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Offers in: Engineering in Industrial Organisation

Description and objectives:

The wood industry generates a large amount of leftover materials, both wood and components in good condition that are not currently being used or properly utilized.

This project aims to improve the circularity of the activity of Arozgi member companies that work with wood, through industrial symbiosis and the development and implementation of a catalog of products and projects on the Circular Market platform, developed by Tecnun.

The phases to be carried out to achieve the objective are as follows:

  • Analysis and identification of resources generated by the companies (products, wastes, components, by-products) but with potential value. This analysis will include both the actual resources and the identification of the best available techniques for their valorization.

  • Evaluation of these resources to facilitate their use, including them in a repository in CircularMarket.

  • Design of the promotion and actions necessary to stimulate the use of such waste as products for other companies and the promotion of symbiosis between them.

Academic supervisor:

Carmen Jaca

Thematic area:

Sustainability and Circular Economy

Area or department:

Industrial Organization Engineering.

Academic supervisor:

Ion Irizar

CEIT Division:

Data Analysis and Information Management

Thematic area:

Artificial Intelligence

Description and objectives:

Many real industrial processes can be mathematically formulated using complex mechanistic models composed of nonlinear differential equations. Although these models are very useful for carrying out design and operation studies, their handicap is their high computational cost which makes them unfeasible for use in real time decision making.

With the advent of Deep Learning techniques, proposals have emerged to reduce the complexity of these models and thus the computational cost. The task of this PFG will be to use the technique known as "physics informed neural networks" to obtain a reduced model of a water treatment plant. The Python environment will be used to carry out the project.

Academic supervisor:

Ion Irizar

CEIT Division:

Data Analysis and Information Management

Thematic area:

Artificial Intelligence

Description and objectives:

Water treatment plants are subject to increasingly demanding operational requirements. It is no longer sufficient to comply with the quality of the treated water, but it is also necessary to do so with the minimum energy consumption. To achieve this, the operators of these processes need to have adequate information to enable them to make better decisions.

The task of this PFG would be to use an already developed WWTP simulator to generate data sets that collect its historical operation. These data sets will then be used to evaluate different Machine Learning classification algorithms with the objective of being able to predict the operational state of the process. The algorithms will be programmed in Python.

Academic supervisor:

Ion Irizar

CEIT Division:

Data Analysis and Information Management

Thematic area:

Artificial Intelligence

Description and objectives:

The detection of rare events in industrial processes is a topic that, with Industry 4.0, has become particularly relevant. Industrial processes are becoming increasingly digitized which brings significant benefits in terms of improved efficiency, but on the other hand makes these systems more vulnerable to cyber-attacks. This is a particularly relevant issue in the case of critical infrastructures such as water treatment facilities.

  • The Codex project is an online platform for research into new methodologies to improve the learning process by increasing the interaction between teachers, learners and the material.

    This project will analyse, design and implement the necessary functionalities so that plugins can be defined in Codex. The plugins will allow new functionalities to be incorporated without the need to change the base code, by defining functions in JavaScript that the plugin user configures in the application. The project will develop the functionalities for its support and an example plugin.

    Examples of plugins that could be defined in the project are: modification of the test interface depending on the type of question, definition of templates with specific interface for certain types of questions, generation of graphs based on the data provided by the application, interaction with the application from different clients such as an MS Office application, etc.

The Codex project is an online platform for research into new methodologies to improve the learning process by increasing the interaction between teachers, learners and the material.

This project will analyse, design and implement the activities necessary for the application of the Codex platform in one of the following topics:

  • information systems (databases and web design)

  • pre-calculus (geometry, trigonometry)

  • programming with Python

The material will consist of short explanations, exercises, problems, videos, animations and scripts of some of the exercises.

Scope: System Dynamics Model applied to the prediction of Coronavirus spread and its consequences

Goal: Develop a SD Model to predict the evolution of a Pandemic based on some key parameters.

How: ting from an existing model, add new loops, stocks and flows to cover other influences associated with the pandemic evolution. Both the old and the new models should be represented using Vensim.

Approach: Test the model against real data obtained from past reports and use the results to tune the new model. The data used will be that available for the Basque Country (EUSKADI).

Requirements: Knowledge about SD modeling; knowledge about VENSIM, access to Data

Deliverables:

  • The SD model in VENSIM;
  • A set of graphs generated by the simulation;
  • The description of parameters settings and the contents of the reports;
  • The comparison between the results generated by the model and the real information obtained by the reports;

Tools to be mastered: VENSIM; VENTITY

Development time: between 2 and 3 months with 20 hours per week dedication

Sources: Initial Vensim Model(https://vensim.com/coronavirus/)

Data for Euskadi covid19: Coronavirus evolution in the Basque Country

General on Epidemics SD modeling: Several articles available on demand

Scope: There has been a lot of recent publications on Pandemics due to the outbreak of covid-19. There is also a high demand of projects for analyzing the evolution of pandemics particularly for new waves of other types of Pandemics.

Goal: The goal of this project is to generate a data base of literature, both scientific and informative (newspaper and web). As a side product, another goal is to generate a taxonomy to facilitate understanding and clustering.

How: Perform a systematic review of the literature and save the relevant results in Mendeley. At the same time perform a literature search using an automatic tool generating another set of results. Compare and unify the results and, most importantly, the taxonomy.

Approach: Design a search query for the retrieval of elements, using google on one side and scientific bases on the other. For the google query, the relevant results should be stored in the Mendeley Digital Library, adding additional metadata, in particular, the taxonomy classification. In parallel, a similar search should be performed using Bibliometrics tool to select, classify the elements extracted from Web of Science and Scopus bases. Finally, the results of both searches should be confronted and combined, generating a single DL and a unique taxonomy.

Requirements:

  • Knowledge about Dl and particularly Mendeley;
  • knowledge on Bibliometrics tool;
  • knowledge in Taxonomies.

Deliverables: A systematic review of the literature describing the search and selection mechanisms, with corresponding taxonomy and the DL.

Tools to be mastered: Mendeley and Bibliometrics

Development time: Estimated between 2 and 3 months

Sources:

  • An initial Mendeley DL
  • An initial taxonomy