Comparative study of estimation methods of NOx emission with selection of input parameters for a coal-fired boiler

2018 ◽  
Vol 35 (9) ◽  
pp. 1779-1790 ◽  
Author(s):  
Beom Seok Kim ◽  
Tae Young Kim ◽  
Tae Chang Park ◽  
Yeong Koo Yeo
Keyword(s):  
Comparative Study ◽  
Nox Emission ◽  
Estimation Methods ◽  
Input Parameters ◽  
Selection Of

Comparative study of four greenness assessment tools for selection of greenest analytical method for assay of hyoscine N-butyl bromide

2021 ◽  
Author(s):  
Mohammed Gamal ◽  
Ibrahim A. Naguib ◽  
Dibya Sundar Panda ◽  
Fatma F. Abdallah
Keyword(s):  
Comparative Study ◽  
Analytical Method ◽  
Assessment Tools ◽  
Butyl Bromide ◽  
Selection Of

The competencies of four greenness assessment tools were tested. AGREE is the best greenness tool while NEMI is the poorest one. AGREE, GAPI, and ESA are reliable greenness tools.

Does knowing overcome wanting? The impact of consumer knowledge and materialism upon credit card selection with young consumers

2015 ◽  
Vol 16 (1) ◽  
pp. 50-70 ◽  
Author(s):  
Jakob Cakarnis ◽  
Steve Peter D'Alessandro
Keyword(s):  
Comparative Study ◽  
Survey Research ◽  
Credit Card ◽  
Design Methodology ◽  
Credit Cards ◽  
Consumer Knowledge ◽  
Content Type ◽  
Young Professionals ◽  
The Impact ◽  
Selection Of

Purpose – This paper investigates the determinants of credit card use and misuse by student and young professionals. Critical to the research is the impact of materialism and knowledge on selection of the appropriate credit card. Design/methodology/approach – This study uses survey research and partial least squares to investigate credit card behaviors of students versus young professionals. Findings – In a comparative study of young professionals and students, it was found that consumer knowledge, as expected, leads to better consumer selection of credit cards. Materialism was also found to increase the motivation for more optimal consumer outcomes. For more experienced consumers, such as young professionals, it was found that despite them being more knowledgeable, they were more likely to select a credit card based on impulse. Originality/value – This paper examines how materialism may in fact encourage some consumers to make better decisions because they are more motivated to develop better knowledge. It also shows how better credit card selection may inhibit impulse purchasing.

Comparative study of brain deformation estimation methods

2006 ◽  
Author(s):  
Fenghong Liu ◽  
Keith D. Paulsen ◽  
Karen E. Lunn ◽  
Hai Sun ◽  
Alexander Hartov ◽  
...  
Keyword(s):  
Comparative Study ◽  
Estimation Methods ◽  
Brain Deformation

Selection of Proper Neural Network Sizes and Architectures—A Comparative Study

2012 ◽  
Vol 8 (2) ◽  
pp. 228-240 ◽  
Author(s):  
David Hunter ◽  
Hao Yu ◽  
Michael S. Pukish, III ◽  
Janusz Kolbusz ◽  
Bogdan M. Wilamowski
Keyword(s):  
Neural Network ◽  
Comparative Study ◽  
Selection Of

Le fonti, i metodi e le narrazioni della storia della videoarte in Italia negli anni Settanta

2017 ◽  
Vol 2 (1) ◽  
Author(s):  
Lisa Parola
Keyword(s):  
Comparative Study ◽  
Contemporary Art ◽  
Narrative Structure ◽  
Video Art ◽  
Wide Range ◽  
History Of ◽  
Selection For ◽  
The Many ◽  
Art And Architecture ◽  
Selection Of

This essay derives from the primary need to make order between direct and indirect sources available for the reconstruction of the history of video art in Italy in the seventies. In fact, during the researches for the Ph.D. thesis it became clear that in most cases it is difficult to define, in terms of facts, which of the different historiographies should be taken into consideration to deepen the study of video art in Italy. Beyond legitimate differences of perspectives and methods, historiographical narratives all share similar issues and narrative structure. The first intention of the essay is, therefore, to compare the different historiographic narratives on Italian video art of the seventies, verifying their genealogy, the sources used and the accuracy of the narrated facts. For the selection of the corpus, it was decided to analyze in particular monographic volumes dealing with the history of the origins of video art in Italy. The aim was, in fact, to get a wide range of types of "narrations", as in the case of contemporary art and architecture magazines, which are examined in the second part of the essay. After the selection, for an analytical and comparative study of the various historiography, the essay focuses only on the Terza Biennale Internazionale della Giovane Pittura. Gennaio ’70. Comportamenti, oggetti e mediazioni (Third International Biennial of Young Painting. January '70. Behaviors, Objects and Mediations, 1970, Bologna), the exhibition which - after Lucio Fontana's pioneering experiments - is said to be the first sign of the arrival of videotape in Italy (called at the time videorecording), curated by Renato Barilli, Tommaso Trini, Andrea Emiliani and Maurizio Calvesi. The narration given so far of this exhibition appeared more mythological than historical and could be compared structurally to that of the many numerous beginnings that historiographyies on international video art identify as ‘first’ and ‘generative’. In the first part of the essay the 'facts' related to Gennaio ’70, as narrated by historiography on video art, are compared. In the second part the survey is carried out through some of the direct sources identified during the research, with the aim of answering to questions raised by the comparison between historiographies. Concluding, it is important to underline that the tapes containing the videos transmitted have not been found and seem to have disappeared since the ending of the exhibition. Nevertheless, the deepening of the works and documentation transmitted during the exhibition is possible thanks to other types of sources which give us many valuable information regarding video techniques and practices at the beginning of 1970 in Italy.

