Optimization of Dividing Wall Distillation Columns

The dividing wall distillation column (DWC) is an energy efficient configuration, capable of a high purity tertiary separation within a single column.1 DWC’s are an alternative to the standard two distillation column configuration. A DWC includes additional degrees of freedom, making modeling and optimization more complex than standard distillation columns.1 This study compiles results from previous DWC pilot columns into a process simulation to validate the method. Three pilot DWC columns were studied - the results of the three DWC column configurations (one four-product DWC and two three-product DWCs) were reconstructed using Aspen PlusTM and the product streams from the resulting simulations were compared to those provided in the authors’ papers.10-12 Each model is optimized using HEEDS®, a multidisciplinary optimization software that tests hundreds of design cases and analyze their results. From a base case simulation, the optimization software varied the DWC design parameters (number of stages, feed location, reboiler duty, etc.) across a specified range. Using the SHERPA optimization method, the objective function of HEEDS® was set to minimize/maximize the key process parameters used to design a DWC. From the simulations, the “best” design is determined, heat transfer is implemented, and a scale-up for each optimized design is conducted. HEEDS® in combination with Aspen PlusTM forms a powerful and efficient tool for the optimization of DWC simulations and designs and the reduction in time and simple user interface allows for many opportunities to test the various complicated design characteristics of the DWC.Chemical Engineerin

Multiobjective Stochastic Optimization of Dividing-wall Distillation Columns Using a Surrogate Model Based on Neural Networks

Surrogate models have been used for modelling and optimization of conventional chemical processes; among them, neural networks have a great potential to capture complex problems such as those found in chemical processes. However, the development of intensified processes has brought about important challenges in modelling and optimization, due to more complex interrelation between design variables. Among intensified processes, dividing-wall columns represent an interesting alternative for fluid mixtures separation, allowing savings in space requirements, energy and investments costs, in comparison with conventional sequences. In this work, we propose the optimization of dividing-wall columns, with a multiobjective genetic algorithm, through the use of neural networks as surrogate models. The contribution of this work is focused on the evaluation of both objectives and constraints functions with neural networks. The results show a significant reduction in computational time and the number of evaluations of objectives and constraints functions required to reaching the Pareto front

Control of Plant Wide Processes Using Fractional Order Controller

Fractional order PID controller is gaining popularity because the presence of two extra degrees of freedom, which have the potential to meet up the extra degrees in terms of uncertainty, robustness, output controllability .In other words, the fractional order PID controller is the generalization of the conventional PID controller. In the current study, the fractional order PID controller is designed and implemented for the complex and plant-wide processes. Distillation is the most effective separation process in the chemical and petroleum industries but with a drawback of energy intensivity To reduce the energy consumption two distillation columns can be combined into one column, which is known as dividing wall distillation column (DWC).Though the control of DWC has been addressed but it requires further R&D efforts considering the complexity in control of this process In this work the DWC is controlled by the advanced control strategy like fractional order PID controller. One of the challenging field in the process control is to design control system for the entire chemical plant. We have presented the control system for the HDA plant by implementing the fractional order PID controller. Both the discussed processes are multi-input-multi-output (MIMO) system and these processes are difficult to tune because of the presence of the interaction between the control loops. For the DWC process, the traditional simplified decoupler is used, while for the HDA plant process the equivalent transfer function model is used to handle the MIMO system. For tuning of the fractional-order PID controllers the optimization techniques have been used. The DWC controllers have been tuned by the ev-MOGA multi objective algorithm and the HDA plant controllers are tuned by the cuckoo search method

Packing Characteristics of Different Shaped Proppants for use with Hydrofracing - A Numerical Investigation using 3D FEMDEM

Imperial Users onl

Single- and multi-objective optimisation of hybrid distillation-pervaporation and dividing wall column structures

The separation of azeotropic mixtures is often energy intensive, thus process intensification (PI) becomes an attractive route to enhance energy efficiency. Two of the most commonly used separation intensifications are dividing wall columns and hybrid distillation-membrane processes. In this work, three typical hybrid distillation structures, distillation followed by pervaporation (D-P), pervaporation followed by distillation (P-D), and distillation followed by pervaporation then by distillation (D-P-D), are considered and compared with a hybrid dividing wall column (H-DWC) structure, which is a highly integrated process combining a dividing wall column and a pervaporation membrane network. The four structures are compared by both single-objective and multi-objective optimisation. It is shown that the D-P-D and H-DWC structures require significantly lower total annualized costs than the other two designs due to requiring smaller membrane area, as these two structures use the membrane only to help the mixture composition cross the azeotropic point

Rigorous Design of Complex Distillation Columns Using Process Simulators and the Particle Swarm Optimization Algorithm

We present a derivative-free optimization algorithm coupled with a chemical process simulator for the optimal design of individual and complex distillation processes using a rigorous tray-by-tray model. The proposed approach serves as an alternative tool to the various models based on nonlinear programming (NLP) or mixed-integer nonlinear programming (MINLP) . This is accomplished by combining the advantages of using a commercial process simulator (Aspen Hysys), including especially suited numerical methods developed for the convergence of distillation columns, with the benefits of the particle swarm optimization (PSO) metaheuristic algorithm, which does not require gradient information and has the ability to escape from local optima. Our method inherits the superstructure developed in Yeomans, H.; Grossmann, I. E.Optimal design of complex distillation columns using rigorous tray-by-tray disjunctive programming models. Ind. Eng. Chem. Res.2000, 39 (11), 4326–4335, in which the nonexisting trays are considered as simple bypasses of liquid and vapor flows. The implemented tool provides the optimal configuration of distillation column systems, which includes continuous and discrete variables, through the minimization of the total annual cost (TAC). The robustness and flexibility of the method is proven through the successful design and synthesis of three distillation systems of increasing complexity.The authors would like to acknowledge financial support from the Spanish “Ministerio de Ciencia e Innovación” (CTQ2009-14420-C02-02 and CTQ2012-37039-C02-02)

Inherently Safer Design and Optimization of Intensified Separation Processes for Furfural Production

Currently furfural production has been the subject of increased interest because it is a biobased chemical able to compete with fossil-based chemicals. Furfural is characterized by flammability, explosion, and toxicity properties. Improper handling and process design can lead to catastrophic accidents. Hence it is of most importance to use inherent safety concepts during the design stage. This work is the first to present several new downstream separation processes for furfural purification, which are designed using an optimization approach that simultaneously considers safety criteria in addition to the total annual cost and the eco-indicator 99. The proposed schemes include thermally coupled configuration, thermodynamic equivalent configuration, dividing-wall column, and a heat integrated configuration. These are compared with the traditional separation process of furfural known as the Quaker Oats Process. The results show that because of a large amount of water present in the feed, similar values are obtained for total annual cost and eco-indicator 99 in all cases. Moreover, the topology of the processes has an important role in the safety criteria. The thermodynamic equivalent configuration resulted as the safest alternative with a 40% reduction of the inherent risk with respect to the Quaker Oats Process, and thus it is the safest option to purify furfural