External Surface Quality of the Graphite Crystallizer as a Factor Influencing the Temperature of the Continuous Casting Process of ETP Grade Copper

Today’s world is a place where lack of electrical energy would be unimaginable for most of society. All the conductors in the world, both aluminum and copper, have their origin in various types of casting lines where the liquid metal after crystallization is being processed into the form of wires and microwires. However, the efficiency of the continuous casting processes of metals and the final quality of the manufactured product strictly depend on the design of the used crystallizers, the materials used during its production and its quality. Research conducted in this paper focuses on the latter, i.e., external surface quality of the graphite crystallizer at the place of contact with the primary cooling system. In order to quantify its influence on the continuous casting process numerical analyses using the finite element method has been conducted, which results have been further confirmed during empirical tests in laboratory conditions. It has been proven with all of the proposed methods that the temperature of the obtained cast rod is closely linked to the aforementioned surface quality, as when its roughness coefficient surpasses a certain value the temperature of the obtained product increases almost twofold from approx. 150–170 °C to 300–320 °C. These values might influence the quality and final properties of the cast rod, the susceptibility to wire drawing process and possible formation of wire drawing defects and therefore be of much importance to the casting and processing industry.

A Model-Based Predictive Controller of the Level of Steel in the Mold with Disturbances Using a Repetitive Structure

Keeping the level of steel in the mold of the continuous casting process constant is fundamental for the quality of the steel produced and, consequently, its commercial value. It is challenging, considering the several disturbances that cause undesired variations in the mold level. The aim of this paper is to apply a repetitive structure composed of two controllers, a generalized predictive controller (GPC) and a repetitive GPC (R-GPC) with constraints to mitigate the bulging and clogging/unclogging disturbances and the casting speed variation in the mold level of the process. The R-GPC controller has the same characteristics as the GPC, such as performance, robustness to disturbances, and insertion of constraints, and its advantage is the elimination of periodic disturbances. The repetitive structure will be implemented with a robustness filter and tuned by a genetic algorithm (GA). The controller tests are performed by simulations of a nonlinear mathematical model of the mold level, validated using real data from the steel industry. The proposed controller reduces the bulging disturbance amplitude by 98.5% and at 25% of the frequency of reversions in the valve. Consequently, the proposed controller allows an increase in the valve life span, a reduction in maintenance costs, and quality improvement in the steel slab.

Analysis of Non-Symmetrical Heat Transfers during the Casting of Steel Billets and Slabs

The current automation of steelmaking processes is capable of complete control through programmed hardware. However, many metallurgical and operating factors, such as heat transfer control, require further studies under industrial conditions. In this context, computer simulation has become a powerful tool for reproducing the effects of industrial constraints on heat transfer. This work reports a computational model to simulate heat removal from billets’ strands in the continuous casting process. This model deals with the non-symmetric cooling conditions of a billet caster. These cooling conditions frequently occur due to plugged nozzles in the secondary cooling system (SCS). The model developed simulates the steel thermal behavior for casters with a non-symmetric distribution of the sprays in the SCS using different boundary conditions to show possible heat transfer variations. Finally, the results are compared with actual temperatures from different casters to demonstrate the predictive capacity of this algorithm’s approach.

Development of a Steel Plant Rescheduling Algorithm Based on Batch Decisions

During the steelmaking and continuous casting process in the steel plant, it is common to encounter delays that affect initial planning. Furthermore, continuous casting machines themselves can lose much of their performance in the event of closure of one or more of their casting strands. The situation that is generated, far from being a planning problem, forces consideration of a vision of cost analysis when deciding changes in the planned sequences. This study presents a detailed analysis of the different circumstances that can cause strands closures or sequence breaks, their consequences and the different options available to minimize losses. Finally, an algorithm capable of analyzing the workshop situation and making the most favorable decision to optimize production is proposed, analyzed and compared with the efficiency of the original scheduling method in a real steel plant. The new algorithm proves its efficiency in all situations, with a time-saving average of 26.41 min per decision taken.