The coexistence of numerous Mississippi Valley-type (MVT) Pb–Zn deposits and oil/gas reservoirs in the world suggests a close genetic relationship between mineralization and hydrocarbon accumulation. The Wusihe MVT Pb–Zn deposits are located along the southwestern margin of the Sichuan Basin. Based on the spatiotemporal relation between Pb–Zn deposits and paleo-oil/gas reservoirs, ore material sources, and processes of mineralization and hydrocarbon accumulation, a new genetic relationship between mineralization and hydrocarbon accumulation is suggested for these deposits. The Wusihe Pb–Zn deposits are hosted in the Ediacaran Dengying Formation dolostone, accompanied by a large amount of thermally cracked bitumen in the ore bodies. The Pb–Zn deposits and paleo-oil/gas reservoirs are distributed along the paleokarst interface; they overlap spatially, and the ore body occupies the upper part of the paleo-oil/gas reservoirs. Both the Pb–Zn ore and sphalerite are rich in thermally cracked bitumen, in which µm sized galena and sphalerite may be observed, and the contents of lead and zinc in the bitumen are higher than those required for Pb–Zn mineralization. The paleo-oil/gas reservoirs experienced paleo-oil reservoir formation, paleo-gas reservoir generation, and paleo-gas reservoir destruction. The generation time of the paleo-gas reservoirs is similar to the metallogenic time. The source rocks from the Cambrian Qiongzhusi Formation not only provided oil sources for paleo-oil reservoirs but also provided ore-forming metal elements for mineralization. Liquid oil with abundant ore-forming metals accumulated to form paleo-oil reservoirs with mature organic matter in source rocks. As paleo-oil reservoirs were buried, the oil underwent in situ thermal cracking to form overpressure paleo-gas reservoirs and a large amount of bitumen. Along with the thermal cracking of the oil, the metal elements decoupled from organic matter and H2S formed by thermochemical sulfate reduction (TSR) and minor decomposition of the organic matter dissolved in oilfield brine to form the ore fluid. The large-scale Pb–Zn mineralization is mainly related to the destruction of the overpressured paleo-gas reservoir; the sudden pressure relief caused the ore fluid around the gas–water interface to migrate upward into the paleo-gas reservoirs and induced extensive metal sulfide precipitation in the ore fluid, resulting in special spatiotemporal associated or paragenetic relations of galena, sphalerite, and bitumen.
The issue of how to fully utilize natural daylighting of public buildings is one of the greatest practical objectives for lighting savings. The rapid and accurate prediction of the daylighting coefficient at the early design stage can provide a quantitative basis for energy-saving optimization. However, it is not comprehensive to determine the design parameters according to experience. The key problem that is still facing designers is the interoperability between building modeling and energy simulation tools. In this paper, an integrated approach using a dataset created by building information modeling and artificial neural network technology is developed for the fast optimal daylight factor prediction of large public spaces at the early design stage. According to this approach, the value of daylight factors is calculated for different windowsill heights, window heights and widths by Autodesk ® Revit and Ecotect Analysis to form a dataset. With this dataset, an artificial neural network model is established using the backpropagation algorithm to predict the relevant design parameters. With their large interior spaces, the reading areas of the aboveground five floors in Chengdu University of Technology Library are selected to carry out the daylight factor experiment and rapid prediction. A total of 495 groups of experimental data are randomly divided into training and testing sets. The root mean squared errors are below 0.1, which indicates a high regression model fitting. A total of 225,369 groups of prepared data are used in the prediction model to obtain the optimal windowsill height (1.0 m), window height (2.4 m) and window width (2.1 m) for five floors in the case of the maximum daylighting coefficient. Finally, a smartphone app is designed to facilitate daylight factor prediction without any experience in modeling and simulation tools, which is simple and available to realize prediction visualization and historical result analysis.
The application of renewable energy can effectively alleviate energy shortages and environmental pollution. However, electric heating and traditional combustion heating are still widely used in western Sichuan. This paper analyzes a techno-economic comparison between a solar-air energy heating system (System 1) and a geothermal-air energy heating system (System 2) designed for Litang. First, the distribution of renewable energy in Litang is analyzed. The system devices and operating modes of the two systems are described. According to the system design results, the paper compares the initial cost, operation cost, and net present value (NPV) of the two systems. The results show that both schemes are feasible from the perspective of technical analysis. While System 2 has lower initial investment, operation cost and higher NPV, which meets the actual needs of the project and local development planning. Using geothermal resources for urban central heating can achieve good energy saving and emission reduction benefits, and provide a reference for improving the proportion of local renewable energy use and optimizing the energy structure.
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