Off-shell fields and conserved currents
Towards establishment of an efficient approach for validation of PWR full core Monte Carlo simulations at hot zero power conditions
Machine learning-enhanced all-photovoltaic blended systems for energy-efficient sustainable buildings
WES-based screening of 7,000 newborns: A pilot study in Russia
Diverse Magnetic Chains in Inorganic Compounds
Influence of Defects and Surfaces on the Electrochemical Performance of MnO2 Cathodes in Rechargeable Alkaline Zn/MnO2 Batteries: A First-Principles Study
Reducing the Environmental Impact of Sewer Network Overflows Using Model Predictive Control Strategy
Abstract This paper proposes a method for reducing the environmental impact of sewer network (SN) overflows. The main objective of the paper is to minimize the wastewater quantity and the pollutant loads that overflow from the SN. The proposed algorithm to achieve this goal is Model Predictive Control using Particle Swarm Optimization as optimization method. It was tested in simulation using a simplified model of the network based on Benchmark Simulation Modelsewer as prediction model, and a forecasted influent. Three cases have been considered: (a) the fitness function is defined as the global yearly overflow volume calculated using equal weights for each tank; (b) the fitness function uses different weights for each tank depending on the medium loads and (c) integrating a penalty term related to the system state at the end of the prediction horizon in the previous fitness function. The simplified model determined a significant reduction of the integration time minimizing the optimization time.