Product category:
Process Monitoring and Optimisation
News Release from: MatrikonOPC | Subject: Rio Tinto Hunter Valley
Edited by the Processingtalk Editorial
Team on 17 November 2006
The Rio Tinto Hunter Valley coal
processing plant
Rio Tinto has improved coal yield and reduced water use in their Hunter Valley Coal Prep Plant by implementing radical control strategies on antiquated controllers!
Implementing advanced control strategies without the risk of extended plant downtime Rio Tinto has improved coal yield and reduced water use in their Hunter Valley Coal Prep Plant by implementing radical control strategies on antiquated controllers! Rio Tinto's Hunter Valley Coal Preparation Plant (HVCPP) processes 15 million tons per year of coal
This article was originally published on Processingtalk on 9 Jul 2008 at 8.00am (UK)
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Such high throughput means that there is potential for significant economic and environmental gains by optimising the major control loops and reducing variation.
However the high throughput is a double-edged sword.
It also means that any downtime required to make such plant modifications will incur significant costs in lost production.
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Rio Tinto partnered with Matrikon to establish a new approach to plant optimisation that had two major advantages over conventional approaches:.
* The control strategies could be proven to work before they were programmed into the plant hardware, removing the risk of extended plant downtime.
* The control strategies could be implemented substantially faster than would be possible using conventional techniques, even on hardware which is outdated and difficult to programme.
This promised to keep disruptions to an absolute minimum.
IMPROVING PLANT PERFORMANCE.
HVCPP was already meeting industry standard performance metrics, but in the spirit of continuous improvement, Rio Tinto Planning and Improvement Superintendent Luke Dimech identified three control systems with room for improvement: dense-medium control, thickener control, and receiver throughput control.
In January 2006, Luke engaged Matrikon to help optimise and tune these three control systems.
After investigating the control system performance, it was soon realized that the best results would come from some radical changes to the control algorithms, as there was little room for improvement using standard PID algorithms on these material recovery processes.
Their highly complex dynamics and hard-to-understand interactions, due to their recycle loops and non-linear processing equipment meant that more sophisticated advanced control algorithms were called for.
IMPLEMENTING ADVANCED CONTROL ON AN AGEING PLANT.
Most existing coal prep plants, including this one, use outdated and rudimentary PLC controls that don't provide any control toolboxes more sophisticated than a standard PID loop.
It is time-consuming, difficult and downright risky to try and trial advanced control strategies using the existing controllers with their limited programming interface.
Unless it could be proven that such radical changes to the controllers algorithms would work, and work first time, there was little hope of getting senior management approval for modifying the process.
With this in mind, Matrikon and Rio Tinto came up with a prototyping method that would ensure 100-per-cent confidence in any new control strategy before modifications were made to the existing controllers code.
This method also allowed implementation with virtually no plant downtime.
"The technique we used for prototyping, then implementing the plant modifications removed almost all the risk that this kind of work normally entails.
Without the novel Matrikon approach we would have had to think twice about whether to go ahead at all with these modifications to our critical plant", said Luke Dimech of Rio Tinto.
This approach was used on all three control systems, but this paper will focus on the dense-medium control system only.
Coal is readied for processing, or beneficiated, through treatment with a dense liquid medium.
Careful control of the density of this medium is important, as constant density is a pre-requisite for the efficient separation of reject material from coal.
The more efficiently the reject material is separated from the coal, the better the coal quality and yield.
The dense-medium control is typically done by two control loops, controlling two splitter gates and one water valve.
In this strategy, the density is controlled (controller: DC) by manipulating the over dense splitter gate.
To increase density, more over-dense medium is diverted to the dense medium circuit.
The level of the dense medium circuit sump (controller: LC) is controlled by manipulating both the water flow and the dense medium splitter gate position.
Mode 1: Filling the sump.
If the level is too low, then the dense medium is diverted to the sump to increase the level with extra water flow.
Mode 2: Emptying the sump.
If the sump level is too high then the water flow is reduced to an operator-set minimum flow and the sump level is reduced by diverting flow away from the sump with the dense medium splitter gate.
The problem with this strategy is that as the sump level moves above and below the setpoint, the control system continuously oscillates between the two control modes.
Because the mode of the level control also has a significant effect on the density of the dense medium, the density oscillates in tandem with the level control action.
Decoupling the level control from the density control can remove the unwanted oscillations.
One way to do this is to exclusively control the level of the sump with the dense-medium splitter gate.
This leaves the water valve and the over-dense splitter gate to control the density.
This can be done by splitting the density controller output and biasing each output to reflect their true contribution to the density.
If these biases are correct, when over-dense medium is directed to the sump to increase density, water addition will decrease accordingly to offset the volume impact of the over-dense medium.
This means density control won't affect sump level control.
Experience also indicated that optimum cleaning of the medium occurred when the gate was 30 per cent open.
Using this knowledge, the dense-medium gate position signal was added as another input to the density-control algorithm.
This new input is used to bias the flow into the dense-medium circuit so the system is always biased toward working with the dense-medium gate in the position that gives optimum performance.
This new strategy sounded great in theory, but a lot of extra biases and tweaks were introduced that needed to be adjusted before the strategy could be commissioned.
Implementing a complex decoupling algorithm like this is fraught with risk and the prospect of costly plant downtime, especially when the existing control hardware is antiquated and unwieldy.
The Matrikon solution was to implement the strategy using ProcessACT, a software prototyping package, and its join-the-dots graphical interface.
This allowed us to quickly create and debug an advanced control strategy offline using advanced control toolboxes.
Instead of translating the strategy into a proprietary programming language, we simply copied the block diagram, dropping prewritten blocks onto the screen and joining them together with arrows as appropriate.
We could even simulate the control system offline by dropping in simulation blocks, further increasing our confidence in the strategy.
Once we were happy with the soft-controller that we had programmed on our laptop, the Matrikon OPC connectivity allowed us to hijack the PLC I/O using nothing more than an Ethernet cable and 10 minutes of configuration.
Once we had control of the I/O, we had control of the plant from our laptop, safe in the knowledge that if anything unexpected happened we could flick the software switch and put the old PLC controllers back in control.
Connected to the plant, the ProcessACT modern user interface allowed us to quickly fine-tune and prove the new strategy on the actual plant.
With the proven control strategy having eliminated the risk from our endeavour, it was time to tackle the task that no one wanted to do: implement it on the old-school PLCs.
This was a painful experience; the programmer still wakes at night with a cold sweat when the interface invades his dreams.
But when you know what your tuning constants and biases are and you are 100-per-cent confident that your control strategy definitely works on this specific plant, the task is much more manageable.
The density now being produced with the new control algorithm, as programmed into the existing controllers, shows a reduced variation, which means Rio Tinto is now separating ash from coal more efficiently, giving a direct improvement to the bottom line.
Control system prototyping software with powerful advanced-control tools, easy-to-use interfaces, and connectivity options that can quickly hijack the I/O from a PLC, means that advanced control is no longer confined to new installations with state-of-the-art hardware, as was proved at Hunter Valley.
The approach of quickly proving advanced control strategies on the actual plant the strategies will be controlling, and then implementing those pre-tuned and fully commissioned strategies directly to a less friendly control platform, minimised risk for all involved.
This new approach to process optimisation helped Rio Tinto to make many operational improvements, including improving its coal yield and reducing water consumption in a drought-prone area.
This could all be done safe in the knowledge they weren't risking any unplanned plant downtime. Request a free brochure from MatrikonOPC ...
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