Biomass Material Replacement Estimation for Low-Impact Product Design
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Solution Overview
Problem
The variability in physical properties of biobased materials makes it difficult to determine appropriate applications on a case-by-case basis.
Innovation Solution
A biomass utilization support device utilizing a machine learning model to estimate appropriate replacement amounts of biobased materials in products, supported by a calculation of environmental impact indicators and collection of related information from the internet.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If biobased materials are used to replace conventional materials in products, then environmental impact is reduced, but determination of appropriate applications becomes difficult due to variability in physical properties
Solution Approach 1:
The system changes the parameters of biobased materials by adjusting replacement amounts to achieve target characteristic values. The estimation unit predicts appropriate replacement amounts based on input parameters (biomass information and product information), and the system calculates characteristic values for different replacement scenarios to find optimal parameters that satisfy product requirements while reducing environmental impact.
Solution Approach 2:
The system introduces an intermediary estimation unit that acts as a mediator between biobased material properties and product requirements. This estimation unit uses machine learning models to predict appropriate replacement amounts, bridging the gap between variable material properties and stable product performance requirements, thereby simplifying the determination process.
2Object-affected harmful factors
If replacement amounts of biobased materials are increased, then environmental impact indicators improve, but product performance may deteriorate
Solution Approach 1:
The system implements feedback by calculating characteristic values for different replacement amounts and comparing them against target values. The estimation unit predicts replacement amounts that will achieve characteristic values meeting product requirements, and the system provides feedback on environmental impact indicators corresponding to these predicted replacement amounts, enabling optimization of both performance and environmental benefits.
Solution Approach 2:
The system dynamically adjusts replacement amounts based on the specific characteristics of the biobased material and the product requirements. Rather than using fixed replacement ratios, the estimation unit predicts optimal replacement amounts tailored to each case, allowing the system to maximize environmental benefits while maintaining product performance through dynamic, case-specific optimization.
Data Source
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AI summary
A biomass utilization support device: acquires biomass information relating to a biobased material and product information for each of a plurality of products including information about materials configuring the products; uses a machine learning model, which has been trained to estimate appropriate values for replacement amounts in a case of replacing a portion of the materials configuring the products with the biobased material, and the acquired biomass information and product information to estimate the appropriate values for each of the plurality of products; calculates, for each of the plurality of products, environmental impact indicators in a case in which a portion of the materials configuring the products has been replaced with the biobased material at the replacement amounts represented by the estimated appropriate values; and outputs support information listing the estimated appropriate values and the calculated environmental impact indicators.