Optimization of Pressure-Swing Distillation by Evolutionary Techniques: Separation of Ethanol-Water and Acetonitrile-Water Mixtures
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Adjay Sagar S
Abstract
Complete separation can be achieved in selective homogeneous azeotropic mixtures by exploiting the pressure sensitive nature of the system. In the present work the optimal number of trays, feed location and reflux ratio for sequential column systems encountered in continuous pressure swing distillation (PSD) have been determined by use of two evolutionary techniques. Two industrially relevant systems: ethanol-water and acetonitrile-water have been considered. The Napthali-Sandholm model is solved to obtain the concentration and temperature profiles. The objective is to minimize the total cost using Genetic Algorithm (GA) and Differential Evolution (DE) for the two azeotropic systems. The techniques offer attractive features like applicability to discontinuous and non-differentiable search spaces.
Appendix
A.1 Costing and design of the Column
The energy costs were calculated according to:
We choose the velocity to be 80% of the flooding velocity.
A heuristic was taken to consider the fixed cost of the pump, energy calculations were done considering the power necessity of the pump to be 8 units of electricity and at a nominal cost of Rs. 6/unit of Electricity.
Assuming depreciation, interest, and maintenance costs of 18%, 15% and 2%, respectively.
A.2 Napthali-Sandholm Method
The Napthali-Sandholm method is one of the most robust techniques and is superior in convergence compared to the successive substitution methods but constraints have to put on the iterated variables for smooth convergence. The simple distillation flow diagram is shown in Figure 14.
Schematic of simple distillation column.
Material Balance Equations:
Summation Equations:
K values are predicted using the UNIQUAC model.
The Jacobian of the Method forms a Block-Tridiagonal Matrix of the form:
Where
If, the vapour flow rates are assumed to be the constant, the energy equations need not be solved for, The MES or the
This iterative scheme is sensitive to the initial Guesses and fails to converge if the initial values are too far from the feasible solution. All simulations have been carried out on MATLAB 2015b. The method discussed before is applicable to simple columns. If the columns are interlinked, then the system no longer remains Tridiagonal [15].
A.3 Summary of fitness functions
Summary of fitness functions tested.
| S.No | P | G | w1 | w2 | w3 | w4 | r1 | r2 | Fitness function | Cost | Distillate | Bottom | Converged fitness value |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 15 | 50 | 1 | 10 | 7.5 | r1+r2 | 0.897 | 0.813 | 1.65 | ||||
| 2 | 30 | 60 | 10 | 10 | - | r1+r2 | 0.804 | 0.731 | 12.87 | ||||
| 3 | 30 | 60 | 10 | - | 7.5 | - | cost | r1*r2 | 0.986 | 0.827 | 6.04 | ||
| 4 | 30 | 60 | - | - | - | - | cost | r1*r2 | 0.989 | 0.866 | 0 | ||
| 5 | 50 | 15 | 10 | 7.5 | - | r1*r2 | 0.989 | 0.866 | 0 | ||||
| 6 | 60 | 30 | 10 | 7.5 | - | r1*r2 | 0.989 | 0.866 | 0 | ||||
| 7 | 60 | 30 | 10 | 7.5 | - | r1+10*r2 | 0.987 | 0.731 | 1.68 | ||||
| 8 | 30 | 60 | 10 | 7.5 | - | r1*10*r2 | 0.989 | 0.867 | 0 | ||||
| 9 | 15 | 30 | 10 | 7.5 | - | r1*10*r2 | 0.989 | 0.867 | 0 | ||||
| 10 | 30 | 45 | 10 | 7.5 | - | r1*10*r2 | 0.989 | 0.865 | 0 | ||||
| 11 | 45 | 60 | 10 | 7.5 | - | r1*10*r2 | 0.989 | 0.865 | 0 |
P = Population Size, G = Generation Size
References
