Hydrocarbon quality prediction

When a customer agrees to purchase gas, product quality is specified in terms of the calorific value of the gas, measured by the Wobbe index (calorific value divided by density), the hydrocarbon dew point and the water dew point, and the fraction of other gases such as Nj, COj, HjS. The Wobbe index specification ensures that the gas the customer receives has a predictable calorific value and hence predictable burning characteristics. If the gas becomes lean, less energy is released, and if the gas becomes too rich there is a risk that the gas burners flame out . Water and hydrocarbon dew points (the pressure and temperature at which liquids start to drop out of the gas) are specified to ensure that over the range of temperature and pressure at which the gas is handled by the customer, no liquids will drop out (these could cause possible corrosion and/or hydrate formation). 

Computer modeling of hydrocarbon pyrolysis is discussed with respect to industrial applications. Pyrolysis models are classified into four groups mechanistic, stoichiometric, semi-kinetic, and empirical. Selection of modeling schemes to meet minimum development cost must be consistent with constraints imposed by factors such as data quality, kinetic knowledge, and time limitations. Stoichiometric and semi-kinetic modelings are further illustrated by two examples, one for light hydrocarbon feedstocks and the other for naphthas. The applicability of these modeling schemes to olefins production is evidenced by successful prediction of commercial plant data. 

Pipeline specifications for water content are needed for a variety of reasons. Among these are the refiner s need for a predictable and high quality of raw materials, the desire of the pipeline company to reduce corrosion potential by eliminating the electrolyte (water) from the corrosion process, and the desire of the pipeline company to construct pipelines on the basis of their capacity to deliver marketable hydrocarbon products rather than waste materials. Failure by the oil producer to meet pipeline specifications for any extended period of time will result in the pipeline company refusing to accept produced oil. This outcome alone forces the oil producer to ensure that emulsions are resolved to reduce BS W in oil to 0.5% or less. Producers pay pipeline tariffs according to their BS W content, so that reductions in BS W below 0.5% reduce the cost of transporting their product to market. If each reduction in BS W below 0.5% is less than the costs necessary to achieve the new BS W standard, the oil producer will pursue that standard. 

Other criticisms19-20 of the VI are that it does not adequately predict or reflect oxidation stability and performance of engine oils or quality. These criticisms merely indicate that at one time expectations of the method far exceeded its real scope. The VI is indeed dependent on hydrocarbon composition (on which all behavior depends), but it should best be regarded as a measure of solely rheological properties, whatever other desirable properties these may subsequently coincide with. The term quality has to be considered carefully as well. Many performance factors do correlate with VI, but not necessarily so. 

To obtain a reasonable quality of the calcidated data for high pressure hydrocarbon jet fires and therewith a reasonable quality of the calculated time-to-loss-of-strength of structure a risk assessor, designer or verifier should focus on the quality of the prediction of the release rate, flame length, radiative heat flux, type of the fire fuel, soot content in the flame and flame emissivity. 

Mass spectrometry and chemometric methods cover very diverse fields Different origin of enzymes can be disclosed with LC-MS and multivariate analysis [45], Pyrolysis mass spectrometry and chemometrics have been applied for quality control of paints [46] and food analysis [47], Olive oils can be classified by analyzing volatile organic hydrocarbons (of benzene type) with headspace-mass spectrometry and CA as well as PC A [48], Differentiation and classification of wines can similarly be solved with headspace-mass spectrometry using unsupervised and supervised principal component analyses (SIMCA = soft independent modeling of class analogy) [49], Early prediction of wheat quality is possible using mass spectrometry and multivariate data analysis [50],... 

There has been a recent attempt to combine MP2/ 6-31G //HF/6-31G calculations with simple carbon substructure parameters as an accurate approach to correlating and predicting the enthalpies of formation of cata- and peri-condensed unsubstituted polycyclic aromatic hydrocarbons. The electronic energies provide the nonadditive electronic effects, and the carbon-structure parameters model the additive effects. The quality of the multilinear regression was... 

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