Velkommen PROST-seminar:
Foredragsholder: Dr. Ernesto Martinez
Tittel: Batch Process Automation using a Re-inforcement Learning/Fuzzy Logic
Based Approach
Abstract: Se nedenfor. Se ogsaa:
http://www.kjemi.unit.no:80/prost/seminars.html
Sted: B343 - lunchrommet (NB! Lunchrommet ved ITK.)
Tid: Onsdag 5/2 kl. 13.00. Foredraget starter kl. 13.30.
Hilsen,
Frode Martinsen
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Abstract
The degree of automation of batch process units is still very low in many
industries. As a result, the key to success in achieving products of high and
reproducible quality depends to a great extent on experienced human operators.
In the current economic climate characterised by intense competition and a
shortening of product cycle life, such a dependence is unsatisfactory and
worrisome.
The talk will adress the issues associated with this state of affairs and
points out some qualities of a human being that could help finding a new avenue
to the problem. Mainly, the capability of learning by interaction with a system
or a simulator of it seems to be the key to achieve a breakthrough in batch
process automation. Learning by interaction has been formalised recently in a
framework known as Re-inforcement Learning. So the application of this new
framework to increase the autonomous operation of a batch unit will be
discussed. To exemplify the ideas, I will use a semi-batch reactor where an
autocatalytic reaction takes place and a process where reaction and
distillation have been integrated.
Re-inforcement Learning (RL) is a broad class of optimal control methods aimed
to learn sequences of actions (policies) from experience, simulation or direct
interaction with the system to be controlled. Designing a RL controller
revolves around the problem faced by an agent that must learn to carry out
tasks through trial-and-error interactions with the system in which is
embedded; reward/punishment is provided for each control action taken by the
controller (agent) at each process state. The core elements for quick learning
using a global control strategy are the search mechanism and the memory
mechanism of the RL learning controller. In the talk the use of fuzzy logic for
implementing both search and memory schemes will be proposed. Results obtained
with the new RL algorithm will be presented.