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Hydrogen Sources
Published in Michael Frank Hordeski, Hydrogen & Fuel Cells: Advances in Transportation and Power, 2020
The fuel dispensing pump is about the size of a washing machine. First, the car is grounded by attaching a wire to the vehicle. The fuel hose nozzle is inserted into the refueling port and locked in place. Filling the car’s tank takes about five or six minutes. The unit produces enough hydrogen to refill a single fuel cell vehicle a day. In Torrance, California, Honda has built a service station that splits water into hydrogen and oxygen using solar power.
The Future of Transportation
Published in Michael Frank Hordeski, Alternative Fuels—The Future of Hydrogen, 2020
The fuel dispensing pump is about the size of a washing machine. First, the car is grounded by attaching a wire to the vehicle. The fuel hose nozzle is inserted into the refueling port and locked in place. Filling the car’s tank takes about five or six minutes. The unit produces enough hydrogen to refill a single fuel cell vehicle a day. In Torrance, California, Honda has built a service station that splits water into hydrogen and oxygen using solar power.
Knowledge-Based Modeling for Computer-Integrated Manufacturing
Published in Ulrich Rembold, Robot Technology and Applications, 2020
The main parts of the planning system (knowledge base) are the world model (product, process, resources) and the submodule for the specification of the assembly task. The plans for the task can be described by an attributed directed graph, which can be separately generated for every product level. The graphs are all connected, to generate a total assembly plan that includes all details. Three types of graphs are distinguished. They can be explained with the example of an assembly of a suds (lye) pump of a washing machine. Details about the entire product, its components, joining surfaces and joining surface matrix are available elsewhere [19]. Figure 3.28 shows the assembly parts of the component bearing cap of the suds pump.
A new fuzzy logic based approach for optimal household appliance scheduling based on electricity price and load consumption prediction
Published in Advances in Building Energy Research, 2022
Sara Atef, Nourhan Ismail, Amr B. Eltawil
On the other hand, the second research question of how to build an integrated home control system was answered by using the predicted data to execute a DR decision-making process using a FIS controller. The proposed FIS controller was used to optimize the daily time schedule for three schedulable devices: washing machine, clothes dryer, and dishwasher. The proposed DR-FIS controller effectively reduced the electricity cost by controlling the usage of smart appliances in a smart home. The load of these appliances was shifted to non-peak periods which resulted in electricity cost savings of 46.8 and 25.8 cents/day. The proposed controller considered only three schedulable appliances. It will be interesting to consider other appliances such as air conditioning and non-shift able devices. Additionally, future work can include investigating the impact of employing the proposed algorithms considering other DR schemes.
Residential Electrical Load Monitoring and Modeling – State of the Art and Future Trends for Smart Homes and Grids
Published in Electric Power Components and Systems, 2020
Xinmei Yuan, Peng Han, Yao Duan, Rosemary E. Alden, Vandana Rallabandi, Dan M. Ionel
In most residential DR studies, the appliance load space needs to be reduced. In [81], the residential load monitoring is divided into two levels: circuit breaker lever (main DR loads) and strip level (backup DR loads) in a bi-level DR control structure. Generally, only schedulable appliances with energy storage capability such as HVAC, water heater, washing machine, clothes dryer, and EV were considered [79, 82–85]. Scheduling of photo-voltaic (PV) and electric water heater (EWH) with battery storage to achieve net-zero-energy housing was shown to alleviate pressure caused by the duck curve and peak demand [86–88]. PV, EWH, and battery scheduling across a distribution line in EnergyPlus and co-simulaton platform shows the benefits of DR in virtual power plants (VPPs) such as maximum PV utilization without curtailing as well as energy and cost savings [88–90].
Assessing environmental impact of textile supply chain using life cycle assessment methodology
Published in The Journal of The Textile Institute, 2018
Shadia Moazzem, Fugen Daver, Enda Crossin, Lijing Wang
A washing process requires the input of water and detergent. Cloth drying and ironing require no input of materials. But all of the processes involved in the use stage require the input of energy. Another important factor is the life time of clothing (Beton et al., 2014). In this study, life time was considered based on the total number of washes over the use stage. Data for electricity, domestic tap water and wastewater (derived from the washing machine) treatment was taken from the Australian Life Cycle Inventory Database, AusLCI (Grant, 2015). It was assumed that the wastewater to be purified in a moderately large municipal wastewater treatment plant (capacity class 2) in Australia. Detergent production process model was developed for this study based on the life cycle study of laundry detergent performed by Procter & Gamble Company (P & G) (Saouter & Hoof, 2002).