About Workshop
Bioprocess 4.0: AI, Digital Twins & Smart Biomanufacturing is a hands-on workshop that introduces participants to AI-enabled bioprocess monitoring, digital twin workflows, and smart biomanufacturing systems. Participants will learn how sensor data, machine learning, predictive analytics, and real-time visualization can improve fermentation, bioreactor performance, yield prediction, anomaly detection, and process optimization.
Using tools such as Python, Google Colab, Scikit-learn, Pandas, Matplotlib, Streamlit, and open-source bioprocess datasets, participants will gain practical exposure to building basic ML models, visualizing process data, and creating simple digital twin-based monitoring dashboards.
Aim
To introduce participants to AI-driven Bioprocess 4.0 by exploring how digital twins, sensor data, machine learning, and smart dashboards can be used for real-time bioprocess monitoring, prediction, and optimization in modern biomanufacturing.
What Participants Will Learn
- Understand the concepts of Bioprocess 4.0, Pharma 4.0, and smart biomanufacturing.
- Learn how AI, IoT, sensors, and automation support modern bioprocess monitoring.
- Explore the role of digital twins in real-time process visualization and decision-making.
- Apply machine learning for bioprocess parameter prediction and optimization.
- Identify anomalies and process deviations using AI-based analytics.
- Build simple workflows for sensor data visualization, ML modeling, and dashboard creation.
Structure
🧬 Day 1: Foundations of Bioprocess 4.0 & Intelligent Monitoring
- Understand Bioprocess 4.0, Pharma 4.0 and smart biomanufacturing concepts
- Learn the role of AI, IoT, sensors and automation in modern biotechnology
- Explore Digital Twin concepts for bioprocess monitoring and control
- Understand real-time data acquisition from fermentation and bioreactor systems
- Learn how predictive analytics supports process efficiency and quality improvement
- Tools covered: Python, Google Colab, Jupyter Notebook, Pandas and Matplotlib
🔬 Hands-on:
Hands-on 1: Visualizing bioprocess sensor data using Python Hands-on 2: Creating a basic real-time monitoring workflow in Google Colab🧫 Day 2: AI & Machine Learning for Bioprocess Optimization
- Learn data preprocessing and feature engineering for bioprocess datasets
- Understand machine learning models for fermentation and bioreactor optimization
- Explore AI-based prediction of yield, pH, temperature, biomass and productivity
- Learn anomaly detection and predictive maintenance for bioprocess systems
- Study real-world applications from pharmaceutical and biotech industries
- Tools covered: Python, Google Colab, Scikit-learn, NumPy and Pandas
🧪 Hands-on:
Hands-on 1: Building a simple ML model for bioprocess parameter prediction Hands-on 2: Performing AI-based optimization of fermentation process conditions🧬 Day 3: Digital Twin Implementation & Smart Biomanufacturing Workflows
- Understand the architecture of Digital Twin systems in biomanufacturing
- Learn how AI-integrated Digital Twins support real-time process decisions
- Explore simulation, process synchronization and cloud-based monitoring
- Learn dashboard development for smart bioprocess visualization
- Explore future trends such as Generative AI, autonomous labs and Industry 5.0
- Tools covered: Python, Streamlit, Plotly Dash, Google Colab and open-source bioprocess datasets
🧫 Hands-on:
Hands-on 1: Developing a simple Digital Twin prototype workflow Hands-on 2: Creating an interactive dashboard for real-time process simulationImportant Dates
Registration Ends
4: 30 PM IST
Workshop Dates
2026-06-08
5: 30PM IST
5: 30PM IST
What You Will Gain
Outcomes
- Understand AI-driven Bioprocess 4.0 and smart biomanufacturing workflows.
- Analyze and visualize bioprocess sensor data using Python.
- Build basic ML models for prediction and process optimization.
- Understand how digital twins support real-time monitoring and decision-making.
- Create simple dashboards for bioprocess visualization.
- Apply AI concepts to fermentation, bioreactor, and biomanufacturing use cases.
Who Should Attend
- Students from biotechnology, life sciences, pharmaceutical sciences, microbiology, bioinformatics, and related fields
- PhD scholars and researchers working in biotechnology, bioprocessing, pharma, or life sciences
- Academicians, faculty members, and trainers interested in smart biomanufacturing
- Industry professionals from biotech, pharma, fermentation, QA/QC, R&D, and biomanufacturing sectors
- Basic knowledge of biotechnology or bioprocessing is helpful
- Prior coding experience is not mandatory
- Familiarity with Python or data analysis will be an added advantage