Current Gas Vs. Ethanol Prices (Oct. 19, 2012)

Ethanol Vs. Gas Price

The below pink line is the commodity/wholesale price of 100% ethanol (E-100). But as a N.Y. Times story explains, ethanol gets less MPG than gasoline. The green line adjusts (increases the price) for the lower energy efficiency of ethanol, allowing an "Apples to Apples" comparison with the wholesale price of gas (blue line). The red line is the average of all U.S. retail pump prices of gasoline.Retail Gasoline Prices: As the below map shows, the current national average (above chart's red line) is skewed upward by spikes in Western prices caused by current refinery problems in California (fires, questionable maintenance).

Current Retail Gasoline Prices by Region The good news is that for most of the U.S., each October marks the annual date where gasoline refiners start switching production to cheaper fuel blends for cooler weather. During summer months, higher cost blends are used to reduce air pollution (e.g., smog). Thus excluding any unforeseen major market event (e.g., war in the Middle East) consumers should expect to see a decrease in pump prices through next Spring.

Ethanol Prices: A simple linear regression statistical analysis shows that U.S. ethanol prices can be almost entirely explained (a R² of .91) by the commodity price of #2 yellow corn (which is not used for human consumption). The extreme volatility in ethanol prices since July is the result of a series of initial over-reactions and then corrections in corn futures markets as to the actual severity of the drought in the Midwest on crop yields. As a result of this price increase, ethanol production levels have decreased ~20% since July. With low or negative profit margins, many ethanol plants have either reduced output or temporarily shut down entirely.

During 2012, we agree with two studies (here and here) that ethanol use has had a negligible impact on retail gas prices. This is because the greatest use of ethanol is a 10% or less blend with gasoline (E-10). Year to date, 10% of the cumulative price differential between the wholesale price of ethanol (adjusted for efficiency) and gasoline is less than 3� per gallon. Also recognizing that without ethanol, fuel blenders would have to substitute higher cost sources of octane additives, the price differential is probably only about 1� per gallon.

However long term, Supporters of ethanol must recognize a clear reality. As long as the adjusted price of ethanol (green line) is higher than the wholesale price of gasoline (blue line), ethanol use will always be criticized and lack acceptance by the U.S. Public even at low E-10 blend levels.

This lower cost can happen in two ways: (1) lowering ethanol's production cost through cheaper feedstocks and better conversion technologies (e.g., cellulosic enzymes); (2) greater auto engine efficiency utilizing ethanol's higher octane levels (smaller engines using turbo-boost).

The Renewable Fuel Standard: The recent volatility and price spikes of ethanol from the Mid-western drought is a good illustration that the future of ethanol is not from corn. Feedstock and production technology diversity is needed from other sources (sorghum, cellulosic). In passing the Renewable Fuels Standard requiring ethanol blending with gasoline, Congress recognized this point by capping the use of corn feedstocks (which current ethanol production has almost approached).

For all the criticism that ethanol use receives, what is generally lost by the general public is the amazing success story that has been so quickly achieved in accomplishing national goals for greater energy security and job creation. This achievement would not have been possible without using the existing corn industry's infrastructure in the Mid-west.

Did you know?: The Renewable Fuels Standard has resulted in a fundamental shift in gasoline formulation. The refining industry has now moved to using predominantly 84 octane "conventional" gasoline and then blending it with higher octane ethanol (around 113) to produce the 87 octane gasoline that is the most popular level with consumers. This change in refining practices is not easily reversed. While other octane enhancers could be used, ethanol's price make it the current lowest cost octane source of choice by refiners.

Data Sources:
Per numerous Sources (DOE, EPA), E-10 (10% ethanol) has ~3% less efficiency than E-0 (zero ethanol). Ethanol on a "net basis" has less BTU content, but higher octane.

-- Wholesale Ethanol prices are from the Chicago Board of Exchange.
-- Wholesale Gasoline prices are from the Chicago Board of Exchange.
-- Retail Gasoline prices are from Bloomberg's survey of national gas prices.
-- Corn Feedstock costs are calculated from Chicago Board of Exchange.
-- Distiller's Dried Grains (DDG) Futures (100 short tons) from CBOE.

-- Real Time Daily Trading Data on energy products.

