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Applied and Environmental Microbiology, November 1998, p. 4555-4565, Vol. 64, No. 11
0099-2240/98/$04.00+0
Copyright © 1998, American Society for Microbiology. All rights reserved.
Use of an Intelligent Control System To Evaluate Multiparametric
Effects on Iron Oxidation by Thermophilic Bacteria
Daphne L.
Stoner,1,*
Karen S.
Miller,1
Dee Jay
Fife,1
Eric D.
Larsen,2
Charles R.
Tolle,2 and
John A.
Johnson2
Biotechnologies Group1
and
Materials Physics Group,2 Idaho
National Engineering and Environmental Laboratory, Lockheed Martin
Idaho Technologies Co., Idaho Falls, Idaho 83415-2203
Received 19 September 1997/Accepted 19 August 1998
 |
ABSTRACT |
A learning-based intelligent control system, the BioExpert, was
developed and applied to the evaluation of multiparametric effects on
iron oxidation by enrichment cultures of moderately thermophilic,
acidophilic mining bacteria. The control system acquired and analyzed
the data and then selected and maintained the sets of conditions that
were evaluated. Through multiple iterations, the BioExpert selected
sets of conditions that resulted in improved iron oxidation rates. The
results obtained with the BioExpert suggested that temperature and pH
were coupled, or interactive, parameters. Elevated temperatures
(51.5°C) in combination with a moderately high pH (pH 1.84) impaired
the growth of and iron oxidation by the enrichment culture.
Moderate-to-high oxidation rates were achieved with a relatively high
pH in combination with a relatively low temperature or, conversely,
with a relatively low pH in combination with a relatively high
temperature. The interactive effect of pH and temperature was not
apparent from the results obtained in an experiment in which
temperature was the only parameter that was varied. When the BioExpert
was applied to a mixed culture containing mesophilic and thermophilic
bacteria, the computer "learned" that pH 1.8, 45°C, and an inlet
iron concentration from 30 to 35 mM were most favorable for iron
oxidation. In conclusion, this study demonstrated that the
learning-based intelligent control system BioExpert was an effective
experimental tool that can be used to examine multiparametric effects
on the growth and metabolic activity of mining bacteria.
 |
INTRODUCTION |
Biological leaching is proving to be
an economically viable approach for the recovery of metals from
low-grade pyritic ores. Mining bioprocesses need to be developed
and evaluated under conditions that more closely represent the
conditions encountered in the real world. Mining bioprocesses are
complex, changing systems with physical and chemical
characteristics and microbial communities that have not been fully
described. Mixed cultures of indigenous iron- and
sulfur-oxidizing acidophilic bacteria mediate the oxidation of pyrite, with the concomitant liberation of metals from the ore.
During biological ore oxidation, the microbial community can change,
the pH of the environment can increase or decrease, temperature
generally increases, dissolved O2 and CO2
concentrations decrease, and the concentration of metals in the
lixivium increases (4, 5, 8, 11, 12, 23, 25, 31).
Due to the elevated temperatures (50 to 60°C and higher) that can be
achieved during biological heap-leaching operations, moderately
thermophilic bacteria can extend the operating temperature range and
improve oxidation efficiency in the heaps (7, 10, 17, 19,
28). Moderately thermophilic bacteria have been isolated from
acidic coal dumps, ore deposits, mining operations, and hot springs
(9, 13, 20, 29, 38, 40). They vary in their abilities to
oxidize iron, sulfur, and pyrite as well as in their abilities to grow
autotrophically or heterotrophically (13, 16, 19, 21, 39).
Temperature, pH, metal concentration, O2 and
CO2 levels, and pulp density are known to affect growth and
mineral oxidation by acidophilic bacteria (16, 19, 22, 26, 29, 30,
39). However, in a mining environment in which any number of
physical and chemical parameters are changing, the extent to which
these parameters interact and impact iron oxidation by moderately
thermophilic bacteria is unknown.
The conventional approach to characterizing the effects of
environmental conditions on microbial activity is to vary one parameter at a time while holding all other conditions constant. Many of these
experiments assume that parameter effects are decoupled or independent
of each other. Experiments that vary one parameter, such as pH,
temperature, or metal concentration, at a time can provide a
considerable amount of data. However, these types of experiments may
not be appropriate for evaluating the metabolic response of
microorganisms to a real-world environment in which, to continue the
example, pH, temperature, and metal concentration are simultaneously
changing. An experimental plan which simultaneously varies more than
one parameter is required to better understand the response of bacteria
to the changing physical and chemical conditions that may be
encountered within a mining environment.
Intelligent control technologies can be designed to handle the
experimental complexities that are associated with examining multiparametric effects on growth and metabolism.
Learning-based intelligent systems require minimal information
prior to implementation. Thus, learning-based systems are the best
technology for characterizing unknown microorganisms. This report
demonstrates the use of a learning-based control system, BioExpert, to
evaluate the combined effects of pH, temperature, and iron
concentration on the oxidation of iron by moderately thermophilic
acidophilic mining bacteria. The BioExpert acquired and analyzed the
data and then automatically selected and maintained the sets of
conditions that were subsequently evaluated. Because multiple
parameters were varied simultaneously by the BioExpert, it was
possible to detect the interactive effects of temperature and pH. These
interactive effects were not apparent from the results obtained from an
experiment in which temperature, but not pH, was varied.
 |
MATERIALS AND METHODS |
Cultures.
Two thermophilic enrichment cultures were provided
by James Brierley (Newmont Technical Services, Englewood, Colo.). These cultures were derived from a gold-leaching operation, and, while they
were handled with aseptic techniques, there was no attempt to obtain
"pure" cultures. The first Newmont culture was used for an initial
characterization study that examined the effects of flow rate and then
temperature on iron oxidation and growth. The second Newmont culture
was used to examine the combined effects of pH, temperature, and inlet
iron concentration. Thiobacillus ferrooxidans, ATCC 23270, was used with the moderately thermophilic Newmont culture for the
mixed-culture experiment. All cultures were grown in an acidic (pH 1.8)
medium containing, per liter, 0.4 g of
(NH4)2SO4, 0.25 g of
MgSO4 · 7H2O, 0.04 g of
K2HPO4, and 0.2 g of yeast extract, as
well as 50 mM FeSO4. The FeSO4 was added as a
filter-sterilized solution after autoclaving the medium. Potential
growth substrates of the moderately thermophilic enrichment culture
were tested in medium containing various combinations of yeast extract
(0.02%), iron (50 mM), tetrathionate (0.32%), and glycerol (0.1%)
(Table 2). Relative growth was assessed by wet-mount light microscopy.
System hardware.
A 2-liter chemostat, with a working volume
of approximately 1,360 ml (BioFlow I; New Brunswick Scientific Co.,
Inc., Edison, N.J.) was equipped with a stirrer, five liquid feeds, and
a heater (Fig. 1) and was modified to
accept remote signals from the computer control system for heating and
stirrer speed. On-line sensors fitted into the stainless steel
headplate of the chemostat measured temperature (Cole-Parmer, Vernon
Hills, Ill.), pH (Ingold Electrodes, Inc., Wilmington, Mass.),
oxidation-reduction potential (Eh) (Ingold Electrodes,
Inc.), and dissolved oxygen (Ingold Electrodes, Inc.). The dissolved
oxygen and pH probes were interfaced to the computer with their
respective transmitters (model 4300 dissolved oxygen transmitter and
model 2300 pH transmitter; Ingold Electrodes, Inc.). The redox and
temperature probes were interfaced to the computer directly. Gas mass
flow controllers were used for air (Sierra Instruments, Monterey,
Calif.) and CO2 (MKS Instruments, Andover, Mass.).

