Soft-sensors to drive manufacturing toward clean production: LCA based on Digital Twin

This study introduces a novel methodological framework that integrates soft-sensor-based Digital Twin (DT) technology with Life Cycle Assessment (LCA), addressing data acquisition challenges and enabling dynamic environmental impact assessment. By aligning the DT standard (ISO 23247) with LCA phases (ISO 14040), the framework provides a real-time environmental analysis model. The methodology's efficacy was demonstrated through a case study on the extrusion of ten PVC materials with varying compositions and rheological properties. A soft sensor was developed to estimate the extruder motor's specific energy consumption based on screw speed, material hardness, and viscosity. Results revealed specific energy consumption ranging from 28.80 kJ/cm 3 for softer PVC to 46.06 kJ/cm 3 for harder PVC at 120 rpm screw speed. The framework facilitated real-time environmental impact quantification, showing Global Warming Potential (GWP100a) between 0.59 and 0.95 kgCO 2 eq per gram of extruded PVC, contingent on material properties and operating conditions. Key outcomes include a real-time environmental impact model with R 2 adj = 0.84 and σ = ±2.20 kJ/cm 3 , a potential GWP100a reduction of up to 16.4% through operating condition optimization, and up to 48.7% through eco-design-driven material selection. This research bridges Industry 4.0 technologies with LCA, offering a dynamic, real-time approach to assess and optimize environmental impacts. It contributes to the transition toward Industry 5.0, paving the way for more sustainable manufacturing processes. • Innovative integration of Digital Twin and Life Cycle Assessment for dynamic environmental analysis. • Development of a soft sensor to estimate energy consumption in PVC extrusion. • Real-time assessment of potential environmental impacts (R 2 adj = 0.84; σ = ±2.20 kJ/cm 3 ). • GWP100a ranges from 0.59 to 0.95 kg CO 2 eq/g depending on material and operating conditions. • Increasing screw speed (100→170 rpm) reduces GWP100a by up to 16.4%

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Publication Details

Journal
Journal of Cleaner Production
Published
2025-03-01
DOI
https://doi.org/10.1016/j.jclepro.2025.145192
Citations
11
Primary Topic
Digital Transformation in Industry
Type
article
Field-Weighted Citation Impact
13.67
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article

Soft-sensors to drive manufacturing toward clean production: LCA based on Digital Twin

Giovanni Lucchetta, Alessandro Manzardo, Enrico Bovo, Mirco Piron
11 citations
Journal of Cleaner Production
Digital Transformation in Industry
13.67
article

Soft-sensors to drive manufacturing toward clean production: LCA based on Digital Twin

Giovanni Lucchetta, Alessandro Manzardo, Enrico Bovo, Mirco Piron
article en
11 citations

Abstract

This study introduces a novel methodological framework that integrates soft-sensor-based Digital Twin (DT) technology with Life Cycle Assessment (LCA), addressing data acquisition challenges and enabling dynamic environmental impact assessment. By aligning the DT standard (ISO 23247) with LCA phases (ISO 14040), the framework provides a real-time environmental analysis model. The methodology's efficacy was demonstrated through a case study on the extrusion of ten PVC materials with varying compositions and rheological properties. A soft sensor was developed to estimate the extruder motor's specific energy consumption based on screw speed, material hardness, and viscosity. Results revealed specific energy consumption ranging from 28.80 kJ/cm 3 for softer PVC to 46.06 kJ/cm 3 for harder PVC at 120 rpm screw speed. The framework facilitated real-time environmental impact quantification, showing Global Warming Potential (GWP100a) between 0.59 and 0.95 kgCO 2 eq per gram of extruded PVC, contingent on material properties and operating conditions. Key outcomes include a real-time environmental impact model with R 2 adj = 0.84 and σ = ±2.20 kJ/cm 3 , a potential GWP100a reduction of up to 16.4% through operating condition optimization, and up to 48.7% through eco-design-driven material selection. This research bridges Industry 4.0 technologies with LCA, offering a dynamic, real-time approach to assess and optimize environmental impacts. It contributes to the transition toward Industry 5.0, paving the way for more sustainable manufacturing processes. • Innovative integration of Digital Twin and Life Cycle Assessment for dynamic environmental analysis. • Development of a soft sensor to estimate energy consumption in PVC extrusion. • Real-time assessment of potential environmental impacts (R 2 adj = 0.84; σ = ±2.20 kJ/cm 3 ). • GWP100a ranges from 0.59 to 0.95 kg CO 2 eq/g depending on material and operating conditions. • Increasing screw speed (100→170 rpm) reduces GWP100a by up to 16.4%

Journal of Cleaner ProductionVol. 498
University of Padua (IT)
Industry, innovation and infrastructure
Openalex Percentile: Top 1%
Digital Transformation in Industry
13.67
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