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Heat Exchanger Network (HEN) synthesis is an important field of study in process engineering. However, obtaining optimal HEN design is a complex task. When mathematically formulated, it may require sophisticated methods to achieve good solutions. The complexity increases even more for large-scale HEN. In this work, a hybrid meta-heuristic method is presented. A rather simple Simulated Annealing approach is used for the combinatorial level, while a strategy named Rocket Fireworks Optimization is developed and applied to the continuous domain. An advantage over other approaches is that the algorithm was written in C++, which is free and faster when compared to many other languages. The developed method was able to provide the lowest costs solutions reported so far to six cases well studied in the literature. An important feature of the approach here proposed is that, differently from other approaches, it does not split HEN into smaller problems during the optimization. This article is protected by copyright. All rights reserved.

Designing effective environmental policies for mitigating global warming is a very challenging task that requires detailed knowledge of the international channels through which goods are traded. This work presents a decision-support tool that minimizes the environmental impact at a global macroeconomic scale by performing changes in the economic sectors of an economy. Our tool combines multi-objective optimization, environmentally extended input–output tables and life cycle assessment within a unified framework. Our results on the U.S. economy to minimize CO2 emissions identify sectors that should be regulated first to reach a given environmental target while maximizing the demand satisfaction. The impact of shale gas on our results is also studied. Our findings show that the application of process systems engineering tools at a macroeconomic level can provide valuable insight for public policy makers into problems of general interest. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3639–3656, 2016

This article proposes a multiobjective optimization model for the design of a macroscopic water system of a Mexican city that solves simultaneously the planning and scheduling of water storage and distribution tasks. The model, which considers rainwater harvesting and reclaimed water reusing as alternative water sources, maximizes the revenues from water sales and minimizes simultaneously the water consumption and land use. A case study based on the city of Morelia in Mexico was solved. It was found that the use of alternative water sources (such as harvested rainwater) along with an appropriate planning and scheduling of storage and distribution tasks have the potential to reduce the pressure over natural reservoirs significantly. Our approach considers simultaneously economic and environmental concerns, thereby contributing to the implementation of more sustainable alternatives in urban water distribution. © 2015 American Institute of Chemical Engineers AIChE J, 61: 2428–2446, 2015