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Machine Learning Tools for Chemical Engineering
Methodologies and Applications
- 1st Edition - May 1, 2025
- Authors: Francisco Javier López-Flores, Rogelio Ochoa-Barragán, Alma Yunuen Raya-Tapia, César Ramírez-Márquez, José Maria Ponce-Ortega
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 9 0 5 8 - 9
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 9 0 5 9 - 6
Machine Learning Tools for Chemical Engineering: Methodologies and Applications examines how Machine Learning (ML) techniques are applied in the field, offering precise, fast, and… Read more
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Request a sales quoteMachine Learning Tools for Chemical Engineering: Methodologies and Applications examines how Machine Learning (ML) techniques are applied in the field, offering precise, fast, and flexible solutions to address specific challenges.
ML techniques and methodologies offer significant advantages (such as accuracy, speed of execution, and flexibility) over traditional modelling and optimization techniques. The book integrates ML techniques to solve problems inherent to chemical engineering, providing practical tools and a theoretical framework combining knowledge modeling, representation, and management, tailored to the chemical engineering field. It provides a precedent for applied Al, but one that goes beyond purely data-centric ML. It is firmly grounded in the philosophies of knowledge modelling, knowledge representation, search and inference, and knowledge extraction and management.
Aimed at graduate students, researchers, educators, and industry professionals, this book is an essential resource for those seeking to implement ML in chemical processes, aiming to foster optimization and innovation in the sector.
- The text highlights the importance of correctly applying machine learning tools in data collection, model development, training, testing, and implementing decision support systems
Graduate and upper undergraduate students, researchers, teachers, and different decision-makers focused on developing Artificial Intelligence (AI) for Chemical Engineering
