Bringing in snowing

LICENSE.txt

@@ -1,6 +1,6 @@
 The MIT License (MIT)
 
-Copyright (c) 2021-2022 Leif-Thore Deck, David Ochsenbein
+Copyright (c) 2021-2023 Leif-Thore Deck, Andraž Košir, David Ochsenbein
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

README.md

@@ -3,17 +3,23 @@
 ![tag](https://img.shields.io/github/v/tag/SPL-ethz/snow) ![pypi](https://img.shields.io/pypi/v/ethz-snow) ![build](https://img.shields.io/github/workflow/status/SPL-ethz/snow/Python%20package) 
 ![Coverage Status](https://coveralls.io/repos/github/SPL-ethz/snow/badge.svg?branch=main) ![](https://img.shields.io/github/stars/SPL-ethz/snow?style=social) ![](https://img.shields.io/github/watchers/SPL-ethz/snow?style=social) ![](https://img.shields.io/github/commit-activity/m/SPL-ethz/snow) ![](https://img.shields.io/github/issues-raw/SPL-ethz/snow) ![](https://img.shields.io/pypi/l/ethz-snow) ![](https://img.shields.io/badge/code%20style-black-black)
 
-SNOW (**S**tochastic **N**ucleation **O**f **W**ater) is an open source modeling framework to simulate the freezing process in a large number of vials, tailored towards pharmaceutical freeze-drying processes. SNOW was developed as part of a research collaboration between ETH Zurich's Separation Processes Laboratory and Janssen (pharmaceutical companies of J&J). It is brought to you by Leif-Thore Deck and Dr. David Ochsenbein. 
+SNOW (**S**tochastic **N**ucleation **O**f **W**ater) is an open source modeling framework to simulate the freezing process in a large number of vials, tailored towards pharmaceutical freeze-drying processes. SNOW was developed as part of a research collaboration between ETH Zurich's Separation Processes Laboratory and Janssen (pharmaceutical companies of J&J). It is brought to you by Leif-Thore Deck, Andraž Košir and Dr. David Ochsenbein. 
 
 ## Description
 
 SNOW is a model capable of simulating the entire freezing process for a batch of vials, from the liquid state to the completely frozen state. It combines balance equations, thermodynamics and a stochastic approach to ice nucleation to provide a comprehensive view of freezing and is entirely derived from first principles. 
 
 In addition to the simulation of the thermal evolution of vials during freezing, SNOW tracks a number of characteristic quantities of the freezing process for each vial. These quantities are the nucleation time, the nucleation temperature and the solidification time. Both nucleation and solidification are correlated with attributes of the frozen product such as the mean crystal size and the product activity of an API. Variability in these quantities among vials may thus be used as surrogate for batch heterogeneity. SNOW consequently may be used in process design and optimization to identify suitable cooling protocols with sufficiently small batch heterogeneity. 
 
-A more detailed description of the modeling framework is presented in a recent publication, which is publicly accessible under https://doi.org/10.1016/j.ijpharm.2021.121276 While version 1.0 is tailored towards simulations of the freezing stage in freeze-drying, version 1.1. was developed for pallet freezing; pallet freezing is for example used in the manufacturing of the Janssen COVID-19 vaccine, which served as case study for the model. Version 1.1 and the case study on the COVID-vaccine are discussed in detail in a recent pre-print at ChemRxiv: https://doi.org/10.26434/chemrxiv-2022-gnwhf
+A more detailed description of the modeling framework is presented in a recent publication, which is publicly accessible under https://doi.org/10.1016/j.ijpharm.2021.121276 While version 1.0 is tailored towards simulations of the freezing stage in freeze-drying, version 1.1. was developed for pallet freezing; pallet freezing is for example used in the manufacturing of the Janssen COVID-19 vaccine, which served as case study for the model. Version 1.1 and the case study on the COVID-vaccine are discussed in detail in a recent scientific publication: https://doi.org/10.1016/j.ijpharm.2022.122051
 
-### Additional features currently supported (as of version 1.1)
+An extension of the modeling framework for the simulation of the freezing process with spatial resolution within individual containers has been implemented in version 2.0 and will be discussed in detail within the scope of a scientific publication.
+
+### Additional features supported by version 2.0
+- Spatial simulation of freezing within individual containers with different dimensionalities (0D, 1D, 2D)
+- Simulation of three process configurations (shelf-ramped freezing, vacuum-induced surface freezing, jacket-ramped freezing)
+
+### Additional features supported by version 1.1
 - Simulation of systems comprising vials arranged in three spatial dimensions
 - Arbitrary initial temperature of the vials
 - Improvements in the numerical implementation (Second method to compare the initial amount of ice formed, faster data saving)
@@ -35,6 +41,6 @@ A more detailed description of the modeling framework is presented in a recent p
 Please report bugs as [Github issues](https://github.com/SPL-ethz/snow/issues/new?assignees=ltdeck&labels=bug) or via [Email](mailto:[email protected]), preferably
 including a screenshot that illustrates the problem.
 
