On-line optimization of biotechnological processes: I. Application to open algal pond

A new on-line optimization and control procedure applicable to biotechnological systems for which a precise mathematical model is unavailable has been developed and tested. The proposed approach is based on an online search for optimum operating conditions by an automatic system using a modified simplex algorithm to which several features have been added to permit real time operation. The simplex algorithm is the upper level of a hierarchical software package in which the other levels are cost evaluation, control, data acquisition, and signal processing. The optimization method was tested in a laboratory minipond for the cultivation of Spirulina platensis. The controlled parameters were light intensity, optical density, pH, and temperature. The proposed optimization method can be applied to other biological processes provided that the pertinent variables can be measured and controlled and the cost function can be defined mathematically.     

Adaptive steady-state optimization of biomass productivity in continuous fermentors

An adaptive steady-state optimization algorithm is presented and applied to the problem of optimizing the production of biomass in continuous fermentation processes. The algorithm requires no modeling information but is based on an on-line identified linear model, locates the optimum dilution rate, and maintains the chemostat at its optimum operating condition at all times. The behavior of the algorithm is tested against a dynamic model of a chemostat that incorporates metabolic time delay, and it is shown that large disturbances in the subtrate feed concentration and the specific growth rate, causing a shift in the optimum, are handled well. The developed algorithm is also used to drive a methylotroph single-cell production process to its optimum.     

Fermentation of Bacillus thuringiensis for exotoxin production: Process analysis study

The exotoxin produced by certain serotypes of Bacillus thuringiensis was used as a means of microbiological control of the larval development of flies. The optimal batch cultivation conditions with respect to pH, temperature, aeration, agitation, and initial concentration of growth-limiting substrate were determined. A dynamic model describing the process was designed and fitted to the experimental data. The application of a method for estimating exotoxin and bacterial concentrations from on-line measurable quantities such as oxygen consumption and heat production is presented.     

Knowledge based control applied to fixed bed pulsed bioreactors

In this work, an expert system was developed and applied for on-line control and supervision of ethanolic fermentation by immobilized Saccharomyces cerevisiae in a fixed-bed pulsed bioreactor of 1.2 l of working volume. A number of experiments with different substrate concentrations (75, 100, 150 and 200 g/l) and hydraulic residence times (2.4, 1.2 and 0.8 h) were carried out. Knowledge-based computer-aided supervision of this process involves accurate on-line measurement of the relevant process variables (temperature, pH, flow rate, carbon dioxide production, etc.). Carbon dioxide production was used for the estimation of the ethanol productivity. The analysis of the measured data allowed to detect states or trends that may be indicative of process or system failures, providing advices and/or alarms. The results showed the reliability of the control system. In previous works, it was proven that pulsing the feed stream highly improves the productivity of fermentation processes carried out in fixed-bed bioreactors [14, 15, 16]. The amplitude and frequency of the pulsation, which is a key factor in the performance of a pulsed feed bioreactor [13], was selected by the control system by using an algorithm allowing the ethanol productivity to be optimized. The pulsation frequency which maximizes the ethanol productivity, presents a high dependency on the hydraulic residence time and the feeding substrate concentration. When increasing the substrate concentration the optimum pulsation frequency also increases; when increasing the hydraulic residence time the optimum pulsation frequency decreases.     

Experimental studies of network parameters and operational variables on recurrent backpropagation neural network adaptive control of penicillin acylase fermentation by Arthrobacter viscosus

This work is to investigate the on-line control of the fermentation by Arthrobacter viscosus. This species of bacteria can secrete penicillin acylase which is a key enzyme in pharmaceutical industry. The growth of more cells during the fermentation will obtain more enzyme. Both the enzyme activity and the cell growth are rather sensitive to the change of pH. Once the pH during a fermentation is not properly controlled, the decay of cells' activity will irreversibly occur. Two peristaltic pumps for supplying acidic and basic solutions, respectively, were connected for the regulation of pH. With superior ability in identification and prediction of dynamic time series, recurrent backpropagation network (RBPN), instead of conventional controllers, was used as the adaptive controller model for the fermentation with dynamic characteristics. Based on a 1-3-1 BPN, a corresponding 4-4-1 RBPN was determined. The deviation of the pH measured at current time from the set point of 7.0, denoted as ( pH(t), was chosen as the input node of the network controller. The output node of this network controller was the predicted flow rate of the peristaltic pump for next control time interval. Such a model was operated by two phases. During the first phase, the network was set as the process model and trained by a fixed set of on-line acquired data. During the second phase, the network was stopped learning and switched to become a predictor, the predicted control action was hence obtained. The optimum sampling time was determined experimentally. To enhance the effective computation of this network, the number of training data was limited. A moving-window type of supplying training data to the network was applied for the on-line learning. The window size was also determined for each learning. With properly chosen network parameters as well as operation conditions, pH of the fermentation was thus well controlled by the RBPN controller.     

