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Model Behavior (model + behavior)
Selected AbstractsKinetic modeling of light limitation and sulfur deprivation effects in the induction of hydrogen production with Chlamydomonas reinhardtii: Part I. Model development and parameter identificationBIOTECHNOLOGY & BIOENGINEERING, Issue 1 2009Swanny Fouchard Abstract Chlamydomonas reinhardtii is a green microalga capable of turning its metabolism towards H2 production under specific conditions. However this H2 production, narrowly linked to the photosynthetic process, results from complex metabolic reactions highly dependent on the environmental conditions of the cells. A kinetic model has been developed to relate culture evolution from standard photosynthetic growth to H2 producing cells. It represents transition in sulfur-deprived conditions, known to lead to H2 production in Chlamydomonas reinhardtii, and the two main processes then induced which are an over-accumulation of intracellular starch and a progressive reduction of PSII activity for anoxia achievement. Because these phenomena are directly linked to the photosynthetic growth, two kinetic models were associated, the first (one) introducing light dependency (Haldane type model associated to a radiative light transfer model), the second (one) making growth a function of available sulfur amount under extracellular and intracellular forms (Droop formulation). The model parameters identification was realized from experimental data obtained with especially designed experiments and a sensitivity analysis of the model to its parameters was also conducted. Model behavior was finally studied showing interdependency between light transfer conditions, photosynthetic growth, sulfate uptake, photosynthetic activity and O2 release, during transition from oxygenic growth to anoxic H2 production conditions. Biotechnol. Bioeng. 2009;102: 232,245. © 2008 Wiley Periodicals, Inc. [source] Using Logistic Regression to Analyze the Sensitivity of PVA Models: a Comparison of Methods Based on African Wild Dog ModelsCONSERVATION BIOLOGY, Issue 5 2001Paul C. Cross Standardized coefficients from the logistic regression analyses indicated that pup survival explained the most variability in the probability of extinction, regardless of whether or not the model incorporated density dependence. Adult survival and the standard deviation of pup survival were the next most important parameters in density-dependent simulations, whereas the severity and probability of catastrophe were more important during density-independent simulations. The inclusion of density dependence decreased the probability of extinction, but neither the abruptness nor the inclusion of density dependence were important model parameters. Results of both relative sensitivity analyses that altered each parameter by 10% of its range and life-stage-simulation analyses of deterministic matrix models supported the logistic regression results, indicating that pup survival and its variation were more important than other parameters. But both conventional sensitivity analysis of the stochastic model which changed each parameter by 10% of its mean value and elasticity analyses indicated that adult survival was more important than pup survival. We evaluated the advantages and disadvantages of using logistic regression to analyze the sensitivity of stochastic population viability models and conclude that it is a powerful method because it can address interactions among input parameters and can incorporate the range of parameter variability, although the standardized regression coefficients are not comparable between studies. Model structure, method of analysis, and parameter uncertainty affect the conclusions of sensitivity analyses. Therefore, rigorous model exploration and analysis should be conducted to understand model behavior and management implications. Resumen: Utilizamos la regresión logística como un método de análisis de sensibilidad par a un modelo de análisis de viabilidad poblacional de perros silvestres Africanos ( Lycaon pictus) y comparamos estos resultados con análisis de sensibilidad convencionales de modelos estocásticos y determinísticos. Coeficientes estandarizados de los análisis de regresión logística indicaron que la supervivencia de cachorros explicaba la mayor variabilidad en la probabilidad de extinción, independientemente de que el modelo incorporara la denso-dependencia. La supervivencia de adultos y la desviación estándar de la supervivencia de cachorros fueron los parámetros que siguieron en importancia en simulaciones de denso-dependencia, mientras que la severidad y la probabilidad de catástrofes fueron más importantes durante simulaciones denso-independientes. La inclusión de la denso dependencia disminuyó la probabilidad de extinción, pero ni la severidad ni la inclusión de denso-dependencia fueron parámetros importantes. Resultados de los análisis de sensibilidad relativa que alteraron cada parámetro