**Implementation of Basic Principles of Econometric Analysis in Petroleum Technology: A Review of the Econometric Evidence**

Constantinos Tsanaktsidis and Konstantinos Spinthiropoulos

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.80510

#### Abstract

In the present study we give the opportunity to understand the physicochemical parameters of distilling petroleum products applying the basic principles of econometric analysis. The quality of the different fuels is expressed by a series of physical, chemical or other characteristics. The connection between production process and quality of fuel is crucial in the field of petroleum technology. It is remarkable that the used method of the regression analysis perfectly illustrates the relationships between the variables in all applied models. Econometrics is one the best methods to study the variation of the physicochemical properties of the oil. The use of econometrics methods in petroleum chemistry turned out to be useful tool in order to prove that there is indeed strong rates volatility and correlation between physicochemical properties of oils with their mixes. In Petroleum Industry the most common types of Diesel fuels are the biodiesel, biomass to liquid or gas to liquid Diesel. The results of our research can be an important tool for the development of software that can anticipate changes of physicochemical properties of petroleum distillate products, taking into account specific parameters.

Keywords: diesel, JP8, biodiesel, econometric, analysis

#### 1. Introduction

Econometric applications are now accepted in order to have safe conclusions in oil technology as well. The use of such applications can provide the necessary information to deal with problems, particularly in terms of fuel quality. The forecast is based on mathematical equations that take

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into account specific fuel price constraints. In this way, it is possible to check the quality of the fuel during the production process and also the quality at its distribution points.

Econometric analysis is used in order to find out whether there is a relationship or not between particular variables. The relative evaluation realization demands the application of statistic and mathematic methods which are focused on the variables features used by the analysis.

The use of linear regression analysis is one of the most famous econometric methods. By using structure and unstructured data we can evaluate empirical research and taking into account the theory we can come up with safe conclusions [1]. An introductory economics textbook describes econometrics as allowing economists "to sift through mountains of data to extract simple relationships [2]. The first known use of the term "econometrics" was by Polish economist Pawel Ciompa in 1910 [3].

As already mentioned above the basic tool for econometric analysis is the linear regression method. With this model we can create models and come up to safe conclusions. The linear regression method can also be easily applied in the case of oil or pure diesel blends. In modern econometrics, other statistical tools are frequently used, but linear regression is still the most widespread method among all. Especially for the study of the physicochemical properties of the oil, meaning diesel and biodiesel, we are dealing with non-chronological data. Econometrics is one the best methods to study the variation of the physicochemical properties of the oil in order to draw strong and reliable conclusions about the quality of the examined mixture and in most cases to able to preview the values of its physicochemical property of the tested model fuels [4]. Estimating a linear regression on two variables can be visualized as fitting a line through data points representing paired values of the independent and dependent variables [4].

Greene [5] suggests that the environmental and energy issues increase the demand for different fuel types of altered and mixed oils. The market of petroleum products is enlarged as a consequence of the changes in the environment of life that have originated from the improvement of the living conditions, the cities expanding as a result of the massive population movement and the expanded basic needs.

Next to this, the national trade balance and the economic development are influenced by this compulsory demand since Greece imports more crude oil products owing to the expansive requirement. Additionally, the transportation energy use of diesel causes a serious environmental pollution by its gases, leading EU countries to command against them and avoid the bad side effects of the polluted environment that are greenhouse effect, acid rain and serious health issues.

So, all the referred arguments make the biodiesel an only-way solution that will replace the diesel oil as long as it is able to respond to the current increased needs of the means of transport and has also the diesel oil characteristics so as to meet the requirements of producing the appropriate energy. Its main features are to be mixable, efficient and stable unexceptionally [6]. Micro emulsions, thermal cracking (pyrolysis), transesterification (alcoholysis) are the basic four proper procedures of producing biodiesel and according to the usual course of things the direct utilization and mixing follows [7].

At this point we should refer that the transesterification of natural and fats oils is the method that is mainly used. So the application of alkalis, acids or enzymes facilitates the process since they implement the catalysis to the transesterification chemical reaction of triglycerides with alcohols (ethanol, propanol, methanol, amyl alcohol or butanol).

The researchers [8] attempt to succeed in defining the accurate new fuels characteristics based on the extracted equations by using the linear regression model which is the basic tool for econometrics. According to their findings, the performance of socially responsible firms is negatively related to an increase of global CO2 emissions. In addition, the methods of econometrics will be applied to the variables targeting to retrace the connection points between the mix and the fuels physicochemical properties in the tested models.

In other words with the use of econometrics methods they to prove that there is indeed strong rates volatility and correlation between physicochemical properties of oils with their mixes. This relationship can be represented by mathematical equations after the application of linear regression [9, 10].

Naturally, as far as mixes are concerned, the Y = ax + b is the suggestive linear equation that is capable of pointing variables reactions. The variables relation could be stated by the simple linear regression model as the following equation shows:

$$Y\_i = b\_0 + b\_i X + \mathcal{U}\_t \tag{1}$$

where Yi: i is the experimental value of the dependent variable physicochemical property as well.

Xi: i is the value of the independent variable Mix.

b0, b1: the straight regression coefficients.

Ut: the equation error.

Finally, the referred estimating process should be applied to fuels identified with similar properties under the advisable temperature that it must be followed at its experiment. The use of econometrics in order to study the physicochemical properties of the oil enables further investigation of the relationship between physicochemical properties in the same or different oil blends. Through the equations it possible to identify in advance where to be expected the change of values of the mixtures and to preview the values of the examined physicochemical properties.

The process of experimenting followed international standards as well as ISO procedures in order for the results to be right. By using econometric methods, the authors have demonstrated that there is a positive relationship between the model variables. The extraction of equations was the main econometric result of the authors' research.

#### 2. Materials

Biodiesel is one of the most well-known and widely used fuels in the world [23]. Speaking about oil and specific for Diesel oil we must mention that in in general is any liquid fuel used in diesel engines. Essentially diesel fuel is a mixture of hydrocarbons which are come from petroleum. Petroleum crude oils are composed of hydrocarbons of three major classes: (1) paraffinic, (2) naphthenic (or cycloparaffinic), and (3) aromatic hydrocarbons. The most common types of Diesel fuels are the biodiesel, biomass to liquid or gas to liquid Diesel.

When we are using the word Biodiesel we first have to give the definition of a particular fuel type [24]. Biodiesel refers to clean burning renewable fuel made using natural vegetable oils and fats. Biodiesel can be used as an alternative solution of the petroleum, diesel fuel or in most cases can be blended with petroleum diesel fuel in any proportion. Of course, this particular type of fuel is used not only on engines but is it also applicable to other uses.

It is known that the process by which the biodiesel is produced involves the esterification of the mother oil (and/or fat) with methanol and with the catalyst. The result may include components such as the residual catalyst, which are not desirable. For example, the presence of glycerol, although separated during the production of biodiesel, is almost certain in the final biodiesel.

Jet Propellant 8 (JP8) is a kerosene based fuel which when blended with specific additives constitutes a suitable material for military applications. This type of fuel has previously been used as a fuel for aircraft. However, its capabilities have also led to its use for ground vehicles [11].

The main reason was that military forces like NATO and especially the United States of America decided to use a specific fuel for their transport and activities. JP8 consists by approximately 99.8% kerosene by weight and is a complex mixture of higher order hydrocarbons, including alkanes, cycloalkanes, and aromatic molecules. JP8 contains three mandatory additives: a fuel system icing inhibitor, a corrosion inhibitor, and a static dissipater additive.

