Run-to-run control of membrane filtration processes

AIChE Journal

Volumn 53, Issue 9, 2007, Pages 2316-2328

  Busch, Jan ^a   Cruse, Andreas ^a,b   Marquardt, Wolfgang ^a  

^a RWTH AACHEN UNIVERSITY   (Germany)

^b UHDE GMBH   (Germany)

Author keywords

Mathematical modeling; Membrane separations; Optimization; Process control; Solid liquid separations

Indexed keywords

COMPUTER SIMULATION; ECONOMIC ANALYSIS; FILTRATION; MATHEMATICAL MODELS; OPTIMIZATION; PROCESS CONTROL;

MEMBRANE SEPARATIONS; PREDICTION QUALITY;

WASTEWATER TREATMENT;

COMPUTER SIMULATION; ECONOMIC ANALYSIS; FILTRATION; MATHEMATICAL MODELS; OPTIMIZATION; PROCESS CONTROL; WASTEWATER TREATMENT;

EID: 34548572009     PISSN: 00011541     EISSN: 15475905     Source Type: Journal    
DOI: 10.1002/aic.11221     Document Type: Article

References (43)

1. Choi H, Zhang K, Dionysiou DD, Oerther DB, Sorial GA. Influence of cross-flow velocity on membrane performance during filtration of biological suspension. J Membrane Sci. 2005;248:189-199.
2. Chen JP, Kim SL, Ting YP. Optimization of membrane physical and chemical cleaning by a statistically designed approach. J Membrane Sci. 2003;219:27-45.
3. Jiang T, Kennedy MD, Guinzbourg BF, Vanrolleghem PA, Schippers JC. Optimising the operation of a MBR pilot plant by quantitative analysis of the membrane fouling mechanism. Water Sci Technol. 2005;51:19-25.
4. Busch J, Cruse A, Marquardt W. Modeling submerged hollow-fiber membrane filtration for wastewater treatment. J Membrane Sci. 2006;288:94-111.
5. Hwang K-J, Yu Y-H, Lu W-M. Cross-flow microfiltration of submicron microbial suspension. J Membrane Sci. 2001;194:229-243.
6. Smith PJ, Vigneswaran S, Ngo HN, Ben-Anim R, Nguyen H. A new approach to backwash initiation in membrane systems. J Membrane Sci. 2006;278:381-389.
7. Blanken B, Betlem BHL, Roffel B. Dynamic optimization of a dead-end filtration trajectory: Blocking filtration laws. J Membrane Sci. 2006;285:90-95.
8. Broeckmann A, Busch J, Wintgens T, Marquardt W. Modeling of pore blocking and cake layer formation in membrane filtration for wastewater treatment. Desalination. 2006;189:97-109.
9. Polyakov YS. Deadend outside-in hollow-fiber membrane filter: Mathematical model. J Membrane Sci. 2006;279:615-624.
10. Gehlert G, Abdulkadir M, Fuhrmann J, Hapke J. Dynamic modeling of an ultrafiltration module for use in a membrane bioreactor. J Membrane Sci. 2005;248:63-71.
11. Marriott J, Sørensen E. A general approach to modelling membrane modules. Chem Eng Sci. 2003;58:4975-4990.
12. Marriott J, Sørensen E. The optimal design of membrane systems. Chem Eng Sci. 2003;58:4991-5004.
13. Mangold M, Ginkel M, Gilles ED. A model library for membrane reactors implemented in the process modelling tool PROMOT. Comput Chem Eng. 2004;28:319-332.
14. Hermia J. Constant pressure blocking filtration laws - Application to power- law non-newtonian fluids. T I Chem Eng-Lon. 1982;60:183-187.
15. Chellam S. Artificial neural network for transient crossflow microfiltration of polydispersed suspensions. J Membrane Sci. 2005;258:35-42.
16. Katsikaris K, Boukouvalas C, Magoulas K. Simulation of ultrafiltration process and application to pilot tests. Desalination. 2005;171:1-11.
17. Geissler S, Wintgens T, Melin T, Vossenkaul K, Kullmann C. Modelling approaches for filtration processes with novel submerged capillary modules in membrane bioreactors for wastewater treatment. Desalination. 2005;178:125-134.
18. Castillo E, Hurwitz AM. Run-to-run process control: Literature review and extensions. J Qual. Technol. 1997;29:184-196.
