Escola Tecnica Superior dEnginyeria Quimica ETSEQ

Tarragona, Spain

Escola Tecnica Superior dEnginyeria Quimica ETSEQ

Tarragona, Spain
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Pozo C.,Escola Tecnica Superior dEnginyeria Quimica ETSEQ | Guillen-Gosalbez G.,Escola Tecnica Superior dEnginyeria Quimica ETSEQ | Sorribas A.,University of Lleida | Jimenez L.,Escola Tecnica Superior dEnginyeria Quimica ETSEQ
Industrial and Engineering Chemistry Research | Year: 2011

The identification of the enzymatic profile that achieves a maximal production rate of a given metabolite is an important problem in the biotechnological industry, especially if there is a limit on the number of enzymatic modulations allowed. The intrinsic nonlinear behavior of metabolic processes enforces the use of kinetic models, such as the generalized mass action (GMA) models, giving rise to nonconvex MINLP formulations with multiple local solutions. In this paper, we introduce a customized spatial branch-and-bound strategy devised to solve efficiently these particular problems to global optimality. A tight MILP-based relaxation of the original nonconvex MINLP is constructed by means of supporting hyperplanes and piecewise linear underestimators. The overall solution procedure is expedited through the use of bound tightening techniques and a special type of cutting plane. The capabilities of the proposed strategy are tested through its application to the maximization of the citric acid production in Aspergillus niger. We also provide a numerical comparison of our algorithm with the commercial package BARON and an outer approximation-based method earlier proposed by the authors. © 2010 American Chemical Society.


Vaskan P.,Escola Tecnica Superior dEnginyeria Quimica ETSEQ | Guillen-Gosalbez G.,Escola Tecnica Superior dEnginyeria Quimica ETSEQ | Kostin A.,Escola Tecnica Superior dEnginyeria Quimica ETSEQ | Jimenez L.,Escola Tecnica Superior dEnginyeria Quimica ETSEQ
Industrial and Engineering Chemistry Research | Year: 2013

We present a decomposition strategy for mixed-integer linear programming (MILP) models that are formulated on the basis of geographic information system (GIS) data. Our algorithm relies on decomposing the MILP into two levels, a master problem and a slave problem between which we iterate until a termination criterion is satisfied. The former is constructed using a K-clustering statistical aggregation method that reduces the computational burden of the model. The solution of this level is used to guide the search in the slave model. A case study that addresses the optimal design of sewage sludge amendment in Catalonia (NE of Spain) is introduced to illustrate the capabilities of the proposed approach. © 2013 American Chemical Society.

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