Book Description

Improper wastewater management could result in significant damage to the treatment plants and the final recipient aquatic ecosystem. In the past, wastewater management did not get much attention from different stakeholders. However, recently a paradigm shift of wastewater and storm water management is evolving from a simple sanitary and flood control, respectively, to a whole environmental protection function. A very important aspect of the sewer systems management policy is to detect and eliminate an illicit intrusion. This PhD research is consisting of two main pillars. In the first pillar, the issues regarding the identification of an illicit intrusion in a sewer system have been addressed, proposing a source identification (SI) methodology. In the second pillar, different innovative methodologies have been proposed to find the optimal placement of a limited number of sensors in the sewer system. In the thesis, the SI is solved through a simulation-optimization model, combining the hydraulic and quality simulation tool Storm Water Management Model (SWMM) with a genetic algorithm code (GALib) as an optimizer. It requires online measurements from some sensors placed on the network. The SWMM does not have the programmer's toolkit. To integrate the SWMM simulator with the proposed automated SI methodology, an ad-hoc toolkit has been developed. A pre-screening procedure, based on the pollution matrix concept and considering the topology of sewers, has been implemented to reduce the computational effort. The SI methodology has been tested on two different networks. One is a literature network taken from the SWMM example manual while the other is one sub-catchment of the real sewer network of Massa Lubrense, a town located near Naples, Italy. The results show that the pre-screening procedure reduces the computational effort significantly, and it has a crucial role in large systems. In investigating the performances of the SI methodology, its sensitivity respect to the genetic algorithm parameters has been verified. Moreover, the influence of the uncertainty of the inflows values and the measurement errors on the results have been investigated. Another core problem associated with the water quality monitoring of sewers is represented by the optimal placement of a limited number of sensors for the early detection of an illicit source. In the thesis, the sensor location is expressed as a single or multi-objective optimization problem and the SWMM is used to extract the water quality data. Different formulations have been proposed and tested. First, an Information Theory (IT) based multi-objective optimization methodology is presented. The IT approach considers two objectives: the Joint entropy, the information content of a set of sensors, which is kept as high as possible; the Total correlation, a measure of redundancy, which is kept as low as possible. In the second multi-objective approach Detection time, to be minimized, and Reliability, to be maximized, are considered. In both cases, the multi-objective problems are solved using the Non-Dominating Sorting Genetic Algorithm-II (NSGA-II). As a third alternative, a single objective Greedy based optimization tool has been tested. The previously considered objectives are also used with different combinations. The Massa Lubrense sewer network is used to test the performances of various proposed procedures. A normalized comparison among all approaches shows that the Greedy based approach could be a handy alternative for optimizing the sensor locations in sewer systems.