Software sensors for fermentation processes

Four software sensors based on standard on-line data from fermentation processes and simple mathematical models were used to monitor a number of state variables in Escherichia coli fed-batch processes: the biomass concentration, the specific growth rate, the oxygen transfer capacity of the bioreactor, and the new R _O/S sensor which is the ratio between oxygen and energy substrate consumption. The R _O/S variable grows continuously in a fed-batch culture with constant glucose feed, which reflects the increasing maintenance demand at declining specific growth rate. The R _O/S sensor also responded to rapid pH shift-downs reflecting the increasing demand for maintenance energy. It is suggested that this sensor may be used to monitor the extent of physiological stress that demands energy for survival.     

Stable adaptive algorithm for simultaneous estimation of time-varying parameters and state variables in aerobic bioprocesses

The application of modern model based control algorithms in the bioprocesses is hampered by the lack of accurate and cheap on-line sensors, capable of providing on-line measurements of the main process variables and parameters. In this paper, a new approach for estimation of immeasurable time-varying parameters and state variable is presented for a class of aerobic bioprocesses using only on-line measurements of the oxygen uptake rate. The approach consists in the design of a new parameter estimator of biomass growth rate and yield coefficient for oxygen consumption on the basis of the theory of adaptive estimation. The dynamical equation of the measurable reaction rate, oxygen uptake rate, is presented as a linear one with respect to the biomass growth rate and the yield coefficient for oxygen consumption. In this way, the structure of the proposed estimator becomes linear time-varying one. After some mathematical transformations, that structure is presented in a form, allowing to be derived the stability conditions using some theoretical results concerning the stability of adaptive observers. The estimates of the yield coefficient for oxygen consumption, the biomass concentration and specific growth rate are obtained then on the basis of the generated estimates using well known kinetic models of bioprocesses. With respect to previous similar approaches, the new estimation algorithm gives stable estimates not only of immeasurable state variable and reaction rates but likewise of an yield coefficient. The behavior of the proposed estimator is studied under inexact initial conditions, step changes of dilution rate and in the presence of measurement noise by simulations using a process model, which belongs to the investigated class of bioprocesses.     

Cole–Cole, linear and multivariate modeling of capacitance data for on-line monitoring of biomass

This work evaluates three techniques of calibrating capacitance (dielectric) spectrometers used for on-line monitoring of biomass: modeling of cell properties using the theoretical Cole–Cole equation, linear regression of dual-frequency capacitance measurements on biomass concentration, and multivariate (PLS) modeling of scanning dielectric spectra. The performance and robustness of each technique is assessed during a sequence of validation batches in two experimental settings of differing signal noise. In more noisy conditions, the Cole–Cole model had significantly higher biomass concentration prediction errors than the linear and multivariate models. The PLS model was the most robust in handling signal noise. In less noisy conditions, the three models performed similarly. Estimates of the mean cell size were done additionally using the Cole–Cole and PLS models, the latter technique giving more satisfactory results.     

Recruitment forecasting of yellowfin tuna in the eastern Pacific Ocean with artificial neuronal networks

The recruitment of yellowfin tuna in the eastern Pacific Ocean is modeled based on oceanographic as well as biological parameters, using two nonlinear autoregressive network models with exogenous inputs (NARX). In the first model (Model 1) the quarterly recruitment is modeled considering eastern Pacific global oceanographic conditions: the Southern Oscillation Index (SOI), the Pacific Decadal Oscillation (PDO), and spawners biomass. In Model 2, recruitment is predicted based on sea surface temperature, wind magnitude, and oceanic current magnitude of a smaller area within the eastern Pacific Ocean, considered as relevant for spawning and recruitment, and total spawners biomass. The correlation coefficient between the ANN recruitment estimate and the “real” recruitment is r > 0.80 in both models. Series of sensitivity analysis suggest that the SOI and the sea surface temperature are the most important variables for the recruitment in Model 1 and Model 2 also show that warm sea surface favors recruitment. A forecasting model under different climatological scenarios indicates that the recruitment of yellowfin tuna could be higher in the period 2015–2020 compared to the ones registered in the period 2009–2013.     

Agricultural disturbance response models for invertebrate and algal metrics from streams at two spatial scales within the U.S.

