Control in bioreactors showing gradients

In large-scale bioreactors gradients often occur as a result of non-ideal mixing. This phenomenon complicates design and control of large-scale bioreactors. Gradients in the oxygen concentration can be modeled with a two-compartment model of the liquid phase. Application of this model had been suggested for the control of the dissolved oxygen concentration with a batch gluconic acid fermentation process as the model system. The control system consists of a controller, an observer and a parameter estimator. In this work, the controller design is reconsidered and, in simulation experiments, the performance of the control system has been investigated. When the parameter values are known, the controller in combination with the observer works adequately. The parameter estimator, however, yields incorrect parameters, which are caused by a coupling between two parameters. This causes a deviation of the estimated states from the process states. The simulation results suggest that a priori knowledge of the parameters is required for application of the model for control and state estimation.     

Observer error in vegetation surveys: a review

Aims Vegetation sampling employing observers is prone to both inter-observer and intra-observer error. Three types of errors are common: (i) overlooking error (i.e. not observing species actually present), (ii) misidentification error (i.e. not correctly identifying species) and (iii) estimation error (i.e. not accurately estimating abundance). I conducted a literature review of 59 articles that provided quantitative estimates or statistical inferences regarding observer error in vegetation studies.Important findings Almost all studies (92%) that tested for a statistically significant effect of observer error found at least one significant comparison. In surveys of species composition, mean pseudoturnover (the percentage of species overlooked by one observer but not another) was 10–30%. Species misidentification rates were on the order of 5–10%. The mean coefficient of variation (CV) among observers in surveys of vegetation cover was often several hundred % for species with low cover, although CVs of 25–50% were more representative of species with mean covers of>50%. A variety of metrics and indices (including commonly used diversity indices) and multivariate data analysis techniques (including ordinations and classifications) were found to be sensitive to observer error. Sources of error commonly include both characteristics of the vegetation (e.g. small size of populations, rarity, morphology, phenology) and attributes of the observers (e.g. mental fatigue, personal biases, differences in experience, physical stress). The use of multiple observers, additional training including active feedback approaches, and continual evaluation and calibration among observers are recommended as strategies to reduce observer error in vegetation surveys.     

Microbial production of enzymes: Nonlinear state and kinetic reaction rates estimation

The nonlinearity of the biotechnological processes and the absence of cheap and reliable instrumentation require an enhanced modelling effort and estimation strategies for the state and the kinetic parameters. This work approaches nonlinear estimation strategies for microbial production of enzymes, exemplified by using a process of lipase production from olive oil by Candida rugosa. First, by using a dynamical mathematical model of this process, an asymptotic observer which reconstructs the unavailable state variables is proposed. The design of this kind of observers is based on mass and energy balances without the knowledge of kinetics being necessary; only minimal information concerning the measured concentrations is used. Second, a nonlinear high-gain observer is designed for the estimation of imprecisely known kinetics of the bioprocess. An important advantage of this high-gain estimator is that the tuning is reduced to the calibration of a single parameter. Numerical simulations in various scenarios are provided in order to test the behaviour and performances of the proposed nonlinear estimation strategies.     

Hybrid bounded error observers for uncertain bioreactor models

In this paper, we build bounded error observers for a common class of partially known bioreactor models. The main idea is to construct hybrid bounded observers “between” high gain observer, which has an adjustable convergence rate but requires perfect knowledge of the model, and asymptotic observer which is very robust towards uncertainty but has a fixed convergence rate. An hybrid bounded error observer which reconstructs the two state variables is constructed considering two steps: first step is similar to a high gain observer meaning that fast convergence rate but error depending on the knowledge of the model are obtained; second step is a switch to an observer similar to the asymptotic one meaning that fixed convergence rate towards an error as small as desired is obtained. Thus, a better convergence rate of estimated variables than the classical asymptotic observer is obtained.     