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FIG. 1.
Schematic diagram of experimental setup of the
chemostat. Arrows indicate the flow of information (set points) from
the computer to the equipment, the action of the equipment which
influences the environment within the chemostat, and the flow of
information (data) from the sensors and off-line measurements to the
computer.
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Computer.
A passive backplane chassis was equipped with a
SB586T series single-board computer (Industrial Computer Source, San
Diego, Calif.) supporting a 233-MHz Intel Pentium processor. An SCXI (signal conditioning extensions for instrumentation) system (National Instruments, Austin, Tex.) provided front-end signal conditioning to an
AT-MIO-16 plug-in data acquisition (DAQ) board. The SCXI modules, along
with the DAQ board, provided a total of 47 analog input, 26 analog
output, and 4 digital input-output channels. The SCXI bus routed
analog, digital, timing, and triggering signals between modules and the
DAQ board. An eight-port serial board was added to give a total of 10 RS-232 serial lines when combined with the computer's two ports. The
computer and all the instruments in the system were protected with a
Fortress uninterruptible power supply (Best Power Technology, Inc.,
Nedecah, Wis.).
System software.
The chemostat was controlled by three
interacting software modules: the BioExpert, the BioController, and the
Diagnostics System (18, 37). Software modules were written
with LabVIEW (National Instruments Corporation), a graphical
programming language that provided a convenient user interface as well
as sophisticated language to interface with the input-output boards
(National Instruments Corporation) for data acquisition and control.
The software modules worked together to acquire and analyze data and
then select and maintain the sets of operating conditions that were tested.
(i) The BioController.
The BioController software module was
similar to many conventional, commercially available set point control
and data acquisition systems. The BioController allowed manual entry of
off-line data, set point control values, time intervals for data
acquisition, liquid-feed concentrations, and file names for data
logging and allowed sensors to be taken off-line for cleaning and
calibration. The BioController regulated conditions by using
computer-controlled pumps for nutrient feeds and pH control (acid and
base pumps), gas flow valves, a heater, and a stirrer. On-line data
such as pH, temperature, and dissolved oxygen concentration and
off-line data such as cell numbers and Fe2+ and
Fe3+ concentration values were acquired, maintained as data
logs, and presented as continually updated graphs on the computer
screen. Gas flow rates were regulated via the gas mass flow controllers but were not integrated into any feedback control loops. Temperature and pH were feedback-controlled parameters that were controlled with
fuzzy-logic controllers. The feed rate and inlet nutrient and iron
concentrations were controlled with an integrated set of pumps with
fuzzy-logic controllers. The pump controllers were automatically
recalibrated to ensure accurate dilution rates and feed concentrations.
Liquid level was maintained by a drain tube located on the side of the chemostat.
(ii) The Diagnostics System.
The Diagnostics System software
module compared the sensor data and set point values and determined if
the data were consistent with the desired chemostat operation. The
Diagnostics System automatically logged computer-generated messages,
such as those dealing with changes in set points and sensors taken
off-line and placed on-line during calibration and error messages.
Observations and comments made by the scientists were manually entered
into the computer but were not utilized by the BioExpert.
(iii) The BioExpert.
The BioExpert was the software control
module that evaluated and supervised the chemostat by using data from
the BioController and the Diagnostics System. Using the on-line and
off-line data and messages concerning changes in set points, the
BioExpert determined whether the chemostat was in transition, at steady
state, or being "washed out." Steady-state determinations were made
by using the following criteria: (i) after set points or flow rates
were changed, a minimum of five residence times must have elapsed with
at least one residence time between sampling events; (ii) redox values, the Fe2+ concentration, the Fe3+ concentration,
the total organic carbon concentration, and the cell numbers must not
have varied more than 10%; and (iii) total iron concentration (the sum
of Fe2+ and Fe3+ concentration values) must
have been within 5% of the set point inlet iron concentration. The
chemostat was defined to be at washout when all of the following
criteria were met: (i) more than two residence times had passed since
the last set point or flow rate was changed, (ii) total organic carbon
decreased by more than 50%, (iii) redox values decreased by more than
25%, (iv) Fe2+ concentration increased by more than 10%,
and (v) Fe3+ concentration decreased by more than 10%. The
chemostat was defined to be in transition whenever a set point was
changed and the conditions for the other two states were not met.
Residence time was defined as the time, in hours, that it takes to
completely replace the working volume of the chemostat
one time.
Residence time = (volume of the reactor/flow rate).
Residence time
is the reciprocal of dilution rate. Dilution rate,
expressed in inverse
hours, was defined as the flow rate divided
by the working volume of
the reactor. The dilution rate of a chemostat
is the nominal growth
rate of a microbial culture (
15).
The equation used by the BioExpert to evaluate the relative
effectiveness of each selected set of parameters was defined as
follows: productivity =
f(
PIRON) + (1
f)(
PCELLS), where 1
f,
PIRON = (Fe
3+ concentration/total
iron concentration) × flow rate, and
PCELLS = (suspended cell density) × flow
rate.
Iron productivity,
PIRON, as defined in this
study, imposed an efficiency constraint that allowed the comparison of
iron oxidation
rates obtained for different inlet iron concentrations.
By defining
f as being equal to 1, productivity was weighted
entirely towards
iron oxidation. Thus, cell numbers were used by the
computer control
system solely for steady-state determinations and had
no bearing
on the selection of parameter sets. To simplify the
reporting
of results, the units associated with the
PCELLS portion of the
productivity statement
have not been included
below.
Initial characterization of culture.
The chemostat was
operated at a temperature of 45°C, an aeration rate of 1 standard
liter per min, a pH of 2.0, and a stirrer speed of 400 rpm in acidic
salts medium containing 50 mM FeSO4. The chemostat was
inoculated with a culture grown at 55°C and then operated in the
batch mode for approximately 2.5 days, by which time the culture had
attained a suspended-cell density of approximately 107
cells/ml. To determine relative growth and iron oxidation rates, the
chemostat was operated in the continuous-flow mode with flow rates, set
by the scientists, that ranged from 6 to 13.5 ml/min (dilution rate
[D] = 0.265 to 0.596/h). To examine the effects of
temperature, the chemostat was operated in the continuous-flow mode
with a flow rate of 7 ml/min (D = 0.309/h) and
temperatures of 40, 30, 50, 45, 55, and 60°C, which were selected in
that order by the scientists.
Multiparametric characterization.
After the initial set of
parameters that were selected by the scientists, the stochastic
learning procedure integrated within the BioExpert software module was
used to select the parameter values and flow rates. The chemostat was
inoculated with a culture grown at 45°C in medium adjusted to pH 1.7 and incubated in the batch mode for approximately 1 day prior to
implementation of the BioExpert. Due to a malfunction of the hard drive
of the computer, the chemostat was restarted in midcourse (run F) with
an inoculum (the culture from the chemostat) that was incubated at
50°C in medium adjusted to pH 1.7.
For chemostat operation, the BioExpert invoked a multistep procedure
that involved two integrated subcontrollers; the flow
rate controller
and the stochastic learning controller. Chemostat
operation was as
follows:
1. Set the algorithm step counter,
k, to
0 (
k = 0). Run the reactor to steady state for a given
temperature, pH, iron concentration,
and flow rate
combination. 2. Calculate the production rate,
P.
P = [Fe
3+ concentration/(Fe
2+
concentration + Fe
3+ concentration)] ×
Fratecurrent. 3. Pick a new
flow rate,
Fratecurrent, with
kL =
k,
k =
kL + 1 (see below,
flow rate controller). 4. Run
the reactor to steady state.
5. Calculate
P. 6. Has the peak
P for
this parameter set been obtained? If
k 
1 and
|
Ptarget
Pcurrent|