Maximizing productivity of a continuous fermenter using nonlinear adaptive optimizing control

The control of a continuously operated fermenter at its maximum productivity level gives rise to a difficult control problem as the location of the optimum operating point changes due to the disturbances. In addition, the fermenter exhibits a change in the sign of the steady state gain near the optimum operating point. This study is aimed at developing an on-line optimizing control scheme that can track the changing location of the steady state optimum so as to maximize the fermenter productivity. A nonlinear Laguerre model, whose parameters are estimated on-line, is used for tracking the optimum operating point. The control at the optimum point is achieved using an adaptive nonlinear MPC strategy that uses the nonlinear Laguerre model for prediction. The efficiency of the proposed algorithm is demonstrated by simulating the control of a continuous fermenter that exhibits shift in the location of the optimum operating point in response to the changes in the maximum specific growth rate. The proposed on-line optimizing control strategy is shown to result in a considerable improvement in the closed loop performance even in the presence of measurement noise.     

Convenient Model To Describe the Combined Effects of Temperature and pH on Microbial Growth

A new model in which the maximum microbial specific growth rate ((mu)(infmax)) is described as a function of pH and temperature is presented. The seven parameters of this model are the three cardinal pH parameters (the pH below which no growth occurs, the pH above which no growth occurs, and the pH at which the (mu)(infmax) is optimal), the three cardinal temperature parameters (the temperature below which no growth occurs, the temperature above which no growth occurs, and the temperature at which the (mu)(infmax) is optimal), and the specific growth rate at the optimum temperature and optimum pH. The model is a combination of the cardinal temperature model with inflection and the cardinal pH model (CPM). The CPM was compared with the models of Wijtzes et al. and Zwietering et al. by using previously published data sets. The models were compared on the basis of the usual criteria (simplicity, biological significance and minimum number of parameters, applicability, quality of fit, minimum structural correlations, and ease of initial parameter estimation), and our results justified the choice of the CPM. Our combined model was constructed by using the hypothesis that the temperature and pH effects on the (mu)(infmax) are independent. An analysis of this new model with an Escherichia coli O157:H7 data set showed that there was a good correspondence between observed and calculated (mu)(infmax) values. The potential and convenience of the model are discussed.     

Selection of optimal DNA oligos for gene expression arrays.

MOTIVATION: High density DNA oligo microarrays are widely used in biomedical research. Selection of optimal DNA oligos that are deposited on the microarrays is critical. Based on sequence information and hybridization free energy, we developed a new algorithm to select optimal short (20-25 bases) or long (50 or 70 bases) oligos from genes or open reading frames (ORFs) and predict their hybridization behavior. Having optimized probes for each gene is valuable for two reasons. By minimizing background hybridization they provide more accurate determinations of true expression levels. Having optimum probes minimizes the number of probes needed per gene, thereby decreasing the cost of each microarray, raising the number of genes on each chip and increasing its usage. RESULTS: In this paper we describe algorithms to optimize the selection of specific probes for each gene in an entire genome. The criteria for truly optimum probes are easily stated but they are not computable at all levels currently. We have developed an heuristic approach that is efficiently computable at all levels and should provide a good approximation to the true optimum set. We have run the program on the complete genomes for several model organisms and deposited the results in a database that is available on-line (http://ural.wustl.edu/~lif/probe.pl). AVAILABILITY: The program is available upon request.     

