Wednesday, September 13, 2017

Iron Ore Mine Productivity


Thirteen iron ore mines, including most of the largest ones globally, were found on Google Maps and then their land areas in square miles were measured using the satellite view.  The Internet was used to identify the mines and also to find the latest available data on the annual amounts of iron ore produced at these mines.

The areas measured and the production data is found in the table below.   With this data, iron ore mine productivity (annual production in metric tons (mt) per square mile) was computed and is shown in the table. 


mine name
country
state /province /region
company
area -square miles
production million mt per year
annual production in mt per square mile
hopedowns 4
au
western australia/pilbara
rio tinto/ hancock
5
43
8,600,000
carajas
bz
para
vale
15
120
8,000,000
mount whaleback
au
western australia/pilbara
bhp billiton
10
77
7,700,000
vargem grande
bz
minas gerais
vale
3
23
7,666,667
mining area c
au
western australia/pilbara
bhp billiton
8
57
7,125,000
yandi
au
western australia/pilbara
bhp billiton
13
80
6,153,846
hamersley  (hub)
au
western australia/pilbara
rio tinto
25
133
5,320,000
minas itabiritos
bz
minas gerais
vale
8
32
4,000,000
christmas creek
au
western australia/pilbara
fortescue
13
50
3,846,154
samarco alegria
bz
minas gerais
vale/bhp billiton
6
22
3,666,667
cloudbreak
au
western australia/pilbara
fortescue
13
40
3,076,923
khumani king
sa
northern cape
assore/ african rainbow minerals
6
13
2,166,667
sishen
sa
northern cape
kumba/anglo american
20
36
1,800,000



average
11
56
5,317,071


Measuring an iron ore mine using Google Maps’ satellite view is straight forward and likely to be accurate.  The production data is less certain as it relies on company reporting, which is hard to verify.  

With these qualifications, if the data in the table are reasonably correct, the data suggest that some mines are more productive than others and that no correlation between the land-area size of a mine and its productivity exists.  Many factors influence mine productivity.  Two reports, one from EY and one from PWC, identify and discuss such factors.  (Click here and here to read these reports.)  What might be interesting is to try to determine what factors at the individual mines listed in the table account for their productivity.


Tuesday, September 5, 2017

Metric Ton per Worker Steel Productivity Comparisons

The Wales National Statistics website (StatsWales) has a data series for crude steel production in the United Kingdom (UK) versus the number of people employed in the iron and steel industry.  (Click here to see this data.)   The data shows that in the late 1970s/early 1980s, the UK had about a 125 metric ton (mt) per worker productivity, whereas by the mid-2010s, the productivity had increased to about 660 mt per worker.  To me this data shows amazing technological progress made by the UK in producing steel from 1970 to 2015.

The following table shows metric tons of steel produced per worker for the UK as well as for Brazil, China, the European Union (EU), Japan, and the United States.  (Links are provided to the data sources.)  One thing that stands out in this table is the relative low productivity of China and Brazil’s steel industry compared to the other countries and the EU.  The Wales data series discussed above shows that the UK reached China’s recent steel-making productivity by 1983 and Brazil’s current productivity by 1987.

country
metric tons (mt) produced
number of workers
mt/worker
data source links
brazil (2016)
33,300,000
111,509
299
click here
china (2015)
803,825,000
3,627,000
222
click here and here
eu (2016)
161,979,000
318,000
509
click here 
japan (2016)
104,780,000
176,000
595
click here
uk (1978)
20,310,000
165,400
123
click here
uk (2014)
12,030,000
18,270
658
click here
usa (2015)
87,000,000
142,000
613
click here



Recently, China announced its intention of reducing the number of workers in their steel industry by 500,000.  (Click here and here to read about this).  Using the data in the table above, I computed that for China to obtain a UK 2014 productivity level (658 mt per worker), China would have had 1,220,772 steel workers in 2015 (803,825,000 mt/1,220,772 workers = 658 mt per worker).  This suggests China would need to reduce its steel worker number by about 2,400,000 (3,627,000 – 1,220,722 = 2,406,228) to obtain UK’s 2014 productivity, far greater than the announced number planned for reduction.

Friday, September 1, 2017

Chemical and Metal Shortage Alert – August 2017

The purpose of this blog is to identify chemical and metal shortages reported on the Internet.  The sources of the information reported here are primarily news releases issued on the Internet.  The issue period of the news releases is August 2017.

Section I below lists those chemicals and metals that were on the previous month’s Chemical and Metal Shortage Alert list and continue to have news releases indicating they are in short supply.  Click here to read the July 2017 Chemical and Metal Shortage Alert list.