-Copyright (c) 2021-2022 Leif-Thore Deck, David Ochsenbein
+Copyright (c) 2021-2023 Leif-Thore Deck, Andraž Košir, David Ochsenbein 
 
 <sub>The snow package logo has been designed using resources from Flaticon.com.</sub>

docs_src/authors.rst

@@ -4,3 +4,4 @@ Contributors
 
 * Leif-Thore Deck <[email protected]>
 * Dr. David R. Ochsenbein <[email protected]>
+* Andraž Košir <[email protected]>

docs_src/changelog.rst

@@ -2,6 +2,19 @@
 Changelog
 =========
 
+Version 2.0
+===========
+
+- New implementation of freezing model for a single container with spatial resolution (termed snowing). The model considers the gradients in temperature and in ice mass that form during freezing. 
+- Currently supported features of the new spatial model:
+    - Simulation of three different freezing process configuration:
+        - Shelf-ramped freezing (commonly used in freeze-drying)
+        - Vacuum-induced surface freezing (process variation where vacuum is applied to promote freezing through surface evaporation)
+        - Jacket-ramped freezing (process variation where vial is surrounded by a temperature-controlled jacket
+    - Simulation of different heat transfer boundary conditions (natural convection, conduction, thermal radiation, surface evaporation)
+    - Simulation with different dimensionalities (0D, 1D, 2D)
+
+
 Version 1.1
 ===========
 
@@ -24,4 +37,4 @@ Version 1.0
     - Arbitrary cooling protocols (i.e., user may choose cooling rate, integrate holding steps and controlled nucleation)
     - Tracking of nucleation times, nucleation temperatures and solidification times for all vials
     - Stochastic nucleation in the form of a Monte Carlo approach as well as controlled nucleation in the form of forced initiation of nucleation at a certain point 
-    - Cubic geometry of vial and rectangular arrangement on the shelf
+    - Cubic geometry of vial and rectangular arrangement on the shelf

docs_src/refs.bib

@@ -13,12 +13,15 @@ @article{deck2022
 abstract = {Freezing and freeze-drying processes are commonly used to improve the stability and thus shelf life of pharmaceutical formulations. Despite strict product quality requirements, batch heterogeneity is widely observed in frozen products, thus potentially causing process failure. Such heterogeneity is the result of the stochasticity of ice nucleation and the variability in heat transfer among vials, which lead to unique freezing histories of individual vials. We present for the first time a modeling framework for large-scale freezing processes of vials on a shelf and publish an open source implementation in the form of a python package on pypi. The model is based on first principles and couples heat transfer with ice nucleation kinetics, thus enabling studies on batch heterogeneity. Ice nucleation is assumed to be an inhomogeneous Poisson process and it is simulated using a Monte Carlo approach. We applied the model to understand the individual pathways leading to batch heterogeneity. Our simulations revealed a novel mechanism how ice nucleation leads to heterogeneity based on thermal interaction among vials. We investigated the effect of various cooling protocols, namely shelf-ramped cooling, holding steps and controlled nucleation, on the nucleation and solidification behavior across the shelf. We found that under rather general conditions holding schemes lead to similar solidification times, as in the case of controlled nucleation, thus identifying a potential pathway for freezing process optimization.}
 }
 
-@article{deck2022_pallet, 
-place={Cambridge}, 
-title={Stochastic ice nucleation governs the freezing
-process of biopharmaceuticals in vials}, 
-DOI={10.26434/chemrxiv-2022-gnwhf}, 
-journal={ChemRxiv}, 
-publisher={Cambridge Open Engage}, 
-author={Deck, Leif-Thore and Ochsenbein, David Richard and Mazzotti, Marco}, 
-year={2022}} 
+@article{deck2022_pallet,
+  doi = {10.1016/j.ijpharm.2022.122051},
+  url = {https://doi.org/10.1016/j.ijpharm.2022.122051},
+  year = {2022},
+  month = sep,
+  publisher = {Elsevier {BV}},
+  volume = {625},
+  pages = {122051},
+  author = {Leif-Thore Deck and David R. Ochsenbein and Marco Mazzotti},
+  title = {Stochastic ice nucleation governs the freezing process of biopharmaceuticals in vials},
+  journal = {International Journal of Pharmaceutics}
+}