A Computer-Aided Methodology for Mixture-Blend Design. Applications to Tailor-Made Design of Surrogate Fuels Academic Article uri icon

abstract

  • © 2018 American Chemical Society. Modern society needs various chemical products for its survival. The chemical products are classified in terms of single species products, multiple species products, and devices. Multiple species products such as mixtures and blends are one of the most widely used chemical products. However, the common design methods for this kind of product are still mostly by trial and error or by rule-based approaches. A computer-aided methodology integrated with experimental verification is presented in this article. In the first step of this methodology, model-based computer-aided techniques are employed to the design of mixtures and blends. In the second step, the properties of the most promising product candidates are verified through experiments and/or rigorous models. The starting point is to analyze the product needs and translate them into target property constraints. A list of molecules that serve as ingredient-chemicals for addition to the blended product together with their pure compound properties are generated using a well-known computer-aided design molecular design technique. A mixed integer nonlinear programming (MINLP) model is established for the selection of the ingredient-chemicals and their compositions in the blended product. The solution methods for the MINLP model are presented. For the first time, phase equilibrium based properties (such as liquid solution activity coefficients) are modeled and solved simultaneously in the MINLP model through the use of UNIFAC model. Finally, the optimization results are verified through experiments and rigorous models. Two application examples highlighting tailor-made surrogate fuel designs of a gasoline blend and a jet-fuel blend are presented.

altmetric score

  • 1.5

author list (cited authors)

  • Zhang, L., Kalakul, S., Liu, L., Elbashir, N. O., Du, J., & Gani, R.

citation count

  • 11

publication date

  • May 2018