On the Impact of Representation and Algorithm Selection for Optimisation in Process Design: Motivating a Meta-Heuristic Framework

In an ideal world, it would be straightforward to identify the most suitable optimisation method to use in the solution of a given optimisation problem. However, although some methods may be more widely applicable than others, it is impossible a priori to know which method will work best. This may be due to the particular mathematical properties of the mathematical model, i.e. the formulation. It may also be due to the representation of the variables in the model. This combination of choices of method, representation and formulation makes it difficult to predict which combination may be best.

[1]  Eric S. Fraga,et al.  Incorporation of dynamic behaviour in an automated process synthesis system , 2000 .

[2]  Arturo Jiménez,et al.  An area targeting algorithm for the synthesis of heat exchanger networks , 2004 .

[3]  B. Lin,et al.  Solving heat exchanger network synthesis problems with Tabu Search , 2004, Comput. Chem. Eng..

[4]  Georg Fieg,et al.  A hybrid genetic algorithm for synthesis of heat exchanger networks , 2009, Comput. Chem. Eng..

[5]  W Morton Optimization of a heat exchanger network superstructure using nonlinear programming , 2002 .

[6]  C. Adjiman,et al.  Global optimization of mixed‐integer nonlinear problems , 2000 .

[7]  Abdellah Salhi,et al.  Tailoring hyper-heuristics to specific instances of a scheduling problem using affinity and competence functions , 2014, Memetic Comput..

[8]  Daniel R. Lewin,et al.  A generalized method for HEN synthesis using stochastic optimization. II. : The synthesis of cost-optimal networks , 1998 .

[9]  Eric S. Fraga Discrete Optimization using String Encodings for the Synthesis of Complete Chemical Processes , 1996 .

[10]  K. I. M. McKinnon,et al.  The simplest examples where the simplex method cycles and conditions where expand fails to prevent cycling , 2000, Math. Program..

[11]  Eric S. Fraga,et al.  Co-operating ant swarm model for heat exchanger network design , 2006 .

[12]  Mauro A.S.S. Ravagnani,et al.  Heat exchanger network synthesis and optimisation using genetic algorithm , 2005 .

[13]  Abdellah Salhi The Ultimate Solution Approach to Intractable Problems , 2010 .

[14]  Eric S. Fraga,et al.  The use of dynamic programming with parallel computers for process synthesis , 1994 .

[15]  Adeniyi J. Isafiade,et al.  Interval based MINLP superstructure synthesis of heat exchanger networks for multi-period operations , 2010 .

[16]  Ankur Pariyani,et al.  Design of heat exchanger networks using randomized algorithm , 2006, Comput. Chem. Eng..

[17]  Eric S. Fraga,et al.  A rewriting grammar for heat exchanger network structure evolution with stream splitting , 2009 .

[18]  Kevin C. Furman,et al.  A Critical Review and Annotated Bibliography for Heat Exchanger Network Synthesis in the 20th Century , 2002 .

[19]  Eric S. Fraga,et al.  A Visual Representation of Process Heat Exchange as a Basis for User Interaction and Stochastic Optimization , 2001 .

[20]  Eric S. Fraga,et al.  Evaluation of hybrid optimization methods for the optimal design of heat integrated distillation sequences , 2003 .

[21]  Serge Domenech,et al.  Process optimization by simulated annealing and NLP procedures. Application to heat exchanger network synthesis , 1997 .