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MINING, METALLURGY,

AND EXPLORATION, INC. 95-19 P.O. BOX 625002 -LITTLETON, COLORADO - 80162-5002

INSTALLATION OF A SUPERVISORY CONTROL SYSTEM AT A GOLD PRESSURE OXIDATION PLANT

M. Spangler

FirstMiss Gold Golconda, NV

For presentation at the SME Annual Meeting Denver, Colorado - March 6-9, 1995

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Abstract FirstMiss Gold installed a supervisory control system

based on Pavilion Technology's Process Insights neural network software and Oil System's PI data historian. The primary goal of the system was to control the acid precon­ditioning circuit. However, our first successful control loop was on the limestone feed rate to the neutralization circuit. The information gained by modeling these circuits and the better control provided by the system has resulted in reagent savings of over $80,000 per month.

OVERVIEW

Getchell Mine

The Getchell Mine, northeast of Winnemucca, NY, was discovered in the 1930s and was initially mined to recover gold from oxide ores. From the 1940s until 1968, when it was shut down, a variety of horizontal and fluid bed roast­ers were used to recover gold from the sulfide ores. In all cases, roasting proved to be difficult, marginally profitable, and environmentally unsound.

The major problem is the nature of the ore. The main ore body is a skarn deposit along the Getchell fault on the east side of the Osgood mountains, a standard Basin and Range block-fault system. The fault provided a conduit for gold-bearing solutions at several different times during the development of the system. Along with gold, silver, mer­cury, iron, molybdenum, thallium, selenium, and antimony came a large quantity of arsenic minerals, primarily realgar (AS2S2) and orpiment (AS2S3)' The latter minerals are the major problem that must be solved to recover the gold. A second problem is the variability of a skarn deposit. Since the mineral-bearing solutions were confined along the fault, the ore body experienced a high thermal and chemical gradient, resulting in ore compositions that can change greatly in a few hundred feet.

In 1989, FirstMiss Gold, a subsidiary of First Mississippi Corporation, began operation of a pressure oxi­dation plant. This technology was chosen as the best avail­able to deal with high-arsenic ores.

Plant Flow sheet

The plant flowsheet has a conventional grinding circuit feeding an acid preconditioning circuit that destroys the carbonate minerals in the ore before feeding three parallel autoclaves. These hold the ore at 210 C (400 F) and 2.8 MPa (410 psi) for 90 minutes while oxygen from a 290 tid (320 stpd) Air Products cryogenic plant reacts with the sulfide minerals. The reaction converts the sulfides into sulfuric acid, ferric iron, and ortho-arsenic acid (H3AS04).

The neutralization circuit is next. Here a staged addition of limestone and milk of lime raises the pH, precipitating the arsenic as a stable blend of ferric and calcium arsenates. The higher pH is also required before cyanidation in a conventional Carbon in Leach (CIL) circuit. Following a standard Zadra strip, the gold is cemented from the pregnant solution with zinc dust. The resulting precipitate is retorted to remove mercury, then fluxed and smelted to oxidize the impurities, produce gold donS. The plant operates at a feed rate of 3000 tid (3300 stpd) and achieves an average recovery of 89% with a head grade of 7g/tonne (0.20 ozlton).

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Supervisory Control System

The supervisory control system [Figure 1] consists of three parts. The PI System from Oil Systems, Inc. acts as the data historian, collecting data from the Bailey Network 90 DCS via its own Computer Interface Unit. Process Insights generates the neural network models, and the Run­time Controller runs current plant data from PI through the model and sends control signals back to PI, which sends them back to the DCS. All three of these software modules run on a Vaxstation 4000, model 60. The workstation is equipped with 40 MB of RAM and two hard drives totaling 1.4 GB of storage.

Bailey

Network 90

DCS

Process Insights *

ontrol C M odel

Run-time Controller *

Plant Bailey Loop

CIU

RS-232

Training

Dataset PI

System *

Control data and outputs

Figure 1; Control system layout. (* This block is software running on a VAX.)

Besides acting as a data historian transform module (via the performance equation package) for the neural network software, we have set up a terminal in the autoclave control room for the operator's use. This has proven very popular, as the operators can build their own customized trend and graphic displays. PI has also made it possible for us to review plant operations in the past, as the system has two years of data on-line in its archive instead of the 26 hours held by our elderly Bailey.

Process Insights, the neural networking software from Pavilion Technologies, actually does the modeling. It can do either a prediction model where it predicts the outputs based on the current inputs, or a control model that can vary the inputs to hold an output at a given value (the mode we use). The software also has a full suite of analysis tools to identify the important variables in the process, the effect of changing them, and to clean up the input data. As this software is very processor-intensive, we recommend that new installations should use DEC ALPHA workstations instead of the older VAX systems.

