CITIS 2021

VII International Conference on Science, Technology, and Innovation for Society (CITIS 2021)

The VII International Conference on Science, Technology and Innovation for Society, CITIS 2021, took place as a virtual edition hosted from Guayaquil on May 26th-28th, 2021. The event, organized by Universidad Politécnica Salesiana, offered the national and international academic community a unified communication platform, aimed at covering the theoretical and practical problems with the greatest impact on modern society through an engineering perspective.

In this seventh edition, dedicated to the 27 years of the life of La Salesiana, the thematic axes were related to the application of science, technological development and innovation in five fundamental pillars of our society: Industry, Mobility, Environmental Sustainability, Information and Telecommunications.

The scientific committee included 112 experts from 18 countries (Argentina, Spain, Mexico, Australia, France, Colombia, Germany, Netherlands, United States, Brazil, United Kingdom, Italy, India, Venezuela, Peru, El Salvador, Chile and Ecuador), who had the responsibility for evaluating, in a ‘double-blind review’ process, the 120 papers received in the conference.

The accepted contributions recreate recent research trends in the fields of software engineering, big data analysis, cloud computing, data engineering, data management and data mining, machine learning, deep learning, artificial intelligence, smart systems, robotics and automation, mechatronic design, and industrial processes design. CITIS 2021 also included six workshop sessions, and several keynote speeches were given on cutting-edge topics related to the thematic axes of the conference. In addition, parallel working sessions were held to strengthen entrepreneurship and innovation skills for engineering students. The 68 papers accepted for presentation and discussion at the Conference were published by Springer Nature in the book series Smart Innovation, Systems and Technologies. The book series Smart Innovation, Systems and Technologies is indexed by SCOPUS, EI Compendex, INSPEC, WTI Frankfurt eG, zbMATH, Japanese Science and Technology Agency (JST), SCImago and DBLP. Also, all books published in the series are submitted for consideration in Web of Science. We acknowledge all of those that contributed to the staging of CITIS 2021 (authors, committees, workshop instructors, and our attendees). We deeply appreciate their involvement and support that was crucial for the success of the event.





Efficient renewable propulsions: energy performance in traffic

Content: The sustainable vehicle traffic of the future will be based on renewable solar and wind (both electric), biological and other energies. Non-fossil vehicles can run on batteries, fuel cells or combustion engines. Fuels made from electricity can be liquid, gas, hydrogen, liquid biological or gaseous. Every propulsion and fuel have their own advantages and disadvantages. Above all, gas and liquids are light and easily stored, whereas storing electricity in batteries is heavy and expensive. However, no form of vehicle propulsion reaches the energy efficiency of the battery electric vehicle (BEV). In this workshop, we will calculate and compare the performances of the different conversion chains (e.g. electricity – hydrogen – gas – combustion) in order to know, once the wind or solar electric power is generated, how to use it in the most efficient way; or given a defined agricultural area, how to use it to drive a maximum distance. In the sustainable traffic of the future, we must use the natural supply of renewable energy in an intelligent, appropriate, and economical way. Required skills/abilities: Basic knowledge of energy engineering. Tools: Calculator or smartphone. Audience: Engineers, technicians, researchers and students of energy, automotive, electrical, and related engineering. Duration: 3 hours Date/Time: May 26, 2021 (9:30-12:30 GMT-5).

Instructor: Klaus Kuhnke, Dr. rer. nat. Hochschule Osnabrück (Osnabrück University of Applied Sciences), Germany

Klaus Kuhnke is a physicist from the University of Göttingen, Germany. He has a PhD in Physics in which he conducted research on laser systems as light sources for high-speed holography. He has been an assistant professor at the National Engineering School of Tunisia, and a scientist at the Institute for Solar Energy Research (ISFH) in Hannover, Germany. He is president of the Osnabrück Solar Energy Association, with teaching and training collaborations at the Universities of Minsk, San Salvador (UCA), Public University of Navarra, Le Havre, Tarbes and Fez (Morocco). He is currently a Professor of Renewable Energies and Physics at the University of Applied Sciences in Osnabrück, Germany. His research focuses on the development of renewable energies and their use for sustainable mobility, as well as on specific applications of wind and photovoltaic energy to support the change of energy model in Germany, a country that plans to close its nuclear power plants by 2022.



