Sewer System Inspection and Maintenance Model for Groundwater Protection

Ivan Milojković1, Jovan Despotović2, Miodrag Popović1

 

1 Institute for the development of water resources "Jaroslav Černi", Jaroslav Černi 80, Belgrade, Serbia; E-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it

2 University of Belgrade - Faculty of Civil Engineering, Bulevar kralja Aleksandra 73, Belgrade, Serbia; E-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it

 

Abstract

Protection of groundwater resources is important for preserving drinking water, especially for karst aquifers which are particularly sensitive to pollution from sewage. The sewer system inspection and maintenance model for groundwater protection has been developed and it consists of three objective functions: regular maintenance, emergency maintenance and investment maintenance. Regular maintenance includes activities related to preventive maintenance, while emergency maintenance includes a variety of activities related to corrective maintenance. Investment maintenance deals with replacement, renovation or restoration of the sewer system. The model includes sewer maintenance, geodetic works, internal pipe inspection using CCTV, external inspection of the sewerage system and hydrodynamic flushing of the pipe network. The model was tested using data from the real system.

Keywords: groundwater; maintenance; sewerage.

 

Introduction

Maintenance of sewer systems is a complex water management task that includes complex activities with the overall aim to protect water resources and the environment. One of the significant areas of water protection is waste water collection and transport from settlements and industries. For the purpose of planning of sewer system maintenance activities, a model was developed based on the sewer system inspection and the considerations of the risk of reduced functionality of the sewer system (Savić, 2009; Ward and Savić, 2012; Ward et al., 2014). The model was tested on a real system that consists of a sewer network which is placed in the vicinity of the water wells that are supplied with water from karstic aquifers. Therefore, the rehabilitation of the sewer system has to be planned in a way that prevents pollution from the wastewater entering the wells (Popović et al., 2013). This paper describes the performance indicators of sanitation, maintenance of sewer systems, external inspection of sewer systems, geodetic surveys, hydrodynamic flushing of the pipe network and CCTV inspection, as well as a model for real sewer system maintenance (Popović et al., 2013) using operational research methods, statistical methods and performance indicators.

 

Materials and Methods

In developing the statistical methods of data collection for the model, sewer system inspection methods (CCTV, surveying methods, etc.) and optimisation were used. A multifunctional optimisation model of the sewer maintenance system consists of three objective functions: 1) Regular maintenance,2) Emergency maintenance and 3) Investment maintenance. Regular maintenance involves regular work on planned (preventive) maintenance of functionality of facilities in terms of preventive actions for all foreseeable problems in the functioning of the sewer system. Emergency maintenance includes activities related to corrective maintenance which aims to reinstate or facilitate the functioning of the sewer system after sudden disturbances in facilities' condition or functionality (Jevtić et al., 2011). Investment maintenance deals with the replacement, renewal or renovation of the sewer facilities. Objective functions describe data and performance indicators that have been developed at the IWA - International Water Association (Matos et al., 2003; Anthony et al., 2003).

The model was developed in two parts as shown in Figure 1. The first part of the model is the data processing part which consists, in part, of geodetic data processing where the existing sewer system is recorded by geodetic methods and data of the sewer system, pipe lengths, diameters and elevations, as well as other geodetic data which are collected and processed. During the inspection of the sewer system, roads and other facilities in the vicinity of the sewer routes that may have an impact on the sewer system were photographed. Then we carried out a CCTV interior inspection of the sewer pipes, manholes and other sewer facilities and assessed the condition of the facilities using WinCan V8 (WinCan8 2013) software. All collected data was then processed calculating the performance indicators (Matos et al., 2003) and then prepared for the second part of the model, which defines criteria functions and alternatives.

The second part of the model is the optimisation module where the multi-criteria optimisation method VIKOR (Opricović, 2009) is applied for selecting the activities that are designed for different types of maintenance, especially for investment maintenance.

The required performance indicators are calculated using variables defined in the IWA (Matos et al., 2003), and the following variables are used:

  1. wC1 Total length of sewer pipes (km)
  2. wD1 Inspection of sewer pipes (km)
  3. wD2 Cleaning of sewer pipes (km)
  4. wD27 Replacement of sewer pipes (km)
  5. wD38 Congestion sewer pipes (No)
  6. wH1 Assessment period (d)
  7. wES Sewer sections to be abandoned (km)

New variable wES - sections to be abandoned, was included in the model (number 7 in the above list). The values of the input data for the operation of the model are given in the Appendix.

Performance indicators that describe maintenance of the sewer system and objective functions are as follows:

  1. Regular maintenance of sewerage
    a) wOp1 - Sewer inspection (% / year)
    b) wOp2 - Sewer cleaning (% / year)
  2. Emergency maintenance of sewerage
    wD38 - Sewer blockages (No)
  3. Investment maintenance of sewerage
    a) wOp23 Sewer replacement (% / year)
    b) wDI Design Indicator (% / year)

 

Fig01
Figure 1: Data processing and multifunctional optimisation.

