Multi-objective optimization for erosion protection of wear-prone components based on optimal prediction model

In the field of shale gas extraction, vulnerable components such as gas extraction pipeline elbows and needle throttle valves are frequently subjected to severe erosive wear due to gas-solid two-phase flow, which often leads to serious production accidents. To address this challenge, this paper proposes a collaborative optimization method integrates machine learning with multi-objective optimization. The erosion characteristics of elbows and needle throttle valves subjected to gas-solid two-phase flow are analyzed, and a high-precision erosion rate prediction model is established based on a data-driven approach. On this basis, a multi-objective optimization framework is constructed. The framework aims to minimize the erosion rate of elbows and achieve a weighted erosion balance in the needle throttling valve area. The control variables used are valve opening, inlet velocity, and particle mass flow rate. By incorporating process constraints (including the feasible domain of parameters and system pressure drop limitations), the engineering feasibility of the optimization results is ensured. The improved NSGA-II algorithm and the Epsilon constraint method are employed to solve the model, yielding the Pareto optimal solution set and optimization results that meet the constraint conditions. The results show that the optimized parameter combinations can significantly reduce the erosion rate of vulnerable components, extend their service life, and meet the requirements of system operating efficiency. This study provides a scientific basis and optimization strategy for the erosion protection of vulnerable components in gas production pipelines, holding significant engineering application value.