Abstract
With the rapid development of industry, problems for the ecological environment are increasing day by day, among which carbon pollution is particularly serious. Product carbon emission accounting is the core of sustainable green design. In this paper, the beer fermentation cylinder is taken as an example for low carbon design to get the best combination of design parameters when the carbon emission is the smallest. The life cycle assessment method is used to calculate the carbon footprint of products. In order to analyse the uncertainty and sensitivity of the method, an uncertainty analysis method using data quality characteristics as input of Monte Carlo is proposed. Sensitivity analysis is carried out by multivariate statistical regression and Extended Fourier Amplitude Sensitivity Test (EFAST). The system boundary of fermentation cylinder is determined and the carbon emissions of life cycle are calculated. The quality characteristics of life cycle inventory data (LCI) data are analysed and Monte Carlo simulation is carried out to quantify the uncertainty of LCI. EFAST is used to calculate the sensitivity of LCI and the results are compared with those of multivariate statistical regression to verify the feasibility of the method. Finally, response surface methodology (RSM) is used to optimize the design of parameters. It provides guidance for the establishment of product carbon emission model and low carbon design.
Highlights
Since the beginning of the 21st century, with the progress of society and the rapid development of industry, a series of environmental problems have been brought about, especially carbon emissions
Chen [9] established the analysis model of sensitivity and uncertainty of DQI-Monte Carlo and analysed the quality of carbon footprint data
The quantitative values of uncertainty and sensitivity of life cycle inventory data (LCI) have not been obtained from the above studies and little research has been done on the carbon footprint of beer fermentation equipment
Summary
Since the beginning of the 21st century, with the progress of society and the rapid development of industry, a series of environmental problems have been brought about, especially carbon emissions. LCA method has some shortcomings [4], among which uncertainty is the main problem affecting the calculation results of carbon footprint, especially the input list. Domestic and foreign scholars have made exploratory research on calculating carbon footprint of products based on LCA. Huang [8] made a quantitative evaluation and proposed to control the data quality of LCA by the CLCD She conducted two simulation tests and got the uncertainty of inventory data by Monte Carlo. Chen [9] established the analysis model of sensitivity and uncertainty of DQI-Monte Carlo and analysed the quality of carbon footprint data. The quantitative values of uncertainty and sensitivity of LCI have not been obtained from the above studies and little research has been done on the carbon footprint of beer fermentation equipment. In order to realize the low-carbon design of beer fermentation cylinder and analyse the influence of design parameters on the carbon footprint, the RSM method is used to carry out parameter optimization test and analysis and the optimal combination of design parameters of the beer fermentation cylinder with the smallest carbon footprint is obtained
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