Papers by JUAN JAVIER HUIZAR ZAMORA
Computers & Chemical Engineering, 1998
In this paper a global optimization algorithm is presented to rigorously solve the MINLP model by... more In this paper a global optimization algorithm is presented to rigorously solve the MINLP model by for the synthesis of heat exchanger networks under the simplifying assumptions of linear area cost, arithmetic mean temperature difference driving forces and no stream splitting. The proposed approach relies on the use of two new different sets of convex underestimators for the heat transfer area. A thermodynamic analysis is used to derive the first set of analytical linear and nonlinear convex underestimators as well as variable bounds and bounds contraction relationships. The second set of convex underestimators is generated by a relaxation of the heat transport equation through the introduction of a new variable, and an inequality that contains a nonconvex term that is subsequently replaced by its concave envelope. Based on these new underestimator functions, the original nonconvex MINLP is replaced by a convex MINLP that predicts tight lower bounds to the global optimum, and which is used in a hybrid branch and bound/outer-approximation search method. Application of the proposed ideas, and the algorithm are illustrated with several numerical examples.
Twitter is one of the most important social network, where extracting useful information is of pa... more Twitter is one of the most important social network, where extracting useful information is of paramount importance to many application areas. Many works to date have tried to mine this information by taking the network structure, language itself or even by searching for a pattern in the words employed by the users. Anyway, a simple idea that might be useful for every challenging mining task-and that at out knowledge has not been tackled yet-consists of predicting the amount of messages (stream volume) that will be emitted in some specific time span. In this work, by using almost 180k messages collected in a period of one week, a preliminary analysis of the temporal structure of the stream volume in Twitter is made. The expected contribution consists of a model based on artificial neural networks to predict the amount of posts in a specific time window, which regards the past history and the daily behavior of the network in terms of the emission rate of the message stream.
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Papers by JUAN JAVIER HUIZAR ZAMORA