Section I: Introduction to Machine Learning for Chemical Engineering
Chapter 1. Introduction to Machine Learning
1.1 Importance of Machine Learning in Artificial Intelligence
1.2 History of Machine Learning in Chemical Engineering
1.3 Emergence of Machine Learning in Chemical Engineering
1.4 Contribution of Machine Learning to Chemical Engineering
1.5 Challenges and Benefits of Implementing Machine Learning in Chemical Engineering
1.6 References
Chapter 2. Data Science in Chemical Engineering
2.1 When should Machine Learning be used?
2.2 Data Science in Chemical Engineering
2.3 Data Availability and Quality Challenges for Machine Learning
2.4 Decision Support Systems
2.5 References
Chapter 3. Fundamentals of Machine Learning Algorithms
3.1 Introduction
3.3 Types of machine learning
3.4 Supervised Machine Learning
3.5 Unsupervised Machine Learning
3.6 Semi-supervised learning
3.7 Reinforcement learning
3.2 Machine Learning Process
3.2.1 Data Collection
3.2.2 Data cleaning
3.2.3 Feature engineering
3.2.4 Evaluation and interpretation of results
3.6 Scaling
3.6.1 Normalization (min-max)
3.6.2 Standardization (Z-score)
3.7 Evaluation metrics
3.7.1 Mean Absolute Error (MAE)
3.7.2 Mean square error (MSE)
3.7.3 Root mean squared error (RMSE)
3.7.4 R-squared or coefficient of determination (R2)
3.8 Some terminology
3.9 References
Section II: Tools and Software
Chapter 4. Machine Learning with Python
4.1 Why is Python?
4.1.1 Python compared to other languages
4.1.2 Install Python
4.1.3 Install Python with Anaconda
4.2. Jupyter Notebook
4.3. Spyder
4.4. NumPy
4.4.1. NumPy Practical Examples
4.5. Pandas
4.5.1. Pandas Practical Examples
4.6. Matplotlib
4.6.1. Matplotlib Practical Examples
4.7. Scikit-Learn
4.7.1. Scikit-learn Practical Examples: 1
4.7.2. Scikit-learn Practical Examples: 2
4.8. Tensorflow – Keras
4.9 References
Chapter 5. Machine Learning with R
5.1 Install R and RStudio
5.1.1 What is R?
5.1.2 How to install RStudio through the official website
5.1.2 What is Anaconda?
5.1.3 How to install RStudio through Anaconda
5.2 R Packages and Tools
5.2.1 R notebook
5.2.2 Caret
5.2.3 caretEnsemble
5.2.4 neuralnet
5.2.5 ggplot2 and plotly
5.3 Conclusions
5.4 References
Section lll: Supervised Learning, Unsupervised Learning and Optimization
Chapter 6 Linear and polynomial regression
6.1 Introduction
6.2 Methods
6.2.1 Simple linear regression
6.2.1.1 Error metrics
6.2.2 Multiple linear regression
6.2.2.1 Variable Selection
6.2.2.1.1 Selection of the Best subset
6.2.2.1.2 Stepwise selection
6.2.2.1.3 Forward Selection
6.2.2.1.4 Backward Selection
6.2.2.1.5 Bidirectional Selection
6.2.2.2 Criteria or metrics for variable selection
6.2.2.2.1 The Daniel-Mallows Cp statistic
6.2.2.2.2 Information criterion
6.2.2.2.3 Akaike information criterion (AIC)
6.2.2.2.4 Bayesian Information Criterion (BIC)
6.2.2.2.5 The adjusted coefficient of determination
6.2.3 Polynomial Regression
6.2.3.1 Extension of the linear model to a polynomial model
6.2.3.1.1 Polynomial regression of one independent variable
6.2.3.1.2 Polynomial regression of two independent variables
6.2.3.2 Selection of polynomial degrees
6.2.3.2.1 Model Comparison by Cross-Validation
6.2.3.2.2 Comparison of models by hypothesis testing
6.3 Implementation and expression of results
6.3.1 Polynomial Regression Example
6.3.1.1 Implementation in Python
6.3.1.2 Implementation in R
6.3.2 Simple linear regression example
6.3.2.1 Implementation in Python
6.3.2.2 Implementation in R
6.3.3 Multiple Linear Regression Example
6.3.3.1 Implementation in Python
6.3.3.2 Implementation in R
6.4 Conclusion
Chapter 7. Support Vector Machines
7.1 Introduction
7.2 Methods
7.2.1 SVM for Classification Tasks
7.2.2 SVR for Regression tasks
7.3 Implementation and expression of results
7.3.1 Case study: Regression problem
7.3.1.1 Python Implementation with Spyder
7.3.1.2 R Implementation with RStudio
7.3.2 Case study: Classification problem
7.3.2.1 Python Implementation
7.3.2.2 Implementation in R
7.4 Conclusions
7.5 References
7.6 SUPPLEMENTARY ONLINE ELEMENTS
Chapter 8. Decision Trees and Random Forests
8.1 Introduction
8.2 Methods
8.2.1 Classification and Regression Trees
8.2.2 Random Forest
8.3 Implementation and expression of results
8.3.1 Case study: Regression problem
8.3.1.1 Python Implementation