[1] Haelssig JB,Considerations for Various Recovery Schemes in Ethanol Production by Fermentation. Industrial & Engineering&Chemistry Research 2008:6185–6191. DOI: 10.1021/ie0715005 .Suche in Google Scholar
[2] Patwardhan AM, Shirsat SP, SD Dawande. Pressure Swing Distillation Simulation and Optimization of Azeotropic IBA + IBAc Mixture. International Journal of Computer Applications :9–13. Vol 66(10), 2013.Suche in Google Scholar
[3] Lewis WK.Dehydrating Alcohol and the like. In: WK Lewis, editors. United States Patent , 1928:676–700. 3.Suche in Google Scholar
[4] Onkar D, Sathe V, Yogesh M. THF-Water mixture separation using Extractive and Azeotropic distillation: Review, Modeling and Simulation in Aspen plus. International Journal of Science and Engineering. 2016;3:10–16.Suche in Google Scholar
[5] Iqbal A, Ahmad SA. Pressure swing distillation of azeotropic mixture – A simulation study. Perspectives in Science. 2016 9;8:4–6. DOI: 10.1016/j.pisc.2016.01.001.Suche in Google Scholar
[6] Yu M, Yang J, Zheng Z. Simulation and optimization for separation of acetonitrile and water by pressure swing distillation and extractive distillation. Journal Of Central South University (Science And Technology) , 2014:2966–2971. Vol 45(9).Suche in Google Scholar
[7] Wang Y, Bu G, Wang Y, Zhao T, Zhang Z, Zhu Z. Application of a simulated annealing algorithm to design and optimize a pressure-swing distillation process. Computers & Chemical Engineering. 2016 12;95:97–107. DOI: 10.1016/j.compchemeng.2016.09.014.Suche in Google Scholar
[8] Modla G. Energy saving methods for the separation of a minimum boiling point azeotrope using an intermediate entrainer. Energy IJSE, 2013:103–109. DOI: 10.1016/j.energy.2012.12.011 .Suche in Google Scholar
[9] Tabari A, Ahmad A. A semicontinuous approach for heterogeneous azeotropic distillation processes. Computers & Chemical Engineering 2015:183–190. DOI: 10.1016/j.compchemeng.2014.12.005 .Suche in Google Scholar
[10] Cortez-Gonzalez J, Segovia-Hernandez JG , Hernandez S, Gutierrez-Antonio C , Briones-Ramírez A ,Rong BG.Genetic Algorithms in the Design of Configurations for Distillation of Quaternary Mixtures using Less than N-1 columns with Thermally Coupling. Computer Aided Chemical Engineering. 2012;30:677–681. DOI: 10.1016/j.compchemeng.2014.12.005.Suche in Google Scholar
[11] Gutiérrez-Antonio C. Multiobjective Stochastic Optimization of Dividing-wall Distillation Columns Using a Surrogate Model Based on Neural Networks. Chemical and Biochemical Engineering Quarterly. 2016 1 3;29(4):491–504. DOI: 10.15255/CABEQ.2014.2132.Suche in Google Scholar
[12] Franke MB. MINLP optimization of a heterogeneous azeotropic distillation process: Separation of ethanol and water with cyclohexane as an entrainer. Computers & Chemical Engineering. 2016 6;89:204–221. DOI: 10.1016/j.compchemeng.2016.03.027.Suche in Google Scholar
[13] Ramanathan SP. Mukherjee S. Dahule RK. Ghosh S. Rahman I. Tambe SS, Ravetkar DD Kulkarni BD Optimization of Continuous Distillation Columns Using Stochastic Optimization Approaches. Chemical Engineering Research and Design. 2001 4;79(3):310–322. DOI: 10.1205/026387601750281671.Suche in Google Scholar
[14] Rahman I, Ahmad A, Kumar P, Kulkarni BD.Optimization of a Continuous Process for the Recovery of Lactic Acid Using Differential Evolution Algorithm. Chemical Product and Process Modeling. 2008 2 3;3(1). DOI: 10.2202/1934-2659.1111.Suche in Google Scholar
[15] Hofeling BS, Seader JD.A modified Naphtali-Sandholm method for general systems of interlinked, multistaged separators. AIChE Journal. 1978 11;24(6):1131–1134. DOI: 10.1002/aic.690240631.Suche in Google Scholar
