Presentacion - Modeling the Sugar Cane Logistics From Farm to Mill

8/12/2019 Presentacion - Modeling the Sugar Cane Logistics From Farm to Mill

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MODELING THE SUGAR CANELOGISTICS FROM FARM TO MILL

Alain Andres Cardona Pineda


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ABSTRACT

A continuous and discrete system gets distinct approach frommodelers, since both have very particular characteristics. Somesimulation tools are specialized in one system or another, but thegreat majority fo-cuses on discrete modeling. However, there aremany hybrid systems where a continuous process inter-faces

with discrete. Despite being possible to model both systems withparticular modeling tools, they have to be properly connected.An example of hybrid system is the ethanol production chain,where the transport of the raw material sugar cane has importantinteractions between the main components of the chain, wherethere is exchange of equipment, transport and interface of

continuous systems with discrete, and vice versa. This studypresents the algorithms and techniques used to model thelogistics operations of the Alcohol/Sugar producer Razen,developed using the SIMIO simulation tool.


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INTRODUCTION

The world watched in recent yearsa growing concern with the issue

of energy, mainly big oildependency.

In Brazil this quest for alternative andsustainable fuels began already in the1980s, during the first global oil crisis,

which led to the develop-ment oftechnologies of production,

distribution and consumption ofethanol.

Ethanol is produced from sugarcane, and this raw material is

grown in large territorialextensions in the main Brazilian

states.


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COMPREHENSIVE DIAGRAM OF THE

CANE TRANSPORTATION PROCESS


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CONCEPTUAL MODEL

The objective of this study is topresent the algorithms and

procedures to model the variousstages of the transport of sugar cane,

since the cane farms until the entryinto mills.


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PROCESSES FROM HARVESTING TO

TRANSPORT

The subsystem of Harvestingcontinuously generates chopped andwhole cane, suffering variations relat-ing to maintenance and other factorsinherent of the production process.


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PROCESSES FROM HARVESTING TO

TRANSPORT

Shattered cane, there is a limitedamount of carts/dollies that should beloaded. When a truck arrives with an

empty dolly, it releases it, andattaches a loaded dolly.


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PROCESSES FROM TRANSPORT TO

RECEPTION

The reception, also performs the dolliesexchange between external and internal

truck fleet. A parking lot is the connectionpoint between the two fleets, serving to

leave empty and loaded dollies waiting for

their trucks.


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PROCESSES FROM RECEPTION TO

THE MILL

The unloading consists of depositing thecane on a mill-hopper. The mill-hopper is

connected to a conveyor belt system whichtransports the cane to the mill inlet.


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CASE STUDY

The presented concepts were applied inthe simulation of one ethanol productionplant that belongs to Razen. Razen is theworlds largest sugarcane processor, with

62 million tons of crushing capacity and

one of the largest producers and exportersof sugar and ethanol.

The goal of the study was to adjust thetruck fleet currently used on the transport

of sugar cane in a productive plant, and also

to re-evaluate the investment planned forthe following year, on the purchase of newtrucks. This case highlights the analytical

approach taken to preserve CAPEXrequirements for fleet vehicles.


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THE MODEL

Razen choose the SIMIO commercialsimulation tool. A new and state of the art

3D rapid modeling simulation software.This is a relatively new tool, but with a

great background, since it is beingdeveloped by the same creators of SLAM,

SIMAN and Arena, well known in thismarket.

The process has eight mechanizedharvesting fronts (chopped cane) and four

fronts of harvest manu-als that feed the

system and ten different types of trucks toperform the transport. The model works

with 32 input variables, 39 output variables,92 auxiliary variables, 19 input tables, and

simulated 240 days (1st season).


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MODEL ANIMATION


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PART OF THE UNLOADING PROCESS

LOGIC

Basically, the model has a continuousportion, representing the plant

processes, and a discrete portion that isthe cane transportation by trucks.


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RESULTS

Identify the smallest fleetable to meet the restrictions .

Based on the experiments, theplanned 2011 CAPEX fleets(deterministic) were reduced from53 trucks to 50. Also, bottleneckswere detected and led to a changeon the reception queues.

The value gained by usingsimulation is that plantadministration gets a predictiverisk analysis. The risk analysiscovers a 240-day.


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CONCLUSIONS

The opportunity to simulate asugar cane logistic system led tothe development of techniquesand algo-rithms to approach theproblem.

The study results were immediatelyimplemented in the process, which iscurrently operating with the

suggested configuration.


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Presentacion - Modeling the Sugar Cane Logistics From Farm to Mill