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Modeling of Ionic Polymer-Metal Composite-Enabled Hydrogen Gas Production

[+] Author Affiliations
Tushar Nagpure, Zheng Chen

Wichita State University, Wichita, KS

Paper No. DSCC2015-9922, pp. V003T39A004; 8 pages
doi:10.1115/DSCC2015-9922
From:
  • ASME 2015 Dynamic Systems and Control Conference
  • Volume 3: Multiagent Network Systems; Natural Gas and Heat Exchangers; Path Planning and Motion Control; Powertrain Systems; Rehab Robotics; Robot Manipulators; Rollover Prevention (AVS); Sensors and Actuators; Time Delay Systems; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamics Control; Vibration and Control of Smart Structures/Mech Systems; Vibration Issues in Mechanical Systems
  • Columbus, Ohio, USA, October 28–30, 2015
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5726-7
  • Copyright © 2015 by ASME

abstract

Hydrogen extraction using water electrolysis, and microbial biomass conversion are clean and minimum-emission option for renewable energy storage applications. Ionic polymer-metal composite (IPMC) is a category of electro-active polymers that exhibits the property of ion migration under the application of external voltage. This property of IPMC is useful in electrolysis of water (H2O) and produce hydrogen (H2) and oxygen (O2) gases. This paper discusses the electrochemical fundamentals of electrolysis, which provides a linear relationship between the flow rate of hydrogen from electrolysis and the source current. An IPMC electrolyzer circuit model is developed to capture the electrical characteristic of IPMC. The model incorporates nonlinear capacitance, pseudo-capacitance, and a nonlinear resistance defined with a polynomial function. A state-space equation is then obtained to simulate the proposed circuit model for electrolysis. Experimental result shows that the flow-rate of hydrogen production is proportional to the system current and the proposed model validates the step-response of the system. The model prediction error is less than 4.5647%.

Copyright © 2015 by ASME

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