质子交换膜燃料电池及电池组模型分析

本文对现有质子交换膜燃料电池以及电池组模型进行比较分析,认为数学模型的建立,可以增加对燃料电池及电池组内部的传递现象和反应机理的认识,同时可以预测电池以及电池组的性能,并且对优化电池结构参数具有指导意义.模型分析包括了现阶段质子交换膜燃料电池单电池模型和电池组模型的基本类别,它们是单电池CFD数值模拟模型、单电池以及电池组性能模拟模型、燃料电池组气体分配模型、系统模型和非稳态模型.比较了几种模型的建模方式及不同模型的应用范围和各自的优缺点.     

Use of monomer fraction data in the parametrization of association theories

Association theories such as the CPA (cubic-plus-association), NRHB (non-random hydrogen bonding) equations of state and the various variants of SAFT (statistical associating fluid theory) have been extensively applied to phase equilibrium calculations. Such models can also be used for estimating the monomer fraction of hydrogen bonding compounds and their mixtures. Monomer fraction data are obtained from spectroscopic measurements and they are available for a few compounds such as pure water and alcohols as well as for some alcohol–alkane and similar mixtures. These data are useful for an understanding of the capabilities and limitations of association models. The purpose of this work is two-fold: (i) to compare the performance of three models, CPA, NRHB and sPC-SAFT, in predicting the monomer fraction of water, alcohols and mixtures of alcohol-inert compounds and (ii) to investigate whether “improved” model parameters can be obtained if monomer fraction data are included in the parameter estimation together with vapor pressures and liquid densities. The expression “improved” implies parameters which can represent several pure compound properties as well as monomer fraction data for pure compounds and mixtures. The accuracy of experimental monomer fraction data is discussed, as well as the role of monomer fraction data in clarifying which association scheme should be used in these equations of state. The results reveal that the investigated association models (CPA, sPC-SAFT and NRHB) can predict, at least qualitatively correct, monomer fractions of associating compounds and mixtures. Only, small differences are observed between the models. In addition, it has been shown that, using a suitable association scheme, a single set of parameters can describe satisfactorily vapor pressures, liquid densities and monomer fractions of water and alcohols. The 4C scheme is the best choice for water, while for methanol there is small difference between the 2B and 3B association schemes.     

Prediction of characteristic properties of crude oil blending with ANN

Mineral oil is one of the most important materials on earth and it is used widely for its several features. Mineral oils derived from petroleum products are commonly used to decrease the friction effects in machine parts and, thus, they both prevent wear/overheating and facilitate power transmission. In this study, various binary mixtures of various base oils (SN-80, SN-100, SN-150, SN-50, SN-500) were prepared at different volumetric ratios. Kinematic viscosity (at 40°C and 100°C), viscosity index, flash point, pour point, and density (at 20°C) measurements were performed for characterization of the prepared mixtures. These values were modeled by an artificial neural network (ANN) and the model was tested with root mean squared error (RMSE), mean absolute percentage error (MAPE, %), and regression coefficient (R) values. A higher value of correlation coefficient and smaller values of MAPE and RMSE indicate that the model performs better. For predicting kinematic viscosity at 40°C, correlation coefficients were calculated for training and testing the network as 0.9999 and 0.9995, respectively. Respective MAPE values were determined as 1.011% and 1.8771%.     

Sequential automated fusion/extraction chromatography methodology for the dissolution of uranium in environmental samples for mass spectrometric determination

