Search results for " Chemical Engineering"
showing 10 items of 2965 documents
State classification for autonomous gas sample taking using deep convolutional neural networks
2017
Despite recent rapid advances and successful large-scale application of deep Convolutional Neural Networks (CNNs) using image, video, sound, text and time-series data, its adoption within the oil and gas industry in particular have been sparse. In this paper, we initially present an overview of opportunities for deep CNN methods within oil and gas industry, followed by details on a novel development where deep CNN have been used for state classification of autonomous gas sample taking procedure utilizing an industrial robot. The experimental results — using a deep CNN containing six layers — show accuracy levels exceeding 99 %. In addition, the advantages of using parallel computing with GP…
Deep learning strategies for automatic fault diagnosis in photovoltaic systems by thermographic images
2021
Abstract Losses of electricity production in photovoltaic systems are mainly caused by the presence of faults that affect the efficiency of the systems. The identification of any overheating in a photovoltaic module, through the thermographic non-destructive test, may be essential to maintain the correct functioning of the photovoltaic system quickly and cost-effectively, without interrupting its normal operation. This work proposes a system for the automatic classification of thermographic images using a convolutional neural network, developed via open-source libraries. To reduce image noise, various pre-processing strategies were evaluated, including normalization and homogenization of pi…
Application of optimized artificial intelligence algorithm to evaluate the heating energy demand of non-residential buildings at European level
2019
Abstract A reliable preliminary forecast of heating energy demand of a building by using a detailed dynamic simulation software typically requires an in-depth knowledge of the thermal balance, several input data and a very skilled user. The authors will describe how to use Artificial Neural Networks to predict the demand for thermal energy linked to the winter climatization of non-residential buildings. To train the neural network it was necessary to develop an accurate energy database that represents the basis of the training of a specific Artificial Neural Networks. Data came from detailed dynamic simulations performed in the TRNSYS environment. The models were built according to the stan…
Neural Networks with Multidimensional Cross-Entropy Loss Functions
2019
Deep neural networks have emerged as an effective machine learning tool successfully applied for many tasks, such as misinformation detection, natural language processing, image recognition, machine translation, etc. Neural networks are often applied to binary or multi-class classification problems. In these settings, cross-entropy is used as a loss function for neural network training. In this short note, we propose an extension of the concept of cross-entropy, referred to as multidimensional cross-entropy, and its application as a loss function for classification using neural networks. The presented computational experiments on a benchmark dataset suggest that the proposed approaches may …
Prediction and qualitative analysis of sensory perceptions over temporal vectors using combination of artificial neural networks and fuzzy logic: Val…
2020
Testing selected optimal descriptors with artificial neural networks
2013
Eleven properties have been modeled with the objective of checking the importance for model purposes of mixed descriptors made of empirical parameters, molecular connectivity indices and random numbers. The mixed descriptors with random indices have a descriptive character which is satisfactorily confirmed by the leave-one-out method of statistical analysis. The introduction of a partition of the set of compounds into training and evaluation sets decreases drastically the probability to find a mixed descriptor with random indices with good model quality. Two properties, the magnetic susceptibility and the elutropic values, insist on having optimal descriptors with random indices. The overal…
Reappraising the appropriate calculation of a common meteorological quantity: Potential Temperature
2020
Abstract. The potential temperature is a widely used quantity in atmospheric science since it is conserved for dry air's adiabatic changes of state. Its definition involves the specific heat capacity of dry air, which is traditionally assumed as constant. However, the literature provides different values of this allegedly constant parameter, which are reviewed and discussed in this study. Furthermore, we derive the potential temperature for a temperature-dependent parameterisation of the specific heat capacity of dry air, thus providing a new reference potential temperature with a more rigorous basis. This new reference shows different values and vertical gradients, in particular in the str…
Density, Viscosity, and Sound Speed of Bis(trifluoromethylsulfonyl)imide-Based Ionic Liquids + 1-Propanol Mixtures
2015
The density, viscosity, and speed of sound of three mixtures formed by 1-propanol and an ionic liquid were measured in a temperature range of 278.15–338.15 K. All measurements were made at atmospheric pressure and covered the entire range of miscible compositions. The three ionic liquids have the same anion but differ in the cation: 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide, [emim][NTf2], 1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide, [bmim][NTf2], and 1-hexyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide, [hmim][NTf2]. The experimental data allowed calculate the molar volume and isentropic compressibility of the mixture and their respective exces…
SVET, AFM and AES study of pitting corrosion initiated on MnS inclusions by microinjection
2003
As pitting is a random phenomenon, it is difficult to predict where a pit will appear on the surface and consequently the use of local probes is rendered difficult. In this work, a new method to study pitting corrosion on a MnS inclusion on 316L stainless steel is proposed. It consists in modifying locally the chemistry in its vicinity by injecting with a microcapillary an aggressive solution of NaCl, H2SO4 or HCl. Once a pit appears, scanning vibrating electrode technique (SVET) is used to follow the current fluctuations over and around the pit when the metal is polarized at a passive potential. In another series of experiments the effect of local activation of MnS inclusion was studied ex…
Technical Note:Study of the Cl−-Induced Breakdown of the Passive Layer on Steel
1991
Abstract The Cl−-induced breakdown of the passive layer on a standard steel has an induction time that increases in a nearly linear fashion with the previous holding time (at the passivation potent...