Yulong Liu

1. Journal Club Process Simulation and Multiobjective Optimization Yulong Liu 2012.11.23

2. Process Simulation Based on Experimental Investigations for 3‑Methylthiophene Alkylation with Isobutylene in a Reactive Distillation Column Yu Liu, Bolun Yang*, and Shasha Li Department of Chemical Engineering, State Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong University, Xi’an 710049, P.R. China Ind. Eng. Chem. Res. 2012, 51, 9803−9811

3. Introduction • The key design factors (number of reactive and nonreactive stages, location of feed stage, column pressure, mass ratio of distillate to feed, and catalyst weight) were optimized. • Higher alkylation selectivity，better catalytic stability and the sulfur content in FCC gasoline declined by more than 99%.

4. Equipment and Procedures

5. Number of Reaction Stages The reaction stage number of 5 was used in further simulation studies.

6. Number of Rectifying Stages and Stripping Stages The rectifying zone of 5 stage was considered in the further simulations The stripping zone of 1 stage was considered in the further simulations

7. Column Pressure A pressure of 0.2 MPa would give the reflux drum temperature of about 325 K; cooling water thus can be used in the condenser in this case.

8. Feed Location and Catalyst Weight The residence time of 3MT in reaction zone was reduced. The residence time of IB in reaction zone was increased.

9. Reflux Ratio

10. Mass Ratio of Distillate to Feed To limit the reboiler duty and to control the sulfur content (less than 10 ppmw) in distillate stream, a D/F ratio of 0.85 was applied during the simulations.

11. Realistic Models for Distillation Columns with Partial Condensers Producing Both Liquid and Vapor Products William L. Luyben* Department of Chemical Engineering Lehigh University Bethlehem, Pennsylvania 18015, United States Ind. Eng. Chem. Res. 2012, 51, 8334−8339

12. Introduction • This paper demonstrates a realistic way to model a partial condenser distillation system using Aspen simulation. • Fixing reflux-drum temperature and selecting a reasonable pressure determines the split between the amount of vapor product and the amount of liquid product. • In the operation of these systems，we usually want to condense as much as possible so as to minimize compression costs of dealing with the vapor product.

13. Column flowsheet

14. Feed Flow Rate Disturbances The realistic situation is when the cooling water flow rate is fixed.

15. Feed Composition Disturbances The realistic situation is when the cooling water flow rate is fixed.

16. Feed Composition Disturbances The most realistic predictions are those given by the Fixed CW model.

17. Multiobjective Evolutionary Optimization of Batch Process Scheduling Under Environmental and Economic Concerns Elisabet Capon-Garcia Dept. of Chemistry and Applied Biosciences, ETH Zurich, Zurich 8093, Switzerland Aaron D. Bojarski, Antonio Espuna, and Luis Puigjaner Dept. of Chemical Engineering, CEPIMA, Universitat Politecnica de Catalunya, ETSEIB, Barcelona 08028, Spain AIChE Journal .2012 Vol. 00, No. 0

18. Introduction • The simultaneous consideration of economic and environmental • objectives in batch production scheduling is today a subject of • major concern. • However, reported computational times were extremely high. Hence, a hybrid strategy has been developed. • Rigorous local search and Genetic algorithm.

19. Objective functions • The batch i production process environmental impact (EnvImi) and batch changeover between i and i` at stage k using cleaning method c environmental impact (EnvImii`kc). • Batch i product benefits (BPi ) and changeover costs between batches i and i` using cleaning method c at each stage k (ChCostii`kc). • The binary variable(Xii`c).

20. Multiobjective genetic algorithm The scheduling problem is formulated using mathematical programming tools, but it is solved using a multiobjective genetic algorithm.

21. Thanks for your attention