Base-Level Cycles and Episodic Coal Accumulation——Case Study of Dongsheng Coalfield in Ordos Basin

The advantage of high-resolution sequence stratigraphy, which takes base-levels as reference, is that it can be applied to continental depositional basins controlled by multiple factors and can effectively improve the accuracy and resolution of sequential stratigraphic analysis. Moreover, the principles of base-level cycles are also suitable for analyzing sequential stratigraphy in continental coal-bearing basins because of their accuracy in forecasting distribution of coal measures. By taking the Dongsheng coalfield in the Ordos basin as an example, the extensive application of base-level cycles in exploration and exploitation of coal is analyzed. The result shows that the Yan’an formation in the Dongsheng area is a long-term base-level cycle which is bordered by nonconformities and made up of five mid-term cycles and 13 short-term cycles. The long-term cycle and the mid-term cycles are obvious in comparison with a transverse profile. The episodic coal accumulation in the Mesozoic Ordos basin means that the deposition of primary matter (peat bogs) of coalification is discontinuous, periodical and cyclical in the evolution of the basin. The episodic accumulation of coal measures in the Yan’an stage is controlled by ascending-descending changes of a long-term cycle and middle-term cycles. Coal measures formed during the early and late periods of the long-term cycle are characterized by multiple layers, big cumulative thickness and poor continuity. Coal measures formed in the mid-term of the long cycle are dominated by good continuity, fewer layers and a small additive thickness, which is favorable for the accumulation of thick and continuous coal measures in the transition stage of mid term base-level cycles.

Correlation methods of base-level cycle based on wavelet neural network

The authors discussed the method of wavelet neural network （WNN） for correlation of base-level cycle. A new vectored method of well log data was proposed. Through the training with the known data set, the WNN can remenber the cycle pattern characteristic of the well log curves. By the trained WNN to identify the cycle pattern in the vectored log data, the ocrrdation process among the well cycles was completed. The application indicates that it is highly efficient and reliable in base-level cycle correlation.

Application of Improved Analysis of Variance to Ionospheric TEC Short-Term Forecast

An improved superposition analysis of periodical wave variance is used for short-term forecast of the ionosphere TEC in this study. Using the ionospheric TEC data provided by IGS as the real value, the forecasting precision of this me-thod at different locations in China with 40 days data is evaluated. The result shows that the improved method has a better forecasting precision which could reach 1.1 TECU. But the forecasting precision still relates to geographical position, it is proportional to longitude and inversely proportional to latitude. Compared with the current-used methods, the improved method has many advantages as higher precision, using fewer parameters and easier to calculate. So, it applied to ionosphere short-term prediction in China very well.

A self-adaptive,data-driven method to predict the cycling life of lithium-ion batteries

Accurately forecasting the nonlinear degradation of lithium-ion batteries(LIBs)using early-cycle data can obviously shorten the battery test time,which accelerates battery optimization and production.In this work,a self-adaptive long short-term memory(SA-LSTM)method has been proposed to predict the battery degradation trajectory and battery lifespan with only early cycling data.Specifically,two features were extracted from discharge voltage curves by a time-series-based approach and forecasted to further cycles using SA-LSTM model.The as-obtained features were correlated with the capacity to predict the capacity degradation trajectory by generalized multiple linear regression model.The proposed method achieved an average online prediction error of 6.00%and 6.74%for discharge capacity and end of life,respectively,when using the early-cycle discharge information until 90%capacity retention.Fur-thermore,the importance of temperature control was highlighted by correlat-ing the features with the average temperature in each cycle.This work develops a self-adaptive data-driven method to accurately predict the cycling life of LIBs,and unveils the underlying degradation mechanism and the impor-tance of controlling environmental temperature.

