Multi-objective optimization through a novel Bayesian approach for industrial manufacturing of Polyvinyl Acetate

ABSTRACT

Manufacturing long-chain branched polymers enhances the quality of products at the cost of production time, translating into high manufacturing costs. In this manuscript, we study the cost-versus-quality trade-off while optimizing the operating conditions for large-scale industrial production of Polyvinyl acetate (PVAc). PVAc polymerization is emulated using a large system of stiff differential equations resulting in time-expensive function evaluation. Thus, we aim to achieve the Pareto optimality using Multi-Objective Bayesian Optimization (MOBO) that introduces q-Expected Hyper-Volume Improvement (qEHVI) as the novel acquisition function. Comparison with Non-dominated Sorting Genetic Algorithm-II (NSGA-II), applied to the high-fidelity model, resulted in a similar Pareto front with only 1% of the high-fidelity calls required by NSGA-II. The results indicate the efficiency of MOBO for PVAc optimization and present a real-world application of a generic method that can be implemented to solve time-expensive multi-objective optimization in manufacturing processes

KEYWORDS:

• Pvac
• multi-objective
• machine-learning
• Bayesian-optimization
• qEHVI
• NSGA-II
• probabilistic
• evolutionary
• design
• manufacturing

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The work was supported by the National Supercomputing Mission, Department of Science and Technology, Government of India [DST/NSM/R&D_HPC_Applications/2021/23].