[16] Beebe AH, Coulter KE, Lindsay RA, Baker EM.Equilibria in Ethanol-Water System at Pressures Less Than Atmospheric. Industrial & Engineering Chemistry 1942:1501–1504.10.1021/ie50396a019 .10.1021/ie50396a019Suche in Google Scholar
[17] Voutsas EC, Pamouktsis C, Argyris D, Pappa GD.Measurements and thermodynamic modeling of the ethanol–water system with emphasis to the azeotropic region. Fluid Phase Equilibria 2011:135–141.10.1016/j.fluid.2011.06.009 66.10.1016/j.fluid.2011.06.009Suche in Google Scholar
[18] Zhang Z, Ming Lv, Huang D, Jia P, Sun D, Li W. Isobaric Vapor–Liquid Equilibrium for the Extractive Distillation of Acetonitrile + Water Mixtures Using Dimethyl Sulfoxide at 101.3 kPa. Journal of Chemical & Engineering Data 2013:3364–3369.10.1021/je400531a 89.10.1021/je400531aSuche in Google Scholar
[19] Babu BV, Angira R, Nilekar A.Computers and Chemical Engineering. 2005;29:1041–1045.10.1016/j.compchemeng.2004.11.010Suche in Google Scholar
[20] Hegerty B , Hung C , Kasprak K.A Comparative Study on Differential Evolution and Genetic Algorithms for Some Combinatorial Problems. proceedings of 8th Mexican International Conference on Artificial Intelligence 2009.Suche in Google Scholar
[21] Skiborowski M, Rautenberg M, Marquardt W., A Hybrid Evolutionary–Deterministic Optimization Approach for Conceptual Design. Industrial & Engineering Chemistry Research. 2015 10 21;54(41):10054–10072.10.1021/acs.iecr.5b01995.10.1021/acs.iecr.5b01995Suche in Google Scholar
[22] Tun LK, Matsumoto H., Application Methods for Genetic Algorithms for the Search of Feed Positions in the Design of a Reactive Distillation Process. Procedia Computer Science. 2013;22:623–632.10.1016/j.procs.2013.09.143.10.1016/j.procs.2013.09.143Suche in Google Scholar
[23] Shahandeh H, Ivakpour J, Kasiri N, Internal and external HIDiCs (heat-integrated distillation columns) optimization by genetic algorithm. Energy. 2014 1;64:875–886.10.1016/j.energy.2013.10.042.10.1016/j.energy.2013.10.042Suche in Google Scholar
[24] Sinnott RK.Coulson and Richardson's Chemical Engineering., 1993;6:567 4th edition Oxford: Pergamon.Suche in Google Scholar
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Artikel in diesem Heft
- Research Articles
- Sensitivity of Effluent Variables in Activated Sludge Process
- Optimization of Pressure-Swing Distillation by Evolutionary Techniques: Separation of Ethanol-Water and Acetonitrile-Water Mixtures
- Phase Split in T-Junction Mini Channel – A Numerical Study
- Simulation and Dynamic Optimization of an Industrial Naphtha Thermal Cracking Furnace Based on Time Variant Feeding Policy
- Mathematical Modeling and Optimization of Syngas Production Process: A Novel Axial Flow Spherical Packed Bed Tri-Reformer
- Estimator Based Inferential Control of an Ideal Quaternary Endothermic Reactive Distillation with Feed-Forward and Recurrent Neural Networks
Artikel in diesem Heft
- Research Articles
- Sensitivity of Effluent Variables in Activated Sludge Process
- Optimization of Pressure-Swing Distillation by Evolutionary Techniques: Separation of Ethanol-Water and Acetonitrile-Water Mixtures
- Phase Split in T-Junction Mini Channel – A Numerical Study
- Simulation and Dynamic Optimization of an Industrial Naphtha Thermal Cracking Furnace Based on Time Variant Feeding Policy
- Mathematical Modeling and Optimization of Syngas Production Process: A Novel Axial Flow Spherical Packed Bed Tri-Reformer
- Estimator Based Inferential Control of an Ideal Quaternary Endothermic Reactive Distillation with Feed-Forward and Recurrent Neural Networks
