№84-11

Innovative approaches to managing deposit formation processes in main pipeline systems

V. Rastsvietaiev1https://orcid.org/0000-0003-3120-4623

O. Aziukovskyi1,   https://orcid.org/0000-0003-1901-4333

M. Babenko1,       https://orcid.org/0000-0003-2309-0291

D. Vasylchenko1,  https://orcid.org/0009-0005-1304-1826

D. Yashyn1             https://orcid.org/0009-0005-4960-5187

1Dnipro University of Technology, Dnipro, Ukraine

Coll.res.pap.nat.min.univ. 2026, 84:149–160

Full text (PDF)

https://doi.org/10.33271/crpnmu/84.149

ABSTRACT

Purpose. To identify the key physicochemical, thermodynamic, and hydrodynamic mechanisms governing deposit formation in trunk pipelines and to develop an integrated system for monitoring, prevention, removal, and prediction of deposits during multiphase transportation.

The methods. A multiscale approach combining numerical modeling, machine learning, and techno-economic analysis was applied. Experiments were conducted in a high-pressure loop with controlled temperature (40–80 °C, up to 100 bar, 0.1–5 m/s) using model fluids containing paraffin, asphaltenes, and water. Computational fluid dynamics (CFD) modeling of multiphase flow was coupled with neural network and LSTM models to predict deposit growth (MAE < 15%). System performance was evaluated through statistical validation and analytical assessment.

Findings. In untreated systems, deposits reached thicknesses of 3.5–4.0 mm, whereas advanced mitigation methods reduced thickness by 58–78%: superhydrophobic coatings (~0.8 mm), nano-inhibitors (~1.1 mm), and AI-assisted monitoring (~1.45 mm). Predictive models identified peak deposition zones and reduced the annual wax flux to < 0.5 g/(m2·day). Compared with conventional strategies, the integrated approach is projected to reduce maintenance costs by 40%, energy consumption by 25%, chemical usage by 60%, downtime by 70%, and greenhouse gas emissions by 32%.

The originality. The dependence of deposit growth on paraffin/asphaltene content, hydrodynamics, temperature gradients, and the composition of the aqueous phase was established. A predictive control framework integrating CFD, machine learning (neural networks, LSTM), real-time monitoring, and nanotechnology-based/eco-friendly inhibitors reduced deposition rates by 60–75% and significantly decreased maintenance costs, energy consumption, and greenhouse gas emissions.

Practical implementation. The proposed system enhances flow assurance, extends pipeline service life, reduces environmental impact, and provides scalable solutions for sustainable pipeline operation.

Keywords: main pipelines, flow reliability, wax deposits, asphaltenes, inorganic scale formation, nanotechnology coatings, smart sensors.

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date of first submission of the article to the publication 01/10/2026
date of acceptance of the article for publication after review – 02/22/2026
date of publication  03/30/2026