scholarly journals Parameter Interaksi Biner Kesetimbangan Uap-Cair Campuran yang Melibatkan Alkohol Rantai Bercabang atau Aseton untuk Optimasi Proses Pemurnian Bioetanol

2019 ◽  
Vol 3 (2) ◽  
pp. 53 ◽  
Author(s):  
Asalil Mustain ◽  
Khalimatus Sa'diyah ◽  
Agung Ari Wibowo ◽  
Dhoni Hartanto
Keyword(s):  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
Interaction Parameters ◽  
Binary Interaction ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Liquid Equilibrium ◽  
Vapor Liquid Equilibrium ◽  
Binary Interaction Parameters ◽  
Vapor Liquid

Parameter interaksi biner kesetimbangan uap-cair campuran yang melibatkan alkohol rantai bercabang atau aseton telah ditentukan pada penelitian ini. Data kesetimbangan uap-cair kondisi isobarik pada tekanan atmosferik telah dipilih total sebanyak 14 sistem. Data kesetimbangan tersebut kemudian dikorelasikan dengan model koefisien aktifitas Wilson, Non-Random Two-Liquid (NRTL) dan Universal Quasi-Chemical (UNIQUAC) untuk diperoleh parameter interaksi binernya. Parameter tersebut ditentukan sebagai fungsi suhu pada penelitian ini untuk meningkatkan kemampuannya dalam aplikasi pada kisaran suhu yang panjang. Korelasi menunjukkan hasil yang baik dikarenakan root mean square deviation (RMSD) antara data eksperimental dengan hasil perhitungan relatif kecil. Sebagai tambahan, perilaku masing-masing sistem biner tersebut juga diamati pada kesempatan ini. Parameter yang diperoleh dapat digunakan untuk optimasi desain kolom distilasi pada proses pemurnian produksi bioetanol.The binary interaction parameters of vapor-liquid equilibrium for the mixtures involving branched-chain higher alcohols or acetone have been determined in this study. Isobaric vapor-liquid equilibrium data at atmospheric pressure have been selected for a total of 14 systems. The VLE data were then correlated with the Wilson, Non-Random Two-Liquid (NRTL) and Universal Quasi-Chemical (UNIQUAC) activity coefficient models to obtain its binary interaction parameters. The parameters were determined as the temperature-dependent in this study to increase its ability in wide temperature range applications. The correlations showed good results because the root mean square deviation (RMSD) between the experimental data and calculation values were relatively low. In addition, the behavior of each binary systems were also observed in this study. The obtained parameters could be used to optimize the distillation column design in the purification process of bioethanol production.

scholarly journals PARAMETER INTERAKSI BINER KESETIMBANGAN UAP-CAIR CAMPURAN ALKOHOL UNTUK OPTIMASI PROSES PEMURNIAN BIOETANOL

2017 ◽  
Vol 5 (2) ◽  
pp. 37-44 ◽  
Author(s):  
Asalil Mustain ◽  
Anang Takwanto ◽  
Dhoni Hartanto
Keyword(s):  
Interaction Parameters ◽  
Binary Interaction ◽  
Primary Alcohols ◽  
Liquid Equilibrium ◽  
Vapor Liquid Equilibrium ◽  
Temperature Function ◽  
Wide Range ◽  
Equilibrium Data ◽  
Binary Interaction Parameters ◽  
Vapor Liquid

In this work, the binary interaction parameters of vapor-liquid equilibrium for the mixtures of primary alcohols (methanol, ethanol, 1-propanol or 1-butanol) with C5 alcohols were obtained. A total of 15 systems that consisted of isobaric vapor-liquid equilibrium data at atmospheric pressure were selected. The binary interaction parameters were determined as temperature function by correlating the selected vapor-liquid equilibrium data using the Wilson, Non-Random Two-Liquid (NRTL) and Universal Quasi-Chemical (UNIQUAC) activity coefficient models. The binary interaction parameters were described as the temperature-dependent to increase the capability of the parameters for the application in wide range of temperature. The correlation showed good results because the root mean square deviation (RMSD) between the calculation values and experimental data were relatively low. The obtained parameters were very useful for optimizing the distillation column in the bio-ethanol purification process.

scholarly journals Isobaric Vapor Liquid Equilibrium Determination for 1,3,5-Trimethylbenzene + Ethanol and 1,3,5-Trimethylbenzene + n-Butanol Binary Systems

2019 ◽  
Vol 64 (4) ◽  
pp. 446-456
Author(s):  
Marilena Nicolae ◽  
Costin Sorin Bîldea
Keyword(s):  
Binary Systems ◽  
Binary Interaction ◽  
Liquid Equilibrium ◽  
Vapor Liquid Equilibrium ◽  
Distillation Columns ◽  
Liquid Liquid Extraction ◽  
Double Phase ◽  
Equilibrium Data ◽  
Binary Interaction Parameters ◽  
Vapor Liquid

The vapor-liquid equilibrium data are necessary for the design of the distillation columns which separate the mixture mesitylene – ABE components resulting from the liquid-liquid extraction of butanol from the ABE using 1,3,5-trimethylbenzene as solvent. In this work, the vapor - liquid equilibrium data is determined for the binary systems: ethanol + 1,3,5-trimethylbenzene and n-butanol + 1,3,5-trimethylbenzene at constant pressure of 93.325 KPa using a double phase circulation apparatus. Thus, P-T-x-y data is determined, which is further processed by regression to determine the binary interaction parameters of the NRTL and UNIQUAC models. Furthermore, the T-x-y diagrams are calculated using the completed thermodynamic models (NRTL and UNIQUAC) and the UNIFAC predictive model, and compared with the experimental diagrams.