8.3.1.2 R Implementation with RStudio
8.3.2 Case study: Classification problem
8.3.2.1 Python Implementation
8.3.2.2 Implementation in R
8.4 Conclusions
8.5 References
8.6 SUPPLEMENTARY ONLINE ELEMENTS
Chapter 9. Deep Learning
9.1. Introduction
9.1.1. Perceptron Operation
9.1.2. Simple Artificial Neural
9.2 Methods
9.2.1 Multilayer Perceptron
9.2.2 Recurrent neural networks
9.2.2.1 Long Short-Term Memory
9.2.2.2. Gated recurrent unit
9.2.3 Convolutional neural networks
9.2.4. Activation functions
9.2.5. Loss function
9.2.6. Optimizers
9.2.7. Hyperparameters
9.3 Implementation and expression of results
9.3.1. Case Study: Regression Problem
9.3.1.1. Database
9.3.1.2. Implementation in Python
9.3.1.3. Implementation in R
9.3.2. Case study 2: Time series forecasting
9.3.2.1. Database
9.3.2.2. Implementation in Python
9.3.2.3 Implementation in R
9.4 Conclusion
9.5 References
9.6 SUPPLEMENTARY ONLINE ELEMENTS
Chapter 10. Clustering and Dimensionality Reduction
10.1 Abstract
10.2 Introduction
10.2.1 Data preprocessing (Feature scaling)
10.2.2 Measures of similarity and dissimilarity
10.2.2.1 Similarity measures
10.2.2.2 Dissimilarity or distance measures
10.2.2.3 Other distance: Dynamic Time Warping (DTW)
10.3 Methods
10.3.1 Partitional clustering
10.3.1.1 k means
10.3.1.2 k-medoid
10.3.2 Hierarchical algorithm
10.3.2.1 Agglomerative Hierarchical
10.3.2.2 BIRCH
10.3.2.3 Divisive Hierarchical
10.3.3 Density-based algorithms
10.3.3.1 DBSCAN
10.3.3.2 OPTICS
10.3.4 Fuzzy algorithms
10.3.4.1 Fuzzy c-means
10.3.1 Other Clustering Algorithms
10.3.1.1 Gaussian Mixture Models
10.1 Principal Component Analysis (PCA)
10.1 Other dimensionality reduction Algorithms
10.1.1 Linear discriminant analysis (LDA)
10.1.2 t-SNE
10.1.3 ISOMAP
10.2 Implementation and expression of results
10.2.1 Implementation in Python
10.2.2 Implementation in R
10.4 Conclusion
10.5 References
10.6 Supplementary online elements
Chapter 11. Machine Learning Model Optimization
11.1 Introduction to Model Optimization Techniques
11.1.1 Overview of Metaheuristic Optimization Techniques
11.2 Methods
11.2.1 Deterministic optimization
11.2.1.1 Implementation and expression of results
11.2.2 Metaheuristic optimization
11.2.2.1 Evolutionary Algorithms (EA)
11.2.2.2 Swarm Intelligence
11.2.2.1 Implementation and expression of results
11.2.3 Hyperparameters optimization
11.2.3.1 Implementation and expression of results
11.3 Conclusion
11.4 References
11.5 SUPPLEMENTARY ONLINE ELEMENTS
Chapter 12. Machine Learning in Chemical Processes
12.1 Introduction
12.1.1 Background
12.1.2 Problem statement
12.1.3 Literature review
12.2 Methods
12.2.1 Linear modeling
12.2.2 Non-linear modeling
12.2.3 Hybrid modeling in decision models
12.3 Conclusions
12.4 References
12.5 SUPPLEMENTARY ONLINE ELEMENTS
Chapter 13. Machine learning in Supply Chain Management
13.1 Introduction
13.1.1 Background
13.1.2 Problem statement
13.1.3 Literature review
13.2 Method
13.2.1 Model-based System
13.2.2 Data-based system
13.2.4 Case study
13.2.5 Implementation
13.3 Discussion and evaluation of results
13.4 Conclusions
13.5 References
13.6 SUPPLEMENTARY ONLINE ELEMENTS
Chapter 14. Machine Learning in Energy Integration
14.1 Introduction
14.1.1 Literature review
14.1.2 Problem statement
14.2. Methodology
14.2.1 Multilayer perceptron (MLP)
14.2.2 Thermal engine simulations
14.2.3 Stages of MLP model building
14.2.4 Evaluation indexes
14.2.5 Overall model
14.3 Results and discussion
14.3.1 Case study
14.3.2 Pinch analysis
14.3.3 MLP model results
14.3.4 Optimization results
14.4 Conclusions
14.5 Nomenclature
14.6 Implementation in Python
14.7 References
14.8 SUPPLEMENTARY ONLINE ELEMENTS
Chapter 15. Machine Learning in Time Series Forecasting
15.1. Introduction
15.1.1. Literature review
15.1.2. Forecasting and deep learning techniques
15.1.3. Problem statement
15.1.4. Case study
15.2. Methodology
15.2.1. Deep learning
15.2.2. Methodology for calculating indicators and the WEF nexus index.
15.3. Results and discussion
15.3.1. Deep learning
15.3.2. Indicators
15.3.3. WEF nexus index
15.4. Conclusions
15.5. Nomenclature
15.6. Implementation in python
15.7. References
15.8. SUPPLEMENTARY ONLINE ELEMENTS
Chapter 16. Machine Learning in Optimal Water Management in the Exploitation of Unconventional Fossil Fuels
