Claudia Gutiérrez-Antonio

Pareto front of ideal Petlyuk sequences using a multiobjective genetic algorithm with constraints

Abstract Petlyuk sequences are a very promissory option to reduce energy consumption and capital costs in distillation. The optimal design of Petlyuk sequence implies determining 8 integers and 3 continuous variables with the minor heat duty and number of stages possible, but reaching the specified purities. Note that the heat duty and the number of stages are variable in competition, since we cannot decrease indefinitely one without increasing the other. In other words, we have a multiobjective problem with constraints. In contrast with other numerical strategies proposed, we consider the search of optimal designs set, from minimum reflux ratio to minimum number of stages and all designs between them. This set of optimal designs can be achieved by means of Pareto front, which represents a set of optimal solutions not dominated for a multiobjective problem. In this work, we implemented a multiobjective genetic algorithm with constraints to obtain the Pareto front of Petlyuk sequences. This algorithm is coupled to Aspen Plus simulator, so the complete MESH equations and rigorous phase equilibrium calculations are used. Results show clear tendencies in the design of Petlyuk sequence, and can be used to develop a short design method. In addition, the tool developed can be used to optimize not only the distillation columns but also complete chemical and petrochemical plants.

Simulation and optimization of a biojet fuel production process

Abstract Aviation sector contributes with 2% of the total CO 2 emissions due to human activities. Moreover, predictions estimate that air traffic will duplicate in the next 20 years, with the corresponding increasing in CO 2 emissions. The International Air Transport Association (IATA) has established four strategies to reduce CO 2 emissions; one strategy is the development of aviation fuel from renewable feedstocks, known as biojet fuel. In 2009 UOP Honeywell received a patent for its process to produce aviation fuel from renewable feedstocks. The process considers the transformation of vegetable oil through hydrogenating, deoxygenating, isomerizing and selective hydrocracking to generate propane and hydrocarbons fuels. The resulting aviation fuel is very similar to the fossil one, with the only difference that the first one does not contain aromatic compounds. Due to this, the ASTM standard established the use of biojet fuel in mixtures with fossil jet fuel with up to 50% of the bio-fuel. Also, it is important to remark that in this moment the process of UOP Honeywell is the only one certified for the production of aviation fuel from renewable feedstocks. In this work we propose a model for the production of biojet fuel, obtaining an estimation of the conversion of the reactions of the process of UOP Honeywell. Also, the optimization of the purification stage is performed using a multiobjective genetic algorithm with constraints, which is coupled to Aspen Plus process simulator, in order to generate results considering the complete models of the process. Results show a high conversion of the vegetable oil (castor oil) to biofuels (biojet fuel and green diesel); also, energy can be generated in the process as result of the conditioning of the stream that is fed to the distillation train.

Speeding up a multiobjective genetic algorithm with constraints through artificial neuronal networks

Abstract Evolutionary algorithms have been recognized to be well suited for multiobjective optimization [1]; their principal disadvantage is the large number of evaluations of objective function required [2], which is accentuated when those are computationally expensive. In this work, we propose the use of artificial neuronal networks, ANN, to speed up a multiobjective genetic algorithm with constraints, with base on the work of Gaspar-Cunha [3]. The neuronal network generates an approximated function of the original objective function, which are switched during the optimization; so, we reduce the evaluations of the original objective function and the computational time. The use of approximated functions created by the ANN allows reaching the optimal zone rapidly. Results show a significant reduction in the number of evaluations of the objective function, as well as in computational time, required to reaching the Pareto front.

EFFECT OF DIFFERENT THERMODYNAMIC MODELS ON THE DESIGN OF HOMOGENEOUS AZEOTROPIC DISTILLATION COLUMNS

An important part in the design of homogeneous azeotropic distillation systems is the choice of the thermodynamic model to describe the vapor-liquid (V-L) equilibrium properties of the fluid mixture. In this work, an analysis of the effect of choosing different thermodynamic models for the V-L equilibrium description of several azeotropic mixtures on the design of distillation columns is presented. The selected models were found to provide a good description of the equilibrium properties of the mixtures; however, when they were applied within a design method for the separation column significant differences were found for the number of trays required for a given distillation task. Some of the difference of the resulting designs can be related to the different residue curve maps provided by each thermodynamic model.