1 ml, then Yes. Pick a new pH, temperature, and
inlet
iron concentration by using the stochastic learning controller
and go
to step 1. Else No. Go to step
3.
Flow rate controller.
The flow rate
controller used within the BioExpert was a hybrid subsystem based on an
expert system and a best-fit control concept. To start the algorithm,
the operator selected the first flow rate for the initial parameter
set, run A. For the subsequent parameter sets, the first flow rate
evaluated in each set of parameters was the last flow rate of the
previous set. For example, the first flow rate evaluated in run D was
the last flow rate evaluated in run C.
The second flow rate is chosen as follows:
If
Frate1 
7 ml/min,
then
Fratetarget = 1.5
Frate1
Else
Fratetarget =
Frate1/1.5
The third flow rate is chosen as:
If
Frate1
Frate2,
then If
P(
Frate1)
P(
Frate2), then
Fratetarget = 1.5
Frate2 Else
Fratetarget =
Frate1/1.5
Else If
P(
Frate2)
P(
Frate1), then
Fratetarget = 1.5
Frate1 Else
Fratetarget =
Frate2/1.5
After three flow rates were evaluated, the flow
controller switched from the expert system to the parabolic fitting
algorithm.
A least-squares parabolic fit for the curve of productivity
versus
flow rate was obtained by using a singular-value decomposition
that produced the pseudo inverse from the left:
P(
Fratetarget) =
b0 + b1Fratetarget +
b2(
Fratetarget)
2.
After a parabolic fit to the data was obtained, the critical
point of
the parabola was used to calculate the next flow rate
target.
Fratetarget =
b1/2
b2
As new flow rates were issued and new fits were calculated, the fits
narrowed in on the optimal choice of the flow rate for
the given set of
parameters. As the BioExpert changed the set
point conditions (pH,
temperature, and iron concentration), the
fitting algorithm reset and
the expert subsystem component of
the flow controller
restarted.
Stochastic learning controller.
A stochastic learning scheme
based on the concepts of Moore (24) and Franklin
(14) was used to select the sets of parameters that were
evaluated. Three variables were tested: temperature, pH, and inlet iron
concentration. The temperature range of 26 to 55°C was based on
experimental data (see below). The iron concentration range of 15 to
100 mM was based on the limits of the peristaltic pumps and expert
knowledge concerning the cultivation of the moderate thermophilic
culture. The pH range of 1.5 to 1.95 was based on expert knowledge. The
lower limit was based on the expected lowest pH at which growth would
be at a rate sufficient to avoid extremely low flow rates. Avoiding the
low flow rates would avoid the dead band limits of the peristaltic
pumps, minimize the possibility of washout, and avoid extremely lengthy
controller trials. The upper pH limit was chosen to avoid the formation
of iron hydroxides. The formation of iron precipitates would decrease
the amount of Fe3+ in solution. Steady state would not be
achieved if the total of Fe2+ and Fe3+
concentrations was not within 5% of the set point inlet iron concentration.
The learning scheme operated simultaneously for the three parameters. A
Gaussian distribution (bell-shaped curve) consisting
of two
half-Gaussian distributions was used. An initial mean was
chosen as an
initial guess at where the productivity (
P) maximum
was
located (Table
1). The initial widths,
i.e., the quasi variances
(
+x,

