Industrial Automation

by Pedro Perdomo A Virtual Sensor Implementation using digital twin models . You need data to train a virtual sensor. This was the main problem to surpass in this project.  So , how do you pretend training  a neural sensor without data collected? We started this project with no data in advanced. Starting to collect data and wait for extreme conditions to occur, ie. operating the column and the plant  over limit conditions was not a good idea for operators. We needed to collect data, good amount of data, but with variety.  We tried to produce this by operating at limits , but it is not always possible. In each condition you have to wait for stabilization and in the other hand, you need to collect samples to the lab. These are real issues once you try it out.  We had a digital model  of the column in a popular simulation software.  This digital model was recently certified by comparing with real data in a very good range of operat...

by Pedro Perdomo 20 years ago, we implemented a virtual sensor using neural networks with no more than a couple of hidden layers. When I trained my first two-layer neural network to predict the content of methane in the bottom of a  deethanizer  column, I realized that more than one layer did not improve my virtual sensor results. Why no limit today in the internal or hidden layers? Why deep learning?   First, computing power has been increasing, so training neural networks with many layers is now feasible. Second, why so many layers if  not  a  big amount  of data available . And third, if you just add layers, you will run into some issues, the neural networks will take a long time to train, some of the layers will become all zero and worst, no better results.  Researchers have developed “tricks” and techniques to get deep neural networks to work. Neural networks with many hidden layers have enabled dramatic improvements in hard p...