en 10% de su rango y análisis de la simulación de etapas de vida de modelos matriciales determinísticos apoyaron los resultados de la regresión logística, indicando que la supervivencia de cachorros y su variación fueron más importantes que otros parámetros. Sin embargo, el análisis de sensibilidad convencional del modelo estocástico que cambiaron cada parámetro en 10% de su valor medio y el análisis de elasticidad indicaron que la supervivencia de adultos fue más importante que la supervivencia de cachorros. Evaluamos las ventajas y desventajas de utilizar la regresión logística para analizar la sensibilidad de modelos estocásticos de viabilidad poblacional y concluimos que es un método poderoso porque puede atender interacciones entre parámetros ingresados e incorporar el rango de variabilidad de parámetros, aunque los coeficientes de regresión estandarizada no son comparables entre estudios. La estructura del modelo, el método de análisis y la incertidumbre en los parámetros afectan las conclusiones del análisis de sensibilidad. Por lo tanto, se debe realizar una rigurosa exploración y análisis del modelo para entender su comportamiento y sus implicaciones en el manejo. [source] Estimating diurnal to annual ecosystem parameters by synthesis of a carbon flux model with eddy covariance net ecosystem exchange observationsGLOBAL CHANGE BIOLOGY, Issue 2 2005Bobby H. Braswell Abstract We performed a synthetic analysis of Harvard Forest net ecosystem exchange of CO2 (NEE) time series and a simple ecosystem carbon flux model, the simplified Photosynthesis and Evapo-Transpiration model (SIPNET). SIPNET runs at a half-daily time step, and has two vegetation carbon pools, a single aggregated soil carbon pool, and a simple soil moisture sub-model. We used a stochastic Bayesian parameter estimation technique that provided posterior distributions of the model parameters, conditioned on the observed fluxes and the model equations. In this analysis, we estimated the values of all quantities that govern model behavior, including both rate constants and initial conditions for carbon pools. The purpose of this analysis was not to calibrate the model to make predictions about future fluxes but rather to understand how much information about process controls can be derived directly from the NEE observations. A wavelet decomposition enabled us to assess model performance at multiple time scales from diurnal to decadal. The model parameters are most highly constrained by eddy flux data at daily to seasonal time scales, suggesting that this approach is not useful for calculating annual integrals. However, the ability of the model to fit both the diurnal and seasonal variability patterns in the data simultaneously, using the same parameter set, indicates the effectiveness of this parameter estimation method. Our results quantify the extent to which the eddy covariance data contain information about the ecosystem process parameters represented in the model, and suggest several next steps in model development and observations for improved synthesis of models with flux observations. [source] Solid-solid reactions in series: A modeling and experimental studyAICHE JOURNAL, Issue 9 2009A. K. Suresh Abstract Reactions among particulate solid phases are important and abundant in many materials, chemical, and metallurgical process industries. Many of these are reaction networks, and not single-step reactions as normally assumed. There is no theoretical framework available for the analysis of such systems, and single-reaction models derived from the gas,solid literature continue to be used. Formation of cement clinker in the rotary cement kiln is a prime example of the genre, in which mechanistic aspects play an important role in determining energy efficiency and the composition and nature of the phases that form. In the present study, we formulate a model within the ambit of the "shrinking core" class of models, for reactions in series among solid phases. The model shows the presence of one or two moving fronts in the reacting particle, depending on the relative rates of the processes involved. A single Thiele-type parameter controls the model behavior, at once describing the relative rates of the intermediate formation and consumption processes, and the diffusion-reaction competition for the product formation step. The model has been shown to reduce to the well known single reaction models at the limits of low and high values of the Thiele parameter. Experimental data have been obtained on the calcia-alumina system, an important one in cement manufacture, in the temperature range 1150,1250°C. The model has been fitted to these data and the kinetic parameters determined. The comparison bears out the salient features of the theory, and shows that a degree of diffusion limitation exists for the intermediate conversion step under these conditions. The diffusivity values estimated are in the range of 10,19 to 10,18 m2/s and agree with values found in