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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The future of manufacturing
This is what the future looks like in 2030: “AI centric control systems run manufacturing plants by optimizing high-level objectives and following constraints and other…
Continue ReadingBridging 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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AI-driven Automation: a Stepping Stone to Autonomous Process Control
Our research mission is to bring the best intelligent autonomy to manufacturing. Undoubtably, AI driven industrial control is a big part of it. At NeurIPS…
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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), Design of Experiments (DoE) is usually conducted before scaling up to production runs. We believe that Bayesian Optimization and its variants could significantly improve…
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Open sourcing A better Penicillin Bioreactor Simulation
For industries like Life Sciences, it is challenging to collect a large amount of data with high quality that are needed for machine learning and autonomous control applications. Instead, we settle with simulations where initial…
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