The ability to predict the amount of time that a light petroleum mixture has been weathered could have many applications, such as aiding forensic investigators in determining the cause and intent of a fire. In our study, an evaporation chamber that permits control of airflow and temperature was constructed and used to weather a model nine-component hydrocarbon mixture. The composition of the mixture was monitored over time by gas chromatography and a variety of chemometric models were explored, including partial least squares (PLS), nonlinear PLS (PolyPLS) and locally weighted regression (LWR or loess). A hierarchical application of multivariate techniques was able to predict the time for which a sample had been exposed to evaporative weathering. A classification model based on partial least squares discriminant analysis (PLS-DA) could predict whether a sample was relatively fresh (< 12 h exposure time) or highly weathered (>20 h exposure time). Subsequent regression models for these individual classes were evaluated for accuracy using the root mean square error of prediction (RMSEP). Prior to regression model calculation, y-gradient generalized least squares weighting (GLSW) was used to preprocess the data by removing variance from the X-block, which was orthogonal to the Y-block. LWR was found to be the most successful regression method, whereby fresh samples could be predicted to within 40 min of exposure and highly weathered samples predicted to within 5.6h. These results suggest that our hierarchical chemometric approach may also allow us to estimate the age of more complicated light petroleum mixtures, such as gasoline.     

Evaporation from Three Single‐Compound Phases in Equilibrium

The evaporation paths in a system of three single‐compound phases in equilibrium were calculated using a phase diagram approach assuming the evaporation rate proportional to the compound's vapor pressure and molecular weight. The variation with time of the weight of the individual phases was linear, while the weight fraction was not. The approach allowed a simple calculation of the fraction of remaining compounds after one of them was exhausted during the evaporation and a convenient graphical illustration of the importance of the relative vapor pressures.     

Chromatographic models for the sorption of neutral organic compounds by soil from water and air.

The solvation parameter model is used to construct models for the estimation of the soil-water and soil-air distribution constants and to characterize the contribution of fundamental intermolecular interactions to the underlying sorption processes. Wet soil is shown to be quite cohesive and polar but relatively non-selective for dipole-type, lone-pair electron and hydrogen-bond interactions. Using a comparison of system constant ratios chromatographic systems employing reversed-phase liquid chromatography on polar bonded phases are shown to provide suitable models for estimating soil-water distribution constants. No suitable gas chromatographic models were found for the soil-air distribution constant but the requirements for such a system are indicated. Models are also provided for adsorption at the air-water interface. Estimation methods based on either the solvation parameter model or chromatographic model reproduce experimental distribution constants for a wide variety of compounds with a similar error (0.2-0.3 log units) to that expected in the experimental data.     

Quantification of naphthalenes in jet fuel with GC x GC/Tri-PLS and windowed rank minimization retention time alignment

Comprehensive, two-dimensional gas chromatography (GC x GC) is used in conjunction with trilinear partial least squares (Tri-PLS) to quantify the percent weight of naphthalenes (two-ring aromatic compounds) in jet fuel samples. The increased peak capacity and selectivity of GC x GC makes the technique attractive for the rapid, and possibly less tedious analysis of jet fuel. The analysis of complex mixtures by GC x GC is further enhanced through the use of chemometric techniques, including those designed for use on 2-D data such as Tri-PLS. Unfortunately, retention time variation, unless corrected, can be an impediment to chemometric analysis. Previous work has demonstrated that the effects of retention time variation can be mitigated in sub-regions of GC x GC chromatograms through the application of an objective retention time alignment algorithm based on rank minimization. Building upon this previous work, it is demonstrated here that the effects of retention time variation can be mitigated throughout an entire GC x GC chromatogram with an objective retention time alignment algorithm based on windowed rank minimization alignment. A significant decrease in calibration error is observed when the algorithm is applied to chromatograms prior to construction of Tri-PLS models. Fourteen jet fuel samples with known weight percentages of naphthalenes (ASTM D1840) were obtained. Each sample was subjected to five replicate five-minute GC x GC separations over a period of two days. A subset of nine samples spanning the range of weight percentages of naphthalenes was chosen as a calibration set and Tri-PLS calibration models were subsequently developed in order to predict the naphthalene content of the samples from the GC x GC chromatograms of the remaining five samples. Calibration models constructed from GC x GC chromatograms that were retention time corrected are shown to exhibit a root mean square error of prediction of roughly half that of calibration models constructed from uncorrected chromatograms. The error of prediction is lowered further to a value that nearly matches the uncertainty in the standard percent weight values (ca. 1% of the median percent volume value) when the aligned chromatograms are truncated to include only regions of the chromatogram populated by naphthalenes and compounds of similar polarity and boiling point.     

