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
To 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
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)
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
A 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
A 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
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
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
The nature of Industrial data
The devil is in the data. Industrial data is known to be one of the complex data forms. We experienced it first-hand