Indirect measurement of water content in an aseptic solid substrate cultivation pilot-scale bioreactor

A lack of models and sensors for describing and monitoring large-scale solid substrate cultivation (SSC) bioreactors has hampered industrial development and application of this type of process. This study presents an indirect dynamic measurement model for a 200-kg-capacity fixed-bed SSC bioreactor under periodic agitation. Growth of the filamentous fungus Gibberella fujikuroi on wheat bran was used as a case study. Real data were preprocessed using previously reported methodology. The model uses CO2 production rate and inlet air conditions to estimate average bed water content and average bed temperature. The model adequately reproduces the evolution of the average bed water content and can therefore be used as an on-line estimator in pilot-scale SSC bioreactors. To obtain a reasonable fit of the bed temperature, however, inlet air humidity measurements will have to be adjusted with a data reconciliation algorithm. Good estimation of temperature is important for the future design of improved water content estimation using state observers. The model also provides insight into understanding the complex behavior of the dynamic system, which could prove useful when establishing advanced model-based operational and control strategies.     

The effect of temperature and pH on the growth of aerobic alkalithermophilic bacteria from hot springs in Buryatia

Growth parameters (temperature and pH) were determined for collection cultures of aerobic heterotrophic bacteria. Analysis of the experimental data with the use of the Rosso model made it possible to calculate the extreme values of temperature and pH permissive for culture growth. The examined cultures were subdivided into three groups with respect to their growth temperature and pH. The first group is represented by the cultures with minimum, maximum, and optimal growth temperatures of < 20, 60-64, and 38-40 degrees C, respectively, and with the optimal growth pH 8.0-8.5. Bacteria of the second group are true alkalithermophilic organisms with a temperature optimum of 45-50 degrees C and pH optimum of 8.5-9.0. The third group includes a culture of a thermophilic alkalitolerant bacterium.     

An algorithm for operating a fed-batch fermentor at optimum specific-growth rate

An algorithm for operating a fed-batch fermentor at an optimum specific fermentation rate is proposed. It does not require on-line measurement of nutrient concentration in the culture medium. An on-line estimate of the specific fermentation rate is sufficient for implementation of this scheme. The algorithm is model independent and works well even with poor estimates of the product yields and the specific fermentation rate. Results of a detailed simulation study are presented for a simple case of optimization of cell-mass production in a fed-batch fermentor. The results clearly demonstrate the efficacy of this algorithm under a wide range of fermentation situations.     

The Effect of Temperature and pH on the Growth of Aerobic Alkalithermophilic Bacteria from Hot Springs in Buryatia

Growth parameters (temperature and pH) were determined for collection cultures of aerobic heterotrophic bacteria. Analysis of the experimental data with the use of the Rosso model made it possible to calculate the extreme values of temperature and pH permissive for culture growth. The cultures examined were subdivided into three groups with respect to their growth temperature and pH. The first group is represented by cultures with minimum, maximum, and optimal growth temperatures of <20, 60–64, and 38–40°C, respectively, and with the optimal growth pH 8.0–8.5. Bacteria of the second group are true alkalithermophilic organisms with a temperature optimum of 45–50°C and a pH optimum of 8.5–9.0. The third group includes a culture of a thermophilic alkalitolerant bacterium.     

Rechnergestützte Führung von Fermentationsprozessen,Teil 2

In the first part of the publication [1] an algorithm for the adaptation of the static optimum was represented. This part demonstrates the application of this algorithim to a specific problem. The problems, which are connected with the application of the choice of the object function, of making the process model available and of the realizable economic effects are elaborated. The example of the tested on-line control of a strirred tank reactor shows the advantages and disadvantages of the used algorithm.     

Adaptive on-line optimizing control of bioreactor systems

Several on-line optimizing control strategies were proposed and tested by computer simulation for the efficient operation of bioreactors. The control task was divided into two, one of which was to search for the optimal operating point and passed the set point to the lower layer of which task was to make the process output follow the set point as soon as possible. It was shown to be effective for the upper layer to express the objective function as a polynomial with respect to the measurement variable and to make use of it for finding the optimum point. Noting that the major dynamic characteristics of bioreactor system is the time-varying and nonlinear nature, the adaptive type control system is in evitable. It was shown to be quite effective to use discrete type self-tuning PID controller and the optimal controller compensated for the interaction between the control loops.Application was made to the cell recycle system for the production of lactic acid and baker's yeast cultivation. I was found from the former application that the control quality can be significantly improved by incorporating the decoupling strategy into the lower layer closed-loop system. It was also found from the latter application that the initial startup period can be significantly reduced by making use of the rough mathematical model.     

