Application of natural language processing and machine learning in prediction of deviations in the HAZOP study worksheet: A comparison of classifiers

Abstract

The HAZOP (Hazard and Operability) study is one of the most well-known approaches in process hazard analysis. The HAZOP study is a systematic procedure a multidisciplinary team uses to find hazards and operability issues through brainstorming. The conventional HAZOP study requires much time, is also knowledge-intensive, and is susceptible to human mistakes. Therefore, there is a significant incentive to automate the HAZOP study. This study investigated the effectiveness of Natural Language Processing (NLP) and Machine Learning (ML) in HAZOP study automation. The case study used in this contribution is based on a conventional HAZOP study report. Initially, the causes were converted into feature vectors using NLP's simple sentence embedding technique (Bag of Words). Random oversampling was employed to manage the limited and imbalanced dataset. Finally, ML classifiers such as Decision Tree, linear Support Vector Machine, Random Forest, Logistic Regression, Gaussian Naïve Bays, and K-Nearest Neighbors were applied to predict deviations. Decision Tree outperformed other classifiers with 92% accuracy. This study's integrative approach applies even to small units and companies with limited training datasets. This is because it does not require a large training dataset.