x), of the distributions or standard
deviations were chosen to
span a reasonable operating range for each
parameter. Stochastic
learning takes place by adjusting the
distributions (i.e., mean
and standard deviation) depending on the
relative production rates.
If the production rate improved with a
tested set point, the new
mean of the distribution was shifted to that
set point. Also,
the standard deviations (right and left widths of the
half-Gaussians)
were changed to reflect the shift of the distribution
towards
the increase in productivity. For example, if an increase in pH
resulted in increased productivity, the right side of the Gaussian
was
increased and the left side (representing the range towards
the lower
pHs) was decreased. If the rate did not improve, the
mean of the
distribution did not change, and the width in the
direction of the set
point was decreased and the other side's
width was increased. To
continue the example, if an increase in
pH did not result in improved
productivity, the mean remained
the same, the right side of the
Gaussian was decreased, and the
left side was increased. The choice for
each new set point was
made with a random-number generator based on
this two-sided Gaussian
distribution.
The basic stochastic control algorithm used was as follows
(
x represents the set point [SetPt] type, i.e., pH,
temperature,
or iron concentration):
Initialize the distributions by using the values in
Table
1 and scaling factors (Sc
x) 0.17 for pH,
1.67 for temperature, and 5.0 for inlet iron concentration
with
SetPt
bestx =
x,
+x =
ix, and

x =
ix Repeat forever the
following: Calculate the standard deviation
change:

x = Sc
x|(SetPt
currentx 
SetPt
bestx)/SetPt
bestx|
If
Pcurrent >
Pbest and If
SetPt
bestx < SetPt
currentx, then
SetPt
bestx = SetPt
currentx,
+x =
+x +