Observer variation in field assessments of vegetation condition: Implications for biodiversity conservation

Summary   Uncertainty in assessments of vegetation condition that are used to inform land management and planning decisions for biodiversity conservation in Australia may lead to unexpected outcomes, including loss of biodiversity. This study investigates observer error in field estimates of vegetation attributes, one component of uncertainty in assessments of vegetation condition. Ten observers conducted vegetation condition assessments using two assessment protocols (BioMetric and Habitat Hectares) on 20 sites in a grassy woodland community. Observers' estimates varied substantially across multiple scoring categories for all vegetation attributes on almost all sites. Across all sites, the average coefficient of variation in total vegetation condition scores was 15–18% for both protocols, with a maximum of 60%. The primary cause of variation in total vegetation condition scores was random error in raw estimates of vegetation attributes, although sensitivity of some highly weighted attributes to error exacerbated variation in some cases. Observers generally agreed on the total scores and ranks of highly degraded (pasture) sites, but were less consistent on other sites. Rank correlations between pairs of observers were stronger for Habitat Hectares, suggesting BioMetric may be slightly more sensitive to observer error. It is recommended that: (i) research is undertaken into methods for reducing observer error; (ii) review is made of the sensitivity of index scoring structures to observer error; (iii) field observers estimate uncertainty around point estimates of vegetation condition; and, (iv) decision-makers explicitly incorporate uncertainty into the decision-making processes and aim for outcomes that are robust to this uncertainty.     

Uniform step-by-step observer for aerobic bioreactor based on super-twisting algorithm

This paper describes a fixed-time convergent step-by-step high order sliding mode observer for a certain type of aerobic bioreactor system. The observer was developed using a hierarchical structure based on a modified super-twisting algorithm. The modification included nonlinear gains of the output error that were used to prove uniform convergence of the estimation error. An energetic function similar to a Lyapunov one was used for proving the convergence between the observer and the bioreactor variables. A nonsmooth analysis was proposed to prove the fixed-time convergence of the observer states to the bioreactor variables. The observer was tested to solve the state estimation problem of an aerobic bioreactor described by the time evolution of biomass, substrate and dissolved oxygen. This last variable was used as the output information because it is feasible to measure it online by regular sensors. Numerical simulations showed the superior behavior of this observer compared to the one having linear output error injection terms (high-gain type) and one having an output injection obtaining first-order sliding mode structure. A set of numerical simulations was developed to demonstrate how the proposed observer served to estimate real information obtained from a real aerobic process with substrate inhibition.     

Interobserver error in grassland vegetation surveys: sources and implications

人类观测误差是植被测量中不可避免的一个问题。我们量化了与高草草原植被长期监测相关的观测者间误差的四个组成部分：忽略误 差、误识别误差、谨慎误差和估计误差。由于观察者会产生误差，我们还评估了地块大小与伪周转率的关系，以及对比了物种组成和丰度的伪变化与四年间植被变化之间的关系。这项研究是在美国堪萨斯州的高草草原国家保护区进行的。监测点包括10个地块，每个地块由一系列的四个嵌套框架(0.01, 0.1, 1和10 m²)组成。在每个嵌套框架中记录了所有的草本物种，并且在10 m²的空间尺度下，视觉估计了7个覆盖类别内的叶面覆盖。总共调查了300个地块(30个地点)，并随机选择28个地块重新进行测量以评估观测者的误差。所有的调查由四名观测者分两组完成。研究结果表明，在10 m²空间尺度上，由忽略误差引起的伪周转率平均为18.6%，而由误识别误差和谨慎误差引起的伪周转率平均值分别为1.4%和0.6%。尽管由重新定位引起的误差可能也起一定的作用，由忽略误差导致的伪周转率随样地面积的减小而增 加。物种组成在四年期间的变化(排除潜在的误识别误差和谨慎误差)为30.7%，其中包括由忽略误差和实际变化引起的伪周转率。18.6%的忽略误差表明四年期间的实际变化只有12.1%。对于估计误差，26.2%会记录为不同的覆盖等级。在四年的时间内，46.9%的记录显示了不同的覆盖等级，这表明两个时间段间覆盖率变化的56%是由于观测者误差造成的。     

Reaction engineering analysis of cellulase production with Trichoderma reesei RUT-C30 with intermittent substrate supply

The effects of varying initial concentrations of microcrystalline cellulose on cellulase production with Trichoderma reesei RUT-C30 as well as the effects of varying lactose and ammonium sulfate concentrations in the feed medium were studied simultaneously in parallel-operated shake flasks and, alternatively, in parallel-operated stirred-tank bioreactors on a 10-mL scale. Fifteen experiments were performed as triplicates in shake flasks as well as in stirred-tank bioreactors in parallel to identify the parameters of second-order polynomials for the estimation of the final filter paper activity of T. reesei RUT-C30 after a process time of 96 h. Even though parameter estimation was not possible based on the results of the shake flasks due to final enzyme activities at or below the detection limit (with the exception of one shake flask), the identification of the second-order polynomial was successful with the results of the parallel-operated stirred-tank bioreactors on a 10-mL scale. Reaction conditions with 53.3 g L^?1 microcrystalline cellulose in the initial medium, no lactose feeding and 3.3 g L^?1 day^?1 intermittent ammonium sulfate addition were estimated to be optimal. The final experimental validation of the optimum substrate supply on a L-scale resulted in the production of 4.88 filter paper units (FPU) mL^?1 with T. reesei RUT-C30 after 96 h. This is an improvement by a factor of 3.6 compared to the reference batch process (1.35 FPU mL^?1).     

