Application of life cycle energy analysis for designing a water distribution network

Purpose: The work here focused on developing a framework for the overall planning of water distribution network (WDN) using life cycle energy analysis (LCEA) method. During the life cycle, aging-based maintenance scheduling was also carried out. Three different networks that have a distinguishing range of pipe diameter sizes and total pipe lengths were selected for study networks in order to evaluate the scale dependency to energy usage trend and design pathway. Methods: The model is built up with a Visual Basic program, and the hydraulic network solver, EPANET 2.0, is linked to support simulation-based environmental assessment. The proposed model determines optimal diameter for minimum annual average energy usage (AAEU) while considering maintenance activities. For optimization, revised harmony search (ReHS) was selected, and also pipe aging and breakage models are used for maintenance scheduling. AAEU is calculated by dividing overall energy usage with life cycle. Proposed model is applied to three different scale WDN and demonstrated for three case studies. In the first case study, the proposed model was verified by comparing results with prior research studies. Then, LCEA was conducted for each of the network. Finally, an optimal design of each network was conducted. Results and discussion: The first case study results matched well to prior research studies, thereby demonstrating that the applicability of the proposed model was verified. Results of the second case study showed that the AAEU is proportional to the scale of network while life cycle is inversely proportional. Maintenance activity had advantage for extending the planning period. Finally, optimal design results in case study 3, both AAEU and life cycle are proportional to the scale of WDN. Comparing second and third case results, AAEU results were mostly influenced by the scale of network, but the length of life cycle was more dependent on the distribution of diameters. In summary, AAEU can be reduced using two methods either by increasing the number of small pipes or extending the life cycle. Conclusions: The elicited results from the three cases are converging towards the importance of the pipe diameter distribution, number of small diameter pipes, and the total length of network. In order to reduce AAEU, the model considered the characteristics of the network and determined whether there are increases in the small diameter pipes or distributed diameters evenly. Results show that the suggested model could constitute an alternative design method for minimizing energy usage.