Confidence interval estimation of mixed oil length with conditional diffusion model
This work addresses a specific economic issue in oil pipeline networks by providing a more reliable estimation method, though it is incremental as it builds on existing diffusion models for a domain-specific application.
The paper tackles the problem of underestimating mixed oil length in oil pipelines by proposing a conditional diffusion model to learn its distribution and estimate confidence intervals, reducing underestimation probability from around 50% to as low as 5% and improving prediction accuracy by at least 10%.
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, they often exhibit an extremely high probability (around 50\%) of underestimating it. This is attributed to their failure to consider the statistical variability inherent in the estimated length of mixed oil. To address such issues, we propose to use the conditional diffusion model to learn the distribution of the mixed oil length given pipeline features. Subsequently, we design a confidence interval estimation for the length of the mixed oil based on the pseudo-samples generated by the learned diffusion model. To our knowledge, we are the first to present an estimation scheme for confidence interval of the oil-mixing 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\%, a substantial reduction compared to 50\%. 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.