A methodology is proposed to aid parameter estimation in fundamental models of pharmaceutical processes. This methodology addresses situations with insufficient data to reliably estimate all parameters, when the estimation is complicated by uncertain independent variables. The proposed method uses an augmented sensitivity matrix to rank the combined set of parameters and uncertain inputs from most estimable to least estimable. An updated mean-squared-error criterion is then used to determine the appropriate parameters and inputs that should be estimated, based on the ranked list. A model for one step in a batch pharmaceutical production process with an uncertain initial reactant concentration is used to illustrate the method, revealing that the initial reactant concentration in each batch should be estimated along with three out of six model parameters. Non-estimable parameters are fixed at their initial values to prevent overfitting. The method will aid error-in-variables parameter estimation in many situations involving limited data.

A review of uncertainty quantification techniques is provided for a variety of situations involving uncertainties in model inputs (independent variables). The situations of interest are divided into three categories: (i) when model prediction uncertainties are quantified based on uncertainties in uncertain inputs, (ii) when parameter estimate uncertainties are calculated by propagation of uncertainties from measured inputs and outputs, and (iii) when model prediction uncertainties are quantified based on corresponding uncertainties in measured inputs and uncertain parameter estimates. For all three situations, linearization-based and Monte Carlo-based techniques are reviewed and details for their corresponding algorithms are presented. Recommendations are provided on which uncertainty quantification techniques are best for different types of chemical engineering models based on the amount of input uncertainty and nonlinearity over the range of plausible input and parameter values.

A dynamic model is proposed to account for shrinkage and swelling during the photopolymerization of 1,6-hexanediol diacrylate (HDDA) with the bifunctional initiator bis-acylphosphine oxide (BAPO) in the presence of oxygen. The model is composed of 14 partial differential equations (PDEs) that are used to track changes in film thickness along with time- and spatially-varying concentrations of monomer, initiator, oxygen, pendant vinyl groups, and seven types of radicals. Shrinkage has a noticeable influence on the model predictions. For a variety of simulated photopolymerization experiments, there is approximate to 9% discrepancy between predicted overall vinyl-group conversions obtained from the current model with shrinkage and a previous model without. Prediction discrepancies become larger for simulated experiments involving thin films (8 mu m) or low light intensities (1200 W m-2). In the future, it will be important to re-estimate the kinetic parameters used in the shrinkage model to obtain accurate model predictions for use in process improvement studies.

A dynamic model is proposed for photopolymerization of 1,6-hexane-diol diacrylate (HDDA) with bifunctional initiator bis-acylphosphine oxide (BAPO) in the presence of oxygen. This partial-differential-equation (PDE) model predicts time- and spatially-varying vinyl-group conversion as well as concentrations of monomer, initiator, oxygen, and seven types of radicals. Experiments to obtain diffusivities of oxygen, BAPO and HDDA are reported. Oxygen-related parameters are estimated using real-time Fourier-transform infrared (FTIR) conversion data. FTIR experiments were conducted using a range of film thicknesses (8-17 μm), BAPO levels (1-4 wt%) and light intensities (200-6000 W/m^2). The model predicts qualitative trends. Conversion predictions for runs with high intensities (≥5000 W/m^2) and high BAPO (4 wt%) are accurate with a root-mean-squared error (RMSE) of 0.04. Larger RMSE (0.13) for runs with lower intensities and BAPO indicates that improved parameter estimates are required. Parameter estimates will be updated using in future using a model that accounts for shrinkage during polymerization.

Error-in-variables model (EVM) methods are used for parameter estimation when independent variables are uncertain. During EVM parameter estimation, output measurement variances are required as weighting factors in the objective function. These variances can be estimated based on data from replicate experiments. However, conducting replicates is complicated when independent variables are uncertain. Instead, pseudo-replicate runs may be performed where the target values of inputs for repeated runs are the same, but the true input values may be different. Here, we propose a method to estimate output-measurement variances for use in multivariate EVM estimation problems, based on pseudo-replicate data. We also propose a bootstrap technique for quantifying uncertainties in resulting parameter estimates and model predictions. The methods are illustrated using a case study involving n-hexane hydroisomerization in a well-mixed reactor. Case-study results reveal that assumptions about input uncertainties can have important influences on parameter estimates, model predictions and their confidence intervals.

A dynamic model is proposed for the photopolymerization of 1,6-hexanediol diacrylate (HDDA) with the bifunctional initiator bis-acylphosphine oxide (BAPO) in the presence of oxygen. The model tracks time-varying concentrations of monomer, oxygen, and different radical end groups using ordinary differential equations. An analytical expression is derived for the mass-transfer of oxygen. Oxygen-related parameters are estimated using real-time Fourier-transform infrared reflection (FTIR) vinyl conversion data, which were collected during polymerization of HDDA films with different thicknesses, initiator concentrations, and light intensities. The resulting model and parameter estimates provide good predictions for experiments involving thin films up to 12 mu m, with BAPO levels ranging from 1 to 4 wt.% and relatively low light intensities (200-1000 W/m(2)).

