## 1 Introduction  
Because of globalization and environmental regulations, chemical process industries are constantly examining all aspects of their processes in an effort to improve their operations to ensure that they remain competitive. In particular, process optimization has been at the forefront of this undertaking. Optimizing process operations in chemical plants involves developing a sufficiently accurate phenomenological, or empirical, model to assist in finding the optimal operating points. In recent years, multiobjective optimization, instead of the minimization of an aggregate objective function used in traditional optimization, has increased in popularity. Indeed, traditional optimization techniques have resolved multiple objectives by combining them into a single objective comprising the weighted sum of the individual objectives, or by considering one objective while treating the others as constraints. Even though single objective optimization is sufficient in many situations, multiobjective optimization provides additional information on the underlying relationships between various objectives. In multiobjective optimization, all objectives are initially considered equally important and the concept of domination serves to circumscribe the Pareto domain using a representative process model. Most often, some of the objective functions conflict such that one objective cannot be improved without deteriorating another. The Pareto domain, comprising nondominated solutions, is obtained without any bias with respect to all objectives and is commonly approximated with a large number of solutions. When the Pareto domain has been obtained, all nondominated solutions are ranked using some preferences expressed by an expert or decision-maker, and the optimal solution is identified. The optimal solution, which is obtained using process models, is then validated on the actual process. Often the solution of the process or plant models used to circumscribe the Pareto domain is computationally expensive, such that determining a sufficiently large number of Pareto-optimal solutions may require an impractically long time. For example, optimization of an ethanol batch fermentation process integrated with a continuous vacuum separation unit performed by combining a Visual Basic for Applications (VBA) code with a Honeywell UniSim(r) Design R430 process flowsheet took a few days to obtain one Pareto domain [1]. One alternative is to use a metamodel, or a surrogate model, where complete or partial operation of the chemical process is modelled with a representative model, often empirical, for which the computational cost would be significantly reduced. One such model is an artificial neural network (ANN), which is a computational network that attempts to mimic the functionality of neurons within the biological central nervous system. In an ANN, adaptable nodes store experiential knowledge acquired via learning algorithms, allowing the network to recognize and predict patterns with no knowledge of the underlying governing equations. This allows ANNs to be used as black-box tools where no prior knowledge about the system is required, thereby achieving high accuracy in multifactorial and nonlinear analysis of complex processes such as fermentation. However, training a neural network requires a good number of representative solutions, which may counteract efforts to reduce the computation time. Strategies to develop effective neural network models for smaller sets of data using a combination of experimental design and stacked neural networks have been previously proposed [2; 3]. In an effort to maximize process information given a small set of data, it is possible to use experimental design. Numerous optimization problems could benefit from such a strategy.