As part of the USGS study of nutrient enrichment of streams in agricultural regions throughout the United States, about 30 sites within each of eight study areas were selected to capture a gradient of nutrient conditions. The objective was to develop watershed disturbance predictive models for macroinvertebrate and algal metrics at national and three regional landscape scales to obtain a better understanding of important explanatory variables. Explanatory variables in models were generated from landscape data, habitat, and chemistry. Instream nutrient concentration and variables assessing the amount of disturbance to the riparian zone (e.g., percent row crops or percent agriculture) were selected as most important explanatory variable in almost all boosted regression tree models regardless of landscape scale or assemblage. Frequently, TN and TP concentration and riparian agricultural land use variables showed a threshold type response at relatively low values to biotic metrics modeled. Some measure of habitat condition was also commonly selected in the final invertebrate models, though the variable(s) varied across regions. Results suggest national models tended to account for more general landscape/climate differences, while regional models incorporated both broad landscape scale and more specific local-scale variables.     

Monitoring of fed-batch <Emphasis Type="Italic">E. coli</Emphasis> fermentations with software sensors

Accurate monitoring and control of industrial bioprocess requires the knowledge of a great number of variables, being some of them not measurable with standard devices. To overcome this difficulty, software sensors can be used for on-line estimation of those variables and, therefore, its development is of paramount importance. An Asymptotic Observer was used for monitoring Escherichia coli fed-batch fermentations. Its performance was evaluated using simulated and experimental data. The results obtained showed that the observer was able to predict the biomass concentration profiles showing, however, less satisfactory results regarding the estimation of glucose and acetate concentrations. In comparison with the results obtained with an Extended Kalman Observer, the performance of the Asymptotic Observer in the fermentation monitoring was slightly better.     

Tradeoffs between fine‐scale site measurements and coarse sensory data for long‐term monitoring of pulsed wetlands

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On-line state estimation of biomass based on acid production in Zymomonas mobilis cultures

The concentrations of biomass, substrate and product are very important state variables of almost every bioprocess and generally unable to be measured directly in?situ due to the lack of reliable sensors. In this paper, an adaptive observer of the biomass concentration is proposed for an anaerobic fermentation process where only the measurement of the acid product is available on-line. The observer was tested to be effective by several experiments under various operating conditions. In this experimental system, an auto-sampling device was connected between the bioreactor for the fermentation of Zymomonas mobilis and a HPLC so that the concentrations of glucose and ethanol could be directly measured through such implementation.     

不同林分起源的相容性生物量模型构建

目前为止已有不同方法构建生物量相容性模型,但不同林分起源的生物量相容性模型很少报道。针对此问题,以150株南方马尾松(Pinus masson iana)地上生物量数据为例,利用比例平差法和非线性联立方程组法建立不同起源地上生物量以及干材、干皮、树枝和树叶各分项生物量相容的通用性模型。根据分配层次不同,两种方法又各自考虑总量直接控制和分级联合控制两种方案。从直径、树高、地径、枝下高和冠幅5个林分变量中选取不同的变量构建一元、二元和三元生物量模型,并利用加权最小二乘回归法消除生物量模型中存在的异方差性。结果为:比例平差法和非线性联立方程组法都能有效保证各分项生物量总和等于总生物量,模型预测精度满足要求。总体而言,非线性联立方程组方法比比例平差方法精度高,同时两种方法中总量直接控制法比分级联合控制法预测效果好;各分项生物量模型本身作为权函数能有效消除异方差;各分项对应的三元生物量模型预测精度最高,其次是二元生物量模型,最低是一元生物量模型,但这些差异不是很大。总之,为权衡考虑模型预测精度和调查成本,建议把直径和树高作为协变量利用总量直接控制非线性联立方程组法对不同起源生物量建模。     

Allometric Models for Tree Volume and Total Aboveground Biomass in a Tropical Humid Forest in Costa Rica1

Allometric equations for the estimation of tree volume and aboveground biomass in a tropical humid forest were developed based on direct measurements of 19 individuals of seven tree species in Northern Costa Rica. The volume and the biomass of the stems represented about two‐thirds of the total volume and total aboveground biomass, respectively. The average stem volume varied between 4 and 11 Mg/tree and the average total aboveground biomass ranged from 4 to 10 mg/tree. The mean specific gravity of the sampled trees was 0.62 ± 0.06 (g/cm³). The average biomass expansion factor was 1.6 ± 0.2. The best‐fit equations for stem and total volume were of logarithmic form, with diameter at breast height (R²= 0.66 ? 0.81) as an independent variable. The best‐fit equations for total aboveground biomass that were based on combinations of diameter at breast height, and total and commercial height as independent variables had R² values between 0.77 and 0.87. Models recommended for estimating total aboveground biomass are based on diameter at breast height, because the simplicity of these models is advantageous. This variable is easy to measure accurately in the field and is the most common variable recorded in forest inventories. Two widely used models in literature tend to underestimate aboveground biomass in large trees. In contrast, the models developed in this study accurately estimate the total aboveground biomass in these trees.     