x, and

x =

x

x Else
SetPt
bestx = SetPt
currentx,
+x =
+x

x, and

x =

x +

x Else
If SetPt
bestx < SetPt
currentx, then
+x =
+x

x and

x =

x +

x Else
+x =
+x +

x and

x =

x

x If
+x < 0.001, then
+x = 0.001 If

x < 0.001, then

x = 0.001 Loop
Evaluation of a mixed culture.
A mixed culture
containing the mesophilic bacterium T. ferrooxidans and
the Newmont culture was evaluated. The chemostat was inoculated with
both cultures and operated in the batch mode for approximately 2 days
at 32°C, a pH of 1.7, and an inlet iron concentration of 50 mM. The
productivity data for the previous runs (A to H) were entered into the
computer, and continuous-flow operation was initiated. The BioExpert
was modified such that the stochastic learning program was used without
the flow controller algorithm. Thus, activities were evaluated at a
single flow rate, in this case 7 ml/min (D = 0.309/h).
After approximately 1 day, the chemostat was returned to the batch mode
because the rate of iron oxidation and cell numbers were extremely low
and washout was possible. After an additional day in batch operation,
the chemostat was restarted. Over a period of 24 weeks, nine sets of
set points were evaluated. The first set (32°C, pH 1.7, and 50 mM
inlet iron concentration) was selected by the scientists; the remaining
eight sets were selected by the BioExpert.
Analytical methods.
Off-line measurements were made for
biomass (cell counts) and dissolved Fe2+ and dissolved
Fe3+ concentrations. To prepare samples for staining, cells
were collected onto black polycarbonate membrane filters (0.2-µm pore
size; Poretics, Livermore, Calif.), washed with water that had been
adjusted to pH 1 with sulfuric acid, and then washed with water that
had been adjusted to pH 11 with NaOH. Cells were stained on the filter with acridine orange for 3 to 5 min with a solution (0.01% final concentration) prepared with water that had been adjusted to a pH of 11 with NaOH. After being stained with acridine orange, filters were
washed with deionized water. For mixed-culture experiments, a
fluorescein-conjugated wheat germ agglutinin (WGA) (Molecular Probes,
Inc., Eugene, Oreg.) staining technique that was based on the method of
Sizemore et al. (32) was used to selectively stain the
Newmont culture. (T. ferrooxidans did not stain with fluorescent lectin). Samples were prepared as described above, and
after the rinse with pH 11 water, the filters were washed with
phosphate-buffered saline (PBS) and stained for 1 to 2 min with WGA
(100 µg/ml final concentration) in PBS. Stained filters were viewed
with an epifluorescence microscope (model IIRS; Carl Zeiss, Inc.,
Thornwood, N.Y.).
The concentrations of Fe
2+ in duplicate samples were
determined by titration with potassium dichromate or potassium
permanganate
(
33). To prepare samples for titration, known
volumes were added
to approximately 15 ml of an acid mixture containing
150 ml of
H
2SO
4 and 150 ml of
H
3PO
4 per liter of ultrapure, deionized
water.
The concentration of dissolved Fe
3+ was determined by UV
absorption spectroscopy at 304 nm (
3,
34). Samples were
prepared
by filtering (0.2-µm-pore-size Acrodiscs) (HT Tufryn; Gelman
Sciences,
Inc., Ann Arbor, Mich.) aliquots and then diluting them as
needed,
typically 1:200 or 1:100, in a diluent which contained, per
liter
of water, 142 g of Na
2SO
4 and 20 ml
of concentrated HCl. Standards
were prepared by dissolving 7.985 mg of
Fe
2O
3 in 10 ml of concentrated
HCl and making
dilutions as appropriate. Water and 71 g of
Na
2SO
4 were added, and the mixture was brought
to 500 ml to make 2 ×
10
4 M Fe
3+.
Neither yeast extract nor Fe
2+ interfered with the UV
absorbance method for the Fe
3+ concentration determination.
Duplicate samples were analyzed
for Fe
2+ and
Fe
3+ concentrations at each sampling period. Total iron
concentration
was calculated by a summation of the average of the
Fe
2+ and Fe
3+ concentration values. This method
was validated by analyzing
samples for total iron concentration by
atomic absorption spectroscopy
(model 5100 spectrometer; Perkin-Elmer
Corp., Norwalk, Conn.).
Estimate of community diversity.
A direct 5S rRNA assay
(36) was used to obtain an estimate of community diversity
within the enrichment cultures. The rRNA was extracted from samples
collected from the chemostat on 9 May (during run A), 4 June (during
run C), 8 August (during run F), and 13 November (during run H) 1996. The RNA extraction procedure was modified to include lysozyme treatment
(2.5 mg/ml in a solution containing 250 mM Tris-HCl and 1.25 mM EDTA).
After treating the solution with lysozyme, 10% sodium dodecyl sulfate
(SDS) was added such that the final lysis solution contained 2% SDS,
200 mM Tris-HCl, and 1 mM EDTA. This entailed adding 0.2 ml of 10% SDS
to every 0.8 ml of lysozyme treatment solution. RNA preparations were
distributed within the single well that ran along the entire top edge
of the gel. After electrophoresis at 65°C for 5 h at 250 V, the
gels were stained with SYBR Green II stain (Molecular Probes).
 |
RESULTS AND DISCUSSION |
Because little was known about the Newmont cultures, they made an
excellent biological system with which to evaluate a learning-based intelligent control system. All that was known were the initial enrichment conditions. The cultures had been derived from a
heap-leaching operation by cultivation at 55°C in acidic (pH 1.8)
medium containing yeast extract (0.01%) and iron (100 mM
Fe2+) (8). The Newmont enrichment cultures
contained organisms that appeared similar to other acidophilic moderate
thermophiles (13, 30, 38). The thermophilic Newmont cultures
required both yeast extract and iron for good growth (Table
2). Without yeast or iron in the culture
medium, little or no growth was observed. Of the organic substrates
tested, only glutathione could substitute for yeast extract
(8). Glycerol, a growth substrate for acidophilic heterotrophic bacteria (36), did not support growth as a
sole carbon source or enhance growth in medium containing yeast extract and iron. As has been found for some other moderately thermophilic bacteria (27), the addition of tetrathionate decreased
growth somewhat (Table 2).
Prior to implementation of the BioExpert as a learning-based
supervisory system, the relative iron oxidation and growth rates were
determined in a continuous culture by using set points selected by the
scientists. At pH 2, 50°C, and 50 mM iron, nominal growth rates
ranged from 0.265 to 0.596 h
1. The behavior of the
chemostat did not follow typical Monod kinetics; both the number of
suspended cells and the Fe3+ concentration decreased as the
flow rate was increased (Fig. 2). While
pH 2 and 50 mM iron were maintained, the effects of temperature were
evaluated during a run of approximately 200 h. At pH 2 and a flow
rate of 7 ml/min, there was little effect of temperature on iron
oxidation rate (Fig. 3). The productivity of the chemostat can also be considered from the perspective of biomass. The greatest number of suspended cells and the highest suspended-cell yield per mole of Fe3+ formed per liter
occurred at 55°C. Higher yields for iron oxidation per suspended cell
occurred at a temperature of either 30 or 60°C (Fig.
4).

