Inference and local influence assessment in a multifactor skew-normal linear mixed model

Abstract

The two challenges facing human life are water and energy. Reverse osmosis (RO) desalination systems are popular owning to their unique advantages. However, robust performance and power supply are the two main challenges in this desalination system. This power is used to drive an induction motor that rotates a centrifugal pump to apply the required back pressure to the RO membrane. To solve these two challenges, a complete RO system powered by a photovoltaic (PV) system was considered, and for each subsystem, a robust controller was designed based on their dynamic models. A fuzzy controller optimized by the invasive weed algorithm (IWA) was designed to track the maximum power in the photovoltaic subsystem under different environmental conditions. A fuzzy-PID controller was used to control the motor-pump subsystem. Furthermore, it is focused on designing a robust controller with the ability to compensate for large set-point changes, reject external disturbances, and cope with parametric uncertainties, such as variations in feed water salinity. Hence, state-dependent Riccati equation control (SDRE) was used to control the reverse osmosis system. The simulation results for different scenarios show that the proposed controller performs well under different operating conditions and can remove the effects of disturbances on the system.