were prepared from returned sludge to activated sludge tank in a municipal wastewater treatment plant. Around 40% of the reactors’ volume was filled with sludge (microorganisms), and the rest of the effective volume was filled with wastewater. Table 3 indicates some necessary details of used sludge.
Table 3

RSM main experiments details

Parameter

Measure

PH

7.2

Temperature (°C)

20

Dissolved oxygen (mg/L)

1.8

Total suspended solid (mg/L)

3500

The aeration pump adjusted to mix the reactor content slowly by aeration and keep the dissolved oxygen near 1.5–2.5 mg/L. In specific periods, pH was kept fixed in the range of 6.8–7.2. The experiments were conducted at an ambient temperature between 21 and 25 °C. Inlet COD concentration was 200 (mg/L) during the adaptation period. At the beginning of each cycle, reactors were fed with 100:5:1, C:N:P ratio wastewater. In the adaptation phase, carbon was from glucose and propylene glycol, and in the main experiments, it was from propylene glycol.

The adaptation nutrients order form glucose and PG feed were in accordance with Fig. 3. In the feeding period, retention time was 7.5 h and 40% of reactors volume could circulate between two reactors. For each input, this would be repeated until FBSSBR reached the steady-state condition. In all of the situations in Fig. 3, removal efficiency was 100%. After microorganism adaptation, in the main experiments (Table 1), PG was used as carbon source. Urea was the primary nitrogen source, and KH2PO4 was the phosphor source. C:N:P ratio was fixed in the range of 100:5:1 during all experiments. Table 4 represents other details about micronutrients, which were used to increase the efficiency and activity of microorganisms.
Fig. 3

Order of nutrient compound input from glucose and PG for adaptation feeding

Table 4

details of used micronutrients

Ingredients

Compound name

Chemical formula

Adaptation concentrate (mg/L)

Carbon source

Glucose

C6H12O6·6H2O

0–200

 

Propylene glycol

C3H4O2

0–200

Nutrients

Urea

H2NCONH2

10

 

Potassium hydrogen phosphate

K2HPO4

1

 

Potassium dihydrogen phosphate

KH2PO4

1

Small nutrients

Magnesium sulphate

MgSO4·7H2O

5

 

Calcium chloride

CaCl2

3.75

 

Iron(III)chloride

FeCl3·6H2O

1

 

Sodium molybdate

Na2MoO4·2H2O

1.26

2.4 Experiments instructions

In this research, all experiments were conducted in line with Standard Methods for the Examination of Water and Wastewater [16].

3 Results and discussions

As mentioned, three main factors (retention time, influent COD, and flow recirculation percentage) were used as independent variables. For each run, removal efficiencies were determined, and they are presented in Table 5. According to Table 5, maximum removal efficiency was 98% in 5 h retention time, 490.92 mg/L of initial COD concentration, and 27.5% recycling flow rate.
Table 5

Removal efficiency of RSM main experiments

Run

Retention time (h)

Influent COD (mg/L)

Flow recirculation percentage

Removal efficiency percentage

1

5

1500

27.50

67

2

3

900

20

70

3

5

1500

27.50

67

4

5

1500

40.11

78

5

3.50

2100

20

30

6

5

1500

27.50

67

7

7.52

1500

27.50

83

8

3.50

900

35

85

9

2.48

1500

27.50

36

10

6.50

2100

35

74

11

5

1500

14.89

64

12

3.50

2100

35

38

13

5

1500

27.50

67

14

6.50

900

35

96

15

6.50

900

20

93

16

5.00

1500

27.50

67

17

6.50

2100

20

56

18

5

1500

27.50

67

19

5

490.92

27.50

98

20

5

2509.08

27.50

45

3.1 Individual effect of each parameter

One dimensional diagram represents each factor’s effect on the removal efficiency percentage separately. In each discussion, two out of three independent variables were considered as a fixed value, and the removal efficiency diagram was illustrated based on effect of one variable.