Optimization of the activity in porous media of proton-generating immobilized enzymatic reactions by weak acid facilitation

In addition to the role of maintaining the pH, buffers can also facilitate the transport of H(+) ions in acid-generating systems. The role of this facilitation in proton transport in porous pellets on acid-generating immobilized enzymic reactions is examined. The activity in these systems can be maximized by a proper control of facilitation, which involves the determination of the appropriate variables out of (1) the concentration of the weak acid, (2) the pH of the medium, (3) the bulk substrate concentration, and (4) the type of weak acid. Since the intrinsic activity (IA) of the immobilized enzyme is such that it exhibits an optimum with respect to the pH, a partial (optimal) removal of diffusional limitation by facilitation maximizes the activity when the bulk pH is larger than this optimum pH. A complete removal of diffusional limitations, however, maximizes the activity when the bulk pH is less than or equal to the above optimum pH. The control of the diffusional resistance can be achieved by controlling the extent of facilitation, hence by adjusting the parameters mentioned above. Computations have been carried out to examine the effect of each of these parameters on the activity of the immobilized enzyme. It is found that when the bulk pH is less than or equal to the optimum pH of the intrinsic activity of the immobilized enzyme, there exists a lower limit on the amount of weak acid required to maximize the activity. However, an optimum amount of weak acid is required to maximize the activity when the bulk pH is higher than that optimum pH. For a given activity the amount of weak acid is minimal if the pK of the weak acid is close to the bulk pH. The effect of coupling between the proton and substrate transport on activity control is also examined and the effect of geometry on activity is evaluated for spherical, cylindrical, and flat-plate configurations.     

Comparison of brain–computer interface decoding algorithms in open-loop and closed-loop control

Neuroprosthetic devices such as a computer cursor can be controlled by the activity of cortical neurons when an appropriate algorithm is used to decode motor intention. Algorithms which have been proposed for this purpose range from the simple population vector algorithm (PVA) and optimal linear estimator (OLE) to various versions of Bayesian decoders. Although Bayesian decoders typically provide the most accurate off-line reconstructions, it is not known which model assumptions in these algorithms are critical for improving decoding performance. Furthermore, it is not necessarily true that improvements (or deficits) in off-line reconstruction will translate into improvements (or deficits) in on-line control, as the subject might compensate for the specifics of the decoder in use at the time. Here we show that by comparing the performance of nine decoders, assumptions about uniformly distributed preferred directions and the way the cursor trajectories are smoothed have the most impact on decoder performance in off-line reconstruction, while assumptions about tuning curve linearity and spike count variance play relatively minor roles. In on-line control, subjects compensate for directional biases caused by non-uniformly distributed preferred directions, leaving cursor smoothing differences as the largest single algorithmic difference driving decoder performance.     

The Model Identification for Small Unmanned Aerial Rotorcraft Based on Adaptive Ant Colony Algorithm

This paper proposes a model identification method to get high performance dynamic model of a small unmanned aerial rotorcraft.With the analysis of flight characteristics,a linear dynamic model is constructed by the small perturbation theory.Using the micro guidance navigation and control module,the system can record the control signals of servos,the state information of attitude and velocity information in sequence.After the data preprocessing,an adaptive ant colony algorithm is proposed to get optimal parameters of the dynamic model.With the adaptive adjustment of the pheromone in the selection process,the proposed model identification method can escape from local minima traps and get the optimal solution quickly.Performance analysis and experiments are conducted to validate the effectiveness of the identified dynamic model.Compared with real flight data,the identified model generated by the proposed method has a better performance than the model generated by the adaptive genetic algorithm.Based on the identified dynamic model,the small unmanned aerial rotorcraft can generate suitable control parameters to realize stable hovering,turning,and straight flight.     

