Managing urban wastewater system using complex adaptive system approach
Sewer networks received little attention in the previous millennium due to the prevailing assumption that the piping infrastructure would continue to carry out the function for which it was designed until failure. As a result, sewer networks across the globe are in poor condition. Moreover, in many developed countries around the world, the problem is further complicated by the fact that it is not easily feasible to repair and replace existing sewer networks because of the higher costs and budget constraints currently placed upon municipal projects. To overcome these issues, decision-makers for utilities have increasingly adopted a proactive asset management approach. The concept behind this approach is to develop a long-term management planning process to renew a sewer network gradually over time, which can be divided into three main steps: (1) predict possible failures in the sewer network throughout the planning period; (2) develop suitable prioritization models that can select the most critical pipes in the network; and (3) evaluate the impacts of different socio-economic factors on developed optimum renewal plans developed during the process. Despite the clear advantages of this approach, models that implement proactive asset management continue to suffer from a number of major drawbacks, which include the following: (1) the developed failure models either ignore operational failures in the network or make many assumptions to simplify their prediction; (2) the available prioritization models do not consider the serviceability of the network, the criticality of the pipes selected, and the life-cycle costs of the network; (3) the prioritization models also ignore budget uncertainties and the impacts of socio-economic factors such as population growth and water consumption; and (4) the asset management models developed to address the limitations of prioritization models are scenario-based and use a monolithic system approach that ignores the feedback loops between the social system, the physical assets, and the financial system. To overcome the above limitations, this dissertation aimed to develop a system-of-systems (SoS) decision support framework and a methodology to aid in efficient management and planning of a wastewater system. The proposed framework focuses on improving different aspects of wastewater system management and planning. First, a stochastic model was developed to determine a blockage in a sewer network in terms of both the physical attributes and the condition of the pipes. Second, a life-cycle multi-objective genetic algorithm model was created to identify optimum renewal strategies by considering their impacts on the long-term rather than the short-term behavior of the sewer network. Third, a hybrid algorithm was developed that combines a single objective ant colony model with the multi-objective genetic algorithm model to overcome the drawbacks of the traditional multi-objective genetic algorithm and to identify critical pipes within the sewer network in accordance with the available funds. Finally, a SoS model was developed by combining the previous models with an urban dynamics model, a water consumption model, and a financial dynamics model using a hybrid agent-based/system dynamic approach. The main advantage of this model is its ability to account for the impacts of the feedback loops between the integrated models on the internal behavior of these models as well as on the behavior of the SoS model as a whole. In addition to the above wastewater management tools, this dissertation also developed two tools for improving the planning process for wastewater systems based on the premise that effective management starts at the planning stage. First, a new algorithm was proposed to layout local sewer networks as a tree graph with minimum length. Second, a spatial model was implemented to layout the network and to select the number, location, capacity, and service area of planned wastewater treatment facilities based on population spatial distribution. Finally, the above proposed approaches are verified and validated following the most recent state-of-art approaches developed for this purpose. First, the behavior of all proposed models is compared with those of the most recent current state-of-art models for external validity. Also, all the proposed models were statistically validated using scenario analysis to consider the uncertainties in the model parameters. In addition to the previous steps, the proposed SoS model is further validated using subject-matter experts.
Kandil, Purdue University.
Off-Campus Purdue Users:
To access this dissertation, please log in to our