Process Systems Engineering for Circular Carbon: Multi-Scale Optimization and Footprint Accounting
Keywords:
Process systems engineering, Circular carbon, Multi-scale optimization, Carbon footprint accounting, Life-cycle assessment, Digital twins, Artificial intelligenceAbstract
This review aims to synthesize recent advances in process systems engineering applied to circular carbon systems, focusing on multi-scale optimization and integrated carbon footprint accounting to support sustainable industrial design. A qualitative review was conducted using 17 peer-reviewed articles published between 2015 and 2025 that explicitly addressed process systems engineering frameworks for circular carbon applications. Data were collected exclusively through literature review, and thematic analysis was performed using NVivo 14 software. Open, axial, and selective coding was employed to identify recurring themes, subthemes, and concepts until theoretical saturation was achieved. The study emphasized multi-scale modeling, optimization frameworks, carbon footprint accounting, and digitalization as analytical categories. Four main themes emerged: multi-scale modeling for circular carbon systems, optimization frameworks and decision analytics, carbon footprint accounting and circular metrics, and digitalization with artificial intelligence integration. Multi-scale modeling enabled the integration of molecular, unit, plant, and supply-chain scales, supporting accurate representation of carbon flows and system interdependencies. Optimization frameworks, including multi-objective, stochastic, and dynamic methods, facilitated trade-off analysis among environmental, economic, and operational objectives. Carbon footprint accounting was increasingly embedded within design and optimization processes, incorporating life-cycle assessment, allocation rules, and circularity metrics. Digitalization and AI enhanced predictive modeling, real-time optimization, adaptive control, and transparency, while blockchain and cloud-based systems supported traceability and collaborative decision-making. Collectively, these approaches demonstrate a convergence toward integrated, data-driven, and sustainable PSE strategies for circular carbon management. Process systems engineering provides a comprehensive, multi-dimensional framework for circular carbon management, linking modeling, optimization, and footprint assessment across scales. Integration of digital and AI tools further enables adaptive, real-time system management. This review highlights methodological advancements, identifies current gaps, and offers directions for future research to advance the design and implementation of sustainable, circular carbon industrial systems.
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