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MOSKopt_Python

Advanced simulation-based optimization framework with AVEVA™ Process Simulation integration, featuring enhanced acquisition functions and robust failure handling.

Project Purpose and Context

MOSKopt_Python is a Python implementation of the MOSKopt (Simulation-Based Stochastic Kriging Optimization framework), designed for chemical process optimization under uncertainty. This implementation extends the original MATLAB version developed by Resul Al. (https://github.com/gsi-lab/MOSKopt) with enhanced features and performance optimizations.

The framework, and example case studies implemented in this repository are described in https://doi.org/10.1016/j.cherd.2026.06.051.

Key Differences from MATLAB Version

  • Enhanced failure handling with smart consecutive failure limits
  • Intelligent restart strategies using successful simulation history (snapshot)
  • Performance optimizations (reduced model refitting, batch predictions)
  • AVEVA Python interface integration with unified base classes
  • Modern Python features (type hints, dataclasses, comprehensive error handling)

Quick Start

1. Download the Repository

# Download as ZIP from GitHub (green "Code" button → "Download ZIP")# Extract the ZIP file to your desired locationcd MOSKopt_Python-main

2. Install the Package

pip install -e .

3. Run Examples

python examples/deterministic_ibuprofen.py
python examples/stochastic_ibuprofen.py

Prerequisites

  • Python 3.10 or Python 3.11 or Python 3.12
  • AVEVA Process Simulation
  • AVEVA Python Interface (simcentralconnect)

AVEVA Custom Libraries Setup for Ibuprofen Example

For the AVEVA Process Simulation examples to run correctly, you need to place the provided simulation library files into your My Thermo Data directory ("C:\Users\xxx\My Thermo Data") on your computer: Pharma.BASE.cmp Pharma.bnk Pharma.lb1 Pharma.lb2

Folder Structure

MOSKopt_Python/
├── core/ │ ├── __init__.py # Package initialization
│ ├── combined_core.cpython-310.pyc # Core implementation (compiled with Python 3.10)
│ ├── combined_core.cpython-311.pyc # Core implementation (compiled with Python 3.11)
│ └── combined_core.cpython-312.pyc # Core implementation (compiled with Python 3.12)
├── examples/ │ ├── deterministic_ibuprofen.py # Deterministic optimization example
│ └── stochastic_ibuprofen.py # Stochastic optimization example
├── simulation/ │ ├── IbuprofenProcessSimulation
│ ├── Pharma/BASE/
│ ├── Pharma.BASE.cmp
│ ├── Pharma.bnk
│ ├── Pharma.lb1
│ ├── Pharma.lb2
├── .gitignore ├── LICENSE ├── MOSKopt_Python Gem Instructions.md # AI Configuration Assistant
├── README.md ├── plot_from_checkpoint.py # Results plotting ├── plot_optimization_results.py # Results plotting ├── requirements.txt # Dependencies
└── setup.py # Package setup

Acknowledgement

This Python implementation was supported by European Marie Skłodowska-Curie network MiEl. The MiEl project received funding by the European Union under the Grant Agreement no. 101073003. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.

Note: Core implementation is compiled to .pyc files for intellectual property protection. Examples and documentation are provided for easy usage.

About

Advanced simulation-based optimization framework with AVEVA™ Process Simulation integration, featuring enhanced acquisition functions and robust failure handling.

