Optimization for large scale process based on evolutionary algorithms: Genetic algorithms
Optimization for large scale process based on evolutionary algorithms: Genetic algorithms
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DOI：
10.1016/j.cej.2006.12.032
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发表时间：
2007-08
影响因子：
15.1
通讯作者：
I. R. S. Victorino;J. P. Maia;E. R. Morais;M. Maciel;R. M. Filho
中科院分区：
文献类型：
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作者：
I. R. S. Victorino;J. P. Maia;E. R. Morais;M. Maciel;R. M. Filho
这项工作的目标是开发一种优化方法，使用遗传算法（GAs），作为进化过程，再加上进化的概念。作为案例研究的大型多相催化反应器被认为是。反应器是管状的，并且使用与自热反应器相同的概念用同心管构建，其中冷却剂流体在外部环形中流动。确定性模型的数学方程是基于反应物和冷却剂流体的守恒原理（质量、能量和动量），并用真实的运行数据进行验证。该模型代表了稳态与活塞流假设，这是相当合理的，由于大流量通常在工业反应器中发现。所需产物是特定的环状醇（CA），出于经济和环境原因，需要使副产物最小化。为此，有必要优化一些重要的操作参数。这个问题是很难解决的，因为反应堆是一个大规模的系统，具有复杂的行为和传统的优化工具，连续二次规划往往失败，在这种情况下，因为局部最小值可能会实现。在这项工作中表明，遗传算法技术可以是有用的CA生产最大化，获得良好的结果与操作改进（减少催化剂速率，以及在不希望的产品率-环烷烃（C））。用于过程优化的GA参数是种群大小，交叉类型与交叉率的变化。使用的编码是二进制形式。结果是相当好的，显示出高性能的CA生产率（相当大的增加CA产量）与分析的操作参数的变化，并显示这种优化程序是非常强大和有效的。结果表明，该方法在处理具有非线性和变量相互作用的复杂行为的大系统中具有很好的应用前景。
This work has as objective the development of an optimization methodology, using Genetic Algorithms (GAs), as evolutionary procedure coupled with the concepts of evolutionary. As case study a large scale multiphase catalytic reactor is considered. The reactor is tubular in shape and is built-up with concentric tubes using the same concept of the auto-thermal reactors, with coolant fluid flow in the external annular. The mathematical equations of the deterministic model are based on conservation principles (mass, energy and momentum) for the reactants and for the coolant fluid and validated with real operational data. The model represents the steady-state with the plug-flow assumption which is quite reasonable due to the large flow rates usually found in industrial reactors. The desired product is a specific cyclical alcohol (CA), and the minimization of the by-products is required for economical and environmental reasons. For that it is necessary to optimize some important operational parameters. This problem is of difficult solution since the reactor is a large scale system with complex behavior and conventional optimization tools as Successive Quadratic Programming tends to fail in such situation since local minima may be achieved. In this work is shown that the Genetic Algorithms technique can be useful to CA production maximization, obtaining good results with operational improvements (reduction in the catalyst rate, as well as in the undesired product rate—cycloalkane (C)). The GA parameters used for the process optimization are population size, crossover types with variation of crossover rates. The used coding was the binary form. The results are quite good, showing high performance in the CA productivity (considerable increase CA production) with changes in the operational parameters analyzed and showing that this optimization procedure is very robust and efficient. The results point out that this technique is very promising to deal with large scale system with complex behavior due to non-linearity and variable interactions.