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Imagen relacionada   PICT Nº 2016-0460: Proyectos de Investigación Científica y Tecnológica (2016)



New computational methods are being developed in order to solve complex problems associated to hydrocarbon transport. Software for onshore and offshore pipelining are being designed and improved. Argentinian cases are studied in detail.

Due to the availabilty of NG fields with high contents of CO2 (up to 60%) in Argentina, we are analysing the feasibility of an innovative process to obtain synthesis gas for methanol production based on combined reforming that will allow taking advantage of these streams without prior CO2 removal.

As to basic research associated to these topics, we are working on multi-objective optimization with metaheuristics for transport problems. Parallel Programming, Numerical Analysis and Artificial Intelligence are the main fields our studies are related to.



Imagen relacionada   Proyectos De Innovacion y Transferencia En Áreas Prioritarias De La Provincia De Buenos Aires (PIT-AP-BA), CIC, ARGENTINA


Software tools are being designed to solve multiple transport instances related to productive and social development, also including pollution effects. We are planning to analyse a couple of crucial routing problems: public bus transport and waste collecting.  An optimizer will contribute to the right planning of these services. The benefits will be optimized for both the citizens and the service providers. In short, our optimizer will allow: I. locating bus stops conveniently, II. proposing alternative itineraries for existing bus lines, III. proposing paths for new bus lines that would cover emerging urban areas whose population is growing and IV. Identifying waste collection places and itineraries.




Grant Code: 24/N026.

Project Leader:Dra. Nélida B. Brignole.
Supporting Organization: SGCyT-UNS.
Period: 01/01/2009 al 31/12/2011.

The general purpose of this research line consists in the development of new computational methods to efficiently solve complex problems of properties’ prediction, simulation and instrumentation design, which commonly arise in the area of Industrial Process’ Engineering.
The re-engineering of our Decision Support System for instrumentation design will be carried on. The main work will be performed on the standardization of simulation modules of industrial pieces of equipment, and on the prediction of properties according to the CAPE-Open specifications.
With respect to the bioinformatics area, machine learning techniques (including genetic programming and neural networks), and scientific computation for the prediction of ADME-Tox parameters, will be applied.




Imagen relacionada  Proyecto Grupo de Investigación PGI 2017, UNS, ARGENTINA


 Sequential and parallel algorithms will be designed and implemented in order to solve efficiently complex problems in the field of engineering. Multi-objective problems will be solved by means of metaheuristics, such as Genetic Algorithms, Ant Colony Optimization and Simulated Annealing, exploring and proposing convenient hybridizations. Besides, Artificial Neural Networks will be applied to modelling and predicting the presence of urban pollutants, like PM10 and NOx.

In particular, the following areas will be addressed: public transport planning, pollution modelling, pipeline network design for hydrocarbon transport, optimization and design of industrial processes related to the use of natural resources. 




  Resultado de imagen    Leibniz Institute of Plant Genetics and Crop Plant Research, Departament of Molecular Genetics, Data Inspection Research Group. Gatersleben, Germany.



Environmental and biomedical sciences share major properties that push utilization of state-of-the-art analysis tools from computational intelligence and machine learning research. Common properties are large numbers of data attributes, attribute correlations caused by spatio-temporal dependencies, and the presence of uncertainty induced by noise and missing values. We plan to combine the learning metrics principle with genetic algorithms for a uniform approach to retrieve relevant subsets of data attributes, and to minimalistic models of associations between different data sets. This way, robust mappings of physico-chemical substance properties to octanol-water partitioning coefficients shall be generated, which are essential building blocks in models of compound transport, e.g. PCB, in the environment. Using the same computational framework, modelling and understanding metabolic processes shall be enhanced by mapping gene- and protein-expression levels to phenotypic properties - a first essential step towards systems biology of stress tolerance in crop plants. The joint development of the new computational methods will allow addressing these and upcoming analytical challenges.