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首页> 外文会议>European Symposium on Computer Aided Process Engineering >Surrogate Models combined with a Support Vector Machine for the Optimized Design of a Crude Oil Distillation Unit using Genetic Algorithms
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Surrogate Models combined with a Support Vector Machine for the Optimized Design of a Crude Oil Distillation Unit using Genetic Algorithms

机译：代理模型与支持向量机结合使用遗传算法的原油蒸馏装置的优化设计

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摘要

This paper introduces a novel optimization-based framework for the design of a crude oil distillation unit. The approach presented integrates surrogate models based on artificial neural networks (ANN) with feasibility constraints generated using a support vector machine (SVM) in order to optimise the column configuration and its operating conditions. The SVM filters infeasible design options from the solution space of the design problem, which reduces the computational effort and ultimately improves the quality of the final solution. Rigorous process simulations are used to build the surrogate model, while pinch analysis is employed to determine the maximum heat recovery and minimum utility costs. The objective is to minimise the total annualized cost, which is optimised by combining a genetic algorithm with the surrogate model. The approach is illustrated in an industrially relevant case study.
机译：本文介绍了一种新颖的基于优化的框架，用于设计原油蒸馏装置。该方法呈现了基于人工神经网络（ANN）的代理模型，使用支持向量机（SVM）产生的可行性约束，以优化列配置及其操作条件。 SVM从设计问题的解决方案空间中滤除了不可行的设计选项，从而降低了计算工作，并最终提高了最终解决方案的质量。严格的工艺模拟用于构建代理模型，而采用捏合分析来确定最大热回收率和最低效用成本。目的是通过将遗传算法与代理模型结合来最大限度地减少年化成本。该方法在工业相关的案例研究中示出。

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