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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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 is needed for machine learning and autonomous control applications. Instead, we settle with simulations where…
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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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The Nature of Industrial Data
The devil is in the data. Industrial data is known to be one of the most complex data forms. We experienced it first-hand building a Near Real-Time…
Continue ReadingProcess Optimization using AI/ML
Techniques like PID control or MPC can track single or multiple setpoints effectively. However, it remains a great challenge to adaptively find the optimal setpoints,…
Continue ReadingClosed-loop AI Key to Transformational Impact
AI systems are becoming available that automate many of the machine learning tasks that could previously only be performed by experienced data scientists. Using these OT-focused AI platforms, engineers and manufacturing subject matter experts can widely apply AI to develop these next generation value control systems. AI applications built by these subject matter experts, just like any other APC will make adoption of AI and the related benefits widespread and mainstream. Highly interpretable and explainable AI is also becoming available to increase the trust and confidence in closed loop AI.
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