3.1.1 The effect of retention time

Figure 4 shows the removal efficiency in different retention times. According to the results, change of retention time between 3.5 and 6.5 h would cause removal efficiency to increase from 51% to nearly 77%. Hence, it can be concluded that in the FBSSBR system, removal efficiency and retention time are directly related. Previous researchers could confirm this augment; for example, in a fixed bed activated hybrid sludge reactor, there was a similar trend of increase in removal efficiency after increasing retention time [15]. This is in that microorganisms have more consumption and more contact with the contamination within the longer retention time.
Fig. 4

The effect of retention time on COD removal efficiency (Influent COD = 1500 mg/L, flow recirculation percentage = 27.5%)

3.1.2 The effect of influent COD concentration

Figure 5 shows the removal efficiency percentage in different influent COD concentrations. The inverse relationship between inlet COD and removal efficiency is apparent because toxicity increases when COD increases. In other words, in high PG concentrations, microorganisms will lose their metabolic. This research attempts to improve the ability of microorganisms in the FBSSBR system with the adaptation phase.
Fig. 5

The effect of influent COD concentration on COD removal efficiency (Retention time = 5 h, flow recirculation percentage = 27.5%)

In Fig. 5, influent COD concentration changes in the range of 900–2100 mg/L. Based on the outcomes, the removal efficiency was 87% for 900 mg/L initial COD concentration, and close to 50% for 2100 mg/L initial COD.

Synthetic PG wastewater treatment in an activated sludge reactor showed the same trend [48]. A Fixed Bed Activated Sludge Hybrid Reactor also showed the reduction of removal efficiency by increasing in influent COD [15]. Wastewater containing PG (Paint industries’ wastewater) treatment with submerged attached growth reactor (SAGR) in both batch and continuous conditions validated this research result.

3.1.3 The effect of flow recirculation percentage

As seen in Fig. 6, removal efficiency and flow recirculation percentage have a direct relation. By increasing the flow recirculation percentage from 20 to 35%, removal efficiency also increased from 64% (540 mg/L output COD) to 73% (405 mg/L output COD). Flow recirculation percentage was investigated for the first time in this research by introducing FBSSBR reactor, which has a positive effect on removal efficiency.
Fig. 6

The effect of flow recirculation percentage on COD removal efficiency (Retention time = 5 h, Influent COD = 1500 mg/L)

3.2 Simultaneous effect of parameters

3.2.1 The effect of retention time and influent COD concentration

The simultaneous effect of retention time and influent COD concentration on COD removal efficiency was shown in Fig. 7. With influent COD concentration changing between 900 and 2100 mg/L and retention time between 3.5 and 6.5 h, removal efficiency was improved from 32% (for 2100 mg/L COD and 3.5 h retention time) to 89.75% (for 900 mg/L COD and 6.5 h retention time). The output COD in 89.75% removal efficiency was 99 mg/L, which complies with environmental regulations such as USEPA and CEPA standards.
Fig. 7

The effect of retention time and influent COD concentration (flow recirculation percentage = 27.5%)

A closer look at Fig. 7 indicates the more significant effect of retention time in comparison with influent COD concentration. This would indicate the importance of finding an optimum level between these two variables.

3.2.2 The effect of retention time and flow recirculation percentage

Figure 8 shows the effect of retention time and flow recirculation percentage on removal efficiency. The retention time ranged from 3.5 to 6.5 h, and flow recirculation percentage ranged from 20 to 35%. These changes caused decline in the COD removal efficiency in the range of 35% (975 mg/L output COD) to 84% (240 mg/L output COD). As shown in Fig. 8, FBSSBR, COD removal efficiency in the system increased by the increase of these two factors. By using the FBSSBR system idea, some non-degraded pollutants entered reactor one again. Therefore, microorganisms had more chances to adapt and remove the COD. Increasing the retention time also gave more exposure time to microorganisms and improved COD removal efficiency.
Fig. 8

The effect of retention time and flow recirculation percentage (influent COD = 1500 mg/L)

3.2.3 The effect of influent COD concentration and flow recirculation percentage

Figure 9 shows the effect of changing in influent COD concentration and flow recirculation percentage. The influent COD concentration range was 900–2100 mg/L, and flow recirculation percentage range was 20–35%. In these conditions, the removal efficiency changed from 48% (1092 mg/L output COD) to 84% (72 mg/L output COD). It seems reduction in influent COD concentration positively affects removal efficiency. This effect is much greater than the flow recirculation percentage effect.
Fig. 9

The effect of influent COD concentration and flow recirculation percentage (retention time = 5 h)

3.3 Cubic diagram

A cubic diagram determines all three variables’ effects on removal efficiency. The effects of Retention time, influent COD concentration, and flow recirculation percentage on COD removal efficiency are shown in Fig. 10.
Fig. 10

The effect of three independent variables

The maximum removal efficiency (97.82%) occurred within the 6.5 h of retention time, 900 mg/L of influent COD concentration, and 35% of flow recirculation percentage. Minimum removal efficiency (27.86%) occurred within the 3.5 h of retention time, 2100 mg/L of influent COD concentration, and 20% of flow recirculation percentage. Output COD in maximum removal was 72 mg/L.