APPLICATION TO NEUTRALIZATION CIRCUIT

Purpose of Circuit

The autoclaves discharge slurry at 38% solids and a pH of 0.5 to 1 with 15 to 45 grams sulfuric acid per liter. How­ever, safe cyanidation requires a pH of over 9.5, preferably about 10.5. The neutralization circuit, [Figure 2] which

consists of four, 570 m3 (150,000 gallon) agitated tanks in series, uses ground limestone to raise the pH from 1 or less to about 5.7 in the first tank. After allowing the reaction to stabilize in the second tank, we then add milk of lime to the third tank to bring the pH to 10.3. Cyanide is added in the fourth tank, and the resulting slurry is pumped to the CIL tanks where the gold dissolves. We can also add water to either the third or fourth tanks if needed to lower the slurry density or viscosity to acceptable values.

Discharge from

Autoclaves

pH= 1

1

Milk of Lime

2 3 4

Neutralization Tanks

pH = 10.3

To elL

Figure 2; Limestone and Neutralization Circuits.

Control Problems

The control problems associated with this seemingly simple circuit fall into two main categories. First, the rea­gents, (limestone and lime) are much different in control effectiveness and reaction rate, and second, nothing about the circuit behaves in a linear fashion.

Cheap limestone, expensive lime: Lime is trucked in dry, slaked on site, then pumped into the neutralization circuit. It costs us $75 !tonne ($68 !ton). Limestone is mined on site about one krn (0.6 mi.) from the mill and ground in two ball mills in the grinding building. Counting grinding costs, limestone costs $4.10/tonne ($3.70/ton). Lab work has established that 1.7 kg of limestone is equivalent to 1 kg of lime in raising pH from 1 to 5.5. The overall effect is that $1 in limestone is worth $10.80 of lime. However, there is a complication.

Nonlinear response: The pH rise from adding limestone [Figure 3] is subject to diminishing returns above a pH of about 5.0. To raise pH to 6.0 requires much more limestone than raising the pH from 5 to 5.5. Raising pH to 6.5 re­quires still more. No amount of limestone will raise pH over 7.0. These effects are due to the precipitation of a variety of metals and the ion exchanging behavior of clays found in the ore.

We also have other, more conventional, control prob­lems. The amount of free acid in the autoclave discharge can vary from 15 to 45 gr.!l, depending on the sulfide content of the feed and, paradoxically, the amount of carbonate in the ore. The temperature in the circuit ranges from 30 to 60C (85 to 140°F) depending on ambient temperature and the condition of the slurry coolers upstream of the neutralization circuit. The throughput of an autoclave varies as a function of its mechanical condition, the ore's sulfide content, and oxygen availability. And autoclaves are shut down for maintenance at least once a week.

2

16 14

"0 12 ~ ·s 10 0-~ 8 1: 6 0)

'05 4 $ 2

0

0

• -----------------------~-------------. • ---------------------.~--------------. .' L'"

::::::::::::::~~.~~~:::~:::::::::::~: ------------

2 4 6 8 10

Slurry pH attained

---O-Lime --.-- Limestone

Figure 3; Relative effectiveness of lime and limestone at raising neutralization pH.

Desired Operation: Overall, our goal is to raise the pH to 10.3 at the minimum cost. This means that we add lime­stone until we reach the point of diminishing returns, then add lime to bring the pH up the rest of the way. Operating experience seemed to show that a pH of 5.5 was the correct setpoint for the first tank, and the operator manually con­trolled to this value by calling the grinding operator and telling him to adjust the speed of the limestone mill's belt feeder up or down depending on where the pH was. This control system resulted in frequent out of specification pH values, rapid swings in pH, and was strictly reactive except in the case of planned shutdowns.

Conventional control systems were unable to improve on the operators. The PID controller block in the Bailey DCS was unable to handle changes that occurred so slowly and with so much lag time. Since so much of the circuit was nonlinear, it was impossible to program a suitable controller by other means that we tried. Therefore, it seemed a natural thing to try with a neural network system.

Process InSights Model

Inputs tried: Based on experience and common sense, we selected a set of variables that seemed likely to influence the operation of the circuit.

Several weeks of data for these inputs was collected in PI and transferred to Process Insights. After cleaning up the data in the preprocessor, several models were run to identify the process delay times. These matched well with our expectations. At this point, several transforms looked useful, (i.e. The model insisted that the free acid content of one autoclave was 5 times more Significant than the free acid of another unit, so we created a transform of "average free acid" to use in subsequent models.) These new inputs were then fed into the next run of the modeL

Inputs and outputs used: After several runs, we chose the following inputs [Table 1] for the control model. Since our version of Process Insights does not allow the easy use of transforms, we recreated the transforms in the PI perfor­mance equation module and transferred them into tags that the run-time controller could read. The value listed under tau is the time lag, given as the number of 10 minute inter­vals before the limestone enters the neutralization tanks. Tag WI:2825.A V is treated as a state variable. The outputs

were the pHs in the first two neutralization tanks. The con­trolled input was the limestone belt feeder speed.

