LnRiLWhlYWRpbmcuaGFzLWJhY2tncm91bmR7cGFkZGluZzowfQ==
LnRiLWZpZWxke21hcmdpbi1ib3R0b206MC43NmVtfS50Yi1maWVsZC0tbGVmdHt0ZXh0LWFsaWduOmxlZnR9LnRiLWZpZWxkLS1jZW50ZXJ7dGV4dC1hbGlnbjpjZW50ZXJ9LnRiLWZpZWxkLS1yaWdodHt0ZXh0LWFsaWduOnJpZ2h0fS50Yi1maWVsZF9fc2t5cGVfcHJldmlld3twYWRkaW5nOjEwcHggMjBweDtib3JkZXItcmFkaXVzOjNweDtjb2xvcjojZmZmO2JhY2tncm91bmQ6IzAwYWZlZTtkaXNwbGF5OmlubGluZS1ibG9ja311bC5nbGlkZV9fc2xpZGVze21hcmdpbjowfQ==
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
Nanocrystalline cellulose is a nano-structured biomaterial often applied in biopolymers to enhance technical performance. Cellulose nanocrystals (CNC) have been obtained in the laboratory from lignocellulosic wastes, e.g., mango seed shells [Ref.1]. Assessing the environmental impacts of novel technologies (low TRL) requires modeling a future industrial production, which can be done using several scale-up techniques. However, these techniques can lead to different LCA results, thus misleading R&D decisions. This study aims to assess the environmental performance of CNC production from mango shells at an early stage (TRL 3) by applying different scale-up approaches. An ex-ante LCA was implemented from cradle-to-gate for 1 kilogram of CNC (dry). Seed shells were considered burden-free since they do not have economic value. CNC production was upscaled by applying: A) Process calculations method [Ref.2] based on lab experiment [Ref.1]; B) Adapted pilot-plant data for CNC production from wood pulp [Ref.3]; C) Adapted techno-economic analysis (TEA) data based on expert estimations [Ref.4]. Model A considers only CNC is produced. Models B and C consider three co-products (CNC, sugar, and gypsum), and the burdens were partitioned using economic allocation. The upscaled LCI models show different features. Model A shows lower electricity use compared to the laboratory but no material reductions. In contrast, Model B shows higher electricity consumption due to an intensive separation technique (ultrafiltration) but considerably reduced acid usage. Model C shows further reductions in electricity and acid usage. The impact assessment was performed for climate change. Model A has the highest impact (137 kg CO2-eq/kg CNC)– with acid consumption as the primary hotspot– followed by B (100 kg CO2-eq)– due to electricity use in ultrafiltration–, and C (83 kg CO2-eq). Our impact scores were higher than literature values (20-25 kg CO2-eq. – Ref.5,3) mainly due to the low yield of CNC from shells (three times lower than wood pulp CNC). There are advantages to using process calculations, e.g., information detailed by unit processes. On the other hand, model A did not capture co-products (not considered at lab-scale) nor reactant reductions due to more efficient agitation. Models B (Pilot-plant data) and C (TEA) provided aggregated data based on a different feedstock. Our analysis shows different scale-up approaches lead to contrasting impact results and hotspots. It highlights the need to improve upscaling in ex-ante LCA, potentially combining various techniques. Refining scale-up modeling shall advance our ability to assess impacts in the early stages and promote sustainable innovation.
Get access to the recording of this presentation:
