In the last posting I described three methods for estimating energy use per worker-hour (EPWH) and the preference was amortizing non-industrial energy supply since, in our opinion, it yields the most accurate estimate of energy use per industrial worker-hour for use in process-based or hybrid economic input-output life-cycle assessment.
We now use this method for an example calculation and accompanying discussion. A short recap to keep us all on the same page.
In this method a value of EPWH is derived from the non-industrial energy supply which includes all primary energy except that supplied to industry. It was defined by the expression
EPWH = (TPES – IPES)/(population x hours / year)
where TPES is a country or region’s total primary energy supply and IPES is industrial primary energy supply. Since IPES is not always readily available, we can approximate it using industrial final consumption (IFC) and total final consumption (TFC) of energy calculated as follows
IPES = TPES x IFC/TFC
This expression assumes the ratio of final consumption to primary energy supply for industry is representative of the ratio of final consumption to primary energy supply for the country.
Now an example comparing the relative energy demands of a worker with a piece of manufacturing machinery operated, in some cases, by a human or, in other cases, totally automated.
The energy use of labor in the United States is significant relative to the energy use of a machine tool and of labor in other major manufacturing countries and regions. The energy use of labor may also be used to more accurately evaluate labor intensive processes and industries.
Though there are significant differences between the capabilities of a worker and a machine tool, it is an interesting exercise to compare their relative energy demands. In the US, electricity production from primary energy is approximately 35% efficient (Source: EIA, 2005, Annual Energy Review 2005, US DOE). This conversion factor is used to compare primary EPWH with machine tool electricity use.
As shown in the figure below, the 2.9 kWh of electricity equivalent EPWH that we equate to 30 MJ of primary EPWH is comparable to the power consumption of an automated milling machine but is considerably less than that of a production scale machining center (Source: Dahmus, J. B., Gutowski, T. G., “An Environmental Analysis of Machining,” In Proc. ASME IMEC, November, 2004). The figure shows the electricity equivalent energy use per worker-hour in the US based on 2004 data as
Comparison of electricity equivalent of machines and labor
compared to the hourly electricity requirements of four common milling machines produced in the years indicated, adapted from Dahmus. Note that this is plotted on a semi-log scale.
There have been a number of comprehensive analyses of machining including all the material production, cutting fluid preparation and machine operation. In addition other studies have looked at the embedded energy of machine tool building, delivery, installation, operation and repair, and, eventually, end of life.
Assuming the manual milling machine requires one worker to operate, a worker-hour contributes 2.9 kWh to the 0.7 kWh the machine consumes directly each hour (from Dahmus, cited above). The actual energy impact of manual milling is therefore 3.5 kWh (person plus machine) or five times greater than previously thought. As a component of process-based LCA, this higher energy use may be reflected in a wide range of products and services.
A decision making application of energy use per worker-hour for the milling machine used in this analysis shows that if a worker is able to operate four or more machines at a time, it is advantageous from an energy point of view to employ the automated milling machine even though it directly uses four times more energy per hour than the manual milling machine. Energy use per part will scale with production rate for each machine. This is illustrated in the figure below.
Electricity equivalent energy use, including labor and machine operation, for manual and automated machine configurations.
This means that, from a trade-off of production vs energy requirements, for a small operation involving few operations it may be advantageous to use a human worker with a manual machine (that is one with little or no automation.) For a more complex series of machining processes, it may be advantageous to use automated machinery attended by a human worker (as opposed to a fully automated autonomous machining line with no human involvement).
There is something missing from this. For example, if you are comparing two automated machines (or more) operating without human assistance then you need some kind of work and tooling transfer system to keep the machines operating from part to part. This is not included in this analysis. A ‘typical’ small part handling robot from Fanuc lists .2kWh as operating requirements. If one of these was required for each automated machine (ie without a human worker) we’d need to add that to the machine requirements. In addition, there might be some other material handling machinery as well adding more to this. That will shift the break-even to the right in the above figure.
In addition, this data on machine energy consumption is from a few years back and there have been improvements in machine energy efficiency since then. However, this would only shift the “break-even” to the left slightly.
The data above is for the US. In countries where the primary energy use per worker hour is different from the US (usually lower, and sometimes substantially lower) this “break-even” point will move further out to the right. Meaning, the worker will be required to attend more automated machines to make the automated production to maintain this advantage.
Major manufacturing countries demonstrate a wide range of energy use per worker-hour values, as shown in the figure below.
Primary energy use per worker-hour in major manufacturing countries and regions
These differences can be attributed to a complex set of factors. A very important factor is undoubtedly population. With the exception of the United States, the five most populous countries evaluated represent the countries with the lowest values for energy per worker-hour.
There is also an inverse relationship between EPWH and ratio of industrial final consumption to total final consumption. For the countries evaluated, this ratio ranges from 19% for the United States to 41% for China. In general, the more a country expends in manufacturing, the less energy is expended per worker-hour. These trends may suggest relationships between service and manufacturing economies and development, or they may simply be attributed to the calculation of EPWH.
The necessity of excluding industrial energy use from the calculations, as discussed earlier in this series, is observed when comparing net importers and net exporters. For example, consider the $214 billion trade deficit between the United States and China in 2006. Energy used in China to manufacture goods for sale in the United States does not contribute to the Chinese EPWH. Meanwhile, energy the United States imports in the form of products can be captured by process-based LCA.
For simplicity, these results do not consider geographic differences in the number of workers employed for any given task or the purchasing power and related energy consumption of industry workers compared to the general population.
Now, this type of analysis raises a lot of questions. Some industrial processes are more labor intensive than others (think apparel manufacturing or electronics assembly.) Different countries have differing impacts due to energy production or efficiencies in delivering energy to industry. How does this play into the discussion? Should we, based on all this, simply export all labor intensive industry to “make our numbers look good?!” The obvious answer should be no. But, we’ll get into that next time.
David Dornfeld is the Will C. Hall Family Chair in Engineering in Mechanical Engineering at University of California Berkeley. He leads the Laboratory for Manufacturing and Sustainability (LMAS), and he writes the Green Manufacturing blog.