Because of high efficiency and cleanness, the simulated moving bed (SMB) is the most important technology in chromatographic separation. SMB system, which contains several sectors of flow rate, the switching time of valves and many other possible influencing variables, is complex, highly sensitive and difficult to control. This paper proposes adjusted fuzzy control; the adjusted fuzzy controller is applied to the SMB system to control the separation concentration by establishing a variable direction correction formula. Compared with the traditional fuzzy controller, the adjusted fuzzy controller does not need to analyze the dynamic direction of the control force in advance, moreover, the control accuracy is high and the fluctuation is small for SMB system.
RajendranA., ParedesG. and MazzottiM., Simulated moving bed chromatography for the separation of enantiomers, J Chromatogr A1216(4) (2009), 709–738.
2.
SchulteM. and StrubeJ., Preparative enantioseparation by simulated moving bedchroma-tography, J Chromatogr A906(1–2) (2001), 399–416, chiralseparations.
3.
ChinC.Y. and WangN.L., Simulated moving bed equipment designs, Sep Purif Rev33(2) (2004), 77–155.
4.
GomesP.S., Advances in Simulated Moving Bed: New Operating Modes; New Design Methodologies; and Product (Flex SMB-LSRE) Development, FEUP Porto, Portugal, 2009, (Ph.D. thesis).
5.
SilvaV.M.T., MincevaM. and RodriguesA.E., Novel analytical solution for a simulated moving bed in the presence of mass-transfer resistance, Ind EngChem Res43(16) (2004), 4494–4502.
6.
LeaoC.P. and RodriguesA.E., Transient and steady-state models for simulated moving bed processes: Numerical solutions, Comput Chem Eng28(9) (2004), 1725–1741.
7.
SuvarovP., KienleA., NobreC., WeireldG.D. and WouwerA.V., Cycle to cycle adaptive control of simulated moving bed chromatographic separation processes, J Process Control24(2) (2014), 357–367.
8.
JiabiD., LingL., DechengY., Determination of Adsorption Isotherm of Column of Simulated Moving Bed, Proceedings of the 36th Chinese Control Conference, pp. 2773–2777, 2017.
9.
OhT.H., OhS.-K., LeeJ.M., Modeling and Optimization of Simulated Moving Bed Chromatography with Side Streams”. 6th International Symposium on Advanced Control of Industrial Processes, pp. 624–629, 2017.
10.
KlattK.-U., HanischF. and DünnebierG., Model-based control of a simulated moving bed chromatographic process for the separation of fructose and glucose, Journal of Process Control12(2) (2002), 203–219.
11.
KimK., KimJ.-I., KimH., YangJ., LeeK.S. and KooY.-M., Experimental verification of bilevel optimizing control for SMB technology, Ind Eng Chem Res49(18) (2010), 8593–8600.
12.
SongI.H., LeeS.B., RheeH.K. and MazzottiM., Optimization-based predictive control of a simulated moving bed process using an identified model, Chemical Eng Science61(8) (2006), 6165–6179.
13.
ShenC.-Y., HuangH.-C. and HwangR.-C., High-Sensitive neural network ammonia sensor based on shear horizontal surface acoustic wave devices,”, J of Chemometrics22 (2008), 548–555.
14.
CoelhoL.C.D., NelsonM.L., FariaR.P.V. and “Separationetc., of tartronic and glyceric acids by simulated moving bed chromatography, Journal of Chromatography A1563 (2018), 62–70.
15.
WeiF., ShiL., WangQ. and ZhaoY., Fast and accurate separation of the paclitaxel from yew extracum by a pseudo simulated moving bed with solvent gradient, Journal of Chromatography A1564 (2018), 120–127.
16.
MaruyamaR.T., KarnalP., SainioT. and RajendranA., Design of bypass-simulated moving bed chromatography for reduced purity requirements, Journal of Chemical Engineering Science205 (2019), 401–413.
17.
KlattK.-U., HanischF. and DünnebierG., Model-based control of a simulated moving bed chromatographic process for the separation of fructose and glucose, Journal of Process Control12(2) (2002), 203–219.
18.
KimK., KimJ.-I., KimH., YangJ., LeeW.S. and KooY.-M., Experimental verification of bilevel optimizing control for SMB technology, Ind Eng Chem Res49(18) (2010), 8593–8600.
