Year: 2026
Type: preprint
Abstract: Life cycle optimization (LCO) selects optimal processes and supply chains from a set of alternatives given environmental constraints and objectives, supporting the design of sustainable systems and their transitions. However, current approaches rely on static assumptions, overlooking two interacting temporal dimensions: temporal distribution (flows are spread over time) and temporal evolution (processes and supply chains change over time). Because distribution dictates flow timing and evolution dictates flow magnitude, both must be considered jointly to accurately assess cumulative environmental impacts and time-specific environmental constraints. We present a framework for time-explicit LCO that captures these interdependencies to determine optimal transition pathways. The framework tracks flow timing relative to process installation and dynamically determines flow magnitudes from vintage-dependent process parameters and time-specific background supply chains. A case study on integrated methanol and pig iron production demonstrates how time-explicit LCO enables optimization against time-resolved environmental impacts and constraints, revealing mitigation strategies inaccessible to static approaches: prioritizing early high-efficiency technology installation to idle older processes as operational savings outweigh embodied impacts of stranded assets, and avoiding transient resource bottlenecks through diversification. To facilitate widespread application, we provide the open-source Python package optimex, enabling practitioners to design transition pathways that rigorously respect environmental limits.
Source: Research Square
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7160527998