Selection of a Mixed-Integer Nonlinear Programming (MINLP) Model of Distillation Column Synthesis by Case-Based Reasoning

Abstract

The paper presents a new application of the case-based reasoning method for finding a mixed-integer nonlinear programming (MINLP) model with superstructure and a solution of the corresponding distillation synthesis problem by suggesting an initial point for performing design and optimization of the system. A case library has been built from earlier published distillation problems with reproducible MINLP models. When solving a new problem, the most similar case to the target is found in the case library during the retrieval process in two steps: (i) first, a set of matching cases is retrieved, using inductive retrieval; (ii) the cases in the retrieved set then are ranked according to their similarity to the target case, using the nearest-neighborhood method.

An online optimization strategy for a fluid catalytic cracking process using a case-based reasoning method based on big data technology

“…The case base should be established to cover all possible problems and variable sets in the application eld. 29 The variables are divided into feed variables, inuencing variables, and product variables. The case model is expressed as follows:…”

“…The case base should be established to cover all possible problems and variable sets in the application field. 29 The variables are divided into feed variables, influencing variables, and product variables. The case model is expressed as follows: C k = {( I k , P k ) → S k } where I is the feed information in the case base, including the feed wax residue ratio, feed flow rate, and feed properties.…”

Innovation in Organization of Design Process in Pulp and Paper Industry Projects

“…Next, the solutions of problems from the retrieved cases are adapted to generate the solution of the current, new problem. The functional steps in CBR are the following (Farkas et al ): New problem presentation (or description) Retrieval of the most similar cases from case‐base Adaptation of the most similar solutions for generating a solution for a new problem Validation of the generated problem Learning from the problem cases, that is, adding the verified solution into the case‐base…”

“…Next, the solutions of problems from the retrieved cases are adapted to generate the solution of the current, new problem. The functional steps in CBR are the following (Farkas et al 2006):…”

Optimisation of thermally coupled distillation sequences with side columns

“…Some works using mathematical optimisation techniques have been reported (Grossmann et al, 2005;Amminudin et al, 2001;Dünnebier and Pantelides, 1999;Farkas et al, 2006;Yeomans and Grossmann, 2000); their solutions have considered both operating and capital costs, but convergence is strongly dependent on simplifying assumptions and the limits and initial values for the search parameters. The degrees of freedom of the side-column distillation systems that can be optimised include the number of stages in each column sections, the reflux ratios and vapour or liquid flowrate interconnections.…”