Research On Carbon Footprint Analysis And Uncertainty Of LCA For Beer Fermentation Equipment

With the progress of science and technology,the rapid development of industry,ecological environment and other problems are increasing day by day,especially the carbon pollution.Accounting product carbon emission is the core of assessing its impact on the environment,guiding,improving and perfecting product green design.Life cycle assessment(LCA)is the mainstream method to evaluate the impact of products on the environment at the present stage.With the wide application of LCA,its theoretical method gradually becomes mature.However,the calculation of carbon footprint of products based on LCA method has certain shortcomings and deficiencies,especially the uncertain problems.This paper mainly analyzes the uncertainty of calculating carbon footprint of products based on LCA method:Firstly,beer fermentation tank is taken as the research object,its life cycle carbon footprint is calculated.According to the beer manufacturing process,the type and capacity of the beer fermentation tank are selected,its system boundary is determined,inventory data of each phase of the life cycle is collected,carbon emission model of each phase of the life cycle is established,and carbon footprint of each phase of the beer fermentation tank is calculated.Secondly,the uncertainty during LCA evaluation is studied,its source is analyzed,and the influencing factors causing the uncertainty of the carbon footprint of beer fermentation tank are determined.Such influencing factors are qualitative and uncontrollable.A method for calculating the combined weight of AHP-rough set is proposed to quantify the weight,the weight of each influencing factor is calculated,the uncertainty of carbon footprint calculation is evaluated.Then,the uncertainty analysis of the list is carried out on the basis of the calculation of the carbon footprint of the beer fermentation tank,The data quality of fermentation tank life cycle inventory(LCI)is evaluated,and the corresponding probability distribution is simulated with Monte Carlo,and the obtained data characteristics are analyzed with uncertainty.Multivariate statistical regression and EFAST methods are used for sensitivity analysis to obtain the list variables of significance of carbon footprint calculation results and the sensitivity of each list variable for output results.Thirdly,the uncertainty and sensitivity of the carbon footprint model based on the LCA method studied,and the applicability and completeness of the current typical carbon footprint model are compared and analyzed.The uncertainty of the model is analyzed,and a sensitivity analysis method was proposed to analyze the variation of the parameters of each model within the range of[-20%,20%].The sensitivity of each model is obtained,and its advantages and disadvantages are compared.Finally,the effects of the design parameters of beer fermentation tank on the carbon footprint are analyzed,including the ratio of diameter to height,cone Angle,wall thickness and material density.Based on the calculation of the carbon footprint in the raw material acquisition stage of the fermentation tank,the response surface method(RSM)is used to optimize the design parameters of the fermentation tank,and the optimal combination of parameters with the minimum carbon emission is obtained.Using RSM and BP neural network to fit the experimental data,the prediction error of BP neural network is smaller and the predicted value is more accurate.