It is also known that JP8 is a fuel that can easily be adapted to any requirements and applications. This is done by adding chemicals with antioxidants.

Its composition is a mixture of petroleum hydrocarbons. In general, the mixture is of the methane series and contains 10–16 carbon atoms per molecule. Of course, the presence of paraffin is a key ingredient in making it used as a fuel in jet engines.

A framework for analyzing unstructured data using statistical methods in order to verify the existence and the type of correlation between the blends pure Diesel and Biodiesel and the respective physicochemical property.

It is also noted, in nowadays, that the statistics science is based on the chemistry and chemical engineering issues, so the interconnection between the statistical models and methods and the chemical experiments is very obvious provided that the chemical experiments and data analysis use them as a guide in the framework of their researches.

Firstly, a significant presentation of this interrelation should take place, and then the calibration matter should be studied. Speaking specifically, the generalized standard addition method (GSAM) will be examined targeting the following areas: initially, the upgrading of statistics field and the future estimation theory and the multicomponent analysis development as well and secondly the registration of the statisticians character in order to achieve a constitutive communication and understanding between the sciences and scientists of the two fields.

During our research we made experiments in order to export a regression equation which describes the variation of the physicochemical property taking into account, in most cases, the parameter of time.

Throughout our experiments we studied the variation of humidity of Diesel fuel with Time [12]; the variation of kinematic viscosity in blends of Diesel fuel with Biodiesel [4]; the variation of humidity and reduction conductivity of JP8 (F34) with time [13]; the variation of heat of combustion in blends of Diesel fuel with Biodiesel [14]; the variation of density in blends of Diesel fuel with Biodiesel [15]; the variation of conductivity in blends of Diesel fuel with Biodiesel [8]; the contribution of different kinds of biodiesel on the conductivity and density of blends of diesel and biodiesel fuels (animal and vegetable) [16]; the variation of humidity of conventional diesel with time [17]; the variation of density of diesel-biodiesel blends across the scale (0–100)% by adding each time 2% biodiesel and then measuring the density of 3 different temperatures (5, 15, 25)�C [9]. The results of our research are based on tables and figures. All tables and related figures are available in the published papers.

#### 3. Results and discussion

In order to determine the suitability of fuel and the reduction of pollutants to the environment, quality control of alternative fuels is considered to be necessary. What we can certainly find almost all of the fuel is the presence of water. In any case, its presence causes problems such as erosion. Our effort is to modify the percentage of dampness in diesel oil through an equation. The whole process involves introducing hydrophilic LPG polymers into diesel fuel samples. Monitoring the whole process resulted in the moisture content being recorded over a period of time, and based on these results, we created an equation. In all cases and throughout our experiment the volume of fuel and mass remain constant [18–21].

Specifically, in this paper we have examined the regression model that we can see below,

$$Y\_i = b\_0 + b\_i \operatorname{Sim}\left(T\_i\right) + \mathcal{U}\_t \tag{2}$$

The adaptability and suitability of the prototype model was confirmed by the application of specific econometric controls. Specifically, we examined the adaptability of the standard residues and their squares in order to check whether or not they are free from serial correlation. We also tested the existence of first order autocorrelation using the Durbin-Watson index [22].

The time (T) and the change in humidity was a basic question that we wanted to answer with this research using the linear regression model. The results showed that the presence of moisture declined over time, even reaching its maximum value in the first hour. Essentially with this equation we can predict humidity values over a period of time. By applying this equation we can know when we can remove the greatest amount of moisture from the fuel.

In our next research we tried to export an equation which describes the variation of kinematic viscosity in blends of diesel fuel with biodiesel. Using specific volume of these blends we determine kinematic viscosity via method ASTM D 445-06 using a capillary glass viscometer in order to study the contribution of quantity of biodiesel and convert the statistical data into mathematic relation as a specific formula, attempting to achieve an empirical evaluation.

Trying to accomplish this, we studied the way how the values of variables are changed and whether a relation exist using dispersion diagrams [4]. From the graphic depiction realized that the relation is linear and they proceeded to regression analysis. The analysis extracted the conclusion that the relation was strong and the values of the dependent variable kinematic viscosity was depended on a large percentage of the values of the mixture of fuels.

As far as the mix is concerned, the authors expected that the upgrade of the animal and vegetable biodiesel would be proportional to the increase of the kinematic viscosity arithmetic outcome due to the linear relation that connects the two variables. Studying on the regression, the authors notice that Eq. (4) has correct signals as they are aware of theory, so that the bigger addition of biodiesel on pure diesel will make the mix quote lower and lower real values to kinematic viscosity. The authors came to the relative conclusion as it is defined by the following: Yi ¼ b<sup>0</sup> þ biX þ Ut.

Yi: i is the observation of the dependent variable kinematic viscosity;

Xi: i is the observation of the independent variable mix;

b0, b1 are the straight regression coefficients and Ut represents the equation error.

$$Y\_i = 40.914 + 0.0540 \,\text{X} \tag{3}$$

We come to the conclusion that there is an actual strong linear relation that connects the above mix with the kinematic viscosity. It is remarkable that the mix independent variable has a strong linear relation to the Kinematic Viscosity. The autocorrelation heteroskedasticity absence that was combined to the strong linear relation of the variables made the authors to conclude that Eq. (3) is able to preview the kinematic viscosity of the model fuels [4].

In 2012, Tsanaktsidis et al. [13] showed that the proposed equation can predict the moisture content and the height of conductivity in JP8 (F34) fuel over time. Essentially an ingredient, called hydrophilic polymer, leads to the reduction or even the elimination of humidity over a certain time. For example over 39% of humidity and 36% conductivity are decreased over 2 hours. The elimination of humidity makes the fuel suit able for car machines and gives combustion with less pollution for the environment. Thus, the quality of the fuel as well as its combustion efficiency can be improved while the reduction of water concentration enhances the secure of the combustion machine's operation.

The preliminary statistical analysis of sequences of humidity Y and time T have shown that the modifications of Y, probably determined by the sine of T. Specifically, in this paper we have examined the regression model in order to investigate the relation between humidity of JP8 fuel and the time, when the volume of the fuel and the mass of the polymer maintain stable. Some diagnostic tests were performed to establish goodness of fit and appropriateness of the model. First, the authors examined whether the standardized residuals and squared standardized residuals of the estimated model were free from serial correlation. In addition, the independence of the standardized residuals was confirmed by the Durbin Watson statistics.

Implementation of Basic Principles of Econometric Analysis in Petroleum Technology: A Review… 45 http://dx.doi.org/10.5772/intechopen.80510

$$Y\_i = b\_0 + b\_i \operatorname{Sim}\left(T\_i\right) + \mathcal{U}\_t \tag{4}$$

More precisely, the results of the current investigation showed that the change of humidity could be described via a regression equation at which the dependent variable is the humidity change and the independent variable is the cosine of the time that the hydrophilic polymer TPA remains at the fuel. The results of the analysis showed that in the long run the presence of moisture decreases. In fact, it reaches its maximum value in the first hour. Hence, with the proposed equation, moisture rates can be predicted for a period of time. Eliminating work makes fuel more suitable for machines and polluting less the environment. Thus, the results of the present study give the possibility of humidity removal via the polymer TPA, which can be reduced [13].

