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首页> 外文期刊>International journal of systems assurance engineering and management >Solution of SNLAE model of backward feed multiple effect evaporator system using genetic algorithm approach
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Solution of SNLAE model of backward feed multiple effect evaporator system using genetic algorithm approach

机译：基于遗传算法的后馈多效蒸发器系统SNLAE模型求解

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This paper presents the genetic algorithm approach to solve the highly complex set of fourteen simultaneous nonlinear algebraic benchmark problem for the backward feed seven effect evaporator. Generally, Newton’s method may be considered a better numerical technique to solve such nonlinear problems. However, a higher number of effects (seven in this case) complicates the evaluation of a 14 × 14 Jacobian matrix that involves determining the analytical derivative of all system variables. Also, such a simultaneous nonlinear algebraic equations model exhibits the problem of divergence and instability when initial values have not been chosen appropriately. In this work, genetic algorithm approach has been demonstrated to be very efficient to solve such complex nonlinear models for a large number of effects in evaporative system without any complications. To make the model more realistic and representative of physico-thermal properties of liquor, boiling point elevation of the liquor during evaporation has been incorporated. Finally, the developed models are solved using genetic algorithm to determine the process variables, namely liquor and steam flow rates, which yield process performance parameters of energy efficiency (steam economy and consumption). The results indicate that a maximum steam efficiency may be achieved for a 50 % steam split in the first two effects.
机译：本文提出了一种遗传算法方法，用于解决后馈七效蒸发器的高复杂度的同时十四个非线性代数基准问题。通常，牛顿法可以被认为是解决此类非线性问题的更好的数值技术。但是，更高数量的影响（在这种情况下为七种）使14×14 Jacobian矩阵的计算复杂化，涉及确定所有系统变量的解析导数。同样，当未适当选择初始值时，这种同时代数非线性代数方程模型也存在发散和不稳定性的问题。在这项工作中，遗传算法方法已被证明非常有效地解决了这种复杂的非线性模型，从而在蒸发系统中产生了许多影响而没有任何复杂性。为了使模型更加逼真并代表液体的物理热性质，已纳入了蒸发过程中液体的沸点升高。最后，使用遗传算法对开发的模型进行求解，以确定过程变量，即液体和蒸汽的流量，从而得出能效（蒸汽经济性和消耗量）的过程性能参数。结果表明，在前两个效果中，将50％的蒸汽分流可达到最大蒸汽效率。

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