Due to the complex shield construction characteristics and the complex effects of geological environment, it is difficult to control the direction of shield tunneling and to determine the reasonable tunneling parameters such as tunneling speed and so on. During the tunneling, shield tunneling machine may rise, shift and snake advance, which are not conducive to control tunnel axis. Aiming at the problem that it is difficult to accurately predict and correct the axis attitude deviation in the shield tunneling process, a prediction of axis attitude deviation and deviation correction method based on data driven during shield tunneling was put forward in this paper. Under certain geological conditions, the relationship between the attitude deviation of the construction axis and tunneling parameters during shield tunneling process with different tunneling mileages is established. Based on the tunneling historical data, the XGBoost prediction model is constructed, and the axis deviation variation is predicted and analyzed with the shield construction parameters. The multi-ring deviation correction parameter calculation model based on the fusion of geometric model and association rules is designed to obtain the internal correlation of the deviation amount, the number of deviation correction rings and the deviation correction parameters of each ring under the maximum deviation of different deviation correction sections, so as to realize the accurate prediction and deviation correction of shield axis deviation in the complex construction process. Under the verification of 155 ring data in a certain subway construction section, the method proposed in this paper has higher prediction accuracy, which is important for improving the safety and quality of shield tunneling. Results from the measured shield construction ring data verify the reasonability of the proposed axis attitude deviation prediction and multi-ring deviation correction method.
Light emitting diode (LED) lamps are now an established lighting technology, which is becoming prevalent in all load sectors. However, LED lamps are non-linear electrical loads, and their impact on distribution system voltage quality must be evaluated. This paper provides a detailed analysis of time domain and frequency domain approaches for developing and evaluating models suitable for use in large scale steady-state harmonic power flow analysis of the low frequency (LF) emission of LED lamps. The considered approaches are illustrated using four general categories of LED lamps, which have been shown to cover the vast majority of LED lamps currently available on the market. The aim is an in-depth assessment of the ability of commonly applied models to represent the specific design characteristics of different categories of LED lamps. The accuracy of the models is quantitatively evaluated by means of laboratory tests, numerical simulations, and statistical analyses. This provides an example, for each LED lamp category, of comprehensive information about the overall accuracy that can be achieved in the general framework of large scale LF harmonic penetration studies, particularly in the assessment of voltage quality in low voltage networks and their future evolution.
The production of a single gas well is influenced by many geological and completion factors. The aim of this paper is to build a production prediction model based on machine learning technique and identify the most important factor for production. Firstly, around 159 horizontal wells were collected, targeting the Duvernay Formation with detailed geological and completion records. Secondly, the key factors were selected using grey relation analysis and Pearson correlation. Then, three statistical models were built through multiple linear regression (MLR), support vector regression (SVR), gaussian process regression (GPR). The model inputs include fluid volume, proppant amount, cluster counts, stage counts, total horizontal lateral length, gas saturation, total organic carbon content, condensate-gas ratio. The model performance was assessed by root mean squared errors (RMSE) and R-squared value. Finally, sensitivity analysis was applied based on best performance model. The analysis shows following conclusions: (1) GPR model shows the best performance with the highest R-squared value and the lowest RMSE. In the testing set, the model shows a R-squared of 0.8 with a RMSE of 280.54 × 104 m3 in the prediction of cumulative gas production within 1st 6 producing months and gives a R-squared of 0.83 with a RMSE of 1884.3 t in the prediction of cumulative oil production within 1st 6 producing months (2) Sensitivity analysis based on GPR model indicates that condensate-gas ratio, fluid volume, and total organic carbon content are the most important features to cumulative oil production within 1st 6 producing months. Fluid volume, Stages, and total organic carbon content are the most significant factors to cumulative gas production within 1st 6 producing months. The analysis progress and results developed in this study will assist companies to build prediction models and figure out which factors control well performance.
To achieve net-zero emissions in air transport industry with defined CO2 mitigation objectives in "Flightpath 2050", electrically powered aircraft as part of electrified aviation become attractive technologies. In recent years, many electric aircraft prototypes for short-haul commuting air transport have been designed. Most of them are supposed to be deployed in the real airports by 2030 -2035. However, the ground power systems and associated electric aircraft charging operation are also essential for realising electrified aviation. Moreover, various renewable generation technologies in terms of PVs and micro wind turbines, electric vehicles and energy storage systems are expected to be integrated into the airport to form a decarbonised microgrid energy system. Therefore, there is a challenge of designing airport microgrids associated with energy scheduling algorithms for electric aircraft charging as well as airport decarbonisation. To overcome this challenge, this paper proposes a multi-agent real-time microgrid energy scheduling solution for electrified air transport. The coordination algorithm between the electric aircraft charging system and the electric vehicles charging in the airport parking lot is developed to enhance the resilience and operational flexibility of the airport microgrid. The stochastic behaviours of the airport passengers and renewable power generation are integrated into the airport microgrid energy management solution. The study shows that the airport microgrid can achieve the resilience through the proposed energy management solution.
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