Constructing first-principles models is usually a challenging and time-consuming task due to the complexity of the real-life processes. On the other hand, data-driven modeling, and in particular neural network models often suffer from issues such as overfitting and lack of useful and highquality data. At the same time, embedding trained machine learning models directly into the optimization problems has become an effective and state-of-the-art approach for surrogate optimization, whose performance can be improved by physics-informed training. In this study, it is proposed to upgrade piece-wise linear neural network models with physics informed knowledge for optimization problems with neural network models embedded. In addition to using widely accepted and naturally piece-wise linear rectified linear unit (ReLU) activation functions, this study also suggests piece-wise linear approximations for the hyperbolic tangent activation function to widen the domain. Optimization of three case studies, a blending process, an industrial distillation column and a crude oil column are investigated. For all cases, physics-informed trained neural network based optimal results are closer to global optimality. Finally, associated CPU times for the optimization problems are much shorter than the standard optimization results.
翻译:由于实际生活过程的复杂性,建立第一原则模型通常是一项艰巨和耗时的任务。另一方面,数据驱动模型,特别是神经网络模型,往往受到过度装配和缺乏有用和高质量数据等问题的影响。与此同时,将经过训练的机器学习模型直接嵌入优化问题已成为一种有效和最先进的代用优化方法,其性能可以通过物理知情培训加以改进。本研究报告建议更新片断线性神经网络模型,以物理知情知识来优化嵌入神经网络模型的问题。除了使用广泛接受和自然的片断线性线性单元(ReLU)激活功能之外,本研究报告还提出了超偏向切线性激活功能的片度直线性近似值,以扩大范围。对三种案例研究、混合过程、工业蒸馏柱和原油柱进行优化的优化进行了优化。对于所有案例,基于物理知情的、经过训练的神经网络的最佳结果都接近于全球优化。最后,与优化相关的CPU问题比标准优化问题要短得多。