Link between Acoustic and Hygrothermal Behavior of Hemp Shiv and Pith Composites

Bio-based materials are an environmentally friendly alternative to classic construction materials, yet their generally low density can lead to poor acoustic properties. The acoustic performance of hemp shiv and sunflower pith composites is therefore analyzed using Kundt’s tube. Although the loose aggregates present an exceptional sound absorbing behavior, it can be notably worsened in the presence of certain binders. The Transmission Loss is nevertheless enhanced by the binders, although it does not exceed 20 dB in most cases. For both properties, the type of binder has been found to be the most influential parameter. Through the Kundt’s tube method, it is also possible to determine the geometrical parameters of the composites’ microstructure, which have been observed to be similar for materials presenting comparable hygrothermal properties and containing the same binder. In a previous work, an experimental correlation was found between the thermal conductivity and the interparticle porosity of the aforementioned composites, which is compared to theoretical thermal conductivity models from literature without finding any apparent correspondence.

Development of Machine Learning Based Propped Fracture Conductivity Correlations in Shale Formations

Propped hydraulic fracturing is a stimulation technique used in tight formations to create conductive fractures. To predict the fractured well productivity, the conductivity of those propped fractures should be estimated. It is common to measure the conductivity of propped fractures in the laboratory under controlled conditions. Nonetheless, it is costly and time-consuming which encouraged developing many empirical and analytical propped fracture conductivity models. Previous empirical models, however, were based on limited datasets producing questionable correlations. We propose herein new empirical models based on an extensive data set utilizing machine learning (ML) methods. In this study, an artificial neural network (ANN) was utilized. A dataset comprised of 351 data points of propped hydraulic fracture experiments on different shale types with different mineralogy under various confining stresses was collected and studied. Several statistical and data science approaches such as box and whisker plots, correlation crossplots, and Z-score techniques were used to remove the outliers and extreme data points. The performance of the developed model was evaluated using powerful metrics such as correlation coefficient and root mean squared error. After several executions and function evaluations, an ANN was found to be the best technique to predict propped fracture conductivity for different mineralogy. The proposed ANN models resulted in less than 7% error between actual and predicted values. In this study, in addition to the development of an optimized ANN model, explicit empirical correlations are also extracted from the weights and biases of the fine-tuned model. The proposed model of propped fracture conductivity was then compared with the commonly available correlations. The results revealed that the proposed mineralogy based propped fracture conductivity models made the predictions with a high correlation coefficient of 94%. This work clearly shows the potential of computer-based ML techniques in the determination of mineralogy based propped fracture conductivity. The proposed empirical correlation can be implemented without requiring any ML-based software.

Modelling the Effect of Solution Composition and Temperature on the Conductivity of Zinc Electrowinning Electrolytes

Zinc electrowinning is an energy-intensive step of hydrometallurgical zinc production in which ohmic drop contributes the second highest overpotential in the process. As the ohmic drop is a result of electrolyte conductivity, three conductivity models (Aalto-I, Aalto-II and Aalto-III) were formulated in this study based on the synthetic industrial electrolyte conditions of Zn (50–70 g/dm3), H2SO4 (150–200 g/dm3), Mn (0–8 g/dm3), Mg (0–4 g/dm3), and temperature, T (30–40 °C). These studies indicate that electrolyte conductivity increases with temperature and H2SO4 concentration, whereas metal ions have negative effects on conductivity. In addition, the interaction effects of temperature and the concentrations of metal ions on solution conductivity were tested by comparing the performance of the linear model (Aalto-I) and interrelated models (Aalto-II and Aalto-III) to determine their significance in the electrowinning process. Statistical analysis shows that Aalto-I has the highest accuracy of all the models developed and investigated in this study. From the industrial validation, Aalto-I also demonstrates a high level of correlation in comparison to the other models presented in this study. Further comparison of model Aalto-I with the existing published models from previous studies shows that model Aalto-I substantially improves the accuracy of the zinc conductivity empirical model.

1D Stochastic Inversion of Airborne Time-Domain Electromagnetic Data with Realistic Prior and Accounting for the Forward Modeling Error

Airborne electromagnetic surveys may consist of hundreds of thousands of soundings. In most cases, this makes 3D inversions unfeasible even when the subsurface is characterized by a high level of heterogeneity. Instead, approaches based on 1D forwards are routinely used because of their computational efficiency. However, it is relatively easy to fit 3D responses with 1D forward modelling and retrieve apparently well-resolved conductivity models. However, those detailed features may simply be caused by fitting the modelling error connected to the approximate forward. In addition, it is, in practice, difficult to identify this kind of artifacts as the modeling error is correlated. The present study demonstrates how to assess the modelling error introduced by the 1D approximation and how to include this additional piece of information into a probabilistic inversion. Not surprisingly, it turns out that this simple modification provides not only much better reconstructions of the targets but, maybe, more importantly, guarantees a correct estimation of the corresponding reliability.