Precise Estimation Of Model Parameters Research Articles

A model-based experimental design approach to assess the identifiability of kinetic models of hydroxymethylfurfural hydrogenation in batch reaction systems

Hydroxymethylfurfural (HMF) is an organic compound that occurs naturally in many foods and is used as feedstock in numerous chemical processes. HMF can be hydrogenated to form 2,5-Dimethylfuran (DMF), which is an important component in biofuel production. To date, several kinetic models have been proposed and studied in literature for this hydrogenation reaction, including power law models based on reaction species and Langmuir-Hinshelwood-Hougen-Watson (LHHW) models. For these models a critical aspect that has not been addressed in literature is related to their practical identifiability, i.e. the estimability of kinetic parameters from experimental data. Also, none of the existing models propose a temperature dependence of the kinetic parameters.A three-step approach is presented in this paper, which exploits model-based design of experiments (MBDoE) techniques to assess the identifiability of candidate kinetic models of HMF hydrogenation in a batch reaction system. The objective is twofold: (1) to propose new kinetic models of HMF hydrogenation where the temperature is explicitly introduced as an experimental design variable and test the practical estimability of kinetic parameters from concentration data only; (2) to identify the most informative regions of the experimental design space, defined by temperature, experiment duration and initial HMF and DMF concentrations, for achieving a precise estimation of model parameters. Together with a-posteriori statistics obtained from parameter estimation from in-silico data, an MBDoE analysis gives a clear representation of the most informative experimental conditions to be explored in the future experimentation underlining distinct areas of practical parametric identifiability.

Online model-based redesign of experiments for improving parameter precision in continuous flow reactors

Online model-based redesign of experiments (OMBRE) techniques reduce the experimental effort substantially for achieving high model reliability along with the precise estimation of model parameters. In dynamic systems, OMBRE techniques allow redesigning an experiment while it is still running and information gathered from samples collected at multiple time points is used to update the experimental conditions before the completion of the experiment. For processes evolving through a sequence of steady state experiments, significant time delays may exist when collecting new information from each single run, because measurements can be available only after steady state conditions are reached. In this work an online model-based optimal redesign technique is employed in continuous flow reactors for improving the accuracy of estimation of kinetic parameters with great benefit in terms of time and analytical resources during the model identification task. The proposed approach is applied to a simulated case study and compared with the conventional sequential model-based design of experiments (MBDoE) techniques as well as the offline optimal redesign of experiments.

Constrained model-based design of experiments for the identification of approximated models

The identification of an approximated model, once an opportune mathematical structure is selected, requires both a precise estimation of its parameters and the determination of the range of conditions in which the model provides accurate predictions, i.e., the domain of model reliability. A variety of model-based design of experiments (MBDoE) techniques are available in the literature for designing highly informative trials for the precise estimation of model parameters. Available MBDoE methods assume that the model structure is exact in the formulation of experimental design metrics. Hence, in the presence of an approximated model, the employment of conventional MBDoE approaches may lead to the collection and fitting of data at conditions where the model performance is very poor, thus leading to the degradation of the fitting performance and a loss of model predictive power. In this work, an iterative framework for the identification of approximated models is proposed in which the MBDoE step is constrained to the domain of model reliability. The method is tested on a simulated case study on the identification of an approximated kinetic model of catalytic ethanol dehydrogenation.

Efficient and Ethical Response-Adaptive Randomization Designs for Multi-Arm Clinical Trials With Weibull Time-to-Event Outcomes

We consider a design problem for a clinical trial with multiple treatment arms and time-to-event primary outcomes that are modeled using the Weibull family of distributions. The D-optimal design for the most precise estimation of model parameters is derived, along with compound optimal allocation designs that provide targeted efficiencies for various estimation problems and ethical considerations. The proposed optimal allocation designs are studied theoretically and are implemented using response-adaptive randomization for a clinical trial with censored Weibull outcomes. We compare the merits of our multiple-objective response-adaptive designs with traditional randomization designs and show that our designs are more flexible, realistic, generally more ethical, and frequently provide higher efficiencies for estimating different sets of parameters.

Bacterial Foraging Algorithm based solar PV parameter estimation

The abundance and non-polluting nature of solar energy has aroused the interest of many researchers. This worldwide attention of photovoltaic panels has led to the need of generating accurate model for solar photovoltaic (PV) module before proceeding to the installation part. However, accurate modeling of solar PV characteristics is difficult; since the manufacturer’s datasheet provides only four values such as Vmp, Imp, Voc, and Isc. Further, for accurate modeling precise estimation of model parameters at different environmental conditions are very essential. On the other hand, optimization technique is a very powerful tool to obtain solutions to complex non-linear problems. Hence, in this paper, Bacterial Foraging Algorithm is proposed to model the solar PV characteristics accurately. A new equation has been evolved to determine the values of Voc, Vmp accurately; since these values decides the closeness of the simulated characteristics. Model parameters are extracted for three different types of solar PV panels. A systematic evaluation and performance comparison of Bacterial Foraging Algorithm with other optimization techniques such as Genetic Algorithm and Artificial Immune System has been done and the best computational technique is derived based on performance criteria such as accuracy, consistency, speed of convergence and absolute error. Extensive computations are carried out for the proposed method, as well as for Genetic Algorithm and Artificial Immune System to substantiate the findings.