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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var __re = new RegExp('^' + "github\\.com" + '

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MOSKopt_Python
Advanced simulation-based optimization framework with AVEVA™ Process Simulation integration, featuring enhanced acquisition functions and robust failure handling.
Project Purpose and Context
MOSKopt_Python is a Python implementation of the MOSKopt (Simulation-Based Stochastic Kriging Optimization framework), designed for chemical process optimization under uncertainty. This implementation extends the original MATLAB version developed by Resul Al. (https://github.com/gsi-lab/MOSKopt) with enhanced features and performance optimizations.
The framework, and example case studies implemented in this repository are described in https://doi.org/10.1016/j.cherd.2026.06.051.
Key Differences from MATLAB Version
  • Enhanced failure handling with smart consecutive failure limits
  • Intelligent restart strategies using successful simulation history (snapshot)
  • Performance optimizations (reduced model refitting, batch predictions)
  • AVEVA Python interface integration with unified base classes
  • Modern Python features (type hints, dataclasses, comprehensive error handling)
Quick Start
1. Download the Repository
# Download as ZIP from GitHub (green "Code" button → "Download ZIP")# Extract the ZIP file to your desired locationcd MOSKopt_Python-main
2. Install the Package
pip install -e .
3. Run Examples
python examples/deterministic_ibuprofen.py
python examples/stochastic_ibuprofen.py
Prerequisites
  • Python 3.10 or Python 3.11 or Python 3.12
  • AVEVA Process Simulation
  • AVEVA Python Interface (simcentralconnect)
AVEVA Custom Libraries Setup for Ibuprofen Example
For the AVEVA Process Simulation examples to run correctly, you need to place the provided simulation library files into your My Thermo Data directory ("C:\Users\xxx\My Thermo Data") on your computer:
Pharma.BASE.cmp
Pharma.bnk
Pharma.lb1
Pharma.lb2
Folder Structure
MOSKopt_Python/
├── core/ │ ├── __init__.py # Package initialization
│ ├── combined_core.cpython-310.pyc # Core implementation (compiled with Python 3.10)
│ ├── combined_core.cpython-311.pyc # Core implementation (compiled with Python 3.11)
│ └── combined_core.cpython-312.pyc # Core implementation (compiled with Python 3.12)
├── examples/ │ ├── deterministic_ibuprofen.py # Deterministic optimization example
│ └── stochastic_ibuprofen.py # Stochastic optimization example
├── simulation/ │ ├── IbuprofenProcessSimulation
│ ├── Pharma/BASE/
│ ├── Pharma.BASE.cmp
│ ├── Pharma.bnk
│ ├── Pharma.lb1
│ ├── Pharma.lb2
├── .gitignore ├── LICENSE ├── MOSKopt_Python Gem Instructions.md # AI Configuration Assistant
├── README.md ├── plot_from_checkpoint.py # Results plotting ├── plot_optimization_results.py # Results plotting ├── requirements.txt # Dependencies
└── setup.py # Package setup

