Inherently Safer Design Approaches and Improvement Strategies in Process Industries

Abstract

Over the last decade, inherent safer design has risen to prominence in any educational and industrial research. Various regulative bodies have mandated that inherently safer design alternatives be thought of. Due to the inherent existence of risk migration, this implementation fails to achieve the objective of minimizing the risk associated with process accidents. This paper examines various inherently safer design methods commonly used in process industries, including Index- based, consequence-based, and Risk-based, as well as the various methodologies and techniques used to implement them. It is also suggested to evaluate the benefits and drawbacks of current procedures, as well as to present alternate innovative approaches to increase the degree of inherent safer design that are not currently used. To enhance safety performance, this paper suggests using Fuzzy Logic, Artificial Neural Network methods, and Machine Learning, among other things