(21c) The Synthesis of Separation Networks with Complex Column

Zhang, L., University of Illinois at Chicago
Moon, J., University of Illinois at Chicago
Linninger, A. A., University of Illinois at Chicago


Separation processes make
up 40%-70% of capital and operating costs of chemical manufacturing.
Distillation accounts for more than 60% of the total process energy for the
manufacture of commodity chemicals. Complex column configurations are estimated
to harness energy savings up to 70%. Therefore, distillation synthesis with
complex is a meaningful target for energy improvements on an industry-wide
scale.  However, design of complex separation system requires (i) structural
decisions such as sequencing of distillation columns for solvent recovery, and
(ii) determination of associated operating conditions (e.g. distillate/bottoms
compositions, reflux ratio etc). For computer tools this process synthesis
constitutes a very formidable challenge. Technically MINLP incorporating binary
as well as continuous variables can address synthesis problem. However,
infeasible operation following from ineffective structural decision may
jeopardize the robustness and convergence of MINLP. In this presentation, we
demonstrate a hybrid algorithm combining stochastic search techniques with
novel formulation of complex distillation configuration. An evolutionary
algorithm will construct automatically structurally different complex
separation network with the complex basic distillation configuration.
Feasibility of the design will be delegated to an advanced feasibility tests
based on temperature collocation of finite elements. Our search algorithm is
capable of solving complex distillative synthesis problem robustly and reliably
without user interactions. Application will demonstrate the algorithms
performance in complex column sequencing problems for solvent recovery.


We propose a novel bi-level
hybrid algorithm for the synthesis of complex column sequences. The master
problem will make structural decisions using evolutionary algorithms, while the
subproblem will rigorously assess the feasibility of each of these structures
using our novel MInimum Bubble Point DIstance (MIDI) Algorithm. The minimum
distance algorithm computes the section temperature profiles by orthogonal finite-element
collocation technique. This minimum bubble point distance is obtained by
solving a gradient based NLP optimization problem. A minimum distance of zero
ensures feasibility, whereas positive values indicate infeasible
specifications. Penalizing the genetic algorithm's objective for infeasible
specifications ensures that the chromosomes are feasible after a few

One of challenge for the
determination of pinch points will also be addressed in the synthesis and
design of complex separation systems. We develop the new format homotopy
continuous methods which can locate all the fixed points starting from pure
components. We also present interval methods, global terrain method, niche
genetic algorithm for validating the results. However, continuous method has to
carefully construct the proper homotopy function. The classification and order
of the fixed points also are given by calculating the eigenvalue and the


The major innovation of the proposed approach is an
evolutionary program combined with a novel temperature collocation algorithm to
systematically build and optimize complex column configurations based on column
sections. Massive problem size reductions due to temperature collocation ensure
the realistic composition profiles of each column in the network without
sacrificing the computational and thermodynamic rigor. The evolutionary
algorithm constructs automatically different separation sequences; feasibility
of the synthesized sequences is ascertained by means of the MIDI algorithm. In
contract to existing approaches, this method synthesizes several clusters of
designs solutions, each one corresponding to regions of local optimality.