PDF Publication Title:
Text from PDF Page: 015
Processes 2022, 10, 812 15 of 19 Processes 2022, 10, x FOR PEER REVIEW 15 of 19 Figure 6. Simulation implementation within the Aspen Plus/Simulink environment of (a) the opti- Figure 6. Simulation implementation within the Aspen Plus/Simulink environment of (a) the optimal mal MPC and (b) fuzzy PD + I to control the adsorption plant cited from Jesse [81]. UL and YL are MPC and (b) fuzzy PD + I to control the adsorption plant cited from Jesse [81]. UL and YL are the the input–output control signals, whereas Ut and Yt are the input–output signals of PSA process. input–output control signals, whereas Ut and Yt are the input–output signals of PSA process. VP, VP, VF and VW are product valve, feed valve and waste valve, respectively. Readapted with per- VF and VW are product valve, feed valve and waste valve, respectively. Readapted with permission mission from Ref. [81]. Copyright 2020 Elsevier. from Ref. [81]. Copyright 2020 Elsevier. 4. Conclusions and Future Work 4. Conclusions and Future Work In this article, we reviewed the numerical research on PSA-process modelling, opti- In this article, we reviewed the numerical research on PSA-process modelling, op- mization and control. A complete PSA-process model consists of a series of PDAEs, in- timization and control. A complete PSA-process model consists of a series of PDAEs, cluding the adsorption bed, auxiliary devices, boundary conditions and performance in- including the adsorption bed, auxiliary devices, boundary conditions and performance dicators. The optimization of PSA is based on the process model and optimization algo- indicators. The optimization of PSA is based on the process model and optimization algo- rithm. The detailed model and surrogate model are two types of special treatment for the rithm. The detailed model and surrogate model are two types of special treatment for the process model. The former solves the process model by discretizing in space or both in process model. The former solves the process model by discretizing in space or both in time time and space simultaneously, while the latter is committed to establishing an input– and space simultaneously, while the latter is committed to establishing an input–output output relationship that is as accurate and simple as possible from data obtained from relationship that is as accurate and simple as possible from data obtained from simulations simulations or experiments instead of solving the equations. As a surrogate model, ANN or experiments instead of solving the equations. As a surrogate model, ANN has excellent has excellent prediction and fitting capabilities, which can provide a large number of sam- prediction and fitting capabilities, which can provide a large number of samples without ples without additional computing-power consumption for the artificial-intelligence- additional computing-power consumption for the artificial-intelligence-based optimization based optimization algorithm. Process control is necessary when facing external disturb- algorithm. Process control is necessary when facing external disturbances or parameter ances or parameter fluctuations. The main reason for the MPC control being superior to fluctuations. The main reason for the MPC control being superior to PID control is owing PID control is owing to its prediction ability. to its prediction ability. With the improvement in computer computing performance, more issues deserve to With the improvement in computer computing performance, more issues deserve to be noticed. First, the overlapping and cooperative efforts of adsorbent research and pro- be noticed. First, the overlapping and cooperative efforts of adsorbent research and process cess development should be achieved. On the one hand, the ideal adsorbent used for tar- development should be achieved. On the one hand, the ideal adsorbent used for target-gas get-gas adsorption should have a large working capacity, fast adsorption/desorption ki- adsorption should have a large working capacity, fast adsorption/desorption kinetics, netics, low adsorption heat, and high selectivity. Moreover, adsorbent materials are re- low adsorption heat, and high selectivity. Moreover, adsorbent materials are required qtuoirbeeditnoebxepeinesxivpe,npsirvaec,tipcraalclyticsahlalypsehda, pheadv,ehastvaebsletatbhlertmhearlmaanldanmdemcheacnhiacnailcpalropproeprteire-s, tiaensd, abnedrebceyrcelcaybclelaibnlethine itnhdeuinstdruiasltrpiarol cpersosc.eTssh.uTsh, uasc,oamcpomrephreenhseivnesiavdesaodrbsoernbtednattdabataas-e bcaosnetcaoinitnaginwinegllw-oerlgl-aonrigzaendi,zedas,ielyasailcyceascsciebslseibdlaetdaaatbaoaubtoauvtaivlabillaebaledasodrsboernbtesntcsancabneboef ogfrgeraetavtavlualeufeorforresreasercahrcehrserasnadnednegningeinereserasnadnsdersveervaesasuaseufsuelfbualsbiassfiosrffourrftuhretrhaedrsaodrsboern-t bdeenvtedleovpemloepnmt enfftoerftfso.rtOs.nOtnhethoetohtehrehrahnadn,d,ththeepprroocceessseess’’engineeringinvolvesccoolulumnn geometry, adsorbent-loading ratio, and time-step settings, which usually have a very deepPDF Image | Numerical Research on the Pressure Swing Adsorption Process
PDF Search Title:
Numerical Research on the Pressure Swing Adsorption ProcessOriginal File Name Searched:
processes-10-00812-v2.pdfDIY PDF Search: Google It | Yahoo | Bing
CO2 Organic Rankine Cycle Experimenter Platform The supercritical CO2 phase change system is both a heat pump and organic rankine cycle which can be used for those purposes and as a supercritical extractor for advanced subcritical and supercritical extraction technology. Uses include producing nanoparticles, precious metal CO2 extraction, lithium battery recycling, and other applications... More Info
Heat Pumps CO2 ORC Heat Pump System Platform More Info
CONTACT TEL: 608-238-6001 Email: greg@infinityturbine.com (Standard Web Page)