Method of Hybrid Adaptive Sampling for the Kriging Metamodel and Application in the Hydropurification Process of Industrial Terephthalic Acid

Abstract

The high-fidelity model is not easy to analyze and optimize because of the high computational cost. When the classical design of experiments is applied to construct the metamodel to replace such a computationally intensive model for analysis or optimization, it usually needs more samples compared with hybrid adaptive sampling to ensure the reliability of the metamodel due to ignoring the system information. In this study, considering the general feature of the chemical model, a new method of hybrid adaptive sampling named hybrid adaptive sampling algorithm based on scores (HASAS) is proposed on the basis of k-nearest neighbor for exploration and nearest neighbor expected improvement for exploitation to enhance the global quality of the metamodel. Furthermore, a weight coefficient is introduced to balance exploration and exploitation during sample placement. Sixteen benchmark cases are utilized to evaluate the performance of the HASAS and four other hybrid adaptive methods. Results show that with the same number of samples, HASAS can perform well in terms of global accuracy on most of them. The effect of the number of initial sample points on HASAS is also discussed. Finally, it is applied for the construction of the Kriging for the hydropurification process of a typical chemical (terephthalic acid) and sensitivity of each variable is done with the metamodel.

A Deep Learning Hybrid Framework Combining an Efficient Evolutionary Algorithm for Complex Many-Objective Optimization of Sustainable Triple CO2 Feed Methanol Production

“…These models possess the capability to approximate the mechanism model in a reduced dimension while ensuring accuracy, thereby mitigating the complexity of many-objective problems while exhibiting strong generalization properties to ensure the efficient convergence of the optimization process. Over the course of time, a multitude of surrogate models have been gradually proposed, including the support vector machine (SVM), , Kriging model, , and artificial neural network (ANN). − An ANN is a crucial component of contemporary computer technology that aims to simulate the structure and functionality of the neural network in the human brain. The simulation comprises a network of interconnected neurons that exhibit strong data processing capabilities and the ability to adapt and learn.…”