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[Display omitted] • Mechanisms controlling lithium enrichment in various geological settings. • Overview of the mining techniques, including beneficiation and extraction. • Recent developments in recycling, environmental impact, and state-of-the-art analytical techniques. • Latest developments and insights in the various sectors related to lithium in the context of clean energy. The energy transition challenges faced by modern civilization have significantly enhanced the demand for critical metals like lithium resulting in improved methods to explore, extract, and utilize these metals. In this comprehensive review, we discuss the different types of lithium resources, factors, and mechanisms controlling lithium enrichment in various geological settings including terrestrial and marine environments. Diverse exploration strategies including geological, geophysical, mineralogical, geochemical, and remote sensing techniques including drone-based techniques for lithium exploration studies in different terranes are summarized. An overview of the mining techniques, including beneficiation and extraction, and their principles, mechanisms, operations, and comparison of the various approaches and compatibility with different types of lithium deposits for obtaining maximum yield are evaluated. Lithium isotopic studies are useful in understanding geological processes such as past weathering events and riverine input into the oceans, as well as in understanding the source of lithium in diverse types of deposits. We also highlight the recent developments in other areas such as recycling, environmental impact, and state-of-the-art analytical techniques for determining lithium in different lithium ore deposits and other geological materials. Our overview provides the latest developments and insights in the various sectors related to lithium and prompt further developments to meet the growing demand for this valuable metal as the world transforms to clean energy. [ABSTRACT FROM AUTHOR]

Abstract Most of the studies on Artificial Neural Network (ANN) models remain restricted to smaller rivers and catchments. In this paper, an attempt has been made to correlate variability of sediment loads with rainfall and runoff through the application of the Back Propagation Neural Network (BPNN) algorithm for a large tropical river. The algorithm and simulation are done through MATLAB environment. The methodology comprised of a collection of data on rainfall, water discharge, and sediment discharge for the Narmada River at various locations (along with time variables) and application to develop a three-layer BPNN model for the prediction of sediment discharges. For training and validation purposes a set of 549 data points for the monsoon (16 June-15 November) period of three consecutive years (1996–1998) was used. For testing purposes, the BPNN model was further trained using a set of 732 data points of monsoon season of four years (2006–07 to 2009–10) at nine stations. The model was tested by predicting daily sediment load for the monsoon season of the year 2010–11. To evaluate the performance of the BPNN model, errors were calculated by comparing the actual and predicted loads. The validation and testing results obtained at all these locations are tabulated and discussed. Results obtained from the model application are robust and encouraging not only for the sub-basins but also for the entire basin. These results suggest that the proposed model is capable of predicting the daily sediment load even at downstream locations, which show nonlinearity in the transportation process. Overall, the proposed model with further training might be useful in the prediction of sediment discharges for large river basins. [ABSTRACT FROM AUTHOR]