THE BENEFITS OF USING AN OPTIMIZATION TOOL AT USIMINAS 1 Adriana Haueisen Pechir 2 Fernando Ferreira Mazzini 3 Luciana Dimas Camillo 4 Luiz Cláudio Costa 5 Abstract The steel industry experienced a hard working time over the past few years. There was an oversupply of steel in the world, a rising in the raw materials prices and a reduction on the final product selling price which provided a decrease in the profits. The new circumstances forced the industries to reduce the production and work with lower productivity in the blast furnaces. Therefore, the biggest challenge was looking for new alternatives to shrink the final product cost. Based on this scenario, Usiminas has chosen to use an integrated optimization software which has broken process paradigms. There were a substantial financial gain and its use gave agility to the process and confidence in the decision-making. Key words: optimization tool, decision-making, limit marginal price 1 Technical contribution to the 6th International Congress on the Science and Technology of Ironmaking ICSTI / 42nd ABM Ironmaking Seminar & 13th ABM Iron Ore Symposium, October 14-18 th, Rio de Janeiro RJ Brazil. 2 Production Engineer, Support of Production Process Management, Usiminas Cubatao, SP, Brazil. 3 ABM membership, Chemical Engineer, Support of Production Process Management, Usiminas Belo Horizonte, MG, Brazil. 4 ABM membership, Chemical Engineer, Reduction Process Management, Usiminas Cubatao, SP, Brazil 5 ABM membership, Metallurgical Engineer., MSc Electric Engineer, Reduction Process Management, Usiminas Cubatao, SP, Brazil
1 INTRODUCTION Usiminas group acts in an integrated way in all stages of steel production. It is composed of companies in steel production, mining, steel distribution and processing segments, manufacturing and sales of stamped steel parts to the automotive industry, value-added products to the capital goods industry, hot-dip galvanizing steel and logistics and distribution. The company has a maximum capacity production of 9.5 million tons per year of steel in two plants: at Ipatinga (MG) and Cubatao (SP). They produce mainly slabs, hot and cold rolled products, being that the last ones can be galvanized or not, depending on their application. The target of the steel industry has always been to produce the quantity of steel required, at the deadline and in the quality specified by the client. Therefore, the new worldwide scenario has been forcing the steel industry to improve its process to produce a more competitive product. The worldwide economy has been in a new context because of the recession that happened in the third trimester of 2008. The international trade almost stopped and the commodities prices decreased. Therefore, in that period the Brazilian industry abruptly reduced the products sales in the internal and external market and the real currency depreciated. In this context, the steel industry was forced to reduce the production. In the end of the first semester of the 2009, the currency started to valorize again. In the following years, this valorization creates another crisis: it opened the Brazilian market to receive imported products whose prices were lower than the Brazilian ones, especially Chinese goods which are assisted by the Government. Otherwise, the raw materials world trend changed through the past years. It is known that they represent around 80% of the hot metal cost and that the fine ores and coals prices have been increasing through the years (figure 1 and 2), as a result of their high demand. Buying alternative raw materials that have more competitive prices should be a good way to reduce extra costs. However, cheaper raw materials have worse quality meaning that it has to change the process related to quality and process restrictions to admit these ones. Figure 1: Average price of iron ore. (4)
Figure 2: Average export prices for metallurgical coal shipped from the USA (5). In such conditions, process optimization became mandatory to maintain the steel producers competitive in their market. In 2000, experts in operation research, involved in a university project and later in a company named n-side, developed SCOOP (Steel Cost Optimization) that integrates the whole steelmaking process and deals as well as the technical as the economical aspects of the production. At that time, the objective of SCOOP was the minimization of the steel production cost by selecting the rights raw material and adapting the process to the current market conditions. Therefore, Usiminas has bet in this tool to change its process. This paper has an overview that has a description of the tool, the steps of its implementation and its financial and operational benefits to the Usiminas. 2 MATERIALS AND METHODS The implementation of SCOOP started in the beginning of 2009. Before that, the good results given by the tool were seen in European companies and in a particular Brazilian company. At that time (during the crisis), this latter used the tool to adapt its production and limit the effect of the crisis. That encouraged Usiminas to buy the package of the tool and started its implementation. The software is an integrated tool which computes the process from the raw materials purchase to the steelmaking shop plant. However, the optimization can also be performed locally at sinter plant, coke plant, blast furnace and steelmaking shop plant separately. The software has detailed information of chemical quality, physical properties and availability of raw materials, thermodynamics, energy and process factors such as productivity, yield, maximum and minimal production of each process and impact of raw material. All the raw materials added in the tool have its chemical and properties data including for example Fe and SiO2 content of iron ores and ash and carbon content of coals. Depending on the raw material, granulometry and screening information also can be requested. Properties for coals and fuels, for instance, CBI, SI, volatile matter, ash content, reflectance, inert content and vitrinite are inputed into the model. In this case, restriction information about the maximum and minimum limits of each propriety in the blend are important due to process restrictions (as oven wall pressure, solidification temperature and fusion temperature which influence the operation of the batteries). Coal properties are also used to predict the coke quality.
