2010
MODELING PROCESS DYNAMICS USING A NOVEL NEURAL NETWORK ARCHITECTURE: APPLICATION TO STIRRED CELL MICROFILTRATION
MHURCHU, Jenny Ni, Greg FOLEY a Josef HAVELZákladní údaje
Originální název
MODELING PROCESS DYNAMICS USING A NOVEL NEURAL NETWORK ARCHITECTURE: APPLICATION TO STIRRED CELL MICROFILTRATION
Autoři
MHURCHU, Jenny Ni, Greg FOLEY (garant) a Josef HAVEL (203 Česká republika, domácí)
Vydání
CHEMICAL ENGINEERING COMMUNICATIONS, TAYLOR & FRANCIS INC, 2010, 0098-6445
Další údaje
Jazyk
angličtina
Typ výsledku
Článek v odborném periodiku
Obor
10400 1.4 Chemical sciences
Stát vydavatele
Spojené státy
Utajení
není předmětem státního či obchodního tajemství
Odkazy
Impakt faktor
Impact factor: 0.913
Organizační jednotka
Přírodovědecká fakulta
UT WoS
000277461700008
Klíčová slova anglicky
Artificial neural network; Dynamic modeling; Flux; Fouling; Stirred cell microfiltration
Štítky
Příznaky
Mezinárodní význam, Recenzováno
Změněno: 20. 8. 2020 11:18, Mgr. Marie Šípková, DiS.
Anotace
V originále
A novel neural network architecture is presented for dynamic process modeling, using stirred cell microfiltration of bentonite suspensions as a model system. Unlike previous studies that include time explicitly as a network input and have a single output at that time, the network architecture presented contains the process variables as inputs and many outputs representing the output (filtrate flux in this case) at different selected times. The network is shown to represent the stirred cell microfiltration of bentonite suspensions over a range of pressures (0.2-1.5bar), initial concentrations (0.5-2.0g/L), stirrer tip speeds (0.04-0.17m/s), membrane resistances (3.09x1010-6.85x1010m-1), pH values (2.5-10.4), and temperatures (20 degrees-24 degrees C) with good accuracy (R2=0.91 on network test data). With this network architecture, it becomes easy to track the time dependence of the relative effect of the various process parameters on the system output. Thus, for example, the network weights show that the effect of stirring rate on flux increases as time progresses, while the opposite effect is seen for membrane resistance, as expected.