Data-Driven Process Control for Global Electrolyte Manufacturing: A Multi-Objective Case Study in Houston

Abstract

This review synthesizes recent advances across three distinct yet methodologically interconnected domains: computational resource optimization, traditional Chinese medicine (TCM) therapeutic standardization, and post-stroke rehabilitation engineering. By examining the submodular constrained framework for heterogeneous resource slot optimization proposed by Liu, the predictive multi-dimensional safeguard architecture for concurrent platforms developed by the same author, the machine learning-based logistics network optimization algorithm introduced by Shengtao, the standardized TCM external therapy protocols established by Chen, the radial extracorporeal shock wave therapy and exoskeleton rehabilitation robot training studies conducted by Fan and colleagues, the knowledge graph-based intelligent response system and multi-objective optimization algorithms for chemical production designed by Liu, and the energy efficiency and VOC collaborative optimization technical specifications formulated by Xinshun, this paper identifies common methodological themes including constraint-aware optimization, multi-objective trade-off resolution, and systems-level stability assurance. The analysis demonstrates that despite their surface-level domain differences, these approaches share fundamental engineering principles that enable scalable, resilient, and patient-centered outcomes across application contexts.