=Paper=
{{Paper
|id=Vol-1814/paper-03
|storemode=property
|title=Analysis of Cranes Control Processes for Converter Production Based on Simulation
|pdfUrl=https://ceur-ws.org/Vol-1814/paper-03.pdf
|volume=Vol-1814
|authors=Anna S. Antonova,Konstantin A. Aksyonov,Olga P. Aksyonova,Wang Kai
}}
==Analysis of Cranes Control Processes for Converter Production Based on Simulation==
https://ceur-ws.org/Vol-1814/paper-03.pdf
Analysis of Cranes Control Processes for
Converter Production Based on Simulation
Anna S. Antonova1 , Konstantin A. Aksyonov1 , Olga P. Aksyonova1 , Wang Kai2
1
Ural Federal University, Yekaterinburg, Russia;
2
Chinese Academy of Social Sciences, Beijing, China,
antonovaannas@gmail.com
Abstract. The paper deals with solution of logistic problem for con-
verter production of metallurgical enterprises. A developed multiagent
simulation model of converter production is applied to solve the problem.
This model allows assessing the different variants of the cranes motion
between steelmaking aggregates. Three variants of the cranes motion
have been considered during experiments with the model. As a result of
experiments, the most effective variant for motion of the cranes has been
revealed. The found variant application provides the cranes with high
and uniform loading and reduced downtime of the continuous casting
machines.
Keywords: Simulation, automated information system, logistic prob-
lem, converter production, multiagent simulation
1 Introduction
Converter production is one of the key stage of the technological chain of the steel
products manufacturing. Within the converter production, steel is obtained from
raw materials with the help of steelmaking aggregates. Steel ladles are delivered
to aggregates via ladle cars and cranes. Typical problem of improving the quality
of products under the converter production is closely joined with the problem
of optimizing the steel smelting technology. Also, a relevant problem is one of
optimizing the motion of vehicles, in particular, cranes. Effectively organized
cranes motion can reduce downtimes of continuous casting machines (CCM).
Downtimes of CCM lead to defects on the melts border, which reduces the total
amount of finished slabs.
We consider application of multiagent simulation [1], [2], [3] to solving the
logistic problem of the cranes motion within one day. Multiagent simulation
model of the cranes motion has been developed with using the simulation module
of a metallurgical enterprise information system. The metallurgical enterprise
information system is a web-oriented one for tracking, monitoring, modelling,
analysis, and improvement processes of the steel products manufacturing [4],
[5], [6], [7].
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2 Problem formulation
The developed model includes three converters and five CCM. Motion of steel
ladles between steelmaking aggregates is carried out by using three cranes and
five ladle cars. The cranes perform the following steps: removing the ladle from
the ladle car, ladle lifting on CCM, and empty ladle removing after the melt
casting ending.
The multiagent simulation model of the cranes motion is intended for evalu-
ating the following variants of the cranes work. The first variant means that a
set of serviced CCM is attached to each crane. The second and third variants
mean that the crane serves CCM in dependence on the proximity to CCM.
Evaluation of the effectiveness is carried out via comparative analysis of load-
ing the cranes and total downtime of CCM. Based on the proposed evaluation
criteria, it is recommended to choose the variant of the cranes motion, which pro-
vides uniform and maximum loading of cranes and decreases the total downtime
of CCM.
3 Development of the simulation model
of converter production
The cranes motion model has been developed using a notation of multiagent
resource conversion processes [4], [5]. Agents in the model are used to implement
the logic to process the orders (steel ladles), ladles cars, and cranes motion
description. Operations in the model are used to visualize duration of the work
of steel making aggregates and vehicles. In the model developed, three orders
are described for determination of the melt, cranes, and ladle cars motion. Order
z1 passes through the model according to the plan for melt processing. Orders
z2 and z3 “Order for ladle car” and “Order for crane” are used to describe the
motion logic of cranes.
The model structure can be divided into five work units: 1) description of
the converters work; 2) description of the ladle cars work; 3) description of the
cranes work; 4) description of the ladles overload; 5) description of the two
streams elements for each CCM: mold, secondary cooling zone, and gas cutting.
A fragment of the model structure with description of two CCM streams is shown
in Fig. 1.
Converters work consists of the following stages: preparation, purging (this
operation may be performed simultaneously on only two converters), operation
after purging, and draining steel operation. After completion of the converter
work, the ladle car delivers a steel ladle into the shop of the converter steel spill.
There, the steel ladle is transferred to the crane. At the end of transferring,
the empty ladle is sent back to the converter, and the crane with the melt is
moved towards the continuous casting machine for steel casting. The number
of CCM for casting depends on the used cranes motion variant. Cranes motion
variants are described in the model via the knowledge base of the agent “Melt’s
distribution on CCM”.
