Design and Implementation of an ANN Based Intelligent System for Real-Time Monitoring and Fault Diagnosis in Pharmaceutical Reverse Osmosis Processes

Abstract

This paper introduces a novel approach for real-time diagnosis of a purified water station designed for pharmaceutical applications. The water is produced using the reverse osmosis principle. Taking into account both the physicochemical properties required by international health regulations and the stringent standards of drug manufacturing, a diagnostic model based on artificial neural networks (ANN) is proposed. The developed intelligent monitoring system employs a multilayer ANN with gradient backpropagation (5-15-5). Its primary objective is to detect and localize potential faults within the water production process. The monitoring focuses on the physicochemical parameters of the purified water. Simulation results demonstrate effective fault detection, characterized by high accuracy, fast response time, and reliable performance.