Lameness detection in dairy cattle: single predictor v. multivariate analysis of image-based posture processing and behaviour and performance sensing

The objective of this study was to evaluate if a multi-sensor system (milk, activity, body posture) was a better classifier for lameness than the single-sensor-based detection models. Between September 2013 and August 2014, 3629 cow observations were collected on a commercial dairy farm in Belgium. Human locomotion scoring was used as reference for the model development and evaluation. Cow behaviour and performance was measured with existing sensors that were already present at the farm. A prototype of three-dimensional-based video recording system was used to quantify automatically the back posture of a cow. For the single predictor comparisons, a receiver operating characteristics curve was made. For the multivariate detection models, logistic regression and generalized linear mixed models (GLMM) were developed. The best lameness classification model was obtained by the multi-sensor analysis (area under the receiver operating characteristics curve (AUC)=0.757±0.029), containing a combination of milk and milking variables, activity and gait and posture variables from videos. Second, the multivariate video-based system (AUC=0.732±0.011) performed better than the multivariate milk sensors (AUC=0.604±0.026) and the multivariate behaviour sensors (AUC=0.633±0.018). The video-based system performed better than the combined behaviour and performance-based detection model (AUC=0.669±0.028), indicating that it is worthwhile to consider a video-based lameness detection system, regardless the presence of other existing sensors in the farm. The results suggest that Θ₂, the feature variable for the back curvature around the hip joints, with an AUC of 0.719 is the best single predictor variable for lameness detection based on locomotion scoring. In general, this study showed that the video-based back posture monitoring system is outperforming the behaviour and performance sensing techniques for locomotion scoring-based lameness detection. A GLMM with seven specific variables (walking speed, back posture measurement, daytime activity, milk yield, lactation stage, milk peak flow rate and milk peak conductivity) is the best combination of variables for lameness classification. The accuracy on four-level lameness classification was 60.3%. The accuracy improved to 79.8% for binary lameness classification. The binary GLMM obtained a sensitivity of 68.5% and a specificity of 87.6%, which both exceed the sensitivity (52.1%±4.7%) and specificity (83.2%±2.3%) of the multi-sensor logistic regression model. This shows that the repeated measures analysis in the GLMM, taking into account the individual history of the animal, outperforms the classification when thresholds based on herd level (a statistical population) are used.     

Demographic noise and resilience in a semi-arid ecosystem model

The scarcity of water characterising drylands forces vegetation to adopt appropriate survival strategies. Some of these generate water–vegetation feedback mechanisms that can lead to spatial self-organisation of vegetation, as it has been shown with models representing plants by a density of biomass, varying continuously in time and space. However, although plants are usually quite plastic they also display discrete qualities and stochastic behaviour. These features may give rise to demographic noise, which in certain cases can influence the qualitative dynamics of ecosystem models. In the present work we explore the effects of demographic noise on the resilience of a model semi-arid ecosystem. We introduce a spatial stochastic eco-hydrological hybrid model in which plants are modelled as discrete entities subject to stochastic dynamical rules, while the dynamics of surface and soil water are described by continuous variables. The model has a deterministic approximation very similar to previous continuous models of arid and semi-arid ecosystems. By means of numerical simulations we show that demographic noise can have important effects on the extinction and recovery dynamics of the system. In particular we find that the stochastic model escapes extinction under a wide range of conditions for which the corresponding deterministic approximation predicts absorption into desert states.     

Raman spectroscopy as a method to replace off-line pH during mammalian cell culture processes

Raman spectroscopy is a robust, well-established tool utilized for measuring important cell culture process variables for example, feed, metabolites, and biomass in real-time. This study further expands the functionality of in-line Raman spectroscopy coupled with partial least squares (PLS) regression modelling to develop a pH measurement tool. Cell line specific models were developed to enhance the robustness for processes with different pH setpoints, deadbands, and cellular metabolism. The modelling strategy further improved robustness by reducing the temporal complexity of pH shifts by splitting data sets into two time zones reflective of major changes in pH. In addition, models were developed to assess if lactate and partial pressure of carbon dioxide (pCO₂) could be used in a PLS model for pH. Splitting the data sets into early and late for the process resulted in errors of 0.035 pH and 0.034 pH for the two respective Raman cell lines models which was within acceptance criteria. The lactate and pCO₂ PLS model with values provided by Raman models had a further 0.001 pH error reduction. This study illustrates the potential to eliminate off-line samples to correct for in-line measurements of pH and further illustrates the capabilities of Raman to measure additional process variables.     

