The ‘Golden Batch’ is your ideal batch, the one that came out perfectly, the way you intended: the recipe is followed perfectly, and the process…
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A blog about news, research, and events at Quartic.ai
Bridge the gap between Process Control and Reinforcement Learning with QuarticGym
Modern process control algorithms are the key to the success of industrial automation. The increased efficiency and quality create value that benefits everyone from the…
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AI-driven Automation: a Stepping Stone to Autonomous Process Control
Our research mission is to bring the best intelligent autonomy to manufacturing. Undoubtedly, AI-driven industrial control is a big part of it. At NeurIPS 2021…
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Optimizing Continuous Manufacturing Processes
This is a joint work of Benjamin Decardi-Nelson, Jerry Cheng, and Mohan Zhang. The future of manufacturing is continuous and autonomous. Compared to batch manufacturing,…
Continue ReadingQuartic.ai advances continuous biomanufacturing; open sources technology to foster innovation
Quartic.ai today announced that it has developed a highly compute-efficient digital twin of a penicillin bioreactor that simulates the industrial-grade Penicillium chrysogenum fermentation. The simulation…
Continue ReadingTo achieve Agility in manufacturing – embrace Variability – Part 1
“Supply chain agility can be defined as an organization’s ability to profitably manufacture and deliver a broad range of high-quality products and services with short…
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Optimization with Offline Reinforcement Learning
We showed that when you are early in your digitalization journey where you only have access to manipulated variables (e.g. sugar feed rate) and the outcome (e.g. yield), you…
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Optimizing DoE and Production Runs with Little Data
For many batch processes (e.g. in Life Sciences, Food & Beverage), the Design of Experiments (DoE) is usually conducted before scaling up to production runs. We believe that Bayesian Optimization and its variants could…
Continue ReadingA Taste of Bayesian Optimization – Part 2
In Part 1, we discussed simple Bayesian optimization (BO), and now we further exam BO by taking into account uncontrollable contextual information and constraints. 1….
Continue ReadingA Taste of Bayesian Optimization – Part 1
Bayesian optimization (BO) is a sample-efficient optimization method focused on solving the problem when the objective function f_0 is unknown or expensive to evaluate. At…
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