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Maria has both a master\\u2019s and bachelor\\u2019s degree in chemical engineering. For some time she was responsible of doing computer process simulations , but she also helped on the implementation of data analytics code to optimize production in chemical plants, while learning about the subject and doing some programming herself. Currently, she is focusing on that area sharpening her skills in data science and wants to also understand the relationship with process automation and control and the technical details to a successful deployment.

Episode Notes

Music used in the podcast: Higher Up, Silverman Sound Studio

Acronyms, Definitions, and Fact Check

AI (Artificial Intelligence)\\u2013 This term is often used to describe machines (or
computers) that mimic \\u201ccognitive\\u201d functions that humans associate with the human
mind, such as \\u201clearning\\u201d and \\u201cproblem solving\\u201d. Alan Turing is said to be the father of
AI and he invented a test to benchmark whether a machine is intelligent or not called
\\u201cThe imitation game\\u201d. (Wikipedia)

Machine Learning - Is the science of getting computers to act without being explicitly
programmed, by accessing data and use it to learn for themselves. In contrast to
software development, no explicit rules are assigned, but rather the algorithm is able
to learn from multiple examples by finding correlations, just like a baby would learn to
walk by observing the world. When a person writes equations or software they are
able to communicate the information that was processed using symbols, while
machine learning is trying to program the process of learning, without communicating
it with symbols. (Stanford with additionsfrom Max Tegmark)

Data Analytics - Process of inspecting, cleansing, transforming, and modelling data
with the goal of discovering useful information, informing conclusions, and supporting
decision-making. The most important step is data preparation before even trying to fit
it to a model and this is the most difficult and time-consuming task in data science. In
manufacturing, it is important to understand the dynamic behaviour of data (How the
time series data of different variables relate to each other), and the context for any
missing data.\\xa0 (Wikipedia)

Neural Network - Neural nets are a means of doing machine learning, in which a
computer learns to perform some task by analysing training examples. Inspired
loosely on the human brain, a neural net consists of thousands or even millions of
simple processing nodes that are densely interconnected and \\u201ccorrelate\\u201d inputs into
outputs\\u201d. Neural nets are good at fitting data with high signal to noise ratio and can
be used with manufacturing time series data for optimization or prediction.
(https://news.mit.edu/2017/explained-neural-networks-deep-learning-0414 with
remarks from Maria).

Process Control - Process control is the ability to\\xa0 monitor and adjust a process to
give a desired output. It is used in industry to maintain quality and improve
performance. Understanding traditional control systems is key to implementing
analytics models in the plant on-line, alongside the different communication protocols. (https://www.haroldbeck.com/process-
control)

Process Simulations - Process simulation is a model-based representation
of chemical, physical, biological, and other technical processes and unit
operations in software. Basic prerequisites for the model are chemical and physical
properties of pure components and mixtures, of reactions, and of mathematical
models which, in combination, allow the calculation of process properties by the
software. It corresponds to scientific software development, since rules are explicitly
programmed.\\xa0 (Wikipedia with remarks from Maria)