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High Oil & Gas Prices -- The Real Issues.

oil prices

For decades, politicians have been promising America oil independence AND lower gasoline prices. But instead of more promises, we need some straight talk on expectations, global realities, and the tough choices we face. The real policy issues that drive today's high oil prices are the consequences of war and a suffocating federal debt -- anything else is a diversion.  
Can We "Drill Baby Drill" to $2 Gas?: A major diversion used in campaign rhetoric on high gas prices is supposedly simple Econ 101 -- Supply and Demand.   Under this argument, if U.S. environmental regulations were reasonable allowing more oil drilling, the increased supply would reduce the price of gas to a $2 range. FACT CHECKing this claim should be really easy by just looking at Canada -- as they have historically produced much more oil than Canadians consume. But Whoops! -- gasoline prices in Canada exactly track those in the U.S. (as they also do in other free market "net oil exporting countries" such as Norway).

On the flip side of political rhetoric, President Obama is taking credit that U.S. oil production is now the highest since 1998. Is this credit deserved?

Of course not. As an article from the Canadian "Oil Patch" explains, the reason oil exploration and production is currently booming in North America is because of high prices. For example, extracting oil from tar sands (the source of the Keystone pipeline project) and other unconventional sources can cost up to $40 a barrel. Without high oil prices, tar sands would not be economic compared to Middle Eastern oil (which costs 50� to $2 per barrel to extract).

While there are tremendous benefits of increasing domestic oil production (e.g., creation of good jobs, greater energy security, decreasing the U.S. trade deficit) -- an expectation of meaningful reductions in oil and gas prices isn't one of them. Simply stated, gasoline prices will always be driven by the world commodity price of oil.

The Real Price Drivers: After decades of stable oil prices, beginning in the early 2000's the combination of several drivers have fundamentally changed global oil markets:
(1) Ongoing war and political unrest in the Middle East; (2) Devaluation of the U.S. Dollar; (3) A dramatic increase in oil demand from developing economies; and possibly
(4) The unprecedented levels of money flowing into oil futures speculation.

A picture can be worth a thousand words. As the above chart illustrates, there have been dramatic consequences from 10 years of war, bad behavior on Wall Street, and the accumulation of massive debt under President Bush and Obama (devaluing our currency). These are the true drivers of high oil prices, not environmental regulations.

War in the Middle East:: Many oil market analysts believe a war between Iran and Israel would immediately send oil prices to $150 per barrel (and already creating a significant risk premium in oil prices). If an Iran/Israeli conflict were to spread throughout the Region (which supplies ~60% of the World's oil market), the consequences on prices would be catastrophic. With the possibility of $6, $8, $10 U.S. gas prices (fill in the blank as your guess is as good as anyone else), foreign policy is very much a domestic economic issue in the U.S. Presidential campaign.

Devaluation of U.S. Dollar: Beginning with the Bush Administration (in 2002) and continuing with Obama, a significant devaluation of the U.S. dollar has occurred. While there are numerous reasons for this, they are generally linked to Federal monetary policies to finance huge federal budget deficits and to spur a weak economy.

With currency devaluation the cost of imported goods (such as oil) increases as the purchasing power of the U.S. dollar is less. In simplistic terms (all things equal), ~33% of the increase in the price of gasoline since 2002 could be explained by currency devaluation. Many market analysts argue that as a result of world central banks flooding the market with money (to finance budget deficits), its not that oil is expensive -- but its money that is cheap.

Increased Demand for Oil from Developing Economies: A major driver in higher oil prices is/will be the continuing dramatic increase in oil demand from developing countries such as China and India -- something that no President has any control over. [British Petroleum (BP) has an excellent 2 minute picture overview of what's occurring in world energy markets on YouTube.]

Oil Futures Speculation & Oil/Gas Prices: Typically, a belief in whether excessive market speculation is causing a significant rise in oil prices depends on what "Political Tribe" you belong to. Democrats believe oil futures markets should be regulated more -- and are raising the current price of gas between $7 (small car) to $14 (truck) when you fill up. Republicans believe this is paranoia and oppose additional regulation (Dodd-Frank).

Thankfully, there is an Easy Button to resolve this. Even if financial market speculation is adversely impacting oil prices, its an "effect" not a "cause". If the issues of war and federal debt were resolved, this would pretty much eliminate the conditions that drive excessive financial speculation.

The Final Question: After listening to all the campaign rhetoric and attack ads, everyone should ask the question:   "What do environmental regulations have to do with war and the Federal debt level, the true drivers of high oil prices?" The answer is simple -- they don't.