3.4 Interaction effects of parameters

Interaction diagrams show the interactive effect of two variables on removal efficiency. These effects can be synergetic (where two variables make a positive effect on each other for removal efficiency) or antagonistic (where two variables tend to weaken positive effects of each other on the removal efficiency).

3.4.1 Interactive effect of retention time and flow recirculation percentage

Figure 11 shows the interaction between retention time and flow recirculation percentage. In this figure, when retention time is 3.5 h, by changing inflow recirculation percentage from 20 to 35%, change in removal efficiency is 47–60%. With 6.5 h retention time and the same situation, there was a shift from 73 to 84% in removal efficiency. The diagram slope in 3.5 h of retention time is 0.87, and 6.5 h of retention time is 0.73. This minimal difference indicates the little effect of these two factors on each other.
Fig. 11

Interactive effect of retention time and flow recirculation percentage (influent COD = 1500 mg/L)

3.4.2 Interactive effect of retention time and influent COD concentration

Figure 12 depicts the interactive effect of influent COD concentration and retention time. In this figure, by applying changes in retention time between 3.5 and 6.5 h, removal efficiency changed from 75 to 92% when the influent COD was 900 mg/L. Moreover, the change in removal efficiency was 33–65% when influent COD was 2100 mg/L. The diagram slope of 900 mg/L of influent COD concentration is 5.67, and in 2100 mg/L of influent COD concentration is 10.67. This difference indicates the antagonistic effect of these two factors on each other. Despite less removal efficiency in higher influent CODs, raise in retention time has stronger effect on samples with higher initial COD.
Fig. 12

Interactive effect of retention time and influent COD concentration (flow recirculation percentage = 27.5%)

3.4.3 Interactive effect of influent COD concentration and flow recirculation percentage

Figure 13 shows the interactive effect of flow recirculation percentage and influent COD concentration. In this figure, where the flow recirculation is 20%, by changing influent COD concentration (between 900 and 2100 mg/L COD), removal efficiency changed from 84.5 to 47%, and when there was 35% flow recirculation percentage, removal efficiency deduced from 90.2 to 59%.
Fig. 13

Interactive effect of influent COD concentration and flow recirculation percentage (retention time = 5 h)

3.5 Mathematical model

After results analysis, Eq. 1 was obtained.
$$\begin{aligned} {\text{Removal}}\,{\text{Efficiency}}\,{\text{percent}} & = +\,86.52052 + 14.73595 \times {\text{Retention}}\,{\text{Time}} - 0.068207\,{\text{Influent}}\,{\text{COD}} \\ & \quad {-}\,1.02736\,{\text{flow}}\,{\text{recirculation}}\,{\text{percentage}} + 3.88889{\text{E}} - 003 \times {\text{Retention}}\,{\text{Tim}}e \\ & \quad \times \,{\text{Influent}}\,{\text{COD}}{-}0.022222\,{\text{Retention}}\,{\text{Time}} \times {\text{flow}}\,{\text{recirculation}}\,{\text{percentage}} \\ & \quad + \,2.22222{\text{E}} - 004\,{\text{Influent}}\,{\text{COD}} \times {\text{flow}}\,{\text{recirculation}}\,{\text{percentage}} \\ & \quad {-}\,1.14133 \times {\text{Retention}}\,{\text{Time}}^{2} + 4.65181{\text{E}} - 006 \times {\text{Influent}}\,{\text{COD}}^{2} \\ & \quad + \,0.026629 \times {\text{flow}}\,{\text{recirculation}}\,{\text{percentage}}^{2} \\ \end{aligned}$$
(1)
Based on Eq. 1, the effect of retention time is more than the other two factors. The comparison between effectiveness of each factor is as the following:
$${\text{Retention}}\,{\text{Time}} > {\text{flow}}\,{\text{recirculation}}\,{\text{percentage}} > {\text{Influent}}\,{\text{COD}}$$

