A variety of methods are available to rank coals in the market. Ranking cannot be simply performed on one parameter. There are many different coal quality parameters that affect the pricing of coal, utilisation performance of coal and the value-in-use of coal. A coal with low ash may still be perceived by a power plant to have low value because it has high moisture and high sulfur. A coal with low HGI may still be valuable because other coal properties such as reactivity compensate for poor pulverising performance. For this blog however, I will primarily look at the ash content in the coal product database. The blog would become too large otherwise!
The simplest ranking method available is to compare the coal quality of the coal of interest (test coal) with a selection of other coals as shown in the figure below. In this scenario, the ash content for the test coal is higher than most of the coals shown.The key problem with this approach is how representative the selected coals are. If the goal is to assess the coal in the world market, then a selection of 25 coals is a crude approach.
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The selection of coals can be compiled using a variety of different reasons. The chart below compares a test coal with 25 other coals with similar calorific value. These specific coals can be seen as direct competitors for the test coal. For this comparison the test coal can be viewed as having low ash as compared to similar energy Australian coals and high ash as compared to similar energy Indonesian coals. Other criteria that could be used for the selection of coals includes coal pricing similarities, shipping distance similarities, ash content etc.
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The bar chart option can be extend by considering all coals in the database. This option is shown below in 2 different charts. The top chart includes variable width bars which are linked to mine export volume. The bottom chart uses the same width for all coal quality data. These charts are a good overview of where a coal sits in the market. These charts are frequently used to show mine cost curve data with mine costs vs cumulative mine production volumes. Ranking can be performed by positioning the test coal ash value within the data. 670 Mt of coal in the market has lower ash content than the test coal. This equates to about 91% of coals in the market have lower ash. Another way to rank the position is to say 182 coals in the market have lower ash. This equates to about 85% of coals in the market have lower ash. The difference in ranking is significant and the 91% ranking is more accurate as it reflects the ash levels in actual coal exports.
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Another option that can be used to rank coals is a boxplot. See below for an example of a boxplot comparing ash contents for coals from different countries and different calorific groups. Note that the calorific groups (labelled as Energy) has to be selected from the legend. The data points represent individual mine coal quality and the box plots show the first quartile (Q1), median, and third quartile (Q3). The upper whisker shows the minimum of (max value, Q3+1.5xIQR). IQR is the interquartile range or (Q3-Q1). The lower whisker is the maximum of (min value, Q1-1.5xIQR).A violin plot could also be used with the boxplot which shows the distribution of the data and the summary statistics shown by a boxplot.
Boxplots are useful for comparing the ash categorised into different groups as shown by the country groups. The boxplot below shows the test coal ash level is higher than most Indonesian coals, and similar to Australian, Russian and South African coals.
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The figure below shows some different categories that can be used in the boxplots:
- coal rank and the
- approximate distance of the coal mine to a power plant in China.
Using categories such as the shipping distance can assist develop marketing strategies and identify competitive advantages for the test coal as compared to others.
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Histograms are a good option to visualise the distribution of coal quality data. The top chart shows ash content that is adjusted for coal export tonnage whereas the bottom chart assumes all mines export the same tonnage. Note that categories have to be selected twice to show in both charts. The bin width is also adjustable by selecting one of the bin width buttons. It might be expected that the data should follow a normal distribution, but the chart below shows that the ash coal quality data does not formal a normal distribution.
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Scatter charts are useful to identify relationships between 2 parameters. See below for a chart comparing ash content with calorific value. The scatter chart is broken into 4 quadrants
- one quadrant grouping higher energy and lower ash coals as compared to the test coal (coloured red). These coals could be considered as having superior quality.
- one quadrant grouping lower energy and higher ash content as compared to the test coal (coloured green). These coals could be considered as having poorer quality.
- the other 2 quadrants have one superior parameter and one poorer quality (coloured orange). These might or might not be considered superior.
The chart shows no significant relationship between ash content and calorific value. The test coal has higher ash than most of the coals in the database.
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Heatmaps can concisely summarise coal quality ranking through simple colour gradients to indicate good performance, a positive attribute or a favourable ranking (e.g. use of green colours) to poor performance, a negative attribute or detrimental ranking (e.g. use of red colours). Heatmaps rely on calculating a score, or a coal rank that can be converted to a colour. There are many potential methods of doing this for coal ranking purpose for example:
CQ Statistics Rank – coal rank can be calculated by using the maximum and minimum of the data and calculating the percentage of the test coal ash within these limits.Minimum and maximum values of countries can be used.
CQ Position Rank – the data is initially sorted in ascending order. The position of the test coal is used with the total number of coals to determine a ranking percentage as compared to a country or in the world market.
Coal Export Rank – The summation of exports of coals with ash less than the test coal is compared with the total exports for each country or the world.
Z Score – calculated the Z-score of the test coal ash using the average and standard deviation of the data.
Plant Limitations – Colour the coal quality parameter as red if it exceeds the plant limits, otherwise colour the parameter green. This method is not shown in the figure below.
The rank assessment can vary significantly between the different methodologies used. The z score option assumes a normal distribution of the data to assess the ranking (or standard deviation from the mean). Since the coal quality distributions do not appear normal, the z score methodology will not convey a good representation of the competitive position of a coal.
The other three ranking methodologies show different grades for the different coal quality parameters for different countries. The coal export rank method identified high HGI for the test coal in the world market (so its a positive attribute) whereas the coal quality statistics method identified the HGI as low (negative attribute). A different ranking was also determined using the CQ position method than the coal export methodology, although the rank percents are similar.
Heatmaps offer a concise way of visualising coal rank. Coal quality parameters, utilisation performance and value-in-use metrics such as generation costs and shipping costs can be compared easily with a heat map.
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Another point to consider when ranking coals is how they compare with domestic coals of countries that produce their own coal as well as import it e.g. China and India.
So which form of coal ranking is the best? I would argue they all have some merit. A comprehensive ranking assessment should include a variety of different charts to fully evaluate the competitive position of a coal in the market. If a simple, concise method is required, then i would suggest a heatmap of coal quality parameters is useful, especially in an executive summary.