Modeling and simulation of solar photovoltaic systems

Content: This workshop aims to show how to model a solar photovoltaic plant (including solar panels, converters and control system), using Simulink (MATLAB’s visual programming environment) and the Simscape Electrical toolbox (library of components for modeling and simulating electrical, electronic and mechatronic systems, including renewable energy systems), in order to simulate the behavior of the installation under different operating conditions, such as incident radiation and temperature. The tools that will be shown in this workshop can be used in teaching and research of solar photovoltaic plants, by allowing to model any installation (with different panels, power levels, applications, etc.) and to evaluate its behavior and that of each of the components under different operating conditions, as a preliminary step for the validation of the design of prototypes of equipment or control systems, without using expensive real equipment. Required skills/abilities: Knowledge of electricity, solar photovoltaic energy and MATLAB is required. Tools: Matlab, Simulink, Simscape Electrical. Audience: Students, teachers, and researchers in electrical engineering, electronics and related fields. Duration: 2.5 hours Date/Time: May 26, 2021 (9:30-12:00 GMT-5).

Instructor: Prof. Dr. Luis M. Fernández Ramírez. Head of the Sustainable and Renewable Electrical Technologies Research Group. Department of Electrical Engineering, Universidad de Cádiz, Spain.

Industrial Engineer (specializing in Electricity) from the University of Seville, Dr. Industrial Engineer from the University of Cadiz and Professor of the Electrical Engineering Department at the University of Cadiz, Spain. He is currently the Coordinator of the PhD Program in Energy and Sustainable Engineering and Head of the Sustainable and Renewable Electrical Technologies Research Group (PAIDI-TEP023). He has worked as a design engineer and M&O manager of a company dedicated to the design of wind turbines, construction and operation of wind farms, wind power developments, etc. He has extensive research experience, supported by publications (140), doctoral theses (8) and research projects/contracts (15) in the development of electrical, electronic and control technologies in the field of smart grids, renewable energies (wind energy and photovoltaic solar energy), energy storage systems, hydrogen technologies, electric vehicles, power electronic converters, intelligent control systems and energy management. He is an evaluator of research projects, member of the Editorial Board of 7 international journals, Guest Editor of 6 Special Issues in international journals, and member of the scientific committee of 8 international conferences. He is also an IEEE Senior Member and member of 2 IEEE Smart Cities Committees.



Use of Neural Networks and Deep Learning for classification and prediction

Content: Artificial Intelligence (AI), understood as the ability to provide computers with elements of human intelligence such as: learning, decision making, classification, memory, and autonomy, among others, is one of the most important disciplines worldwide. The governments of the major powers, the most important companies and the main universities of the world dedicate a great part of their research to the applications of intelligent systems in the diverse areas of daily life. One of the most ambitious AI techniques are neural networks, since they allow learning, through data and observations, behavioral patterns that could be imperceptible by other systems. Additionally, in recent years it has been enhanced with the creation of new techniques based on Deep Learning, which has allowed it to be massively used in many industrial, business, medical, educational, and artistic applications. In this theoretical-practical workshop, the principles of neural networks operation will be known, and practical activities will be carried out using the Python programming language, which is the most widely used at the moment, and through a simple environment based on the use of a web browser (Colaboratory). Required skills/abilities: Use of internet browsers, basic knowledge of algebra and basic notions of statistics. Tools: Python through Google Colaboratory, which are free tools that require no installation or configuration ( The programs and data to be used will be provided through a link to a Colaboratory Notebook. Audience: Academic community in general. Duration: 4 hours Date/Time: May 26, 2021 (9:00-13:00 GMT-5).

Instructor: PhD. Francklin Rivas Echeverría, Universidad Técnica Federico Santa María, Chile.

