  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.