16.1 Introduction
16.2 Problem statement
16.3. Methodology
16.3.1. Artificial Neural Network (ANN)
16.3.2. Normalization
16.3.3. Evaluation Metric
16.3.4. Hyperparameter Optimization
16.3.5. Mathematical Model
16.4. Case study
16.5. Results and discussion
16.5.1. Data Preparation
16.5.2. ANN Model
16.5.3. Optimization Results
16.6. Conclusions
16.7. Nomenclature
16.8. References
Chapter 17. Challenges and Future Scope
17.1 Introduction
17.2 Examples and applications
17.3 References
Appendix
A1. Building an ANN with PyTorch
A2. ANN optimization with reluMIP
Chapter 1. Introduction to Machine Learning
1.1 Importance of Machine Learning in Artificial Intelligence
1.2 History of Machine Learning in Chemical Engineering
1.3 Emergence of Machine Learning in Chemical Engineering
1.4 Contribution of Machine Learning to Chemical Engineering
1.5 Challenges and Benefits of Implementing Machine Learning in Chemical Engineering
1.6 References
Chapter 2. Data Science in Chemical Engineering
2.1 When should Machine Learning be used?
2.2 Data Science in Chemical Engineering
2.3 Data Availability and Quality Challenges for Machine Learning
2.4 Decision Support Systems
2.5 References
Chapter 3. Fundamentals of Machine Learning Algorithms
3.1 Introduction
3.3 Types of machine learning
3.4 Supervised Machine Learning
3.5 Unsupervised Machine Learning
3.6 Semi-supervised learning
3.7 Reinforcement learning
3.2 Machine Learning Process
3.2.1 Data Collection
3.2.2 Data cleaning
3.2.3 Feature engineering
3.2.4 Evaluation and interpretation of results
3.6 Scaling
3.6.1 Normalization (min-max)
3.6.2 Standardization (Z-score)
3.7 Evaluation metrics
3.7.1 Mean Absolute Error (MAE)
3.7.2 Mean square error (MSE)
3.7.3 Root mean squared error (RMSE)
3.7.4 R-squared or coefficient of determination (R2)
3.8 Some terminology
3.9 References
Section II: Tools and Software
Chapter 4. Machine Learning with Python
4.1 Why is Python?
4.1.1 Python compared to other languages
4.1.2 Install Python
4.1.3 Install Python with Anaconda
4.2. Jupyter Notebook
4.3. Spyder
4.4. NumPy
4.4.1. NumPy Practical Examples
4.5. Pandas
4.5.1. Pandas Practical Examples
4.6. Matplotlib
4.6.1. Matplotlib Practical Examples
4.7. Scikit-Learn
4.7.1. Scikit-learn Practical Examples: 1
4.7.2. Scikit-learn Practical Examples: 2
4.8. Tensorflow – Keras
4.9 References
Chapter 5. Machine Learning with R
5.1 Install R and RStudio
5.1.1 What is R?
5.1.2 How to install RStudio through the official website
5.1.2 What is Anaconda?
5.1.3 How to install RStudio through Anaconda
5.2 R Packages and Tools
5.2.1 R notebook
5.2.2 Caret
5.2.3 caretEnsemble
5.2.4 neuralnet
5.2.5 ggplot2 and plotly
5.3 Conclusions
5.4 References
Section lll: Supervised Learning, Unsupervised Learning and Optimization
Chapter 6 Linear and polynomial regression
6.1 Introduction
6.2 Methods
6.2.1 Simple linear regression
6.2.1.1 Error metrics
6.2.2 Multiple linear regression
6.2.2.1 Variable Selection
6.2.2.1.1 Selection of the Best subset
6.2.2.1.2 Stepwise selection
6.2.2.1.3 Forward Selection
6.2.2.1.4 Backward Selection
6.2.2.1.5 Bidirectional Selection
6.2.2.2 Criteria or metrics for variable selection
6.2.2.2.1 The Daniel-Mallows Cp statistic
6.2.2.2.2 Information criterion
6.2.2.2.3 Akaike information criterion (AIC)
6.2.2.2.4 Bayesian Information Criterion (BIC)
6.2.2.2.5 The adjusted coefficient of determination
6.2.3 Polynomial Regression
