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Steel plant model for optimisation of steel plant logisticsDanieli Corus and Systems Navigator have developed a Steel Plant Model (SPM) to solve bottlenecks and optimise logistics in both existing and new steel plants. The SPM is based on a three-dimensional (3D) layout where all movements, actions and interactions of all units, including cranes and ladles, maintenance activities and unexpected breakdowns, are simulated. Alternative scenarios with, for instance, different numbers of plant units, ladles, cranes, etc, can be added, modifi ed or removed. This makes it very easy to determine, for example, where bottlenecks are, what the optimum amount of ladles is or what the return on investments will be, for example, with the addition of another converter or caster. The SPM has been validated for several steel plants with widely ranging characteristics and layouts.

Ever since the steelmaking process became industrialised, steelmakers have looked for ways to optimise the

logistics in the plant. Process engineers have traditionally used rules of thumb and static calculations to predict and analyse logistics, but these gave static results that could not take into account unexpected events like failures or cranes with two jobs at the same time. Analysis of logistics depended on the experience of the process engineer, since the static calculations and rules of thumb can never produce unexpected and original results.

In 2011, Danieli Corus, together with software consultancy company Systems Navigator, decided to develop a logistics model for steel plants. The Steel Plant Model (SPM) can cope with daily practice in the steel plant by simulating every event. It is the tool for process engineers to get a new and better insight into steel plant logistics.

In 2013, the SPM was validated for the fi rst time for a European steel plant, and in 2014 the Tata Steel plant in Port Talbot, UK, was simulated in order to analyse logistics issues, compare possible solutions and quantify the results. SPM simulations were also used for the improvement of an existing Indian steel plant, as well as for Indian and Russian greenfi eld steel plant designs from Danieli Linz, Austria.

DESCRIPTION OF THE MODELThe SPM consists of two parts: the actual model and the Human Machine Interface (HMI). The model is object-orientated and is built in an existing simulation software package that also provides three-dimensional (3D) animations. The HMI runs in the program Scenario Navigator, where all input for the model can be given. After the simulation, the output of the simulated scenario is presented in an orderly way[1].

Authors: Frank Schrama, Daan Merkestein, Mart Jansen, Walter Vortreffl ich and Bart van den Berg Danieli Corus, Systems Navigator and Danieli Linz Technology

r Fig 1 Three-dimensional layout in the SPM

STEELMAKING AND CASTING

In the SPM, the whole steel plant, from the hot metal bay to the casting bay, is built in 3D. This model uses the actual distances, sizes and velocities (see Figure 1). Every object in the steel plant (such as equipment, ladles and cranes) is simulated, including its process time, speed, movements, maintenance and unexpected breakdowns. The model can also take probability distributions of various process parameters into account. Furthermore, all objects in the model are able to communicate with each other. The combination of all these aspects makes the model simulation very realistic, sensitive and accurate.

The model calculates chronologically and is object-orientated. The heats are requested in series (of a certain steel grade) and are assigned to a caster. Then the heats are created at the beginning of the plant as hot metal in a torpedo or ladle. During the simulation, a certain heat (before the converter as hot metal and after the converter as steel) will go through every process step required for its steel grade. When more than one option exists for a certain step in the process (for example, two different a

r Fig 1 Three-dimensional layout in the SPM

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MAIN KEY PERFORMANCE INDICATORS (KPIs)Because the SPM simulates the whole plant, all customer-required key performance indicators (KPIs) can be logged. KPIs that are always logged in each simulation include:` Production` Number of heats` Used quantity of raw products` Utilisation of all equipment (units, cranes and ladles)` Daily production (also per caster)` Ladle full and empty times` Waiting times (per station and per heat)` Quantity of heats per route.

For specifi c cases, specifi c KPIs can be added to the model as desired.

CASE STUDYIntroduction To show the approach and results of a simulation, a case study for a fi ctional steel plant is presented. This plant contains the following major equipment:` 2 Hot metal pits (HMP)` 2 Hot metal desulphurisation (HMD) stations` 2 Converters (BOF)` 2 Ladle furnaces (LF)` 1 Vacuum degasser (VD)` 2 Continuous casting machines (CCM)

The plant has three bays and two cranes in each bay. The jobs of these cranes, as well as other process parameters, are implemented utilising the knowledge of Danieli Linz, combined with the experience of Danieli Corus. Furthermore, two hot metal ladles and nine steel ladles are in use. The heat size is 160t.

