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[P] Anomaly detection and forecasting of permeate breakthrough

In this work we have evaluated various methods to predict when there is permeate breakthrough in a biochemical production process. An autoencoder model seems quite promising, but should be combined with conventional statistic process control metrics to increase its robustness.

Likewise, the Exponential moving average (EMA) and Long short-term memory (LSTM) provide different outcomes. The EMA smooths the time series data and gives the trend over time. This combined with the LSTM enables us to make future predictions on the permeate values in the future.

The entire code for this project can be found in my github repo.

submitted by /u/PriceTT
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.