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Hit rate is a key performance metric in predicting process product quality inintegrated industrial processes. It represents the percentage of productsaccepted by downstream processes within a controlled range of quality. However,optimizing hit rate is a non-convex and challenging problem. To address thisissue, we propose a data-driven quasi-convex approach that combines factorialhidden Markov models, multitask elastic net, and quasi-convex optimization. Ourapproach converts the original non-convex problem into a set of convex feasibleproblems, achieving an optimal hit rate. We verify the convex optimizationproperty and quasi-convex frontier through Monte Carlo simulations andreal-world experiments in steel production. Results demonstrate that ourapproach outperforms classical models, improving hit rates by at least 41.11%and 31.01% on two real datasets. Furthermore, the quasi-convex frontierprovides a reference explanation and visualization for the deterioration ofsolutions obtained by conventional models.

A Novel Black Box Process Quality Optimization Approach based on Hit Rate