Scaling-up vaccine production: implementation aspects of a biomass growth observer and controller

This study considers two aspects of the implementation of a biomass growth observer and specific growth rate controller in scale-up from small- to pilot-scale bioreactors towards a feasible bulk production process for whole-cell vaccine against whooping cough. The first is the calculation of the oxygen uptake rate, the starting point for online monitoring and control of biomass growth, taking into account the dynamics in the gas-phase. Mixing effects and delays are caused by amongst others the headspace and tubing to the analyzer. These gas phase dynamics are modelled using knowledge of the system in order to reconstruct oxygen consumption. The second aspect is to evaluate performance of the monitoring and control system with the required modifications of the oxygen consumption calculation on pilot-scale. In pilot-scale fed-batch cultivation good monitoring and control performance is obtained enabling a doubled concentration of bulk vaccine compared to standard batch production.     

Perfusion bioreactors for the production of recombinant proteins in insect cells

Conclusion High density perfusion culture of insect cells for the production of recombinant proteins has proved to be an attractive alternative to batch and fed-batch processes. A comparison of the different production processes is summarized in Table 3. Internal membrane perfusion has a limited scale-up potential but appears to the method of choice in smaller lab-scale production systems. External membrane perfusion results in increased shear stress generated by pumping of cells and passing through microfiltration modules at high velocity. However, using optimized perfusion strategies this shear stress can be minimized such that it is tolerated by the cells. In these cases, perfusion culture has proven to be superior to batch production with respect to product yields and cell specific productivity. Although insect cells could be successfully cultivated by immobilization and perfusion in stationary bed bioreactors, this method has not yet been used in continuous processes. In fluidized bed bioreactors with continuous medium exchange cells showed reduced growth and protein production rates.For the cultivation of insect cells in batch and fedbatch processes numerous efforts have been made to optimize the culture medium in order to allow growth and production at higher cell densities. These improved media could be used in combination with a perfusion process, thus allowing substantially increased cell densities without raising the medium exchange rate. However, sufficient oxygen supply has to be guaranteed during fermentation in order to ensure optimal productivity.     

Nonlinear predictive control for maximization of CO2 bio-fixation by microalgae in a photobioreactor

In the framework of environment preservation, microalgae biotechnology appears as a promising alternative for CO₂ mitigation. Advanced control strategies can be further developed to maximize biomass productivity, by maintaining these microorganisms in bioreactors at optimal operating conditions. This article proposes the implementation of Nonlinear Predictive Control combined with an on-line estimation of the biomass concentration, using dissolved carbon dioxide concentration measurements. First, optimal culture conditions are determined so that biomass productivity is maximized. To cope with the lack of on-line biomass concentration measurements, an interval observer for biomass concentration estimation is built and described. This estimator provides a stable accurate interval for the state trajectory and is further included in a nonlinear model predictive control framework that regulates the biomass concentration at its optimal value. The proposed methodology is applied to cultures of the microalgae Chlorella vulgaris in a laboratory-scale continuous photobioreactor. Performance and robustness of the proposed control strategy are assessed through experimental results.     

Submerged cultivation of Stephania glabra (Roxb.) Miers cells in different systems: Specific features of growth and accumulation of alkaloid stepharine

Strains of a Stephania glabra suspension culture grown in flasks and two types of bioreactors (laboratory-scale bubble and pilot-scale stirred reactors) have been compared according to their growth characteristics and accumulation of the alkaloid stepharine. The best characteristics have been recorded for strains 113 and 261. In the case of batch cultivation in flasks, the maximal accumulation of dry biomass by these strains reaches 19–21 g/l; that of the alkaloid stepharine, 0.30–0.35% of dry biomass. The used strains differ in their response to cultivation scale-up from flasks to bioreactors, strain 254 displaying the lowest adaptation to such changes. A bubble reactor is the most beneficial system for submerged cultivation of S. glabra. The absence of detectable stepharine synthesis on the background of a considerable decrease in all growth characteristics of the cultures has been observed when using a pilot stirred bioreactor. The batch cultures of strains 113 and 261 in a bubble bioreactor accumulate 11–16 g/l of dry biomass containing 0.05–0.16% of the alkaloid. It has been shown that strains 113 and 261 retain satisfactory physiological characteristics in a semi-flow regime of a bubble bioreactor. This scale-up scheme can be used for further industrial cultivation.     

