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Ind. Eng. Chem. Res. 2007, 46, 5100-5107
Prediction of Emulsion Stability via a Neural Network-Based Mapping Technique Ubiratan F. de Souza, Frank H. Quina, and Roberto Guardani* Oxiteno S.A. Maua´ -SP, Brazil; Instituto de Quı´mica, UniVersity of Sa˜ o Paulo, Sa˜ o Paulo-SP, Brazil; and Chemical Engineering Department, UniVersity of Sa˜ o Paulo, Sa˜ o Paulo-SP, Brazil
Stability is a key property related to the production and use of a number of products that are commercialized in the form of emulsions. However, because of the complexity of these systems, in most cases there is no readily available means to predict emulsion stability. Thus, product formulation development is based on laboratory evaluations of emulsion stability, e.g., by measuring the time required for the emulsion to break according to standardized procedures. These tests are time-consuming and subject to visual inaccuracies between different operators. In this work, a neural network-based model is tested as a tool for predicting the emulsification properties of mixtures of surfactants, organic solvents, and organic compounds used as active ingredients in pesticide formulations. The model is able to predict the emulsion-breaking height, a standard measure of the system’s stability. Six physicochemical properties were used as descriptors for the active ingredients: octanol/water partition coefficient, molar volume, refractive index, density, Hildebrand solubility parameter, and Henry’s constant. The solvents and their mixtures were described by their density, Hildebrand solubility parameter, and surface tension. Other input variables include water hardness and the concentrations of active ingredient and surfactant. The output variable was the volume percentage of cream formation after dispersion of the emulsifiable concentrate in water, represented by the “emulsion-breaking height”, predicted by the neural network model. In a different approach, nonionic surfactants were described by their average hydrophilic-lipophilic balance (HLB) number. The loss of information in the neural network resulted in inaccurate estimates of cream formation. Nevertheless, with this approach, the neural network was able to discriminate between regions of emulsion stability and instability. Application of the method enabled the construction of phase diagrams for the prediction of the optimum surfactant mixture(s) for emulsification of a given combination of solvent and active ingredient. The method can reduce the time needed for formulation development, minimize the exposure of the formulator to chemicals, and avoid unnecessary production of effluent in product test facilities. 1. Introduction Stability is a key property related to the production and use of a number of products that are commercialized in the form of emulsions. Emulsion stability can affect not only the appearance of products after storage for a given time but also the efficiency of the dispersal of active ingredients. Emulsion formation requires the presence of interfaces in a fluid system containing at least two phases, which can be exclusively liquid phases or can include solids and/or gas. The properties of an emulsion are affected by a number of factors related to the properties of its individual constituents, such as density, viscosity, or solid particle size, and to the interactions among the different constituents, such as solubility, surface or interfacial tension, and chemical interactions. These properties and their interactions are responsible for maintaining the emulsion in a stable condition by preventing processes such as droplet coalescence, breaking, cream formation, or flocculation1 that are associated with emulsion destabilization. The most commonly used parameter in emulsion formulation is the hydrophilic-lipophilic balance (HLB).2 The HLB is related to the ratio between the hydrophilic and lipophilic parts of the surfactant molecules that play an essential role in promoting emulsification in such multiphase systems. The HLB is one in a number of criteria that are useful as guidelines in emulsion production and handling and is normally employed * Corresponding author. Chemical Engineering Department, University of Sa˜o Paulo, Av. Luciano Gualberto 380 Tv 3, 05508-900 Sa˜o Paulo-SP, Brazil. Phone: +55-11-3091-1169. Fax: +55-11-38132380. E-mail:
[email protected].
to estimate whether a specific surfactant can be added to a given system in order to obtain a stable emulsion. A comprehensive description of fundamental and technological aspects of surfactants is presented in the literature.3 An important limitation of empirical criteria like the HLB is that different surfactants having the same HLB value can show significantly different behavior because of differences in interactions related to the chemical nature of the substances. This deficiency can be even more evident in systems that contain, in addition to the two immiscible phases and surfactant, other components such as cosurfactants, stabilizers, active substances dissolved in one of the fluids, or solid particles. In such cases, criteria based on physical-chemical properties are of limited application and prediction methods rely primarily on empirical correlations. Most of the published work refers to emulsions of pharmaceutical interest and focuses on the development of models for evaluating the effect of different surfactants or surfactant mixtures on pseudo-ternary diagrams for specific water, oil, and surfactant mixtures. In most cases, the behavior of specific water-surfactant mixtures is relatively well-understood from independent experiments and empirical models can be used to describe the effects of cosurfactants. In the last 10 years, there have been a number of reports of different empirical models. Mixture planning techniques were applied in a study on formulation optimization of agrochemical emulsions, and experimental data were fit to quadratic and cubic correlations of the factors.4 Multilinear regression techniques were used with physical-chemical descriptors of the components calculated by a molecular modeling technique to predict microemulsion
10.1021/ie070337a CCC: $37.00 © 2007 American Chemical Society Published on Web 06/21/2007
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existence regions in pseudo-ternary phase diagrams.5 Multilinear regression models and polynomial fits have the advantage of generating coefficients that can be explicitly associated with the descriptors of the system or to the interactions between them, thus enabling comparisons of the relative importance of the factors involved. However, given the complexity of their nature, emulsions are not adequately described by such models, which has motivated the application of neural networks (NNs) to predict emulsion characteristics. A neural network model was fit to experimental data for microemulsion systems containing lecithin, isopropyl myristate, water, and 11 cosurfactants.6 Four physical-chemical descriptors were used as inputs, and one output variable was adopted as an indicator of emulsion formation. The model was able to predict correctly 91.6% of the data from a validation set. Polynomial and NN models for the prediction of the stability of lipophilic semisolid emulsion systems were compared employing the dynamic oscillatory parameter as an indicator of stability at different times.7 Both the polynomial and NN models provided good agreement with experimental data. NN-based approaches were also used to model a system consisting of ethyl oleate, water, and two surfactants.8 The concentrations of the constituents and the mean HLB value were used as input variables, and the output variable served as an indicator of four possible states: homogeneous solution, microemulsion, water-in-oil emulsion, or oil-in-water emulsion. According to the authors, the model was able to classify correctly 90.5% of the data in their validation set. A comparison between a response surface method and a NN model for the optimization of the formulation of a pharmaceutical gel, based on laboratory experiments, was also reported.9 The NN model showed superior performance in predicting most of the variables adopted as responses in formulation preparation. According to the authors, the small inaccuracies observed for some of the model output variables were due to the small number of experiments. A NN optimized by a genetic algorithm was used to predict the different regions in a pseudo-ternary system containing five components: water, ethyl oleate, sorbitan monolaurate, polyoxyethylene 20 sorbitan monooleate, and cosurfactant.10 The model was based on the mixture formulation and on physical-chemical descriptors of the cosurfactants. The model correctly predicted 82.2% of the data points for the microemulsion region in their validation set. The results of the works cited above indicate that the complex nature of emulsion systems can be adequately described by NNlike models that are capable of associating specific patterns of the descriptors with characteristics of the system by taking into account nonlinear relationships among the factors. A specific characteristic of the present study is that, unlike most other published studies, model fitting is not based on experimental data obtained from experiments designed to generate information for model fitting and performed under controlled laboratory conditions. Our study takes advantage of historical data. For this reason, a significantly larger amount of experimental data was available for the NN fitting study, allowing the development of a more representative model. However, even with the standard procedures adopted by the company, long series of historical data are subject to a number of random and systematic perturbations. Furthermore, the obvious tendency to perform most of the tests with formulations that are most commonly commercialized by the company means that the distribution of the data is not always homogeneous over the entire range of the variables. Nonetheless, the data do include a relatively large variety of chemical substances (different active ingredients, solvents, surfactants, and cosurfactants), for which
a number of relevant physical-chemical descriptors and properties were selected as model input variables. A standard procedure-based indicator, the volume percentage of cream formation after dispersion of the emulsifiable concentrate in water, was the output variable associated with emulsion stability. 2. Experimental Section The experimental procedure and criteria adopted in the industry for the evaluation of the physical characteristics of emulsions are defined by the Brazilian Association of Technical Standards.11 The procedure is similar to equivalent ASTM standard procedures such as E1116-9812 and consists of the visual observation of the static emulsion, recording the fraction of emulsion volume occupied by the cream phase as a function of time. Cream formation is an indication that the emulsion is unstable. The volume fraction occupied by the cream is read in a glass cylinder and is denominated the “emulsion-breaking height”. The procedure for preparation of an emulsion sample involved the preparation of a “concentrate”, which is a mixture of three components: an active ingredient, an emulsifier, and a solvent. The concentrate was then diluted 1:100 (volume basis) in water to a final volume of 250 mL. Two standard water samples were used in the dilution: “soft water”, equivalent to 20 mg/kg of calcium carbonate, and “hard water”, equivalent to 342 mg/kg of calcium carbonate. Each sample was placed in a closed glass cylinder shaken by rotation (30 times, 2 s intervals between rotations). After shaking, 100 mL of the sample was placed in a static glass cylinder and the emulsion-breaking height was read at different times. The whole procedure was carried out at a temperature of 30 °C. According to the criteria adopted, an emulsion was labeled as “approved” if no cream formation is observed after 2 h. The minimal reading in the cylinder corresponds to a cream volume of 0.05 mL. If any trace of interface was observed, the value 0.01 mL was recorded. If cream was present, but the reading was