Quantitative structure-property relationship study on the determination of binding constant by fluorescence quenching

Models to predict binding constant (logK) to bovine serum albumin (BSA) should be very useful in the pharmaceutical industry to help speed up the design of new compounds, especially as far as pharmacokinetics is concerned. We present here an extensive list of logK binding constants for thirty-five compounds to BSA determined by florescence quenching from the literature. These data have allowed us the derivation of a quantitative structure-property relationship (QSPR) model to predict binding constants to BSA of compounds on the basis of their structure. A stepwise multiple linear regression (MLR) was performed to build the model. The statistical parameter provided by the MLR model (R = 0.9200, RMS = 0.3305) indicated satisfactory stability and predictive ability for the model. Using florescence quenching spectroscopy, we also experimentally determined the binding constants to BSA for two bioactive components in traditional Chinese medicines. Using the proposed model it was possible to predict the binding constants for each, which were in good agreement with the experimental results. This QSPR approach can contribute to a better understanding of structural factors of the compounds responsible for drug-protein interactions, and be useful in predicting the binding constants of other compounds.      

Polydimethylsiloxane-based permeation passive air sampler. Part I: Calibration constants and their relation to retention indices of the analytes

A simple and cost effective permeation passive sampler equipped with a polydimethylsiloxane (PDMS) membrane was designed for the determination of time-weighted average (TWA) concentrations of volatile organic compounds (VOCs) in air. Permeation passive samplers have significant advantages over diffusive passive samplers, including insensitivity to moisture and high face velocities of air across the surface of the sampler. Calibration constants of the sampler towards 41 analytes belonging to alkane, aromatic hydrocarbon, chlorinated hydrocarbon, ester and alcohol groups were determined. The calibration constants allowed for the determination of the permeability of PDMS towards the selected analytes. They ranged from 0.026 cm² min⁻¹ for 1,1-dichloroethylene to 0.605 cm² min⁻¹ for n-octanol. Further, the mechanism of analyte transport across PDMS membranes allowed for the calibration constants of the sampler to be estimated from the linear temperature programmed retention indices (LTPRI) of the analytes, determined using GC columns coated with pure PDMS stationary phases. Statistical analysis using Student's t test indicated that there was no significant difference at the 95% probability level between the experimentally obtained calibration constants and those estimated using LTPRI for most analyte groups studied. This correlation allows the estimation of the calibration constants of compounds not known to be present at the time of sampler deployment, which makes it possible to determine parameters like total petroleum hydrocarbons in the vapor phase.     

Indirect photolysis of organic compounds: prediction of OH reaction rate constants through molecular orbital calculations

The MOOH approach is a perturbational molecular orbital method to predict rate constants of indirect photolysis of organic compounds through reaction with OH radicals. It employs the semiempirical AM1 scheme as the underlying quantum chemical model. The original method introduced by Klamt has been reparametrized using an up-to-date set of 675 compounds with experimental rate constants and outperforms the prominent Atkinson increment scheme for this training set as well as for an extended set of 805 compounds, yielding an overall root-mean-square error of 0.32 log units. The discussion includes detailed comparative analyses of the model performances for individual compound classes. The present model calibration refers mainly to monofunctional compounds but performs already reasonably well for multifunctional compounds. For predictive applications, both the Atkinson scheme and the alternative, independent AM1-MOOH model can be used as components of a consensus modeling approach, arriving at increased confidence in cases where the different models agree.     

