CHE 636 - AI Applications in Chemical and Bioengineering

Credits: 3

Level: Undergraduate

Description: AI Applications in Chemical and Bioengineering introduces undergraduate students to data-driven, symbolic, and hybrid artificial intelligence methods for modeling, monitoring, design, control, optimization, and decision-making in chemical and bioengineering systems. The course is motivated by the growing role of AI, automation, and data analytics in Industry 4.0, where modern processes generate large volumes of data from experiments, sensors, simulations, imaging systems, and plant operations. The course first covers AI paradigms, data pipelines, statistical foundations, feature engineering, model validation, and reproducible Python-based workflows. It then introduces supervised and unsupervised learning, time-series modeling, computer vision, and AI-assisted process and product design. A central theme of the course is integrating first-principles chemical engineering knowledge, including mass and energy balances, kinetics, and transport phenomena, with machine learning through gray-box and physics-informed modeling. Through case studies in chemical and bioengineering applications, students learn to map engineering problems to appropriate AI task types and to evaluate AI models with attention to accuracy, interpretability, robustness, safety, and responsible deployment.

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CHE 561 - Chemical Process Dynamics and Control

Credits: 3

Level: Undergraduate

Description: The chemical engineering curriculum is mostly focused on fundamental principles of thermodynamics, kinetics, and transport phenomena that govern chemical processes, as well as the design of continuous reaction and separation systems for steady-state operation. Chemical Process Dynamics and Control is concerned with the fact that real processes rarely operate under steady-state conditions, either due to process upsets or deliberate changes in operating steady states. This requires the design and implementation of automated control systems for enforcing the desired operating conditions. The key behind this is the concept of feedback, which, in addition to making engineering systems work robustly and efficiently, is ubiquitous in chemical and energy systems. The course is integrative, building on the content of all other chemical engineering courses and adding a fair number of new concepts. Its first part covers the analysis of the dynamic behavior of chemical processes. For small changes around a steady-state, this is adequately captured by the solution of linear ordinary differential equation (ODE) models. The second part introduces the concepts of feedback and a closed-loop system (a process under a feedback controller) and covers the analysis of closed-loop behavior under different controllers, as well as the design of controllers that enforce desired performance.

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CHE 835 - Chemical Engineering Analysis

Credits: 3

Level: Graduate

Description: This course covers the mathematical formulation of chemical engineering problems and explores how to solve them using analytical, numerical, and data-driven methods. The emphasis is on analytical and numerical solutions of ordinary differential equations (ODEs) and partial differential equations (PDEs) arising in transport-reaction systems. Data-driven and machine learning-based approaches will be discussed only briefly. The course builds upon the student’s skills in applied mathematics, scientific computing, and chemical process analysis. Concepts such as transport phenomena and chemical reactions are emphasized with mathematical modeling and solution methods. The intimate connection of fundamental scientific principles with practical engineering problem-solving is demonstrated and experienced through class discussions, reading assignments, and homework problems. 

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CHE 875 - Graduate Seminar in Chemical Engineering

Credits: 1

Level: Graduate

Description: Discussion of current advances and research in chemical engineering and related fields, highlighted through weekly seminars by invited scientists and professors from universities, national laboratories, and industry. Students prepare brief questions, engage in moderated Q&A, and submit short reflections to strengthen critical analysis and scientific communication.