Section II lists the new chemicals and metals (not on the July alert).  Also provided is some explanation for the shortage and geographical information.  This blog attempts to list only actual shortage situations – those shortages that are being experienced during the period covered by the news releases.  Chemicals and metals identified in news releases as only being in danger of being in short supply status are not listed.

Section I. 

Cobalt:  global; mining not keeping up with demand
Graphite electrodes:  global; supply not keeping up with demand
Zinc:  global; supply not keeping up with demand
      
Section II.   Shortages Reported in August not found on the Previous Month’s List

Magnesium: United States; supply not keeping up with demand
Methyl methacrylate (MMA): United States; production not keeping up with demand
Rebar steel: Russia; production not keeping up with demand
Stainless steel: Japan; supply not keeping up with demand

Reasons for Section II shortages can be broadly categorized as: 

1.  Mining not keeping up with demand: none
2.  Production not keeping up with demand:  methyl methacrylate; rebar steel
3.  Government regulations: none
4.  Sources no longer available: none
5.  Insufficient imports: none
6.  Supply not keeping up with demand:  magnesium; stainless steel


Saturday, August 26, 2017

Wacker and Shin Etsu Expanding Silicone Production to Meet Rising Demand

Two global chemical companies, the German company Wacker and the Japanese company Shin Etsu, are experiencing strong demand for silicone products and in response to that demand are increasing silicone production capacities.

Wacker indicates that customer demand for silicones is high.  Silicone sales increases have been in the 10% range, higher than for overall company sales.  Leading the way are silicones used in the construction, electronics, and automotive sectors.   The capacities of silicone production plants in the United States, Norway, South Korea, and Brazil have been increased.  Wacker has recently opened a research and development center in the United States focusing on silicone products.

Shin Etsu is expanding silicone production capacity at its Gumma and Niigata plants in Japan.   The company is also expanding the capacity of its silicone monomer and polymer production plants in Ragong Province, Thailand (monomers: from 70,000 to 105,000 metric tons per year; polymers: from 54,000 to 74,000 metric tones per year).  Shin Etsu is the second largest global producer of silicones with 20% of the market.  (Dow Corning has the largest share.)  The company is also increasing its silicone-related research and development efforts.   Shin Etsu expects a rising global demand for high-performance silicone products.  The company has been experiencing an average 13% increase in silicone sales in recent years.

Silicones are used in thousands of products spread across several sectors such as construction, transportation, energy, health, and food.    Increasing demand for silicones products in these sectors should be a good indicator of global economic growth.   Click here to go to a website maintained by the European trade association CES that provides a good overview of what silicones are and their uses.


Wednesday, August 9, 2017

Estimating a Desalination Plant Size for a Given Output

I searched the Internet to identify desalination plants located around the world.   (At The International Desalination Association website, 18,426 plants are estimated to have been in existence in 2015 – click here to go to this data.)  I identified 20 plants that I was able to find on Google Maps.  I then measured the areas of each plant (using the Satellite view) and graphed the measured area against each plant’s reported water output in millions of gallons per day.  The following graph shows the results of plotting the measured areas against the reported outputs (mgd = millions of gallons per day):




The graph is based on the data in the following table:

mgd/day
acres
25.7
5.4
27.0
6.9
36.0
10.4
52.0
16.2
91.9
18.8
15.9
21.3
92.5
22.0
36.1
22.2
137.4
25.5
84.5
26.4
75.7
30.4
72.1
31.9
47.0
33.1
66.1
35.3
132.1
43.5
72.3
43.7
165.1
49.2
158.5
50.2
168.0
52.4
264.2
85.3


So, what is the value of doing this?  One value might be using the results to estimate a desalination plant size for a given output.   Doing a regression analysis of the correlation between the measured plant sizes in acres and the reported plant outputs in millions of gallons per day (the data in the table above) gives an R square value of 0.7603, indicating some correlation between the two in a series of measurements.   The equation for the resulting regression line is y = 2.9013x – 0.3923 (the graph above provides the R square value, the regression line, and the line's equation).  Using the equation (and assuming the correlation is sufficient),  a plant size for a 100 mgd output would be about 35 acres and a plant size for an output of 150 mgd would be about 52 acres.

It seems to me that using Google Maps to look at chemical sites (such as desalination plants) by satellite photographs has a lot of potential uses, such as what is described above.  I was hoping to find a tipping point such that at a certain plant size, the output suddenly substantially increases, but was not able to get enough data for larger-size plants.