Optimization of Sour Water Stripping Unit Using Artificial Neural Network–Particle Swarm Optimization Algorithm

Abstract

Sour water stripping can treat the sour water produced by crude oil processing, which has the effect of environmental protection, energy saving and emission reduction. This paper aims to reduce energy consumption of the unit by strengthening process parameter optimization. Firstly, the basic model is established by utilizing Aspen Plus, and the optimal model is determined by comparative analysis of back propagation neural network (BPNN), radial basis function neural network (RBFNN) and generalized regression neural network (GRNN) models. Then, the sensitivity analysis of Sobol is used to select the operating variables that have a significant influence on the energy consumption of the sour water stripping system. Finally, the particle swarm optimization (PSO) algorithm is used to optimize the operating conditions of the sour water stripping unit. The results show that the RBFNN model is more accurate than other models. Its network structure is 5-66-1, and the expected value has an approximately linear relationship with the output value. Through sensitivity analysis, it is found that each operating parameter has an impact on the sour water stripping process, which needs to be optimized by the PSO algorithm. After 210 iterations of the PSO algorithm, the optimal system energy consumption is obtained. In addition, the cold/hot feed ratio, sideline production position, tower bottom pressure, hot feed temperature, and cold feed temperature are 0.117, 18, 436 kPa, 146 °C, and 35 °C, respectively; the system energy consumption is 5.918 MW. Compared with value of 7.128 MW before optimization, the energy consumption of the system is greatly reduced by 16.97%, which shows that the energy-saving effect is very significant.