Wind power technology is developing rapidly in China. Especially in the north of China, decentralized wind power (DWP) has developed rapidly. However, wind curtailment exceeds 4% of the gross generation, which has become an important obstacle restricting the development of DWP in 2019. Technical schemes are effective solutions to promote decentralized wind power consumption (DWPC). Moreover, there are few economic analyses of DWP projects with key technologies. This paper summarizes the typical technical schemes and analyzes the economy of a DWP grid-connected project with typical technical schemes by combining methods of the net present value (NPV) and real options (RO). It appears that the DWP project can be profitable with an NPV of about CNY 4.3721 million and an option value of CNY 16.6022 million in the case of a feed-in tariff of 0.34 CNY/kWh. Feed-in tariff and equipment investment costs are the most relevant parameters for economic feasibility according to the sensitivity analysis. Furthermore, 30.73 million kWh connects to the Mengxi grid each year. The project has an annual maximum CO₂ emission reduction of about 31 thousand tons and an SO₂ of about 0.1 thousand tons. In addition, it also realizes fine social benefits, including the support of local residents and the improvement of local new urbanization levels. Finally, this paper puts forward policy suggestions for the development of DWP mainly from an economic point of view.

Coal seam water injection is widely applied to prevent mine disasters. In the traditional water migration model of water-injected coal, from ignoring capillary force in the seepage stage to generating the capillary dynamic during the spontaneous imbibition state, no transition state exists from 0 to generating capillary force. In this paper, on the basis of theoretical analysis, an equivalent capillary term is introduced to modify the Forchheimer model, and then a seepage-transition-spontaneous imbibition model that is suitable for water migration of water-injected coal is proposed. The formulas for the upper critical point velocity v1, lower critical point velocity v2, and water migration range of the transition state are derived. An analysis experiment of the spontaneous imbibition state in the coal rock water injection process and a monitoring experiment of the water injection process based on nuclear magnetic resonance method are conducted to verify the accuracy of the model. The results show that the accuracy of the water migration model, which takes the transition section into account, exceeds 0.99, representing a 12% improvement compared to the fitting precision of the water injection model that does not consider the transition section. The water migration process of water-injected coal cannot be accurately reflected without considering the transition section. In this paper, the water migration model of water-injected coal is improved, which provides theoretical support for a deeper understanding of the water injection mechanism and for enhancing the extraction efficiency of the coal seam.

Coal and gas outburst is a major mine disaster affected by complex coupled factors, bringing obstacles to disaster prevention. To address low accuracy and poor generalization of traditional single-algorithm prediction models, this paper constructs a two-layer Stacking ensemble learning identification model for outburst risk. RF, SVM and AdaBoost serve as base models, and WOA-LightGBM acts as the meta-model. Based on measured data of a Shanxi coal mine, Spearman correlation analysis and RF dimensionality reduction remove redundant features; Borderline-SMOTE balances imbalanced samples with few severe-risk data. Accuracy, macro-precision, recall and F1-score evaluate model performance after parameter optimization. Results show that the proposed Stacking model reaches 0.9770 accuracy, outperforming single machine learning models and other intelligent algorithms. It presents minor index fluctuations with strong stability and correctly identifies all eight practical engineering cases. Combining feature engineering and Stacking learning effectively captures nonlinear relations between influencing factors and risk levels. The model owns high precision and robustness, offering reliable technical support for coal and gas outburst prediction and control.

In an era characterized by frequent global supply chain disruptions, manufacturers increasingly explore strategies to safeguard operational continuity, including equity participation in upstream suppliers. This paper investigates partial vertical ownership (PVO), defined as a manufacturer’s partial equity stake in an upstream supplier that supports strategic cooperation and risk information access without full vertical integration. We model a single manufacturer, an unreliable primary supplier with private disruption risk information, and a reliable backup supplier. Relative to a non-ownership (NO) baseline, PVO is modeled as a benchmark governance mechanism that can improve the manufacturer’s information set before backup capacity is reserved. The analytical results show that expected profit is convex in the unit stockout penalty under the specified convex capacity cost structure and deterministic demand assumptions. They also show that the information dividend from PVO is governed by the variance of the disruption probability, while its economic magnitude depends on the scale of stockout losses, backup procurement costs, and the equity return condition. By benchmarking PVO against a clarified option contract strategy, we identify parameter regions in which equity-based information access can outperform contractual hedging. The study reframes PVO as a conditional operational hedging instrument and clarifies the assumptions under which it can strengthen supply chain resilience.