聚合物电解质膜电解
资本成本
制氢
质子交换膜燃料电池
电解
电解水
工艺工程
堆栈(抽象数据类型)
高温电解
废物管理
工程类
环境科学
氢
计算机科学
化学工程
燃料电池
电气工程
化学
电解质
有机化学
程序设计语言
物理化学
电极
摘要
The Department of Energy (DOE) has identified hydrogen production by electrolysis of water at forecourt stations as a critical technology for transition to the hydrogen economy; however, the cost of hydrogen produced by present commercially available electrolysis systems is considerably higher than the DOE 2015 and 2020 cost targets. Analyses of proton-exchange membrane (PEM) electrolyzer systems indicate that reductions in electricity consumption and electrolyzer stack and system capital cost are required to meet the DOE cost targets. The primary objective is to develop and demonstrate a cost-effective energy-based system for electrolytic generation of hydrogen. The goal is to increase PEM electrolyzer efficiency and to reduce electrolyzer stack and system capital cost to meet the DOE cost targets for distributed electrolysis. To accomplish this objective, work was conducted by a team consisting of Giner, Inc. (Giner), Virginia Polytechnic Institute & University (VT), and domnick hunter group, a subsidiary of Parker Hannifin (Parker). The project focused on four (4) key areas: (1) development of a high-efficiency, high-strength membrane; (2) development of a long-life cell-separator; (3) scale-up of cell active area to 290 cm2 (from 160 cm²); and (4) development of a prototype commercial electrolyzer system. In each of the key stack development areas Giner and our team members conducted focused development in laboratory-scale hardware, with analytical support as necessary, followed by life-testing of the most promising candidate materials. Selected components were then scaled up and incorporated into low-cost scaled-up stack hardware. The project culminated in the fabrication and testing of a highly efficient electrolyzer system for production of 0.5 kg/hr hydrogen and validation of the stack and system in testing at the National Renewable Energy Laboratory (NREL).
科研通智能强力驱动
Strongly Powered by AbleSci AI