Lab-scale fermentation tests of microchip with integrated electrochemical sensors for pH, temperature, dissolved oxygen and viable biomass concentration

This article shows the development and testing of a microchip with integrated electrochemical sensors for measurement of pH, temperature, dissolved oxygen and viable biomass concentration under yeast cultivation conditions. Measurements were done both under dynamic batch conditions as well as under prolonged continuous cultivation conditions. The response of the sensors compared well with conventional measurement techniques. The biomass sensor was based on impedance spectroscopy. The results of the biomass sensor matched very well with dry weight measurements and showed a limit of detection of approximately 1 g/L. The dissolved oxygen concentration was monitored amperometrically using an ultra-microelectrode array, which showed an accuracy of approximately 0.2 mg/L and negligible drift. pH was monitored using an ISFET with an accuracy well below 0.1 pH unit. The platinum thin-film temperature resistor followed temperature changes with approximately 0.1 degrees C accuracy. The dimensions of the multi sensor chip are chosen as such that it is compatible with the 96-well plate format.     

Climate more important than soils for predicting forest biomass at the continental scale

Above-ground biomass in forests is critical to the global carbon cycle as it stores and sequesters carbon from the atmosphere. Climate change will disrupt the carbon cycle hence understanding how climate and other abiotic variables determine forest biomass at broad spatial scales is important for validating and constraining Earth System models and predicting the impacts of climate change on forest carbon stores. We examined the importance of climate and soil variables to explaining above-ground biomass distribution across the Australian continent using publicly available biomass data from 3130 mature forest sites, in 6 broad ecoregions, encompassing tropical, subtropical and temperate biomes. We used the Random Forest algorithm to test the explanatory power of 14 abiotic variables (8 climate, 6 soil) and to identify the best-performing models based on climate-only, soil-only and climate plus soil. The best performing models explained ~50% of the variation (climate-only: R² = 0.47 ± 0.04, and climate plus soils: R² = 0.49 ± 0.04). Mean temperature of the driest quarter was the most important climate variable, and bulk density was the most important soil variable. Climate variables were consistently more important than soil variables in combined models, and model predictive performance was not substantively improved by the inclusion of soil variables. This result was also achieved when the analysis was repeated at the ecoregion scale. Predicted forest above-ground biomass ranged from 18 to 1066 Mg ha⁻¹, often under-predicting measured above-ground biomass, which ranged from 7 to 1500 Mg ha⁻¹. This suggested that other non-climate, non-edaphic variables impose a substantial influence on forest above-ground biomass, particularly in the high biomass range. We conclude that climate is a strong predictor of above-ground biomass at broad spatial scales and across large environmental gradients, yet to predict forest above-ground biomass distribution under future climates, other non-climatic factors must also be identified.     

基于机载激光雷达的小兴安岭温带森林组分生物量反演

使用小兴安岭温带森林机载遥感-地面观测同步试验获取的机载激光雷达(light detection and ranging, Lidar)点云数据和地面实测样地数据, 估测了典型森林类型的树叶、树枝、树干、地上、树根和总生物量等组分的生物量。从激光雷达数据中提取了两组变量(树冠高度变量组和植被密度变量组)作为自变量, 并采用逐步回归方法进行自变量选择。结果表明: 激光雷达数据得到的变量与森林各组分生物量有很强的相关性; 对于针叶林、阔叶林和针阔叶混交林三种不同森林类型生物量的估测结果是: 针叶林优于阔叶林, 阔叶林优于针阔叶混交林; 不区分森林类型的各组分生物量估测与地面实测值显著相关, 模型决定系数在0.6以上; 区分森林类型进行建模可以进一步提高生物量的估测精度。     

Sensor combination and chemometric variable selection for online monitoring of Streptomyces coelicolor fed-batch cultivations

Fed-batch cultivations of Streptomyces coelicolor, producing the antibiotic actinorhodin, were monitored online by multiwavelength fluorescence spectroscopy and off-gas analysis. Partial least squares (PLS), locally weighted regression, and multilinear PLS (N-PLS) models were built for prediction of biomass and substrate (casamino acids) concentrations, respectively. The effect of combination of fluorescence and gas analyzer data as well as of different variable selection methods was investigated. Improved prediction models were obtained by combination of data from the two sensors and by variable selection using a genetic algorithm, interval PLS, and the principal variables method, respectively. A stepwise variable elimination method was applied to the three-way fluorescence data, resulting in simpler and more accurate N-PLS models. The prediction models were validated using leave-one-batch-out cross-validation, and the best models had root mean square error of cross-validation values of 1.02 g l⁻¹ biomass and 0.8 g l⁻¹ total amino acids, respectively. The fluorescence data were also explored by parallel factor analysis. The analysis revealed four spectral profiles present in the fluorescence data, three of which were identified as pyridoxine, NAD(P)H, and flavin nucleotides, respectively.     