 

The Multifunctional Optimisation model was developed in a large number of phases (Milojković et al., 2015a, b). The formulas listed below show the way in which the Multifunctional Optimisation model calculates the selected performance indicators that are used to derive a compromised solution for sewer maintenance, for each sewer section separately.

wOp1 - Inspection of sewer pipes (% / year)

wOp1 = (wD1x365 / wH1) / wC1 x100 (1)

wOp2 - Cleaning of sewer pipes (% / year)

wOp2 = (wD2 x 365 / wH1) / wC1 x 100 (2)

wOp23 - Replacement of sewer pipes (% / year)

wOp23 = (wD27 x 365 / wH1) / wC1 x 100 (3)

wDI - Design Indicator (designed abolition of sewer section) (% / year)

wDI = (wES x 365 / wH1) / wC1 x 100 (4)

The model included variable WD38 which is the result of the analysis of CCTV records. A method for multi-criteria optimisation VIKOR, that is used here, has been applied to define a multicriteria optimal solution and it is frequently used, alone or together with other methods (Gwo-Hshiung et al., 2002; Opricović and Gwo-Hshiung, 2004, 2007; Opricović, 2009).

In the case of the Multifunctional Optimisation model, the following Equation is used:

wOp = vkoaєAi(f1 (a), f2 (a), ... , fn (a)) (5)

Where:

wOp – is the operator for decision making based on the sewerage performance indicators

A – is the set of alternative sewer sections

a = (x1,x2,....) – is an alternative that has been obtained for certain values of the variables (x) system

fi – i criteria function

vko – is the operator for multi-criteria optimal solution

In particular, for the Multifunctional Optimisation model, the equation is as follows:

wOp = vkoaєAi(f1 (wOp1), f2 (wOp2), f3 (wD38), f4 (wOp23), f5 (wDI)) (6)

Where: wOp1, wOp2, wD38, wOp23, wDI – are performance indicators of sewerage .

In this case, alternative solutions are different sections of the sewer network. We are looking for the most problematic sections of the sewer system in terms of interventions on regular, emergency and investment maintenance, with priority given to investment maintenance being the most expensive for the investor.

Criteria function, as a representative of maintenance of sewer systems, used different IWA performance indicators of sewer systems. We proposed a new variable - indicator wDI for investment maintenance and input data for calculating performance indicators based on the number of expected congestion of sewer pipes per year. Criteria functions are as follows:

wOp1- Sewer inspection (%/year)

wOp2- Sewer cleaning (%/year)

wD38- Sewer blockages (No)

wOp23- Sewer replacement (%/year)

wDI- Design Indicator (%/year)

Evaluation of alternatives is carried out according to the above criteria functions. Criteria functions can be expressed in the form of quantitative economic indicators, technical indicators, quantitative and qualitative indicators (scores or points). Values of parameters of the criteria functions that determine the model are extremised as follows: 1 - maximum value of the function as the most suitable; 0 - minimum value of the function as the most favorable. In this model, the value for parameter extremisation for each criteria function is 1. The input for the other results, the optimisation part of the model, after data processing in the first part of the model, as shown in Figure 2.

 

Fig02
Figure 2: Results of the first part of the Multifunctional Optimisation model.

 

Results and Discussion

After designing the sewer system maintenance, pipelines which were included in the investment maintenance were obtained. Results are completely new pipelines that have improved the functioning of the existing sewer system and are a part of the investment maintenance, taking over the previous function of the sewers. Derived sections that were built in the first half of 2014 are entirely those of investment maintenance, no buildings were constructed during this period that would have required new sections of the sewer system.

 

Table 1: Final weight value criteria for ranking different alternatives
Tab01

 

In the calibration phase, a new criteria function was added to the model: wDI-Design Indicator (sewer section planned to be abandoned), which enabled precise operation of the model. The wD38 variable, was entered into the model since it is more convenient for operation. After a series of iterations, where the weights of the criteria function were varied, the calibrated model led to the weights presented in Table 1, ranking I. Regarding investment maintenance of the sewer system, after calibration the model achieved appropriate accuracy. The model was verified in a real sewer system (Table 2).

 

Table 2: Comparison of planned and executed state of investment maintenance after the final iteration of the model
Tab02

 

Conclusion

Objective functions: regular, emergency and investment maintenance of the developed model are described. The developed sewer system inspection and maintenance model provides opportunities for big savings, especially in investment maintenance. Instead of building a new sewerage system, being that the entire sewer network was in poor condition, only part of the existing sewer was repaired. The data are represented through performance indicators: wOp1-Sewer inspection; wOp2-Sewer cleaning; wD38-Sewer blockages; wOp23-Sewer replacement; wDI Design Indicator, along with a multi-criteria optimisation environment gives good results in savings for the investors. The new wDI Design Indicator gives a different approach to investment maintenance and opens up new perspectives for the sewer system maintenance planning. Performance Indicator wDI provides great opportunities to save on investment maintenance. In addition, the investment maintenance has a crucial influence on regular and emergency maintenance. The costs of regular maintenance are lowered to a minimum because the length of the investment maintenance section is reduced.

 

Acknowledgements

The authors are grateful to the Ministry of Education, Science and Technological Development of Republic of Serbia that the project TR 37014 allowed the realization of this research.

 

References

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Appendix: The values of the input data for the calculation of criteria functions for different alternatives
Tab03