Acknowledgement
This Python implementation was supported by European Marie Skłodowska-Curie network MiEl. The MiEl project received funding by the European Union under the Grant Agreement no. 101073003. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.
Note: Core implementation is compiled to .pyc files for intellectual property protection. Examples and documentation are provided for easy usage.
About
Advanced simulation-based optimization framework with AVEVA™ Process Simulation integration, featuring enhanced acquisition functions and robust failure handling.
Resources
Readme
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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})();
(function(){
try {
var __m = "*";
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MOSKopt_Python
Advanced simulation-based optimization framework with AVEVA™ Process Simulation integration, featuring enhanced acquisition functions and robust failure handling.
Project Purpose and Context
MOSKopt_Python is a Python implementation of the MOSKopt (Simulation-Based Stochastic Kriging Optimization framework), designed for chemical process optimization under uncertainty. This implementation extends the original MATLAB version developed by Resul Al. (https://github.com/gsi-lab/MOSKopt) with enhanced features and performance optimizations.
The framework, and example case studies implemented in this repository are described in https://doi.org/10.1016/j.cherd.2026.06.051.
Key Differences from MATLAB Version
  • Enhanced failure handling with smart consecutive failure limits
  • Intelligent restart strategies using successful simulation history (snapshot)
  • Performance optimizations (reduced model refitting, batch predictions)
  • AVEVA Python interface integration with unified base classes
  • Modern Python features (type hints, dataclasses, comprehensive error handling)
Quick Start
1. Download the Repository
# Download as ZIP from GitHub (green "Code" button → "Download ZIP")# Extract the ZIP file to your desired locationcd MOSKopt_Python-main
2. Install the Package
pip install -e .
3. Run Examples
python examples/deterministic_ibuprofen.py
python examples/stochastic_ibuprofen.py
Prerequisites
  • Python 3.10 or Python 3.11 or Python 3.12
  • AVEVA Process Simulation
  • AVEVA Python Interface (simcentralconnect)
AVEVA Custom Libraries Setup for Ibuprofen Example
For the AVEVA Process Simulation examples to run correctly, you need to place the provided simulation library files into your My Thermo Data directory ("C:\Users\xxx\My Thermo Data") on your computer:
Pharma.BASE.cmp
Pharma.bnk
Pharma.lb1
Pharma.lb2
Folder Structure
MOSKopt_Python/
├── core/ │ ├── __init__.py # Package initialization
│ ├── combined_core.cpython-310.pyc # Core implementation (compiled with Python 3.10)
│ ├── combined_core.cpython-311.pyc # Core implementation (compiled with Python 3.11)
│ └── combined_core.cpython-312.pyc # Core implementation (compiled with Python 3.12)
├── examples/ │ ├── deterministic_ibuprofen.py # Deterministic optimization example
│ └── stochastic_ibuprofen.py # Stochastic optimization example
├── simulation/ │ ├── IbuprofenProcessSimulation
│ ├── Pharma/BASE/
│ ├── Pharma.BASE.cmp
│ ├── Pharma.bnk
│ ├── Pharma.lb1
│ ├── Pharma.lb2
├── .gitignore ├── LICENSE ├── MOSKopt_Python Gem Instructions.md # AI Configuration Assistant
├── README.md ├── plot_from_checkpoint.py # Results plotting ├── plot_optimization_results.py # Results plotting ├── requirements.txt # Dependencies
└── setup.py # Package setup

Acknowledgement
This Python implementation was supported by European Marie Skłodowska-Curie network MiEl. The MiEl project received funding by the European Union under the Grant Agreement no. 101073003. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.
Note: Core implementation is compiled to .pyc files for intellectual property protection. Examples and documentation are provided for easy usage.
About
Advanced simulation-based optimization framework with AVEVA™ Process Simulation integration, featuring enhanced acquisition functions and robust failure handling.
Resources
Readme
License
Activity
Custom properties
Stars
5 stars
Watchers
0 watching
Forks
0 forks
Report repository
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Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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MOSKopt_Python
Advanced simulation-based optimization framework with AVEVA™ Process Simulation integration, featuring enhanced acquisition functions and robust failure handling.
Project Purpose and Context
MOSKopt_Python is a Python implementation of the MOSKopt (Simulation-Based Stochastic Kriging Optimization framework), designed for chemical process optimization under uncertainty. This implementation extends the original MATLAB version developed by Resul Al. (https://github.com/gsi-lab/MOSKopt) with enhanced features and performance optimizations.
The framework, and example case studies implemented in this repository are described in https://doi.org/10.1016/j.cherd.2026.06.051.
Key Differences from MATLAB Version
  • Enhanced failure handling with smart consecutive failure limits
  • Intelligent restart strategies using successful simulation history (snapshot)
  • Performance optimizations (reduced model refitting, batch predictions)
  • AVEVA Python interface integration with unified base classes
  • Modern Python features (type hints, dataclasses, comprehensive error handling)
Quick Start
1. Download the Repository
# Download as ZIP from GitHub (green "Code" button → "Download ZIP")# Extract the ZIP file to your desired locationcd MOSKopt_Python-main
2. Install the Package
pip install -e .
3. Run Examples
python examples/deterministic_ibuprofen.py
python examples/stochastic_ibuprofen.py
Prerequisites
  • Python 3.10 or Python 3.11 or Python 3.12
  • AVEVA Process Simulation
  • AVEVA Python Interface (simcentralconnect)
AVEVA Custom Libraries Setup for Ibuprofen Example
For the AVEVA Process Simulation examples to run correctly, you need to place the provided simulation library files into your My Thermo Data directory ("C:\Users\xxx\My Thermo Data") on your computer:
Pharma.BASE.cmp
Pharma.bnk
Pharma.lb1
Pharma.lb2
Folder Structure
MOSKopt_Python/
├── core/ │ ├── __init__.py # Package initialization
│ ├── combined_core.cpython-310.pyc # Core implementation (compiled with Python 3.10)
│ ├── combined_core.cpython-311.pyc # Core implementation (compiled with Python 3.11)
│ └── combined_core.cpython-312.pyc # Core implementation (compiled with Python 3.12)
├── examples/ │ ├── deterministic_ibuprofen.py # Deterministic optimization example
│ └── stochastic_ibuprofen.py # Stochastic optimization example
├── simulation/ │ ├── IbuprofenProcessSimulation
│ ├── Pharma/BASE/
│ ├── Pharma.BASE.cmp
│ ├── Pharma.bnk
│ ├── Pharma.lb1
│ ├── Pharma.lb2
├── .gitignore ├── LICENSE ├── MOSKopt_Python Gem Instructions.md # AI Configuration Assistant
├── README.md ├── plot_from_checkpoint.py # Results plotting ├── plot_optimization_results.py # Results plotting ├── requirements.txt # Dependencies
└── setup.py # Package setup

Acknowledgement
This Python implementation was supported by European Marie Skłodowska-Curie network MiEl. The MiEl project received funding by the European Union under the Grant Agreement no. 101073003. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.
Note: Core implementation is compiled to .pyc files for intellectual property protection. Examples and documentation are provided for easy usage.
About
Advanced simulation-based optimization framework with AVEVA™ Process Simulation integration, featuring enhanced acquisition functions and robust failure handling.
Resources
Readme
License
Activity
Custom properties
Stars
5 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
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} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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BranchesTags
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MOSKopt_Python
Advanced simulation-based optimization framework with AVEVA™ Process Simulation integration, featuring enhanced acquisition functions and robust failure handling.
Project Purpose and Context
MOSKopt_Python is a Python implementation of the MOSKopt (Simulation-Based Stochastic Kriging Optimization framework), designed for chemical process optimization under uncertainty. This implementation extends the original MATLAB version developed by Resul Al. (https://github.com/gsi-lab/MOSKopt) with enhanced features and performance optimizations.
The framework, and example case studies implemented in this repository are described in https://doi.org/10.1016/j.cherd.2026.06.051.
Key Differences from MATLAB Version
  • Enhanced failure handling with smart consecutive failure limits
  • Intelligent restart strategies using successful simulation history (snapshot)
  • Performance optimizations (reduced model refitting, batch predictions)
  • AVEVA Python interface integration with unified base classes
  • Modern Python features (type hints, dataclasses, comprehensive error handling)
Quick Start
1. Download the Repository
# Download as ZIP from GitHub (green "Code" button → "Download ZIP")# Extract the ZIP file to your desired locationcd MOSKopt_Python-main
2. Install the Package
pip install -e .
3. Run Examples
python examples/deterministic_ibuprofen.py
python examples/stochastic_ibuprofen.py
Prerequisites
  • Python 3.10 or Python 3.11 or Python 3.12
  • AVEVA Process Simulation
  • AVEVA Python Interface (simcentralconnect)
AVEVA Custom Libraries Setup for Ibuprofen Example
For the AVEVA Process Simulation examples to run correctly, you need to place the provided simulation library files into your My Thermo Data directory ("C:\Users\xxx\My Thermo Data") on your computer:
Pharma.BASE.cmp
Pharma.bnk
Pharma.lb1
Pharma.lb2
Folder Structure
MOSKopt_Python/
├── core/ │ ├── __init__.py # Package initialization
│ ├── combined_core.cpython-310.pyc # Core implementation (compiled with Python 3.10)
│ ├── combined_core.cpython-311.pyc # Core implementation (compiled with Python 3.11)
│ └── combined_core.cpython-312.pyc # Core implementation (compiled with Python 3.12)
├── examples/ │ ├── deterministic_ibuprofen.py # Deterministic optimization example
│ └── stochastic_ibuprofen.py # Stochastic optimization example
├── simulation/ │ ├── IbuprofenProcessSimulation
│ ├── Pharma/BASE/
│ ├── Pharma.BASE.cmp
│ ├── Pharma.bnk
│ ├── Pharma.lb1
│ ├── Pharma.lb2
├── .gitignore ├── LICENSE ├── MOSKopt_Python Gem Instructions.md # AI Configuration Assistant
├── README.md ├── plot_from_checkpoint.py # Results plotting ├── plot_optimization_results.py # Results plotting ├── requirements.txt # Dependencies
└── setup.py # Package setup

