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Short course

From Data to Dynamics: Advanced Process Modelling

Five-day short course on data-driven modelling, latent variable methods, machine learning and computational fluid dynamics for chemical engineers.
From Data to Dynamics: Advanced Process Modelling

Five-day short course delivered at the Department of Chemical Engineering, NIT Calicut (16—20 February 2026). The first three days cover data-driven foundations: handling noisy, correlated process data, principal component analysis and partial least squares, and nonlinear models from decision trees to neural networks. The last two days cover computational fluid dynamics, from the finite volume method and meshing to multiphase flow models and industrial case studies. Each day combines lectures with hands-on practical sessions.

What you will gain

Participants learn to map theoretical concepts to real-world industrial problems, and to critically assess the validity, assumptions, and limitations of process models. The course brings empirical data science and engineering first principles together.

Schedule

Four hours per day, 16—20 February 2026.

  • Day 1: Data handling, noise management, multivariate analysis, and exploratory work on process datasets.
  • Day 2: Principal component analysis and partial least squares for process monitoring.
  • Day 3: Tree-based models, neural networks, and interpreting machine learning models.
  • Days 4—5: CFD fundamentals, multiphase flow simulation, and numerical methods.

Who it is for

Final-year undergraduates and postgraduates in chemical engineering, as professional development or elective coursework.

Before you start

A chemical engineering foundation, calculus and linear algebra, and basic programming skills. Some familiarity with statistics and machine learning is recommended.

Instructor

Ranjeet Utikar, Associate Professor at Curtin University and head of the SMILE lab.

Course site with notes, slides and datasets: apm.smilelab.dev