Weidong Li

Refined oil depots are critical nodes in energy supply chains. Tank truck queuing increases costs and idling carbon emissions while reducing customer satisfaction. This research proposes a modified finite capacity Markovian queuing system (M/M/c/N) with random service interruptions and establishes a multi-objective model covering expected cost, carbon emissions and customer satisfaction. We derive core metrics and formulate corresponding unit time objective functions. The Bayesian optimization-based NSGA-II (BO-NSGA-II) hybrid algorithm is adopted to solve the proposed model. Comparisons against four algorithms are conducted using four multi-objective evaluation metrics: hypervolume (HV), Inverted Generational Distance (IGD), generational distance (GD) and spacing, which confirm that BO-NSGA-II achieves a balanced overall performance. Its HV reaches 0.657, while IGD (0.0066), GD (0.0009) and spacing (0.0069) remain low, demonstrating broad Pareto front coverage, high convergence accuracy and uniform solution distribution. Trade-off analysis indicates that the algorithm entails a marginal expected cost increase of merely 1.15% compared with the minimum expected cost solution obtained by MOEA/D. Meanwhile, carbon emissions drop to 11.25 kgCO₂/h and customer satisfaction rises to 0.9769, achieving coordinated economic, environmental and service benefits. Sensitivity analysis further identifies the optimal loading bay configuration range of 2 to 4. Insufficient allocation leads to congestion, while excessive allocation causes equipment idling and higher emissions. Blind expansion also raises the expected cost. This study provides methodological references for the collaborative optimization of oil depot resource allocation, low-carbon scheduling and service performance.

With the expansion of ammonia energy applications, long-distance liquid ammonia pipelines are expected to support large-scale cross-regional ammonia transport. In the liquid ammonia pipeline commissioning process, gaseous ammonia purging involves ammonia–nitrogen mixing and possible liquefaction, while liquid ammonia injection may induce flashing and severe local cooling, all of which can affect commissioning safety. To characterize these thermodynamic phenomena, a transient gas–liquid two-phase flow model was established and validated using OLGA 2022.1.0 software for simulating the long-distance liquid ammonia pipeline commissioning. The model adopts the cross-sectionally averaged one-dimensional approach. A volume-corrected Soave–Redlich–Kwong (SRK) equation of state for ammonia was adapted, validated, and used to generate OLGA-compatible thermodynamic property tables. The results show that, during gaseous ammonia purging, a higher flowrate shortens the displacement time by accelerating nitrogen removal, and this effect is more pronounced at higher ambient temperatures due to enhanced molecular diffusion. Along the pipeline, pressure gradually decreases from frictional resistance, with a steeper drop near the outlet caused by gas acceleration, and temperature gradually approaches ambient through heat exchange with the pipe wall and surrounding soil. A high gaseous ammonia flowrate can cause partial liquefaction, regasification, and temperature fluctuations. During liquid ammonia injection, local condensation and slight liquid accumulation occur before the liquid front arrives, and the low-temperature region moves with the liquid front. The liquid ammonia mass flowrate has the strongest influence on the injection process, as it reduces the completion time but increases the outlet temperature, outlet pressure, and the low-temperature risk downstream of the valve. Therefore, it should be controlled within an appropriate range to balance efficiency and low-temperature safety risks. This work provides a rapid and efficient prediction model for key thermo-hydraulic parameters during liquid ammonia pipeline commissioning, and the overall analyses offer insights for on-site process design and safety control.

The centrifugal compressor is the heart that drives the operation of natural gas pipeline systems. Under low-throughput conditions, natural gas often returns back to the compressor through the surge control valve to increase the flow rate and avoid surge. However, how to reasonably determine the opening of the surge control valve is still an important problem in production. To predict the opening of the surge control valve in a centrifugal compressor, this work proposes a BP neural network optimized by the grey wolf optimizer (GWO). Five key parameters, including compressor shell vibration, power turbine speed, compressor inlet pressure, compressor outlet temperature, and gas turbine power, are determined to be key factors correlated to the opening of the surge control valve, and the relationships of these parameters are physically analyzed from a physical perspective. Compared with the other five parallel models, the GWO–BP method effectively optimizes the initial weights and thresholds of the neural network, reduces the probability of falling into a local optimum, and significantly improves prediction accuracy and stability. The root mean square error (RMSE), determination coefficient (R-square), and mean absolute error (MAE) of the GWO–BP model are all the best fit, and the predicted and actual openings of the surge control valve match well, with the average relative deviation being 4.65%, indicating that the GWO–BP model proposed in this paper has a good ability to predict the opening of surge control valves.

