Heat exchangers play an important role in offshore oil and gas production and offshore low-temperature waste heat power generation. Heat exchanger health management mainly relies on performance parameter monitoring; however, accurate fault mode identification remains a challenge and the diagnostic results are difficult to interpret. This paper proposed an interpretable intelligent diagnosis method for heat exchangers based on Shapley Additive exPlanation and eXtreme Gradient Boosting. Firstly, secondary parameters characterizing the health state of heat exchangers are constructed based on online monitored data such as pressure and temperature. Then, the multi-dimensional feature vector is constructed by integrating the online monitored data and secondary parameters. The XGBoost fault diagnosis model for leakage and scaling faults is established with the multi-dimensional feature vector as the input. To improve the model performance on diagnosis, a grid search optimization algorithm is adopted. After the fault diagnosis of a heat exchanger is completed, the SHAP method is introduced to analyze the contribution of features by quantifying their influence on the fault diagnosis results. Finally, the effectiveness of the proposed method is verified by the heat exchanger fault simulation experiment. The proposed method achieved a diagnosis accuracy of 99.79%, which has better performance than the traditional fault diagnosis method.

Geothermal energy is not only environmentally friendly and has low carbon but also represents a nonintermittent technology. It is anticipated that its utilization in human society will experience rapid growth in the coming decades. Absorption refrigeration can be employed for the recovery of geothermal resources. However, the low performance of absorption refrigeration systems has hindered their development. In order to advance absorption refrigeration technology suitable for geothermal energy recovery, this study developed a mathematical model for an absorption refrigeration system using 55% lithium bromide (LiBr) and 35% lithium chloride (LiCl) solutions as working fluids. The impact of various parameters, including cooling water conditions, chilled water conditions, heat source parameters, and working fluid concentration, on the thermodynamic performance, system efficiency, and exergy efficiency of a single-effect absorption refrigeration system was analyzed. The results reveal that the cooling water inlet temperature and flow, chilled water inlet temperature, heat source inlet temperature, and working fluid concentration are the primary factors affecting the thermodynamic performance and exergy efficiency of the absorption refrigeration system. The coefficient of performance (COP) of the system increases with higher cooling water flow, chilled water inlet temperature, and working fluid concentration, but decreases with higher cooling water inlet temperature. Within a certain temperature range, the COP increases with higher heat source temperature. The effective COP (ECOP) increases with higher cooling water inlet temperature, cooling water flow, chilled water inlet temperature, and working fluid concentration, while it decreases with higher heat source temperature. The highest COP values for the LiBr and LiCl solution single-effect absorption refrigeration systems are 0.795 and 0.785, respectively, while the highest ECOP values are 0.694 and 0.684, respectively.

The subsea clamp connector is susceptible to sealing and locking failures over its lifetime in harsh marine environments and complex loading conditions, posing a serious challenge to the safe development of subsea oil and gas fields. Accurately predicting the reliability of the subsea clamp connector under realistic and complex operating conditions is therefore an important guarantee of its safe operation. Considering the main structural characteristic parameters of the subsea clamp connector, this paper conducts a reliability analysis using finite element numerical simulation combined with multiple response surface methodology (MRSM), based on the seal failure and yield failure criteria. The applicability has been verified through the application of subsea clamp connector in the Bohai Sea. The results show that the failure probability of the system is mainly affected by the radius of the seal, the contact angle of the upper and lower flanges and internal pressure. Considering the influence of various factors, the reliability of the connector was calculated to be 98.73%, and the reliability was verified by the sealing performance test. This paper provides a practical method for the reliability analysis of the subsea clamp connector structure under the comprehensive consideration of multiple factors, and provides a new technology to ensure the safe operation of subsea oil and gas fields.

T-junctions are important structures used in a number of industries to separate gas and liquid. This work studied the effect of the orientation of the side arm on the separation efficiency using a computational fluid dynamics (CFD) approach, and a new mechanical model is developed based on force analysis to predict the liquid carryout threshold. Laboratory experiments from published works are used to verify the CFD simulation and the new model. In this work, the angle of the side arm to the horizontal plane, $\mathsf{\alpha }$, and the angle of the side arm to the main arm’s axial direction, $\mathsf{\beta }$, are investigated. The results show that with increasing $\mathsf{\beta }$, the liquid carryover threshold increases accordingly, demonstrating that the liquid can be more easily carried to the side arm, while the liquid-carrying performance in the side arm is not sensitive to the inclination angle, $\mathsf{\beta }$. Hence, in the new model, the inclination angle of is ignored. Experimental data are collected to validate the new model. The results show that this model can accurately predict the liquid carryover threshold, and the relative error is 4.16%.

Seabed topography is a key factor affecting the layout of underwater production systems. Developing a more scientific, intelligent, and integrated layout optimization method is the key to optimizing the layout of underwater production systems. To address the challenge of acquiring a more scientific, intelligent, and integrated optimization method, this paper proposes a multi-level integrated optimization model that incorporates three-dimensional seabed topography, obstacle areas, target locations, pipeline paths, and manifold connection relationships, with the primary objective of minimizing total investment cost. A hybrid algorithm combining H-MOPSO (Hierarchical Multi-Objective Particle Swarm Optimization) with K-means-ILP clustering, dynamic programming, and TEWA* pathfinding is raised to collaboratively solve for the global optimal layout, achieving a coupled “target grouping-manifold connection-path optimization” design. Based on the actual oilfield seabed topography and target data, this paper carries out case analysis and algorithm comparison experiments. The results show that the optimization method in this paper can significantly improve the layout economy and cost accuracy under the premise of meeting the engineering constraints. Among them, the PLEM parallel connection method reduces the pipeline laying cost by 25.72% and the overall layout investment cost by 5.39% compared with the traditional manifold series scheme.

Reciprocating compressors, which serve as core equipment in the petrochemical and natural gas transmission sectors, operate under prolonged variable loads and high-frequency impact conditions. Critical components, such as valves and piston rings, are prone to failure. Existing fault diagnosis methods suffer from inadequate spatio-temporal feature extraction and neglect spatio-temporal correlations. To address this, this paper proposes a spatio-temporal feature fusion-based fault diagnosis method for reciprocating compressors. This method constructs a spatio-temporal feature fusion model (STFFM) comprising three principal modules: First, a spatio-temporal feature extraction module employing a multi-layered stacked bidirectional gated recurrent unit (BiGRU) with batch normalisation to uncover temporal dependencies in long-term sequence data. A graph structure is constructed via k-nearest neighbours (KNN), and an enhanced graph isomorphism network (GIN) is integrated to capture spatial domain fault information variations. Second, the spatio-temporal bidirectional attention-gated fusion module employs a bidirectional multi-head attention mechanism to enhance temporal and spatial features. It incorporates a cross-modal gated update mechanism and learnable weight parameters to dynamically retain the highly discriminative features. Third, the classification output module enhances the model’s generalisation capability through multi-layer fully connected layers and regularisation design. Research findings demonstrate that this approach effectively integrates spatio-temporal coupled fault features, achieving an average accuracy of 99.14% on an experimental dataset. This provides an effective technical pathway for the precise identification of faults in the critical components of reciprocating compressors.