Selective Electroenzymatic Oxyfunctionalization by Alkane Monooxygenase in a Biofuel Cell

Aliphatic synthetic intermediates with high added value are generally produced from alkane sources (e.g., petroleum) by inert carbon–hydrogen (C?H) bond activation using classical chemical methods (i.e. high temperature, rare metals). As an alternative approach for these reactions, alkane monooxygenase from Pseudomonas putida (alkB) is able to catalyze the difficult terminal oxyfunctionalization of alkanes selectively and under mild conditions. Herein, we report an electrosynthetic system using an alkB biocathode which produces alcohols, epoxides, and sulfoxides through bioelectrochemical hydroxylation, epoxidation, sulfoxidation, and demethylation. The capacity of the alkB binding pocket to protect internal functional groups is also demonstrated. By coupling our alkB biocathode with a hydrogenase bioanode and using H₂ as a clean fuel source, we have developed and characterized a series of enzymatic fuel cells capable of oxyfunctionalization while simultaneously producing electricity.     

Surfactants Synthesis Using Petroleum Fractions and Crude Oil: Application in Microemulsion Formulation

Surfactant synthesis was realized from Algerian crude oil and petroleum fractions. To predict the composition (wt%) in paraffins, naphtenes, and aromatics, the crude oil and petroleum fractions were first characterized using the n-d-PA empirical method of Robert. The characterization showed a good level in aromatics compounds which give high yield in the sulfonation reaction by oleum. The synthesized surfactants were characterized by spectroscopic techniques (UV, FTIR) and by critical micelle concentration (CMC), Krafft temperature, solubility (in aqueous and in salt solution), molecular weight, and matter actives (%) measurements. The formulation of microemulsion using synthesized surfactant from plat format petroleum fraction showed a Winsor III type system. The effect of salinity demonstrates the existence of an optimal value of NaCl concentration for which the interfacial tension takes the lowered value (10^?4 mN/m). Viscosity measurements confirm that the formulated microemulsion has a Newtonian behavior.     

Influence of chelate ring interactions on copper(II) chelate stability studied by connectivity index functions

Linear models for estimation of the first (K1), second (K2), and overall stability constant (beta2) based on the valence connectivity index of the third order ((3)chi(v)) were developed and checked on four sets of copper(II) chelates (with diamines, N-alkylated glycines, and naturally occurring amino acids, including their mixed complexes). Univariate models were valid when log K1 and log K2 values were linearly correlated, i.e., when there was no interaction between chelate rings. The univariate models proved applicable for estimation of all three stability constants of complexes with diamines and N-alkylated glycines, but for complexes with amino acids additional terms were needed (bivariate models). Models reproduced stability constants with an error usually less than 0.3 log K units.     

High-speed gas chromatographic separations with diaphragm valve-based injection and chemometric analysis as a gas chromatographic “sensor”

A high-speed gas chromatography system, the gas chromatographic sensor (GCS), is developed and evaluated. The GCS combines fast separations and chemometric analysis to produce an instrument capable of high-speed, high-throughput screening and quantitative analysis of complex chemical mixtures on a similar time scale as typical chemical sensors. The GCS was evaluated with 28 test mixtures consisting of 15 compounds from four chemical classes: alkanes, ketones, alkyl benzenes, and alcohols. The chromatograms are on the order of one second in duration, which is considerably faster than the traditional use of gas chromatography. While complete chromatographic separation of each analyte peak is not aimed for, chemical information is readily extracted through chemometric data analysis and quantification of the samples is achieved in considerably less time than conventional gas chromatography.

Calibration models to predict percent volume content of either alkanes or ketones were constructed using partial least squares (PLS) regression on calibration sets consisting of the five replicate GCS runs of six different samples. The percent volume content of the alkane and ketone chemical classes were predicted on five replicate runs of the 22 remaining samples ranging from 0 to 50 or 60% depending on the class. Root mean square errors of prediction were 2–3% relative to the mean percent volume values for either alkane or ketone prediction models, depending on the samples chosen for the calibration set of that model. The alkyl benzenes and alcohols present in the calibration sets or samples were treated as variable background interference. It is anticipated that the GCS will eventually be used to rapidly sample and directly analyze industrial processes or for the high throughput analysis of batches of samples.     