Linking camera‐trap data to taxonomy: Identifying photographs of morphologically similar chipmunks

Remote cameras are a common method for surveying wildlife and recently have been promoted for implementing large‐scale regional biodiversity monitoring programs. The use of camera‐trap data depends on the correct identification of animals captured in the photographs, yet misidentification rates can be high, especially when morphologically similar species co‐occur, and this can lead to faulty inferences and hinder conservation efforts. Correct identification is dependent on diagnosable taxonomic characters, photograph quality, and the experience and training of the observer. However, keys rooted in taxonomy are rarely used for the identification of camera‐trap images and error rates are rarely assessed, even when morphologically similar species are present in the study area. We tested a method for ensuring high identification accuracy using two sympatric and morphologically similar chipmunk (Neotamias) species as a case study. We hypothesized that the identification accuracy would improve with use of the identification key and with observer training, resulting in higher levels of observer confidence and higher levels of agreement among observers. We developed an identification key and tested identification accuracy based on photographs of verified museum specimens. Our results supported predictions for each of these hypotheses. In addition, we validated the method in the field by comparing remote‐camera data with live‐trapping data. We recommend use of these methods to evaluate error rates and to exclude ambiguous records in camera‐trap datasets. We urge that ensuring correct and scientifically defensible species identifications is incumbent on researchers and should be incorporated into the camera‐trap workflow.     

Strong observer effect on tree microhabitats inventories: A case study in a French lowland forest

Validating biodiversity indicators requires an analysis of their applicability, their range of validity and their degree of correlation with the biodiversity they are supposed to represent. In this process, assessing the magnitude of observer effect is an essential step, especially if non-specialist observers are involved. Tree microhabitats – woodpecker cavities, cracks and bark characteristics – are reputed to be easily detected by non-specialists as microhabitat observation does not require prior forestry or ecology knowledge. We therefore quantified the probabilities of true and false positive detections made by observers during inventories.Within two 0.5 ha plots in a forest reserve that has not been harvested for at least 150 years, 14 observers with various backgrounds visually inventoried microhabitats on 106 oak (Quercus petraea and Quercus robur) and beech (Fagus sylvatica) trees. We used parametric and Bayesian statistics to compare these observers’ recorded observations with results from an independent census.The mean number of microhabitats per tree varied widely among observers – from 1.4 to over 3. Only five observers reported a mean number of microhabitats per tree that was statistically equivalent to the reference census. The probability of true detection also varied among observers for each microhabitat (from to 0 to 1) as did the probability of false positive detection (from 0 to 0.7). These results show that microhabitat inventories are particularly prone to observer effects.Such strong observer effects weaken the usefulness of microhabitats as biodiversity indicators. If microhabitat inventories are to be developed, we recommend controlling for observer effects by (i) defining standard operating procedures and multiplying the number of observer training sessions and of consensual standardization censuses; (ii) using pairs of observers to record microhabitats whenever possible (though the efficiency of this method remains to be tested); (iii) planning fieldwork so that the factors of interest are not confused with observer effects; and (iv) integrating observer profiles into the statistical models used to analyze the data.     

Identifying Sources of Variation in Sheet Metal Stamping

Manufacturers using traditional process control charts to monitor their sheet metal stamping processes often encounter out-of-control signals indicating that the process mean has changed. Unfortunately, a sheet metal stamping process does not have the necessary adjustability in its process variable input settings to allow easily correcting the mean response in an out-of-control condition. Hence the signals often go ignored. Accordingly, manufacturers are unaware of how much these changes in the mean inflate the variance in the process output. We suggest using a designed experiment to quantify the variation in stamped panels attributable to changing means. Specifically, we suggest classifying stamping variation into three components: part-to-part, batch-to-batch, and within batch variation. The part-to-part variation represents the short run variability about a given stable or trending batch mean. The batch-to-batch variation represents the variability of the individual batch mean between die setups. The within batch variation represents any movement of the process mean during a given batch run. Using a two-factor nested analysis of variance model, a manufacturer may estimate the three components of variation. After partitioning the variation, the manufacturer may identify appropriate countermeasures in a variation reduction plan. In addition, identifying the part-to-part or short run variation allows the manufacturer to predict the potential process capability and the inherent variation of the process given a stable mean. We demonstrate the methodology using a case study of an automotive body side panel.     

