The multiple-effect falling-film evaporator (MEFFE), as a widely used process in industry, is usually affected by strong disturbances, which make it very difficult to control. Several control strategies have been applied to MEFFE; however, most of them did not focus on the disturbance rejection, which may lead to poor control performance when disturbances occur. Although model predictive control (MPC) shows the ability to decouple multivariable and control complex processes, it rejects disturbances merely through feedback regulation and does not deal with disturbances directly. To improve disturbance rejection in MEFFE, a control scheme based on MPC and disturbance observer is proposed in this paper. The scheme combines a feedforward compensation part-based on disturbance observer and a feedback regulation part using MPC. The performance of disturbance rejection is analysed in both a nominal case and a model mismatch case. The results of simulation illustrate that the proposed method obtained remarkable superiority in disturbance rejection compared with the MPC method in MEFFE control.
BakkerHHCMarshCParamalingamS. (2006) Cascade controller design for concentration control in a falling-film evaporator. Food Control17: 325–330.
2.
BenneMGrondinPBChabriatJP. (2000) Artificial neural networks for modeling and predictive control of an Industrial evaporation process. Journal of Food Engineering46(4): 227–234.
3.
ChenFLuFJiangB. (2014) Adaptive compensation control of the quadrotor helicopter using quantum information technology and disturbance observer. Journal of the Franklin Institute351(1): 442–455.
4.
ChenMChenWWuQ (2014) Adaptive fuzzy tracking control for a class of uncertain MIMO nonlinear systems using disturbance observer. Science China Information Sciences57(1): 1–13.
5.
CutlerCRRamakerDL (1980) Dynamic matrix control – a computer control algorithm. In: Proceedings of American Control Conference, pp. 281–286.
6.
DuCLiHThumCK. (2010) Simple disturbance observer for disturbance compensation. IET Control Theory Applications4(9): 1748–1755.
7.
FanJB (2009) Working principle and key points of multiple-effect falling film evaporators. Equipment Application and Research32(242): 46–49 (in Chinese).
8.
FindeisenRAllgöwerF (2002) An introduction to nonlinear model predictive control. In:21st Benelux Meeting on Systems and Control, 11: 119–141.
9.
KangMJParkJHKimJH (2011) A disturbance observer design for backlash compensation. International Journal of Control, Automation and Systems9(4): 742–749.
10.
KarimiMJahanmiriAAzarmiM (2007) Inferential cascade control of multi-effect falling-film evaporator. Food Control18(9): 1036–1042.
11.
KatsuraSYS (2013) A decoupling controller design and analysis of multilateral control using disturbance observer in modal space. Advanced Robotics27(1): 71–80.
12.
KatsuraSIrieKOhishiK (2006) Advanced motion control by multi-sensor based disturbance observer. IEE Transactions on Electrical and Electronic Engineering1(1): 112–115.
13.
KayaDIbrahimHSarac (2007) Mathematical modeling of multiple-effect evaporators and energy economy. Energy32: 1536–1542.
14.
KimKSRewKHKimS (2010) Disturbance observer for estimating higher order disturbances in time series expansion. IEEE Transactions on Automatic Control55(8): 1905–1911.
15.
KothareMVBalakrishnanVMorariM (1994) Robust constrained model predictive control using linear matrix inequalities. In: American Control Conference, Baltimore, MD, pp. 440–444.
16.
LahtinenST (2001) Identification of fuzzy controller for use with a falling-film evaporator. Food Control12(3): 175–180.
17.
LiuDY (2000) Some issues in design of multiple-effect falling film evaporators. Chemical Equipment and Pipeline37(3): 11–12 (in Chinese).
18.
OhnishiESK (2013) Analysis the robustness of control systems based on disturbance observer. International Journal of Control86(10): 1733–1743.
19.
PonciaGBittantiS (2001) Multivariable model predictive control of a thermal power plant with built-in classical regulation. International Journal of Control74(11): 1118–1130.
20.
RawlingsB (2000) Tutorial overview of model predictive control. IEEE Control Systems Magazine20(3): 38–52.
21.
ShahGEngellS (2013) Multivariable MPC design based on a frequency response approximation approach. In: European Control Conference (ECC), Zurich, pp, 13–18.
22.
SmithPDSwartzCLEHarrisonSTL (2000) Control and optimization of a multiple effect evaporator. Proceedings of the South African Sugar Technologists’ Association74: 274–279.
23.
StefanovZI (2004) Fundamental modeling and control of falling film evaporators. PhD thesis, Texas Tech University, Lubbock, TX.
24.
StefanovZHooKA (2005) Control of a multiple-effect falling-film evaporator plant. Industrial & Engineering Chemistry Research44(9): 3146–3158.
25.
YanchoVTTsvetanDT (2008) Model predictive control of a lyophilization plant. In: 4th International IEEE Conference: Intelligent Systems, 3(20): 13–18.
26.
YangJZhengWX (2014) Offset-free nonlinear MPC for mismatched disturbance attenuation with application to a static var compensator. IEEE Transactions on Circuits and Systems II – Express Briefs61(1): 49–53.
27.
YangJLiSSunC. (2013a) Nonlinear-disturbance-observer-based robust flight control for airbreathing hypersonic vehicles. IEEE Transactions on Aerospace and Electronic Systems49(2): 1263–1275.
28.
YangJLiSYuX (2013b) Sliding-mode control for systems with mismatched uncertainties via a disturbance observer. IEEE Transactions on Industrial Electronics60(1): 160–169.
29.
YangJSuJLiS. (2014) High-order mismatched disturbance compensation for motion control systems via a continuous dynamic sliding-mode approach. IEEE Transactions on Industrial Informatics10(1): 604–614.
