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APPLIED AND ENVIRONMENTAL MICROBIOLOGY, Nov. 1998, p. 4555–4565 0099-2240/98/$04.0010 Copyright © 1998, American Society for Microbiology. All Rights Reserved.

Vol. 64, No. 11

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

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. Moderateto-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. 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

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 * 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@inel.gov. 4555

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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 z 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.). 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 Fe21 and Fe31 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.

APPL. ENVIRON. MICROBIOL. TABLE 1. Initial Gaussian distributions x

Gaussian parameter

Temp (°C)

pH

Inlet iron concn (mM)

x s1x s2x

45 5.0 5.0

1.8 0.5 0.5

50 15.0 15.0

(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 Fe21 concentration, the Fe31 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 Fe21 and Fe31 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) Fe21 concentration increased by more than 10%, and (v) Fe31 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 5 (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 5 f(PIRON) 1 (1 2 f)(PCELLS), where 1 $ f, PIRON 5 (Fe31 concentration/total iron concentration) 3 flow rate, and PCELLS 5 (suspended cell density) 3 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 continuousflow mode with flow rates, set by the scientists, that ranged from 6 to 13.5 ml/min (dilution rate [D] 5 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 5 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 5 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 5 [Fe31 concentration/(Fe21 concentracurrent . tion 1 Fe31 concentration)] 3 F rate current , with kL 5 k, k 5 kL 1 1 (see below, flow rate 3. Pick a new flow rate, F rate controller).

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TABLE 2. Growth on selected medium components Culture

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 a b

Medium components presenta 21

Fe

Yeast extract

X X X X X X X X

Tetrathionate

X X

X X X X X X

X X X X X X X X

Glycerol

X X X X X X X X

Growthb

1 1 1111 111 11 11 11 1111 111 1 1 1 1 1 1 1

X, component present. Relative growth.

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 uPtarget 2 Pcurrentu # 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: 1 # 7 ml/min, then If F rate target 1 5 1.5 F rate F rate Else target 1 5 F rate /1.5 F rate

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.

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: F, cell numbers; ■, Fe31 concentration; Œ, Fe21 concentration; 3, iron production rate [D(Fe31)] 5 (dilution rate)(Fe31 concentration). The third flow rate is chosen as: 1 2 If F rate # F rate , then 1 2 ) # P(F rate ), then If P(F rate target 2 5 1.5 Frate F rate Else target 1 5 F rate /1.5 F rate Else 2 1 ) # P(F rate ), then If P(F rate target 1 5 1.5 F rate F rate Else target 2 5 F rate /1.5 F rate

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 target ) 5 b0 1 decomposition that produced the pseudo inverse from the left: P(F rate target target 2 1 b2(F rate ) . After a parabolic fit to the data was obtained, the critical b1 F rate target 5 point of the parabola was used to calculate the next flow rate target. F rate 2b1/2b2 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 param-

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: F, cell numbers; ■, Fe31 concentration; Œ, Fe21 concentration.

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APPL. ENVIRON. MICROBIOL. 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 (Scx) 0.17 for pH, 1.67 for temperature, and 5.0 for inlet iron concentration with x SetPt best 5m ˆ x, s1x 5 s xi , and s2x 5 s xi

FIG. 4. Yield relationships at different temperatures. F, suspended-cell yield per mole of Fe31 formed per liter; ■, iron oxidized per suspended cell. eters 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 Fe31 in solution. Steady state would not be achieved if the total of Fe21 and Fe31 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 (sx1, sx2), 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

Repeat forever the following: x x 2 SetPt best )/ Calculate the standard deviation change: Ds x 5 Sc xu(SetPt current x SetPt best u If Pcurrent . Pbest and x x If SetPt best , SetPt current , then x x 5 SetPt current , s1x 5 s1x 1 Ds x, and s2x 5 s2x 2 Ds x SetPt best Else x x SetPt best 5 SetPt current , s1x 5 s1x 2 Ds x, and s2x 5 s2x 1 Ds x Else x x If SetPt best , SetPt current , then s1x 5 s1x 2 Ds x and s2x 5 s2x 1 Ds x Else s1x 5 s1x 1 Ds x and s2x 5 s2x 2 Ds x If s1x , 0.001, then s1x 5 0.001 If s2x , 0.001, then s2x 5 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 5 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 Fe21 and dissolved Fe31 concentrations. To prepare samples for staining, cells were collected onto black polycarbonate membrane filters (0.2-mm 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

TABLE 3. Effects of multiparametric changes on growth and iron oxidation Values at maximum productivityd

Parameter set Run

Temp (°C)

pH


Flow rates (ml/min)

A B C D E F G H

45 51.5 45 40.7 53.3 53.3 39.7 39.9

1.8 1.84 1.8 1.9 1.64 1.64 2.3 1.7

50 47.15 50 34.48 60.65 60.65 45.5 39.94

4, 6, 9, 7.03b 7, 2b 7, 4.67, 10.5, 7.45b 7.45, 4.93, 11.1, 12, 11.2b 11.1, 7.4, 12, 9.6b 4.5, 7.5, 11.25, 9.17, 12, 8.52b 8.55, 5.73, 12, 12.75, 15.4, 21 5.2, 7.8, 11.7, 9.19b

a

Biomass (cells/ml)

Fe31 concn (mM)

Fe21 concn (mM)

Fe31/Fe21 concn ratio

Iron conversion (%)