Sensitivity analysis: Part of the attraction of Process Insights is the sensitivity analysis. This has allowed us to evaluate circuits with an eye to finding bottlenecks. Unfortunately, the operating requirements of other circuits restricts our ability to adjust the parameters of this ciruit. The sensitivity analysis was more interesting in the conditioning circuit, which is discussed later.

Table 1; Final Limestone Model

Tag Name

Input Variables WC:2825 LS:Geho.ca LS:FAave.ca

AC:ddens.ca

TI:7010 TI:7056

LA:ls150

LA:lsc02

State variable

Tau

-4 -2 o

o

o o

o

o

WI:2825.AV -3

Outputs AR:7011 AR:7021

Results

o o

Description

Speed of belt feeder to ball mills. Sum of autoclave feed rates. Average free acid content of auto-

clave discharge Average discharge density (% solids)

out of autoclaves Temperature of feed to neutralization Temperature of feed out of

neutralization Percent of limestone passing 150

mesh Carbonate content of limestone

Belt scale to ball mills; 10 pt moving average.

pH in #1 neutralization tank pH in #2 neutralization tank

We used more limestone and less lime: The run-time controller had interesting effects. As you can see, [Figure 4] the model used somewhat more limestone than had been the case previously. However, it used substantially less lime. This tells us that we were not as far up the diminish­ing returns curve as we should have been. The net result was a $31,000 per month reduction in total reagent costs. These alone would have paid for the entire supervisory control system in 6 months.

Q)

c 0 400 50 -U)

350 Q)

Q) 45 E E 300 ::i

..J 250 40

200 35 ~ c

150 -0 30 en - i p,,,,,,,,, I Mig h" ..... 100 .Q U)

goes on line 25 ..J .Q 50 ..J

0 20

5/93 7/93 9/93 11/93 1/94 3/94

---I-- Limestone --0- Lime

Figure 4; Limestone and Lime usage.

Other benefits: Also to our benefit was more consistent operation. [Table 2] There have been fewer pH excursions since the system came on line, reflected in a smaller range between the minimum and maximum pH values, and a

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smaller standard deviation. These improvements not only included the first tank where the limestone is added, but also the third tank where the lime is added, even though Process Insights does not directly control the lime addition. Smoothing out the preceding stage has allowed the conventional PID control from the DCS to control the lime addition quite well, whereas it had previously been unable to maintain acceptable pH values.

Table 2; Comparison of Control of Neutralization Tank #1

General Statistics

Mean Standard Deviation Variance Range Minimum Maximum Standard Error Median Mode Kurtosis Skewness

Operators

4.79 0.764 0.584

3.9 1.6 5.5

0.083 5.0 5.2

8.106 -2.879

Proc. Insights

5.38 0.407 0.166

2.3 3.7

6.01 0.044

5.5 5.8

2.282 -1.205

ANOVA results. F Statistic = 39.21 F crit = 3.90 P-value = 3.14 X 10-9

Remaining Problems

Financial optimization: The next step, which we hope is addressed in the next revision of Process Insights is to actually optimize our costs. Our goal is to raise pH from 1 to 10.3 at the minimum cost. Given the nonlinear behavior of our circuit we have not established that we are at the optimum point. The current version of Process Insights does not have a way of weighting inputs to accomplish this. Furthermore, it does not output a control equation that could be fed into a separate nonlinear programming routine to solve for this optimum point. This feature is planned for a future release of the software.

Oscillation problem: The model did tend to oscillate from time to time, where from one cycle of the run-time con­troller to another the output will alternately overshoot and undershoot even dUling steady-state operation. Part of this problem was due to a problem with the run-time controller, which has since been mostly fixed. We still have occasional periods of oscillation, but they are usually related to plant disturbances and quickly damp out.

Have to take the model off-line to retrain: One unforeseen problem is that we have to take the model off-line for two weeks if we decide that we need to train a new model. While the model is running there is too little variation in the circuit to produce a data set suitable for training a model.

Hard to predict how well a given model will control: Contrary to our expectations, a model that trains well does not necessarily control well. Our best models have consis­tently had r-values from 0.6 to 0.8. Models with correla­tions of 0.9 or higher have generally failed to control at all. This was not what we expected, although Pavilion assured us that it was not an unusual occurrence. However, as we have not found any way other than on-line testing to see if we have a good model, this does add to the time required to set up a working control model.