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Current Ethanol Vs. Gas Prices (July 6, 2012)

Ethanol Vs. Gas Price

The below pink line is the commodity/wholesale price of ethanol. But as a N.Y. Times story explains, ethanol gets less MPG than gasoline. The green line adjusts (increases the price) for the lower energy efficiency of ethanol, allowing an "Apples to Apples" comparison with the wholesale price of gas (blue line). The red line is the average U.S. retail pump price of gasoline.Current Retail Gasoline Prices by RegionThe Bad News: On July 6 -- the price of ethanol (green line) was a whopping ~71� per gallon higher than gas on a wholesale price comparison (blue line). Incredibly, this price is higher than the retail price of gas (which includes taxes, transportation costs).

Not as Bad News: Most gasoline contains 10% or less ethanol (E-10). Thus, as this high cost ethanol is blended with gasoline, most people could be paying ~7� per gallon more when they fill up.

Better News: The current "price premium" between wholesale ethanol and gasoline has not existed for the entire year. From March to May, ethanol prices (green line) were less than gas. On a year-to-date basis, the price premium for E-10 is ~1.5� per gallon.

Hopeful Consumer News: U.S. ethanol inventories remain at near record high levels (excess supply). If inventories remain high, the current price premium may never fully make it to consumer's pocketbooks. Rather, ethanol companies may have to "eat" some of this higher cost, reducing their margin and profit levels to sell product.

What's Going On?: Prior to June, ethanol prices were trending lower in expectations of reduced ethanol feedstock costs from the largest corn crop planted in the U.S. since 1937. However, recent drought conditions in many mid Western States have caused commodity corn futures prices to skyrocket.

Currently, almost all ethanol in the U.S. is produced from corn feedstock. Until the Industry transforms itself to next generation feedstocks (e.g., sugar cane, sweet sorghum, cellulosic sources as is being done in Florida), ethanol prices will be highly dependent on the market price of corn. ^{(1) (2)}

⁽¹⁾ A simple linear regression of corn (x) and ethanol (y) prices using 2012 data resulted in a correlation R² of .8378 with a dependent variable value of y= 0.8893x + 0.7536.
⁽²⁾ "Corn Feedstock Cost" is the estimated "Net Costs" reflecting co-product of DDGS (Distiller Grains). The calculation methodology is simplistic, using 70% of commodity corn prices.

Data Sources:
Per numerous Sources (DOE, EPA), E-10 (10% ethanol) has ~3% less efficiency than E-0 (zero ethanol). Ethanol on a "net basis" has less BTU content, but higher octane.

-- Wholesale Ethanol prices (pink line) are from the Chicago Board of Exchange.
-- Wholesale Gasoline prices (blue line) are from the Chicgo Board of Exchange.
-- Retail Gasoline prices (red line) are from Bloomberg's survey of national gas prices.
-- Corn Feedstock costs (orange line) are calculated from Chicago Board of Exchange.
-- Dried Distillers Grains data from Chicago Board of Exchange.

-- Real Time Daily Trading Data on energy products.

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Why Oil &Gas Prices are High in 5 Pictures (Updated)

Update: British Petroleum (BP) has released an excellent 2 minute overview using pictures of what's occurring in world energy markets on YouTube. Also, World oil prices (Brent) have dropped to $89 per barrel. ⁽¹⁾

History: After decades of stable oil prices, beginning in the early 2000's the combination of several drivers have fundamentally changed global oil markets: (1) Ongoing war and political unrest in the Middle East; (2) Devaluation of the U.S. Dollar; (3) A dramatic increase in world oil consumption from developing economies; and possibly, (4) The unprecedented levels of money flowing into oil speculation markets since banking deregulation.