scholarly journals GNINA 1.0: molecular docking with deep learning

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Andrew T. McNutt ◽  
Paul Francoeur ◽  
Rishal Aggarwal ◽  
Tomohide Masuda ◽  
Rocco Meli ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
Computational Cost ◽  
Scoring Function ◽  
Binding Pocket ◽  
Protein Docking ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Autodock Vina

AbstractMolecular docking computationally predicts the conformation of a small molecule when binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline as they determine the fitness of sampled poses. Here we describe and evaluate the 1.0 release of the Gnina docking software, which utilizes an ensemble of convolutional neural networks (CNNs) as a scoring function. We also explore an array of parameter values for Gnina 1.0 to optimize docking performance and computational cost. Docking performance, as evaluated by the percentage of targets where the top pose is better than 2Å root mean square deviation (Top1), is compared to AutoDock Vina scoring when utilizing explicitly defined binding pockets or whole protein docking. Gnina, utilizing a CNN scoring function to rescore the output poses, outperforms AutoDock Vina scoring on redocking and cross-docking tasks when the binding pocket is defined (Top1 increases from 58% to 73% and from 27% to 37%, respectively) and when the whole protein defines the binding pocket (Top1 increases from 31% to 38% and from 12% to 16%, respectively). The derived ensemble of CNNs generalizes to unseen proteins and ligands and produces scores that correlate well with the root mean square deviation to the known binding pose. We provide the 1.0 version of Gnina under an open source license for use as a molecular docking tool at https://github.com/gnina/gnina.

Fast collocation for Moho estimation from GOCE gravity data: the Iran case study

2020 ◽  
Vol 221 (1) ◽  
pp. 651-664
Author(s):  
H Heydarizadeh Shali ◽  
D Sampietro ◽  
A Safari ◽  
M Capponi ◽  
A Bahroudi
Keyword(s):  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
Seismic Data ◽  
Active Faults ◽  
Earth’S Crust ◽  
Arabian Plate ◽  
Moho Depth ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Earth's Crust

SUMMARY The study of the discontinuity between crust and mantle beneath Iran is still an open issue in the geophysical community due to its various tectonic features created by the collision between the Iranian and Arabian Plate. For instance in regions such as Zagros, Alborz or Makran, despite the number of studies performed, both by exploiting gravity or seismic data, the depth of the Moho and also interior structure is still highly uncertain. This is due to the complexity of the crust and to the presence of large short wavelength signals in the Moho depth. GOCE observations are capable and useful products to describe the Earth’s crust structure either at the regional or global scale. Furthermore, it is plausible to retrieve important information regarding the structure of the Earth’s crust by combining the GOCE observations with seismic data and considering additional information. In the current study, we used as observation a grid of second radial derivative of the anomalous gravitational potential computed at an altitude of 221 km by means of the space-wise approach, to study the depth of the Moho. The observations have been reduced for the gravitational effects of topography, bathymetry and sediments. The residual gravity has been inverted accordingly to a simple two-layer model. In particular, this guarantees the uniqueness of the solution of the inverse problem which has been regularized by means of a collocation approach in the frequency domain. Although results of this study show a general good agreement with seismically derived depths with a root mean square deviation of 6 km, there are some discrepancies under the Alborz zone and also Oman sea with a root mean square deviation up 10 km for the former and an average difference of 3 km for the latter. Further comparisons with the natural feature of the study area, for instance, active faults, show that the resulting Moho features can be directly associated with geophysical and tectonic blocks.

scholarly journals A Variable Data Fusion Approach for Electromechanical Impedance-Based Damage Detection

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4204
Author(s):  
Shishir Kumar Singh ◽  
Rohan Soman ◽  
Tomasz Wandowski ◽  
Pawel Malinowski
Keyword(s):  
Data Fusion ◽  
Damage Detection ◽  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
Health Monitoring ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Damage Scenarios ◽  
Electromechanical Impedance ◽  
Fusion Approach

There is continuing research in the area of structural health monitoring (SHM) as it may allow a reduction in maintenance costs as well as lifetime extension. The search for a low-cost health monitoring system that is able to detect small levels of damage is still on-going. The present study is one more step in this direction. This paper describes a data fusion technique by combining the information for robust damage detection using the electromechanical impedance (EMI) method. The EMI method is commonly used for damage detection due to its sensitivity to low levels of damage. In this paper, the information of resistance (R) and conductance (G) is studied in a selected frequency band and a novel data fusion approach is proposed. A novel fused parameter (F) is developed by combining the information from G and R. The difference in the new metric under different damage conditions is then quantified using established indices such as the root mean square deviation (RMSD) index, mean absolute percentage deviation (MAPD), and root mean square deviation using k-th state as the reference (RMSDk). The paper presents an application of the new metric for detection of damage in three structures, namely, a thin aluminum (Al) plate with increasing damage severity (simulated with a drilled hole of increasing size), a glass fiber reinforced polymer (GFRP) composite beam with increasing delamination and another GFRP plate with impact-induced damage scenarios. Based on the experimental results, it is apparent that the variable F increases the robustness of the damage detection as compared to the quantities R and G.

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