Professor Francklin Rivas Echeverría is a Systems Engineer (1993), Master in Control Engineering (1996) and Doctor in Applied Sciences (2000). He is a lawyer with Cum Laude distinction (2017). He has a Diploma in Administrative Law, one in Strategic Management and another in Research Methodology. He is Academic and Assistant Director of Outreach at Universidad Técnica Federico Santa María, Chile; Retired Professor of the School of Systems Engineering at Universidad de Los Andes; Professor at Pontificia Universidad Católica del Ecuador headquarter Ibarra; Visiting Professor at Yachay Tech, Ecuador. He has published more than 250 scientific articles in international journals, books and conference proceedings. He is co-author or editor of 15 books. He has been invited to give lectures and tutorials in several parts of the world. He has been a consultant for the United Nations Organization (UNO), Kuwait Oil Company (KOC), Halliburton, Venezuelan oil industry, steel companies, Aluminum companies, Mass Transportation Systems, Health Systems, different public organizations, and universities. He has directed more than 70 projects or thesis at bachelor, master, and doctorate levels. His main line of research is related to Artificial Intelligence and its applications. He was recognized by the International Magazine “Gerente” for being among the 100 most successful Venezuelan Managers. He was recognized by the International Magazine “Gerente” for being among the 100 most successful Venezuelan Managers.



Stochastic modeling of heterogeneous materials for bioengineering applications

Content: Unlike classical homogeneous materials, the numerical simulation of heterogeneous materials represents a challenge from the computational point of view. The study of the mechanical behavior of heterogeneous materials usually considers the separation of the domain scale into two; the micro- and macro-scales. The development of these models couples the micro-scale characterization with the homogenized response at the macro-scale level. This approach is computationally expensive as it requires not only a conformal description of the material morphology and constituent behavior at the microstructure level but also the solution of problems with auxiliary boundary conditions on volume representative elements. To overcome these difficulties, different strategies have been recently proposed that consider the description of an intermediate scale, or meso-scale, where the level of detail of the material is smoothed and consequently the computational cost of the homogenization process for the response to external mechanical stresses is alleviated. In this context, this workshop aims to introduce participants to the concepts and techniques used in the development of heterogeneous material models. Specifically, we will focus on the construction of stochastic models that describe the spatial variability of the mechanical properties of healthy or diseased bone tissues under the hypothesis of isotropy or orthotropy, in order to explore the influence of the parameters that define the meso-structure of the material on the uncertainties of the effective response to external load stresses. Required skills/abilities: Basic programming, preferably using Matlab. Tools: Matlab Audience: Undergraduate or graduate students and research professors with interest in materials modeling with applications in bioengineering. Duration: 3 hours Date/Time: May 26, 2021 (14:30-17:30 GMT-5).

Instructor: PhD. José A. Alvarado Contreras. School of Mechanical Engineering, Universidad de Los Andes, Venezuela.

He graduated in Mechanical Engineering and had a master’s degree in Applied Mathematics in Engineering from Universidad de Los Andes in Venezuela. He obtained a PhD degree in Civil Engineering from the University of Waterloo in Canada. He spent 3 years as a postdoctoral researcher at San Diego State University in the United States in mathematical modeling of sintering processes affected by gravity acceleration funded by the National Aeronautics and Space Administration (NASA). He is currently a Professor at the School of Mechanical Engineering of Universidad de Los Andes and Coordinator of the master’s program in Mechanical Engineering at the same institution. He is a visiting professor at Moscow Engineering and Physics University (Russia) and at Universidad Politécnica Salesiana (Ecuador). He has taught undergraduate courses in Strength of Materials, Mechanics of Materials, Mechanical Design and Materials Science. He has also taught graduate courses in Numerical Methods, Mechanics of Continuous Media, Solid Mechanics and Finite Elements. He has dedicated his research work to the modeling of the mechanical behavior of materials with emphasis on multiscale modeling, continuum damage mechanics and consolidation of porous materials.



Tools for supporting state of the art reviews

Content: The preparation of a state of the art, based on a literature review, is a basic activity in any academic or research work. From these, it is possible to detect gaps in the current development of knowledge, and therefore, to justify the purpose of the work to be done and formulate the starting hypotheses. It is desirable that literature reviews are systematic, complete, explicit and reproducible, i.e., that they are transparent in the development of the procedure carried out and the sources used. To this end, this workshop will present a procedure that will allow participants to acquire basic skills for the development of a state of the art on the research topic of their interest, using tools for the management, analysis and review of bibliographic references in academic or research work. Audience: Academic community in general. Duration: 3 hours Date/Time: May 26, 2021 (2:00-5:00 GMT-5).