19.
SongI.H., LeeS.B., RheeH.K. and MazzottiM., Optimization-based predictive control of a simulated moving bed process using an identified model, Chemical Eng Science61(8) (2006), [45] 6165–6179.
20.
Andrade NetoA.S., SecchiA.R., SouzaM.B.Jr. and BarretoA.G.Jr., Nonlinear model predictive control applied to the separation of praziquantel in simulated moving bed chromatography, J Chromatogr A1470 (2016), 42–49.
21.
NogueiraI.B.R., RibeiroA.M., MartinsM.A.F., RodriguesA.E., KoivistoH. and LoureiroJ.M., Dynamics of a True Moving Bed separation process: Linear model identification and advanced process control, J Chromatogr A1504 (2017), 112–123.
22.
MunS. and WangN.H.L., Improvement of the performances of a tandem simulated moving bed chromatography by controlling the yield level of a key product of the first simulated moving bed unit,”, Journal of Chromatography A1488 (2017), 104–112.
23.
LeeJ.W. and Seidel-MorgensternA., Model Predictive Control of Simulated Moving Bed Chromatography for Binary and Pseudo-binary Separations: Simulation Study, IFAC-PapersOnLine51(18) (2018), 530–535.
24.
YanZ., WangJ.-S., WangS.-Y., LiS.-J., WangD. and SunW.-Z., Model predictive control method of simulated moving bed chromatographic separation process based on sub space system identification, Mathematical Problems in Engineering2019(7) (2019), 1–24.
25.
WuT.-Y., ChenC.-M., WangK.-H., MengC. and WangE.K., A provably secure certificate less public key encryption with keyword search, Journal of the Chinese Institute of Engineers42(1) (2019), 20–28.
26.
ChenC.-M., WangK.-H., YehK.-H., XiangB. and WuT.-Y., Attacks and solutions on a three-party password-based authenticated key exchange protocol for wireless communications, Journal of Ambient Intelligence and Humanized Computing10(8) (2019), 3133–3142.
27.
ChenC.-M., XiangB., LiuY. and WangK.-H., A secure authentication protocol for internet of vehicles, IEEE Access7(1) (2019), 12047–12057.
28.
WuJ.M.-T., LinJ.C.-W., TamrakarA., High-Utility Itemset Mining with Effective Pruning Strategies, ACM Transactions on Knowledge Discovery from Data, 2019. https://doi.org/10.1145/3363571
29.
PanJ.-S., KongL., SungT.-W., TsaiP.-W. and SnaselV., α-Fraction First Strategy for Hirarchical Wireless Sensor Neteorks, Journal of Internet Technology19(6) (2018), 1717–1726.
30.
PanJ.-S., LeeC.-Y., SghaierA., ZeghidM. and XieJ., Novel systolization of subquadratic space complexity multipliers based on toeplitz matrix–vector product approach, IEEE Transactions on Very Large Scale Integration Systems27(7) (2019), 1614–1622.
31.
Balas ValentinaE., LangH.J., JasonG. and Tsung-ChihL., Special issue on fuzzy theoretical model analysis for signal processing, Journal of Intelligent & Fuzzy Systems37(4) (2019), 4407–4411.
32.
PengW., WeiL. and WenlongS., Fast, accurate normalized cross-correlation image matching, Journal of Intelligent & Fuzzy Systems37(4) (2019), 4431–4436.
33.
LiangM.-T., LiangR.-C., HuangL.-R., LiangK.-Y., ChienY.-L. and LiaoJ.-Y., Supercritical fluids as the desorbent for simulated moving bed – Application to the concentration of triterpenoids from Taiwanofugus camphorate, Journal of the Taiwan Institute of Chemical Engineers45 (2014), 1225–1232.
34.
LiangM.-T., LiangR.-C., HuangL.-R., HsuP.-H., WuY-H. and YenH.-E., Separation of sesamin and sesamolin by a supercritical fluid-simulated moving bed, American Journal of Analytical Chemistry3 (2012), 931–938.
35.
LiangM.-T., LiangR.-C., WangL.-Y., YenH.-E. and LeeK.-T., Effect of temperaturevariation on the separation of sesamin and sesamolin by simulated moving bed, Journal of Chemistry and Chemical Engineering5 (2011), 479–486.
