TECHNICAL REPORT ON PBL
Course: 20BT2015 – Bioprocess Principles
Through
Project:
AI-Integrated Smart Bioreactor with Cloud and Digital Twin Features
a report submitted by
MOHAMMED SUHAIL A (URK23BT3008)
in partial fulfillment for the award of the degree
of
B.Tech. BIOTECHNOLOGY
Under the Faculty In charge
Dr. Satya Sundar Mohanty
DIVISION OF BIOTECHNOLOGY
KARUNYA INSTITUTE OF TECHNOLOGY AND SCIENCES
(Deemed-to-be-University)
Karunya Nagar, Coimbatore – 641 114, INDIA
January 2025
Introduction
Bioreactors are indispensable tools in biotechnology, enabling the controlled cultivation of
microorganisms or cells for the production of bio-based products such as biofuels, pharmaceuticals,
and industrial enzymes. Effective operation of a bioreactor requires real-time monitoring and
control of critical parameters like temperature, pH, dissolved oxygen, and nutrient levels (Shuler &
Kargi, 2017). Traditionally, bioreactors rely on on-site manual supervision or basic preprogrammed control systems, which can be inefficient and resource-intensive.
The integration of emerging technologies like Artificial Intelligence (AI), Internet of Things (IoT),
and cloud computing offers a revolutionary approach to bioreactor management. Cloud-connected
bioreactors enable remote monitoring and control, fostering collaboration among teams across
different locations. Additionally, digital twins—virtual replicas of physical systems—offer a
unique opportunity to simulate and optimize bioprocess conditions before real-world
implementation (Rao et al., 2023).
This project aims to develop an AI-integrated, cloud-enabled bioreactor with digital twin
capabilities. The system will feature adaptive learning algorithms that optimize control parameters
based on historical data. Students will have the ability to remotely monitor and control the
bioreactor from anywhere, such as their hostel rooms, using real-time cloud updates.
Problem Statement
Current bioreactor systems are limited by their dependence on on-site supervision, lack of adaptive
learning, and restricted data accessibility. These limitations hinder collaborative research,
especially in academic settings, where budget constraints often prevent the deployment of
advanced systems.
Key challenges include:
1. How can we create a bioreactor system that allows students to remotely monitor and
control fermentation processes, enabling seamless operation from hostel rooms or other
locations?
2. How can cloud integration be leveraged to store fermentation data for collaborative
research and real-time analysis across teams globally?
3. Can a digital twin of the bioreactor be developed to virtually simulate fermentation
conditions, reducing the risk of real-world experimentation failures?
4. How can adaptive learning algorithms enhance bioprocess control by continuously
optimizing parameters based on historical and real-time data?
By addressing these challenges, the proposed project aims to democratize access to advanced
bioprocessing technologies, fostering innovation and collaboration in academic and small-scale
industrial settings.
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Figure 1: Preliminary Idea brainstorming
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Proposed Methodology
1. Bioreactor Design:
o Assemble a small-scale bioreactor (2–3 L capacity) with sensors for temperature,
pH, dissolved oxygen, and turbidity.
o Include actuators (heating pad, cooling fan, air pump) for auto-regulation of
environmental conditions.
2. IoT Integration:
o Use a microcontroller (e.g., Raspberry Pi or Arduino) for interfacing sensors and
actuators.
o Install a Wi-Fi module (e.g., ESP8266) for cloud connectivity.
3. Cloud Integration:
o Develop a cloud-based dashboard (e.g., Google Firebase or AWS IoT Core) to log
and visualize fermentation data.
o Enable real-time notifications for critical parameter deviations (e.g., temperature out
of range).
4. Digital Twin Development:
o Create a virtual model of the bioreactor using simulation software (e.g., MATLAB,
Simulink, or Python-based frameworks).
o Use the digital twin to predict fermentation outcomes by varying parameters
virtually before real-world implementation.
5. Adaptive Learning Implementation:
o Train an AI model using data from previous fermentation experiments to predict
optimal parameter settings.
o Deploy the model on the microcontroller to adjust bioreactor conditions
dynamically.
6. Remote Accessibility:
o Build a user-friendly mobile or web-based application to allow students to control
the bioreactor remotely and receive real-time updates.
Expected Outcomes
1. A functional, AI-integrated bioreactor with remote control capabilities accessible via
cloud-based platforms.
2. A digital twin that facilitates virtual experimentation and reduces the risks associated with
real-world trials.
3. Adaptive learning algorithms that continuously improve bioreactor performance.
4. Enhanced accessibility for students to operate the bioreactor from any location, promoting
convenience and collaborative learning.
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References
Rao, S., Prasad, V., & Deshmukh, M. (2023). Digital twins in bioprocessing: Applications and
challenges. Journal of Bioprocess Technology, 12(3), 45-56.
https://doi.org/10.1016/j.jbt.2023.03.001
Shuler, M. L., & Kargi, F. (2017). Bioprocess engineering: Basic concepts (3rd ed.). Pearson
Education.
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