Devaluation of U.S. Dollar: Beginning with President Bush's Administration (in 2002) and continuing under President Obama, a significant devaluation of the U.S. dollar has occurred. With currency devaluation the cost of imports (such as oil) increases. In simplistic terms (all things being equal), ~33% of the increase in the price of gasoline could be explained by currency devaluation. Increased Demand for Oil from Developing Economies: A major driver in higher oil prices is/will be the continuing dramatic increase in oil demand from developing countries such as China and India -- something that no President has any control over. Oil Futures: If you don't understand financial futures/derivatives, don't feel alone. When Warren Buffet was asked about what he thinks of derivatives he responded, "I don't understand them". For most people, an opinion on futures trading will depend on which Tribe they belong to (Democrats believe they should be regulated better, Republicans are anti-regulation). While the impact on oil prices from futures trading may be unclear, one fact is crystal: Their use exploded in the early 2000's after banking de-regulation (where now ~60% to 70% of trading volume is by financial institutions). But the above chart is just the tip of the iceberg, where the vast majority of futures trading is unreported (OTC markets). The problem is the lack of transparency/disclosure -- nobody really knows or can know if excessive speculation is occurring.

Can We "Drill Baby Drill" to $2 Gas?: A central theme in this year's election campaign rhetoric on high gas prices is supposedly simple Econ 101 -- Supply and Demand. Under this argument, if environmental regulations were reasonable allowing more oil drilling, the increased supply would reduce the price of gas to a $2 range. Since the economics are so simple, we should be able to just look at the pump price savings in Canada -- as they produce much more oil than Canadians consume. But Whoops! -- gasoline prices in Canada exactly track those in the U.S.

Conclusion: While there are numerous benefits of increasing domestic oil production (e.g., creation of good jobs, greater energy security, improving the U.S. trade deficit) -- expectations of meaningful reductions in oil and gas prices isn't one of them. Simply stated, gasoline prices will always be driven by the world price of oil (an internationally traded commodity).

Extra Credit: If you want to be an A+ Student on market drivers for oil and gasoline prices, read this article from the Canadian "Oil Patch". The reason oil exploration is currently booming in Canada and the U.S. is because of high prices. For example, extracting oil from tar sands (the source of the Keystone project) can cost up to $40 per barrel. Without high oil prices, tar sands would not be economic compared to Middle Eastern oil (which costs 50� to $2 per barrel to extract).

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Simulation is the best way to allocate trucks to shovels in open-pit mine operations

1. The problem

Efficient open-pit mine operations maintain a steady ore feed to the extraction plant (the �bottleneck� as stated in one of my previous postings in this BLOG). This can be guaranteed by allocating sufficient resources (trucks and shovels) to the appropriate circuits. Since hauling represents 50% or more of the total operating costs, economic penalties are incurred when extra resources are assigned. Therefore, an important operational objective is to feed the plant with minimum resources. 

To do this, a two-stage problem is usually formulated:

1. Allocation: Trucks are assigned to shovels according to performance variables of the shovel, desired production levels, and truck cycle times. Successful truck allocation can have a significant impact on the overall performance of the mine. The allocation process is based on historical information and is performed usually at the beginning of each shift.
2. Dispatching: Is a real-time decision making process that dynamically allocates trucks in response to unexpected events or changes in the planned scheme.

Cycle times play an important role in truck allocation and refers to the sum of: travel time from the dumping point to shovel, waiting time at the shovel, loading time, and travel time from the shovel to the dumping point. Due to the inherent variability of mining operations, cycle times are stochastic in nature. To mitigate the risk of not meeting the ore demand due this variability, mine managers often assign extra trucks to haul ore material. This inefficient approach generates long truck queues throughout the mine. As well, when fewer trucks are available for hauling waste material the overall mine planning is negatively affected.

So, the allocation problem can be stated as: �Given a number of available trucks at the beginning of the shift, how to allocate them to have a steady ore feed to the processing plant and to maximize the waste removal.�

2. The usual solutions

Typically, dispatchers allocate trucks at the beginning of the shifts based on historical data and experience. This heuristic approach is inefficient since it relies on the dispatchers' experience, which varies among shifts. On the other hand, many authors affirm that initial truck allocation can be improved by using mathematical programming. There are two main methods proposed: deterministic and stochastic.

Multiple problems arise when using these approaches:

• It is quite difficult (in my opinion almost impossible) to state an accurate mathematical model to represent mine operations.
• Most of the assumptions made to build these models are unrealistic and tend to oversimplify the reality.
• Truck allocation can only be implemented using a discrete number of trucks, and therefore fractional results are not acceptable. Approximations of these fractional results can make the solution non-optimal or unfeasible. As well, integer programming can make problems computationally intractable.
• Deterministic approaches do not include the inherent variability of mining processes (average cycle time is used to state deterministic problems). Random changes due to variability can make the deterministic optimal solution non-optimal (and in some cases unfeasible!)
• It is not always clear if working with stochastic programming will provide an appreciable benefit as a worthwhile trade-off for its complexity. Stochastic programming typically demands heavy computer resources, particularly if there are many realizations to be evaluated.
• In both cases, there is no assurance that the model will converge to the optimum solution within a reasonable number of iterations.