Continuing our research and using specific volume of blends of Diesel fuel with Biodiesel we determined heat of combustion, in order to study the contribution of each kind of Biodiesel and we converted the statistical data into mathematic relations as a specific formula, attempting to achieve an empirical evaluation. The linear regression model was selected in order to correctly evaluate the values of the dependent variable (relative to the corresponding values of the independent variable). The preliminary statistical analysis of our series has led to the conclusion that the relation that links the variables of our model is linear [14].

We have created two blends which were blend 1, which referred to the pure diesel with the animal fats biodiesel and the blend 2 which referred to the pure diesel and the vegetable biodiesel. The two blends were studied in terms of the heat of combustion value in order to find the equation that might define the blends heat of combustion value. Studying on the regression, we notice that the Eqs. (5) and (6) had correct signals as the blend value increase should quote positively the heat of combustion variable.

$$\text{Heat of }\begin{array}{c}\text{Combustion } 1 = & 40.914 + 0.0540 \text{ } blend \text{ } 1 \end{array} \tag{5}$$

$$\begin{array}{cccc} \text{Heat of } \multicolumn{3}{c}{\text{Combustion 2}} & = & \text{39.297} + 0.062 & \text{blend } \multicolumn{3}{c}{\text{2}} \end{array} \tag{6}$$

By studying the relationship of the variables initially with diagrams, we have concluded that the relationship between fuel (Diesel + Animal Fat Biodiesel = Mixture 1 and Diesel + Vegetable Biodiesel = Mixture 2) is linear. The strong linear positive relation of the variables made us conclude that the Eqs. (5) and (6) were able to preview the heat of combustion of the model fuels [14].

Furthermore Tsanaktsidis et al. [15] tried to export equations that would be able to describe the variation of density in blends of Diesel fuel with Biodiesel. Using specific volume of those blends, we determined density (while temperature maintain stable), in order to study the contribution of each kind of Biodiesel and we converted the statistical data into mathematic relations as a specific formula, attempting to achieve an empirical evaluation.

In that study [15] we used pure Diesel and two kinds of Biodiesel; Biodiesel by vegetables (vegetable oil fuel) and Biodiesel by animal fats. The total volume of each blend was 100 mL and in every measurement the volume of Biodiesel was changing. In the third blend the percentage of each kind of Biodiesel was 50% of the total volume of Biodiesel (for example 20% blend included: 80 mL Diesel, 10 mL Vegetable Biodiesel and 10 mL Animal Fats Biodiesel).

We measured density via the method ASTM D 1298-99 firstly in pure Diesel and then in all blends. The preliminary statistical analysis of the density series and the mix have showed that the variations of the variable den (Υ) were defined by the rates of the variable mix (Χ) introducing linear equation for the three mix types that we took into account. We observe that Eqs. (2) and (4) has the appropriate signals as the mix rate increase should give plus rate to density variable.

The examination of the relation of the Diesel pure fuel to a specific mix ratio of fats and vegetable oils Biodiesel was implemented under stable climate conditions that were measured per mix in order to avoid the mistake risk of sample rate output. Eq. (7) that refers to the 1st mix (pure Diesel and vegetable oils biodiesel) attempts to explain the relation between the pure Diesel and the vegetable oils biodiesel, that were the Y and the Χ accounting the chemicals attributes of the variables that they express. We observed that the Eqs. (7) and (8) had the appropriate signals as the mix rate were increased should gave plus rate to density variable.

$$Density = 0.817461 + 0.00521 \text{ mix} \, 1 \text{ or } \, Y = 0.817461 + 0.00521 \tag{7}$$

With reference to the 2nd mix (pure Diesel and vegetable oils biodiesel), the equation had the appropriate signals and the relation to the dependent variable were statistically significant for the 2nd fuel mix, on account of the reasons we have already referred.

$$\text{Density} = 0.830749 + 0.000312 \text{ mix2 or } \text{Y} = 0.830749 + 0.000312 \tag{8}$$

For the 3rd mix (pure Diesel and aggregation of fats and vegetable oils biodiesel), the next equation were registered, which explained the linear relation of the model variable.

$$\text{Density} = 0.826332 + 0.000417 \text{ mix3 or } \text{Y} = 0.826332 + 0.000417 \tag{9}$$

Based on the Eqs. (7), (8) and (9) we shown with can said that the dependent variable was statistically significant to each of the three mixes we have studied.

Taking into account the chemical features of the diesel fuels, fats and vegetable oils Biodiesel, and considering that the observation temperature is 15�C, we conclude that we might have high density predictability for all the mix types that we used [15] meaning the 1st the 2nd and the 3rd one. The equation application might only achieve quite perfect success in predicting the suitability of the final fuel in terms of the every time examined characteristic.

In our next research we tried to export a regression equation which describes the variation of conductivity in blends of Diesel fuel with Biodiesel. Using specific volume of those blends, we determined conductivity, in order to study the contribution of each kind of Biodiesel and we converted the statistical data into mathematic relations as a specific formula, attempting to achieve an empirical evaluation [8].

We used pure Diesel and one kind of Biodiesel; Biodiesel by Vegetables (soybean oil- vegetable oil fuel). Those samples met the specifications of Diesel fuel and Biodiesel Standards. The total volume of each blend was 100 mL (for example 20% blend included: 80 mL Diesel and 20 mL Vegetable Biodiesel) and in every measurement the volume of Biodiesel was changing.

Studying on the regression, we noticed that the Eq. (10) had correct signals. The bigger addition of Biodiesel on pure Diesel would make the mix quote lower and lower real prices to the Conductivity.

$$\text{Conductivity} = 1691.409 - 17.504 \text{ mix or } Y = 1691.409 - 17.504 \tag{10}$$

In the framework of our research on the fuels relation (diesel + biodiesel = blend or mix), we concluded that there were an actual strong linear relation that connects the above mix with the Conductivity. It was considerable that the mix independent variable has a linear relation to the Conductivity.

That model meaning Eq. (10) probably might give not give us secure conclusions because of the substantial part of the humidity in the conductivity factor. The humidity factor importantly influences the conductivity values so that an additional measurement will ensure the inferences in this research field.

One of the filed that needs more attention and further study is the contribution of different kinds of biodiesel on the conductivity and density of blends of diesel and biodiesel fuels (animal and vegetable). In the framework of this research we attempted to substantiate the existence and the type of the correlation between the blends of pure Diesel and Biodiesel (Vegetable and Animal Biodiesel) and the Density as well as the conductivity.

We decided to adopt for this purpose the Regression Analysis as the best model that would support this study. The basic target of this method was the potentiality of the precise evaluation of the dependent variable prices regarding the specific independent variables prices. The preliminary statistical analysis of the Density (Y1) and the Conductivity (Y2) with respect to the fuel volume fractions (X) (fuels) series has directly showed that the relation that connects each variable is linear [16].

Pure diesel and biodiesel (vegetable and animal) were used. Those samples met the specifications of diesel fuel and biodiesel standards described above. Eleven different blends were used. The total volume of each blend was 100 mL and in each blend the volume fraction of diesel/biodiesel was different (100% diesel, 90% diesel-10% biodiesel, 80% diesel-20% biodiesel, 70% diesel-30% biodiesel, 60% diesel-40% biodiesel, 50% diesel-50% biodiesel, 40% diesel-60% biodiesel, 30% diesel-70% biodiesel, 200% diesel-80% biodiesel, 10% diesel-90% biodiesel, and 100% biodiesel).

