Decoding AI for Batch Process Optimization

Batch processing is one of the most common manufacturing approaches amongst many industries such as Biopharmaceuticals, Pharmaceuticals, and Specialty Chemicals. A batch reactor is the heart of any batch processing plant, and it is important to have more visibility and control of the operating parameters of a batch process in order to achieve higher yield, better product quality, reduction in batch variabilities, cycle time reduction, and faster time-to-market.

In this upcoming webinar, we are going to delve into the AI strategy for achieving the above objectives for batch process optimization. We are going to take examples of one of the most complex batch reactors in the industry i.e. Bioreactor. Our objective is to provide the understanding of the AI-based approach to optimize a batch reactor which has complex parameters, processes and control challenges.

𝐋𝐚𝐲𝐞𝐫𝐬 𝐨𝐟 𝐀𝐈 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐰𝐞 𝐚𝐫𝐞 𝐠𝐨𝐢𝐧𝐠 𝐭𝐨 𝐬𝐡𝐚𝐫𝐞:

• Identifying the problem
• Defining the objective
• Type of data for analytics
• Data contextualization
• 1st principle modeling
• Hybrid modeling framework
• Data driven control strategy
  
  

𝐖𝐡𝐨 𝐬𝐡𝐨𝐮𝐥𝐝 𝐚𝐭𝐭𝐞𝐧𝐝?

This webinar is for all the learning enthusiasts. However, if you fall into any of the following categories, you should strongly consider attending:

• MSAT Engineer / Lead
• Manufacturing / Production / Site Head
• Process Data Analysts
• Digital Transformation Lead / Head
• Biologics / Biosimilar Production Head
• Cell and Gene Therapies Production Lead
• Process Engineer