Acknowledgement
This Python implementation was supported by European Marie Skłodowska-Curie network MiEl. The MiEl project received funding by the European Union under the Grant Agreement no. 101073003. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.
Note: Core implementation is compiled to .pyc files for intellectual property protection. Examples and documentation are provided for easy usage.
About
Advanced simulation-based optimization framework with AVEVA™ Process Simulation integration, featuring enhanced acquisition functions and robust failure handling.
Resources
Readme
License
Activity
Custom properties
Stars
5 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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MOSKopt_Python
Advanced simulation-based optimization framework with AVEVA™ Process Simulation integration, featuring enhanced acquisition functions and robust failure handling.
Project Purpose and Context
MOSKopt_Python is a Python implementation of the MOSKopt (Simulation-Based Stochastic Kriging Optimization framework), designed for chemical process optimization under uncertainty. This implementation extends the original MATLAB version developed by Resul Al. (https://github.com/gsi-lab/MOSKopt) with enhanced features and performance optimizations.
The framework, and example case studies implemented in this repository are described in https://doi.org/10.1016/j.cherd.2026.06.051.
Key Differences from MATLAB Version
  • Enhanced failure handling with smart consecutive failure limits
  • Intelligent restart strategies using successful simulation history (snapshot)
  • Performance optimizations (reduced model refitting, batch predictions)
  • AVEVA Python interface integration with unified base classes
  • Modern Python features (type hints, dataclasses, comprehensive error handling)
Quick Start
1. Download the Repository
# Download as ZIP from GitHub (green "Code" button → "Download ZIP")# Extract the ZIP file to your desired locationcd MOSKopt_Python-main
2. Install the Package
pip install -e .
3. Run Examples
python examples/deterministic_ibuprofen.py
python examples/stochastic_ibuprofen.py
Prerequisites
  • Python 3.10 or Python 3.11 or Python 3.12
  • AVEVA Process Simulation
  • AVEVA Python Interface (simcentralconnect)
AVEVA Custom Libraries Setup for Ibuprofen Example
For the AVEVA Process Simulation examples to run correctly, you need to place the provided simulation library files into your My Thermo Data directory ("C:\Users\xxx\My Thermo Data") on your computer:
Pharma.BASE.cmp
Pharma.bnk
Pharma.lb1
Pharma.lb2
Folder Structure
MOSKopt_Python/
├── core/ │ ├── __init__.py # Package initialization
│ ├── combined_core.cpython-310.pyc # Core implementation (compiled with Python 3.10)
│ ├── combined_core.cpython-311.pyc # Core implementation (compiled with Python 3.11)
│ └── combined_core.cpython-312.pyc # Core implementation (compiled with Python 3.12)
├── examples/ │ ├── deterministic_ibuprofen.py # Deterministic optimization example
│ └── stochastic_ibuprofen.py # Stochastic optimization example
├── simulation/ │ ├── IbuprofenProcessSimulation
│ ├── Pharma/BASE/
│ ├── Pharma.BASE.cmp
│ ├── Pharma.bnk
│ ├── Pharma.lb1
│ ├── Pharma.lb2
├── .gitignore ├── LICENSE ├── MOSKopt_Python Gem Instructions.md # AI Configuration Assistant
├── README.md ├── plot_from_checkpoint.py # Results plotting ├── plot_optimization_results.py # Results plotting ├── requirements.txt # Dependencies
└── setup.py # Package setup