In this research we had six different blends of biodiesel. The mixes were studied in terms of their Conductivity and Density values in order to find an equation that shows the relationship between Conductivity and Density of the created mix as a function of the blend's constitution. As far as the produced mix is concerned, the "mix", our last assumption is that the upgrade of biodiesel (vegetable or animal or 50% vegetable with 50% animal biodiesel) would be proportional to the increase in Density and Conductivity due to the numerical result of a linear relationship between the two variables.

$$\text{Density} = 0.005 \text{mix} + 0.8175 \text{ } Y\_1 = 0.005 \text{mix} + 0.8175 \tag{11}$$

$$\text{Conductivity} = 844.55 \text{mix} \ - 83.63 \text{ or } \ Y\_2 = 844.55 \text{mix} - 83.63 \tag{12}$$

The tested model was statistically significant at 1% level and we came to the decision that the selected regression model for the above mixes were suitable to account for an important part of the Density and Conductivity variability. We proceed to a closer examination of blends of pure diesel with animal biodiesel. The mixes were studied in terms of the Conductivity and the Density. As far as the produced mix were concerned, the "mix", we expected that the upgrade of animal biodiesel would be proportional to the increase in Conductivity and also in Density due to the numerical result of a linear relationship between the two variables.

$$\text{Density} = 0.004m\text{ix} + 0.8215\text{ }or\\ Y\_1 = 0.004m\text{ix} + 0.8215\tag{13}$$

$$\text{Conductivity} = \ 2.789 \text{mix} - 29.364 \text{ or } \ Y\_2 = 2.789 \text{mix} - 29.364 \tag{14}$$

The tested model was statistically significant at 1% level and we came to the decision that the selected regression model for the above mixes were suitable to account for an important part of the Density and Conductivity variability.

Throughout the rest of that paper [16], we continued to our analysis with the examination of blends of pure diesel with Biodiesel (vegetable 50% + animal 50% biodiesel). Due to the linear relationship between the two variables our regression analysis has shown that the Eqs. (15) and (16) had correct signals.

$$Density = 0.005 mix + 0.8149 \text{ or } Y\_1 = 0.005 mix + 0.8149.$$

$$M\ddot{x} = Pure \ Diesel + Biodiesel \ (50\% Ainimal \ Biodiesel + 50\% Vegetable \ Biodiesel \ )$$

$$Conductivity = 11.605 mix - 178.4 \ or \ Y\_2 = 11.605 mix - 178.64 \ .$$

$$M\ddot{x} = Pure \ Diesel + Biodiesel \ (50\% Ainimal \ Biodiesel + 50\% Vegetable \ Biodiesel) \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \$$

The tested model was statistically significant at 1% and based on that procedure we are able to come to secure conclusions. Moreover with this procedure it is possible the further study of the biodiesel use with lower density and Conductivity. We consider that this scientific study can contribute to the today's industry sector in terms of the exploitation of the alternative biodiesel fuel. Finally due to fact that the biodiesel cost is lower than the pure diesel the utilization possibility in a wide range reduces the production cost and makes a final fuel product that is friendlier to the environment [16].

In our next research we studied the possibility of exporting a regression equation which could describe the variation of humidity of conventional diesel with time. A hydrophilic polymers TPA (Thermal Polyaspartic Anion) and natural resin from halepius Pines tree were used to eliminate humidity from conventional diesel. At both cases, where TPA as well as natural resin was used as additive, the hydrophilic polymers just blended, mechanically, with the diesel and after several mixing times were removed from this. The elimination of humidity made the fuel suitable for car machines and gave combustion with less pollution for the environment [17].

The preliminary statistical analysis of sequences of humidity Y and time T have shown that the modifications of Y, probably determined by the sine of T. Specifically, in this research we have examined the regression model

$$Y\_i = \ \ b\_0 + b\_i \operatorname{Sim}\left(T\_i\right) + \mathcal{U}\_t \tag{17}$$

in order to investigate the relation between humidity of JP8 fuel and the time, when the volume of the fuel and the mass of the polymer maintain stable.

As far as for the statistical investigation for the variables like time T and resin we came to the conclusion that the relationship is not linear. So, in order to proceed our research we have <sup>2</sup> examined the regression model Yi <sup>¼</sup> <sup>b</sup><sup>0</sup> <sup>þ</sup> <sup>b</sup>1ðTiÞ þ <sup>b</sup>2ð Þ Ti <sup>þ</sup> ut in order to investigate the relation between humidity (Y), Time (Ti) and Time 2 (Ti) and the result of our analysis have shown the following equation:

$$Humidity \left(\frac{mg}{g}\right) = -137.58 + 1.33Time + 0.004Time^2 \tag{18}$$

The results of the current investigation have shown that the change of humidity could be described via a regression equation at which the dependent variable was the humidity change and the independent variable was the cosine of the time that the hydrophilic polymer ΤΡΑ remains at the fuel.

Thus, the results of this research [17] gave the possibility of humidity removal via the polymers TPA and RESIN. Hence the quality of the fuel as well as its combustion efficiency can be improved while significant problems can be avoided because of the presence of water in the combustion machine.

Moreover, the properties of the fuel were not influenced by the use of the polymer. Via the equation, the value of humidity in a fuel can be calculated in the frames of the experiment time scale, without the use of experimental process, only by maintaining the parameters of the experiment (temperature 25�C and polymer use) in the proposed proportion.

The present study also investigated how fuel's humidity changes by the time (T) that the resin mass remains at the fuel, using a regression model. Because of the nonlinear relation we can say with certainty that the humidity can be predicted with safety (almost 86% at the time) when using Eq. (18).

The use of biodiesel fuel is becoming increasingly imperative nowadays and it is necessary to know the change of density. In our next research we have studied the variation of density of diesel-biodiesel blends across the scale (0–100)% by adding each time 2% biodiesel and then measuring the density of three different temperatures (5, 15, 25)�C covering and the usual scale of temperatures the use of mixtures of diesel-biodiesel. Through the extraction of equations can be known in advance the relationship of density of diesel-biodiesel blend, and temperature that is used.

Based on these fuels created 50 diesel-biodiesel blends content (0-2-4-6 .... 100) % v/v, at three different temperatures (5, 15, 25)�C to cover all common temperature scale used dieselbiodiesel mixtures. Then we proceed to the determination of the density of these mixtures. The determination is conducted through the ASTM D-1298 method (ASTM D1298-99, 2005) with measurements by means of BS718:1960LSOSP hydrometers. These measurements are reduced to a temperature of 15�C, at which they also constitute the value of fuel density, while they are expressed in kg/L. The measurement scale of these hydrometers is between 0.6 and 1.1.

In order to verify the existence and the type of correlation between the blends pure Diesel and Biodiesel and the Density we decided to use the Regression Analysis as the best method that would support this study. The preliminary statistical analysis of the Density (Y) with respect to the fuel volume fractions (X) (fuels) series had directly showed that the relation that connects each variable were linear for its temperature meaning 5, 15 and 25�C [9].