3.6 Analysis of variance

The ANOVA test strategy is based on using data to calculate the effect of each factor on the results. Table 6 indicates the ANOVA analysis result. The mean square and Fisher value are some of the factors of this table, but the important column in this table is the P value column. The P value of the model was below 0.0001, which shows that the presented mathematical model is statistically significant.
Table 6

Analysis of variance results

Source

Sum of squares

df

Mean squares

F value

P value

prob > F

Model

6918.37

9

768.71

76.54

< 0.0001

A-retention time

224,360

1

2243.60

223.40

< 0.0001

B-influent COD

4048.41

1

4048.41

403.11

< 0.0001

C-flow recirculation percentage

334.07

1

334.07

33.26

0.0002

AB

98.00

1

98.00

9.76

0.0108

AC

0.50

1

0.50

0.050

0.8279

BC

8.00

1

8.00

0.80

0.3931

A2

95.04

1

95.04

9.46

0.0117

B2

40.42

1

40.42

4.02

0.0726

C2

32.33

1

32.33

3.22

0.1030

Residual

100.43

10

10.04

  

Lack of fit

100.43

5

20.09

  

Pure error

0.000

5

0.000

  

Cor total

7018.80

19

   

3.7 Optimum condition and validation investigation

The optimum condition occurred in the 3.52-h of retention time, 1667.76 mg/L of influent COD concentration, and 23.23% of flow recirculation percentage. Due to this condition, based on the mathematical equation, the optimum removal efficiency was 47%. Based on experimental results, this removal efficiency had less than 5% error, and this error showed the model’s accuracy. The experimental results confirmed the model results ultimately, as seen in Fig. 14.
Fig. 14

Predicted values versus actual values

4 Conclusions

This research studied FBSSBR’s practical factors simultaneously. The RSM model was successfully applied to investigate the effect of three independent factors. The retention time was the most effective factor. Influent COD concentration by changing the environment’s toxicity was the next effective factor. Approximately 97.82% was the highest removal efficiency. Among the other biological reactors, FBSSBR was successful in PG treatment.

Notes

Compliance with ethical standards

Conflict of interest

The authors declare they have no conflict of interest.