19. Castillo E, Yeh J-Y. An adaptive run-to-run optimizing controller for linear and nonlinear semiconductor processes. IEEE T Semiconduct, M. 1998;11:285-295.
20. Patel NS, Jenkins ST. Adaptive optimization of run-to-run controllers: The EWMA example. IEEE T Semiconduct, M. 2000;13:97-107.
21. Srinivasan B, Bonvin D. Interplay between identification and optimization in run-to-run optimization schemes. In: Proceedings of the American Control Conference (Anchorage); 2002:2174-2179.
22. Zhang C, Deng H, Baras JS. Run-to-run control methods based on the DHOBE alogrithm. Automatica. 2003;39:35-45.
23. Gao W, Engell S. Iterative set-point optimization of batch chromatography. In: ESCAPE. 2004;14:661-667.
24. Bonné D, Jørgensen SB. Batch to batch improving control of yeast fermentation. In: ESCAPE. 2001;11:621-626.
25. Xiong Z, Zhang J. A batch-to-batch iterative optimal control strategy based on recurrent neural network models. J Process Contr. 2005;15:11-21.
26. Srinivasan B, Primus CJ, Bonvin D, Ricker NL. Run-to-run optimization via control of generalized constraints. Control Eng Pract. 2001;9:911-919.
27. François G, Srinivasan B, Bonvin D, Barajas JH, Hunkeler D. Runto-run adaptation of a semiadiabatic policy for the optimization of an industrial batch polymerization process. Ind Eng Chem Res. 2004;43:7238-7242.
28. Vassiliadis VS, Sargent RWH, Pantelides CC. Solution of a class of multistage dynamic optimization problems. 2. Problems with path constraints, lnd Eng Chem Res. 1994;33:2123-2133.
29. Oldenburg J, Marquardt W, Heinz D, Leineweber DB. Mixed-logic dynamic optimization applied to batch distillation process design. AIChE J. 2003;49:2900-2917.
30. Bard Y. Nonlinear Parameter Estimation. New York and London: Academic Press; 1974.
31. Rao CV, Rawlings JB, Mayne DQ. Constrained state estimation for nonlinear discrete-time systems: stability and moving horizon approximations. IEEE T Automat. Contr. 2003;48:246-258.
32. Ljung L. System Identification. Englewood Cliffs: Prentice Hall; 1987.
33. Wittenmark B. Adaptive dual control methods: An overview. In: 5th IFAC Symposium on Adaptive Systems in Control and Signal Processing; 1995:67-73.
34. Roberts PD. An algorithm for steady-state system optimization and parameter estimation. Int J Systems Sci. 1979;10:719-734.
35. Roberts PD. Coping with model-reality differences in industrial process optimisation - A review of integrated system optimisation and parameter estimation. Comput Ind. 1995;26:281-290.
36. Findeisen R, Allgöwer F. Computational delay in nonlinear model predictive control. In: Proc Int Symp Adv Control of Chemical Processes; 2003.
37. Gray NF. Biology of Wastewater Treatment; No 4 of Series on Environmental Science and Management. Imperial College Press; 2004.
38. Wintgens T, Melin T, Schäfer A, Khan S, Muston M, Bixio D, Thoeye C. The role of membrane processes in municipal wastewater treatment reclamation and reuse. Desalination. 2005;178:1-11.
39. Husain H, Côté P. Membrane bioreactors for municipal wastewater treatment. Wat Qual Int. 1999;March/April:19-22.
40. Engelhardt N, Firk W, Warnken W. Integration of membrane filtration into the activated sludge process in municipal wastewater treatment. Wat Sc Tech. 1998;38:429-436.
41. Stephenson T, Brindle K, Judd S, Jefferson B. Membrane Bioreactors for Wastewater Treatment. IWA Publishing; 2000.
42. Yang W, Cicek N, Hg J. State-of-the-art of membrane bioreactors: Worldwide research and commercial applications in North America. J Membrane Sci. 2006;270:201-211.
43. Lübbecke S, Vogelpohl A. Wastewater treatment in a biological high-performance system with high biomass concentration. Water Res. 1995;29:793-802.