The principal formulas are to find out DI, CSR and CRI of coke. The possibility of feeding the model with Usiminas formulas is a great benefit in the tool. You can also establish chemical restrictions for intermediate products such as mix of coals, blend to sinter plant and blast furnace. Availability and costs of raw materials are other important data that must be load in the tool. For all raw materials that can be purchased there are FOB (free on board) and freight costs information. The last one is related with the source and route of each raw material. All other costs (as operating costs) are considered in the model in order to compose the final price of the intermediate and final products. The system implementation was done following some steps during 18 months. After the initial meetings the first step was to collect all the necessary chemical data, define the chemical and operational restrictions and the impact of raw materials in the process in order to set the system parameters. It must have had at that time all the thermodynamics and basic prediction chemical formulas. The prototype of the tool was ready. The second stage was to improve the prototype by adapting the model to the specific situation of Usiminas and by implementing the custom models owned by Usiminas. At that point, individual meetings with each plant were made to upgrade the model. Using the real data collected, the answers returned by the tool were analyzed and validated by the experts of each plant. If it was not coherent, the data, restrictions and formulas were reviewed and new answers were analyzed until the final answer became logical. This step lasted several weeks until the integrated model returned a good response, similar to the real process. The first version of the software was ready to use. The next step would be to determine the potential people who would be responsible to its use. Several access keys were created and the coaching was initiated to help people to explore the tool benefits. The training was conducted by N-Side consultants. Along its achievement, new versions of the tool were implemented to adapt it to Usiminas process. One particularity of its process is that the company has two plants (Ipatinga and Cubatão) and the model should be able to integrate the two processes and it should be ready to share raw materials, have different routes and incorporate the particularity of each plant, always keeping the plants together in the tool. Therefore, the tool was adapted to have corporate data. Once the tool was completely ready to give a response coherent with the real process, it gives the best raw material combination that returns the lowest final product cost at any change in the process, restrictions or raw material prices. Another benefit of the tool is to give the best price when the raw material turns attractive based on its quality. It is called Limit Marginal Price. However, it is important to remember that the software is only a support tool and the experts with all their experience should evaluate the final answer of the model. After its implementation, Usiminas started its use as a support to plan production and reduce costs. 3 RESULTS AND DISCUSSION
Nowadays, the tool is satisfactory used by process people to change and adapt the process and by procurement experts in order to help them to purchase raw materials. Many are the benefits given by the tool for Usiminas Company. Before the implementation of the tool, the production planning and raw material purchase planning were made locally by individual excel files. It made the process slow and created a lot of rework. Now, the study is faster and the tool gives an integrated answer in seconds. Another benefit and one of the most significant example was a reduction in the final cost ofsteel by reducing the production cost of coke. During the 2011 economical crisis, the focus was to reduce costs and the tool helped the company to find out a way to do it. Many scenarios were made and they focused in raw materials purchase. In all scenarios the impact of the changes in the cost and quality of coke, sinter and hot metal were watched. As Usiminas iron ore comes mainly from few particular mines and the coal prices are very expressive, the biggest impacts in the final cost come from the coals purchase. Having that in mind, some restrictions have been opened in the model, mainly in the coal mix properties. In this way, the model was able to suggest the consumption of buy alternative coals with quality poorer than the standard ones and the mix was still able to meet the restrictions. Once the software is complete and returns some important information in the scenarios, each area can follow some vital data of their process like sulfur and ash content in the mix, volatile matter, the properties responsible for the coke quality as CBI, SI, basicity index and the coke qualities itselves. The scenarios showed that a higher percentage of coals of Group A in the mix would help Usiminas to reduce costs. The optimal percentage returned by the model was higher than the actual values. Following the recommendation of the software, the percentage of the Group A coals in the coal mix in the batteries has been increased. In the beginning of 2011, the average percentage used of this coal group was about 20%. This percentage increased to values around 35% and 38% in Ipatinga and Cubatao respectively, as it is showed in figures 3 and 4. Figure 3: Percentage of Group A in the coal mix for Cubatao.
