Soft Sensor Development for Real-Time Process Monitoring of Multidimensional Fractionation in Tubular Centrifuges

Efficient and accurate determination of the degree of substitution of cellulose acetate using ATR-FTIR spectroscopy and machine learning

Multiple linear regression models were trained to predict the degree of substitution (DS) of cellulose acetate based on raw infrared (IR) spectroscopic data. A repeated k-fold cross validation ensured unbiased assessment of model accuracy. Using the DS obtained from 1H NMR data as reference, the machine learning model achieved a mean absolute error (MAE) of 0.069 in DS on test data, demonstrating higher accuracy compared to the manual evaluation based on peak integration. Limiting the model to physically relevant areas unexpectedly showed the [Formula: see text] peak to be the strongest predictor of DS. By applying a n-best feature selection algorithm based on the F-statistic of the Pearson correlation coefficient, several relevant areas were identified and the optimized model achieved an improved MAE of 0.052. Predicting the DS of other cellulose acetate data sets yielded similar accuracy, demonstrating that the developed models are robust and suitable for efficient and accurate routine evaluations. The model solely trained on cellulose acetate was further able to predict the DS of other cellulose esters with an accuracy of [Formula: see text] in DS and model architectures for a more general analysis of cellulose esters were proposed.

Real-Time Modeling of Volume and Form Dependent Nanoparticle Fractionation in Tubular Centrifuges

A dynamic process model for the simulation of nanoparticle fractionation in tubular centrifuges is presented. Established state-of-the-art methods are further developed to incorporate multi-dimensional particle properties (traits). The separation outcome is quantified based on a discrete distribution of particle volume, elongation and flatness. The simulation algorithm solves a mass balance between interconnected compartments which represent the separation zone. Grade efficiencies are calculated by a short-cut model involving material functions and higher dimensional particle trait distributions. For the one dimensional classification of fumed silica nanoparticles, the numerical solution is validated experimentally. A creation and characterization of a virtual particle system provides an additional three dimensional input dataset. Following a three dimensional fractionation case study, the tubular centrifuge model underlines the fact that a precise fractionation according to particle form is extremely difficult. In light of this, the paper discusses particle elongation and flatness as impacting traits during fractionation in tubular centrifuges. Furthermore, communications on separation performance and outcome are possible and facilitated by the three dimensional visualization of grade efficiency data. Future research in nanoparticle characterization will further enhance the models use in real-time separation process simulation.

Virtual Soft Sensor of the Feedstock Composition of the Catalytic Reforming Unit

The paper discusses a method for obtaining a matrix of individual and group composition of a hydrotreated heavy gasoline fraction in industrial conditions based on the fractional composition obtained by the distillation method according to the ASTM D86 (the Russian analogue of such a standard is GOST 2177). A method for bounds estimation of the retention index (RI) change is considered on the basis of the symmetry of the RI change range relative to its arithmetic mean. Implementation of this method is performed by simulation of individual composition of C6–C12 feedstock of the catalytic reforming unit in the software package. For this purpose, the boiling curve of individual composition of hydrocarbon mixture is converted into the corresponding curve of fractional composition. The presented technique of creating a virtual soft sensor makes it possible to establish a correct relationship between the fractional composition and the individual hydrocarbon composition obtained according to the IFP 9301 (GOST R 52714) (Russian GOST R 52714 and international IFP 9301 standards for the determination of individual and group composition of hydrocarbon mixtures by capillary gas chromatography). The virtual soft sensor is based on chemical and mathematical principles. The application of this technique on the data of a real oil refinery is shown. Obtaining accurate data by means of a virtual soft sensor on the individual composition of feedstock will make it possible to optimize the catalytic reforming process and thus indirectly improve its environmental friendliness and enrichment efficiency.