# Modeling of Electric Energy Consumption during Dairy Wastewater Treatment Plant Operation

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## Abstract

**:**

## 1. Introduction

^{3}to 12,867 hm

^{3}, while the global amount of sewage from the dairy sector reached 28.1 hm

^{3}in 2016 [1].

_{5}) and chemical oxygen demand (COD), range from 4 to as much as 10 times higher than in municipal wastewater [2,3]. Dairy wastewater is also characterized by a high concentration of phosphorus. Due to the high concentration of pollutants and strict requirements regarding the quality of treated wastewater, the amount of energy used is much higher in comparison with municipal sewage. Energy consumption is also influenced by the high variability of sewage composition and quantity over time [4,5]. Both the volume flow rate and the pollutant load from dairy wastewater treatment plants (WWTPs) depend on the production profile, which may often change daily [6]. About 90% of organic contaminants in dairy wastewater come from residual products washed out during the operation of cleaning-in-place (CIP) stations [7].

^{3}of treated wastewater [17].

## 2. Materials and Methods

#### 2.1. Study Site

^{3}d

^{−1}and a 11,500 person equivalent (PE). The average daily flow during the research period was 653 m

^{3}d

^{−1}(301 ÷ 1154 m

^{3}d

^{−1}). The value of BOD

_{5}changed from 1067 to 2240 mgO

_{2}dm

^{−3}, while that of COD varied from 1429 to 2945 mg O

_{2}dm

^{−3}. The concentration of total nitrogen (N

_{tot}) fluctuated within the range of 32 ÷ 64 mg N dm

^{−3}, while the concentration of total phosphorus (P

_{tot}) was from 5 to 24 mg P dm

^{−3}.

#### 2.2. Electric Energy Measuring Methods

#### 2.3. Sampling of Sewage and Analytical Procedures

_{5}, COD, N

_{tot}, and P

_{tot}. Sewage quantity and meteorological conditions were also monitored. The performed analysis allowed for the creation of a database containing the following parameters:

- $T$—air temperature,
- ${L}_{BOD5}$, ${L}_{COD}$, ${L}_{Ntot}$, ${L}_{Ptot}$—pollutant (BOD
_{5}, COD, N_{tot}, and P_{tot}) loads after the averaging tank (point I), - ${L}_{DAF.BOD5}$, ${L}_{DAF.COD}$, ${L}_{DAF.Ntot}$, ${L}_{DAF.Ptot}$—pollutant loads after DAF treatment (point II),
- ${L}_{BIO.BOD5}$, ${L}_{BIO.COD}$, ${L}_{BIO.Ntot}$, ${L}_{BIO.Ptot}$—pollutant loads after biological treatment in SBRs (point III).

#### 2.4. Modeling of Electric Energy Consumption

- modeling of ${E}_{TOT}$ was carried out based on ${E}_{BIO}$ and $T$ (the least squares linear regression);
- a qualitative analysis of ${L}_{DAF.BOD5}$, ${L}_{DAF.Ntot}$, ${L}_{DAF.Ptot}$, and $Q$ was carried out. ${L}_{DAF.COD}$ was used as an independent variable (quantile and quantile segmented regression methods);
- modeling of ${E}_{BIO}$ was carried out based on the ${L}_{DAF.COD}$ and $T$ (the least squares linear regression method);
- a qualitative analysis of ${L}_{DAF.COD}$ was carried out based on ${L}_{COD}$ (quantile regression and segmented quantile regression methods);
- modeling of ${E}_{DAF}$ was carried out based on $Q$ (the least squares linear regression method).

## 3. Results

#### 3.1. Total Electric Energy Consumption

#### 3.2. Electric Energy Consumption during Biological Treatment

^{−1}, results in a reversal of the nature of ${E}_{BIO}$’s dependence on ${L}_{DAF.COD}$ in relation to T. For loads smaller than ${L}_{DAF.COD}=430.222$ kgd

^{−1}, a decreasing temperature results in an increase in electric energy consumption; for larger loads, it results in a reduction of electric energy consumption.

