Population balance modelling of crystallization

Version 1.0.1 (17.1 KB) by Reza Andalibi

Solution of population balance equation using direct quadrature method of moments (DQMOM) for precipitation/crystallization processes

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Updated 20 Jun 2020

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This package presents the fully-commented workflow for the Solution of population balance equation using the direct quadrature method of moments (DQMOM) applied to precipitation/crystallization processes. The thermodynamic driving forces are calculated by coupling to PHREEQC. Please contact me if you need the uncertainty/sensitivity analysis workflow codes as well.

Please cite our work if you use this package or part of it:

M. Reza Andalibi, Paul Bowen, Agnese Carino, Andrea Testino, Global uncertainty-sensitivity analysis on mechanistic kinetic models: From model assessment to theory-driven design of nanoparticles (2020), Computers & Chemical Engineering Volume 140, 2 September 2020, 106971, https://doi.org/10.1016/j.compchemeng.2020.106971

Please watch the video tutorials on youtube:
https://youtu.be/azlVANAsq90
https://youtu.be/usH6JdmmqaU

Cite As

Andalibi, M. Reza, et al. “Global Uncertainty-Sensitivity Analysis on Mechanistic Kinetic Models: From Model Assessment to Theory-Driven Design of Nanoparticles.” Computers & Chemical Engineering, vol. 140, Elsevier BV, Sept. 2020, p. 106971, doi:10.1016/j.compchemeng.2020.106971.

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MATLAB Release Compatibility

Created with R2017b

Compatible with any release

Platform Compatibility

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PBE_wokflow_for_publication_V1

Version	Published	Release Notes
1.0.1	20 Jun 2020	Video tutorials added.	Download
1.0.0	20 Jun 2020		Download

MLA	Andalibi, M. Reza, et al. “Global Uncertainty-Sensitivity Analysis on Mechanistic Kinetic Models: From Model Assessment to Theory-Driven Design of Nanoparticles.” Computers & Chemical Engineering, vol. 140, Elsevier BV, Sept. 2020, p. 106971, doi:10.1016/j.compchemeng.2020.106971.
APA	Andalibi, M. R., Bowen, P., Carino, A., & Testino, A. (2020). Global uncertainty-sensitivity analysis on mechanistic kinetic models: From model assessment to theory-driven design of nanoparticles. Computers & Chemical Engineering, 140, 106971. Elsevier BV. Retrieved from https://doi.org/10.1016%2Fj.compchemeng.2020.106971
BibTeX	@article{Andalibi_2020, doi = {10.1016/j.compchemeng.2020.106971}, url = {https://doi.org/10.1016%2Fj.compchemeng.2020.106971}, year = 2020, month = {sep}, publisher = {Elsevier {BV}}, volume = {140}, pages = {106971}, author = {M. Reza Andalibi and Paul Bowen and Agnese Carino and Andrea Testino}, title = {Global uncertainty-sensitivity analysis on mechanistic kinetic models: From model assessment to theory-driven design of nanoparticles}, journal = {Computers {\&} Chemical Engineering} }