In this research we had almost 50 different blends of biodiesel for its temperature. The mixes were studied in terms of their Density values in order to find an equation that shown the relationship between Densities of the created mix as a function of the blend's constitution. As far as the produced mix was concerned, the "mix", our last assumption was that the upgrade of biodiesel would be proportional to the increase in Density due to the numerical result of a linear relationship between the two variables. We came to the relative conclusion as it was defined by the following equations:

$$\text{mix01} = 0.8114 + 0.0013 \cdot density \,\text{Constant temperature } 5^{\circ} \text{C} \tag{19}$$

$$\text{maxim02} = 0.8282 + 0.001 \text{ density} \\ \text{Constant temperature } 15^{\circ} \text{C} \tag{20}$$

$$\text{maxim03} = 0.8262 + 0.0008 \text{ (density)} \,\text{Constant temperature } 25^{\circ}\text{C} \tag{21}$$

The proposed methodology can be used in the bio fuels industry for the prediction of variation of the density of mixtures of diesel-biodiesel in a temperature scale is the most common in use for these fuels. Moreover based on this procedure we are able to come to secure conclusions when the values of density according to the mixes where measured between 5 and 25�C. If we overcome these limits then we will face autocorrelation and heteroskedasticity problems. In this case our model will have no predictive ability and will essentially reject as unacceptable [9].

#### 4. Conclusion

Based on our results the variation of the physicochemical properties of the oil can be predicted. This can be done using the equations generated during our investigations. The predictive capacity of these equations is valid only if specimens and mixtures follow specific rules, such as those during the experiments we conducted. With these studies we came to the conclusion that it is given the opportunity to develop software in order to study the changes of physicochemical properties of petroleum distillate products. The development of such an application would help us to know in advance the variation of the physicochemical properties. This implementation would be important not only for researchers but also for the respective control bodies as regards the quality of the final product at each stage to the final consumer. Taking into account such equations and having knowledge of oil technology, we can predict the prices per fuel mix and, accordingly, accept it or reject it.

## Acknowledgements

At this point we would like to stress that without the use of the facilities of Technological Education Institute of Western Macedonia and specific the laboratory of Qualitative Fuel Control ISO 9001: 2008 this study would not be possible.

#### Author details

Constantinos Tsanaktsidis<sup>1</sup> \* and Konstantinos Spinthiropoulos<sup>2</sup>

\*Address all correspondence to: prof.tsanaktsidis@gmail.com

1 Technological Education Institute of Western Macedonia, Department of Pollution Control and Technologies, Kozani, Greece

2 Technological Education Institute of Western Macedonia, Department of Accounting and Finance, Kozani, Greece

## References


using synthetic and natural hydrophilic polymers as additives. Petroleum Science and Technology. 2016; in press


**Chapter 5**

Provisional chapter

**Biological Treatment of Petrochemical Wastewater**

DOI: 10.5772/intechopen.79655

Petrochemical wastewater is inherent to oil industries. The wastewater contains various organic and inorganic components that need to be well managed before they can be discharged to any receiving waters. The complexity of the wastewater and stringent discharge limit push the development of wastewater treatment by combinations of different methods. Biological wastewater treatments that have been well developed for organic and inorganic wastewater treatment are thus a potential method for petrochemical wastewater management. This chapter summarizes the commonly applied petrochemical wastewater pretreatment methods prior biological treatments and compares different biological treatment systems' performance such as biological anaerobic, aerobic and integrated systems. Two case studies are presented for a high chemical oxygen demand (COD) contents petrochemical wastewater treatment in full-scale by applying Biowater Technology's biofilm system continuous flow intermittent cleaning (CFIC) and a pilotscale study by an integrated anaerobic and aerobic biofilm system hybrid vertical anaerobic biofilm (HyVAB). Both processes showed substantial (over 90%) COD removal, while the HyVAB system produced high methane content biogas that can be potentially used as an energy source. Studies of degradation of certain toxic chemicals, such as aromatic compounds in petrochemical wastewater, by the advanced treatment systems incorporat-

Keywords: petrochemical wastewater, anaerobic digestion, aerobic digestion, biofilm

Increasing consumption of oil in modern society has led to more oil/oil refinery waste generation. The oil processing wastewater/waste has high concentrations of aliphatic, aromatic petroleum

> © 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited.

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

Biological Treatment of Petrochemical Wastewater

Nirmal Ghimire and Shuai Wang

Nirmal Ghimire and Shuai Wang

http://dx.doi.org/10.5772/intechopen.79655

Abstract

Additional information is available at the end of the chapter

ing specific organisms can be of good research interest.

reactor, integrated system

1. Introduction

Additional information is available at the end of the chapter

## **Biological Treatment of Petrochemical Wastewater**

Nirmal Ghimire and Shuai Wang

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.79655

#### Abstract

Petrochemical wastewater is inherent to oil industries. The wastewater contains various organic and inorganic components that need to be well managed before they can be discharged to any receiving waters. The complexity of the wastewater and stringent discharge limit push the development of wastewater treatment by combinations of different methods. Biological wastewater treatments that have been well developed for organic and inorganic wastewater treatment are thus a potential method for petrochemical wastewater management. This chapter summarizes the commonly applied petrochemical wastewater pretreatment methods prior biological treatments and compares different biological treatment systems' performance such as biological anaerobic, aerobic and integrated systems. Two case studies are presented for a high chemical oxygen demand (COD) contents petrochemical wastewater treatment in full-scale by applying Biowater Technology's biofilm system continuous flow intermittent cleaning (CFIC) and a pilotscale study by an integrated anaerobic and aerobic biofilm system hybrid vertical anaerobic biofilm (HyVAB). Both processes showed substantial (over 90%) COD removal, while the HyVAB system produced high methane content biogas that can be potentially used as an energy source. Studies of degradation of certain toxic chemicals, such as aromatic compounds in petrochemical wastewater, by the advanced treatment systems incorporating specific organisms can be of good research interest.

Keywords: petrochemical wastewater, anaerobic digestion, aerobic digestion, biofilm reactor, integrated system

#### 1. Introduction

Increasing consumption of oil in modern society has led to more oil/oil refinery waste generation. The oil processing wastewater/waste has high concentrations of aliphatic, aromatic petroleum

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

hydrocarbons, etc. Direct discharge of this will affect plants and aquatic life of surface and ground water sources. Due to its organic origination, complex nature, and toxic effects, wastewater treatment prior to discharge is obligatory. The biological treatment process is normally applied to reduce the effects of petrochemical waste.

Stringent regulations have motivated researchers to design advanced treatment facilities to give high treatment efficiency, low maintenance, footprint, and operational costs. Biological anaerobic, anoxic, and aerobic digestion (or a combination of each other) have been implemented to treat petrochemical wastewater. Optimizing pretreatment process using physicochemical processes is also important for getting suitable pretreatment wastewater for efficient biological secondary treatment. An overview and update of the petrochemical wastewater treatment processes will contribute to the knowledge development both theoretically and practically.

In this section, the petrochemical wastewater treatment by biological processes is shortly reviewed and discussed. Section 2 introduces the petrochemical wastewater sources and their components in general. Section 3 introduces the normally applied pretreatment process prior to biological treatment processes. Section 4 presents the commonly applied anaerobic, aerobic, and combined anaerobic and aerobic biological systems for petrochemical wastewater treatment. Section 5 shows two case studies on the petrochemical wastewater treatment using Biowater Technology AS's continuous flow intermittent cleaning (CFIC) and hybrid vertical anaerobic biofilm (HyVAB) processes. Section 6 summarizes challenges and further studies in the petrochemical wastewater treatment.

#### 2. Petrochemical wastewater

Petrochemical wastewater is a general term of wastewater associated with oil-related industries. The sources of petrochemical wastewater are diverse and can originate from oilfield production, crude oil refinery plants, the olefin process plants, refrigeration, energy unities, and other sporadic wastewaters [1, 2]. The compositions of wastewater from different sources consist of varying chemicals and show different toxicity and degradability in terms of biological treatment. In this chapter, to better compare the treatment efficiency with varying pretreatment processes, the petrochemical wastewater has been categorized to oilfield-produced wastewater, petrochemical refinery, and oily wastewater based on the originates.