Sedimentary system characteristics and depositional filling model of Upper Permian——Lower Triassic in South Yellow Sea Basin

In the South Yellow Sea Basin,Mesozoic–Paleozoic marine strata are generally well developed with large thickness,and no substantial breakthroughs have been made in hydrocarbon exploration.Through research,it is believed that the Upper Permian–Lower Triassic can be regarded as a long-term base-level cycle.Based on drilling data,characteristics of the lithology–electric property combination cyclicity,and the special lithology,the long-term base-level cycle was divided into five medium-term base-level cycles(MC1–MC5).On this basis,the Permian–Triassic sedimentary systems and their filling model were analyzed in accordance with the change of base-level cycle and transition of sedimentary environment,as well as characteristics of the drilling sedimentary facies and seismic facies.The results show that there were six sedimentary systems(fluvial,delta,tidal flat,open platform,restricted platform,and continental shelf)developed in the Upper Permian–Lower Triassic,the sedimentary systems were distributed such that the water was deep in the northwest and shallow in the southeast,and there were two base-level cycle filling models(a relatively stable tidal flat facies and a rapidly transgressive continental shelf facies to stable platform facies)developed in the Upper Permian–Lower Triassic.These models can provide a basis for evaluation of the Mesozoic–Paleozoic hydrocarbon geology in the South Yellow Sea Basin.

On the analysis of the high-resolution sequence stratigraphy and coal accumulating law of jurassic in Ordos Basin

The strata of Jurassic was divided into three tectonic sequences and eight se- quences of third rank,according to the developing feature of the tectonic inconformity and the transforming feature of the depositional system tracts.Also the identification and the division of the base-level cycle of different period were carried through.Therefore three cycles of super period,eight cycles of long period,twenty-four cycles of middle period and some cycles of short period were identified.From the overall character of the coal-accu- mulation in the Mesozoic,we can see that the Yan'an formation is of the best nature of coal bearing.When the coal bearing systems of Jurassic were depositing,the Ordos area is the coal accumulating basin of terrene of large scale and located in the same tectonic unit.But the local structure of different part and the paleolandform are different in the basin which resulted in the difference of the depositional environment.So the layer number and the distribution of the thickness of the coal beds are different in the different part of the ba- sin.The coal-accumulating action migrated regularly along with the development,evolve- ment and migration of the depositional systems.The layer numbers of the coal beds, which can be mined,are more in the north and west fringe of the basin,whose distributing area is extensive,and they are more steady in the landscape orientation,also the total thickness is great.Therefore the nature of coal bearing and the coal-accumulating action of different part changed obviously in the space in Ordos area.

HIGH-RESOLUTION SEQUENCE STRATIGRAPHIC ANALYSIS OF THE PERMIAN IN THE WESTERN SHANDONG AND HUAINAN-HUAIBEI COALFIELDS

The sedimentary characteristics and their combination succession of the Permian in the Shandong nd Huainan-Huaibei coalfields are analyzed. The mid-and short-term stratigraphic base-level cycles are identified. High-resolution sequences are divided based on the above results. The study shows that the stratigraphic base-level cyclic method is an efficient way in the determination of the high-resolution sequences, especially in the classification of tbe terrestrial and transitional depositional succession.

Data-driven modeling of power generation for a coal power plant under cycling

Increased penetration of renewables for power generation has negatively impacted the dynamics of conventional fossil fuel-based power plants.The power plants operating on the base load are forced to cycle,to adjust to the fluctuating power demands.This results in an inefficient operation of the coal power plants,which leads up to higher operating losses.To overcome such operational challenge associated with cycling and to develop an optimal process control,this work analyzes a set of models for predicting power generation.Moreover,the power generation is intrinsically affected by the state of the power plant components,and therefore our model development also incorporates additional power plant process variables while forecasting the power generation.We present and compare multiple state-of-the-art forecasting data-driven methods for power generation to determine the most adequate and accurate model.We also develop an interpretable attention-based transformer model to explain the importance of process variables during training and forecasting.The trained deep neural network(DNN)LSTM model has good accuracy in predicting gross power generation under various prediction horizons with/without cycling events and outperforms the other models for long-term forecasting.The DNN memory-based models show significant superiority over other state-of-the-art machine learning models for short,medium and long range predictions.The transformer-based model with attention enhances the selection of historical data for multi-horizon forecasting,and also allows to interpret the significance of internal power plant components on the power generation.This newly gained insights can be used by operation engineers to anticipate and monitor the health of power plant equipment during high cycling periods.