One of the key discoveries in the history of mankind was the casting process. Beginning with copper and then bronze and gold, humans learned to melt various metals, finally reaching iron, which allowed them to develop new weapons and tools. However, large-scale production of cast iron only became a reality as of the second half of the twentieth century, with the widespread use of cupola and electric furnaces (induction or arc).
New cast iron applications were discovered and its use was expanded throughout the development of the industry. The way in which it is produced directly impacts its properties such as malleability, resistance to high temperatures and durability, increasing its flexibility and versatility. These characteristics come together to create the major advantage of cast iron: low-cost production of parts with complex shapes.
However, in order to maintain this advantage of being a low-cost product in an increasingly competitive world, it is necessary to optimize some aspects of production, such as the purchase of raw materials and charge definition, allocation of production among the different production units and the configuration of important process variables.
A good definition of which raw materials to buy and their use is essential for the company’s competitiveness, since these account for most of the cost of cast iron. The characterization of raw materials thus plays an important role in this decision, and their trade-offs must be very carefully analyzed. In addition, this decision is very dynamic and may vary depending on the company’s demand profile at the time, the availability of its production units and the status of the raw materials market.
Metallic charge is a good example. At times, the price of scrap can be more competitive. At others, the use of solid pig iron may be more feasible. In addition to price, the chemical composition and values for some of these materials’ properties are the main decision drivers, such as, for example, density and moisture.
Still with regard to raw materials, defining the coke purchase is probably the biggest decision regarding cupola furnaces. The ash and volatile matter content, granulometry and coke resistance have a direct impact on the combustion efficiency of the furnace, and, consequently, on the coke rate. Another important impact of coke comes from its sulfur content, which can increase or reduce the need for desulfurization.
Finally, it is necessary to choose the best way to purchase additive materials, whether they are carburisers, desulfurizing agents, ferrous materials, such as ferrosilicon and ferrochrome, or non-ferrous materials, such as nickel and copper.
In an iron foundry, it is common to have different melting processes: electric furnaces, which use electricity as an energy source, and cupola furnaces, which use coke combustion to generate energy. In addition, these furnaces can have different characteristics such as volume and power.
Thus, it is extremely important to dynamically define how to allocate production by responding to fluctuations in prices (electricity and coke) as well as the material characteristics (e.g. carburizers’ efficiency). Also, production capacities and the capability of a facility to produce a metal with certain chemical composition may interfere with this decision.
The way the process is configured must also be optimized. For example, the temperature of the metal produced must take into account energy demand, thermal losses between the productive units and temperature restrictions at the time of pouring. Other important factors are slag generation and its basicity, the blowing of air and oxygen in the cupola furnaces, and the hot heel in the electric furnaces.
The process of ensuring that all of these decisions in a foundry are made in the best way is extremely complex. However, with the aid of mathematical models and data analysis, decision makers can leave the job of finding the best solution to an optimization software and only focus on analyzing the solutions and building scenarios.
With extensive knowledge in process modeling, Cassotis has a solution for optimizing the foundry process. Through a non-linear model, which integrates technical knowledge from literature and the analysis of historical data validated by the experience of process engineers, the aforementioned decisions can be streamlined, helping companies to achieve increasingly better results.
In addition, the Quandec tool is used in most projects, which facilitates the analysis of results, comparison of different scenarios and maintenance of data in a simple and concise manner. This is all to ensure that users can make the best use of their time and the model’s potential!
If you are interested in this type of model, check out our website and feel free to contact us!
Author: Cassiano Lima - Senior Consultant at Cassotis Consulting
Co-author: Fabio Silva - Senior Manager at Cassotis Consulting
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