Oilfield-produced wastewater is generated in crude oil extraction from oil wells that contain high concentrations of artificial surfactants and emulsified crude oil characterized of high COD and low biodegradability [3]. It is produced during oil extraction in oil fields and contains complex recalcitrant organic pollutants such as polymer, surfactants, radioactive substances, benzenes, phenols, humus, polycyclic aromatic hydrocarbons (PAHs), and different kinds of heavy mineral oil [4, 5]. Table 1 presents the commonly found compositions of wastewater obtained from oilfield production.

Petroleum refinery wastewater is generated in oil refinery processes that produce more than 2500 refined products. The wastewater can be from cooling systems, distillation, hydrotreating, and


Table 1. Wastewater parameter form oilfield production [6].

desalting. The compositions of the refinery wastewater can vary depending upon the operational units for different products at specific time and locations. Different concentrations of ammonia, sulfide, phenols, Benzo, and other hydrocarbons are normally present in such wastewater [7, 8].

The oily wastewater is defined here to be any wastewater that does not clearly belong to the two categories mentioned earlier. This wastewater can be from petrochemical-related industries such as from oil transportation tank, garage oil wastewater, etc. The composition of such wastewater is diverse with high COD that can be over 15 g/L [9].

#### 3. Pretreatment process for biological stabilization

Wastewater from petrochemical industries consists of different chemicals. The treatment processes depend and are specialized by wastewater sources, discharge requirements, and treatment efficiencies. Normally, pretreatment processes are applied in the treatment of petroleum refinery wastewater before it is sent to biological process for organic elimination [8]. A primary treatment includes the elimination of free oil and gross solids; elimination of dispersed oil and solids by flocculation, flotation, sedimentation, filtration, microelectrolysis, etc.; increasing the biodegradability of wastewater, etc. [8]. This chapter lists a few commonly applied methods for petrochemical wastewater pretreatment.

#### 3.1. Physical treatment

Depending on the wastewater characteristics, physical treatment such as adsorption by active carbon, copolymers, zeolite, etc. can be used for removing hydrocarbons in the petrochemical wastewater [6]. Evaporation is proposed to remove oil residuals in saline wastewater. Dissolved air flotation (DAF) is commonly used for wastewater containing oil/fat as well as suspended solids, which can also be applied for petrochemical wastewater.

Microfiltration (MF) and ultrafiltration (UF) are also applicable for pretreatment before the wastewater passes through, for example, reverse osmosis (RO) process for reusing purposes [10].

#### 3.2. Chemical treatment

Enhancing hydrolysis by adding chemicals for removing the long-chain organics, toxic material, or suspended solids can increase the Biochemical Oxygen Demand (BOD) ratio of the wastewater. Three chemical treatment processes are listed here.

Micro-aeration breaks down high hydrocarbon content components from wastewater, which leads to easily biodegradable organic generation. At a dissolved oxygen (DO) concentration from 0.2 to 0.3 mg/L, the hydrolysis of wastewater organics is enhanced. The BOD/COD ratio is increased and SO4� reduction in wastewater is inhibited. Low H2S generation due to SO4� reduced reduction can benefit subsequent biological treatment by lowering inhibitory effects. Benzene ring organics', such as benzene, toluene, ethylbenzene, and xylenes, treatability in the biological stage can be improved [11].

Coagulation-flocculation for specific petrochemical wastewater treatment, such as purified terephthalic acid (PTA) production wastewater; the wastewater contains aromatic compounds such as p-toluic acid, benzoic acid, 4-carboxybenzaldehyde, phthalic acid (PA), and terephthalic acid (TA), etc. Ferric chloride is found to be the most effective coagulant with COD removal efficiency at 75.5% at wastewater COD of 2776 mg/L and dose of pH 5.6. Adding cationic polyacrylamide improves the sludge filtration [12]. Certain streams that combine coagulation and flocculation as pretreatment followed by MF and UF achieved significant suspended solid removal [10].

Ozonation for wastewater that contains phenol, benzoic acid, aminobenzoic acid, and petrochemical industry wastewater containing acrylonitrile butadiene styrene (ABS) at 30 min and 100–200 mg O3/h showed an increased BOD/COD ratio from 20 to 35% [13].

#### 3.3. Other treatment

Microelectrolysis of petrochemical wastewater has been tested with positive effects on the COD removal as well as increasing the BOD-to-COD ratio levels [14].

#### 4. Biological treatment of petrochemical wastewater

Biological treatment incorporates actions of different microbes to eliminate organics and stabilize hazardous pollutants in petrochemical wastewater. Stringent environmental standards and recycling of water for reuse have shifted focus to biological treatments because of its cost and pollutant removal efficiency. As the nature of petrochemical wastewater is very complex, biological treatment to remove pollutants still has challenges despite immense potentials. Complex structures of aromatic, polycyclic, and heterocyclic ringed chemicals are known to be restraint to biological degradation [15]. However, recent research activities have produced notable removal percentages of pollutants from petrochemical wastewater [16].

Anaerobic digestion (AD), aerobic digestion, or an integration of both methods is commonly applied in biological processes to treat petrochemical wastewater.

#### 4.1. Anaerobic process

Anaerobic digestion has the advantages of producing methane as a renewable energy, requiring less space and having lower sludge generation than aerobic process. A literature review of anaerobic digestion on the petrochemical wastewater is given in Table 2. Petrochemical wastewater treated in anaerobic baffled reactor (ABR), sequence batch, and up-flow sludge blanket reactor (UASB) was commonly applied. It shows that organics in the petrochemical wastewater could be partially anaerobic digested at a removal efficiency depending on the chemical constituents, reactor type, operational conditions (temperature, loading rate, etc.), and wastewater sources [24].

COD removal efficiency is used here as a general parameter to assess the performance of different systems. Crude oil extraction of light, medium, and heavy petroleum wastewater treatment by different anaerobic digestion systems at mesophilic or thermophilic conditions showed that in batch test over 56–71% COD removal was achievable at thermophilic condition [1, 18] (Table 2), while UASB system can achieve over 93% COD removal at mesophilic conditions for wastewater from light petroleum extraction (Table 2). It seems light petroleum extraction wastewater was generally easily degradable (over 71–93% removal) compared to the medium and heavy oil extraction wastewater. The setup of plug flow pattern and granular sludge application in UASB might also enhance the interaction between wastewater and organisms, giving higher efficiency. The removal efficiency decreases as the loading rate increases, indicating the inhibition effects to the organisms.

Medium- and heavy oil-produced wastewater treatment efficiency was relatively low. Batch system gives generally a better treatment efficiency for these two wastewaters at about 50–60%


\*Water from light petroleum, medium petroleum and heavy petroleum, respectively.

\*\*Water from medium petroleum and heavy petroleum, respectively.

\*\*\*Water from light petroleum, medium petroleum, respectively.

Table 2. Overview of anaerobic treatment of petrochemical wastewater.

removal (Table 2), while UASB shows low efficiency at around 20–30% removal efficiency. The effects of toxic chemicals in the wastewater and high content of large organic molecules can be the reason for low efficiency.

