Modeling and optimization is crucial to smart chemical process operations. However, a large number of nonlinearities must be considered in a typical chemical process according to complex unit operations, chemical reactions and separations. This leads to a great challenge of implementing mechanistic models into industrial-scale problems due to the resulting computational complexity. Thus, this paper presents an efficient hybrid framework of integrating machine learning and particle swarm optimization to overcome the aforementioned difficulties. An industrial propane dehydrogenation process was carried out to demonstrate the validity and efficiency of our method. Firstly, a data set was generated based on process mechanistic simulation validated by industrial data, which provides sufficient and reasonable samples for model training and testing. Secondly, four well-known machine learning methods, namely, K-nearest neighbors, decision tree, support vector machine, and artificial neural network, were compared and used to obtain the prediction models of the processes operation. All of these methods achieved highly accurate model by adjusting model parameters on the basis of high-coverage data and properly features. Finally, optimal process operations were obtained by using the particle swarm optimization approach.

建模和优化对于智能化学过程操作至关重要。然而，根据复杂的单元操作，化学反应和分离，在典型的化学过程中必须考虑大量的非线性。由于由此产生的计算复杂性，这导致了将机械模型应用于工业规模问题的巨大挑战。因此，本文提出了一种将机器学习与粒子群优化相结合的有效混合框架，以克服上述困难。进行了工业丙烷脱氢工艺，证明了该方法的有效性和有效性。首先，基于通过工业数据验证的过程机械仿真生成了一个数据集，该数据集为模型训练和测试提供了足够而合理的样本。其次，比较了四种最著名的机器学习方法，即K最近邻，决策树，支持向量机和人工神经网络，并使用它们来获得过程操作的预测模型。所有这些方法都是通过在高覆盖率数据和适当特征的基础上调整模型参数来实现高度精确的模型。最后，通过使用粒子群优化方法获得了最佳的工艺操作。