Productivity (ml/min)a

7.6 3 107 2.91 3 105 7.33 3 107 1.62 3 107

45.08 4.24 42.88 16.28

6.95 40 8.7 18.9

6.48 0.11 4.93 0.86

86.64 9.58 83.13 46.28

6.31 0.19 6.193 5.183

2.98 3 107 2.28 3 107 3.14 3 107

35.56 5.4 27.74

26.375 42.5 12.875

1.35 0.127 2.154

57.42 11.27 68.29

4.891 1.4 6.32

Productivity 5 {([Fe31]/([Fe21] 1 [Fe31])} 3 flow rate. Values were obtained at the flow rate for which maximum productivity was achieved. c Liquid feed system was unable to deliver the calculated flow rate. d Run E was incomplete, and no data were recorded. b

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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

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 Fe31 to Fe21 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.

phosphate-buffered saline (PBS) and stained for 1 to 2 min with WGA (100 mg/ml final concentration) in PBS. Stained filters were viewed with an epifluorescence microscope (model IIRS; Carl Zeiss, Inc., Thornwood, N.Y.). The concentrations of Fe21 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 H2SO4 and 150 ml of H3PO4 per liter of ultrapure, deionized water. The concentration of dissolved Fe31 was determined by UV absorption spectroscopy at 304 nm (3, 34). Samples were prepared by filtering (0.2-mm-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 Na2SO4 and 20 ml of concentrated HCl. Standards were prepared by dissolving 7.985 mg of Fe2O3 in 10 ml of concentrated HCl and making dilutions as appropriate. Water and 71 g of Na2SO4 were added, and the mixture was brought to 500 ml to make 2 3 1024 M Fe31. Neither yeast extract nor Fe21 interfered with the UV absorbance method for the Fe31 concentration determination. Duplicate samples were analyzed for Fe21 and Fe31 concentrations at each sampling period. Total iron concentration was calculated by a summation of the average of the Fe21 and Fe31 concentration values. This method was validated by analyzing samples for total iron concentration by atomic absorption spectroscopy (model 5100 spectrometer; PerkinElmer Corp., Norwalk, Conn.).

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 (Fe31 concentration/total iron concentration) 3 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.

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APPL. ENVIRON. MICROBIOL.

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.

in acidic (pH 1.8) medium containing yeast extract (0.01%) and iron (100 mM Fe21) (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 learningbased 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 h21. The behavior of the chemostat did not follow typical Monod kinetics; both the number of suspended cells and the Fe31 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 Fe31 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). 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

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TABLE 4. Effect of multiparametic changes on growth and iron oxidation at flow rates at or near 7 ml/min Concn (mM) of:

Test

Temp (°C)

pH


Flow rate (ml/min)

No. of cells (106)

Fe31

Fe21

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

40 30 50 45 55 60 45 45 51.5 45 45 40.7 53.3 53.3 39.9 32 38.7 34 45 43 36.8 45.9 43.6 44

2 2 2 2 2 2 1.8 1.8 1.84 1.8 1.8 1.9 1.64 1.64 1.7 1.7 1.69 1.69 1.87 1.94 1.85 1.87 1.8 1.81

50 50 50 50 50 50 50 50 47.15 50 50 34.48 60.65 60.65 39.94 50 17.14 53.61 51.33 16.82 31.05 21.74 28.9 34

7 7 7 7 7 7 7.03 7 7 7 7.45 7.4 7.4 7.5 7.8 7 7 7 7 7 7 7 7 7

9.9 4.71 18.7 14.2 21.2 1.7 76 105 4.4 82.9 73.3 29 85.1 70.4 31.5 35.8 2.13 1.0 66.5 54.7 23 83.8 45.9 44.8

21.96 19.66 23.64 19.94 20.74 12.22 45.08 48.8 10.27 43.12 42.88 27.7 46.36 48.32 26.92 22.04 0.769 2.45 32.28 14.19 16.14 20.66 27.04 34.2

29.20 32.33 26.60 31.00 29.80 38.20 6.95 2.10 37.75 8.40 8.70 6.15 14.25 15.25 14.38 29 17 52 18.88 1.8 15.50 1.38 1.53 1.43

a

[Fe31]/[Fe21]

% Iron oxidized

Productivitya

0.75 0.61 0.89 0.64 0.70 0.32 6.49 23.24 0.27 5.13 4.93 4.50 3.25 3.17 1.87 0.76 0.05 0.05 1.71 7.88 1.04 15.03 17.73 24

42.9 37.8 47.1 39.1 41.0 24.2 86.6 95.9 21.4 83.7 83.1 81.8 76.5 76.0 65.2 43.2 4.3 4.5 63.1 88.7 51.0 93.8 94.7 96.0

3.005 2.647 3.294 2.740 2.873 1.697 6.091 6.711 1.497 5.859 6.193 6.065 5.660 5.701 5.084 3.023 0.303 0.315 4.417 6.212 3.571 6.563 6.626 6.720

Productivity 5 {([Fe31]/([Fe21] 1 [Fe31])} 3 flow rate.

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 Fe31). During these attempts the chemostat approached a washout condition, with cell density and Fe31 concentration decreasing even at 2 ml/ min (D 5 0.088 h21). 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 Fe31 to Fe21 concentration at the optimum flow rate for that set of conditions (Fig. 5A). Although run H had a lower ratio of Fe31 to Fe21 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 Fe31 to Fe21 concentration. 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 Fe31/Fe21 concentration ratio (0.958), Fe31 concentration (0.881), and the percentage of iron that was oxidized (0.853). Negative correlation values between pH and suspended-cell density (20.229) and productivity (20.566) were observed, which indicated that at the higher pH values, growth and metabolism were impaired.

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 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). 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

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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.

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 mixedculture 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. 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

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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 (20.338), the ratio of Fe31 to Fe21 concentration (20.328), and percent iron oxidized (20.338). 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 5 0.309 h21). 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. 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-AC01-94ID13223. We are grateful to James Brierley, Newmont Technical Services, for the bacterial cultures. We thank James Brierley and Robert S. Cherry for valuable discussions.

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