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.
Scope:
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 generations.
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 entropy.
Significance:
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.