Optimal scheduling of converter oxygen based on particle swarm optimization

The air separation plants of iron and steel enterprises are characterized by a high oxygen-emission rate and high comprehensive energy consumption. To solve this problem, a converter oxygen scheduling model was established based on particle swarm optimization (PSO) with the goal of reducing the fluctuation of the total oxygen consumption and saving system energy consumption in the converter. With the full consideration of constraints, such as the constant duration of blowing intervals, compliant starting time of each blowing interval, molten steel temperature above 1250 °C, and minimal variation before and after converter scheduling, PSO based on integer space was used to solve the hypothesis. With the air separation plant of a large domestic iron and steel enterprise as a case study, Pipeline Studio software was used to establish the oxygen transmission and distribution model, and the energy-saving performance of the converter oxygen scheduling was verified. The results show that the optimal scheduling of converter oxygen based on PSO can arrange oxygen for a single converter as much as possible during the study period; moreover, the optimal scheduling can effectively reduce the overlapping time of oxygen blowing in multiple converters, reduce the fluctuation of the total oxygen amount, and alleviate the contradiction between oxygen supply and demand. The oxygen emission of the pipeline transmission and distribution system before and after the dispatch is reduced from 1242.1 m3 to 0 within the 120 min simulation period; the corresponding air separation system oxygen production energy consumption saves 1192.42 kW·h; the compression energy consumption of the oxygen compressor increases by 41 kW·h; and the total energy saving of the system is 1151.42 kW·h. Based on comprehensive calculations, optimal scheduling of converter oxygen based on PSO is applied throughout the year. The oxygen transmission and distribution pipeline system is expected to reduce the total amount of oxygen emission by 5.44×106 m3 and save the total energy consumption by 5.22×106 kW·h.