Oil and Gas Petro chemical Pulp and Paper Water and wastewater Metal Industries
Applications
Data pre- ML model AI algorithm Optimizer DSS
Data
processing development development
Modelling &
APC
Simulation
Dr. Senthilmurugan Subbiah, Department of Chemical Engineering, IITG.
Application of Al and ML in chemical engineering
Introduction
Objective of course
• To introduce the Machine learning model development lifecycle
• To introduce Artificial intelligence and ML
• Demonstrate the Application of AI& ML chemical engineering
• Decision support system
• Process control & real-time optimization
• Modelling and simulation
• Industrial example
• Oil & Gas
• Petro Chemical
• Pulp and paper
• Water and wastewater
• Metal Industries
January 15, 2024 | Slide 2
Syllabus
Application of Al and ML in chemical engineering (2-0-2-6)
• Introduction to AI and ML • Introduction to software tools used in AI
• Types of learning problems
& ML
• Python scikit-learn
• Supervised, Unsupervised,
• (https://scikit-learn.org/stable/)
Semi-supervised
• MATLAB Statistics and Machine
• Regression and Classification
Learning Toolbox
• Overview of optimization (https://in.mathworks.com/product
techniques s/statistics.html);
• Application in chemical • Complete machine learning
engineering process demo – Simple
regression/classification example
in Python/MATLAB
January 15, 2024 | Slide 3
Syllabus
• Overview of Machine learning process • Data preprocessing:
lifecycle • Data visualization (line plot, scatter
• Data collection, plot, histogram)
• Preprocessing, • Outlier detection (z-score, Inter-
• Exploratory Data Analysis (EDA) Quartile Range (IQR))
• Model development (training, • Smoothing techniques (moving
validation), average, exponential average,
• Deployment
Savitzky–Golay (SG) filter)
• Data scaling (Standardization,
• Maintenance
Normalization)
• Process lifecycle in MATLAB, Python
January 15, 2024 | Slide 4
Syllabus
• Dimensionality reduction • Model selection
• PCA : Principle Component Analysis
• ICA : Integrated Component Analysis • Hybrid Cross-Validation methods
• t-SNE : t- distributed Stochastic • Leave-one-out cross-
Neighbour Embedding
• UMAP : Uniform Manifold Approximation validation (LpOCV)
Projection • K-fold
• Feature extraction, selection
• Model Evaluation & identification • Time-series cv
• Performance metrics • Nested cv
• Regression - MAE, MSE, RMSE
• Classification - Precision, Recall, F1 • Hyperparameter Tuning
score, Receiver operating characteristic
(RUC) curve,
• Residual analysis
January 15, 2024 | Slide 5
Syllabus
• Model development: • Regression
• Classification • Linear regression
• Logistic regression • simple
• Naïve bayes classifier • multiple
• K-nearest neighbors • Kernel
• Support vector machines • Regression analysis
• Decision trees • Decision tree-based regression
• Random forests models
• Boosting Algorithms
January 15, 2024 | Slide 6
Syllabus
• Time series forecasting –
• Autoregressive (AR) model
• Neural Network:
• Moving average (MA) model • Introduction (Perceptron,
• Autoregressive moving average (ARMA) model
• Autoregressive integrated moving average
multilayer perceptron)
(ARIMA) model
• Backpropagation
• Seasonal autoregressive integrated moving
average (SARIMA) model • Extreme Learning Machines (ELM)
• Vector autoregressive (VAR) model
• Vector error correction (VECM) model • Convolutional Neural Network
• Autoregressive Integrated Moving Average (CNN)
eXogenous Variable Models (ARIMAX)
• Seasonal Auto-Regressive Integrated Moving • Recurrent Neural Network (RNN);
Average with eXogenous factors (SARIMAX)
• Vector Autoregressive Moving Average • Long Short Term Memory
(VARMA)
• Vector Autoregression Moving-Average with (LSTM)
Exogenous Regressors (VARMAX)
January 15, 2024 | Slide 7
Syllabus
• Unsupervised Techniques: • Use cases in Chemical engineering:
• K-means • decision support system,
• Hierarchical and spectral • process control & real time
clustering, optimization
• Density-based spatial clustering of • modeling and simulation
applications with noise (DBSCAN)
• Affinity propagation
• Apriori algorithm
January 15, 2024 | Slide 8
References:
1. Hastie, T., Tibshirani, R., Friedman, J.H., The Elements of Statistical Learning Data
Mining, Inference, and Prediction, Second Edition, 2009
2. Abu-Mostafa, Y.S., Magdon-Ismail, M., Hsuan-Tein, L., Learning from Data.
AMLBook, 2012
3. Bishop, C., Pattern Recognition and Machine Learning. Springer-Verlag, 2006
4. Gareth, J., Witten, D., Hastie. T., Tibshirani, R., An Introduction to Statistical
Learning with Applications in R, Springer-Verlag, 2013
5. Müller, A. C., Gudio, S., Introduction to Machine Learning with Python, O'Reilly
Media, Inc., 2016
6. Shalev-Shwartz, S. and Ben-David, S., Understanding Machine Learning: From
Theory to Algorithms, Cambridge University Press., 2014
January 15, 2024 | Slide 9
Evaluation criteria
Mid Semester
Evaluation Marks
Mid Semester 20
End Semester 30
Assignment 10
Quiz 10
Project 30
Attendance As per institute norms
January 15, 2024 | Slide 10
TA Details
S.No TA Name Email ID Phone
1 B. Sai Mukesh bsm.reddy@iitg.ac.in 7075570330
Reddy
2 Bijoyendra Sharma bsharma@iitg.ac.in 7577814920
January 15, 2024 | Slide 11
Class Schedule
Monday (4:00 pm – 5:00 pm) Tuesday (2:00 pm – 4:00 pm) Wednesday (4:00 pm – 5:00 pm)
1 2 3
January
8 9 10
15 Holiday 16 17
22 23 24
February 5 6 7
12 13 14
19 20 21
26 Mid term 27 Mid term 28 Mid term
January 15, 2024 | Slide 12
Monday (4:00 pm – 5:00 pm) Tuesday (2:00 pm – 4:00 pm) Wednesday (4:00 pm – 5:00 pm)
March 4 5 6
11 12 13
18 19 20
25 Holiday 26 27
April 1 2 3
8 9 10
15 Holiday 16 17
22 23 24
29 End sem 30 End sem
January 15, 2024 | Slide 13
Monday (4:00 pm – 5:00 pm) Tuesday (2:00 pm – 4:00 pm) Wednesday (4:00 pm – 5:00 pm)
May 1 End sem
6 7 8
13 14 15
20 21 22
January 15, 2024 | Slide 14
