International Conference on Biotechnology in Sustainable Chemical Engineering - (ICBSCE-26)


19th - 20th December, 2026 | Kathmandu, Nepal

Multi-format (In-person/Virtual)

Registration Options

Explore conference registration categories designed for every mode of participation.

Important Dates

Pre-registration Deadline

19th November, 2026

Paper Submission Deadline

24th November, 2026

Last Date Of Registration

4th December, 2026

Date Of Conference

19th - 20th December, 2026

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Conference Session Tracks

SDG Wheel

Aligned with

UN Sustainable Development Goals

This conference contributes to global sustainability by aligning its research discussions and academic sessions with key United Nations Sustainable Development Goals. It fosters knowledge exchange, innovation, and collaborative engagement.

SDG 7 SDG 7 — Affordable and Clean Energy
SDG 9 SDG 9 — Industry, Innovation and Infrastructure
SDG 12 SDG 12 — Responsible Consumption and Production
SDG 13 SDG 13 — Climate Action
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All Session Tracks

Track 01
Advancements in Predictive Modeling for Bioprocess Optimization

This track focuses on the latest methodologies in predictive modeling that enhance bioprocess optimization in sustainable chemical engineering. Participants will explore case studies demonstrating the application of these models in real-world scenarios.

Track 02
Machine Learning Techniques in Biotechnology Applications

This session will delve into the application of supervised and unsupervised learning techniques in biotechnology. Emphasis will be placed on novel algorithms that improve the efficiency of bioprocesses and resource allocation.

Track 03
Deep Learning Innovations in Chemical Engineering

This track will highlight the transformative impact of deep learning on chemical engineering processes. Presentations will cover advancements in model architectures and their implications for bioprocess modeling and simulation.

Track 04
Anomaly Detection in Bioprocess Systems

This session will address the critical role of anomaly detection in maintaining the integrity of bioprocess systems. Experts will present techniques that leverage data analytics to identify and mitigate potential failures.

Track 05
Feature Extraction Techniques for Enhanced Bioprocess Monitoring

This track will explore innovative feature extraction methods that facilitate improved monitoring of bioprocesses. Discussions will include the integration of these techniques with industrial IoT systems for real-time analytics.

Track 06
Workflow Automation in Sustainable Chemical Engineering

This session will focus on the implementation of workflow automation technologies in the field of sustainable chemical engineering. Attendees will learn about tools and frameworks that streamline bioprocess operations and enhance productivity.

Track 07
Digital Twin Technologies in Biotechnology

This track will examine the role of digital twin technologies in simulating and optimizing bioprocesses. Presentations will highlight case studies that demonstrate the effectiveness of digital twins in predictive maintenance and system monitoring.

Track 08
Green Chemistry Approaches in Bioprocess Engineering

This session will discuss the integration of green chemistry principles in bioprocess engineering. Participants will explore strategies for reducing environmental impact while maintaining efficiency in chemical production.

Track 09
Analytics for Energy-Efficient Bioprocesses

This track will focus on the application of advanced analytics to achieve energy efficiency in bioprocesses. Experts will present methodologies that optimize energy consumption while ensuring process sustainability.

Track 10
Resource Allocation Strategies in Biotechnology

This session will explore innovative resource allocation strategies that enhance the sustainability of biotechnological processes. Discussions will include optimization techniques that balance economic and environmental considerations.

Track 11
Simulation Techniques for Bioprocess Modeling

This track will delve into the latest simulation techniques used for bioprocess modeling. Participants will gain insights into how these techniques can improve the accuracy and reliability of bioprocess predictions.