SymbiosisMatch: An AI-augmented decision-support tool for energy and carbon symbiosis in industrial parks
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Abstract
Early-stage screening of industrial symbiosis is constrained by incomplete data and limited representation of energy storage. This study presents SymbiosisMatch, a browser-based decision-support framework for identifying energy and carbon symbiosis opportunities in industrial parks. It combines editable park profiles with deterministic, storage-aware multi-criteria scoring of 15 solution archetypes, separate robustness and confidence indicators, and physical-output safeguards. An optional large language model interprets results without modifying calculations. Evaluation uses an illustrative mixed-industrial-park profile, four controlled storage configurations, and a contrasting electricity-focused scenario. Battery and thermal storage contributions change compatible solution scores by 6–10 points, altering heat-recovery and electricity-flexibility priorities while leaving unrelated scores unchanged. Sensitivity analysis shows that stable rankings can coexist with low input confidence, underscoring the distinction between compatibility and supporting evidence. Unsupported impact estimates are withheld when necessary physical inputs are unavailable. SymbiosisMatch provides traceable candidate prioritisation and identifies information needs before detailed technical and economic feasibility assessment.
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© 2026 The authors. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License.
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