During shale gas field production, wellhead throttle valves are prone to erosion caused by solid particles carried in the gas stream, posing significant safety risks. Existing studies on erosion primarily focus on simple structure like elbows and tees, while research on gas–solid two-phase flow erosion in needle throttle valves remains limited. This paper establishes a numerical model based on the CFD-DPM approach, integrating actual shale gas field production conditions to investigate the erosion behavior of needle throttle valves under varying openings, particle sizes, inlet velocities, and particle mass flow rates. The results show that the valve spool consistently exhibits the highest erosion rate among all components, with the most severe erosion localized at its front end. At a 1/4 opening, particles colliding with the spool exhibit significantly increased frequency and energy when re-entering the upstream pipeline, raising the risk of blockages. Furthermore, when the opening exceeds 2/4, the valve chamber experiences higher erosion rates than the upstream and downstream pipelines. This study provides critical insights for optimizing valve design and maintenance strategies, thereby enhancing service life and ensuring safe shale gas production.

The transition towards carbon neutrality and sustainable urban development necessitates innovative strategies for managing multi-energy supply stations (MESS), which integrate oil, gas, electricity, and hydrogen to support diversified energy demands. Existing revenue prediction models for MESS lack interpretability and multi-energy adaptability, hindering actionable insights for sustainable operations. This study proposes a novel Shapley additive explanations (SHAP)-driven machine learning framework for multi-energy supply station revenue forecasting. By leveraging real-world consumption data from Hangzhou West Lake Tanghe Station, we constructed a dataset with nine critical parameters, including energy types, transaction frequency, and temporal features. Four machine learning models—decision tree regression, random forest (RF), support vector regression, and multilayer perceptron—were evaluated using MAE, MSE, and R² metrics. The RF model achieved an R² of 0.98, demonstrating superior accuracy in predicting hourly gross transaction values. SHAP analysis further identified consumption volume and transaction frequency as the most influential factors, providing actionable insights for operational optimization. This research not only advances the scientific management of MESS but also contributes to carbon emission reduction by enabling data-driven resource allocation. The proposed framework offers policymakers and industry stakeholders a scalable tool to accelerate urban energy transitions under carbon neutrality goals, bridging the gap between predictive analytics and sustainable infrastructure planning.