Table 1 shows the average process times (µ) of every piece of equipment, as well as the standard deviation (σ). In this case it is assumed that the process times of a certain unit are normally distributed. Absolute minimum processing times are added to avoid non-realistic situations. There is no difference between the process times for the different steel grades. Since the caster can adjust its speed based on steel availability (these casters can slow down to 66.7% of their original speed; this is called starving). Its process time is the minimum time it would cost to cast a full steel ladle at maximum speed. If starving does not help and the casting series breaks, the total net restart time of the caster (including ramping up for the fi rst slab) is 45 minutes.Base case and validation The steel plant is now created in 3D in the model and all parameters, routings, crane movements and priorities, are implemented (for the sake of readability only the most important parameters are mentioned in this article). Figure 2 gives a layout overview of this plant. With this layout (the third CCM does not yet participate) the base case scenario is run. This base case scenario mirrors the steel plant practice over the whole

stirring stations), the model will choose the best option for the heat, based on availability and priority.

Transportation of the ladles, by cranes and transfer cars, is actually simulated in the SPM, instead of simplifying transportation to a time delay. This means that crane and transfer car limitations, such as being unable to pass or being occupied, are fully simulated. Smart Cranes in the SPM are, based on priorities and timing, able to take over each other’s jobs or move each other aside. The SPM is unique in including the transportation in the simulation at this high level of detail.

The SPM is able to predict the steel production of a simulated plant for a given period and gives a wide range of production data of the complete plant, and information about every single object (including cranes and ladles) in the simulation. This information includes utilisation, capacity, idle times, maintenance and task breakdowns.

When the SPM is running, the simulation can be viewed in real or accelerated time in 3D from every desired angle or point. Simply ‘walking through’ the steel plant is also possible, making it possible to detect and visualise logistic problems in a plant.

Plant unit µ, min σ, minHMD 30 2.25BOF 36 1.25LF 32 1.25VD 27 1.75CCM 45 –

r Fig 2 Layout overview of fi ctional steel plant in the SPM

r Table 1 Process times and standard deviations of the equipment

STEELMAKING AND CASTING

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a

simulation time period and the results are used to identify bottlenecks and analyse the equipment/crane utilisations and ladle empty/full times.

In the case of an existing plant, the results of this scenario would be compared with the actual data, in order to validate the model (in addition, extra scenarios of known cases would be run for further validation of the model).

Simulation of the base case resulted in an annual steel production of 2.79Mt. Figure 3 shows the Gantt chart of an average day. The chart clearly shows that the bottleneck in this plant is at the casters, since they are running continuously, while the converters have space for increased production. The average utilisation for both casters in a whole year is 98%. Analysis of a third caster Since both casters are fully utilised, the only way to increase annual production is to add another caster (if the steel grade mix is not to be changed). However, when a second scenario is run with an additional caster, the total annual production is only 2.82Mt, which is not a signifi cant increase. The utilisation of the casters (preparation, casting and starving) is decreased, as can be seen in Figure 4. The average caster utilisation is now 83%.Analysis of a third hot metal ladle Since adding a third caster does not increase production as desired, the new bottleneck must be found. When studying the life animation of the third caster scenario or analysing the utilisations or Gantt charts, the new bottleneck appears to be at Bay I. Figure 5 gives the full and empty times of the two hot metal ladles during the third caster scenario. It shows that in almost all cases the time a hot metal ladle is empty is 14 minutes, which is exactly the time

r Fig 3 Gantt chart of one random day for the base case. (Inside the blocks, grey represents actual process time and white represents waiting time for a crane to pick up a ladle or for CCM starving)

r Fig 4 Utilisation of the casters for the scenario in which a third CCM is added to the base case

r Fig 3 Gantt chart of one random day for the base case. (Inside the blocks, grey represents actual process

r Fig 4 Utilisation of the casters for the scenario in which a third CCM

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it takes to bring a ladle that was just emptied in the converter back to the HMP and have it refi lled. The full times of the hot metal ladles are strongly dependant on the desulphurisation time (which, in this example, was normally distributed).