A dynamic model is proposed for photopolymerization of 1,6-hexanediol diacrylate (HDDA) using bifunc-tional initiator bis-acylphosphine oxide (BAPO). The proposed model accounts for branching, backbiting and cyclization reactions, and for diffusion-dependent reaction rates during photopolymerization. The proposed model contains 40 adjustable kinetic and free-volume parameters. Experimental data available for parameter estimation are vinyl group conversions obtained using a variety of light intensities and exposure times, and monomer conversions for three experiments. Systematic parameter ranking and estimation is used to evaluate the influence of phenomena included in the model on the quality of the fit. Estimation and ranking results indicate that branching, backbiting, and cyclization reactions have important influences on conversion. Reactions involving two large molecules and propagation reactions become diffusion-dependent. Incorporating diffusion-dependent initiator efficiency results in improved model predictions.(c) 2022 Elsevier Ltd. All rights reserved.

Error-in-variables model (EVM) methods require information about variances of input and output measured variables when estimating the parameters in mathematical models for chemical processes. In EVM, using replicate experiments for estimating output measurement variances is complicated because true values of inputs may be different when multiple attempts are made to repeat an experiment. To address this issue, we categorize attempted replicate experiments as: (i) true replicates (TRs) when uncertain inputs are the same in replicated runs and (ii) pseudo replicates (PRs) when measured inputs are the same, but unknown true values of inputs are different. We propose methodologies to obtain output measurement variance estimates and associated parameter estimates for both situations. We also propose bootstrap methods for obtaining joint-confidence information for the resulting parameter estimates. A copolymerization case study is used to illustrate the proposed techniques. We show that different assumptions noticeably affect the uncertainties in the resulting reactivity-ratio estimates.

Error-in-variables model (EVM) methods require information about input and output measurement variances when estimating model parameters. In EVM, using replicate experiments for estimating output measurement variances is complicated, because true values of inputs may be different when multiple attempts are made to repeat an experiment. To address this issue, we categorize attempted replicate experiments as: i) true replicates (TRs) when uncertain inputs are the same in replicated runs and ii) pseudo-replicates (PRs) when measured inputs are the same, but unknown true values of inputs are different. We propose methodologies to obtain output measurement variance estimates and associated parameter estimates for both situations. We also propose bootstrap methods for obtaining joint-confidence information for the resulting parameter estimates. A copolymerization case study is used to illustrate the proposed techniques. We show that different assumptions noticeably affect the uncertainties in the resulting reactivity-ratio estimates.

Sulfur compounds are essential for many industries and organisms; however, they cause serious respiratory problems in human beings. Therefore, determination of sulfur concentration is of paramount importance. The research approach in the field of detecting contaminants has led to smaller systems that provide faster and more effective ways for diagnosis purposes. In this study, a novel portable amperometric graphene oxide-protein biosensor platform is investigated. The main characteristic of this structure is the implementation of a microfluidic configuration. With albumin metalloprotein as the biorecognition element, graphene oxide was synthesized and characterized by transmission electron microscopy and Fourier-transform infrared spectroscopy (FTIR). Albumin protein was stabilized on the surface of graphene oxide by the application of the N-(3-dimethylamionpropyl)-N-ethylcarbodiimide hydrochloride/N-hydroxysuccinimide method. The stabilization was confirmed by FTIR and electrochemistry analyses. The calibration curve of sulfur concentration was determined. When the graphene oxide-protein complex was stabilized by nephion on the surface of the microfluidic system, the response time reduced to 50 Sec, which is a relatively faster response among the similar studies and validated the significant effect of the microfluidic system. The nanosystem had an optimized pH of 7.4 and exhibited high sensitivity in determining sulfide. The results confirm that the portable graphene oxide-protein nanosystem has a fast and accurate response in detecting sulfide.

This study reports the fabrication of a new cathode electrode assembly using polyaniline (PANI) and graphene on a stainless steel mesh (SSM) as an alternative for the conventional expensive cathode of microbial electrolysis cells (MECs). With respect to the previous efforts to propose an efficient and cost-effective alternative for platinum (Pt) catalysts and cathode electrodes, the present study investigates the assessment of different catalysts to elucidate the potential of the modified SSM cathode electrode for larger-scale MECs. In the case of feeding dairy wastewater to the MEC, the maximum hydrogen production rate and COD removal were obtained by SSM/PANI/graphene cathode and had the values 0.805 m(3) H-2 m(-3) anolyte day(-1) and 82%, respectively, at the applied potential of 1 V. These values were only 20% and 7% lower than those of the MEC with Pt on the carbon cloth cathode, respectively. The coulombic efficiencies of SSM/Pt and SSM/PANI/graphene were seen to be 64.48% and 56.67%, respectively. It was also concluded that the fabrication cost of the modified cathode was 50% lower than the conventional cathodes with Pt on the carbon cloth. Finally, the evaluation of the modified cathode performance was achieved based on Fourier transform infrared spectroscopy, linear sweep voltammetry, scanning electron microscopy, and atomic force microscopy. (C) 2019, The Society for Biotechnology, Japan. All rights reserved.