Instructor: PhD. Manuel Díaz-Madroñero – Research Centre on Production Management and Engineering (CIGIP), Universitat Politècnica de València, España.

Manuel Diaz-Madroñero is Associate Professor in the Department of Business Management of the Universitat Politècnica de València (UPV), Spain. He teaches subjects related to Information Systems, Operational Research and Operations Management and Logistics. He is member of the Research Centre on Production Management and Engineering (CIGIP) of the UPV. He has participated in different research projects funded by the European Commission, the Spanish Government, the Valencian Regional Goverment and the UPV. As a result, he has published (in collaboration) more than forty articles in different indexed journals and international conferences. He is co-author of the book Operations Research Problems: Statements and Solutions (Springer, 2014). His research areas include production planning and transportation, fuzzy mathematical programming and robust optimization, multicriteria decision making and sustainable operations management.


Simulation tools in electric traction

Content: This workshop will show different simulation tools with application for electric traction systems. The use of these tools allows the analysis of different aspects such as power losses, harmonic distortion and efficiency that are present in the topologies of three-phase converters used for the control of motors in an electric vehicle system. The first part of the session will contemplate an analysis of the different converter topologies with VSI voltage source, CSI current source and impedance networks (Z, Qzsi) and their different modulation techniques used in electric traction, by using the PSIM simulation tool. The second part, will deal with the use of silicon carbide (SiC) devices in converters and the use of different tools to develop specific models with the operating characteristics of these devices which allow working at a higher switching frequency, higher temperature range and reduce power losses, which would mean an increase in efficiency in the power system part. In addition, different methodologies will be analyzed for the development of simulations of the different elements that make up the electric traction by using different tools to obtain results and efficiency of energy consumption. Required skills and abilities: Basic knowledge of electronics and hybrid and electric vehicle configurations. Tools: PSIM, ADVISOR, MATLAB. Audience: Students of Electronic Engineering, Electrical Engineering and Automotive Engineering. Duration: 3 hours Date/Time: May 26, 2021 (2:30-5:30 GMT-5).

Instructor: PhD. Efrén Fernández Palomeque – Universidad del Azuay, Ecuador.

He graduated Electronic Engineer from Universidad del Azuay. He has a Master’s Degree in Industrial Control and Automation from ESPOL, and is also a PhD in Electronic Engineering in the area of electric traction system from the Polytechnic University of Catalonia. He has worked as a professor at Universidad Espíritu Santo, Universidad Internacional del Ecuador and Universidad Politécnica Salesiana in Cuenca. He is currently a full professor at Universidad del Azuay and a member of the ERGON Research Group attached to the Automotive Mechanics Engineering program. He is the director of the Master in Electric Propulsion Systems attached to the graduate department of Universidad del Azuay. He has served as Technical Trainer of the company Cise Electronics (United States), Technical Advisor of the company EAATA (Barcelona, Spain) and founder of the Ecuadorian brand Dr. ECUS Solutions. He has represented Ecuador in different international congresses and has several publications in magazines related to the area of electric mobility and optimization of converter topologies in electric traction. He has 10 years of experience in the development and implementation of projects focused on electronic systems in automobiles and optimization in the conversion of conventional automotive systems to electric.



“Artificial Intelligence: Deciding like people”

Systems based on Artificial Intelligence have reached levels of competence in decision making that in many areas equal, if not surpass, those of people. They usually incorporate algorithms that, although can increase the capacity and efficiency of people in their respective contexts of action, could also replace them, something that notably worries the whole society. Thus, avoiding possible dysfunctions in these systems, both in terms of operation and substitution of people, is a priority social, scientific, and technological objective, which makes it more necessary than ever to have models that include all the richness and variety of the decision problems to be faced. These models, because their final objective is to be implemented on computers, must incorporate elements and specificities that until now had not been necessary to consider. This talk is devoted to describing all these aspects, as well as the opportunities and threats that the omnipresence of Artificial Intelligence in our lives represents for the future, even in the present.

