arXiv:2308.07822v2 Announce Type: replace Abstract: The transformation towards renewable energy and feedstock supply in the chemical industry requires new conceptual process design approaches. Recently, breakthroughs in artificial intelligence offer opportunities to accelerate this transition. Specifically, deep reinforcement learning, a subclass of machine learning, has shown the potential to solve complex decision-making problems and aid sustainable process design. We survey state-of-the-art research in reinforcement learning for process design through three major elements: (i) information r
The accelerating transition to renewable energy and feedstock, coupled with recent AI breakthroughs, is driving the need for sophisticated new process design methods within the chemical industry.
This development indicates AI's growing role in optimizing critical industrial processes for sustainability, potentially reshaping manufacturing paradigms and resource allocation.
Traditional process design methodologies are being augmented or replaced by AI-driven approaches, particularly deep reinforcement learning, offering more efficient and sustainable solutions.
- · Chemical industry (early adopters)
- · AI/ML solution providers
- · Renewable energy sector
- · ESG-focused investors
- · Companies slow to adopt AI in R&D
- · Inefficient conventional design firms
- · Carbon-intensive legacy industries
Wider adoption of deep reinforcement learning for industrial process optimization.
Accelerated innovation in sustainable materials and energy production methods.
Reduced environmental footprint and increased efficiency across heavy industries, contributing to broader climate goals and new competitive landscapes.
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