Process Synthesis and Design using Modified Differential Evolution (MDE)

A large number of process synthesis and design problems in chemical engineering can be modeled as mixed integer nonlinear programming (MINLP) problems. They involve continuous (floating point) and integer variables. A common feature of this class of mathematical problem is the potential existence of non-convexities due to the particular form of the objective function and/or the set of constraints. Due to their combinatorial nature, these problems are considered to be difficult. In the present study, a novel modified differential evolution (Angira & Babu, 2005) is used for solving process synthesis and design problems. To illustrate the applicability and efficiency of modified differential evolution (MDE), two test problems on process synthesis and design have been solved. These problems arise from the area of chemical engineering, and represent difficult non-convex optimization problems, with continuous and discrete variables. The performance of MDE is compared with that of Genetic Algorithm, Evolution Strategy, and MINLP-Simplex Simulated Annealing (M-SIMPSA). INTRODUCTION Process synthesis can be defined as the selection, arrangement, and operation of processing units so as to create an optimal scheme. In other words, it is an act of determining the optimal interconnection of processing units as well as the optimal type and design of units within a process system. The interconnection of processing units is called the structure of the process system. When the performance of the system is specified, the structure of the system and the performance of the processing units are not determined uniquely. This task is combinatorial and open-ended in nature and has received a great deal of attention over the past twenty-five years (Nishida et al., 1981). The use of mathematical programming techniques for process synthesis has received considerable attention over the last two decades. For example, nonlinear programming (NLP) technique for heat exchanger networks (Floudas et al, 1986), and mixed integer nonlinear programming (MINLP) models for structural flowsheet optimization (Kocis & Grossmann, 1987, 1988, 1989; Floudas et al., 1989) to name a few. The major reason for this increased interest lies in the fact that mathematical programming techniques provide Session: Process Synthesis and Energy Management Process Synthesis and Design using Modified Differential Evolution (MDE) Rakesh Angira and B. V. Babu Department of Chemical Engineering, Birla Institute of Technology & Science, Pilani – 333031, India a Email: [email protected]; b Email: [email protected]