Optimization of hydrotransport system parameters taking into account reliability criteria

Abstract

The article explores the problem of improving the reliability and efficiency of hydrotransport systems, which play a crucial role in many industrial processes involving the movement of large volumes of solid-liquid mixtures. These systems operate under complex conditions, being exposed to intensive hydroabrasive wear and variable loads. Recent studies highlight the need to move beyond traditional deterministic models that estimate average wear, as they fail to capture the stochastic nature of degradation under real operating conditions. Instead, probabilistic models based on the theory of stochastic processes and Markov chains have proven effective in predicting the reliability and remaining service life of pipelines and slurry pumps.
The aim of the study is to enhance the operational reliability of hydrotransport systems by developing mathematical and optimization models that consider the randomness of wear and performance parameters. Particular attention is given to selecting rational configurations of pump stations and determining system layouts that minimize risks of failure. The methodology integrates monitoring of hydraulic parameters, statistical analysis of wear data, and modeling of component failures.
The research results demonstrate that modeling hydroabrasive wear as a stochastic process with independent increments enables more accurate prediction of service life and failure probability. The paper presents analytical expressions for key reliability indicators and illustrates how different system configurations—single-stage, multi-stage, with or without redundancy—impact overall system availability. Optimization techniques, including nonlinear programming and simulation, are applied to determine ideal pump placement and sump volume. The proposed approach offers practical recommendations for maintenance scheduling, spare part management, and cost-effective system design. It is concluded that accounting for stochastic factors significantly enhances system resilience and economic performance in long-term operation.