Dynamic operability analysis of an industrial membrane separation process

Abstract Dynamic operability analysis is applied to an industrial whey ultrafiltration process with 12 stages to determine the best feedback control structure. Feedback control is used to maintain the properties of the product stream within their desired ranges. The process is inherently difficult to control because of the extensive recycling of retentate and permeate streams. Using all-pass factorization of the process dynamics models and the Internal Model Control framework, the best achievable closed-loop performances of mitigating the effects from variations in the flowrate and compositions of whey from different control system designs are determined. The best control performance can be achieved by manipulating the recycle flowrate at the 12th stage. The control performance is further improved after a buffer tank is installed upstream of the process. However, control performance will deteriorate with operating time as a result of long-term membrane fouling. When necessary, membrane washing in the middle of process operation should be used to restore control performance.

[1]  Munir Cheryan,et al.  Factors affecting the mechanism of flux decline during ultrafiltration of Cottage cheese whey. , 1980 .

[2]  Jie Bao,et al.  CFD modelling of reverse osmosis membrane flow and validation with experimental results , 2007 .

[3]  Jie Bao,et al.  Dynamic response of a high-pressure reverse osmosis membrane simulation to time dependent disturbances , 2006 .

[4]  Jie Bao,et al.  Effects of recycle ratios on process dynamics and operability of a whey ultrafiltration stage , 2009 .

[5]  S. Skogestad,et al.  Buffer Tank Design for Acceptable Control Performance , 2003 .

[6]  Christos Georgakis,et al.  A new measure of process output controllability , 1998 .

[7]  Munir Cheryan,et al.  Ultrafiltration of Acid Whey in a Spiral‐Wound Unit: Effect of Operating Parameters on Membrane Fouling , 1983 .

[8]  Carlos E. Garcia,et al.  Internal model control. A unifying review and some new results , 1982 .

[9]  Jie Bao,et al.  A unified model of the time dependence of flux decline for the long-term ultrafiltration of whey , 2009 .

[10]  A. Fane,et al.  The cleaning of ultrafiltration membranes fouled by protein , 1993 .

[11]  William L. Luyben,et al.  Plantwide Process Control , 1998 .

[12]  Andrew L. Zydney,et al.  Constant Cwall ultrafiltration process control , 1997 .

[13]  Ioannis K. Kookos,et al.  An Algorithm for Simultaneous Process Design and Control , 2001 .

[14]  Stanley M. Walas,et al.  Chemical Process Equipment : Selection and Design , 1988 .

[15]  Jie Bao,et al.  Analysis of the dynamic response of a reverse osmosis membrane to time-dependent transmembrane pressure variation , 2005 .

[16]  Pierre Aimar,et al.  Mass transfer limitations during ultrafiltration of cheese whey with inorganic membranes , 1988 .

[17]  David L. Ma,et al.  On the computation of disturbance rejection measures , 2000 .

[18]  Sigurd Skogestad,et al.  L1/Q Approach for efficient computation of disturbance rejection measures for feedback control , 2007 .

[19]  Munir Cheryan,et al.  Ultrafiltration and Microfiltration Handbook , 1998 .

[20]  Toraj Mohammadi,et al.  Chemical cleaning of ultrafiltration membranes in the milk industry , 2007 .

[21]  Jie Bao,et al.  Whey protein concentrate production by continuous ultrafiltration: Operability under constant operating conditions , 2007 .

[22]  G. Schock,et al.  Mass transfer and pressure loss in spiral wound modules , 1987 .

[23]  A.J.B. van Boxtel,et al.  Dynamic optimization of a one-stage reverse-osmosis installation with respect to membrane fouling , 1992 .

[24]  David F. Fletcher,et al.  Computational fluid dynamics modelling of flow and permeation for pressure-driven membrane processes , 2002 .

[25]  S. M. Walas Rules of thumb: selecting and designing equipment , 1987 .

[26]  Gun Trägårdh,et al.  The effect of protein fouling in microfiltration and ultrafiltration on permeate flux, protein retention and selectivity: A literature review , 1993 .

[27]  Ram Lavie,et al.  Dynamics of plants with recycle , 1982 .

[28]  Ian Postlethwaite,et al.  Multivariable Feedback Control: Analysis and Design , 1996 .

[29]  Imad Alatiqi,et al.  System identification and control of reverse osmosis desalination , 1989 .

[30]  Christos Georgakis,et al.  An Optimization-Based Approach for the Operability Analysis of Continuously Stirred Tank Reactors , 2001 .

[31]  Sigurd Skogestad,et al.  A systematic approach to the design of buffer tanks , 2000 .

[32]  J. A. Bandoni,et al.  Integrated flexibility and controllability analysis in design of chemical processes , 1997 .

[33]  John D. Perkins,et al.  Optimization as a tool for design/control integration , 1994 .

[34]  Jie Bao,et al.  Process Dynamic Controllability Analysis Based on All-Pass Factorization , 2005 .

[35]  P. Gerla,et al.  Hollow fiber and spiral cheese whey ultrafiltration: minimizing controlling resistances , 2005 .

[36]  Jie Bao,et al.  A dynamic operability analysis approach for nonlinear processes , 2007 .

[37]  Abderrahim Abbas,et al.  Model predictive control of a reverse osmosis desalination unit , 2006 .

[38]  A J Mawson,et al.  Membrane Cleaning in the Dairy Industry: A Review , 2005, Critical reviews in food science and nutrition.

[39]  S. Skogestad,et al.  Controllability measures for disturbance rejection , 1992 .

[40]  W. T. Hanbury,et al.  Spiral wound modules performance an analytical solution: Part II , 1991 .