Chromatographic separation processes are described by nonlinear partial differential and algebraic equations, which may result in high computational cost, hindering further online applications. To decrease the computational burden, different data-driven modeling approaches can be implemented. In this work, we investigate different strategies of data-driven modeling for chromatographic processes, using artificial neural networks to predict pseudo-dynamic elution profiles, without the use of explicit temporal information. We assess the performance of the surrogates trained on different dataset sizes, achieving good predictions with a minimum of 3400 data points. Different activation functions are used and evaluated against the original high-fidelity model, using accuracy, interpolation, and simulation time as performance metrics. Based on these metrics, the best performing data-driven models are implemented in a process optimization framework. The results indicate that data-driven models can capture the nonlinear profile of the process and that can be considered as reliable surrogates used to aid process development.

中文翻译：

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评估色谱分离过程的数据驱动建模方法


色谱分离过程由非线性偏微分和代数方程描述，这可能会导致高计算成本，从而阻碍进一步的在线应用。为了减轻计算负担，可以实施不同的数据驱动建模方法。在这项工作中，我们研究了色谱过程数据驱动建模的不同策略，使用人工神经网络来预测伪动态洗脱曲线，而无需使用显式的时间信息。我们评估了在不同数据集大小上训练的代理的性能，以至少 3400 个数据点实现了良好的预测。使用不同的激活函数，并根据原始高保真模型进行评估，使用准确率、插值和仿真时间作为性能指标。基于这些指标，在流程优化框架中实施性能最佳的数据驱动模型。结果表明，数据驱动模型可以捕获过程的非线性曲线，并且可以将其视为用于辅助过程开发的可靠替代指标。