3. The usual suspects: normal and exponential distributions

Typically, stochastic truck allocation models are formulated based on the following assumptions in regard with the variables (e.g. truckloads, cycle times, loading times):

• are normally distributed (models based on �queuing theory� assume that service times are exponentially distributed),
• have known standard deviation, and
• vary independently from each other.

These assumptions make models simpler to be stated and computationally tractable. Most textbooks and papers present these neat formulations without questioning the validity of the assumptions.

The problem is that these distributions (i.e. normal and exponential) have no relation at all with what happens in the reality! Indeed, the range of a normal distribution is from positive to negative infinity. To my understanding there are not negative times . . . are they?

As well, the assumption that all variables are independent it is not true since there is always a certain degree of correlation among them.

Finally, there is no reason to expect that real-world stochastic processes vary in accordance to some theoretical distribution. Those who are familiarized with distribution fitting know that sometimes a triangular approximation is needed, or a empirical distribution is the best alternative. There is no way to mathematically model these situations without doing the above detailed unrealistic assumptions.

4. Computer Simulation: the right tool to decide truck allocation

Is there a way to decide truck allocation in a more accurate and efficient way? The answer is YES!

Computer Simulation has been widely used to produce experimental data for validating and evaluating different operating policies and dispatching algorithms without interfering in the real mine operations. As well, computer simulation can be used for a careful evaluation of possible combination of shovels and trucks in order to achieve minimum production costs and reduce capital expenditures.

Depending on the completeness of the simulation model, mine operations can be digitally imitated with great accuracy without the need of making unrealistic assumptions. One of the most important aspects of simulation, is the reliability of the results produced! In particular, let suppose that we have a comprehensive simulation model that imitates the real mine operations in a highly accurate way. Let�s assume as well that the model has been properly validated and calibrated and it is ready to use. Can we use this model to find the best truck allocation? Again, the answer is YES.

The simulation model can be used to test different allocation schemes and find the most appropriate one, considering the whole variability inherent to mine operations (e.g. loading times, equipment down time, travel times).

HERE you can find an example of a standardized and highly efficient model that can be adapted to any mid-size mine. This model has a built-in �optimizer� that performs an �intelligent search� among different allocation configurations to find the best one.

In conclusion, truck allocation plays an important role in reducing the operational costs in open-pit mine operations and maintaining a steady ore feed to the extraction plant. Different ways to perform truck allocation can be used ranging from manual allocation (using the dispatcher�s experience) to allocation assisted by complex mathematical programming. Usually these mathematical models are stated based on unrealistic assumptions that prevent them to achieve the right results. Computer simulation can help us in this endeavour by accurately imitating the real operations including all the variability. Finally, the good news is that SmartSimulation has a very comprehensive simulation model that can accurately find the best truck allocation!

Rene Alvarez, IE, MEng
www.SmartSimulation.ca
 

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Current Ethanol Vs. Gas Prices (May 25, 2012)

Ethanol Vs. Gas Price

Tracking gasoline prices versus ethanol prices. The pink line is the commodity price of ethanol (which has less efficiency than gas, E-0). The green line line adjusts for this lower efficiency, allowing an "Apples to Apples" comparison with the commodity RBOB price of gas (the blue line). The red line is the average retail price of gasoline in U.S.

As of May 25, 2012 -- ethanol (adjusted for efficiency) is 8� per gallon higher than gasoline on a wholesale price comparison. However, since most gasoline contains only 10% or less ethanol (E-10), this price differential at the pump is currently eight tenths of a penny (.8�).

Current Retail Gasoline Prices by Region Data Sources:
Per numerous Sources (DOE, EPA), E-10 (10% ethanol) has ~3% less efficiency than E-0 (zero ethanol). Ethanol on a "net basis" has less BTU content, but higher octane.

-- Wholesale Ethanol prices (pink line) are from the Chicago Board of Exchange.
-- Wholesale Gasoline prices (blue line) are from the Chicgo Board of Exchange.
-- Retail Gasoline prices (red line) are from Bloomberg's survey of national gas prices.