APPLICATION TO CONDITIONING CIRCUIT

Purpose of Circuit

This circuit acidifies the slurry to destroy carbonates before pumping it into the autoclaves. The circuit consists of a 570 M3 (150,000 gal) surge tank followed by 3, 265 M 3 (70,000 gal) tanks in series. Sulfuric acid (93%) is added to the fIrst conditioning tank. This acid is usually our largest single reagent cost. Process Insights was originally purchased expressly to control this circuit after an initial study showed that substantial cost savings were possible.

Control Problems

Although we have been able to train a model for any given ore stockpile, this requires about two weeks. As a given stockpile lasts no more than four weeks, and it is not uncommon to start blending in other stockpiles during this time, we have been unable to develop a useful control model that will work reliably across stockpiles. Part of the problem has been the changing clay contents of the ore. The k-feldspar clays freely absorb acid. Also, we have recently discovered that we have organic carbon present in the ore which causes inaccurate carbonate assays. As these contaminants strongly vary throughout the ore zone, we have not been able to find a cross-stockpile model.

Since we have now proved that the lab results that we were using to try to run the plant were random variables, we are investing in an oxygen analyzer that will sample the autoclave vent gas. The vent gas is a mixture of oxygen and carbon dioxide, therefore we can derive the amount of carbonates in the autoclave feed from the oxygen content of the vent gas. We hope that this will give us a better value to use in a control scheme, as we can then control conditioning to give a desired carbon dioxide content in the autoclave's vapor space.

Further complicating our efforts is that Process Insights expects to control an output to a fixed value. However, the desired value for our major control variable, autoclave feed pH, varies with the ore characteristics. On one ore, the autoclaves may run fine with a feed pH of 5.8. On the next stockpile, the feed pH may have to be 4.8 to maintain the same throughput and oxidation. This does not lend itself to easy control.

Results Using Process Insights

Even though a control model has eluded us, we have been able to make some money from Process Insights. Sensitivity analyses of the various models consistently showed that the feed pH was highly sensitive to the tem­peratures of the conditioning tanks [TI:3016, TI:3026, and TI:3036 in Table 3]. This is an indirect mechanism, as higher temperatures allowed a higher autoclave feed pH without having excess C02 quench the reaction in the autoclaves. This led to a plant-scale test using boiler steam to heat the slurry in the surge tank before it entered the conditioning tanks. The results showed a substantial drop in acid consumption. Therefore we spent about $40,000 to route waste steam from our flash tanks to the surge tank, raising the average temperature from 38C to 55C (lOO°F to 130°F.) This has reduced acid use about 20%, resulting in a savings of $54,000 per month. Figure 5 shows the ratio of actual acid use to the usage expected based on plant history.

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Table 3; Conditioning sensitivity results.

rank# input name tau# Avg.

1 AI:3012. -1 0.489 2 TI:3016. 0 0.314 3 CD:D_T1.CA 0 -0.306 4 TI:3026. 0 0.283 5 FR:3007. -3 -0.169 6 U:3038. 0 -0.082 7 U:2748. 0 -0.071 8 AC:3001. -2 0.041 9 CD:D_ST.CA 0 -0.069 10 02:DEMAND 0 -0.048 11 FR:3007. 0 -0.056 12 DI:2664. -3 -0.054 13 TI:3036. 0 0.038 14 AC:P _AVE.CA 0 0.043 15 FC:2665. -3 0.040 16 LS:GEHO.CA 0 -0.023

1.20

1.10

1.00

0 0.90 .... 1\1 0.80 a:

0.70

~ .~. / \ \/ "\ . • •

.... 1"'"'"'"., . ,~ .\1

V 0.60

First full month with steam on. 0.50

9/93 11/93 1/94 3/94 5/94 7/94

Figure 5; Results of Adding Steam to Conditioning.

FUTURE AUTOCLAVE MODELING

We are presently working on modeling some of the details of autoclave operation. Our goal is to ultimately use the system to control the feed rate and oxygen supply to an autoclave so as to maintain the maximum feed rate without exceeding either the oxygen supply or having EMF go out of spec. Our testing has shown that we will have to model each autoclave separately, as running all three in one model just doesn't work. This, in turn, means that we need external logic to juggle three models competing for the same resources. We are looking into the ExSys rule-based AI system as the code to do this.

SUMMARY

Although not entirely without problems, Process Insights has been successful in controlling the limestone circuit. In the first year of operation it has been directly responsible for over $85,000 per month in cost savings. The system has also highlighted other operational problems that we are now able to address which will further improve operations and profitability in the near future.