PhD. José Luis Verdegay – Computer Science and Artificial Intelligence Department, Universidad de Granada, Spain

José Luis Verdegay is a Professor of Computer Science and Artificial Intelligence at the University of Granada (UGR) in Spain, and Director of the Decision Modeling and Optimization Research Group. He has held numerous positions at that University, in different ministerial agencies of the Spanish government and in the TEMPUS Office of the European Union. Between 2008 and 2015 he was a delegate of the Rector of the UGR for ICT. Since 2015 he has been regional director of the Ibero-American University Association for Postgraduate Studies (AUIP). Author of more than 400 scientific publications, he has an h-index of 54. On the other hand, he has the special teaching category of Visiting Professor at the Technological University of Havana, the Central University of Las Villas, and the University of Holguin (Cuba). He is a Fellow of the International Fuzzy Systems Association (IFSA), IEEE Senior Member, Honorary Member of the Academy of Mathematics and Computer Science of Cuba and Distinguished Guest of the National University of Trujillo (Peru). Among his topics of interest are decision and optimization problems in Artificial Intelligence, and the design, implementation, and application of Autonomous Decision Systems in the tourism sector and intermodal transportation.

“Towards the Supply Chain 4.0”

This session will define and address the concept of Industry 4.0 (I4.0) from the perspective of production management and engineering. Concretely, the main factors that facilitate the transformation towards I4.0 as well as the main barriers to its implementation will be identified. Also, the current state of supply chain 4.0 implementations will be considered from the perspective of sustainability but also from the perspective of the main digital and production technologies.

PhD. Josefa Mula – Department of Business Management, Universitat Politècnica de València (UPV), Spain

Professor of the Department of Business Management at the Universitat Politècnica de València (UPV). She is a member of the Research Centre in Production Management and Engineering (CIGIP) of the UPV. Her teaching and research interests focus on production engineering and management, operations research and supply chain optimisation and simulation. She is editor-in-chief of the International Journal of Production Management and Engineering. She regularly serves as an associate editor, guest editor, member of scientific committees of international journals and conferences, and as a reviewer of scientific journals. She is co-author of more than 100 articles published in international books and high-quality journals. Currently, she is principal investigator of the national project “Optimisation of zero-defect enabling production technologies for supply chains 4.0 (CADS4.0)” (RTI2018-101344-B-I00) and investigator of the European H2020 project “Industrial data services for quality control in smart manufacturing (i4Q)” (958205).

“Solar houses of the future: Technical and business aspects”

The solar houses of the future are already a reality. The development of Building Integrated Photovoltaics (BIPV) has made it possible to replace conventional building materials with photovoltaic materials on parts such as roofs, skylights, facades, pergolas, sliding patio doors and terrace floors. Photovoltaic modules are increasingly being incorporated from the earliest stages in the design and construction of new buildings as their primary source of electricity or as an energy-saving strategy. In this talk, you will learn about these photovoltaic materials, their characteristics, and possible applications as a business opportunity.

PhD. Luis Fernando Mulcue Nieto – Universidad Autónoma de Manizales, Colombia

Luis Fernando Mulcue Nieto has a PhD in Physics, a PhD in Engineering, a Master’s degree in Physics, a Master’s degree in Photovoltaic Solar Energy, and is a Physical Engineer. He has more than 20 distinctions in the fields of engineering, physics, renewable energy and teaching, awarded by institutions in the United States, Europe and Latin America. He has research experience in the fields of nanotechnology, renewable energy, solar photovoltaics and the integration of the latter to architecture (Building Integrated Photovoltaics, BIPV). He has developed and published scientific models and technical standards in BIPV. He has also researched the design of more efficient and economical solar cells for BIPV using nanotechnology. He is currently CEO and founder of the consulting firm BIPV Solar Consulting LLC based in Miami, USA (

“The challenges of the automobile to face the decarbonization of transport”

Environmental and legislative requirements, associated with the reduction of greenhouse gases, are leading the automotive industry to a gradual process of orderly transition from conventional internal combustion engine propulsion systems to zero-emission propulsion systems. In this regard, batteries and hydrogen fuel cells are seen as the technologies with the greatest potential for incorporation into zero-emission vehicle powertrains. In December 2019, the European Commission presented the European Green Deal as the new European growth strategy that sets out a clear agenda to make Europe by 2050 the world’s first climate-neutral continent. This fundamental transformation of the European economy into a decarbonized and competitive economy requires fundamental changes and innovative technologies. Thus, clean hydrogen and its energy carriers were identified as a priority area where the European Union (EU) needs resources to develop these technologies and commercial applications.