## 4. Final Modeling Scheme and Discussion

- The increased electric energy consumption in the biological treatment process causes a decrease in the temperature dependence of the total electric energy consumption. After exceeding the value of ${E}_{BIO}=1001.5\text{}kWh\xb7{d}^{-1}$, the reverse dependence can be observed, i.e., with increasing temperature, total energy consumption grows. Mamais et al. [19] have also reported an increase in the electric energy consumption during summer periods. This is due to an increase of endogenous respiration when the temperature rises.
- Quantile regression models confirm the interdependence of wastewater parameters after the DAF process. Due to the ease of measurement, ${L}_{DAF.COD}$ can be used to qualitatively describe other wastewater parameters. Along with the increase in $T$, the influence of ${L}_{DAF.COD}$ on electric energy consumption during biological treatment increases. When exceeding the load ${L}_{DAF.COD}=430.2\text{}kg\xb7{d}^{-1}$, the relationship between ${E}_{BIO}$ and ${L}_{DAF.COD}$ with respect to temperature reverses. Lowering the air temperature increases energy consumption for smaller loads, and decreases electric energy consumption for larger pollutant loads. According to Niu et. al. [20], in order to reduce electric energy consumption, the pollutant load in the wastewater discharged into biological treatment should be increased. The authors point out that a properly loaded WWTP is characterized by lower energy consumption than an underloaded one. Therefore, it is important to control the pollutant load discharged into the biological treatment, depending on the air temperature. This can be realized by controlling the flotation efficiency.
- The results of the qualitative analysis make it possible to predict the range of variability of the ${L}_{DAF.COD}$ by measuring the COD parameter in the wastewater before the DAF process. Parameters such as ${L}_{COD}$ and ${L}_{DAF.COD}$ can be easily monitored during all of the treatment processes. The COD parameter in raw sewage was also mentioned by Huang et al. [21] as being important to the modeling of electric energy consumption. It is used to predict not only the electric energy consumption but also the parameters of treated wastewater.

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**Scheme of the dairy wastewater treatment plant (WWTP) (facilities and sampling points): 1—raw sewage, 2—screen and pumping station, 3—sand trap, 4—averaging tank, 5—dissolved air flotation (DAF), 6—sequence batch reactors (SBRs), 7—treated sewage; 8—excess and flotation sludge for treatment; I, II, III—sampling points.

**Figure 4.**Analysis of sewage parameters after DAF: (

**a**) flow, (

**b**) BOD

_{5}, (

**c**) total nitrogen, (

**d**) total phosphorus.

**Figure 9.**The main modeling scheme: (

**A**)—model for ${E}_{TOT}$, (

**B**)—model for ${E}_{BIO\text{}}$, (

**C**)—model for ${L}_{DAF.COD}$.

Parameter | Equations | Conditions |
---|---|---|

$Q$ | $\{\begin{array}{c}222.337+0.331\times {L}_{DAF.COD}\\ 507.627+0.409\times {L}_{DAF.COD}\end{array}$ | $\begin{array}{c}\tau =0.1\\ \tau =0.9\end{array}$ |

${L}_{DAF.BOD}$ | $\{\begin{array}{c}25.415+0.431\times {L}_{DAF.COD}\\ 46.046+0.757\times {L}_{DAF.COD}\end{array}$ | $\begin{array}{c}\tau =0.1\\ \tau =0.9\end{array}$ |

${L}_{DAF.Ntot}$ | $\{\begin{array}{c}1.031+0.009\times {L}_{DAF.COD}\\ -159.964+1.135\times {L}_{DAF.COD}\\ 4.507+0.094\times {L}_{DAF.COD}\end{array}$ | $\begin{array}{c}\tau =0.1;{L}_{DAF.COD}<1544.635\\ \tau =0.1;{L}_{DAF.COD}<1544.635\\ \tau =0.9\end{array}$ |

${L}_{DAF.Ptot}$ | $\{\begin{array}{c}0.633+0.002\times {L}_{DAF.COD}\\ -9.319+0.011\times {L}_{DAF.COD}\\ 5.060+0.011\times {L}_{DAF.COD}\end{array}$ | $\begin{array}{c}\tau =0.1;{L}_{DAF.COD}<1060.207\\ \tau =0.1;{L}_{DAF.COD}\ge 1060.207\\ \tau =0.9\end{array}$ |

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**MDPI and ACS Style**

Żyłka, R.; Dąbrowski, W.; Malinowski, P.; Karolinczak, B. Modeling of Electric Energy Consumption during Dairy Wastewater Treatment Plant Operation. *Energies* **2020**, *13*, 3769.
https://doi.org/10.3390/en13153769

**AMA Style**

Żyłka R, Dąbrowski W, Malinowski P, Karolinczak B. Modeling of Electric Energy Consumption during Dairy Wastewater Treatment Plant Operation. *Energies*. 2020; 13(15):3769.
https://doi.org/10.3390/en13153769

**Chicago/Turabian Style**

Żyłka, Radosław, Wojciech Dąbrowski, Paweł Malinowski, and Beata Karolinczak. 2020. "Modeling of Electric Energy Consumption during Dairy Wastewater Treatment Plant Operation" *Energies* 13, no. 15: 3769.
https://doi.org/10.3390/en13153769