The article examines an approach based on the construction of intelligent virtual analyzers, which calculate the values of difficult-to-measure quality indicators based on the current readings of technological parameter sensors. A methodology for forming such models under conditions of noise, incomplete, and temporarily dissynchronized data is proposed. A probabilistic maximum algorithm is used to clarify time delays, partial least squares regression for working with multicollinear inputs, nonlinear transformations, analysis methods, and training sampling formation. It has been shown that the implementation of the developed virtual analyzer allows for a decrease in the average error of quality forecasting by tens of percent, stabilizes the refining regime, and ensures a reduction in energy consumption and losses of the target product.
| Mualliflar | Ortikov, Elbek |
|---|---|
| Jurnal | Techscience.uz - техника фанлари долзарб масалалри |
| Nashr sanasi | 2025-12-27 |
| Jild | 3 |
| Son | 12 |
| Betlar | 103-111 |
| Til | Rus |
| DOI | 10.47390/ts-v3i12y2025n11 |
DOI: 10.47390/ts-v3i12y2025n11 · Maqolaning asl sahifasi
intelligent control, refining of vegetable oils, virtual analyzers, oil quality assessment, intelligent diagnostic system., интеллектуальное управление, рафинация растительных масел, виртуальные анализаторы, оценка качества масла, интеллектуальная диагностическая система.
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