Roberto Muñoz-Guerrero

Bio: Roberto Muñoz-Guerrero is an academic researcher from Instituto Politécnico Nacional. The author has contributed to research in topics: Polymer electrolyte membrane electrolysis & Alternative energy. The author has an hindex of 4, co-authored 4 publications receiving 113 citations.

Development of a Laparoscopic Box Trainer Based on Open Source Hardware and Artificial Intelligence for Objective Assessment of Surgical Psychomotor Skills

Abstract: Background. A trainer for online laparoscopic surgical skills assessment based on the performance of experts and nonexperts is presented. The system uses computer vision, augmented reality, and art...

Dynamic Model of a PEM Electrolyzer based on Artificial Neural Networks

Abstract: Hydrogen production by electrolysis is emerging as one of the most promising ways to meet future fuel demand; likewise, the development of models capable of simulating the operation of electrolysis devices is indispensable in the efficient design of power generation systems, reducing manufacturing costs and resources savings. The nonlinear nature of the Artificial Neural Network (ANN) plays a key role at the development of models for predicting the performance of complex systems. The behavior of a Polymer Electrolyte Membrane (PEM) Electrolyzer of three cell stack (100 cm 2 of active area) was modeled successfully using a Multilayer Perceptron Network (MLP). This dynamic model has been trained to learn the internal relationships that govern this electrolysis device and predict its behavior without any physical equations. The electric current supply and the operation temperature were used as input vector able to predict each cell voltage behavior. A reliable accuracy (< 2%) was reached in this work after comparing the single cell performance of the real electrolyzer versus the ANN based model. This predictive model can be used as a virtual device into a more complex energy system.

Wind turbine design with emphasis on Darrieus concept Ion Paraschivoiu

Abstract: Wind energy is the fastest growing alternate source of energy in the world since its purely economic potential is complemented by its great positive environmental impact. The wind turbine, whether it may be a Horizontal-Axis Wind Turbine (HAWT) or a Vertical-Axis Wind Turbine (VAWT), offers a practical way to convert the wind energy into electrical or mechanical energy. Although this book focuses on the aerodynamic design and performance of VAWTs based on the Darrieus concept, it also discusses the comparison between HAWTs and VAWTs, future trends in design and the inherent socio-economic and environmental friendly aspects of wind energy as an alternate source of energy.

Fundamentals, materials, and machine learning of polymer electrolyte membrane fuel cell technology

Abstract: Polymer electrolyte membrane (PEM) fuel cells are electrochemical devices that directly convert the chemical energy stored in fuel into electrical energy with a practical conversion efficiency as high as 65%. In the past years, significant progress has been made in PEM fuel cell commercialization. By 2019, there were over 19,000 fuel cell electric vehicles (FCEV) and 340 hydrogen refueling stations (HRF) in the U.S. (~8,000 and 44, respectively), Japan (~3,600 and 112, respectively), South Korea (~5,000 and 34, respectively), Europe (~2,500 and 140, respectively), and China (~110 and 12, respectively). Japan, South Korea, and China plan to build approximately 3,000 HRF stations by 2030. In 2019, Hyundai Nexo and Toyota Mirai accounted for approximately 63% and 32% of the total sales, with a driving range of 380 and 312 miles and a mile per gallon (MPGe) of 65 and 67, respectively. Fundamentals of PEM fuel cells play a crucial role in the technological advancement to improve fuel cell performance/durability and reduce cost. Several key aspects for fuel cell design, operational control, and material development, such as durability, electrocatalyst materials, water and thermal management, dynamic operation, and cold start, are briefly explained in this work. Machine learning and artificial intelligence (AI) have received increasing attention in material/energy development. This review also discusses their applications and potential in the development of fundamental knowledge and correlations, material selection and improvement, cell design and optimization, system control, power management, and monitoring of operation health for PEM fuel cells, along with main physics in PEM fuel cells for physics-informed machine learning. The objective of this review is three fold: (1) to present the most recent status of PEM fuel cell applications in the portable, stationary, and transportation sectors; (2) to describe the important fundamentals for the further advancement of fuel cell technology in terms of design and control optimization, cost reduction, and durability improvement; and (3) to explain machine learning, physics-informed deep learning, and AI methods and describe their significant potentials in PEM fuel cell research and development (R&D).

Renewable energy: Present research and future scope of Artificial Intelligence

Abstract: The existence of sunlight, air and other resources on earth must be used in an appropriate way for human welfare while still protecting the environment and its living creatures. The exploitation of sunlight and air as a substantial Renewable Energy (RE) source is an important research and development domain over past few years. The present and future overtaking in RE mainly comprises of (i) the development of novel technology for optimum production from the available natural resources (ii) environmental awareness, and (iii) the better management and distribution system. Like other domains (food, health, accommodation, safety, etc.), Artificial Intelligence (AI) could assist in achieving the future goals of the RE. Statistical and biologically inspired AI methods have been implemented in several studies to achieve common and future aims of the RE. The present study summarizes the review of reviews and the state-of-the-art research outcomes related to wind energy, solar energy, geothermal energy, hydro energy, ocean energy, bioenergy, hydrogen energy, and hybrid energy. Particularly, the role of single and hybrid AI approaches in research and development of the previously mentioned sources of RE will be comprehensively reviewed.