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Online mass and energy balance simulation system for sugar cane industry

Grant number: 13/50781-9
Support type:Research Grants - Innovative Research in Small Business - PIPE
Duration: August 01, 2014 - July 31, 2016
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computer Systems
Cooperation agreement: FINEP - PIPE/PAPPE Grant
Principal researcher:Claudio Adriano Policastro
Grantee:Claudio Adriano Policastro
Company:Atenatec Soluções Tecnológicas Ltda. - ME
City: São Carlos


The objective of this project is to develop a set of phenomenological computational models to allow the simulation of biochemical process for the production of cellulosic ethanol by mass and energy balance. A second objective of this project, is the development of a computational model to allow the simulation of material residence and flow, in order to simulate a process delay (time it takes to change a material output flow in response to an incoming flow change) enabling the online process simulation in a more reliable way, even for large sugar cane industrial plants. The computational models proposed will be developed in an environment of rapid prototyping (MATLAB) and will later be translated into a computer language (C++) and integrated in the existing Process Simulation System for Sugar Cane Industry (BdME), owned by the proponent of this project, developed employing 100% national labor and technology as a~ result of research supported by FAPESP (PIPE 2007/52004-9 and 2009/51657-4 ) and CNPq (Rhae 555716/2009-0), which is an innovative commercial product and unique in the market, used by large sugar cane groups as: Raizen, Sao Martinho, Guarani, Odebrecht Agroindustrial and Petrobras Biocombustivel, for improvement projects and performance management of industrial processes for the production of first generation ethanol, sugar and bioelectricity. This project will enable the development and a major innovation to the existing simulator. It is noteworthy that the use of a tool like BdME enables the reduction of operating costs by reducing losses caused by process deviations and by detecting bottlenecks before they occur, allowing maneuvers that overcome the drawbacks caused by changes on operational conditions, on raw material flow, and on raw material properties. (AU)

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