Acknowledgement
This Python implementation was supported by European Marie Skłodowska-Curie network MiEl. The MiEl project received funding by the European Union under the Grant Agreement no. 101073003. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.
Note: Core implementation is compiled to .pyc files for intellectual property protection. Examples and documentation are provided for easy usage.
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})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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MOSKopt_Python
Advanced simulation-based optimization framework with AVEVA™ Process Simulation integration, featuring enhanced acquisition functions and robust failure handling.
Project Purpose and Context
MOSKopt_Python is a Python implementation of the MOSKopt (Simulation-Based Stochastic Kriging Optimization framework), designed for chemical process optimization under uncertainty. This implementation extends the original MATLAB version developed by Resul Al. (https://github.com/gsi-lab/MOSKopt) with enhanced features and performance optimizations.
The framework, and example case studies implemented in this repository are described in https://doi.org/10.1016/j.cherd.2026.06.051.
Key Differences from MATLAB Version
  • Enhanced failure handling with smart consecutive failure limits
  • Intelligent restart strategies using successful simulation history (snapshot)
  • Performance optimizations (reduced model refitting, batch predictions)
  • AVEVA Python interface integration with unified base classes
  • Modern Python features (type hints, dataclasses, comprehensive error handling)
Quick Start
1. Download the Repository
# Download as ZIP from GitHub (green "Code" button → "Download ZIP")# Extract the ZIP file to your desired locationcd MOSKopt_Python-main
2. Install the Package
pip install -e .
3. Run Examples
python examples/deterministic_ibuprofen.py
python examples/stochastic_ibuprofen.py
Prerequisites
  • Python 3.10 or Python 3.11 or Python 3.12
  • AVEVA Process Simulation
  • AVEVA Python Interface (simcentralconnect)
AVEVA Custom Libraries Setup for Ibuprofen Example
For the AVEVA Process Simulation examples to run correctly, you need to place the provided simulation library files into your My Thermo Data directory ("C:\Users\xxx\My Thermo Data") on your computer:
Pharma.BASE.cmp
Pharma.bnk
Pharma.lb1
Pharma.lb2
Folder Structure
MOSKopt_Python/
├── core/ │ ├── __init__.py # Package initialization
│ ├── combined_core.cpython-310.pyc # Core implementation (compiled with Python 3.10)
│ ├── combined_core.cpython-311.pyc # Core implementation (compiled with Python 3.11)
│ └── combined_core.cpython-312.pyc # Core implementation (compiled with Python 3.12)
├── examples/ │ ├── deterministic_ibuprofen.py # Deterministic optimization example
│ └── stochastic_ibuprofen.py # Stochastic optimization example
├── simulation/ │ ├── IbuprofenProcessSimulation
│ ├── Pharma/BASE/
│ ├── Pharma.BASE.cmp
│ ├── Pharma.bnk
│ ├── Pharma.lb1
│ ├── Pharma.lb2
├── .gitignore ├── LICENSE ├── MOSKopt_Python Gem Instructions.md # AI Configuration Assistant
├── README.md ├── plot_from_checkpoint.py # Results plotting ├── plot_optimization_results.py # Results plotting ├── requirements.txt # Dependencies
└── setup.py # Package setup