6.2.3.1 Extension of the linear model to a polynomial model
6.2.3.1.1 Polynomial regression of one independent variable
6.2.3.1.2 Polynomial regression of two independent variables
6.2.3.2 Selection of polynomial degrees
6.2.3.2.1 Model Comparison by Cross-Validation
6.2.3.2.2 Comparison of models by hypothesis testing
6.3 Implementation and expression of results
6.3.1 Polynomial Regression Example
6.3.1.1 Implementation in Python
6.3.1.2 Implementation in R
6.3.2 Simple linear regression example
6.3.2.1 Implementation in Python
6.3.2.2 Implementation in R
6.3.3 Multiple Linear Regression Example
6.3.3.1 Implementation in Python
6.3.3.2 Implementation in R
6.4 Conclusion
Chapter 7. Support Vector Machines
7.1 Introduction
7.2 Methods
7.2.1 SVM for Classification Tasks
7.2.2 SVR for Regression tasks
7.3 Implementation and expression of results
7.3.1 Case study: Regression problem
7.3.1.1 Python Implementation with Spyder
7.3.1.2 R Implementation with RStudio
7.3.2 Case study: Classification problem
7.3.2.1 Python Implementation
7.3.2.2 Implementation in R
7.4 Conclusions
7.5 References
7.6 SUPPLEMENTARY ONLINE ELEMENTS
Chapter 8. Decision Trees and Random Forests
8.1 Introduction
8.2 Methods
8.2.1 Classification and Regression Trees
8.2.2 Random Forest
8.3 Implementation and expression of results
8.3.1 Case study: Regression problem
8.3.1.1 Python Implementation
8.3.1.2 R Implementation with RStudio
8.3.2 Case study: Classification problem
8.3.2.1 Python Implementation
8.3.2.2 Implementation in R
8.4 Conclusions
8.5 References
8.6 SUPPLEMENTARY ONLINE ELEMENTS
Chapter 9. Deep Learning
9.1. Introduction
9.1.1. Perceptron Operation
9.1.2. Simple Artificial Neural
9.2 Methods
9.2.1 Multilayer Perceptron
9.2.2 Recurrent neural networks
9.2.2.1 Long Short-Term Memory
9.2.2.2. Gated recurrent unit
9.2.3 Convolutional neural networks
9.2.4. Activation functions
9.2.5. Loss function
9.2.6. Optimizers
9.2.7. Hyperparameters
9.3 Implementation and expression of results
9.3.1. Case Study: Regression Problem
9.3.1.1. Database
9.3.1.2. Implementation in Python
9.3.1.3. Implementation in R
9.3.2. Case study 2: Time series forecasting
9.3.2.1. Database
9.3.2.2. Implementation in Python
9.3.2.3 Implementation in R
9.4 Conclusion
9.5 References
9.6 SUPPLEMENTARY ONLINE ELEMENTS
Chapter 10. Clustering and Dimensionality Reduction
10.1 Abstract
10.2 Introduction
10.2.1 Data preprocessing (Feature scaling)
10.2.2 Measures of similarity and dissimilarity
10.2.2.1 Similarity measures
10.2.2.2 Dissimilarity or distance measures
10.2.2.3 Other distance: Dynamic Time Warping (DTW)
10.3 Methods
10.3.1 Partitional clustering
10.3.1.1 k means
10.3.1.2 k-medoid
10.3.2 Hierarchical algorithm
10.3.2.1 Agglomerative Hierarchical
10.3.2.2 BIRCH
10.3.2.3 Divisive Hierarchical
10.3.3 Density-based algorithms
10.3.3.1 DBSCAN
10.3.3.2 OPTICS
10.3.4 Fuzzy algorithms
10.3.4.1 Fuzzy c-means
10.3.1 Other Clustering Algorithms
10.3.1.1 Gaussian Mixture Models
10.1 Principal Component Analysis (PCA)
10.1 Other dimensionality reduction Algorithms
10.1.1 Linear discriminant analysis (LDA)
10.1.2 t-SNE
10.1.3 ISOMAP
10.2 Implementation and expression of results
10.2.1 Implementation in Python
10.2.2 Implementation in R
10.4 Conclusion
10.5 References
10.6 Supplementary online elements
Chapter 11. Machine Learning Model Optimization
11.1 Introduction to Model Optimization Techniques
11.1.1 Overview of Metaheuristic Optimization Techniques
