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Level I: Find all possible basics configurations. Implicit Differentiation. Mixed Integer Linear Program. Basic Config. Column Section profile:. Level II: Identify the feasible complex column. Pinch Point. Supply. D. Temperature. Design Specification. Feasible. BPD.

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**Level I: Find all possible basics configurations**Implicit Differentiation Mixed Integer Linear Program Basic Config. Column Section profile: Level II: Identify the feasible complex column Pinch Point Supply D Temperature Design Specification Feasible BPD Level III : Obtain optimal designs Output Input Flow rate tray# S3 Specification Output B Comp. profile S2 S4 S1 Each individual different design (sequence + operating conditions) Each random initial guess for integer variables, x, will generate one possible structure K individuals MASTER A Basic Structure Population Feasibility test High performance Inverse problem DESIGN AND SYNTHESIS OF COMPLEX COLUMN NETWORKS WITH GLOBAL FEASIBILITY TEST Gerardo J. Ruiz, Seon B. Kim, and Andreas A. LinningerLaboratory for Product and Process Design, Departments of Chemical and Bio-Engineering, University of Illinois at Chicago, Chicago, IL 60607, USAGeneral on Separations (02H00), AIChE Annual Meeting, Nashville, TN, Nov 9, 2009 Poster No. 335o Feasible Case MinBPD = 0 TB TA x2 dB dA Methodology - Complex Column Network Synthesis Motivation and Objectives x1 Motivation Distillation occupies in chemical process: • 40-70% of capital and operating costs • 60% of the total process energy • 4% of total energy consumption in United States • Atmospheric carbon emissions There is a need for a redefinition of the design objectives for industrial separations with a new focus on energy conservation and the emission reduction using complex column configurations have the potential of achieving up to 70% energy savings over simple column networks Inverse Design of Distillation Column Global Design Procedure Temperature Collocation of a General Column Section • Starting with the desired design specification of product purity requires that each column of the network is feasible • Feasible design – Intersection of profiles of both adjacent sections • Profile Intersection Index - bubble point distance (BPD) Basic complex column configurations Generic Structure Synthesis Network Task Optimization Basic Complex Configuration: Quaternary System Using Difference Point Equations Find Feasibility of Two Column Sections Objectives • Develop computer-aided systematic design procedures to prevent numerical failures associated with the extraordinary sensitivity of column profile calculations • Massive size reductions enabled by a new column profile computation algorithm called Temperature Collocation • Synthesize separation networks with realistic column profiles • Realizable column profiles validated with industrially accepted simulation software such as AspenPlus Considering Operating and Capital Cost Obtain Optimal Design Rigorous Feasibility Test Reduced Search Space According to the general definition of the minimum bubble point distance approach, a complex column k is feasible if and only if the sum of all minimum profile distances of any pair of equivalent rectifying, r, and stripping, s, column sections is within a small tolerance of zero () as in expression Bubble Point Temperature Distance map shows the minimum composition distance between two adjacent sections. The minimum bubble point distance (BPD) of 10-8 is localized at r= 2.35, xD3=0.0081 and BPT= 72.6 C. C Conclusions Case Study 1 – Complex Column Network for Quaternary Mixtures Separation Case Study 2 - Initialization of Complex Distillation Networks with AspenPlus Simulator • Temperature collocation and minimum bubble point distance algorithm were effective to find a feasible separation by intercepting profiles. • The first case study demonstrates the potential to save 72% in energy using a complex column network compared to the simple column network. • The second case study demonstrates the current state of the art of separation synthesis in conjunction with computer simulations to fully integrate complex separation networks. • The seamless integration of rigorous flowsheet simulators to validate the predictive results of our scientific method was demonstrated. A quaternary mixture of pentane, hexane, heptane, and octane was studied. In the complex network, it uses two simple columns for pre-fraction to complex column. As a result, the best energy efficient complex column network saves up to 72% of the operating cost in terms of vapor flowrate. AspenPlus Simulation of Composition and Temperature Profiles Column II Column III Column I Column II Column III Column I Acknowledgements • Dr. Angelo Lucia (University of Rhode Island) • Dr. Diane Hildebrandt (University of the Witwatersrand) • DOE Grant: DE-FG36-06GO16104 • Dr. Rakesh Agrawal (Purdue University) • Dr. Chau-Chyun Chen (Aspen Tech.) Temperature Collocation Composition Profiles Column II Column III Column I References Optimal operating conditions for 9 selected Networks Complex Network Agrawal, R. (2003). "Synthesis of multicomponent distillation column configurations." AIChE J49(2): 379-401. Tapp, M., S.T. Holland, D. Hildebrandt, and D. Glasser, Column Profile Maps. 1. Derivation and Interpretation.I&EC Research, 2004. 43(2): p. 364-374. Zhang, L. and A. A. Linninger (2004). "Temperature collocation algorithm for fast and robust distillation design." I&EC Research43(12): 3163-3182. Zhang, L. and A. A. Linninger (2006). "Towards computer-aided separation synthesis." AIChE J52(4): 1392-1409. Simple Network

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