-- Real Time Daily Trading Data on energy products.

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Current Ethanol Vs. Gas Prices (May 11, 2012)

Ethanol Vs. Gas Price

Tracking gasoline prices versus ethanol prices. The pink line is commodity price of ethanol (which has less efficiency than gas, E-0). The green line line adjusts for this lower efficiency, allowing an "Apples to Apples" comparison with the commodity RBOB price of gas (the blue line). The red line is the average retail price of gasoline in U.S.

As of May 11, 2012 -- ethanol (adjusted for efficiency) and gasoline are exactly equal ($3.00) on a wholesale price comparison.

Data Sources:
Per numerous Sources (DOE, EPA), E-10 (10% ethanol) has ~3% less efficiency than E-0 (zero ethanol). Ethanol on a "net basis" has less BTU content, but higher octane.

-- Wholesale Ethanol prices (pink line) are from the Chicago Board of Exchange.
-- Wholesale Gasoline prices (blue line) are from the Chicgo Board of Exchange.
-- Retail Gasoline prices (red line) are from Bloomberg's survey of national gas prices.

-- Real Time Daily Trading Data on energy products.

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Current Ethanol Vs. Gas Prices (5/04/12)

Ethanol Vs. Gas Price

Tracking gasoline prices versus ethanol prices. The pink line is commodity price of ethanol (which has less efficiency than gas, E-0). The green line line adjusts for this lower efficiency, allowing an "Apples to Apples" comparison with the commodity RBOB price of gas (the blue line). The red line is the average retail price of gasoline in U.S.

As of May 4, 2012 -- ethanol is about 16� per gallon more expensive than gasoline on a wholesale price comparison.

Data Sources:
Per numerous Sources (DOE, EPA), E-10 (10% ethanol) has ~3% less efficiency than E-0 (zero ethanol). Ethanol on a "net basis" has less BTU content, but higher octane.

-- Wholesale Ethanol prices (pink line) are from the Chicago Board of Exchange.
-- Wholesale Gasoline prices (blue line) are from the Chicgo Board of Exchange.
-- Retail Gasoline prices (red line) are from Bloomberg's survey of national gas prices.

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Current Ethanol Vs. Gas Prices (4/20/2012)

Ethanol Vs. Gas Price

Tracking gasoline prices versus ethanol prices. The pink line is commodity price of ethanol (which has less efficiency than gas, E-0). The green line line adjusts for this lower efficiency, allowing an "Apples to Apples" comparison with the commodity RBOB price of gas (the blue line). The red line is the average retail price of gasoline in U.S.

As of April 20, 2012 -- ethanol is about 8� per gallon cheaper than gasoline on a wholesale price comparison.

Data Sources:
Per numerous Sources (DOE, EPA), E-10 (10% ethanol) has ~3% less efficiency than E-0 (zero ethanol). Ethanol on a "net basis" has less BTU content, but higher octane.

-- Wholesale Ethanol prices (pink line) are from the Chicago Board of Exchange.
-- Wholesale Gasoline prices (blue line) are from the Chicgo Board of Exchange.
-- Retail Gasoline prices (red line) are from Bloomberg's survey of national gas prices.

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Is it possible to accurately estimate production in mining operations?

1. Mines are �Complex Systems�

Mine operations consist of multiple �stochastic dependent processes� interconnected through a wide range of complex relationships. 

Let�s analyze this proposition.

1.1. Mining processes are stochastic in nature

Variability is inherent to mining operations. Each mining process is subject to variability. Examples of this inherent variability are:

• Time to load truck 'A' is not equal to the time to load truck 'B'. 
• Travel time between an extraction point and the crusher, varies for the same truck �A� during the day. 
• Even if trucks �A� and �B� are of the same model type, travel times are different for the same pair origin-destination.
• Big particles generate unplanned stops at the crusher. These stops generate uneven conveyor loading rates.

In the presence of variability delays, slowness, and lags in production appear.

1.2. Mine processes are 'dependent events'

As in many production environments, mine operations are composed by a sequence of interconnected dependent processes. Let�s consider the following sequence: 

Extraction ? Truck loading ? Transportation ? Truck unloading ? crushing

Upstream delays due to variability will negatively affect flow and generate lags downstream. For example, delays in the truck loading process will delay the arrival of loaded trucks to the crusher.