Potential effects of interaction between CO2 and temperature on forest landscape response to global warming

Projected temperature increases under global warming could benefit southern tree species by providing them the optimal growing temperature and could be detrimental to northern species by exposing them to the supra optimal growing temperatures. This benefit-detriment trade-off could increase the competitive advantage of southern species in the northern species range and cause the increase or even dominance of southern species in the northern domain. However, the optimum temperature for photosynthesis of C3 plants may increase due to CO₂ enrichment. An increase in the optimum temperature could greatly reduce the benefit-detriment effect. In this study, we coupled a forest ecosystem process model (PnET-II) and a forest GAP model (LINKAGES) with a spatially dynamic forest landscape model (LANDIS-II) to study how an optimum temperature increase could affect forest landscape response due to global warming. We simulated 360 years of forest landscape change in the Boundary Water Canoe Area (BWCA) in northern Minnesota, which is transitional between boreal and temperate forest. Our results showed that, under the control scenario of continuing the historic 1984–1993 mean climate (mainly temperature, precipitation and CO₂), the BWCA will become a spruce-fir dominated boreal forest. However, under the scenario of predicted climatic change [the 2000–2099 climates are predicted by Canadian Climate Center (CCC), followed by 200 years of continuing the predicted 2090–2099 mean climate], the BWCA will become a pine-dominated mixed forest. If the optimum temperature increases gradually with [CO₂] (the increase in optimum temperature is assumed to change gradually from 0 °C in year 2000 to 5 °C in year 2099 when [CO₂] reaches 711 ppm and stabilizes at 5 °C after year 2099), the BWCA would remain a fir-dominated boreal forest in areas with relatively high water-holding capacity, but not in areas with relatively low water-holding capacity. Our results suggest that the [CO₂] induced increases in optimum temperature could substantially reduce forest landscape change caused by global warming. However, not all tree species would be able to successfully adapt to future warming as predicted by CCC, regardless of optimum temperature acclimations.     

On-line monitoring and controlling system for fermentation processes

A personal computer-based on-line monitoring and controlling system was developed for the fermentation of microorganism. The on-line HPLC system for the analysis of glucose and ethanol in the fermentation broth was connected to the fermenter via an auto-sampling equipment, which could perform the pipetting, filtration and dilution of the sample and final injection onto the HPLC through automation based on a programmed procedure. The A/D and D/A interfaces were equipped in order to process the signals from electrodes and from the detector of HPLC, and to direct the feed pumps, the motor of stirrer and gas flow-rate controller. The software that supervised the control of the stirring speed, gas flow-rate, pH value, feed flow-rate of medium, and the on-line measurement of glucose and ethanol concentration was programmed by using Microsoft Visual Basic under Microsoft Windows. The signal for chromatographic peaks from on-line HPLC was well captured and processed using an RC filter and a smoothing algorithm. This monitoring and control system was demonstrated to be effective in the ethanol fermentation of Zymomonas mobilis operated in both batch and fed-batch modes. In addition to substrate and product concentrations determined by on-line HPLC, the biomass concentration in Z. mobilis fermentation could also be on-line estimated by using the pH control and an implemented software sensor. The substrate concentration profile in the fed-back fermentation followed well the set point profile due to the fed-back action of feed flow-rate control.     

Precision Sensing by Two Opposing Gradient Sensors: How Does Escherichia coli Find its Preferred pH Level?

It is essential for bacteria to find optimal conditions for their growth and survival. The optimal levels of certain environmental factors (such as pH and temperature) often correspond to some intermediate points of the respective gradients. This requires the ability of bacteria to navigate from both directions toward the optimum location and is distinct from the conventional unidirectional chemotactic strategy. Remarkably, Escherichia coli cells can perform such a precision sensing task in pH taxis by using the same chemotaxis machinery, but with opposite pH responses from two different chemoreceptors (Tar and Tsr). To understand bacterial pH sensing, we developed an Ising-type model for a mixed cluster of opposing receptors based on the push-pull mechanism. Our model can quantitatively explain experimental observations in pH taxis for various mutants and wild-type cells. We show how the preferred pH level depends on the relative abundance of the competing sensors and how the sensory activity regulates the behavioral response. Our model allows us to make quantitative predictions on signal integration of pH and chemoattractant stimuli. Our study reveals two general conditions and a robust push-pull scheme for precision sensing, which should be applicable in other adaptive sensory systems with opposing gradient sensors.