Information Technology Reference
In-Depth Information
with the smoothing parameter τ (see Figure 10) even though the continuous trajectory of
T evap is only marginally modified by the smoothing method. Similarly and importantly,
the optimal parameters estimated are quite robust regarding the smoothing parameter.
6
Conclusions
In summary, we carried out the parameter estimation accompanied by sensitivity anal-
ysis for a hybrid evaporating system using a smoothing approach. We first investigated
a smooth approximated model by means of simulation of the evaporator dynamics for
different values of the smoothing parameter. Performing the sensitivity analysis with
respect to the parameters to be estimated we could evaluate the usability of the mea-
surement of a certain variable for the parameter estimation. The sensitivity with respect
to the smoothing parameter was studied to evaluate the suitability of the smooth model
for the purpose of parameter estimation. The proposed method allowed to successfully
identify the parameter values. The results turned out to be quite robust against the vari-
ation of the smoothing parameter. In the future, the extension of the model will be made
to optimize the operations of the evaporation system.
References
1. Barton, P.I., Lee, C.K.: Modeling, simulation, sensitivity analysis, and optimization of hybrid
systems. ACMT. Model. Comp. S. 12, 256-289 (2002)
2. Bahl, V., Linninger, A.A.: Modeling of Continuous-Discrete Processes. In: Di Benedetto,
M.D., Sangiovanni-Vincentelli, A. (eds.) HSCC 2001. LNCS, vol. 2034, pp. 387-402.
Springer, Heidelberg (2001)
3. Mehrmann, V., Wunderlich, L.: Hybrid systems of differential-algebraic equations - analysis
and numerical solution. J. Process Contr. 19, 1218-1228 (2009)
4. Goebel, R., Sanfelice, R.G., Teel, A.R.: Hybrid dynamical systems. IEEE Control Syst. Mag.,
28-93 (2009)
5. Sonntag, C., Su, W., Stursberg, O., Engell, S.: Optimized start-up control of an industrial-
scale evaporation system with hybrid dynamics. Control Eng. Pract. 16, 976-990 (2008)
6. Pappala, V.S., Erlich, I.: A new approach for solving the unit commitment problem by adap-
tive particle swarm optimization. In: 2008 IEEE Power and Energy Society General Meeting,
pp. 1-6 (2008)
7. Biegler, L.T.: Nonlinear Programming: Concepts, Algorithms, and Applications to Chemical
Processes. SIAM and MOS (2010)
8. Sonntag, C., Stursberg, O., Engell, S.: Dynamic optimization of an industrial evaporator
using graph search with embedded nonlinear programming. In: 2nd IFAC Conference on
Analysis and Design of Hybrid Systems, pp. 211-216 (2006)
9. Barton, P.I., Lee, C.K., Yunt, M.: Optimization of hybrid systems. Comp. Chem. Eng. 30,
1576-1589 (2006)
10. Till, J., Engell, S., Panek, S., Stursberg, O.: Applied hybrid system optimization: An empiri-
cal investigation of complexity. Control Eng. Pract. 12, 1291-1303 (2004)
11. de Prada, C., Cristea, S., Rosano, J.J.: Optimal start-up of an evaporation station. In:
8th International IFAC Symposium on Dynamics and Control of Process Systems, vol. 3,
pp. 115-120 (2007)
 
Search WWH ::




Custom Search