Evaluation of hybrid optimization methods for the optimal design of heat integrated distillation sequences (original) (raw)
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The optimal design and synthesis of distillation systems remains one of the most challenging problems in process engineering. The goal of this paper is to introduce an evolutionary approach for the optimization of the total energy consumption of distillation systems with constraints. Moreover, the contribution of this paper is a novel constraint handling technique that manages design goals as equality constraints, such as the purity and the recovery of the final components. In the literature of these problems prevail the use of inequality constraints; although easy to apply they may lead the search to suboptimal solutions. The case study is a distillation column sequence (DCS) for the separation of four components; this problem is easy to describe yet complex to solve so our approach can show its advantages. The evolutionary algorithm Boltzmann Univariate Marginal Distribution Algorithm, (BUMDA), performs the optimization. AspenONE c software is used for the rigorous evaluation of the fitness function of the population. The results show the efficacy performance of the proposed approach reaching near optimal designs in less than 3000 function evaluations.
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The optimal design of hybrid separation systems is a highly non-linear and multivariable problem, and the objective function (total annual cost. TAC) used as optimization criterion is generally non-convex with several local optimums. Particularly, genetic algorithms optimization methods are very attractive for engineering applications, due to its reliability and simplicity in numerical implementation. In this work, we have studied the design of the hybrid distillation/melt crystallization process with conventional and thermally coupled distillation using as a design tool a multi objective genetic algorithm with restrictions. Results indicate that the energy consumption and TAC of the hybrid separation systems can be reduced significantly using coupled distillation systems. The feasibility of this approach is demonstrated by design of a hybrid distillation/melt crystallization process for separation of a ternary isomer mixture, using thermally coupled distillation sequences.
2009
The design and optimization of a coupled distillation system is a non-linear and multivariable problem. The complexity of this kind of problem leads to the high difficulty for solving it. This paper addresses the application of genetic algorithms to the optimization of intensified distillation systems for quaternary distillations. For that purpose, we used a multi-objective genetic algorithm with restrictions, written in Matlab TM coupled to process simulator Aspen Plus TM for the evaluation of the objective function.
Systematic optimization methodology for heat exchanger network and simultaneous process design
Computers & Chemical Engineering, 2016
Distillation units require huge amounts of energy for the separation of the multicomponent mixtures involved in refineries and petrochemical industries. Keeping the same separation standards, energy savings can be achieved in multiple ways. The overall efficiency of the distillation column system is determined from the trade-offs of the Operating Expenditures (OPEX) and Capital investment cost (CAPEX), as there is a strong interaction between the distillation columns and the Heat Exchanger Network (HEN) of the interconnecting streams. Therefore, the highly complex optimization of this trade-off necessitates moving towards process integration solutions. Individually, both distillation columns and HEN are well-defined rigorous problems and numerous methodologies have been developed for finding optimal solutions. The integrative problem however is larger in scale, computational demanding and less robust. In this paper, a systematic optimization methodology for process integration of a multicomponent distillation column complex is presented. The highly nonlinear and time consuming rigorous models of the distillation column are being substituted with simple surrogate models that generate operating responses with adequate accuracy. Moreover, another surrogate model is included in the Mixed Integer Non-Linear Programming (MINLP) formulation to enhance the accuracy of the results by considering the phase changes of the process streams involved in the HEN. The methodology is applied on two case studies of the aromatics separation PARAMAX complex and the results illustrate significant reductions on the Total Annualized Cost. With a scope limited to the benzene and toluene columns, the gain reaches about 15%.
AIChE Journal, 2012
Nonazeotropic multicomponent mixtures are often separated into products by distillation configurations containing multiple distillation columns. One method of calculating the minimum vapor duty of a configuration is to sequentially calculate the minimum vapor duty of each mixture as it is split into two streams within a given column starting from the feed column. The other method simultaneously manipulates all the splits to yield the overall minimum vapor duty of the entire configuration. Of these two methods, the sequential minimization is attractive as it can be analytically solved. However, through extensive computations, we find that the sequential minimization method is not a valid substitute for the simultaneous minimization method. As the number of components in the feed increases, the fraction of the basic configurations for which sequential method yields a reasonable estimate decreases rapidly, thereby emphasizing the need for a more robust and reliable global optimization algorithm. V V