Research On Monitoring Method And Application Of Station Carbon Footprint Based On Time Dimension

The view that large quantities of greenhouse gases caused by human activities make global warming accelerated has been universally recognized.Facing the increasingly serious environmental and resource pressures,a “low carbon revolution” with the core of high efficiency,low emissions has been launched in the world.Manufacturing industry is the key area of carbon emissions.As a manufacturing power,it is of great significance to study the “low-carbon emissions” of production process from a variety of angles for our country.Although,the study on carbon footprint is increasing in recent years,most of them concentrate on the accounting and optimization control of products carbon footprint in the full life cycle based on spatial dimension.It is almost nonexistent to research the monitor methods of carbon footprint in the station's production process based on the time dimension.On the basis of the above facts,based on the time dimension,the study that realizes the real-time and effective monitoring of the carbon footprint in the station's whole production process by the theory of statistical process control has been carried out.Its purpose is to provide reference for the enterprise's work on “low-carbon emissions”.The main contents of this paper are as follows:(1)Summarizing the study results of carbon footprint that based on spatial dimension,analyzing the carbon footprint based on the spatial dimension and time dimension and presenting the conception of carbon footprint based on the time dimension.At last,an issue is proposed creatively based on the time dimension,which realizes the real-time and effective monitoring of the carbon footprint in the station's whole production process by the method of statistical process control.(2)Analyzing the application methods of the SPC control chart,expounding its limitations and the relevant solutions are compared.Finally,the bayesian estimation theory is confirmed to make up for the defect under the condition of insufficient carbon footprint samples in the early years of the production.(3)Proposing station carbon footprint calculation model based on the time dimension.The model identifies the system boundary and provides an accurate quantitative method for the study of station carbon footprint.(4)Considering the limitations of the traditional SPC control chart in the absence of carbon footprint samples and carbon footprint's priori information in the process of actual production,the station carbon footprint's statistical process control models based on conjugate bayesian estimation and bayesian assumption are proposed respectively.The two kinds of models are used to estimate the distribution parameters of station carbon footprint's overall data and optimize the boundaries of traditional SPC control chart.As a consequence,the defect that the traditional SPC control charts has a poor monitoring ability under the condition of small sample is redeemed.(5)Taking the screen printing station in H instrument group for example,the validities of the above three models are proved.By comparing the application effect of statistical process control models that based on two kinds of bayesian estimation theories,the conclusion that the statistical process control based on conjugate bayesian estimation should be applied preferentially when the priori information exists is reached.(6)On the basis of the above three models,the station carbon footprint monitoring system is developed.The system has realized the functions of analysis,real-time monitoring,alarm about the carbon footprint in the station's whole production process and been of great value for the H instrument group's work on “low-carbon emissions”.