Intercriteria Analysis to Diagnose the Reasons for Increased Fouling in a Commercial Ebullated Bed Vacuum Residue Hydrocracker.

The intercriteria analysis developed on the base of intuitionistic fuzziness and index matrices was applied to evaluate processing data of the LUKOIL Neftohim Burgas H-Oil ebullated bed vacuum residue hydrocracker with the aim of revealing the reasons for increased fouling registered during the 3rd cycle of the H-Oil hydrocracker. It was found that when the ratio of the Δ of the 1st reactor to the Δ of the 2nd reactor gets lower than 2.0, an excessive H-Oil equipment fouling occurs.

Role of Catalyst in Optimizing Fluid Catalytic Cracking Performance During Cracking of H-Oil-Derived Gas Oils.

Three H-Oil gas oils, heavy atmospheric gas oil (HAGO), light vacuum gas oil (LVGO), heavy vacuum gas oil (HVGO), and two their blends with hydrotreated straight run vacuum gas oils (HTSRVGOs) were cracked on two high unit cell size (UCS) lower porosity commercial catalysts and two low UCS higher porosity commercial catalysts. The cracking experiments were performed in an advanced cracking evaluation fluid catalytic cracking (FCC) laboratory unit at 527 °C, 30 s catalyst time on stream, and catalyst-to-oil (CTO) variation between 3.5 and 7.

Commercial Investigation of the Ebullated-Bed Vacuum Residue Hydrocracking in the Conversion Range of 55-93.

The LUKOIL Neftohim Burgas vacuum residue hydrocracking has increased the vacuum residue conversion from 55 to 93% as a result of a proper feed selection, optimal catalyst condition, and the use of a Mo nanodispersed catalyst. It was found that the feed colloidal instability index estimated from the feed saturates, aromatics, resins, and asphaltenes (SARA) data negatively correlated with the conversion. Correlations based on the use of the nonlinear least-squares method, which relates the density to the aromatic structure contents for the straight run and hydrocracked vacuum residues, were developed.

Application of the Intuitionistic Fuzzy InterCriteria Analysis Method with Triples to a Neural Network Preprocessing Procedure.

The approach of InterCriteria Analysis (ICA) was applied for the aim of reducing the set of variables on the input of a neural network, taking into account the fact that their large number increases the number of neurons in the network, thus making them unusable for hardware implementation. Here, for the first time, with the help of the ICA method, correlations between triples of the input parameters for training of the neural networks were obtained. In this case, we use the approach of ICA for data preprocessing, which may yield reduction of the total time for training the neural networks, hence, the time for the network's processing of data and images.

Intercriteria analysis of calorimetric data of blood serum proteome.

Background: Biological microcalorimetry has entered into a phase where its potential for disease diagnostics is readily recognized. A wide variety of oncological and immunological disorders have been characterized by differential scanning calorimetry (DSC) and characteristic thermodynamic profiles were reported. Now the challenge before DSC is not the experimental data collection but the development of analysis protocols for reliable data stratification/classification and discrimination of disease specific features (calorimetric markers).