A scenario with a third hot metal ladle active has a huge effect on annual production – increasing it by 32% to 3.67Mt. The average utilisations for the different pieces of equipment of the base case compared to the base case with a third caster and a third hot metal ladle show a better overall utilisation (see Figure 6). The casters no longer have to run full time, while the other (already existing) equipment is utilised more effi ciently.

In order to prove that the third hot metal ladle should already be implemented in the base case before adding the third caster, another scenario (base case plus a third hot metal ladle) was run. The total production does not increase, thus proving that the casters are the bottleneck in the base case scenario.Steel ladles optimisation For the new layout (the base case plus a third caster and a third hot metal ladle) the steel plant needs to be optimised. This is done by running different scenarios with small changes (for example, different steel grade mix, alternative routing, shorter process times – when and where possible – and more steel ladles). As an example, the optimum number of steel ladles is determined for the new layout. Scenarios with different numbers of steel ladles are run (in the base case nine steel ladles were used).

Figure 7 gives the results in annual production for the scenarios with different numbers of steel ladles.

An optimal production of 3.84Mt/y is reached with 10 steel ladles and more do not add signifi cantly to the annual production. When more steel ladles are used, the number of emptied ladles that fi rst go to the preheating station before being refi lled again at the converter also increases. The utilisation of the crane that handles the empty steel ladles (the left crane in Bay II) also increases, together with the number of steel ladles since more movements are required. The average preheating time for a steel ladle increases from 13 minutes for 8 ladles, to 15 minutes for 10 ladles, to 25 minutes for 12 ladles. Using too many ladles would therefore result in an unnecessary high usage of the preheating facility, which would lead to an increase in energy consumption. Using the right amount of steel ladles leads to cost-effi cient plant practice. Case study conclusion Based on the results of the SPM simulations, a third caster should be added, the number of hot metal ladles in operation should be increased to three and the number of steel ladles in operation should

r Fig 7 Annual production, utilisation of left crane in Bay II and the percentage of steel ladles that go via the preheater for scenarios with different numbers of steel ladles

r Fig 6 Average utilisation of major equipment for the base case compared to the scenario where a third CCM and a third hot metal ladle are added

r Fig 6 Average utilisation of major equipment for the base case

r Fig 7 Annual production, utilisation of left crane in Bay II and the

r Fig 5 Full and empty times of the hot metal ladles

STEELMAKING AND CASTING

be increased to 10. This would result in a 38% increase in total annual production from 2.79Mt to 3.84Mt.

Note that since this is a fictional case, the steel plant is not as detailed as a real one and should therefore be considered as an example of the possibilities of the SPM rather than proof of its accuracy.

CONCLUSIONSThe SPM has the following benefits:` It identifies bottlenecks and logistics issues in a

simulated steel plant` Different scenarios can easily be tested and compared,

enabling analysis and solving of bottlenecks` It can predict the result of changes in the process (like

an extra converter, a different routing, an extra steel ladle and different crane priorities)

` It can predict the capacity of designed greenfield steel plants

` It can visualise in 3D and real time the routings in a plant and the actual problems

` It can quickly give concrete answers and solutions` Transportation and its limitations are taken into

account by simulation` It gives advice on CAPEX and helps optimise OPEX.

FUTURE DEVELOPMENTSThe SPM has been in use since 2011. Since then, the library of developed objects and logic has grown, making the SPM more complete and client-orientated. This results in increased client satisfaction and faster results.

The next step in the development is to extend the SPM functionality with a planning tool, the SPP, where maintenance- and planning-based questions can be evaluated and solved. The SPP will work with real-time plant data and can be used to analyse the impact of different scenarios in a short time base, such as a day. MS

REFERENCES[1]Systems Navigator. Scenario Navigator. [Online] 2014. [Cited: 15 Sept 2014] http://scenarionavigator.systemsnavigator.com/

Frank Schrama and Bart van den Berg are with Danieli Corus, Velsen-Noord, the Netherlands. Daan Merkestein and Mart Jansen are with Systems Navigator, Delft, the Netherlands, and Walter Vortrefflich is with Danieli Linz Technology, Linz, Austria.

CONTACT: comms.office@danieli-corus.com

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