11.2 Methods
11.2.1 Deterministic optimization
11.2.1.1 Implementation and expression of results
11.2.2 Metaheuristic optimization
11.2.2.1 Evolutionary Algorithms (EA)
11.2.2.2 Swarm Intelligence
11.2.2.1 Implementation and expression of results
11.2.3 Hyperparameters optimization
11.2.3.1 Implementation and expression of results
11.3 Conclusion
11.4 References
11.5 SUPPLEMENTARY ONLINE ELEMENTS
Chapter 12. Machine Learning in Chemical Processes
12.1 Introduction
12.1.1 Background
12.1.2 Problem statement
12.1.3 Literature review
12.2 Methods
12.2.1 Linear modeling
12.2.2 Non-linear modeling
12.2.3 Hybrid modeling in decision models
12.3 Conclusions
12.4 References
12.5 SUPPLEMENTARY ONLINE ELEMENTS
Chapter 13. Machine learning in Supply Chain Management
13.1 Introduction
13.1.1 Background
13.1.2 Problem statement
13.1.3 Literature review
13.2 Method
13.2.1 Model-based System
13.2.2 Data-based system
13.2.4 Case study
13.2.5 Implementation
13.3 Discussion and evaluation of results
13.4 Conclusions
13.5 References
13.6 SUPPLEMENTARY ONLINE ELEMENTS
Chapter 14. Machine Learning in Energy Integration
14.1 Introduction
14.1.1 Literature review
14.1.2 Problem statement
14.2. Methodology
14.2.1 Multilayer perceptron (MLP)
14.2.2 Thermal engine simulations
14.2.3 Stages of MLP model building
14.2.4 Evaluation indexes
14.2.5 Overall model
14.3 Results and discussion
14.3.1 Case study
14.3.2 Pinch analysis
14.3.3 MLP model results
14.3.4 Optimization results
14.4 Conclusions
14.5 Nomenclature
14.6 Implementation in Python
14.7 References
14.8 SUPPLEMENTARY ONLINE ELEMENTS
Chapter 15. Machine Learning in Time Series Forecasting
15.1. Introduction
15.1.1. Literature review
15.1.2. Forecasting and deep learning techniques
15.1.3. Problem statement
15.1.4. Case study
15.2. Methodology
15.2.1. Deep learning
15.2.2. Methodology for calculating indicators and the WEF nexus index.
15.3. Results and discussion
15.3.1. Deep learning
15.3.2. Indicators
15.3.3. WEF nexus index
15.4. Conclusions
15.5. Nomenclature
15.6. Implementation in python
15.7. References
15.8. SUPPLEMENTARY ONLINE ELEMENTS
Chapter 16. Machine Learning in Optimal Water Management in the Exploitation of Unconventional Fossil Fuels
16.1 Introduction
16.2 Problem statement
16.3. Methodology
16.3.1. Artificial Neural Network (ANN)
16.3.2. Normalization
16.3.3. Evaluation Metric
16.3.4. Hyperparameter Optimization
16.3.5. Mathematical Model
16.4. Case study
16.5. Results and discussion
16.5.1. Data Preparation
16.5.2. ANN Model
16.5.3. Optimization Results
16.6. Conclusions
16.7. Nomenclature
16.8. References
Chapter 17. Challenges and Future Scope
17.1 Introduction
17.2 Examples and applications
17.3 References
Appendix
A1. Building an ANN with PyTorch
A2. ANN optimization with reluMIP
- No. of pages: 352
- Language: English
- Edition: 1
- Published: May 1, 2025
- Imprint: Elsevier Science
- Paperback ISBN: 9780443290589
- eBook ISBN: 9780443290596
FL
Francisco Javier López-Flores
Francisco Javier López Flores received his Master’s and Ph.D. degrees from the Chemical Engineering Department at the Universidad Michoacana de San Nicolás de Hidalgo in Mexico in 2020 and 2024, respectively. His research interests include process optimization, energy integration, planning strategies, and machine learning. He has published more than ten scientific papers and presented his research at ten international and regional conferences.