Acoustic emission (AE) is a passive non-destructive testing (NDT) method that allows for online monitoring of oil tank bottom corrosion without production shutdown. However, AE signals are susceptible to ambient noise interference, causing the AE inspection system to mistakenly identify noise as corrosion signals, which significantly reduces AE inspection performance. Therefore, it is important to distinguish between AE signals caused by corrosion and those caused by noise. To address this, an AE inspection platform for vertical atmospheric tank corrosion is established. Six common noise sources in field AE inspections, including mechanical vibration and friction, fluid and raining disturbance, external impacts, and oil leakage are simulated. The impacts of these noises on AE location events are analyzed. Variational mode decomposition (VMD) and dispersion entropy (DE) are used to extract multi-domain features of AE signals. An improved distance evaluation (IDE) algorithm is then introduced to obtain a highly correlated feature subset. A support vector machine (SVM) model optimized by the bald eagle search (BES) algorithm is proposed to identify different noise sources. Field experiments demonstrate that for mechanical friction, external impacts, and effective corrosion signals, the proposed method achieves identification accuracy of 92.95% and 94.00% in the training and test sets, respectively. This proves the reliability of the BES-SVM model, which uses multi-domain features for AE source identification in oil tank bottom corrosion inspections. Moreover, the impacts of the optimization algorithm, feature selection algorithm, and feature type on AE source identification are further investigated.

A pressure regulator is essential for pressure control in a gas transmission system. The traditional maintenance approaches for pressure regulators involve equipment disassembly that disrupts normal production. In response, this paper proposes a support vector machine (SVM) model improved by the grey wolf optimization algorithm (GWO) and autoencoder (AE), i.e., the AE-GWO-SVM model. It achieves intelligent fault diagnosis on pressure regulators based on operation data collected by the SCADA system. Firstly, a number of actual pressure regulator faults are counted, and it is found that internal and external leakages are key problems faced by pressure regulators. To address the limited fault data available in field practice, a dataset of pressure regulator faults is constructed using numerical simulation. Additionally, 18 new features are obtained through feature combination, and an improved distance evaluation method (IDE) is utilized to select highly correlated features as input for the machine learning model. Furthermore, an autoencoder (AE) is employed to overcome the interference from abnormal data, which significantly enhances the fault identification process of GWO-SVM. To verify the performance of the AE-GWO-SVM model, an experimental platform for pressure regulators is designed and constructed. Compared with conventional SVM, the accuracy of the AE-GWO-SVM model increases from 60.4% to 86.7%, indicating its strong diagnosis capability for pressure regulator faults.

Wax deposition of diesel oil and contamination of gasoline by diesel wax deposit is a severe problem in sequential transportation of product oil in pipes. However, it has long been neglected by the pipeline transportation industry. In response, this work aims to present a unique perspective on wax deposition of diesel oil and consequent contamination of gasoline. A cold finger apparatus was designed and constructed. Model oil composed of diesel and refined wax was prepared for wax deposition. Shear dispersion was excluded for wax deposition of diesel oil. Moreover, dissolution experiments of diesel wax deposit in gasoline were conducted. It was found that the dissolution rate increases with oil temperature and decreases with cold finger temperature and shear stress. Analysis on gasoline quality after diesel wax deposit dissolution indicated that contamination of gasoline caused by diesel wax deposit severely deteriorates its distillation range, but the octane number remains acceptable. This work features an interesting investigation on the wax-deposition mechanism of diesel oil, dissolution characteristics of diesel wax deposit, as well as quality deterioration of subsequent gasoline. It would be helpful in scheduling a product oil-transportation program.

Contamination of gasoline by wax deposit of diesel is a severe problem in sequential transportation of product oil pipelines in cold areas. However, most works on wax deposition are focused on crude oil. In response, this paper aims to investigate wax deposition from a unique perspective of diesel oil in sequential transportation. To this end, a cold finger apparatus was designed and constructed. It is found that the wax deposition rate of diesel oil increases with oil temperature and wax content, and decreases with cold finger temperature. A non‐monotonic variation trend is observed against shear stress. To predict diesel wax deposition rate, a back propagation (BP) neural network optimized by bald eagle search (BES) algorithm is proposed. Grey relational analysis (GRA) is employed to get the highly relevant factors as input parameters of the developed model. Prediction accuracy and generalization ability of the BES‐BP model is experimentally verified. This work will be helpful to schedule the transportation program of product oil to avoid contamination of gasoline by diesel wax deposit.

Most treatments for spinal cancer are accompanied by serious side effects including subsequent tumor recurrence, spinal cord compression, and tissue adhesion, thus a highly effective treatment is crucial for preserving spinal and neurological functionalities. Herein, trilayered electrospun doxorubicin@bovine serum albumin/poly(ε-caprolactone)/manganese dioxide (DOX@BSA/PCL/MnO₂) nanofibers with excellent antiadhesion ability, dual glutathione/hydrogen peroxide (GSH/H₂O₂) responsiveness, and cascade release of Mn²⁺/DOX was fabricated for realizing an efficient spinal tumor therapy. In detail, Fenton-like reactions between MnO₂ in the fibers outermost layer and intra-/extracellular glutathione within tumors promoted the first-order release of Mn²⁺. Then, sustained release of DOX from the fibers' core layer occurred along with the infiltration of degradation fluid. Such release behavior avoided toxic side effects of drugs, regulated inflammatory tumor microenvironment, amplified tumor elimination efficiency through synergistic chemo-/chemodynamic therapies, and inhibited recurrence of spinal tumors. More interestingly, magnetic resonance and photoacoustic dual-modal imaging enabled visualizations of tumor therapy and material degradation in vivo, achieving rapid pathological analysis and diagnosis. On the whole, such versatile hierarchical-structured nanofibers provided a reference for rapid and potent theranostic of spinal cancer in future clinical translations.