Building AI-Driven Digital Twins for the Process Industry
C3 Splitter Optimization and Fault Detection
Bridge the gap between HYSYS simulations and industrial reality using Physics-Informed AI and MATLAB
About
About the Book
Why settle for a model that works only on paper?
In 2026, the gap between "Simulation" and "Reality" is the biggest hurdle in process optimization. Most engineers are trained in "Clean" simulations (Aspen Plus, HYSYS), but real plants are messy.
This workshop report, designed by Dr. Kamal Al-Malah, bridges that gap. You won't just learn AI; you will learn Physics-Informed AI. By the end of this course, you will have the tools to build a virtual instrument that "sees" through sensor noise to predict equipment failure before it happens.
What’s Included:
- Theory-to-Code Audit: Mathematical mapping of FUG and Kern-Seaton models.
- GPR Implementation: Using Gaussian Process Regression as a physics-informed filter.
- Interactive MATLAB Tools: Source code for the C3 Splitter Mini-Simulator.
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Packages
Pick Your Package
All packages include the ebook in the following formats: PDF
Standard Workshop Report ($45)
Minimum price
Suggested price$45.00Bridging Simulation and Industrial Reality The theoretical foundation for building AI-driven Digital Twins. Physics-Informed Modeling: C3 Splitter mapping using FUG and Kern-Seaton models. Fault Detection: Deep analysis of reboiler fouling and sensor drift. AI Logic: Step-by-step methodology for Gaussian Process Regression (GPR) filtering. Note: Includes the full PDF report. Select the Professional Tier for MATLAB source code and datasets.
$20.00
Professional Implementation Tier ($145)
Minimum price
Suggested price$145.00Implementation Toolkit: MATLAB Source Code & Datasets Get the complete technical environment to replicate all digital twin simulations: Interactive GUI: Full MATLAB Mini-Simulator for C3 Splitter optimization. GPR Scripts: Precise code for filtering industrial sensor noise. Data Generators: Tools for modeling fouling and sensor drift. Datasets: Raw and corrupted industrial data for hands-on testing. Ideal for professionals deploying Physics-Informed AI in the process industry.
$79.99
- MATLAB GUIShortCut Column App
- C3_MonteCarlo_DataGenMonte Carlo Data Generator
- MATLAB for Corrupt Process Data% CORRUPT_PROCESS_DATA: Toolbox-free version % Applies fouling, drift, and manual Gaussian noise (40dB SNR).
- CreateCorruptDataThe main script for reading clean data and generating the corrupted data. cleanData = readtable('C3_MonteCarlo_Data.csv'); % Run the function (passing the table as an argument)
- PlotDatagrpIt bridges the gap between industrial reality and theoretical physics by using a Gaussian Process Regression (GPR) model to reconstruct the true process signal from a cloud of noisy measurements
- Corrupted Indus Data5000 datapoints for corrupted (dirty) industrial data as a result of reboiler fouling, sensor drift, and stochastic process shocks
- C3_MonteCarloDataThis large synthetic dataset serves as the training fuel for the digital twin. • Stochastic inputs: MATLAB’s randn (or sobolset for quasi‑random sampling) is used to vary: o Feed composition (zₚᵣₒₚᵧₗₑₙₑ = 0.6 ± 0.1) o Feed temperature o Column pressure • Outputs: Distillate purity and reboiler duty.
Author
About the Author
Dr. Al-Malah graduated from Oregon State University in 1993, and his area of specialty during M.S. and Ph.D. programs dealt with protein interactions and behavior at interfaces in biological systems. He currently researches in the modeling, simulation, and optimization aspects of physical/biophysical systems and characterization of molecular properties within the dome of chemical, biochemical, pharmaceutical, and food engineering.
Dr. Al-Malah is the sole book author with renowned publishers as shown below:
Books Authored:
1. Al-Malah, K., “Chemical Reaction Engineering with MATLAB: Modeling, Simulation, and Design”. Wiley & Sons, Inc. In Press (May 2026).
2. Al-Malah, K., "Non-Conventional Materials: Nanomaterials, Metamaterials, and Smart materials, Characterization and Synthesis". Bentham Science, Inc. May 2025. https://benthambooks.com/book/9789815324273/preface/
3. Al-Malah, K., “MATLAB-based Computations of Chemical Engineering Principles”. Wiley & Sons, Inc. (May 2025). https://www.wiley.com/en-us/MATLAB-based+Computations+of+Chemical+Engineering+Principles-p-9781394308828
4. Al-Malah, K., “Machine and Deep Learning Using MATLAB Algorithms and Tools for Scientists and Engineers”, 1st Edition. Wiley & Sons, Inc. December, 2023. https://www.amazon.com/Machine-Deep-Learning-Using-MATLAB/dp/1394209088/.
5. Al-Malah, K., “Aspen Plus: Chemical Engineering Applications”, 2nd Edition. Wiley & Sons, Inc. (October, 2022). https://www.wiley.com/en-ae/Aspen+Plus:+Chemical+Engineering+Applications,+2nd+Edition-p-9781119868699
6. Al-Malah, K. “Prediction of Critical Temperature and Pressure of Hydrocarbons Using Simple Molecular Properties” in: Advanced Aspects of Engineering Research (Editor Gnana Sheela K), Vol. 10, 29 April 2021, Page 130-148. https://doi.org/10.9734/bpi/aaer/v10/8994D.
7. Al-Malah, K., “Aspen Plus: Chemical Engineering Applications”, Wiley & Sons, Inc. (2016). (http://eu.wiley.com/WileyCDA/WileyTitle/productCd-1119131235.html) and (http://www.amazon.com/Aspen-Plus-Chemical-EngineeringApplications/dp/1119131235/).
8. Al-Malah, K., “MATLAB®: Numerical Methods with Chemical Engineering Applications”, McGraw Hill, Inc. (2013).
(http://www.mcgrawhill.ca/professional/products/9780071831284/). http://www.amazon.ca/Numerical-Methods-Chemical-EngineeringApplications/dp/0071831282.
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