Dynamic Modeling of Multioperating Conditions for a Three-Converter Gas Holder System in Steel Industry by Using Time-Varying Dynamic Bayesian Networks

The multiple joint Linz-Donawitz converter gas (LDG) holder systems are usually employed to alleviate the LDG fluctuation in steel enterprises. A dynamic modeling method based on time-varying dynamic Bayesian network (TVDBN) is proposed for a three-converter gas holder system (TCGHS). Considering the connectivity, the difference in pressure, and the interlocking rules between different gas holders, the operating conditions are defined in this article by physical mechanism analysis of the operation of this gas holder system. Based on them, the TVDBN’s structural model with a mixture of continuous and discrete variables is constructed to describe their transition process through the change of the network structures and the uncertainty relationship of the process variables. The Gaussian regression network is designed for depicting the quantitative uncertain relationship between the gas holder level and gas generation as well as consumption flow rate under each operating condition. Furthermore, as several operating conditions occur rarely due to the operation mechanism, an online parameter learning method is proposed to learn these rare operating conditions according to new coming data. The simulation experiments are performed for a TCGHS of a steel plant in China based on actual data. The results show that the proposed method can not only accurately identify the joint operating conditions of the gas holders and their conversion process but also obtain higher prediction accuracy of gas holder levels compared with the state-of-the-art comparative methods, providing sound support for LDG scheduling.

I. Introduction

Gas holders are important storage and buffer devices for by-product gases in the steel industry [1], [2]. The Linz-Donawitz converter gas (LDG), produced in the steelmaking process, is first charged into the gas holders to buffer the impact of LDG rapid and intermittent generation on the pipeline system and then delivered to other production users through the pressurizer and pipeline networks for gas recycling. In the steel industry, the actual system usually contains multiple converter gas holders that run jointly, where multiple pressure sections are set up for each gas holder, and the pressure levels in every gas holder are different. The gas holder level is a key operational index of the LDG system, so it is crucial to establish an accurate mathematical model for monitoring and forecasting it [3], [4]. However, the operation process of the gas holder system presents complex transition characteristics of multiple operating conditions, posing a great challenge to their accurate modeling.