As the main energy-consuming equipment, the reheating furnace plays an important role in the scheduling of continuous casting and hot rolling for a steel mill. This paper studies the energy consumption of cold and hot slab conversion mode in a reheating furnace, forming four conversion relationships, with the goal of minimizing the coupling function between the maximum completion time of the hot rolling and the energy waste of the reheating furnace, and modelling the scheduling of a continuous casting–hot rolling process. An improved ordered pair bat algorithm is used to solve for special process conditions of the proposed scheduling model. The effectiveness of the proposed model and algorithm is verified by solving the different slab charging sequence and conversion mode. Moreover, a comparative study of different types of batches is also conducted, and it is found that the model can save more than 5000 GJ per month for the steel mill, which can achieve the goal of effective energy saving and increasing enterprise profit.

The hot rolled strip is a basic industrial product whose surface quality is of utmost importance. The condition of hot rolling work rolls that have been worn for a long time is the key factor. However, the traditional scheduling method controls risks to the surface quality by setting fixed rolling length limits and penalty scores, ignoring the wear condition differences caused by various products. This paper addresses this limitation by reconstructing a hot rolling-scheduling model, after developing a model for pre-assessment of the risk to surface quality based on the Weibull failure function, the deformation resistance formula, and real production data from a rolling plant. Additionally, Ant Colony Optimization (referred to as ACO) is employed to implement the scheduling model. The simulation results of the experiments demonstrate that, compared to the original scheduling method, the proposed one significantly reduces the cumulative risk of surface defects on products. This highlights the efficacy of the proposed method in improving scheduling decisions and surface quality of hot rolled strips.

Aiming at the problem that previous product quality prediction models cannot directly assess product quality before hot rolling, this paper presents a joint model for process parameters generation and quality prediction based on Conditional Generative Adversarial Nets and Artificial Neural Network. After generated actual process parameters by the generation module, this model would predict the quality of products ahead of schedule according to the generated parameters before hot rolling, which do not rely on inputting actual process parameter online anymore. Finally, the model has been trained and tested with the actual data from a certain hot rolling plant. The experimental results show that the generated process parameters agree well with actual production and quality prediction accuracy can meet the production requirements, which confirms the proposed model can be applied to simulate the actual rolling process and predict strip quality ahead of hot rolling production, providing a reference for the adjustment of planning and scheduling in the future.

Aiming at the current shortcoming of relying on online control of process parameters in hot rolling process, we proposed a digital twin model based on CGAN. This model can simulate the rolling process according to rolling information such as production conditions, working environment, material properties, and rolling sequence. For model training, testing and validation, we collected actual production data of the hot rolling mill is collected. The results show that the process parameters generated by the model are close to those of the real process, which proves it can reflect the hot rolling process and could be used for future simulation, quality prediction and production optimization of the hot rolling process.