Affiliations and expertise
Universidad Michoacana de San Nicolás de Hidalgo, MexicoRO
Rogelio Ochoa-Barragán
Rogelio Ochoa-Barragán earned his Ph.D. and Master’s degrees in Chemical Engineering from the Universidad Michoacana de San Nicolás de Hidalgo in Mexico in 2020 and 2024, respectively. His current research focuses on process optimization, energy network management, social justice, and machine learning. His work has been presented at eleven international and regional conferences. He has published more than ten scientific articles and contributed to two books.
Affiliations and expertise
Universidad Michoacana de San Nicolás de Hidalgo, MexicoAR
Alma Yunuen Raya-Tapia
Alma Yunuen Raya Tapia is currently pursuing a Ph.D. in Chemical Engineering at the Universidad Michoacana de San Nicolás de Hidalgo. She earned her Master of Science in Chemical Engineering in 2021 with honors, following a degree in Chemical Engineering from the Technological Institute of Lázaro Cárdenas in 2019. Her research focuses on materials synthesis, photocatalysis, dye degradation, wastewater treatment, and the strategic planning of the water-energy-food nexus, combined with machine learning techniques. She has published more than ten scientific articles and presented her work at five international and national conferences.
Affiliations and expertise
Universidad Michoacana de San Nicolás de Hidalgo, MexicoCR
César Ramírez-Márquez
César Ramírez-Márquez is a Postdoctoral Fellow at the Chemical Engineering Department of the Universidad Michoacana de San Nicolás de Hidalgo, Mexico. He earned his Ph.D. from the University of Guanajuato, Mexico, in 2020. His current research focuses on the production of materials for the solar energy industry and base chemicals in the chemical industry. He has published more than 55 journal papers, six book chapters, presented his work at over fifteen international and regional conferences, and holds four patents.
Affiliations and expertise
Postdoctoral Fellow, Universidad Michoacana de San Nicolás de Hidalgo, MexicoJP
José Maria Ponce-Ortega
José María Ponce-Ortega is a Professor in the Chemical Engineering Department at the Universidad Michoacana de San Nicolás de Hidalgo, Mexico. He earned his Ph.D. and Master’s degrees in Chemical Engineering from the Institute of Technology of Celaya, Mexico, in 2009 and 2003, respectively. He completed postdoctoral research at Texas A&M University, USA, and served as a visiting scholar at Carnegie Mellon University, USA. Dr. Ponce-Ortega is a full professor and a member of the National Research System of Mexico. His research focuses on the optimization of chemical processes, sustainable design, energy, mass, water, and property integration, and supply chain optimization. He has published more than 310 papers, five books, and 60 book chapters. He has supervised 30 Ph.D. students and 50 Master’s students and secured funding for 15 research projects totaling approximately $1,000,000. Dr. Ponce-Ortega serves on the editorial boards of Clean Technologies and Environmental Policy and Process Integration and Optimization for Sustainability, and is a subject editor for Sustainable Production and Consumption, as well as associate editor in Frontiers in Chemical Engineering.
Affiliations and expertise
Professor, Chemical Engineering Department, Universidad Michoacana de San Nicolás de Hidalgo, Mexico