The development of a method for the qualitative and quantitative determination of petroleum hydrocarbon components using thin-layer chromatography with flame ionization detection

An analytical scheme to determine groups of petroleum hydrocarbon compounds in crude oil was developed and used for the qualitative and quantitative characterization of crude oil samples from the Shengli oilfield, the second largest oilfield in China. Crude oil samples were fractionated and analyzed by thin-layer chromatography with flame ionization detection (TLC-FID). Relative standard deviation (RSD) values for retention time, peak height and half peak width were less than 5.2% for all classes of compounds, based on nine independent replicates. The crude oil light fraction was further analyzed by GC–MS and the majority of identified compounds were methyl- or hydro-derivatives of long-chain hydrocarbons and aromatic compounds. The external standard method used in the present study can lower detection limits of petroleum hydrocarbon compound classes to 20.0 mg L⁻¹, and the crude oil concentration in the range of 30 and 35,000 mg L⁻¹ has a high linear correlation (r² > 0.97, P < 0.05) with peak area. A comparison between elution chromatography (EC) and TLC-FID regarding the recovery of petroleum hydrocarbon compounds was carried out with aged crude oil contaminated soils of 50, 80, 200 and 300 mg g⁻¹. The tested TLC-FID method showed a 10% higher recovery for total extractable materials than the reference EC method. The calibration factor was fraction-dependent and varied with the recovery rate of TLC/EC. Regarding the tested extraction procedures, accelerated solvent extraction (ASE) had a higher extraction efficiency for crude oil contaminated soils than Soxhlet and ultrasonic extractions.     

Application of Head-Space Analysis with Passive Adsorption for Forensic Purposes in the Automated Thermal Desorption-Gas Chromatography-Mass Spectrometry System

Offenders often use petroleum products as accelerants to commit an arson. A multi-component mixture of organic compounds isolated from the fire debris significantly differs from the initial one. These changes result from many factors, i.e. evaporation of the volatile components or presence of some organic compounds in the sample, which do not originate from the petroleum product (e.g. extinguishing medium components, products of pyrolysis of the burned material). The aim of presented research was to evaluate the influence of these factors. A series of experiments were carried out, and the analytical procedure was worked out. The headspace analysis with passive adsorption or the liquid extraction with ultrasonification was used for separation and concentration of the analytes. The mixture of organic compounds (accelerants, pollutants) was identified by the use of gas chromatography hyphenated with mass spectrometry. Principal component analysis (PCA) and likelihood ratio approach (LR) were applied to distinguish between evaporated samples of kerosene and diesel fuel. The elaborated procedure was verified both, witch inter-laboratory tests and using it for examinations of simulated fire debris.     

Degradation behavior of maleated polyethylene/ground tire rubber thermoplastic elastomers with and without stabilizers

This work investigates the effects of different degradation processes (weathering, thermal ageing and re-extrusion) on maleated polyethylene (MAPE), as well as MAPE/ground tire rubber (GTR) thermoplastic elastomers with and without stabilizers (thermal stabilizer, UVA and HALS). Mechanical characterizations revealed that GTR addition reduced compounds stability against thermal ageing at both 80 and 90 °C. But addition of stabilizers increased thermal stability of the MAPE phase. However, the results suggest that the stabilizers had no effect on the GTR phase. Mechanical characterizations also showed that weathering caused degradation in MAPE, while presence of stabilizers decreased the effects of degradation. Addition of GTR also increased significantly the stability of MAPE against weathering (due to presence of carbon black in GTR). Re-extrusion resulted in lower MAPE modulus (due to decreased molecular weight), while the modulus of GTR filled compounds increased. Re-extrusion also led to decreased GTR average particle size, especially at higher GTR contents. Morphological analyses showed that thermal ageing and re-extrusion did not change significantly blend morphology, but weathering, on the other hand, led to severe degradation of both MAPE and GTR filled compounds surface. Compression set of MAPE/GTR compounds was found to decrease with increasing degradation level (due to crosslinking). Finally, sample density slightly increased with degradation level (also due to crosslinking).