I have been evaluating different approaches to calculate the “Well-to-Wheels” efficiency and the pollution intensity of various renewable and low-pollution alternative transportation fuels. The Well-to-Wheels approach is an energy counting method that attempts to account for every iota of energy consumed to get fuel from its original source (the well) to its ultimate transportation application (the wheel). Using this approach, gasoline does not simply contain the energy that is released when it is burned in an engine. Instead, all the energy required for raw petroleum to become gasoline and then to reach the engine is considered: drilling, refining, transportation, etc.
The Well-to-Wheels approach is critical when comparing gasoline internal combustion engines to alternative prime mover and energy carrier combinations for transportation; especially electric cars or vehicles that run on fuel made from grid electricity (for example, hydrogen generated from water electrolysis). A perfect Well-to-Wheels comparison would provide a true energy efficiency comparison between competing transportation technologies. However, it is very challenging to count up all the little pieces of energy consumption to make a complete comparison.
Pollution intensity is another important fuel evaluation metric that provides comparisons of equivalent carbon dioxide greenhouse gas produced per kilowatt-hour of electricity consumed for different energy sources. While electric cars themselves produce no greenhouse gasses, the electrical energy used to charge their batteries likely came from a power plant that did produce emissions. For two equivalent transportation scenarios, were fewer pollutants generated by burning gasoline in a car engine or by making the electricity in an electric car’s battery at a power plant?
The pollution intensity and the Well-to-Wheel efficiency are two important values that facilitate informed comparison of different transportation energy choices, and they are both very challenging to calculate. In my review of methods, I encountered the Earth Notes: Great Brittan Grid Intensity Web site, which provides a helpful and continuously-updated pollution intensity tally for the United Kingdom’s utility grid. I like that this site features a continuously updating signal, designed like a stop light, telling casual users the best times to run energy-intense domestic tasks (like laundry washers and driers, for example). Below this signal, the data are broken into details that energy geeks like me love to scour over to extract trends.
This split display allows people to make informed decisions about when to schedule energy-intense activities to minimize environmental impact while allowing researchers to compare new technologies based on how much pollution they might generate (or offset).
Are they robots or androids? We're not exactly sure. Each talking, gesturing Geminoid looks exactly like a real individual, starting with their creator, professor Hiroshi Ishiguro of Osaka University in Japan.
Truchard will be presented the award at the 2014 Golden Mousetrap Awards ceremony during the co-located events Pacific Design & Manufacturing, MD&M West, WestPack, PLASTEC West, Electronics West, ATX West, and AeroCon.
For industrial control applications, or even a simple assembly line, that machine can go almost 24/7 without a break. But what happens when the task is a little more complex? That’s where the “smart” machine would come in. The smart machine is one that has some simple (or complex in some cases) processing capability to be able to adapt to changing conditions. Such machines are suited for a host of applications, including automotive, aerospace, defense, medical, computers and electronics, telecommunications, consumer goods, and so on. This discussion will examine what’s possible with smart machines, and what tradeoffs need to be made to implement such a solution.