1.3. Complex interrelationships

Operation managers know how difficult mine operations are. Synchronizing the different processes is a complex endeavor. Examples of this complexity are:

• The same truck �A� could serve different pairs origin-destination during the same day
• Crusher operation is highly dependent on the rock fragmentation process
• Unplanned truck downtime affects the dispatch process
• Unplanned crusher stops generate truck lines at the crusher, and could reduce shovel�s utilization upstream

2. Aggregate Variability of the Mine as a �System�

Mine production as a whole, also presents statistical fluctuations so-called 'Aggregated variability'.

2.1 Why we need to know the aggregate variability?

For planning, budgeting, and control purposes, operations managers need to estimate the production volume for a given period of time (e.g. month, year). 

Due to the existence of aggregate variability production volumes could take on multiple values. In other words, it is a random variable with an unknown probability distribution.

Being a random variable, we cannot calculate/estimate production volumes with 100% certainty. A typical approach is to use averages to make this estimation. There are two important problems with this approach: 

• Using averages totally ignores the presence of variability throughout the system, and
• The average is only one of the infinite possible values that this random variable can take on.

2.2. How to calculate the aggregated variability?

Unfortunately, the aggregated variability is not equal to the sum of the variability in each of the dependent processes. As well, it is almost impossible to develop an analytical or mathematical model to represent mine operations. To calculate the aggregate variability, it is necessary to use sophisticated computational tools such as 'Computer Simulation'.

2.3 How Computer simulation works?

Computer simulation consists in developing a computational model of the mine. The model is composed of a set of entities. These entities represent elements of the mine (e.g. trucks, shovels). Each entity has its own characteristics and could be tracked during the simulation. Entities are interconnected through a series of hypotheses about the mine operations expressed as mathematical, logical, and statistical relationships among them.

Processes are represented through probability distributions. Therefore, during the simulation, a single process could take on different values. In order to allow the model to generate these random values, data collection in the field is necessary. Based on the collected data, the modeler fits probability distributions and populates the model with them. In this way, during each run the model replicates the real operations using the same variability observed in the reality.

For example, when simulating the truck loading process, the 'entity truck' is positioned near to the 'entity shovel'. The model generates a random loading time keeping the truck near the shovel for this simulated time. The model also generates a random value to simulate the tons of material to be loaded in the truck. This value will be stored in the 'entity truck'.

The model is capable of recording and storing a large number of data, allowing the modeler to process it when the run is completed. 

The model should be validated and calibrated to accurately represent the reality. Once calibrated and validated, the model can be used to estimate the aggregate variability of the process. To do this it is necessary to run the same scenario multiple times. Modern computers allow the modeler to run long periods of time in seconds. This means that a year can be simulated many times in a few minutes.

Each run will generate one value for the production volume. Collecting these values for a series of runs, allows the modeler to build a probability distribution. Using this distribution, different statistical analyses can be performed (e.g. confidence intervals).

Simulation models can also be used to perform experiments in order to fully understand the system performance and evaluate different operational strategies.

HERE you can find more information about a comprehensive mine simulation model.

3. In Summary

Due to the inherent variability of mining processes, aggregate variability appears making it difficult to estimate the production level for a given period of time.

Averages hide and ignore the presence of variability throughout the system and therefore lead to errors when used to estimate production levels.

So, is it possible to accurately estimate production in mining operations? The answer is yes. The use of computer simulation is the appropriate way to do it.

Rene Alvarez, IE, MEng

www.SmartSimulation.ca

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Where should the 'bottleneck' be located in mine operations? . . . crusher conveyor, shovels, trucks?

What is a bottleneck?

A 'bottleneck' is a stage of the production chain (a sub-process), that constraints the throughput of the whole process. Its capacity is smaller than the capacity of the up-stream and down-stream sub-processes.

Why bottlenecks appear?

When a process has no variability (as in bottling lines) there are no bottlenecks. However, in the presence of variability bottlenecks constitute an inevitable fact. It is the nature of the beast!

Bottlenecks appear every time there is a process composed by Dependent Stochastic Events . . . as in mining!

There is a number of reasons why there are bottlenecks in a production chain:

• Inadequate design/planning 
• Unplanned events that constraint the production 
• The inherent variability of the processes 

Since bottlenecks are a fact of the reality, we have to learn how to deal with them.

Are bottlenecks a bad thing?