References

  1. 1.
    Azizpour F, Qaderi F (2019) Optimization modeling and uncertainty investigation of phenolic wastewater treatment by photocatalytic process in cascade reactor. Environ Dev Sustain.  https://doi.org/10.1007/s10668-019-00480-8 CrossRefGoogle Scholar
  2. 2.
    Abdel-Fatah MA, Shaarawy HH, Hawash SI (2019) Integrated treatment of municipal wastewater using advanced electro-membrane filtration system. SN Appl Sci.  https://doi.org/10.1007/s42452-019-1178-9 CrossRefGoogle Scholar
  3. 3.
    Alamdar R, Kumar V, Moghtaderi T, Naghibi SJ (2019) Groundwater quality evaluation of Shiraz City, Iran using multivariate and geostatistical techniques. SN Appl Sci 1:1367.  https://doi.org/10.1007/s42452-019-1108-x CrossRefGoogle Scholar
  4. 4.
    Albuquerque MTD, Antunes IMHR, Oliveira NP, Pelletier G (2019) Impact of sewage effluent discharges prediction using QUAL2Kw in a sensitive protected area: Portugal. SN Appl Sci.  https://doi.org/10.1007/s42452-019-1095-y CrossRefGoogle Scholar
  5. 5.
    Amritha A, Sundararajan M, Rejith RG, Mohammed-Aslam MA (2019) La-Ce doped TiO2 nanocrystals: a review on synthesis, characterization and photocatalytic activity. SN Appl Sci 1:1441.  https://doi.org/10.1007/s42452-019-1455-7 CrossRefGoogle Scholar
  6. 6.
    Asadi P, Amini Rad H, Qaderi F (2019) Comparison of Chlorella vulgaris and Chlorella sorokiniana pa.91 in post treatment of dairy wastewater treatment plant effluents. Environ Sci Pollut Res.  https://doi.org/10.1007/s11356-019-06051-8 CrossRefGoogle Scholar
  7. 7.
    Babanezhad E, Amini Rad H, Hosseini Karimi SS, Qaderi F (2017) Investigating nitrogen removal using simultaneous nitrification-denitrification in transferring wastewater through collection networks with small-diameter pipes. Water Pract Technol 12:396–405.  https://doi.org/10.2166/wpt.2017.044 CrossRefGoogle Scholar
  8. 8.
    Babanezhad E, Qaderi F, Salehi Ziri M (2018) Spatial modeling of groundwater quality based on using Schoeller diagram in GIS base: a case study of Khorramabad, Iran. Environ Earth Sci 77:339.  https://doi.org/10.1007/s12665-018-7541-0 CrossRefGoogle Scholar
  9. 9.
    Bezerra MA, Santelli RE, Oliveira EP, Villar LS, Escaleira LA (2008) Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta 76:965–977CrossRefGoogle Scholar
  10. 10.
    Cardoso CMM, Zavarize DG, Lago PA, Pedroza MM, Brum SS, Mendonça ARV (2019) Evaluating adsorbent properties of drinking water treatment plant sludge-based carbons activated by K2CO3/CH3COOH: a low-cost material for metal ion remediation. SN Appl Sci 1:686.  https://doi.org/10.1007/s42452-019-0709-8 CrossRefGoogle Scholar
  11. 11.
    Chiavola A, Farabegoli G, Antonetti F (2014) Biological treatment of olive mill wastewater in a sequencing batch reactor. Biochem Eng J 85:71–78CrossRefGoogle Scholar
  12. 12.
    Faghih Nasiri E, YousefiKebria D, Qaderi F (2018) An experimental study on the simultaneous phenol and chromium removal from water using titanium dioxide photocatalyst. Civil Eng J 4(3):585–593.  https://doi.org/10.28991/cej-0309117 CrossRefGoogle Scholar
  13. 13.
    Faghih Nasiri E, Yousefi Kebria D, Qaderi F (2019) The degradation of phenol in water solution by immobilized TiO2 photocatalysis. J Civil Environ Eng 48(93):43–49Google Scholar
  14. 14.
    Fakhru’razi A, Pendashte A, Abdullah CL, AwangBiak DR, Madaeni SS, Abidin ZZ (2009) Review of technologies for oil and gas produced water treatment. J Hazard Mater 170:530–551CrossRefGoogle Scholar
  15. 15.
    Farzadkia M, Rezaei Kalantary R, Mousavi G, Jorfi S, Gholami M (2010) The effect of organic loading on propylene glycol removal using fixed bed activated sludge hybrid reactor. Chem Biochem Eng Q 24(2):227–234Google Scholar
  16. 16.
    Greenberg AE, Clesceri LS, Eaton AD (2000) Standard methods for the examination of water and wastewater, 20th edn. American Public Health, WashingtonGoogle Scholar
  17. 17.
    Khalegh R, Qaderi F (2019) Optimization of the effect of nanoparticle morphologies on the cost of dye wastewater treatment via ultrasonic/photocatalytic hybrid process. Appl Nanosci.  https://doi.org/10.1007/s13204-019-00984-9 CrossRefGoogle Scholar
  18. 18.
    Krithika D, Philip L (2015) Treatment of wastewater from water-based paint industries using submerged attached growth reactor. Int Biodeterior Biodegrad 107:31–41CrossRefGoogle Scholar