Optimal designs for population pharmacokinetic studies of the partner drugs co-administered with artemisinin derivatives in patients with uncomplicated falciparum malaria

BackgroundArtemisinin-based combination therapy (ACT) is currently recommended as first-line treatment for uncomplicated malaria, but of concern, it has been observed that the effectiveness of the main artemisinin derivative, artesunate, has been diminished due to parasite resistance. This reduction in effect highlights the importance of the partner drugs in ACT and provides motivation to gain more knowledge of their pharmacokinetic (PK) properties via population PK studies. Optimal design methodology has been developed for population PK studies, which analytically determines a sampling schedule that is clinically feasible and yields precise estimation of model parameters. In this work, optimal design methodology was used to determine sampling designs for typical future population PK studies of the partner drugs (mefloquine, lumefantrine, piperaquine and amodiaquine) co-administered with artemisinin derivatives.MethodsThe optimal designs were determined using freely available software and were based on structural PK models from the literature and the key specifications of 100 patients with five samples per patient, with one sample taken on the seventh day of treatment. The derived optimal designs were then evaluated via a simulation-estimation procedure.ResultsFor all partner drugs, designs consisting of two sampling schedules (50 patients per schedule) with five samples per patient resulted in acceptable precision of the model parameter estimates.ConclusionsThe sampling schedules proposed in this paper should be considered in future population pharmacokinetic studies where intensive sampling over many days or weeks of follow-up is not possible due to either ethical, logistic or economical reasons.

Pharmacodynamic Analysis of Recombinant Human Erythropoietin Effect on Reticulocyte Production Rate and Age Distribution in Healthy Subjects

To evaluate the effect of recombinant human erythropoietin (rHuEPO) on the reticulocyte production rate and age distribution in healthy subjects. Extensive pharmacokinetic and pharmacodynamic data collected from 88 subjects who received a single subcutaneous dose of rHuEPO (dose range 20-160 kIU) were analysed. Four nonlinear mixed-effects models were evaluated to describe the time course of the percentage of reticulocytes and their age distribution in relation to rHuEPO pharmacokinetics. Model A accounted for stimulation of the production of progenitor cells in bone marrow, and model B implemented shortening of differentiation and maturation times of early progenitors in bone marrow. Model C was the combination of models A and B, and model D was the combination of model A with an increase in the maturation times of the circulating reticulocytes. Model evaluation was performed using goodness-of-fit plots, a nonparametric bootstrap and a posterior predictive check. Model D was selected as the best model, and evidenced accurate and precise estimation of model parameters and prediction of the time course of the percentage of reticulocytes. At baseline, the estimated circulating reticulocyte maturation time was 2.6 days, whereas the lifespan of the precursors in the bone marrow was about 5 days. The rHuEPO potency for the stimulatory effect (7.61 IU/L) was higher than that for the increase in reticulocyte maturation times (56.3 IU/L). There was a significant 1- to 2-day lag time in the reticulocyte response. The effect of rHuEPO on the reticulocyte age distribution consisted of a transient increase in the reticulocyte maturation time from baseline up to 6-7 days, occurring 1 day after administration. The dose-dependent amplitude of the changes in the age distribution lasted for 12-14 days. The model-predicted peak increase in the reticulocyte release rate ranged from 140% to 160% of the baseline value and was maximal on days 7-8 following rHuEPO administration. A semiphysiological model quantifying the effect of rHuEPO on the reticulocyte production rate and age distribution was developed. The validated model predicts that rHuEPO increases the reticulocyte production rate and modifies the reticulocyte age distribution in a dose-dependent manner.

Long-Memory versus Option-Implied Volatility Predictions

Volatility is a critical parameter in virtually all option pricing models. But the closer we look at volatility, the harder it seems to be to model it correctly. The constant volatility assumption of early pricing models is clearly inadequate. GARCH family models make volatility a function of the asset price process, and stochastic volatility models bring in a second stochastic factor that affects volatility movements. These approaches make sense in theory, but empirically volatility shocks seem to be too persistent to be consistent with them. A further confounding factor is that implied volatilities extracted from option prices in the market are widely thought to give more accurate predictions of future realized volatility, but don9t obey any of these models exactly. In this article, Li examines a volatility model with “long memory,” meaning that it can be made to fit the slow-decay feature of market volatilities. He also introduces a much more extensive data series, with intraday observations every five minutes. Using such a dense price series, realized volatility becomes observable, and the vast number of data points allows precise estimation of model parameters. For exchange rates on the deutsche mark, the yen and the British pound, the ARFIMA (“Autoregressive Fractionally Integrated Moving Average”) model is shown to give a better fit to volatility behavior than the alternative model, and it beats implied volatility substantially in the standard tests of forecasting performance.

Error analysis of predicted fate of anthracene in a simulated pond

AbstractStatistical regressions that relate rates of transport processes to chemical characteristics of polycyclic aromatic hydrocarbons were used to estimate parameters in a model that predicts the fate of a simulated anthracene spill onto the surface (25 m2) of a 12.5 m3 pond ecosystem. During 60 d following a simulated spill of 2.0 g/m2 anthracene, predicted concentration in sediments peaked at 550 μg/m2. Predicted bioaccumulation was maximal in rooted macrophytes (0.35 μg/g dry weight), followed by benthic invertebrates (0.23 μg/g dry weight) and zooplankton (0.04 μg/g dry weight). Precision of predicted concentrations depends upon precise estimation of model parameters that define maximum water solubility and that regulate photolytic degradation, volatilization, sorption and bioaccumulation of anthracene. In 200 simulations, values of 29 model parameters were chosen at random from distributions with standard deviations that ranged between 0.5 and 53% of their means. Results demonstrate greater precision in predictions of the relative distribution of anthracene among water, sediments and total biota than precision of predicted concentrations of individual pond components.