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FIG. 2.
Steady-state data obtained each time the chemostat
reached biological equilibrium during a run of 200 h in which the
flow rate was varied. Symbols: , cell numbers; , Fe3+
concentration; , Fe2+ concentration; ×, iron
production rate [D(Fe3+)] = (dilution
rate)(Fe3+ concentration).
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FIG. 3.
Steady-state data obtained each time the chemostat
reached biological equilibrium during a run of 200 h in which the
temperature was varied. Symbols: , cell numbers; ,
Fe3+ concentration; , Fe2+ concentration.
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FIG. 4.
Yield relationships at different temperatures. ,
suspended-cell yield per mole of Fe3+ formed per liter;
, iron oxidized per suspended cell.
|
|
When the chemostat was operated under the control of the learning-based
control algorithm, the BioExpert selected the sets of parameters (pH,
temperature, and iron concentration) and the flow rates to be evaluated
(Table 3). The parameters were selected from the following ranges that had been fixed by the scientists: pH
1.65 to 1.95, 26 to 55°C, and 15 to 100 mM inlet iron concentration. The upper and lower limits for pH and inlet iron concentration were
based on the "expert knowledge" of the scientists. The upper and
lower limits for temperature and flow rates were based on experimental
data obtained in this study. Except when noted in Table 3, the data
reported are the values obtained when the chemostat was operated at the
flow rate that achieved the maximum iron production for that particular
set of conditions. The scientists selected the parameters for runs A
and C. The BioExpert selected the sets of parameters for runs B, D, E,
F, and G. For run H, the BioExpert had requested a pH of 1.95; however,
the scientists selected pH 1.7 in order to evaluate a set of conditions
within the region of low pH and low temperature. Two attempts were made
to operate the chemostat at the parameters selected for run B
(51.5°C, pH 1.84, 47.15 mM Fe3+). During these attempts
the chemostat approached a washout condition, with cell density and
Fe3+ concentration decreasing even at 2 ml/min
(D = 0.088 h
1). Between the two attempts
to operate the chemostat under the conditions of run B, the chemostat
was operated at the set points for run A. Run C was a repeat of run A
and was used to recover from the near-washout conditions encountered
during run B. Although three flow rates were tested for run E, the
final flow rate was not evaluated due to a malfunction in the hard
drive of the computer. Run F was a repeat of run E to obtain a complete
set of data.
In this study, the computer was programmed to select conditions that
were favorable for iron productivity, that is, to select conditions
that would optimize iron oxidation. Conditions evaluated during runs A
and C had the greatest ratio of Fe3+ to Fe2+
concentration at the optimum flow rate for that set of conditions (Fig.
5A). Although run H had a lower ratio of
Fe3+ to Fe2+ concentration, the productivity
value of run H (Fig. 5A) was comparable to those obtained for runs A
and C because of the higher optimum flow rate that was achieved. The
data obtained with the BioExpert suggested that pH and temperature were
coupled parameters. While pH alone appeared to have a significant
impact on iron oxidation (compare runs D, G, and H), the combined
effects of a relatively high pH (pH 1.84) and temperature (51.5°C) of
run B resulted in washout conditions in the chemostat (Fig. 5B and
6B). Run F (pH 1.64, 53.3°C, 60.65 mM
Fe) produced a moderate productivity value and ratio of
Fe3+ to Fe2+ concentration.

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FIG. 5.
Continuous-space plot depicting the parameter sets that
were evaluated and the results that were obtained. The size of a sphere
is proportional to the suspended-cell density, and the color of a
sphere indicates the ratio of Fe3+ to Fe2+
concentration. (A) The continuous-space plot has been positioned to
allow the viewing of the data with respect to temperature (x
axis), pH (y axis), and inlet iron concentration
(z axis). (B) The continuous-space plot has been rotated to
provide a top-down view, which emphasizes the data in relation to
temperature and pH.
|
|

