Flare Gas Flow Rate Estimation Using Multimodal Deep Learning

Abstract

In refinery operations, flare gas is generated as a byproduct. It is harmful to human health and the environment and causes secondary issues such as noise and unpleasant odors. Flare stacks are commonly used to combust and neutralize flare gas before releasing it into the atmosphere. Accurate monitoring of flare gas flow rate is essential for flare gas reduction and recovery, but installing flow meters is costly. This study proposes a method to estimate flare gas flow rate using the flare images and suppression steam flow rate. Flare images are processed with a convolutional neural network (CNN) to extract spatial features, while suppression steam time-series data are processed with a long short-term memory (LSTM) network to capture temporal dynamics. These features are fused and passed through fully connected layers to regress the flare gas flow rate. To address data imbalance due to the infrequent occurrence of flare events, we designed a custom loss function that assigns higher weights to high-flow samples while penalizing overestimation when low-flow samples are incorrectly predicted as high flow. Furthermore, we employed data augmentation, preprocessing techniques, and feature engineering to improve prediction accuracy.