Digitalização e Modelação de Bioprocessos

Objectives

The students should acquire fundamental concepts of computational modeling applied to biological processes, including digital models, digital shadows, and digital twins, and be able to distinguish between mechanistic/first principles (white box), empirical/data-driven (black box), and hybrid models (grey box), focusing on their application to upstream and downstream processes. Students should also develop skills in model construction, calibration, and analysis, as well as in interpreting results for bioprocess monitoring and control.

Syllabus

1. Introduction to bioprocess digitalization: Concepts of digital model, digital shadow, and digital twin. Role of digitalization in Industry 4.0 and in Bioengineering. 2. Bioprocess modelling: classification and applications based on the level of i) knowledge/interpretation; ii) detail and scope and iii) on time dependencies. Understand key differences and typical applications. 3. Modelling of cell culture processes: Microbial and cell growth models; inhibition kinetics and substrate consumption; modelling of batch, fed-batch, and continuous cultures. 4. Modelling of purification processes: Models for cell lysis, membrane processes (concentration and diafiltration), and chromatography. 5. Parameter estimation, sensitivity, and uncertainty analysis: Methods for model calibration and variability quantification. 6. Advanced digital applications: Implementation of digital shadows, digital twins and integration with real-time data.

Prerequisites

Fluency in quantitative reasoning and fundamentals of bioprocess engineering. Prior knowledge of differential calculus, statistics, and basic scientific programming skills, preferably in Python (or MATLAB).

Cross Competence Component

The course enables the development of transversal competences from the following groups: i) Critical thinking and problem-solving, developed through the analysis, formulation, and validation of models applied to complex biological processes; ii) Teamwork, fostered by the collaborative development of modelling projects, requiring planning, effective communication, and shared decision-making; iii) Scientific communication, both oral and written, cultivated through the preparation and delivery of a technical report, oral presentation, and the individual mini-viva; iv) Autonomy and time management, stimulated by the planning and scheduling of the project throughout the semester, with structured deliverables and feedback; v) Creativity and innovative thinking, applied in the design and optimization of computational solutions for modelling cultivation and purification processes; and, vi) Information and media literacy, developed through the search, critical assessment, and integration of relevant scientific sources, as well as the use of digital tools for simulation and data analysis. These skills are integrated into the course content and teaching methodologies, contributing to a solid technical and transversal education essential for professionals in computational biotechnology and digital bioprocess engineering. The assessment percentage associated with these skills is around 30%.

Laboratorial Component

The laboratory component of the curricular unit is delivered through computational simulations of biotechnological processes, conducted during theoretical-practical classes and as part of the group project. These sessions take place in the Information Technology Laboratory (LTI), where students implement mechanistic models of cultivation and purification processes, analyse experimental data, perform parameter estimation, and simulate the dynamic behaviour of systems using environments such as Python or MATLAB. This approach promotes the development of practical skills in modelling, data analysis, and interpretation, which are essential in digital bioprocess engineering.

Programming And Computing Component

The curricular unit has a strong computational component, focused on the implementation and analysis of bioprocess models. Students use programming languages and tools such as Python and/or MATLAB to develop models and simulations, estimate parameters, perform sensitivity analyses, and explore process scenarios. This component supports the development of scientific programming skills and computational engineering practices applied to bioengineering.

Ethical Principles

All members of a group are responsible for the group’s work In any assessment every student shall honestly disclose any help received and sources used. In an oral assessment, every student shall be alble to present and answer questions about the entire assignment and solution.