Decarbonizing the American Industrial Base: Ai-Enabled Process Optimization for Resilient Photovoltaic (Pv) Manufacturing Supply Chains

Authors

  • S M Mainul Islam

DOI:

https://doi.org/10.22178/acta.25.5.31

Keywords:

Photovoltaic Manufacturing, Artificial Intelligence, Process Optimization, Supply Chain Resilience, Industrial Decarbonization, Machine Learning, Clean Energy Manufacturing, Sustainable Manufacturing

Abstract

The United States faces dual imperatives of industrial decarbonization and supply chain resilience, particularly acute in photovoltaic manufacturing where over 80% of global production concentrates in Asia. This research examines how artificial intelligence-enabled process optimization can simultaneously advance domestic PV manufacturing competitiveness and carbon reduction objectives. We investigate machine learning applications across the PV manufacturing value chain—from polysilicon production through module assembly—identifying opportunities where AI optimization reduces energy consumption, material waste, and production defects while improving throughput and quality. Through analysis of manufacturing data from pilot facilities and modeling of AI intervention scenarios, we demonstrate that integrated AI optimization can reduce PV manufacturing energy intensity by 18-25%, decrease material waste by 15-22%, and improve first-pass yield by 12-18% compared to conventional process control. The research develops a framework for AI-enabled manufacturing optimization that addresses both operational efficiency and supply chain resilience through predictive maintenance, adaptive quality control, and dynamic resource allocation. Key findings indicate that AI optimization delivers greatest impact in energy-intensive polysilicon and wafer production stages where process parameters critically affect both quality and energy consumption. However, successful implementation requires substantial data infrastructure investment, skilled workforce development, and integration with existing manufacturing execution systems. This work contributes to industrial decarbonization literature by demonstrating quantifiable pathways for AI to reduce manufacturing carbon intensity while supporting reshoring objectives, providing actionable guidance for policymakers designing industrial policy and manufacturers pursuing sustainability goals.

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Published

2024-12-25

How to Cite

S M Mainul Islam. (2024). Decarbonizing the American Industrial Base: Ai-Enabled Process Optimization for Resilient Photovoltaic (Pv) Manufacturing Supply Chains. Acta Scientiae, 25(5), 596–613. https://doi.org/10.22178/acta.25.5.31

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Section

Articles