A novel framework for technical performance evaluation of water distribution networks based on the water-energy nexus concept

Today energy recovery using Micro-Hydropowers (MHPs) in Water Distribution Networks (WDN) is a well-known approach for recycling the wasted energy in infrastructures as a sample of circular economy. Likewise, in this study for the first time a framework for evaluation of WDN for energy harvesting have been designed with the application of statistical optimization, simulation, and artificial intelligence concepts. In this study, after modelling a WDN in Mashhad, Iran, with Environmental Protection Agency Network Evaluation Tool (EPANET) software, the potential of energy recovery using MHP technology was optimized with the application of Design of Experiment (DOE) methods, including Taguchi and Response Surface Methodology (RSM) and then the model prediction ability was improved by Artificial Neural Network (ANN) technique. Results of this investigation revealed that the combination of Taguchi and RSM methods could successfully optimize the energy recovery potential with consideration of improving the hydraulic parameters of WDN. With the application of RSM and Taguchi, high potential positions for MHP placement are detected and analyzed based on a high-performance operational decision-making methodology. According to Artificial Intelligence (AI) computations, energy har-vesting and hydraulic responses can be estimated with more than a 99 % correlation coefficient. Also, it shows that the soft-operator can be executed to control the features of MHPs in WDNs. The outputs of this research demonstrated that MHP harvested energy is more than 400KW for the run time of this study with consideration of hydraulic parameters.

Automated summary

This study presents a framework for evaluating energy recovery in water distribution networks (WDN) using Micro-Hydropowers (MHPs). The framework combines statistical optimization, simulation, and artificial intelligence concepts. The researchers optimized the energy recovery potential using MHP technology and improved the model prediction ability using Artificial Neural Network (ANN) technique. The results showed that the combination of Taguchi and Response Surface Methodology (RSM) successfully optimized the energy recovery potential and identified high potential positions for MHP placement.