Energy integration of a hydrotreating process for the production of biojet fuel

Biojet fuel has been identified as the most promissory alternative to reduce CO2 emissions in the aviation sector, which contributes about 2% of the total emissions of carbon dioxide. There are several processes available for the production of biojet fuel; nevertheless, the hydrotreating process is one of the most promising, since it can be adapted to the existing refinery infrastructure and it is also certified by ASTM. Biojet fuel, nevertheless, is still not economically competitive with the conventional, petroleum-based jet fuel. Thereby, in this work we propose the energy integration of the hydrotreating process considering Jatropha Curcas as renewable raw material. We present a kinetic model for the reactive section, in order to estimate the energy released by the process, which is used to partially satisfy the energy requirements of the purification section. Finally, the effect of the energy integration on the price of biojet fuel is analyzed. Results show that the cost per liter of biojet fuel is very close to the cost of the fossil one, when the energy generated in the process is used. Thus, through a proper energy integration on the production process, the cost of biojet fuel can be competitive with that of the fossil jet fuel.

Optimal planning for the supply chain of biofuels for aviation in Mexico

There are great challenges to replace fossil fuels by biofuels, including the development of efficient technologies and adequate strategic planning. For the manufacture of biofuels at the industrial scale, there is a need to optimize the overall associated supply chain. Among biofuels, the global market for the aviation biofuel has increased drastically, mainly due to the forecasted growth of the aviation sector. Nevertheless, the supply chain for the aviation biofuel has specific characteristics, which are different from the case of bioethanol and biodiesel. Therefore, this paper presents a general optimization approach for optimizing the supply chain of aviation biofuels. A case study is presented accounting for the projection of the Mexican government for implementing the use of aviation biofuel in the airport network. The application of the proposed approach generates very interesting solutions, which are grouped in a Pareto front; from these results, it can be highlighted that the demand for aviation biofuel in the Mexican market can be satisfied with a gross economic benefit of M US

Design of Reactive Distillation with Thermal Coupling for the Synthesis of Biodiesel using Genetic Algorithms

1681 per year, and savings of 93% of carbon dioxide emissions. The generated information can contribute to the establishment of the sustainable supply chain for aviation biofuels in Mexico.

Dividing wall distillation columns for separation of azeotropic mixtures: feasibility procedure and rigorous optimization

Abstract The esterification of lauric acid and methanol is explored using a thermally coupled distillation sequence with a side rectifier and the Petlyuk distillation column. The study was conducted using as a design tool a multi objective genetic algorithm with restrictions. The product of the esterification can be used as biodiesel. It was found that the thermally coupled distillation sequence involving a side rectifier can produce biodiesel with a high purity (around 0.999) and also pure water, and the excess of methanol is recovered in a side rectifier. The results indicate that the energy consumption of the complex distillation sequence with a side rectifier can be reduced significantly by varying operational conditions. These reductions in energy consumption can be interpreted as reductions in carbon dioxide emissions.

Analysis of alternative non-catalytic processes for the production of biodiesel fuel

Abstract In this work, we propose the use of dividing wall distillation columns, DWC, to separate azeotropic mixtures. First, we present a procedure to guarantee the feasibility of the desired split, based on material balances. Once the feasibility is verified, a preliminary design is calculated considering the DWC as an arrangement of three disengaged conventional distillation columns. Then, this design is the initial solution of the optimization procedure: a multiobjective genetic algorithm with constraints coupled to Aspen Plus [1]. This algorithm is used to find optimal designs of DWC for two azeotropic mixtures of industrial importance, with just one distillation border at the operating pressure. Results show that use the DWC for separation of azeotropic mixtures is feasible, and important energy savings, until 50%, can be obtained in comparison with conventional sequences. In addition, tendencies in the location of interconnection, side and feed streams were founded. In general, results provide information that can be used for developing a short design method for these schemes.

Modelling of the hydrotreating process to produce renewable aviation fuel from micro-algae oil

One of the most common supercritical processes for the production of biodiesel fuel involves the use of methanol as reactant. Besides obtaining biodiesel fuel, glycerol is also produced. To avoid the production of glycerol as by-product, alternative reactants for the production of the biofuel have been proposed in recent years. As expected, the use of different reactants may have an impact on the separation processes required to obtain biodiesel fuel complying with international standards. Thus, in this work flowsheets for the different supercritical processes for the production of biodiesel are proposed and analyzed in a simulation environment. The analyzed processes are then compared in terms of energy requirements, total annual costs, and environmental impact. It has been found that the two-step processes show advantages in terms of CO2 emissions, but in terms of total annual cost the one-step processes are better, showing potential for low CO2 emissions. Nevertheless, the processes in one-step (with methanol or methyl acetate) result in lower CO2 emissions and TAC if they are operated at lower temperature. Acetic acid process is the more energy-intensive and expensive of the four processes.