review and comparison between different surrogate models for analysis of catalytic fixed-bed reactor

The need for accurate, fast and low-cost methods to predict characteristics of complex chemical reactions has led to the use of various models in fields of science and engineering# chemical engineering and its subcategories are not excluded from this rule# in chemical engineering, surrogate models are used for various purposes, including modeling, optimization and, etc# latin hypercube is used as a promised sampling method since it has better performance compared to other sampling techniques based on our previous study# in the current study different types of alternative models: linear regression, support vector regression (svr), multi-layer perceptron neural network (mlp), radial neural network are studied and compared based on error rates# results indicated that mlp provides the best ability to predict the steady-state behavior temperature, pressure, mole fraction of components, maximum temperature of a direct dimethyl ether synthesis in the fixed-bed reactor#