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FIG. 6.
Continuous-space plot depicting the parameter sets that
were evaluated and the results that were obtained. The size of a sphere
is proportional to the suspended-cell density, and the color of a
sphere indicates the productivity of the chemostat, which was defined
as (Fe3+ concentration/total iron concentration) × flow
rate. (A) The continuous-space plot has been positioned to allow the
viewing of the data with respect to temperature (x axis), pH
(y axis), and inlet iron concentration (z axis).
(B) The continuous-space plot has been rotated to provide a top-down
view, which emphasizes the data in relation to temperature and pH.
|
|
The computer evaluated changes in suspended-cell density values when
identifying the "state" of the chemostat, that is, whether the
chemostat was in transition, steady state, or washout. Suspended-cell density was not used for the selection of the sets of parameters that
were evaluated. Nevertheless, any of the measured values, such as
suspended-cell density, can be discussed in the context of the
conditions that were evaluated. The highest suspended-cell density
occurred during runs A and C, and the lowest occurred during run B
(Fig. 5 and 6). The data obtained at the optimum flow rate for each set
of conditions showed that there was moderate correlation between
suspended-cell density and iron productivity (0.698). There were high
correlation values between suspended-cell density and the
Fe3+/Fe2+ concentration ratio (0.958),
Fe3+ concentration (0.881), and the percentage of iron that
was oxidized (0.853). Negative correlation values between pH and
suspended-cell density (
0.229) and productivity (
0.566) were
observed, which indicated that at the higher pH values, growth and
metabolism were impaired.
The potential for change is the reason that enrichment cultures,
instead of axenic cultures, were used in this study. There was the
possibility that the enrichments were mixed cultures and that the
community structure and its associated metabolic activity would change
with time. The control algorithm within the BioExpert does not assume
that there is a single set of parameters that results in the "best"
productivity. The optimum set of conditions can change with time. Thus,
the BioExpert can adapt what it has "learned" as the community
structure evolves. It was assumed that this type of community,
with its increased complexity, would present control challenges
more representative of those presented by industrial heap-leaching
operations than would axenic cultures.
The Newmont culture appeared stable with regard to species composition.
All of the denaturing gradient gel electrophoresis (DGGE) profiles
(Fig. 7) were rather simple and similar
to one another in spite of the time span between the first and last
samples and the variety of conditions that were evaluated during
this time interval. The DGGE 5S rRNA profile of the chemostat sample collected during run A was similar to the profile obtained for the sample collected during run C. There was consistency between runs A
and C in spite of run B, a run which had resulted in washout of the
chemostat. All profiles were similar to an earlier 5S rRNA profile that
was obtained for a batch culture (December 1995) that had been
inoculated with the first Newmont enrichment culture (36).
In all profiles, the 5S rRNA bands migrated to the lower region of the
gel. This is the region to which the 5S rRNA species of other
moderately thermophilic bacteria (36) as well as
Bacillus subtilis and Bacillus cereus
(35) migrate. No bands were visible in the upper regions of
the gel, the region to which the 5S rRNA species of gram-negative,
acidophilic, mesophilic bacteria such as T. ferrooxidans, Thiobacillus thiooxidans, and
Acidiphilium spp. migrate (36).

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FIG. 7.
Negative images of SYBR II-stained gels obtained for
chemostat samples collected during run A, 9 May 1996 (A); run C, 4 June
1996 (B); run F, 16 August 1996 (C); and run H, 13 November 1996 (D).
The 5S rRNA bands are positioned approximately in the middle of the
gels. The dark bands in the upper regions of the gel have been assigned
to the higher-molecular-weight RNA species, i.e., 16S and 23S rRNAs,
while the bands clustered within the lower regions of the gels have
been assigned to the tRNA species.
|
|
To evaluate the response of the supervisory control system to a
major shift in community structure, the BioExpert was challenged with a mixed culture containing mesophilic T. ferrooxidans and the moderately thermophilic Newmont culture.
There was the possibility that the BioExpert would search and find a
set of parameters that would allow both cultures to be maintained
within the chemostat. However, for this to occur, the productivity of
the mixed culture would have to be greater than the productivity of a
culture in which either the moderate thermophilic or mesophilic culture
predominated. The "history" of the chemostat, i.e., the previous
productivity values and the conditions under which they were achieved
(runs A to H), was entered into the BioExpert. While it can be assumed that this would bias the system towards the Newmont culture, the inclusion of these data eliminated the need to reevaluate these sets of parameters.
The chemostat was started at 32°C, pH 1.7, and an inlet iron
concentration of 50 mM, conditions selected by the scientists to allow
the growth of both cultures. The sets of parameters that were evaluated
and the results obtained for the mixed-culture experiment correspond to
tests 16 to 24 in Table 4. Initially, T. ferrooxidans was the dominant species in the
chemostat (Fig. 8). The BioExpert
selected and evaluated two sets of parameters (tests 17 and 18) that
resulted in poor growth by either culture prior to selecting conditions
that were favorable to the Newmont enrichment culture. Iron oxidation
improved after test 18 when conditions favorable for the Newmont
culture were selected and continued to improve as the BioExpert
continued to select sets of parameters from within smaller and smaller
ranges (Fig. 9). Near the end of the
mixed-culture experiment, the BioExpert was selecting inlet iron
concentrations around 30 mM and achieving iron oxidation rates that
exceeded 90% conversion of available iron (tests 22 to 24, Table 4;
Fig. 9C). Even with the major shift in community structure that
resulted from the addition of the mesophilic T. ferrooxidans, the BioExpert eventually selected parameter sets
that were conducive to the moderately thermophilic culture.

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FIG. 8.
Changes in total suspended-cell density determined by
acridine orange direct counts (AODC) and moderate thermophile
suspended-cell densities determined by fluorescein isothiocyanate
(FITC)-conjugated WGA direct counts as pH, temperature, and iron
concentration were varied. Test numbers correspond to the test numbers
in Table 4.
|
|