Different ways of modeling spatial-frequency uncertainty in visual signal detection

Inferior human signal-detection behavior compared with that of ideal observers has been explained by intrinsic uncertainty of the human observer with respect to certain signal parameters. One way to model this uncertainty is to assume that the observer simultaneously monitors multiple channels, corresponding to possible parameters. However, it is also conceivable to assume that an observer, uncertain about which channel to monitor, chooses a suboptimally tuned single filter. Finally, uncertainty may also cause the filter underlying a single channel to broaden. In this paper these different models are investigated with respect to spatial-frequency uncertainty for matched filters detecting Gabor signals. All three mechanisms predict a decrease in detection performance. However, it is shown that the resulting psychometric functions are different. While the slopes increase with uncertainty for the multiple-channel models, they decrease for a randomly chosen single channel. Broadening a single filter leads to parallel psychometric functions.     

Regime analysis and scale-down: Tools to investigate the performance of bioreactors

Scale-up and optimization of biotechnological processes on a large scale tend to be more methodically approached than the application of rules of thumb, experience, and trial and error. Methods frequently used in chemical engineering are adopted in biochemical engineering and are employed with great effect. A summary is given of methods and rules of thumb used in scaling up chemical processes. A procedure to scale up and optimize bioreactors is presented. It is based on the so-called scale-down approach. Some elements of this procedure, viz. theoretical regime analysis and small-scale investigations, are extensively demonstrated by examples. It is shown that a regime analysis based on characteristic times can give a quick estimation of the performance of bioreactors. Small-scale experiments based on the result of such analysis or on the results of a dimensional analysis can give valuable information for scale-up or optimization fermentation processes.     

Parallel steady state studies on a milliliter scale accelerate fed‐batch bioprocess design for recombinant protein production with Escherichia coli

In general, fed‐batch processes are applied for recombinant protein production with Escherichia coli (E. coli). However, state of the art methods for identifying suitable reaction conditions suffer from severe drawbacks, i.e. direct transfer of process information from parallel batch studies is often defective and sequential fed‐batch studies are time‐consuming and cost‐intensive. In this study, continuously operated stirred‐tank reactors on a milliliter scale were applied to identify suitable reaction conditions for fed‐batch processes. Isopropyl β‐d ‐1‐thiogalactopyranoside (IPTG) induction strategies were varied in parallel‐operated stirred‐tank bioreactors to study the effects on the continuous production of the recombinant protein photoactivatable mCherry (PAmCherry) with E. coli. Best‐performing induction strategies were transferred from the continuous processes on a milliliter scale to liter scale fed‐batch processes. Inducing recombinant protein expression by dynamically increasing the IPTG concentration to 100 µM led to an increase in the product concentration of 21% (8.4 g L^?1) compared to an implemented high‐performance production process with the most frequently applied induction strategy by a single addition of 1000 µM IPGT. Thus, identifying feasible reaction conditions for fed‐batch processes in parallel continuous studies on a milliliter scale was shown to be a powerful, novel method to accelerate bioprocess design in a cost‐reducing manner. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1426–1435, 2016     

Detection and Plant Monitoring Programs: Lessons from an Intensive Survey of Asclepias meadii with Five Observers

Monitoring programs, where numbers of individuals are followed through time, are central to conservation. Although incomplete detection is expected with wildlife surveys, this topic is rarely considered with plants. However, if plants are missed in surveys, raw count data can lead to biased estimates of population abundance and vital rates. To illustrate, we had five independent observers survey patches of the rare plant Asclepias meadii at two prairie sites. We analyzed data with two mark-recapture approaches. Using the program CAPTURE, the estimated number of patches equaled the detected number for a burned site, but exceeded detected numbers by 28% for an unburned site. Analyses of detected patches using Huggins models revealed important effects of observer, patch state (flowering/nonflowering), and patch size (number of stems) on probabilities of detection. Although some results were expected (i.e. greater detection of flowering than nonflowering patches), the importance of our approach is the ability to quantify the magnitude of detection problems. We also evaluated the degree to which increased observer numbers improved detection: smaller groups (3–4 observers) generally found 90 – 99% of the patches found by all five people, but pairs of observers or single observers had high error and detection depended on which individuals were involved. We conclude that an intensive study at the start of a long-term monitoring study provides essential information about probabilities of detection and what factors cause plants to be missed. This information can guide development of monitoring programs.