Fracture Network Characterization by Analyzing Flowback Salts: Scale-Up of Experimental Data

2017 ◽  
Author(s):  
Ashkan Zolfaghari ◽  
Yingzhe Tang ◽  
Jia He ◽  
Hassan Dehghanpour ◽  
Doug Bearinger ◽  
...  
Keyword(s):  
Experimental Data ◽  
Scale Up ◽  
Fracture Network ◽  
Network Characterization

Experimental and CFD Evaluation of a Bubble Column Reactor

2010 ◽  
Author(s):  
Sean M. McGuffie ◽  
Mike A. Porter ◽  
Dennis H. Martens
Keyword(s):  
Experimental Data ◽  
Bubble Column ◽  
Scale Up ◽  
Bubble Column Reactor ◽  
Column Reactor ◽  
Cfd Models ◽  
Design Variables ◽  
Input Parameters ◽  
Multi Phase Flow ◽  
Multi Phase

During the scale-up design of a slurry bubble column reactor from a pilot demonstration facility to a production reactor, the design team used computational fluid dynamics (CFD) as a tool to quantify design variables, such as gas holdup and liquid velocities/structural pressures within the reactor. At the time of the analysis, all available physics models for modeling the multi-phase flow had significant limitations that would require “tuning” of the CFD input parameters to ensure confidence in the results. The authors initially conducted a literature search to find data that could be used to calibrate the model. While a wide variety of literature is available, none provided the exact data required for model calibration. For this reason, the authors constructed a test column and performed experiments to derive data for tuning the CFD models. Statistical analysis of the experimental data provided distributions on the input parameters of interest. CFD studies were then used to tune the CFD input parameters to match the experimental data. A correlation was developed, tested and verified. This correlation was then used to provide confidence in the results of the design analysis performed on the scaled up reactor.

Homogeneous Photoreactions for AOTs: Reactor Analysis and Design

2002 ◽  
Vol 5 (2) ◽  
Author(s):  
C. A. Martín ◽  
R. J. Brandi ◽  
O. M. Alfano ◽  
A. E. Cassano
Keyword(s):  
Mathematical Modeling ◽  
Experimental Data ◽  
Computer Simulation ◽  
Mathematical Description ◽  
Design Methodology ◽  
Reaction Kinetic ◽  
Scale Up ◽  
Reactor Performance ◽  
Analysis And Design ◽  
Additional Information

AbstractThis paper presents the most important technical tools that are needed for designing homogeneous photoreactors using computer simulation of a rigorous mathematical description of the reactor performance. Employing intrinsic reaction kinetic models and parameters derived from properly analyzed laboratory information, it is shown that is possible to scale up reactors with no additional information and without resorting to empirically adjusted correcting factors. The method is illustrated with two processes of degradation of organic pollutants as typical applications of the newly developed Advanced Oxidation Technologies. Two reactors, having pilot plant sizes, are modeled to show the proposed approach. Predictions from the models are compared with experimental data obtaining reasonable good results. They provide confidence on mathematical modeling as a design methodology for homogeneous photochemical reactors.

scholarly journals STUDY THE GROWTH KINETICS OF PEDIOCOCCUS ACIDILACTICI WITH ESTIMATION OF KINETIC PARAMETERS AND APPLIED IN LARGE SCALE PEDIOCIN PRODUCTION

2016 ◽  
Vol 9 (5) ◽  
pp. 130
Author(s):  
Barnali Mandal
Keyword(s):  
Experimental Data ◽  
Lactic Acid ◽  
Kinetic Parameters ◽  
Growth Kinetics ◽  
Large Scale ◽  
Scale Up ◽  
Small Scale ◽  
Pediococcus Acidilactici ◽  
Monod Model ◽  
Kinetics Of

ABSTRACTObjectives: The aim of the study was to determine the growth kinetics of Pediococcus acidilactici using a mathematical model for large scale pediocinproduction.Methods: Growth kinetics of P. acidilactici has been studied for pediocin production in small scale batch fermenter (Erlenmeyer flask) using meatprocessing waste medium. The experiments have been conducted with varying the concentrations of glucose, protein, and lactic acid. A mathematicalmodel has been developed to describe growth rate, products (pediocin and lactic acid) formation rate, and substrates (glucose and protein) utilizationrate. Monod model for dual substrates (glucose and protein) has been used with considering lactic acid inhibition. Luedeking-Piret model has beenintroduced to describe the production of pediocin and lactic acid.Results: The values of kinetic parameters have been determined using experimental data and model equations. The model prediction has beencompared satisfactorily with the experimental data for the validation of the model.Conclusions: The developed model was satisfactorily validated to scale up the production of pediocin.Keywords: Pediococcus acidilactici, Pediocin, Meat processing waste, Monod model, Luedeking-Piret model, Kinetic parameters.

scholarly journals The Use of 3D Convolutional Autoencoder in Fault and Fracture Network Characterization

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Feng Xu ◽  
Zhiyong Li ◽  
Bo Wen ◽  
Youhui Huang ◽  
Yaojun Wang
Keyword(s):  
Pattern Recognition ◽  
Spatial Structure ◽  
Seismic Data ◽  
Fracture Network ◽  
Spatial Dimension ◽  
3D Seismic ◽  
Structure Information ◽  
Convolutional Autoencoder ◽  
Network Characterization ◽  
3D Seismic Data

Conventional pattern recognition methods directly use 1D poststack data or 2D prestack data for the statistical pattern recognition of fault and fracture network, thereby ignoring the spatial structure information in 3D seismic data. As a result, the generated fault and fracture network is not distinguishable and has poor continuity. In this paper, a fault and fracture network characterization method based on 3D convolutional autoencoder is proposed. First, in the autoencoder training frame, 3D prestack data are used as input, and the 3D convolution operation is used to mine the spatial structure information to the maximum and gradually reduce the spatial dimension of the input. Then, the residual network is used to recover the input’s details and the corresponding spatial dimension. Lastly, the hidden features extracted by the encoders are recognized via k -means, SOM, and two-step clustering analysis. The validity of the method is verified by testing the seismic simulation data and applying real seismic data. The 3D convolution can directly process the seismic data and maximize the prestack texture attributes and spatial structure information provided by 3D seismic data without dimensionality reduction and other preprocessing operations. The interleaving convolution layer and residual block overcome low learning and accuracy rates due to the deepening of networks.

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