The Upper Carboniferous (Westphalian A) Kozlu Formation consisting chiefly of sandstone, thick coal layers, shale, and conglomerate levels crops out in the Zonguldak-Amasra Basin, NW Anatolia. The Kozlu Formation contains a total of 20 mineable coal layers with the thickness varying from 0.5 to 6 m. The lower level of formation is represented by lacustrine deposits, whereas the upper part is made up of thick flood plain sediments and meandering river deposits bearing laterally continuous coal levels. In this study, coal samples taken from coal layers within the Kozlu Formation at the Kozlu Underground Coal Mining site were evaluated using the data obtained from pyrolysis/total organic matter (TOC), gas chromatography, and gas chromatography–mass spectrometry. The average total organic matter (TOC) value of Kozlu coals is 40.28%. The coals are characterized by relatively high hydrogen index (HI) value (average 262 mg HC/gTOC) and very low oxygen index value (2 mg CO2/gTOC). Pyrolysis data indicate that coals contain dominantly type II and less amount of type III kerogen, and T max values range from 454 to 468 °C. In gas chromatographs, the recorded distribution consists predominantly of low-carbon-numbered n-alkanes and subordinately of high-carbon-numbered n-alkanes and the terrigenous/aquatic ratio value is very low (0.05–0.09). Pristane abundance is greater than that of phytane, and Pr/Ph ratios are in the range of 1.11–1.60. The sterane abundances in the Kozlu coals are in the following order: C29 > C28 > C27. Coals have high C19 and C20 tricyclic terpane concentrations and high (C19 + C20)/C30 ratio, high C30* (diahopane) and C29Ts concentrations, and high C30*/C29Ts ratio and low C31R/C30 and C29/C30 hopane ratios. The dibenzothiophene-to-phenanthrene ratio of Kozlu coals is found to be very low (0.04–0.14). Based on the pyrolysis and biomarker data, the Kozlu coals are interpreted as being deposited in a suboxic–oxic continental environment in which there is effective input of clay and dominantly terrestrial (with significant lipid-rich components) and bacterial organic matter. High T max values, CPI values close to 1, low moretane/hopane (0.23–0.12), equilibrated 22S/(22S + 22R) homopane (for C32), 20S/(20S + 20R) C29 sterane (0.52–0.54) and TA(I)/TA(I + II) steroid ratios, high ββ/(ββ + αα) sterane (0.51–0.55), C30*/C29Ts, C30*/(C30* + C30H), MPI-3(α/β) (1.24–1.41), and MDR and MA(I)/MA(I + II) steroid are indicative of mature–late mature organic matter. R o values between 0.9 and 1.25% determined from T max (454–468 °C) values indicate “high-volatile bituminous B–medium-volatile bituminous” rank for Kozlu coals. Kozlu coals having HI values (up to 331 mg HC/gTOC) extremely higher than those of classical coals indicates that these coals have significant oil and gas generation potential, and high S 1 (average 6.04 mg HC/g rock) and S 2 (110.08 mg HC/g rock) values imply that they generate notably high amount of liquid hydrocarbons and still have generation potential.
Electricity generation from renewable energy sources is increased day by day. Accurate estimation of electricity generation from the renewable energy sources which have intermittent and variable characteristics is a requirement to ensure stable operation of the electrical grid. In this study, a multi-layer artificial neural network (ANN) system, which is supported by meteorological forecasting data, has been proposed to predict day ahead hourly solar radiation. In this context, the ANN system which operates by based on cause-effect relationship has been designed. In order to increase accuracy of the solar radiation prediction of the designed ANN, a similar day selection algorithm has been developed. A unique ANN has been constituted for each season by evaluating the seasons within itself. The designed ANN model has been designed, trained and tested in MATLAB simulation environment without using codes of the MATLAB ANN toolbox. Day ahead hourly solar radiation of Trabzon province has been predicted by the proposed ANN. The accuracy of the predictions has been evaluated by the mean absolute percentage error (MAPE), the root means squared error (RMSE), the mean absolute error (MAE) and the correlation coefficient (r) performance measures.
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