A Genetic Algorithm Based Approach to Coalescence Parameters: Estimation in Liquid‐Liquid Extraction Columns

Abstract

The population balance model is a useful tool for the design and prediction of a range of processes that involve dispersed phases and particulates. The inverse problem method for the droplet population balance model is applied to estimate coalescences parameters for two-phase liquid-liquid systems. This is undertaken for two systems, namely toluene/water and n-butyl acetate/water in a rotating disc contactor (RDC), using a droplet population balance model. In the literature, the estimation procedure applied to this problem is often based on the deterministic optimization approach. These methods generate instabilities near a local minimum, inevitably requiring information about the derivatives at each iteration. To overcome these limitations, a method providing an estimate for the coalescences parameters is proposed. It is based on a simple and adapted structure of the genetic algorithm, for this particular problem. The agreement between the experimental observations and the simulations is encouraging and, in particular, the models used have proven to be suitable for the prediction of hold-up and Sauter diameter profiles for these systems. Finally, these results demonstrate that the optimization procedure proposed is very convenient for estimating the coalescences parameters for extraction column systems.

Thermodynamic Modeling of the Use of Alkane Mixtures as Working Fluids in Absorption Cycles

“…The choice of this method was mainly based on the fact that the global techniques for this type of problem have not been much tested, contrary to deterministic approaches which are based on the known interval Newton , , homotopy continuation , and Lipschitz algorithm methods, although the involved mathematics are very complex. Therefore, this has encouraged the authors (who already have past experience in using the genetic algorithm‐based method , ) to use it again for the present problem. It is of a stochastic nature, does not require complex mathematics and can be considered as based on a global search with several advantages, such as: the absence of any assumptions concerning the function to be optimized; a compromise is made between the new and the current points in the search domain; the search space is scanned according to probabilities, hence randomizing the problem; local minima problems are avoided by considering several solutions at the same time using information of current search points for the subsequent searches; a direct search technique character, excluding the use of other functions or derivatives as is the case for many optimization methods. …”

Evolutionary Prediction of Biohydrogen Production by Dark Fermentation

“…The genetic algorithm (GA) and genetic programming (GP) are important optimization processes based on the genetic and natural selection . The ability to present a simple relationship and no assumption for a base form is considered the main advantages of this method . The gene expression programming (GEP) model was proposed by Ferreira and is currently being used to solve several engineering problems .…”