PhD. José María López Martínez – University Institute of Automobile Research (INSIA), Technical University of Madrid, Spain

PhD in Industrial Engineering, Director of INSIA, Director of the INSIA Environmental Impact Unit and Professor at the Technical University of Madrid (UPM). He has extensive research experience in the field of alternative vehicle propulsion systems (hybrid, electric and fuel cell) and pollutant emissions due to road traffic, as evidenced by his publications, books and conference papers on these topics. His most recent research activity is focused on the development of models of hybrid and electric vehicle propulsion systems and their integration and validation in vehicles, as well as in the field of pollutant emissions and biofuels in diesel and natural gas engines. He is also Director of the Master in Automotive Engineering and the Master in Hybrid and Electric Vehicle Engineering, both INSIA and UPM degrees, and member of national and international automotive technical committees.

“Exploring the value of analytics to improve decision-making in asset management”

Nowadays many companies are shifting their focus from capital expenditure to operation and maintenance (O&M) costs, as such asset management has received increasing attention. The emergence of industry 4.0 contributed to the hype that the large volumes of data obtained with smart sensors are sufficient to create autonomous and effective predictive maintenance algorithms. However, real-world applications have shown the need to combine the new data-driven approaches with the existing engineering knowledge. In fact, asset management is a multidisciplinary field and to provide the required level of service in the most cost-effective manner, engineering, technology and management must work as a whole. In this talk, we present applications of data-driven approaches which leverage the domain expert knowledge in order to tackle real-world asset management challenges. The examples cover: (i) improved fault detection and diagnosis, (ii) accurate estimation of the remaining useful life, and (iii) efficient maintenance policies boosted by analytical-driven insights. We will also highlight the ongoing challenges on this research line at INESC TEC.

PhD. Luis Guimarães – Department of Industrial Engineering, Universidade do Porto, Portugal

Luis Guimarães is a Professor at the Department of Industrial Engineering and Management of the Faculty of Engineering of the University of Porto, senior researcher at the Industrial Engineering and Management group of the research institute INESC TEC and co-founder of LTPlabs a management consulting firm. He’s main area of activity is Operations Research/Computer Science. Most of his research is problem-driven and aims to develop advanced analytic solutions to be applied in real-world problems. In this context, he has collaborated in more than 30 industry-based research and consulting projects with various companies in the areas of process industry, transportation, retail and energy. He is part of the research team of XFLEX a project recently approved in the H2020 program in which he will lead the research on innovative solutions to optimize maintenance plans and decrease the outage time and increase the availability of flexible hydropower plants. Furthermore, he has been the project leader of several industry collaboration projects at INESC TEC in the area of asset management with companies such as EDP Renováveis, EDP Produção, EDP Distribuição and REN Portgás. Author of several publications in international journals in the field of Operations Research.

Computer vision systems and agriculture 4.0

The agri-food industry faces the challenge of increasing productivity and food quality while optimizing the use of inputs. Therefore, new technologies such as computer vision are being applied to develop non-destructive methods, precision agriculture, etc., with which to automate and accelerate planting, harvesting and post-harvest operations. These activities form a new branch of Industry 4.0 called Agriculture 4.0; characterized by integrating data collection and processing to decision processes on field activities or the food industry. This talk will address the basics of obtaining and applying different computer vision techniques to the analysis of agri-food products as well as machine learning and big data methods coupled to these techniques.

