Convolutional architectures for virtual screening

Background: A Virtual Screening algorithm has to adapt to the different stages of this process. Early screening needs to ensure that all bioactive compounds are ranked in the first positions despite of the number of false positives, while a second screening round is aimed at increasing the prediction accuracy. Results: A novel CNN architecture is presented to this aim, which predicts bioactivity of candidate compounds on CDK1 using a combination of molecular fingerprints as their vector representation, and has been trained suitably to achieve good results as regards both enrichment factor and accuracy in different screening modes (98.55% accuracy in active-only selection, and 98.88% in high precision discrimination). Conclusion: The proposed architecture outperforms state-of-the-art ML approaches, and some interesting insights on molecular fingerprints are devised

Data-Driven Methods for the State of Charge Estimation of Lithium-Ion Batteries: An Overview

In recent years, there has been a noticeable shift towards electric mobility and an increasing emphasis on integrating renewable energy sources. Consequently, batteries and their management have been prominent in this context. A vital aspect of the BMS revolves around accurately determining the battery pack’s SOC. Notably, the advent of advanced microcontrollers and the availability of extensive datasets have contributed to the growing popularity and practicality of data-driven methodologies. This study examines the developments in SOC estimation over the past half-decade, explicitly focusing on data-driven estimation techniques. It comprehensively assesses the performance of each algorithm, considering the type of battery and various operational conditions. Additionally, intricate details concerning the models’ hyperparameters, including the number of layers, type of optimiser, and neuron, are provided for thorough examination. Most of the models analysed in the paper demonstrate strong performance, with both the MAE and RMSE for the estimation of SOC hovering around 2% or even lower

Data-driven sensors and their applications

Virtuální senzory jsou postupně se rozšiřující technikou v oblasti průmyslových měření. Jedná se o počítačové programy, které za pomoci dříve získaných dat poskytují další údaje podobně jako klasické hardwarové senzory. Tyto údaje získávají pomocí prediktivních modelů založených na metodách strojového učení jako jsou například neuronové sítě nebo support vector machines. Tato práce obsahuje především rešerši fungování, struktur a tvorby virtuálních senzorů. Dále popisuje strojové učení, rozdělení jeho algoritmů a seznamuje s metodami běžně využívanými v oblasti virtuálních senzorů. Ke konci autor popisuje jejich možný budoucí vývoj a směr dalších aplikací.Soft sensors are a gradually expanding technique in the field of industrial measurement. These sensors are computer programs that provide additional data using previously acquired data in a similar way to conventional hardware sensors. The additional data is obtained using predictive models based on machine learning methods such as neural networks or support vector machines. This work mainly includes a research on the function, structure and creation of soft sensors. It also describes machine learning, the distribution of its algorithms and introduces the methods commonly used in the field of virtual sensors. Towards the end, the author describes possible future development of soft sensors and the direction of further applications.

Illuminating Strain Fields Generated by Hydraulic Fracturing: from Modeling of Fiber-Optic Response to Fracture Geometry Inversion

The use of fiber-optics in reservoir surveillance can bring valuable insights to fracture geometry and fracture-hit identification, stage communication, and perforation cluster fluid distribution in hydraulic fracturing processes. However, the complexity of multiple information streams associated with realistic field data makes interpretation challenging for engineers and geoscientists. In this work, I generate distributed strain sensing (DSS)/low-frequency distributed acoustic sensing (LF-DAS) synthetic data of a cross-well fiber deployment. This data incorporates the physics governing hydraulic fracturing treatments. Forward modeling streamlines the interpretation task by exploring data richness, which has the potential to improve completion design and optimize production. Forward modeling relies on analytical and numerical solutions. The analytical solution is developed by coupling two models: a 2D fracture (e.g., Khristianovic-Geertsma-de Klerk [KGD]) and Sneddon’s induced stress. DSS is estimated using the plane strain approach that combines calculated stresses and rock properties (e.g., Young’s modulus and Poisson’s ratio). In turn, the numerical solution is implemented using the displacement discontinuity method (DDM) with net pressure and/or shear stress as the boundary condition. In this case, the fiber gauge length concept is incorporated deriving displacement (i.e., DDM output) in space to obtain DSS values. In both methods, LF-DAS is estimated by the differentiation of DSS in time. My simulator models classic features present in field data including: the heart-shaped pattern from a fracture approaching the fiber, stress shadow, and fracture hits. Incorporating shear stress in simulation creates strain time histories that entail complexities beyond those observed in cases in which tensile stress is the unique failure mechanism, thus highlighting the significant impact promoted by natural fractures. Moreover, a large gauge length (i.e., popular 10 m size used in the field) can mask strain data richness, distorting intrinsic characteristics of fracture systems. Fracture corridor extension signature, occasionally observed in LF-DAS field data when pumping stops, is verified in synthetic results for small pressure drop gradients, revealing that fractures continue to propagate in this scenario. Quantitatively, fracture geometry characterization is improved by estimating width in multiple locations as time increases, with the support of deep learning (DL) algorithms I developed using data from multiple monitor wells. The model framework captures a wide range of relevant phenomena and provides a solid foundation for generating accurate and rich synthetic data representing multiple distinct scenarios leading to interpretation optimization. Also, the development of specific packages (commercial or otherwise) that explicitly model DSS/LF-DAS, incorporating the impact of fracture opening and slippage, is still in its infancy. This project is novel in this regard, and it opens new avenues of research and applications of synthetic DSS/LF-DAS in hydraulic fracturing processes