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Morales-Acosta D.,CONACYT | Morales-Acosta D.,Research Center en Quimica Aplicada | Rodriguez-Varela F.J.,CINVESTAV | Benavides R.,Research Center en Quimica Aplicada
International Journal of Hydrogen Energy

The electrochemical evaluation of Pt nanoparticles deposited on ordered mesoporous carbon (OMC) and multiwalled carbon nanotubes (MWCNT), by the polyol method was reported in this work. Their performances were evaluated for methanol, ethanol and ethylene glycol electro-oxidation reaction (MOR, EOR, and EGOR, respectively). The results have been compared with those of a Pt/Vulcan XC-72 catalyst prepared by the same procedure. OMC was synthesized via self-assembly in aqueous solution, as an alternative to other conventional preparation methods. MWCNT were synthesized by the spray pyrolysis technique. The three carbon supports were chemically modified prior to the Pt deposition and the changes on structural properties of OMC and MWCNT due such surface functionalization were characterized. Cyclic voltammetry results demonstrated that Pt/OMC has a lower on-set potential, delivering a higher anodic peak current density for the MOR, EOR and EGOR. Moreover, the jf/jb ratios at Pt/OMC were higher than those determined for Pt/MWCNT and Pt/Vulcan for the anodic reactions. The improvement in catalytic activity has been attributed to the mesoporous structure of OMC, which promoted a greater support-catalysts interaction. The use of OMC enhanced the tolerance to carbonaceous intermediates, thus inhibiting the self-poisoning of Pt nanoparticles. In addition, Pt/OMC exhibited electrochemically active surface area losses after accelerated degradation tests of 12%, a lower value compared with that of Pt/Vulcan (36%). Copyright © 2015 Hydrogen Energy Publications, LLC. Source

Chavez-Olivares C.,CONACYT | Chavez-Olivares C.,Aguascalientes Institute of Technology | Reyes-Cortes F.,Autonomous University of Puebla | Gonzalez-Galvan E.,Autonomous University of San Luis Potosi
International Journal of Advanced Robotic Systems

The stiffness controller proposed by Salisbury is an interaction control strategy designed to achieve a desired form of static behavior as regards the interaction of a robot manipulator with the environment. The main idea behind this approach is the simulation of a multidimensional linear spring - or linear elastic material - using the difference between the actual position of the end-effector and a constant position (relaxed point), multiplied by a constant stiffness matrix. In this paper, this idea is generalized with the objective of proposing a controller structure that includes a family of stiffness models based on the idea of linear elastic materials. The new controller structure also includes a damping term in order to have control over energy dissipation, as well as a term added for the purpose of compensating the gravity forces of the links. The stability analysis of the proposed controller was performed in the Lyapunov sense. The new stiffness controller is presented as a case study and compared to other cases, such as the Salisbury controller (Cartesian PD) and the tanh-tanh controller. Experimental results using a three degrees-offreedom direct-drive robot for the evaluation of controllers in a constrained motion task are presented. Source

Perez-Rodriguez R.,CIMAT | Hernandez-Aguirre A.,CIMAT | Jons S.,CONACYT
International Journal of Advanced Manufacturing Technology

In manual order-picking systems such as picker-to-parts, order pickers walk through a warehouse in order to pick up articles required by customers. Order batching consists of combining these customer orders into picking orders. In online batching, customer orders arrive throughout the scheduling. This paper considers an online order-batching problem in which the turnover time of all customer orders has to be minimized, i.e., the time period between the arrival time of the customer order and its completion time. A continuous estimation of distribution algorithm-based approach is proposed and developed to solve the problem and implement the solution. Using this approach, the warehouse performance can be noticeably improved with a substantial reduction in the average turnover time of a set of customer orders. © 2015, Springer-Verlag London. Source

The flexible jobshop scheduling problem permits the operation of each job to be processed by more than one machine. The configuration mentioned generally seeks to minimize the completion time of all jobs known in the literature as 'makespan'. We propose an Estimation of Distribution Algorithm for Sequencing, AEDS for simplicity and functionality. The AEDS attempts to find a relationship or interaction between the input variables, jobs, operations and shifts to optimize the output variable of real manufacturing processes, the makespan. In this sense the AEDS algorithm is used to guide the search and to solve the problem. In the algorithm, three graphical models were used to find better solutions. To set off-duty hours for operators before starting their activities in each shift as an input parameter and its development through the AEDS algorithm is a novelty of this research on the current research work. The comparison between AEDS and a genetic algorithm shows the effectiveness of AEDS solving the problem statement. Using the AEDS proposed, the performance of real manufacturing processes can be improved significantly when different machines are assigned to different schedules. Copyright © 2015 CEA. Publicado por Elsevier España, S.L. Source

Borunda M.,CONACYT | Borunda M.,Electric Research Institute of Mexico | Jaramillo O.A.,National Autonomous University of Mexico | Reyes A.,Electric Research Institute of Mexico | Ibarguengoytia P.H.,Electric Research Institute of Mexico
Renewable and Sustainable Energy Reviews

For the last years, the research and development in the field of Renewable Energy has been growing due to the need of Renewable Energy as an extended and reliable source of energy. However, the implementation of renewable energy has many complex problems not easily solved with conventional methods. Recently, Artificial Intelligence techniques such as Artificial Neural Networks, Fuzzy Logic and Genetic Algorithms, have been widely used to deal with these problems in the field of Renewable Energy. Nevertheless, issues with a degree of uncertainty need Bayesian Networks since this is one of the most effective theories to face them. This technique can contribute to the Renewable Energy harnessing and other open issues on this field. In this work we show the state of the art of the applications of Bayesian Networks in Renewable Energy, such as solar thermal, photovoltaic, wind, geothermal, hydroelectric energies and biomass. Additionally, we include related topics such as energy storage, smart grids and energy assessment. We classify the literature by areas considering three main subjects: resource evaluation, operation, and applications, and in each section we describe the possible directions to be taken in the research of the field. We find that the main applications are done for forecasting, fault diagnosis, maintenance, operation, planning, sizing and risk management. We conclude that Bayesian Networks are a promising tool for the field of Renewable Energy with potential applications due to their versatility. © 2016 Elsevier Ltd. All rights reserved. Source

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