Accelerating Flash Calculation through Deep Learning Methods

TL;DR

This paper introduces a deep learning approach to accelerate flash calculations, demonstrating improved speed and accuracy over traditional methods like Newton's and Sparse Grids, with robust results validated against experimental data.

Contribution

The paper presents a novel deep learning model for flash calculation acceleration, outperforming existing methods in speed and accuracy, and thoroughly investigates factors affecting model performance.

Findings

Deep learning model achieves faster flash calculations.

Results align well with experimental data.

Model demonstrates robustness across tests.

Abstract

In the past two decades, researchers have made remarkable progress in accelerating flash calculation, which is very useful in a variety of engineering processes. In this paper, general phase splitting problem statements and flash calculation procedures using the Successive Substitution Method are reviewed, while the main shortages are pointed out. Two acceleration methods, Newton's method and the Sparse Grids Method are presented afterwards as a comparison with the deep learning model proposed in this paper. A detailed introduction from artificial neural networks to deep learning methods is provided here with the authors' own remarks. Factors in the deep learning model are investigated to show their effect on the final result. A selected model based on that has been used in a flash calculation predictor with comparison with other methods mentioned above. It is shown that results from the optimized deep learning model meet the experimental data well with the shortest CPU time. More comparison with experimental data has been conducted to show the robustness of our model.