Confidence interval estimation of mixed oil length with conditional diffusion models

Accurately estimating the mixed oil length plays a big role in the economic benefit for oil pipeline network. While various proposed methods have tried to predict the mixed oil length, none of them explicitly consider the statistical variability inherent in the estimation, nor the associated risk of underestimating the mixed oil length. To address such gap, we propose to use the conditional diffusion models to learn the distribution of the mixed oil length given pipeline features. Subsequently, we design a confidence interval estimation for the mixed oil length based on the pseudo-samples generated by the learned diffusion models. To our knowledge, we are the first to present an estimation scheme for confidence interval of the mixed oil length that considers statistical variability, thereby reducing the possibility of underestimating it. When employing the upper bound of the interval as a reference for excluding the mixed oil, the probability of underestimation can be as minimal as 5%. Furthermore, utilizing the mean of the generated pseudo samples as the estimator for the mixed oil length enhances prediction accuracy by at least 10% compared to commonly used methods.