We use to think that bottlenecks are a bad thing. Often the word 'bottleneck' has a negative connotation in our minds. However, in production systems the 'bottleneck' plays an invaluable dual role.

1. it sets the production pace, and 
2. it allows the manager to program the operations by subordinating everything to the bottleneck. 

In summary, bottlenecks are NOT a problem. Bottlenecks are a FACT of the reality!

Should we eliminate the bottlenecks?

Mining operations are not bottling production processes. Inherent variability in each sub-process (e.g. loading, unloading, transportation) is present all the time.

When trying to achieve the goal of 'no bottlenecks', what happens is that 'floating bottlenecks' appear. Managing moving/floating bottlenecks in mining is a nightmare!

A simple example will help us to understand this: let's simulate a process line composed by four sub-processes. Processing times in each sub-process is simulated using dice. Since the variability in this case is big (1 to 6), a bottleneck will appear in the line. Since capacity and variability are equal for all the 4 stations (1 die), you will see that the bottleneck moves from one sub-process to other over time: 'floating bottlenecks'.

This proves that trying to 'balance the capacity' in en each of the sub-processes to 'eliminate the bottlenecks' is futile. The reason: variability.

The good news is that mining operations are easier to manage when having a bottleneck. Indeed, the manager can synchronize the operations by planning for the 'bottleneck' and subordinating the rest of the processes to it.

In summary, the bottleneck CONTROLS the system's flow.

Where to locate the bottleneck?

I think that the location of the bottleneck is a strategic decision.

For example, if the decision is to have the truck fleet as the bottleneck, it will become very difficult to synchronize the operations to achieve a smooth production.

Some argue that the bottleneck should be located at the operational point where the capital cost to rectify that bottleneck becomes uneconomic. Most mines will got through a series of operational modifications to increase the bottleneck capacity until such a point is reached.

An expert told me once: "The only commanding process at the mine should be the processing plant capacity. Everything else should be driven by that capacity" . . . and I totally agree!


I often suggest managers should locate the bottleneck in the crusher at the entrance of the processing plant, and program the rest of the sub-processes in a way that the crusher is fed as smoothly, uniformly, and continuously, as possible.

The reason is simple: since there are always other operations inside the mine which are different from the ore feed (e.g. site preparation, stock), you will always have extra resources when needed (e.g. trucks).

Should all processes in the mine be busy all the time?

Having all resources busy at all time should not be the goal. The goal should be having the bottleneck busy all time! 

Since by definition all other resources/sub-processes in the mine have more capacity than the bottleneck, if you keep them busy all the time, no synchronicity will be achieve and lines/overstock will appear all over the place.

How to administrate the bottleneck?

Managers should put emphasis in augmenting the capacity of the bottleneck, and reduce its variability. To reduce the variability Six-sigma & TOC tools work at its best!

Remember what Dr. Eli Goldratt said: "an hour lost on a bottleneck is an hour lost to the whole plant". 

On the other hand, if you increase the capacity of a resource up-stream from the bottleneck, you will get over-stock. 

Finally, if you increase the capacity of a sub-process downstream from the bottleneck, nothing will happen!

A VP of Operations complained that "we doubled the transport capacity, but we are loading dirt onto the trucks". Well, in his case the transport capacity was not the bottleneck . . .

In summary:

1. Decide where to locate the 'bottleneck' 
2. Program the operations in such way that the 'bottleneck' is working smoothly at maximum capacity 
3. Take measures (using continuous improvement techniques) to reduce variability and increase capacity in the 'bottleneck' 

How can I plan the production in the presence of bottlenecks?

Planning the system's flow is simpler if you 'plan the flow at the bottleneck' and make the whole system to be synchronized with it!

Eli Goldratt describes this process as 'Drum-Buffer-Rope'. I can summarize it as follows: Let the bottleneck set the pace of the production and let the rest of the sub-processes work full capacity ONLY when it is required by the bottleneck. The rest of the time they should be ready to produce but idle in order not to waste resources and generate overstock. This is know as the road-runner rule.

Can Computer Simulation aid in planning the operations?

Totally! SmartSimulation has developed a computer simulation program to help managers in planning mine operations and test different scenarios. Computer simulation becomes a powerful tool to analyse different production scenarios, and to decide where to locate the bottleneck.

More information can be found HERE.

Rene Alvarez, IE, MEng

www.SmartSimulation.ca

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