Figure 4: Percentage of Group A in the coal mix for Ipatinga One of the biggest benefits of the tool is the capacity of weighing up the quality, process factors and price and to give an optimum solution for the scenario, computing the impacts not only on the coke but on the hot metal as well. So, we could see the impact of the change in the coke yield, for example, once it depends on the coal volatile matter. In the simulation the optimal coke yield was lower. However, it was obtained without violating any quality and process restrictions inputted in the model inclusively blast furnaces restrictions. All the changes in the process were possible because of the low level production at that moment. Because of the lower productivity of the blast furnaces, they accept a poorer coke quality. The change in the process was watched by the team-work and the results were exactly the decrease of the coke cost. The tool was also used to simulate the new iron ore quality scenario: the raw material became poorer in Fe content and with a higher SiO 2 content. The higher content of SiO 2 increases the SiO 2 content in the sinter and finally raises the slag rate in the blast furnaces. It occurs because the SiO 2 and others elements are impurities. However, the slag production process is a procedure that requests a lot of extra energy that should be saved. The process is using the energy which comes from coke and PCI, the fuel rate, to produce a secondary product that does not have the same value as steel. When there are a higher content of impurities, the process needs a higher fuel rate and coke and PCI are expensive energy sources. So the challenge is always keeping the fuel rate in the lowest levels possible. In the tab 1 it is showed the Fe and SiO 2 content in the fine iron ore of three mainly suppliers of Usiminas. These values are inputted in the model. Tab 1: SiO2 and Fe Content of fine iron ores. Fine iron Ore Fe content SiO 2 Content Supply 1 Iron ore 1 64.3% 5.1% Supply 2 Iron ore 2 63.5% 6.0% Supply 3 Iron ore 3 63.0% 6.1% Each iron ore has its own route, price and quality and before using the tool, the analysis between these three suppliers was rough and the result should not be effective.
One kind of study that is frequently made has the objective of determining the attractive price of the raw materials. It analyses the Limit Marginal Price and it was made for example for the Iron Ore 3. The scenario was used to negotiate the contract with the supplier and a fair price was determined. With the new price, the analysis was made from the raw materials purchase to the blast furnaces. The tool suggested the Supplier 3 as the source of all fine ore consumed at Cubatao and part of it consumed at Ipatinga. Even though the SiO 2 content is higher, the Iron Ore 3 has a more competitive price that compensated the increase in the slag rate and in the energy required. The slag rate restriction was not violated and the sinter price was reduced because of the lowest price of this iron ore. 4 CONCLUSIONS As the tool has an overview of the process, contemplating and returning all the vital processes parameters not only in Coke Plant but all over the steel making process, the tool gave to Usiminas the confidence on the decision-making. Furthermore, it turned the procedure of controlling data and process quicker, as it has the same function of separately excel files. In the past, the decision of opting for a certain solution instead of another one was based only on the product cost and the impacts in the process. By using the tool, it became possible to study much more variables associated with the change, regarding that it considers the integrated process. This deeper analyze allows a decision-making done more well founded. It helped Usiminas to reduce costs and showed that a change in the process is possible. Even the change means quality reduction in any part of the process, the software is capable of computing the changes at each part of the process and we are responsible to tell if the adjustment, for instance at blast furnace, is possible or not in the real procedure. REFERENCES 1 FUNDAP, Grupo de Conjuntura. Crise e Pós-crise: O impacto sobre as grandes empresas brasileiras de capital aberto. Disponível em: <http://www.fundap.sp.gov.br/debatesfundap/pdf/conjuntura/crise%20e%20p%c3% B3s-crise.pdf> Acesso em: 10 abril 2012. 2 Instituto Brasileiro de Siderurgia. A Industria do Aço no Brasil e os efeitos da crise. Siderurgia em Foco, n. 10, maio 2009. Disponível em: <http://www.acobrasil.org.br/siderurgiaemfoco%5csiderurgia10.pdf> Acesso em: 10 abril 2012. 3 InfoMoney. Siderúrgicas Iniciam bem o ano de 2011, após 2010 negativo para o setor. Disponível em: <http://economia.uol.com.br/ultimasnoticias/infomoney/2011/01/17/ siderurgicas-iniciam-bem-o-ano-de-2011-apos-2010- negativo-para-o-setor.jhtm> Acesso em: 10 abril 2012.
4 Instituto Brasileiro de Mineração. Informações e Análises da economia Mineral Brasileira. Disponível em: < http://www.ibram.org.br/sites/1300/1382/00001455.pdf> Acesso em: 18 abril 2012. 5 Metals Consulting International. Metallurgical Coal Prices. Disponível em: <http://www.steelonthenet.com/files/metallurgical_coal.html> Acesso em: 18 abril 2012. 6 N-Side. Benefits, SCOOP. Disponível em: <http://www.scoop4steel.com/pdf/benefits.pdf> Acesso em: 17 abril 2012.