Application of fuzzy logic and machine learning techniques to improve inherently safer design in process safety management: A brief study

Abstract

AbstractOver the last decade, one of the most significant areas under focus in process safety management was developing an inherently safer design. The main objective of having an inherently safer design is to avoid hazards and risks from developing in the first place, rather than to reduce them after they have already occurred. A number of strategies, including index‐based and other types, are used in today's process industries. This paper provides a brief overview of the current inherent design methods used in the process industries. This study also details how new technologies such as fuzzy logic and machine learning are used in the improvement of inherently safer designs. Traditional safety evaluation methods have flaws such as poor accuracy, large human element influence, which can affect the degree of safety. Inherently safer design prediction was modeled using various machine learning techniques like random forest, support vector machine (SVM), and K‐neighborhood algorithm. Accuracy obtained for the sample prediction of upper flammability limit while using random forest algorithm was found to be more efficient while comparing with K‐neighborhood and support vector machine algorithms. Accuracy obtained was in the range of 90%–95% for each epoch. The accuracy of the model will always be dependent on the type of parameters that we select for prediction. By considering more safety parameters and efficient machine learning algorithms for training models, we can develop systems with high accuracy predictions for inherently safer process plants.