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Fig. 1. Fragment of the model structure with description of two CCM streams.
The knowledge base of the agent “Melt’s distribution on CCM” has been
described using If-Then rules with system variables. During simulation, each
rule with performed initial conditions is stored in a calendar. The calendar is
a queue that contains an ordered list of the rules. According to the rule, one
of the following actions should perform at a certain time: check the condition
of operation or agent launch, perform operation input steps, perform operation
output steps, and perform agent steps.
4 Analysis of experiments results
The first variant of the cranes motion (variant A) assumes that each crane
serves strictly assigned CCM: crane No1 serves CCM No1 and No5, crane No2
serves CCM No2 and No3, crane No3 serves CCM No1. In this case, there is no
intersection between the cranes motion.
The second variant of the cranes motion (variant B) assumes that the crane
serves CCM depending on crane’s current location and proximity to CCM that
24
requires service: crane No1 serves CCM No1 and No2, crane No2 serves CCM
No2, No3 and No4, crane No3 serves CCM No4 and No5. The third variant of
the cranes motion (variant C) assumes that the crane serves CCM depending
on crane’s current location and proximity to CCM that requires service: crane
No1 serves CCM No1, No2 and No3, crane No2 serves CCM No3, No4 and No5,
crane No3 serves CCM No4 and No5. In these cases, there is an intersection
between the cranes motion. The final selection of CCM is carried out based on
the current location of crane.
We consider experiments with the developed model in the optimization mod-
ule of the metallurgical enterprise information system. A series of the experi-
ments was implemented, in which three cranes motion variants have been con-
ducted and analyzed. As a result of experiments, the following output character-
istics have been evaluated: the loading of cranes and the total CCM downtime.
In Figure 2, the loading of cranes for variant A of the cranes motion is shown.
Fig. 2. The loading of cranes for variant A cranes motion, percentage.
In Figure 3, the loading of cranes for variant B of the cranes motion is shown.
Fig. 3. The loading of cranes for variant B cranes motion, percentage.
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In Figure 4, the loading of cranes for variant C of the cranes motion is shown.
Fig. 4. The loading of cranes for variant C cranes motion, percentage.
As figures show, all variants of the cranes motion provide the high and uni-
form cranes loading, but the variant B ensures the maximum cranes loading.
The total CCM downtime is one of the output model characteristics. For
each variant of the cranes motion, four expeiments with the simulation model
have been conducted with the same initial conditions. Experimental results are
presented using the diagram, which enables to see the average downtime and the
range of values of this variable for each experiment.
In Figure 5, the total CCM downtime for variant A of the cranes motion is
shown.
Fig. 5. The total CCM downtime for variant A cranes motion, minutes.
26
In Figure 6, the total CCM downtime for variant B of the cranes motion is
shown.
Fig. 6. The total CCM downtime for variant B cranes motion, minutes.
In Figure 7, the total CCM downtime for variant C of the cranes motion is
shown.
Fig. 7. The total CCM downtime for variant C cranes motion, minutes.
As figures show, all variants of the cranes motion provide the low total CCM
downtime, but the variant B ensures the lowest total CCM downtime. Fluc-
tuations in average values of the CCM downtime and changes in the downtime
ranges are associated with presence in the simulation model stochastic variables,
namely, the processing time of melts on steelmaking aggregates.
5 Conclusion and future work
As a result of analysis of the logistic processes for converter production, a sim-
ulation model of the converter production has been developed with the use of
the simulation module of the metallurgical enterprise information system. Eval-
uation of three variants of the cranes motion has been carried out in the model
using the agent knowledge base containing algorithms of the cranes motion from
27
the ladle car to CCM. The use of agent-based modeling technology provides the
flexibility of the developed model in terms of changes in algorithms of the cranes
motion.
As a result of the experiments, the following recommendations have been
obtained. More effective crane motion is one, in which, first, the crane serves
CCM depending on crane’s current location and proximity to CCM that requires
service and, second, the following relationship between the cranes and CCM is
observed: crane No1 serves CCM No1 and No2, crane No2 serves CCM No2,
No3, and No4, crane No3 serves CCM No4 and No5. The use of this variant
ensures high and uniform cranes loading and low total downtime of CCM.
The aim of future research is to apply the query builder and the data prepa-
ration modules of the metallurgical enterprise information system on the stage
of receiving input modelling data from real production.
Acknowledgments. The work was supported by Act 211 Government of the
Russian Federation, contract no. 02.A03.21.0006.
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