Acknowledgement
This Python implementation was supported by European Marie Skłodowska-Curie network MiEl. The MiEl project received funding by the European Union under the Grant Agreement no. 101073003. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.
Note: Core implementation is compiled to .pyc files for intellectual property protection. Examples and documentation are provided for easy usage.
About
Advanced simulation-based optimization framework with AVEVA™ Process Simulation integration, featuring enhanced acquisition functions and robust failure handling.
Resources
Readme
License
Activity
Custom properties
Stars
5 stars
Watchers
0 watching
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0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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MOSKopt_Python
Advanced simulation-based optimization framework with AVEVA™ Process Simulation integration, featuring enhanced acquisition functions and robust failure handling.
Project Purpose and Context
MOSKopt_Python is a Python implementation of the MOSKopt (Simulation-Based Stochastic Kriging Optimization framework), designed for chemical process optimization under uncertainty. This implementation extends the original MATLAB version developed by Resul Al. (https://github.com/gsi-lab/MOSKopt) with enhanced features and performance optimizations.
The framework, and example case studies implemented in this repository are described in https://doi.org/10.1016/j.cherd.2026.06.051.
Key Differences from MATLAB Version
  • Enhanced failure handling with smart consecutive failure limits
  • Intelligent restart strategies using successful simulation history (snapshot)
  • Performance optimizations (reduced model refitting, batch predictions)
  • AVEVA Python interface integration with unified base classes
  • Modern Python features (type hints, dataclasses, comprehensive error handling)
Quick Start
1. Download the Repository
# Download as ZIP from GitHub (green "Code" button → "Download ZIP")# Extract the ZIP file to your desired locationcd MOSKopt_Python-main
2. Install the Package
pip install -e .
3. Run Examples
python examples/deterministic_ibuprofen.py
python examples/stochastic_ibuprofen.py
Prerequisites
  • Python 3.10 or Python 3.11 or Python 3.12
  • AVEVA Process Simulation
  • AVEVA Python Interface (simcentralconnect)
AVEVA Custom Libraries Setup for Ibuprofen Example
For the AVEVA Process Simulation examples to run correctly, you need to place the provided simulation library files into your My Thermo Data directory ("C:\Users\xxx\My Thermo Data") on your computer:
Pharma.BASE.cmp
Pharma.bnk
Pharma.lb1
Pharma.lb2
Folder Structure
MOSKopt_Python/
├── core/ │ ├── __init__.py # Package initialization
│ ├── combined_core.cpython-310.pyc # Core implementation (compiled with Python 3.10)
│ ├── combined_core.cpython-311.pyc # Core implementation (compiled with Python 3.11)
│ └── combined_core.cpython-312.pyc # Core implementation (compiled with Python 3.12)
├── examples/ │ ├── deterministic_ibuprofen.py # Deterministic optimization example
│ └── stochastic_ibuprofen.py # Stochastic optimization example
├── simulation/ │ ├── IbuprofenProcessSimulation
│ ├── Pharma/BASE/
│ ├── Pharma.BASE.cmp
│ ├── Pharma.bnk
│ ├── Pharma.lb1
│ ├── Pharma.lb2
├── .gitignore ├── LICENSE ├── MOSKopt_Python Gem Instructions.md # AI Configuration Assistant
├── README.md ├── plot_from_checkpoint.py # Results plotting ├── plot_optimization_results.py # Results plotting ├── requirements.txt # Dependencies
└── setup.py # Package setup

Acknowledgement
This Python implementation was supported by European Marie Skłodowska-Curie network MiEl. The MiEl project received funding by the European Union under the Grant Agreement no. 101073003. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.
Note: Core implementation is compiled to .pyc files for intellectual property protection. Examples and documentation are provided for easy usage.
About
Advanced simulation-based optimization framework with AVEVA™ Process Simulation integration, featuring enhanced acquisition functions and robust failure handling.
Resources
Readme
License
Activity
Custom properties
Stars
5 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages