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Product formulation design has seen an increasing attention because of the rising demand for application-specific products such as paints, adhesives, coating chemicals, detergents, disinfectants, pharmaceuticals, etc. However, new product formulation design is becoming increasingly difficult in today's markets due to tough competition. To survive and succeed, companies should be able to design new products in a short pace. Failure to do so can be very costly, not only in terms of market share lost, but also in the investment made to develop a product. Given that traditional product development methods are very slow, and cannot fulfill today's needs, a methodology is presented here to efficiently design new product formulations based on a combination of experimental designs, neural networks and optimization techniques. The methodology is applied on a case study that involves disinfectants' formulations. The framework takes advantage of all previous experiments for the next product formulation design, and archives experimental results of the existing project and uses them to retrain a model for future projects. The results show that the use of the proposed methodology significantly reduces the time and cost of product formulation. Although the methodology is applied to the case study of disinfectant formulation, it can be easily adopted to the design of other products.

Combining Design of Experiments Techniques, Connectionist Models, and Optimization for the Efficient Design of New Product Formulations