  19. 19.
    Pajoum Shariati F, Qaderi F, Haeri H (2019) Using moving bed biofilm reactor including kaldness media in treatment of wastewater containing light component petroleum. J Civil Environ Eng 49:1–19Google Scholar
  20. 20.
    Qaderi F, Ayati B, Ganjidoust H (2011) Role of moving bed biofilm reactor and sequencing batch reactor in biological degradation of formaldehyde wastewater. Iran J Environ Health Sci Eng 8(4):295–306Google Scholar
  21. 21.
    Qaderi F, Babanezhad E (2017) Prediction of the groundwater remediation costs for drinking use based on quality of water resource, using artificial neural network. J Clean Prod 161:840–849.  https://doi.org/10.1016/j.jclepro.2017.05.187 CrossRefGoogle Scholar
  22. 22.
    Qaderi F, Sayahzadeh AH, Azizi M (2018) Efficiency optimization of petroleum wastewater treatment by using of serial moving bed biofilm reactors. J Clean Prod 192:665–677.  https://doi.org/10.1016/j.jclepro.2018.04.257 CrossRefGoogle Scholar
  23. 23.
    Qaderi F, Sayahzadeh AH, Azizpour F, Vosughi P (2018) Efficiency modeling of serial stabilization ponds in treatment of phenolic wastewater by response surface methodology. Int J Environ Sci Technol.  https://doi.org/10.1007/s13762-018-1816-6 CrossRefGoogle Scholar
  24. 24.
    Qaderi F, Asadi P, Tamadoni A, Azizi M (2018) Evaluation of sustainability of development in zone 22 of Tehran by ecological footprint method. Geogr Dev Iran J 16(50):231–245.  https://doi.org/10.22111/gdij.2018.3575 CrossRefGoogle Scholar
  25. 25.
    Qaderi F, Ayati B, Ganjidoust H, Sarraf-Mamoory R (2015) Investigation of kinetic and intermediate products of acid orange 7 removal by hybrid ozonation/photocatalytic processes. Modares J Civil Eng 15(2):79–89Google Scholar
  26. 26.
    Qaderi F, Sayahzadeh AH, Ebrahimi GM (2019) Optimization of effective environmental parameters on Astrazon Red GTL removal by dominant species Bacillus and Aeromonas: in a concurrent culture study. J Mol Cell Res 32(1):1–15Google Scholar
  27. 27.
    Qaderi F, Ayati B (2014) Comparison of MBBR and SBAR in treating toxic formaldehyde wastewater. J Civil Environ Eng 44(74):99–106Google Scholar
  28. 28.
    Qaderi F, Ayati B, Ganjidoust H (2012) Comparing the efficiency of MBBR and SBR in treating wastewater containing formaldehyde. Amirkabir J Civil Eng 43(2):43–50Google Scholar
  29. 29.
    Qaderi F, Ayati B, Ganjidoost H, Sarraf MR (2015) Treatment of wastewater containing acid orange 7 using ozonation process and determination of the intermediate by-products. J Water Wastewater 26(2):13–23Google Scholar
  30. 30.
    Robinson T, McMullan G, Marchant R, Nigam P (2001) Remediation of dyes in textile effluent: a critical review on current treatment technologies with a proposed alternative. Biores Technol 77:247–255CrossRefGoogle Scholar
  31. 31.
    Sezgin N, Tonuk GU (2013) Anaerobic treatability of wastewater contaminated with propylene glycol. Bull Environ Contam Toxicol 91(3):320–323CrossRefGoogle Scholar
  32. 32.
    Shariati SRP, Bonakdarpour B, Zare N, Ashtiani FZ (2011) The effect of hydraulic retention time on the performance and fouling characteristics of membrane sequencing batch reactors used for the treatment of synthetic petroleum refinery wastewater. Biores Technol 102:7692–7699CrossRefGoogle Scholar
  33. 33.
    Sheikholeslami M (2017) Magnetic field influence on CuO–H2O nano fluid convective flow in a permeable cavity considering various shapes for nanoparticles. Int J Hydrogen Energy 42:19611–19621CrossRefGoogle Scholar
  34. 34.
    Sheikholeslami M (2018) Solidification of NEPCM under the effect of magnetic field in a porous thermal energy storage enclosure using CuO nanoparticles. J Mol Liq 263:303–315CrossRefGoogle Scholar
  35. 35.
    Sheikholeslami M (2018) Numerical modeling of nano enhanced PCM solidification in an enclosure with metallic fin. J Mol Liq 259:424–438CrossRefGoogle Scholar
  36. 36.
    Sheikholeslami M (2018) Numerical simulation for solidification in a LHTESS by means of nano-enhanced PCM. J Taiwan Inst Chem Eng 86:25–41CrossRefGoogle Scholar
  37. 37.
    Sheikholeslami M (2018) Influence of magnetic field on Al2O3–H2O nanofluid forced convection heat transfer in a porous lid driven cavity with hot sphere obstacle by means of LBM. J Mol Liq 263:472–488CrossRefGoogle Scholar
