Vapour–liquid equilibrium (VLE) modeling is of paramount importance since it affects the efficiency of downstream processing during product recovery in the Fischer–Tropsch synthesis. multi-layer perceptron neural network (MLPNN) was used to simultaneously model VLE of 1533 gas-liquid solubilities divided over sixty binary systems at pressures up to 5.5 MPa and temperatures from 293 to 553 K using literature data. The network was trained using the Levenberg–Marquardt algorithm in MATLAB® for developing and optimizing the model while Bayesian regularization was used to improve the performance of the network. Results obtained from the network suggest that the MLPNN has a better capability in estimating VLE when compared to conventional thermodynamic models.
Eze, P. C., & Masuku, C. M. (2018). Vapour–liquid equilibrium prediction for synthesis gas conversion using artificial neural networks. South African Journal of Chemical Engineering, 26, 80–85. https://doi.org/10.1016/j.sajce.2018.10.001