Monte Carlo Simulation of the Mechanical Processing of Bulk Materials with Fluctuating Compositions - Compositional Probability Density

Karim Khodier; Tobias Krenn; Lisa Kandlbauer; Lisa Tatschl; Renato Sarc

doi:10.1016/B978-0-323-95879-0.50021-7

Monte Carlo Simulation of the Mechanical Processing of Bulk Materials with Fluctuating Compositions - Compositional Probability Density

Karim Khodier
, Tobias Krenn
, Lisa Kandlbauer
, Lisa Tatschl
, Renato Sarc

Chair of Waste Processing Technology and Waste Management (515)

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Research

Abstract

Compositions are omnipresent in process engineering, and often directly interact with process performance. In this work, a Monte Carlo based approach for considering their impact in the case of fluctuating compositions is presented. Based on experimental data, the first component – a model for the compositional probability density is targeted. A variety of descriptive approaches that involve parametric and non-parametric density estimations is discussed. Finally, for a D-dimensional composition, a multivariate Gaussian kernel density estimation for a bijective projection on D-1 linearly independent statically constrained coordinates in combination with boundary reflection is presented as a suitable approach.

Original language	English
Title of host publication	Computer Aided Chemical Engineering
Pages	121-126
Number of pages	6
Volume	51
DOIs	https://doi.org/10.1016/B978-0-323-95879-0.50021-7
Publication status	Published - Jan 2022

Publication series

Name	Computer Aided Chemical Engineering
Volume	51
ISSN (Print)	1570-7946

Bibliographical note

Publisher Copyright:
© 2022 Elsevier B.V.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 12 Responsible Consumption and Production

Keywords

compositional data
kernel density estimation
mixed solid waste
Monte Carlo simulation
reflection

Access to Document

10.1016/B978-0-323-95879-0.50021-7

Cite this

Khodier, K., Krenn, T., Kandlbauer, L., Tatschl, L., & Sarc, R. (2022). Monte Carlo Simulation of the Mechanical Processing of Bulk Materials with Fluctuating Compositions - Compositional Probability Density. In Computer Aided Chemical Engineering (Vol. 51, pp. 121-126). (Computer Aided Chemical Engineering; Vol. 51). https://doi.org/10.1016/B978-0-323-95879-0.50021-7

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abstract = "Compositions are omnipresent in process engineering, and often directly interact with process performance. In this work, a Monte Carlo based approach for considering their impact in the case of fluctuating compositions is presented. Based on experimental data, the first component {\textendash} a model for the compositional probability density is targeted. A variety of descriptive approaches that involve parametric and non-parametric density estimations is discussed. Finally, for a D-dimensional composition, a multivariate Gaussian kernel density estimation for a bijective projection on D-1 linearly independent statically constrained coordinates in combination with boundary reflection is presented as a suitable approach.",

keywords = "compositional data, kernel density estimation, mixed solid waste, Monte Carlo simulation, reflection",

author = "Karim Khodier and Tobias Krenn and Lisa Kandlbauer and Lisa Tatschl and Renato Sarc",

note = "Publisher Copyright: {\textcopyright} 2022 Elsevier B.V.",

year = "2022",

month = jan,

doi = "10.1016/B978-0-323-95879-0.50021-7",

language = "English",

volume = "51",

series = "Computer Aided Chemical Engineering",

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Monte Carlo Simulation of the Mechanical Processing of Bulk Materials with Fluctuating Compositions - Compositional Probability Density. / Khodier, Karim; Krenn, Tobias; Kandlbauer, Lisa et al.
Computer Aided Chemical Engineering. Vol. 51 2022. p. 121-126 (Computer Aided Chemical Engineering; Vol. 51).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Research

TY - CHAP

T1 - Monte Carlo Simulation of the Mechanical Processing of Bulk Materials with Fluctuating Compositions - Compositional Probability Density

AU - Khodier, Karim

AU - Krenn, Tobias

AU - Kandlbauer, Lisa

AU - Tatschl, Lisa

AU - Sarc, Renato

PY - 2022/1

Y1 - 2022/1

N2 - Compositions are omnipresent in process engineering, and often directly interact with process performance. In this work, a Monte Carlo based approach for considering their impact in the case of fluctuating compositions is presented. Based on experimental data, the first component – a model for the compositional probability density is targeted. A variety of descriptive approaches that involve parametric and non-parametric density estimations is discussed. Finally, for a D-dimensional composition, a multivariate Gaussian kernel density estimation for a bijective projection on D-1 linearly independent statically constrained coordinates in combination with boundary reflection is presented as a suitable approach.

AB - Compositions are omnipresent in process engineering, and often directly interact with process performance. In this work, a Monte Carlo based approach for considering their impact in the case of fluctuating compositions is presented. Based on experimental data, the first component – a model for the compositional probability density is targeted. A variety of descriptive approaches that involve parametric and non-parametric density estimations is discussed. Finally, for a D-dimensional composition, a multivariate Gaussian kernel density estimation for a bijective projection on D-1 linearly independent statically constrained coordinates in combination with boundary reflection is presented as a suitable approach.

KW - compositional data

KW - kernel density estimation

KW - mixed solid waste

KW - Monte Carlo simulation

KW - reflection

UR - https://www.scopus.com/pages/publications/85135334262

U2 - 10.1016/B978-0-323-95879-0.50021-7

DO - 10.1016/B978-0-323-95879-0.50021-7

M3 - Chapter

VL - 51

T3 - Computer Aided Chemical Engineering

SP - 121

EP - 126

BT - Computer Aided Chemical Engineering

ER -

Monte Carlo Simulation of the Mechanical Processing of Bulk Materials with Fluctuating Compositions - Compositional Probability Density

Abstract

Publication series

Bibliographical note

UN SDGs

Keywords

Access to Document

Other files and links

European Symposium on Computer Aided Process Engineering

Monte Carlo Simulation der mechanischen Behandlung von Schüttgütern mit fluktuierender Zusammensetzung - Wahrscheinlichkeitsdichte der Zusammensetzung

Cite this

Monte Carlo Simulation of the Mechanical Processing of Bulk Materials with Fluctuating Compositions - Compositional Probability Density

Abstract

Publication series

Bibliographical note

UN SDGs

Keywords

Access to Document

Other files and links

Activities

European Symposium on Computer Aided Process Engineering

Monte Carlo Simulation der mechanischen Behandlung von Schüttgütern mit fluktuierender Zusammensetzung - Wahrscheinlichkeitsdichte der Zusammensetzung

Cite this