Hydrocarbon Generation from Low-Mature Saline Lacustrine Sediments Studied Using Machine Learning and Chemometric Methods: The Succession of the Sikeshu Sag, Junggar Basin, NW China.

Significant attention has been given to the extensive development of saline environments in petroliferous basins. Further exploration and studies have discovered that saline environments, such as those for the deposition of source rocks in the Paleogene Anjihaihe (E ) Formation of the Sikeshu Sag, are ubiquitous in terrestrial lake basins. Previous studies have suggested that the oil reservoirs in the Sikeshu Sag and its peripheral regions are predominantly derived from the black mudstone and coal measures of the Lower Jurassic Badaowan (J ) Formation.

Missing data recovery combined with Parallel factor analysis model for eliminating Rayleigh scattering in the process of detecting pesticide mixture.

Excitation-Emission Matrix (EEM) fluorescence spectroscopy combined with Parallel factor analysis (PARAFAC) provides a widely used method to extract useful information containing unknown components. However, the inherent scattering especially Rayleigh scattering will influence the accuracy of PARAFAC so that appropriate procedure to the scattering becomes an essential problem when processing the EEM data. Many methods have been proposed to solve the problems about eliminating scattering.

A GA-BP method of detecting carbamate pesticide mixture based on three-dimensional fluorescence spectroscopy.

In this paper, we have proposed a method to detect a mixture of carbamate pesticides using a back propagation network (BP), which is optimized by genetic algorithm (GA) for quantitative analysis. This method aims to combine the advantages of BP and GA to remedy their drawbacks. The training samples were taken as input, some performance indexes such as the predicted values, iteration time, mean squared error, correlation coefficient and recovery rate were compared between BP neural network and the constructed GA-BP model to evaluate the performance of two neural networks.

Comparison of several third-order correction algorithms applied to fluorescence excitation-emission-sample data array: Interference-free determination of polycyclic aromatic hydrocarbons in water pollution.

Interference-free determination of polycyclic aromatic hydrocarbons (PAHs) in water pollution is proposed based on third-order correction algorithms with quadrilinear component modeling applied to the constructed four way fluorescence excitation-emission-sample data array with higher accuracy and better predictive ability than second-order (three-dimension) correction. Alternating weighted residue constraint quadrilinear decomposition (AWRCQLD), quadrilinear parallel factor analysis (4-PARAFAC), alternate penalty quadrilinear decomposition (APQLD) and alternate penalty trilinear decomposition (APTLD) are applied to acenaphthene (ANA), naphthalene (NAP) and fluorene (FLU) respectively. Fulvic acid affects PAHs determination seriously in real-world situation, so it is simulated as an interfering agent.