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FIG. 9.
Continuous-space plot depicting the parameter sets that
were evaluated and the results that were obtained with a mixed culture
containing T. ferrooxidans and the moderately
thermophilic culture. The size of a sphere is proportional to the
suspended-cell density, and the color of a sphere indicates the
productivity of the chemostat (for the definition of productivity, see
the legend to Fig. 6). (A) The continuous-space plot has been
positioned to allow the viewing of the data with respect to temperature
(x axis), pH (y axis), and inlet iron
concentration (z axis). (B) The continuous-space plot has
been rotated to provide a top-down view, which emphasizes the data in
relation to temperature and pH. (C) The continuous-space plot has been
rotated to provide an end view, which emphasizes the data in relation
to inlet iron concentration and pH. The continuous-space plot has been
rotated to provide a side view, which emphasizes the data in relation
to inlet iron concentration and temperature.
|
|
The highest temperature evaluated during the mixed-culture experiment
was 45.9°C. This upper temperature limit may have decoupled the
interactive effects between temperature and pH. In the mixed-culture experiment, there were positive and moderately high correlation values
between pH and suspended-cell density (0.746), productivity (0.757),
and the percent iron that was oxidized (0.757). These results are in
contrast to those from the earlier experiment in which negative
correlation values between the set point pH and suspended-cell density
and iron oxidation percentage were observed. A finite capacity to
oxidize iron at the set flow rate (dilution rate) was implied by the
observed negative correlation values between inlet iron concentration
and productivity (
0.338), the ratio of Fe3+ to
Fe2+ concentration (
0.328), and percent iron
oxidized (
0.338). Increasing the inlet concentration of iron
above that amount resulted in a decreased percentage of iron oxidized.
To acquire an overall perspective on the results that were obtained
throughout this study, a data set (Table 4) was compiled from each test
that was conducted at a flow rate at or near 7 ml/min (D = 0.309 h
1). This includes data from the experiment
examining the effects of temperature (tests 1 to 6), from individual
tests during runs A to H that were done at or near 7 ml/min (tests 7 to
15), and from the mixed-culture experiment (tests 16 to 24). Data were included for test 9 (which had occurred during run B, the run that
resulted in washout) even though the chemostat did not achieve steady
state at 7 ml/min.
For the compiled data set, there were high correlation coefficients
between suspended-cell density and productivity (0.829), total oxidized
iron (0.827), and the percent iron that was oxidized (0.831). The
compiled data indicated that pH was certainly an important, but not the
only important, parameter affecting metabolic activity and growth (Fig.
10). Consistently high productivity
values were achieved around pH 1.8, a temperature of 45°C, and an
inlet iron concentration between 20 and 40 mM. The compiled data set also brings into perspective the experiment in which only a single parameter, iron concentration, was varied. Initially, it was concluded that the culture was able to oxidize iron over a broad range of temperature and that maximum growth occurred at 55°C. However, after
reviewing the results obtained with the BioExpert, it was concluded
that the moderate oxidation efficiencies (less than half of the iron
was oxidized) and the relatively low cell yields in the earlier
experiment may have been due to the pH at which this experiment was
conducted.

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FIG. 10.
Continuous-space plot for the composite data set for
all tests with a flow rate at or near 7 ml/min. The size of a sphere is
proportional to the suspended-cell density, and the color of a sphere
indicates the productivity of the chemostat (for the definition of
productivity, see the legend to Fig. 6). (A) The continuous-space plot
has been positioned to allow the viewing of the data with respect to
temperature (x axis), pH (y axis), and inlet iron
concentration (z axis). (B) The continuous-space plot has
been rotated to provide a top-down view, which emphasizes the data in
relation to temperature and pH. (C) The continuous-space plot has been
rotated to provide an end view, which emphasizes the data in relation
to inlet iron concentration and pH. The continuous-space plot has been
rotated to provide a side view, which emphasizes the data in relation
to inlet iron concentration and temperature.
|
|
A system such as the BioExpert would also be useful for examining the
suitability of mining bacteria for leaching processes. For example, the
apparent sensitivity of the Newmont culture to pH, particularly at high
temperatures, may make it unsuitable for mining bioprocesses. In this
study, the range of pH values (1.65 to 2) over which significant
changes in iron metabolism were observed is much narrower than one
would expect in a mining operation (1, 2, 6, 8). During a
study of the oxidation of sulfidic-nickel-based tailings (1)
and one evaluating the oxidation of copper- and iron-containing
sulfidic ores (6), pH swings between 2 and 4 were observed.
In one of these studies (6), pH swings occurred in spite of
sulfuric acid amendment.
In summary, the study demonstrated the use of an intelligent control
system, the BioExpert, as an experimental tool that can be used
to examine multiparametric effects on microbial activity. By
simultaneously changing three parameters, pH, temperature, and
inlet iron concentration, the BioExpert demonstrated that temperature
and pH appeared to be coupled parameters and that there appeared to be
a finite capacity to oxidize iron.
 |
ACKNOWLEDGMENTS |
This work was supported by the Department of Energy, Office of
Energy Research, Basic Energy Sciences to the Idaho National Engineering and Environmental Laboratory under contract
DE-AC01-94-ID13223.
We are grateful to James Brierley, Newmont Technical Services, for the
bacterial cultures. We thank James Brierley and Robert S. Cherry for
valuable discussions.
 |
FOOTNOTES |
*
Corresponding author. Mailing address: Biotechnologies
Group, Idaho National Engineering and Environmental Laboratory,
Lockheed Martin Idaho Technologies Co., P.O. Box 1625, Idaho
Falls, ID 83415-2203. Phone: (208) 526-8786. Fax: (208) 526-0828. E-mail: dstoner{at}inel.gov.
 |
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Applied and Environmental Microbiology, November 1998, p. 4555-4565, Vol. 64, No. 11
0099-2240/98/$04.00+0
Copyright © 1998, American Society for Microbiology. All rights reserved.
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