269057 Chemical Product Design Using Chemometric Technique in Property Cluster Space

Sunday, October 28, 2012
Hall B (Convention Center )
Subin Hada and Mario Richard Eden, Department of Chemical Engineering, Auburn University, Auburn, AL

Recently, significant research in the chemical industries are focused efficient design/synthesis of new product, this is, the search for the most appropriate new materials or mixture of materials with specifically tailored properties that will exhibit and/or cause the desired behavior. Hence, product design is now being considered as an emerging paradigm in the field of chemical engineering because it requires a different set of tools and skill from other problems encountered in chemical engineering. In product design, we do not know the identity of the final product but we have some idea of how we want it to behave. Examples of chemically formulated products include pharmaceuticals, nano-structured, semi-conductors, household products, proteins, fuel mixtures, and many more.

In this work, a logical and systematic approach to solve chemical product design problems by integrating a larger space of knowledge emerging at the interface of fields that traditionally has not been incorporated into chemical engineering science and practice. Interdisciplinary methods and tools that extend through multivariate statistics, applied mathematics and computer science are encompassed. Methodologies and techniques such as characterization based group contribution method, chemometric technique, reverse problem formulation and property clustering techniques are combined with existing Computer Aided Molecular/Mixture Design (CAMD/CAMbD) algorithms to design chemical products in computationally efficient manner that provide optimum performance in terms of customer requirements.

Two types of product design are targeted: mixture design and molecular design. In mixture design, the property based framework is combined with multivariate statistical technique and applied in a reverse problem formulation on chemical product design problems by systematic and insightful use of past data describing the properties of the raw materials, their blend ratios, and the process conditions during the production of a range of product grades to achieve new and improved products. Projection methods, like principal component analysis (PCA) and partial least squares (PLS) are applied to identify the underlying relationships necessary for simultaneous optimization of all three variables. The method and concept is illustrated using a polymer blending problem.

In molecular design, a multivariate characterization technique like infrared spectroscopic is utilized to describe the molecular architecture of a set of representative samples followed by decomposition techniques such as principal component analysis to find the underlying latent variable models that describe the molecule’s properties. Characterization based group contribution method is applied to design new molecules that were not present in the original training set and that satisfies specific product property requirements. The concept and the solution methodology are demonstrated using an example of biodiesel additive formulation.

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