the literature for similar systems. The rate constant for the intermediate conversion step is of the order of 10,6 s,1. This being among the first such determinations, this value awaits confirmation from other studies. © 2009 American Institute of Chemical Engineers AIChE J, 2009 [source] Modeling Postfire Response and Recovery using the Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS),JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 3 2009Kristina Cydzik Abstract:, This paper investigates application of the Army Corps of Engineers' Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) to a burned watershed in San Bernardino County, California. We evaluate the HEC-HMS' ability to simulate discharge in prefire and postfire conditions in a semi arid watershed and the necessary parameterizations for modeling hydrologic response during the immediate, and subsequent recovery, period after a wildfire. The model is applied to City Creek watershed, which was 90% burned during the Old Fire of October 2003. An optimal spatial resolution for the HEC-HMS model was chosen based on an initial sensitivity analysis of subbasin configurations and related model performance. Five prefire storms were calibrated for the selected model resolution, defining a set of parameters that reasonably simulate prefire conditions. Six postfire storms, two from each of the following rainy (winter) seasons were then selected to simulate postfire response and evaluate relative changes in parameter values and model behavior. There were clear trends in the postfire parameters [initial abstractions (Ia), curve number (CN), and lag time] that reveal significant (and expected) changes in watershed behavior. CN returns to prefire (baseline) values by the end of Year 2, while Ia approaches baseline by the end of the third rainy season. However, lag time remains significantly lower than prefire values throughout the three-year study period. Our results indicate that recovery of soil conditions and related runoff response is not entirely evidenced by the end of the study period (three rainy seasons postfire). Understanding the evolution of the land surface and related hydrologic properties during the highly dynamic postfire period, and accounting for these changes in model parameterizations, will allow for more accurate and reliable discharge simulations in both the immediate, and subsequent, rainy seasons following fire. [source] Approximation and complexity trade-off by TP model transformation in controller design: A case study of the TORA system,ASIAN JOURNAL OF CONTROL, Issue 5 2010Zoltán Petres Abstract The main objective of the paper is to study the approximation and complexity trade-off capabilities of the recently proposed tensor product distributed compensation (TPDC) based control design framework. The TPDC is the combination of the TP model transformation and the parallel distributed compensation (PDC) framework. The Tensor Product (TP) model transformation includes an Higher Order Singular Value Decomposition (HOSVD)-based technique to solve the approximation and complexity trade-off. In this paper we generate TP models with different complexity and approximation properties, and then we derive controllers for them. We analyze how the trade-off effects the model behavior and control performance. All these properties are studied via the state feedback controller design of the Translational Oscillations with an Eccentric Rotational Proof Mass Actuator (TORA) System. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source] Parametric study of a 6-column countercurrent solvent gradient purification (MCSGP) unit,BIOTECHNOLOGY & BIOENGINEERING, Issue 5 2007Lars Aumann Abstract The novel "multicolumn countercurrent solvent gradient purification" (MCSGP) process has been modeled for the purification of a polypeptide mixture characterized by a strong non-linear competitive adsorption isotherm. As a model system, the purification of an industrial polypeptide mixture containing 46% of the hormone calcitonin has been selected. The many impurities contained in the mixture have been lumped into three key impurities, which are selected as the ones eluting closer to the main component. The simulation model allows for a better understanding of the complex operating behavior of the multicolumn system, which has been experimentally investigated in a previous work. Through a systematic parametric analyses of the model behavior, the main operating parameters controlling the process performance in terms of purity and yield are investigated. The study of internal liquid and adsorbed phase concentration profiles along the unit for the different operating conditions allow elucidating the working principle of the new separation process. It is found that the MCSGP unit achieves much higher yields for a given product purity than the corresponding single-column batch units. Biotechnol. Bioeng. 2007;98: 1029,1042. © 2007 Wiley Periodicals, Inc. [source] | |||||||||||||