Optimization of acid gas sweetening technology based on big data

Optimization of acid gas sweetening technology based on big data

Decision making

Carbon dioxide

Big data

Desulfurization

Energy utilization

Genetic algorithms

Multiobjective optimization

Gases

Air purification

Dynamic models

In this paper, an optimization method based on big data was proposed to improve the desulfurization selectivity and reduce the energy consumption of traditional acid gas sweetening technologies. At first, decision-making parameters which have significant effects on the performance indexes were identified by analyzing the sweetening process. Then, a dynamic model of unscented Kalman filter neural network was built to describe the potential rules of the sweetening process. And finally, a preference-based multi-objective optimization was adopted to address the issue of excessive removal of H2S and CO2 in the original process. The multi-objective optimization was carried out on the model by using the non-dominated sorting genetic algorithm with the concentration of H2S and CO2 approaching 2.5 mg/m3 and 2% respectively as the objective functions. In this way, the optimal process parameters were obtained. The real production data of a certain acid gas sweetening plant from January to December in 2014 were acquired for simulation experiments with the first 80% samples as the training set while the left as the testing set. It is shown that the dynamic model can better present the production rules of the sweetening process

that based on the optimization results, it is recommended to decrease the temperature of primary absorption column, increase the temperature of secondary absorption column, raise the pressure of flash drum and reduce the circulation rate of amine solution appropriately

that after the optimization, the desulfurization selectivity is improved significantly with H2S concentration of the purified gas rising from 0.62 to 3.22 mg/m3, and the CO2 concentration rising from 1.19% to 1.99%

and that the circulation rate of amine solution drops by 16.67%, the steam consumption decreases, and the production rate of purified gas increases by 0.8%. On the whole, the target of production increase and energy consumption decrease is reached. 2016, Natural Gas Industry Journal Agency. All right reserved.

107-114

9

36

Gu, Xiaohua

Qiu, Kui

Li, Taifu

Wang, Kan

Tang, Haihong

Shang, Jianfeng

Natural Gas Industry

2016

journalArticle

10000976

10.3787/j.issn.1000-0976.2016.09.013

http://dx.doi.org/10.3787/j.issn.1000-0976.2016.09.013