#### 4.2. Aerobic process

Aerobic process has been applied widely in petrochemical wastewater treatment attributed to its features of easy operation, less sensitiveness to toxic effects, higher organisms' growth rate, etc. than the anaerobic system. Different aerobic reactors such as traditional active sludge, contact stabilization active sludge, sequence batch reactor (SBR) that applies active sludge and biological aerated filter (BAF), membrane bioreactor (MB), moving bed biofilm reactor (MBBR), aerobic submerged fixed-bed reactor (ASFBR) that applies biofilm, etc. have been



Table 3. Overview of aerobic treatment process of petrochemical wastewater.

tested to treat petrochemical wastewater from varying sources and presented in Table 3. Generally higher COD and chemical removal efficiencies by aerobic process are achieved than the anaerobic processes (Tables 2 and 3). The sludge retention time, hydraulic retention time, dissolved oxygen level, feed to organism ratio, and temperature are some of the important factors that determine the treatment efficiency.

Petroleum refinery wastewater COD removal was generally high from 70 to 98% in the mentioned aerobic system (Table 3), which in anaerobic system is from 70 to 93%. The contact and extended active sludge process can achieve high COD removal rate of 89–95% (Table 3) at a feed to microorganism ratio of 0.38 [25]. The applied aeration to the mixed liquor and the sludge recycle rate was found to be critical parameters in the successful optimization of the contact stabilization process. The treatment efficiency of NH4-N, H2S, and TSS were also high [25]. Traditional SBR has relatively lower treatment efficiency at 80% COD removal (Table 3).

The membrane reactors such as BAF, cross-flow membrane bioreactor (CF-MBR), membrane sequencing batch reactor (MSBR), and hollow fiber ultrafiltration membrane bioreactor (HF-UF MBR) including ultrafiltration MBR systems treating higher OLR or food to organisms' ratio can achieve over 80% COD removal (Table 3). MBBR system applying biofilm can achieve 74% COD removal at a high OLR of 4.2 kg COD/m3 ˜d (Table 3). It also can be seen that NH4-N and H2S removal are above 60% that cannot be obtained in anaerobic system. The Total Organic Compounds (TOC) and oil removal are also better than the anaerobic system.

Oilfield wastewater is relatively reluctant to aerobic digestion due to the complex ingredient. The removal efficiency of such water has a COD removal at around 30–74% (Table 3) by BAF, MBBR, etc. Active sludge process seems to handle well the wastewater and achieve high total petroleum hydrocarbon (TPH) removal.

The oily wastewater COD removal is generally high by using different aerobic methods, indicating its easily degradable nature (Table 3). The case study in Section 5 presents the advanced biofilm technology named CFIC process by Biowater Technology AS. The full-scale plant data show consistently high COD removal efficiency over 90%.

#### 4.3. Integrated biological process

The treatment efficiencies of individual anaerobic and aerobic systems show good capability in treating certain petrochemical wastewater. An integrated system combining anaerobic and aerobic processes can possibly take the advantages of both and achieve even better removal efficiency for chemicals that are not easily degraded by either anaerobic or aerobic process. An integrated system that is focused in this chapter can be a hybrid reactor consisting of an anaerobic and an aerobic system in a vertical design, such as a hybrid vertical flow anaerobic aerobic biofilm reactor (HyVAB) [9], provided by Biowater Technology AS, or a combination of different treatment processes in series, for example, a system consists of traditional anaerobic reactor and an aerobic stage in series. The performance of integrated systems for petrochemical refinery, oilfield-produced wastewater, and other oily wastewaters is presented in Table 4. The integrated system could effectively remove easily degradable COD in the anaerobic stage first and convert it to biogas with the residual COD and other chemicals such as ammonium, sulfide, etc. degraded in the aerobic stage (Table 4).

Hybrid system combining UASB and aerobic stage treating oilfield wastewater showed good effects on COD removal by enabling acidification prior to the aerobic stage where organisms are actively reacting with organic chemicals. The COD removal rates were over 70–95%. Oil and ammonia removal was also recorded over 87% (Table 4).


Table 4. Overview of integrated treatment process of petrochemical wastewater.

For petrochemical refinery treatment, direct discharge of treatment effluents after combining anaerobic and aerobic MBBR system is possible. The PAH removal reached even 100% by combining the UASB and packed bed biofilm reactor (PBBL) at 0.5 kg COD/m<sup>3</sup> ˜d (Table 4).

The pilot study of hybrid vertical flow anaerobic biofilm (HyVAB) treating oily wastewater had substantially high organic loading rate over 23 kg COD/m<sup>3</sup> ˜d. The COD removal efficiency was consistently good over 86% [9]. A case study based on this HyVAB concept is followed in the next section with detailed performance data presentations and discussions.

#### 5. Petrochemical wastewater treatment case study

Petrochemical wastewater of different sources, such as from manufacturing industries, auto repair shops, and washing water of oil tanks, is collected and delivered to a full-scale aerobic treatment plant at Bamble, Norway, for resource recovery and biological stabilization. The collected wastes are stored in storage tanks before being distilled to extract oil residuals. The wastewater after oil extraction still contains high COD and is therefore further treated by biological processes. The full-scale CFIC plant was designed and delivered by Biowater Technology AS and has been running continuously for 3 years. A pilot study of the integrated system HyVAB was also carried out on site of the full-scale plant running with the same feed water and the results showed good performance and can be referred to [9]. In this chapter, the full-scale CFIC operation data and a continuous study of HyVAB applying pure oxygen as aeration media are presented.

#### 5.1. Full-scale CFIC treating petrochemical wastewater

The full-scale plant applies continuous flow intermittent cleaning biofilm (CFIC) technology. The CFIC technology is an advanced biofilm system based on MBBR concept. It is compact and is operated with alternating a normal and a washing mode while continuously feeding the reactor. CFIC contains highly packed biofilm carriers (over 90% filling ratio) to a degree that oxygen is utilized efficiently by enhancing gas transfer and limiting carriers' movement in the reactor. The biofilm grows in condition of sufficient oxygen, organic substrates, and nutrients. Excess aerobic sludge grown on the carriers' surface is washed off during the intermittent washing that helps maintain a thin and effective biofilm.

#### 5.1.1. System layout

The full-scale plant layout is shown in Figure 1. Distilled wastewater is pumped to a conditioning chamber where nutrients are dosed and pH is corrected. Effluent from CFIC goes through chemical precipitation and DAF to remove solids before being discharged to the sea. Sludge is temporally stored and dewatered to be tanked away for specific treatment.

The full-scale system is treating wastewater of fluctuating concentrations with COD concentration ranging from 7 to 35 g/L at a designed daily flow rate of 240 m3 /d. The wastewater pH is around 5 and a total dissolved solid content of 4 g/L. BWTS® (Biowater Technology AS) with a surface area of 650 m<sup>2</sup> /m<sup>3</sup> is applied as biofilm carriers in CFIC (Figure 1).

#### 5.1.2. Operational results and discussion

Operational data of the full-scale plant in 2017 is summarized here. The COD feed to the reactor and the final effluent after DAF is shown in Figure 2 together with removal efficiency. It shows that on average over 90% feed COD was removed by the system. At the early days of the year, sludge flocculation process chemical dosing was not well established; the total COD

Figure 1. Up, layout of the full-scale CFIC plant with 1. Storage tank; 2. CFIC reactor; 3. DAF; 4. Sludge storage tank; 5. Dewatered sludge tanker. Down, applied BWTS® biofilm carriers.

