Twin-Screw Food Extrusion: Control Case Study

Transcription

1 Twin-Screw Food Extrusion: Control Case Study Joel Schlosburg May 12 th,25 HOWARD P. ISERMANN DEPARTMENT OF CHEMICAL & BIOLOGICAL ENGINEERING RENSSELAER POLYTECHNIC INSTITUTE TROY, NY 1218

2 Contents Motivation & Past Study Model Development SISO Control RGA: MVSISO Pairing SVD: MVSISO Performance Disturbance Rejection Possibilities for Modification

3 Motivation To produce a control problem based on a real-life experimental and industrial operation. Provide system parameters that can be modeled and controlled, while challenging the student on concepts of control stability and design choices.

4 Examined Possible Systems for Case-Study Project Anesthetic Drug Infusion Straightforward biomedical application, but with s in transfer function matrix. May be a interesting module, but RGA would be too simple for case-study project. Mechanical Ventilator Complex biomechanical application that is based on sinusoidal inputs and split-second time-frames. Desalination Plant A common chemical engineering operation, though large system needs to be reduced from a 6x6 system.

5 Twin-Screw Cooing Food Extruder Common food processing unit, mostly in baing industry. Fast-speed bioreactor with heating, cooling, compressing, mixing, evaporating, cutting, and aerating in one unit. Twin-screw is now becoming more common, as it is easier to manipulate a number of parameters.

6 Previous Control Wor Wor involving the twin-screw extruder include: Dr. Rosana Moreira at Texas A&M (Schonauer 1995, 1996, & 1997) University of Newcastle in Australia (Wang 21, 24) Dr. Steven Mulvaney at Cornell University (Lu 1993, Singh 1994, and Haley 2) Control primarily MPC and GPC, with the exception of PID control by Singh and Mulvaney (1994), for which this study is based. Previous control inputs and outputs on this system include: Inputs: screw speed, motor torque, specific mechanical energy, liquid injection rate, moisture content, individual zone and overall jacet temperature, die pressure, and product temperature. Outputs: color of extrudate, bul density, expansion (diameter, lineal, ratio), texture (breaing strength), water solubility index, water absorption index, gelatinization, dextrinization, sensory attributes, dimensional (diameter and length), and surface texture, motor torque, screw speed, and product or outlet temperature.

7 Plant Transfer Functions BT MC SS 1) 1)(127.1s (149.6s s 2.4 1) 1)(13.4s (29.6s s.12 1) 1)(26.9s (79.4s 1).87(123.2s 1) (17.45s 1) 14.6s.32( PT MT A C B D

8 Model Development

9 Model Transfer Functions (SS Step)

10 Model Development SS-MT loop has inverse response and second order dynamics that require modeling using figures 3-9, 3-11 to determine τ n and τ p. Must first assume ζ1. SS-PT loop is simple first order. MC-MT loop has positive numerator dynamics, but modeled as first order plus time delay. MC-PT loop is first order plus time delay.

11 Model Transfer Functions (MC Step)

12 Final Empirical Models MC SS PT MT 1 84s 2.4e s s.87e 1) (14.2s 1) 15s.32( 39s 39s 2 MC SS s 2.4e 1) 1)(13.4s (29.6s.12 1) 1)(26.9s (79.4s 1)e.87(123.2s 1) (17.45s 1) 14.6s.32( PT MT 39s 39s 2 Model Plant

13 SISO Tuning Parameters Loop name (input-output) c τ I (s) τ D (s) Optimal Experimental λ Range SS-MT λ sec SS-PT λ sec MC-MT λ sec MC-PT λ sec

14 SS-MT SISO

15 SS-PT SISO

16 MC-MT SISO

17 MC-PT SISO

18 Relative Gain Analysis Gain array λ Λ λ λ λ λ 11 & λ 22 are closer to one, and therefore are the better control loop pairings. Being closer to one allows the closed-loop performance to better match the open-loop performance. This means the two control loops are: SS controlling MT MC controlling PT Since <λ<1, our closed loop may be too aggressive in their control action. To prevent instability and overshoot, c was detuned by the λ value.

19 Optimal RGA Control System

20 SVD Analysis G* S o G S 1 i 1/ / G UΣV T T σ max σmin 2.25 SVD based on scaled gain matrix G* SVD matrices calculated in Matlab. Strongest step directions are MT decrease and PT increase.

21 SVD Simultaneous Step Changes Y*.25 S 1 o U *

22 SVD Simultaneous Step Changes Y*.25 S 1 o U *

23 RGA Validation Y*.25 S 1 o U *

24 Disturbance Rejection Barrel jacet temperature is disturbance rejection. Increase is barrel temperature obviously should have a direct impact on product temperature. Barrel temperature was originally a manipulated input in Singh (1994), but that choice was designed for minimal loop interaction. This diminishes the choice necessary in the RGA, and not the best casestudy choice.

25 Disturbance Rejection (Cont d.)

26 Conclusions and Suggestions Stable and flexible bidirectional control of both motor torque and product temperature. Consistent issues of slight overshoot, but not outside of reasonable percentage. More complicated modeling of the positive numerator dynamics could improve control, but may be beyond ability of students beyond guess-and-chec. However, simplified modeling of loop shows the sacrifices necessary with plant-model mismatch, while still able to achieve stable control.

27 References 1. Bequette BW. 23. Process Control: Modeling, Design, and Simulation. Prentice Hall: Upper Saddle River, NJ. 2. Haley TA, and Mulvaney SJ. 2. On-line system identification and control design of an extrusion cooing process: Part I System Identification. Food Control. 11: Haley TA, and Mulvaney SJ. 2.. On-line system identification and control design of an extrusion cooing process: Part II Model predictive and inferential control design. Food Control. 11: Lu Q, Mulvaney SJ, Hsieh F, and Huff HE Model and strategies for computer control of a twin-screw extruder. Food Control. 4: Schonauer S, and Moreira RG Development of a fixed-gpc controller for a food extruder based on PQA- Part I: System identification. Transactions of the Institute of Chemical Engineers. 73(c): Schonauer S, and Moreira RG A variable restrictive valve as an extra independent control variable for food extrusion process. Food Science and Technology International. 2: Schonauer S, and Moreira RG Dynamics analysis of on-line product quality attributes for automation of food extruders. Food Science and Technology International. 12: Singh B, and Mulvaney SJ Modeling and process control of twin-screw cooing food extruders. Journal of Food Engineering. 23: Wang L, Gawthrop P, Chessari C, Podsiadly T, and Giles A. 24. Indirect approach to continuous time system identification of food extruder. Journal of Process Control. 14: Wang L, Chessari C, and Karpiel E. 21. Inferential control of product quality attributes application to food cooing extrusion process. Journal of Process Control. 11: