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.

翻译：命中率是综合工业过程中预测过程产品质量的关键性能指标，它表示下游工序在可控质量范围内所接受产品的百分比。然而，命中率优化是一个非凸且具有挑战性的问题。为解决这一问题，我们提出了一种数据驱动的拟凸方法，该方法结合了因子隐马尔可夫模型、多任务弹性网络和拟凸优化。该方法将原始非凸问题转化为一组凸可行问题，从而实现最优命中率。通过蒙特卡洛模拟和钢铁生产的实际实验，我们验证了凸优化性质和拟凸前沿。结果表明，该方法优于经典模型，在两个真实数据集上命中率分别提升至少41.11%和31.01%。此外，拟凸前沿为传统模型所求解的退化现象提供了参考解释和可视化。