Figure 2. Feed and effluent total COD and COD removal efficiency.

removal was fluctuating around 80–90%. When the system was stabilized even high COD feed from 100 to 200 days did not reduce treatment efficiency. The high removal efficiency indicates that CFIC is a stable and robust system.

The suspended solid content of the final effluent shows that the average value was within 100 mg/L (Figure 3). The CFIC system running in normal mode generally worked as a filter bed which retains suspended solid in the reactor. When washing mode starts, raised water level in the reactor coupled with increased aeration induces a well-mixed moving bed biofilm system. The extra biofilm/sludge in carrier voids are washed off due to intensified shear force and are carried out of the system by continuous effluents. The washing washes away on average 30% of the total solids on the biofilm carriers.

#### 5.2. Pilot study of HyVAB treating petrochemical wastewater using pure oxygen

The concept of the HyVAB system is illustrated in Figure 4. The system consists of a bottom anaerobic and a top aerobic biofilm stage in a vertical mode. Biogas generated from the anaerobic stage can be collected through the three-stage separator. Due to the close integration of two processes, the dissolved gases (methane, H2S, etc.) in liquid that are generated in the AD stage will not be released to the atmosphere but captured and oxidized by aerobic organisms, avoiding a commonly observed emission problem in anaerobic treatment plants [43]. Returning of the excess aerobic sludge to the AD stage by gravity where the solids undergo stabilization simplifies the sludge treatment which also contributes to methane production. The detailed longer-term pilot study with reactor layout and performance can be referred to [9], where air was applied as aeration source.

This chapter presents the pilot study of pure oxygen effects on HyVAB performance. Oxygen aerations were known to be less energy intensive, high in efficiency, and give good biofilm development due to its close contact with biofilm layers. Results show that the HyVAB COD removal using air and pure oxygen reached similar ratios on average 94 and 85% for the soluble and total feed COD removal, respectively. Oxygen aeration minimized the flushing

Figure 3. Effluent suspended solid concentration after DAF.

Figure 4. Sketch of the HyVAB (hybrid vertical anaerobic biofilm) bioreactor with the anaerobic stage at the bottom and a CFIC stage at the top. Numbers are sampling points.

effects on biofilm carriers and reduced the effluent suspended solid to 500 mg/L and effluent pH was overall 1.1 less than applying air aeration.

#### 5.2.1. Experiment management

The anaerobic stage was filled with granular sludge, with relatively equal size (˜2 mm) from an industrial wastewater treatment facility. Similar biofilm carriers (Figure 1) were used in the aerobic stage. Pure oxygen was applied as aeration oxygen source and air washing was introduced intermittently during the washing mode in the study. The pilot was running continuously for 115 days at 21 ° 2˛C.

#### 5.2.2. Operational results and discussion

With OLR increased gradually to close to 30 kg COD/m<sup>3</sup> ˝d at lower HRT of 15 h, the HyVAB system still performed well with over 90% soluble COD removal when the oxygen aeration was introduced after 32 days (Figure 5). The air aeration was conducted before 31 days and the results were treated as reference. With oxygen aeration, the anaerobic stage generated high

Figure 5. COD removal at different organic loading rate (OLR) and HRT.

Figure 6. Biomass yield at different OLR and with/without oxygen aeration, vertical line separate air and oxygen aeration.

methane content biogas (82%) and the soluble COD removal efficiency was comparable with air aeration (Figure 5).

The sludge yield with oxygen aeration was at 0.04 g VSS/g CODremoved and less variations showed comparing to the air aeration stage (Figure 6). The reasons can be that the fine bubbles of the aeration from oxygen did not give high shear force on the biofilm to scratch it off. The low mixing effects also retained the solids in the reactor. The low sludge yield at high organic loading rate indicates high efficiency of the HyVAB system in removing petrochemical organic substances. Consistently lower effluents of less than 500 mg/L were observed with oxygen aeration.

Some petrochemical wastewater contains high salinity and nutrients such as ammonia and phosphate, especially after anaerobic treatment. The high content of dissolved solids might

Figure 7. pH variations in different streams, vertical line separate air and oxygen aeration.

precipitate on biofilm carriers when pH is high and temperature is in good range. Oxygen aeration showed good pH control effects compared with air aeration which induced high pH (over 8.5) (Figure 7) in the aerobic stage. Good biofilm development was observed in the pilot test with such petrochemical wastewater and the scaling effects on carriers were minimal with oxygen aeration.

#### 6. Conclusions and further study

Biological treatment of petrochemical wastewater is an economic and efficient waste stabilization method. The treatment of wastewater containing organic contaminants of refractory nature can be ineffective in biological treatments [44]. The challenges are as follows: (1) activated sludge method can fail while treating strong petrochemical wastewater with high COD concentration (>10 g/L) and contain some aromatic compounds (phenol and its derivatives, etc.); [45] (2) variations in the strength of the organic load due to various sources of petrochemical refinery can cause shock to the biomass; (3) petrochemical wastewater contains large amounts of volatile organic compounds (VOCs) and can cause odor and air pollutions around the biological treatments, and aerobic treatments like activated sludge should not be considered in this case; (4) oil, fat, and grease can cause floatation of the sludge and this can cause sludge washout ultimately failing the treatment system.

Application of certain organisms for specific wastewater components' treatment after secondary biological treatment can be a topic in the future. Isolation of specific bacteria to treat recalcitrant compounds can lead to effective removal, for example, the bacterium Pseudomonas putida to degrade phenolic compounds [7]. Integrated biological system showed general better performance for treating petrochemical wastewater; the synergistic effects of organisms of different originals such as from anaerobic combining aerobic might facilitate the recalcitrant organic removal. Also, reactor modification and microorganisms' isolation to handle complex petroleum wastewater treatment can be of great interest in coming days to reduce extratreatment costs.

#### Acknowledgements

The authors would like to thank for the funding provided by Skattefunn No. 265293 and University of South-eastern Norway.

#### Conflict of interest

There is no conflict of interest.

#### Notes/Thanks/Other declarations

The authors would like to thank Norsk Spesialolje for supporting this research during pilot study and by providing operational data. Also, our thank goes to University of South-eastern Norway for research cooperation and Praxair for supporting on the pilot study.

### Author details

Nirmal Ghimire<sup>1</sup> \* and Shuai Wang<sup>2</sup>

\*Address all correspondence to: nirmal.ghimire@ku.edu.np


#### References


[44] Bahri M, Mahdavi A, Mirzaei A, Mansouri A, Haghighat F. Integrated oxidation process and biological treatment for highly concentrated petrochemical effluents: A review. Chemical Engineering and Processing Process Intensification. 2018;125:183-196. DOI:

[45] Debellefontaine H, Chakchouk M, Foussard J, Tissot D, Striolo P. Treatment of organic aqueous wastes: Wet air oxidation and wet peroxide oxidation. Environmental Pollution.

10.1016/j.cep.2018.02.002

1996;92(2):155-164

74 Petroleum Chemicals - Recent Insight

*Edited by Mansoor Zoveidavianpoor* 

A vast amount has been writen about petroleum fuels, including books and guidelines; hence, we thought it timely to produce a book *Petroleum Fuels: Recent Updates*, which covers the most important areas in the topic. In its pages, we tried to include advances toward green and sustainable viable products in terms of biodiesel production and chemical transformation. Te book contains rich extracts from experts in the fuel feld, including technical/environmental and econometric aspects.

Published in London, UK © 2019 IntechOpen © Bobby Ware / iStock