Simulation-based fault propagation analysis - Application on hydrogen production plant

Abstract

Production of hydrogen from water through the Cu-Cl thermochemical cycle is a relatively new technology. The main advantages of this technology over existing ones are higher efficiency, lower costs, lower environmental impact, and reduced greenhouse gas (GHG) emissions. Considering these advantages, the usage of this technology in industries such as nuclear and oil is increasing. Due to different hazards involved in hydrogen production, design and implementation of hydrogen plants require provisions for safety, reliability, and risk assessment. However, a very less research is done from the safety point of view. This paper introduces fault semantic network (FSN) as a novel method for fault diagnosis and fault propagation analysis by using interactions among process variables. The interactions among process variables are estimated through genetic programming (GP) and neural network (NN) modeling. The effectiveness, feasibility, and robustness of the proposed method have been demonstrated on simulated data obtained from the simulation of hydrogen production process in AspenHysys software. The proposed method has successfully achieved reasonable detection and prediction of non-linear interaction patterns among process variables. © 2013 Springer-Verlag.