Predictive modeling for mitigating fugitive emissions in industrial valve seal stacks: a comparative analysis of configuration efficacies

This study investigates the development and application of advanced predictive modeling techniques to address the critical environmental challenge of fugitive emission mitigation in industrial valve seal stacks, specifically in the context of the oil and gas sector. Emphasizing the reduction of greenhouse gas emissions, the research systematically evaluates the effectiveness of multiple seal-stack configurations in minimizing emissions. The experimental framework utilizes argon gas as a surrogate for methane to simulate real-world scenarios. The research employs a comprehensive suite of predictive models, including advanced statistical and machine learning algorithms such as linear regression, ridge regression, Lasso (least absolute shrinkage and selection operator), MARS (multivariate adaptive regression splines), and elastic net. These models are rigorously tested to ascertain their predictive accuracy in estimating the emission levels of two different seal-stack arrangements. Each seal stack contains five individual seals of PTFE and AFLAS in different sequences. The MARS model, identified for its superior performance, is then applied to predict the efficacy of various seal-stack configurations against the stringent ISO 15848–1 standards for allowable emission limits. The results of this comparative analysis offer critical insights into the optimal combination of seal stacks, contributing significantly to the advancement of environmental sustainability practices in industrial applications. This research not only provides a methodological framework for predictive analysis in this domain but also underscores the importance of integrating environmental considerations into industrial design and operation.