Utilizing Data-Driven Models to Predict Brittleness in Tuscaloosa Marine Shale: A Machine Learning Approach

2021 ◽  
Author(s):  
Jamal Ahmadov
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Brittleness Index ◽  
Estimation Methods ◽  
Gradient Boosting ◽  
Average Error ◽  
Support Vector ◽  
Marine Shale ◽  
Effective Manner ◽  
Selection Of

Abstract The Tuscaloosa Marine Shale (TMS) formation is a clay- and liquid-rich emerging shale play across central Louisiana and southwest Mississippi with recoverable resources of 1.5 billion barrels of oil and 4.6 trillion cubic feet of gas. The formation poses numerous challenges due to its high average clay content (50 wt%) and rapidly changing mineralogy, making the selection of fracturing candidates a difficult task. While brittleness plays an important role in screening potential intervals for hydraulic fracturing, typical brittleness estimation methods require the use of geomechanical and mineralogical properties from costly laboratory tests. Machine Learning (ML) can be employed to generate synthetic brittleness logs and therefore, may serve as an inexpensive and fast alternative to the current techniques. In this paper, we propose the use of machine learning to predict the brittleness index of Tuscaloosa Marine Shale from conventional well logs. We trained ML models on a dataset containing conventional and brittleness index logs from 8 wells. The latter were estimated either from geomechanical logs or log-derived mineralogy. Moreover, to ensure mechanical data reliability, dynamic-to-static conversion ratios were applied to Young's modulus and Poisson's ratio. The predictor features included neutron porosity, density and compressional slowness logs to account for the petrophysical and mineralogical character of TMS. The brittleness index was predicted using algorithms such as Linear, Ridge and Lasso Regression, K-Nearest Neighbors, Support Vector Machine (SVM), Decision Tree, Random Forest, AdaBoost and Gradient Boosting. Models were shortlisted based on the Root Mean Square Error (RMSE) value and fine-tuned using the Grid Search method with a specific set of hyperparameters for each model. Overall, Gradient Boosting and Random Forest outperformed other algorithms and showed an average error reduction of 5 %, a normalized RMSE of 0.06 and a R-squared value of 0.89. The Gradient Boosting was chosen to evaluate the test set and successfully predicted the brittleness index with a normalized RMSE of 0.07 and R-squared value of 0.83. This paper presents the practical use of machine learning to evaluate brittleness in a cost and time effective manner and can further provide valuable insights into the optimization of completion in TMS. The proposed ML model can be used as a tool for initial screening of fracturing candidates and selection of fracturing intervals in other clay-rich and heterogeneous shale formations.

A Comparative Study of CIPN Symptom Estimation Methods Based on Machine Learning

2021 ◽  
Author(s):  
Satoki Hamanaka ◽  
Wataru Sasaki ◽  
Tadashi Okoshi ◽  
Jin Nakazawa ◽  
Kaori Yagasaki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Estimation Methods

scholarly journals An artificial neural network model for predicting characteristic input parameters for physics based modelling of drying process

2019 ◽  
Author(s):  
Ankita Sinha ◽  
Atul Bhargav
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Estimation Methods ◽  
Drying Process ◽  
Many Instruments ◽  
Input Parameters ◽  
Artificial Neural

Drying is crucial in the quality preservation of food materials. Physics-based models are effective tools to optimally control the drying process. However, these models require accurate thermo-physical properties; unavailability or uncertainty in the values of these properties increases the possibility of error. Property estimation methods are not standardized, and usually involve the use of many instruments and are time-consuming. In this work, we have developed an experimentally validated deep learning-based artificial neural network model that estimates sensitive input parameters of food materials using temperature and moisture data from a set of simple experiments. This model predicts input parameters with error less than 1%. Further, using input parameters, physics-based model predicts temperature and moisture to within 5% accuracy of experiments. The proposed work when interfaced with food machinery could play a significant role in process optimization in food processing industries.

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