Numerical simulation of cooling performance in microchannel heat sinks made of AlN ceramics

Microsystem Technologies - The problem of generating a high amount of heat in microelectronic equipment should be minimized properly. Allowing systems to run for long periods of time in high...     

Application of artificial neural network for prediction of 10 crude oil properties

This study aims to develop an industrially reliable and accurate method to estimate crude oil properties from their Fourier transform infrared spectroscopy (FTIR) spectra. We used the complete FTIR spectral data of selected crude oil samples from seven different Canadian oil fields to predict 10 important crude oil properties using artificial neural networks (ANNs). The predicted properties include specific gravity, kinematic viscosity, total acid number, micro carbon content, and production of light and heavy naphtha, Kero, and distillate in oil refineries. The 107 different (65 light oil and 42 heavy/medium oil samples) crude oil samples used in this study came from seven oil fields and reservoirs across Canada. In line with standard practice, we used 80% of the dataset for training the ANN models and used the remaining 20% of the crude oil samples to test the models. In the ANN analysis, the mean squared error (MSE) was used as the loss function in models, and the mean absolute prediction error (MAPE) was used as a reference to compare the performance of different neural networks constructed with different numbers of layers. This work demonstrates that FTIR spectroscopy is a promising technique that provides rapid and accurate estimates for the oil properties of interest to the industry. A comparison of the values predicted by the validated ANN models and their corresponding measured (actual) values showed excellent prediction with the acceptable range of error (below 15%) aimed for by our industry partner for all properties except viscosity, for which building models based on the natural logarithmic values of measured viscosities significantly improved the results.     

Deep chemometrics using one-dimensional convolutional neural networks for predicting crude oil properties from FTIR spectral data

The determination of physicochemical properties of crude oils is a very important and time-intensive process that needs elaborate laboratory procedures. Over the last few decades, several correlations have been developed to estimate these properties, but they have been very limited in their scope and range. In recent years, methods based on spectral data analysis have been shown to be very promising in characterizing petroleum crude. In this work, the physicochemical properties of crude oils using Fourier transform infrared (FTIR) spectrums are predicted. A total of 107 samples of FTIR spectral data consisting of 6840 wavenumbers is used. One dimensional convolutional neural networks (CNNs) were used employing FTIR spectral data as the one-dimensional input and Keras and TensorFlow were used for model building. The Root Mean Square Error decreased from 160 to around 60 for viscosity when compared to previous machine learning methods like partial least squares (PLS), principal component regression (PCR), and partial least squares regression with genetic algorithm (PLS-GA) on the same data. The important hyper-parameters of the CNN were optimized. In addition, a comparison of results obtained with different neural network architectures is presented. Some common preprocessing techniques were also tested on the spectral data to determine their impact on model performance. To increase interpretability, the intermediate neural network layers were analyzed to reveal what the convolutions represented, and sensitivity analysis was done to gather key insights about the wavenumbers that were the most important for prediction of the crude oil properties using the neural network.     

Challenges and Strategies for Optimizing Corrosion and Biodegradation Stability of Biomedical Micro- and Nanoswimmers: A Review

The last two decades have witnessed the emergence of micro- and nanoswimmers (MNSs). Researchers have invested significant efforts in engineering motile micro- and nanodevices to address current limitations in minimally invasive medicine. MNSs can move through complex fluid media by using chemical fuels or external energy sources such as magnetic fields, ultrasound, or light. Despite significant advancements in their locomotion and functionalities, the gradual deterioration of MNSs in human physiological media is often overlooked. Corrosion and biodegradation caused by chemical reactions with surrounding medium and the activity of biological agents can significantly affect their chemical stability and functional properties during their lifetime performance. It is therefore essential to understand the degradation mechanisms and factors that influence them to design ideal biomedical MNSs that are affordable, highly efficient, and sufficiently resistant to degradation (at least during their service time). This review summarizes recent studies that delve into the physicochemical characteristics and complex environmental factors affecting the corrosion and biodegradation of MNSs, with a focus on metal-based devices. Additionally, different strategies are discussed to enhance and/or optimize their stability. Conversely, controlled degradation of non-toxic MNSs can be highly advantageous for numerous biomedical applications, allowing for less invasive, safer, and more efficient treatments.