Mutational approach for N2-fixing and P-solubilizing mutant strains of Klebsiella pneumoniae RSN19 by microwave mutagenesis

Nitrogen (N) fixing Klebsiella pneumoniae RSN19 has high inorganic phosphorus (P) solubilizing capability, but its N₂-fixing capability is limited. In order to acquire a P-solubilizing mutant strain with high efficiency N-fixing capability, different microwave irradiation intensities and durations were tested on RSN19 in an attempt to produce mutants with improved N₂-fixation and P-solubilization capabilities. The effect of microwave irradiation power and time were studied and the microwave mutagenesis parameters were optimized. Nitrogenase activity was tested on the mutant strains by acetylene reduction method; and their P-solubilizing capability and genetic stability were determined. The results indicated that the best conditions for microwave mutagenesis that produced better performed mutant strains were 250W, 36 s. Under these conditions a maximum positive mutation rate of 1.66% was obtained, resulting in five genetically stable strains with promoted nitrogenase activity which was designated as RSM-219, RSM-206, RSM-224, RSM-225 and RSM-275. Subculture tests showed that RSM-219 and RSM-206 were genetically stable mutant strains with higher nitrogenase activity and phosphate solubilizing capabilities than the original strain. Both RSM-219 and RSM-206 performed better than the original strain under N-free conditions when supplied with calcium phosphate only, and produced greater increases in the biomass of alfalfa seedlings.     

Macroclimate associated with urbanization increases the rate of secondary succession from fallow soil

To examine the impact of projected climate changes on secondary succession, we exposed the same fallow soil with a common seed bank to an in situ gradient of urban to rural macroenvironments that differed in temperature and CO₂ concentration ([CO₂]). This gradient was established at three locations: Baltimore city center (urban), a city park on the outskirts of Baltimore (suburban), and an organic farm 87 km from the Baltimore city center site (rural). Over a five-year period, the urban site averaged 2.1°C warmer and had a [CO₂] that was ~20% higher than at the rural location, indicating that this gradient was a reasonable surrogate for projected changes in those variables for this century. Previous work had demonstrated that other abiotic variables measured across the transect, including tropospheric ozone and nitrogen deposition, did not differ consistently. The first year of exposure resulted in (two- to threefold) greater aboveground biomass in the urban relative to the rural site, but with uniform species composition across sites. Simple regression of abiotic variables indicated that temperature and vapor pressure deficit (VPD) were the best predictors of plant biomass among locations. Stepwise multiple regressions were also performed to analyze the effect of more than one macroenvironmental variable on total plant biomass. The combination of daily CO₂ concentration and nighttime temperature explained 87% (P < 0.01) of the variability in total biomass between sites. After five years, the species demography of the plant communities had changed significantly, with a greater ratio of perennials to annuals for the urban relative to the rural location. Greater first-year biomass and litter accumulation at the urban site may have suppressed the subsequent seed germination of annual species, accelerating changes in species composition. If urban macroenvironments reflect future global change conditions, these data suggest a faster rate of secondary succession in a warmer, higher [CO₂] world.     

营养盐加富和鱼类添加对浮游植物群落演替和多样性的影响

浮游植物是水体生态系统敞水区最重要的初级生产者,其组成与多样性反映了群落的结构类型和存在状态。通过围隔实验,模拟水库春季发生的营养盐加富和鱼类放养的干扰,分析在这两种干扰下的浮游植物群落演替过程中优势种和稀有种的变化,并通过以丰度与生物量为变量的香农和辛普森多样性指数的计算,分析浮游植物群落演替过程中的多样性变化特征。结果表明,营养盐加富干扰下的浮游植物群落的优势种变化和演替更为明显,营养盐加富与鱼类添加对浮游植物群落多样性变化的影响符合中度干扰理论。在优势种优势度变化较大的浮游植物群落演替过程中,多样性指数与浮游植物生物量有较高的负相关性。在浮游植物群落演替过程中,香农和辛普森多样性指数的变化趋势基本一致,采用丰度与生物量为变量的两种多样性指数的计算结果对实验系统中浮游植物群落多样性的分析结果没有明显的影响。