PhD. Wilson Manuel Castro Silupu – Universidad Nacional de Frontera, Perú

Dr. Wilson Manuel Castro Silupu is Principal Professor and Research Professor of the Faculty of Food Industry Engineering at the Universidad Nacional de Frontera – Sullana – Peru. He obtained his PhD in Food Science, Technology and Management at the Polytechnic University of Valencia – Spain (Cum Laude Mention), as well as a Master’s Degree in Food Science and Engineering from the University Institute of Food Engineering Research for Development (IuIAD) of the Polytechnic University of Valencia (UPV) – Spain (with Honors). Dr Wilson Castro’s research work has focused on the application of non-invasive technologies such as hyperspectral imaging, multispectral imaging, near infrared spectroscopy, among others coupled with machine learning techniques for the chemometric analysis of food. As a result of this research he has more than thirty publications in several journals indexed in Scopus and WoS and three chapters of specialized books. He is reviewer of different Journals such as Scientific Reports, Food Engineering, Food International Research among others.

“Multi-objective computational intelligence for engineering systems”

Processes and systems in the field of engineering are becoming increasingly complex, making their optimization, modelling or simulation difficult under some circumstances. An alternative to deal with such complexities is the use of computational intelligence tools (e.g., artificial neural networks, evolutionary computation, and fuzzy systems). In the use of such tools, it is common to seek satisfying numerous conflicting objectives and requirements by optimizing a single performance indicator. The solution indicated by this performance indicator may sometimes not be satisfactory. In such a case, a multi-objective optimization may be desirable, since it determines a set of solutions with different degree of compromise between the conflicting objectives. In this talk, we will discuss how multi-objective optimization techniques can be implemented in different computational intelligence tools for engineering systems.

PhD. Gilberto Reynoso Meza – Escola Politécnica, Pontifícia Universidade Católica do Paraná (PUCPR), Brazil

Gilberto Reynoso Meza received his PhD in Automation, Robotics and Industrial Informatics from Universitat Politècnica de València (2014), and the degrees of master of science with a specialization in Automation (2005) and Mechanical Engineering Administrator (2001) from Tecnológico de Monterrey. He is an adjunct professor of the Programa de Pós-Graduação em Engenharia de Produção e Sistemas (PPGEPS), form Pontifícia Universidade Católica do Paraná (PUCPR), Brazil. He is an external professor of the Master in Electronics and Automation from Universidad Politécnica Salesiana (UPS), Ecuador. His current research interests are related to the development of multi-objective optimization techniques for engineering design and machine learning for industrial processes. His main research interests are computational intelligence, intelligent control, multi-objective optimization, multi-criteria decision making, evolutionary algorithms and machine learning.

“Reduction of pollutant emissions associated with failures not detected by the On-Board Diagnostic System”

Most of the faults that affect the level of pollutant emissions of the vehicle are reported to the driver by the on-board diagnostic (OBD) system. However, there are faults that are not recognized by this system that imply that the driver, in some cases, operates the car without observing a possible reduction in engine performance and an increase in harmful exhaust gases. Despite its importance, not enough attention has been paid to this problem in the scientific literature. In this context, this talk exposes the methodology and results of previous research that pursued the reduction of pollutant emissions of a spark-ignition engine that was induced to controlled faults not detected by the OBD system, and for which an optimal operating configuration was defined by design of experiments. The exposition also involved a multi-criteria decision analysis using the analytical hierarchy process (AHP) for the selection of the defects and regulations applied to the experiment.

MSc. Jairo Castillo Calderón – Universidad Nacional de Loja, Ecuador

Professor Jairo Castillo Calderón holds a master’s degree in Automotive Systems from Escuela Politécnica Nacional (2016), Automotive Mechanical Engineer from Universidad Politécnica Salesiana (2013), Master Technician in Automotive Electronics from Cise Electronics, and is currently pursuing a Master’s degree in Electric Propulsion Systems at the University of Azuay. Since May 2016, he has been a professor at Universidad Nacional de Loja (UNL) in the Electromechanical Engineering and Automotive Mechanics Engineering careers. He has directed degree projects at the undergraduate and graduate level. He was responsible for the technological careers of the Faculty of Energy, Industries and Non-Renewable Natural Resources of the UNL during 2017 and 2018. He was a promoter and author of the design of the Automotive Engineering career at UNL where he currently serves as academic manager. He has participated in and directed research projects in the field of automotive engineering, artificial intelligence, mechanical design, and fluid mechanics, in addition to publishing works of high academic and scientific value in the form of scientific articles and book chapters in the same area.



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