High CO₂ emissions and energy consumption have greatly restricted the development of China’s iron and steel industry. Two alternative ironmaking processes, top gas recycling-oxygen blast furnace (TGR-OBF) and COREX^®, can reduce CO₂ emissions and coking coal consumption in the steel industry when compared with a conventional blast furnace (BF). To obtain parameters on the material flow of these processes, two static process models for TGR-OBF and COREX were established. Combining the operating data from the Jingtang steel plant with established static process models, this research presents a detailed analysis of the material flows, metallurgical gas generation and consumption, electricity consumption and generation, comprehensive energy consumption, and CO₂ emissions of three integrated steel plants (ISP) equipped with the BF, TGR-OBF, and COREX, respectively. The results indicated that the energy consumption of an ISP with the TGR-OBF was 16% and 16.5% lower than that of a conventional ISP and an ISP with the COREX. Compared with a conventional ISP, the coking coal consumption in an ISP with the TGR-OBF and an ISP with the COREX were reduced by 39.7% and 100% respectively. With the International Energy Agency factor, the ISP with the TGR-OBF had the lowest net CO₂ emissions, which were 10.8% and 35.0% lower than that of a conventional ISP and an ISP with the COREX. With the China Grid factor, the conventional ISP had the lowest net CO₂ emissions—2.8% and 24.1% lower than that of an ISP with the TGR-OBF and an ISP with the COREX, respectively.

The endpoint carbon content is an important target of converters. The precise prediction of carbon content is the key to endpoint control in converter steelmaking. In this study, a real-time dynamic prediction of the carbon content model for the second-blowing stage of the converter steelmaking process was proposed. First, a case-based reasoning (CBR) algorithm was used to retrieve similar historical cases and their corresponding process parameters in the second blowing stage, based on the process parameters of the new case in the main blowing stage. Next, a long short-term memory (LSTM) model was trained by using process parameters of similar cases from the previous moment as the input and the carbon content for the next moment as the output. Finally, the process parameters of the new case were input into the trained LSTM model to produce a real-time dynamic prediction of the carbon content in the second blowing stage. Actual production data were used for the verification, and the results showed that the prediction errors of the proposed model within the ranges of (−0.005, 0.005), (−0.010, 0.010), (−0.015, 0.015) and (−0.020, 0.020) were 25%, 54%, 71%, and 91% respectively, which were higher than the prediction accuracies of the traditional carbon integral model, cubic model, and exponential model.

Here, we present a simulation-based solution for a multi-crane-scheduling problem derived from a steelmaking shop. This problem features non-conflict constraint between cranes, station-capacity constraint, and jobs with inaccurate release times and different temporal scheduling objectives. The predictive–reactive rescheduling strategy was applied to solve the problem. The problem was modelled considering different temporal objectives for the jobs and workload objective for the cranes and the model was solved by a heuristic. In the simulation, the jobs were not directly given but generated by a job-prediction method. The cranes’ moving behaviours were controlled by a designed crane trajectory solution. Experimental tests were conducted using data from the site and the results show that the proposed crane-scheduling solution provided better scheduling results than both the exhaustive method and the method that is used in the production field. The best predictive spans for the jobs in this specific crane-scheduling problem were found to be 7–14 min. The real-time performance of the crane-scheduling solution is demonstrated to highly guarantee its practicability.