scholarly journals Model-Free Learning Control of Chemical Processes

10.5772/5287 ◽  
2008 ◽  
Author(s):  
S. Syafiie ◽  
F. Tadeo ◽  
E. Martinez
Keyword(s):  
Learning Control ◽  
Chemical Processes ◽  
Model Free

Model-Free Learning Control of a Nonlinear CSTR system

2021 ◽  
Author(s):  
Leighton Estrada-Rayme ◽  
Paul Cardenas-Lizana
Keyword(s):  
Learning Control ◽  
Model Free ◽  
Cstr System

scholarly journals Closed-loop separation control using machine learning

2015 ◽  
Vol 770 ◽  
pp. 442-457 ◽  
Author(s):  
N. Gautier ◽  
J.-L. Aider ◽  
T. Duriez ◽  
B. R. Noack ◽  
M. Segond ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feedback Control ◽  
Genetic Programming ◽  
Closed Loop ◽  
Recirculation Zone ◽  
Low Frequency ◽  
Learning Control ◽  
Separation Control ◽  
Periodic Forcing ◽  
Model Free

We present the first closed-loop separation control experiment using a novel, model-free strategy based on genetic programming, which we call ‘machine learning control’. The goal is to reduce the recirculation zone of backward-facing step flow at $\mathit{Re}_{h}=1350$ manipulated by a slotted jet and optically sensed by online particle image velocimetry. The feedback control law is optimized with respect to a cost functional based on the recirculation area and a penalization of the actuation. This optimization is performed employing genetic programming. After 12 generations comprised of 500 individuals, the algorithm converges to a feedback law which reduces the recirculation zone by 80 %. This machine learning control is benchmarked against the best periodic forcing which excites Kelvin–Helmholtz vortices. The machine learning control yields a new actuation mechanism resonating with the low-frequency flapping mode instability. This feedback control performs similarly to periodic forcing at the design condition but outperforms periodic forcing when the Reynolds number is varied by a factor two. The current study indicates that machine learning control can effectively explore and optimize new feedback actuation mechanisms in numerous experimental applications.

scholarly journals Pyrolysis and Combustion Kinetics of Disposable Surgical Face Mask Produced During Covid-19 Pandemic

2021 ◽  
Author(s):  
Arshad Adam Salema ◽  
Yasmin Mohd Zaifullizan ◽  
Wong Wai Hong
Keyword(s):  
Weight Loss ◽  
Face Mask ◽  
Chemical Processes ◽  
X Rays ◽  
Combustion Kinetics ◽  
Heating Rates ◽  
Safe Disposal ◽  
Model Free ◽  
The Face ◽  
Kinetics Of

Abstract In order to prevent the spread of Covid 19, most countries have made face masks compulsory. Millions of face mask are disposed of daily in the community. Therefore, the aim of the present paper is to study the thermo-chemical (pyrolysis and combustion) behavior of the face mask for its safe disposal. The kinetic parameter activation energy was calculated using both the model-based Coats–Redfern method and model-free methods (Flynn-Wall-Ozawa, Kissinger-Akihara-Sunose, and Starink) at four different heating rates (5, 10, 15, and 20 °C/min). Physical morphology with elemental analysis was performed using field-emission scanning electron microscopy and energy-dispersive X-rays. Results have shown that face masks decompose in the temperature range of 320–480 °C during pyrolysis with a maximum derivative weight loss of 2.5 %/°C. Combustion took place between 200 and 370 °C with a maximum derivative weight loss of 1.25 %/°C. The average activation energies calculated using model-free methods for pyrolysis and combustion were ~135 kJ/mol and ~65 kJ/mol, respectively. The leftover residue for both pyrolysis and combustion was in the range of 1.35 to 3.50 wt.%. In conclusion, thermo-chemical processes are a promising method for the safe disposal of face mask waste.

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