Machine learning and first principle models are two widely discussed approaches for process optimization nowadays. First principle models, which are also referred as dynamic models,…
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Bridging the gap between AI and industrial controls
Our paper titled SMPL: Simulated Industrial Manufacturing and Process Control Learning Environments has recently been accepted by NeurIPS 2022, Datasets and Benchmarks Track. In this…
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Manufacturing needs MVDA: An introduction to modern, scalable multivariate data analysis
Although there are speed efficiencies that can be achieved by successfully digitizing the enterprise, the competitive advantages will be gained by achieving a new level…
Continue ReadingThe Current Challenges of Life Science Manufacturing Require the Adoption of Modern Solutions
In the last 2 – 3 years, Life Science industry supply chain disruptions highlighted the need for high-performance, distributed manufacturing that can accelerate the delivery…
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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,…
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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…
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It’s time to ditch Apache Spark and adopt Dask
This isn’t a comparison between Apache Spark vs. Dask. But if you are interested in that, Dask is humble enough to include that in their…
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