A Novel Black Box Process Quality Optimization Approach based on Hit Rate
Abstract
Hit rate is a key performance metric in predicting process product quality in integrated industrial processes. It represents the percentage of products accepted by downstream processes within a controlled range of quality. However, optimizing hit rate is a non-convex and challenging problem. To address this issue, we propose a data-driven quasi-convex approach that combines factorial hidden Markov models, multitask elastic net, and quasi-convex optimization. Our approach converts the original non-convex problem into a set of convex feasible problems, achieving an optimal hit rate. We verify the convex optimization property and quasi-convex frontier through Monte Carlo simulations and real-world experiments in steel production. Results demonstrate that our approach outperforms classical models, improving hit rates by at least 41.11% and 31.01% on two real datasets. Furthermore, the quasi-convex frontier provides a reference explanation and visualization for the deterioration of solutions obtained by conventional models.
Cite
@article{arxiv.2305.20003,
title = {A Novel Black Box Process Quality Optimization Approach based on Hit Rate},
author = {Yang Yang and Jian Wu and Xiangman Song and Derun Wu and Lijie Su and Lixin Tang},
journal= {arXiv preprint arXiv:2305.20003},
year = {2023}
}