  38. 38.
    Sheikholeslami M (2018) Finite element method for PCM solidification in existence of CuO nanoparticles. J Mol Liq 265:347–355CrossRefGoogle Scholar
  39. 39.
    Sheikholeslami M, Rokni HB (2018) Magnetic nanofluid flow and convective heat transfer in a porous cavity considering Brownian motion effects. Phys Fluids 30(1):1–8.  https://doi.org/10.1063/1.5012517 CrossRefGoogle Scholar
  40. 40.
    Sheikholeslami M, Ghasemi A (2018) Solidification heat transfer of nanofluid in existence of thermal radiation by means of FEM. Int J Heat Mass Transf 123:418–431CrossRefGoogle Scholar
  41. 41.
    Sheikholeslami Z, YousefiKebria D, Qaderi F (2018) Nanoparticle for degradation of BTEX in produced water; an experimental procedure. J Mol Liq 246:476–482.  https://doi.org/10.1016/j.molliq.2018.05.096 CrossRefGoogle Scholar
  42. 42.
    Sheikholeslami Z, YousefiKebria D, Qaderi F (2018) Investigation of photocatalytic degradation of BTEX in produced waterusing γ-Fe2O3 nanoparticle. J Therm Anal Calorim.  https://doi.org/10.1007/s10973-018-7381-x CrossRefGoogle Scholar
  43. 43.
    Sheikholeslami M, Mahian O (2019) Enhancement of PCM solidification using inorganic nanoparticles and an external magnetic field with application in energy storage systems. J Clean Prod 215:963–977CrossRefGoogle Scholar
  44. 44.
    Sheikholeslami M (2019) New computational approach for exergy and entropy analysis of nanofluid under the impact of Lorentz force through a porous media. Comput Methods Appl Mech Eng 344:319–333MathSciNetCrossRefGoogle Scholar
  45. 45.
    Sheikholeslami M (2019) Numerical approach for MHD Al2O3–water nano fluid transportation inside a permeable medium using innovative computer method. Comput Methods Appl Mech Eng 344:306–318MathSciNetCrossRefGoogle Scholar
  46. 46.
    Singh M, Srivastava RK (2011) Sequencing batch reactor technology for biological wastewater treatment: a review. Asia Pac J Chem Eng 6:3–13CrossRefGoogle Scholar
  47. 47.
    Taghizadeh M, Kebria DY, Qaderi F (2019) Benzene and toluene removal from saline water with coupled membrane process and nanophotocatalyst. J Pet Res 27(10300695):168–179Google Scholar
  48. 48.
    Talaiekhozani A, Jorfi S, Fulazzaky MA, Ponraj M, Abd Majid MZ, Navarchian AH, Talaie MR, Zare S (2014) Lab-scale optimization of propylene glycol removal from synthetic wastewater using activated sludge reactor. Desalin Water Treat 52(19–21):3585–3593CrossRefGoogle Scholar
  49. 49.
    Tamadoni A, Qaderi F (2019) Optimization of soil remediation by ozonation for PAHs contaminated soils. Ozone Sci Eng.  https://doi.org/10.1080/01919512.2019.1615865 CrossRefGoogle Scholar
  50. 50.
    Tavakoli Moghadam M, Qaderi F (2019) Modeling of petroleum wastewater treatment by Fe/Zn nanoparticles using the response surface methodology and enhancing the efficiency by scavenger. Results Phys 15:102566–102576.  https://doi.org/10.1016/j.rinp.2019.102566 CrossRefGoogle Scholar
  51. 51.
    Wang HY, Hu YN, Cao GP, Yuan WK (2011) Degradation of propylene glycol wastewater by Fenton’s reagent in a semi-continuous reactor. Chem Eng J 170:75–81CrossRefGoogle Scholar
  52. 52.
    Weidhaas J, Lin LL, Buzby K (2016) A case study for orphaned chemicals: 4-methylcyclohexane methanol (MCHM) and propylene glycol phenyl ether (PPH) in riverine sediment and water treatment processes. Sci Total Environ 574:1396–1404CrossRefGoogle Scholar
  53. 53.
    Weste R, Banton M, Hu J, Klapacz J (2014) The distribution, fate, and effects of propylene glycol substances in the environment. Rev Environ Contam Toxicol 232:107–138Google Scholar
  54. 54.
    Yavari SM, Qaderi F (2018) Determination of thermal pollution of water resources caused by Neka power plant through processing satellite imagery. Environ Dev Sustain.  https://doi.org/10.1007/s10668-018-0272-2 CrossRefGoogle Scholar
  55. 55.
    Zar T, Graeber C, Perazella MA (2007) Recognition, treatment, and prevention of propylene glycol toxicity. Semin Dial 20(3):179–284CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mahdi Ghaderi
    • 1
    Email author
  • Amin Tamadoni
    • 2
  • Asieh Mahdizadeh
    • 2
  1. 1.Mechanical Engineering DivisionTorbat-e Jam High Educational ComplexTorbat-e JamIran
  2. 2.Faculty of Civil EngineeringBabol Noshirvani University of TechnologyBabolIran

Personalised recommendations