基于时间维的工位碳足迹监控方法及其应用研究

发布时间：2019-06-15 06:52

【摘要】：人类活动产生大量的温室气体造成全球变暖加速的观点已得到普遍认同。面对越来越严重的环境和资源压力,全球范围内已经掀起了一场以高能效、低排放为核心的“低碳革命”。制造业是碳排放的重点领域,我国作为制造业大国,从多种角度研究如何实现制造过程中的“低碳减排”具有重要意义。虽然近年来对碳足迹的研究日益增多,但均是基于空间维研究产品全生命周期内碳足迹的核算与优化控制,而在时间维上对工位生产过程中的碳足迹监控方面的研究少之又少。基于以上事实,本文从时间维出发,研究通过统计过程控制理论实现对工位生产全过程碳足迹的实时、有效监控,为企业的低碳减排工作提供参考。本文的主要工作内容如下:(1)总结回顾了基于空间维的碳足迹的研究成果,对碳足迹的空间维与时间维进行比较分析,给出了基于时间维的碳足迹定义;创造性地提出了从时间维出发,通过统计过程控制理论实现对工位生产全过程碳足迹的有效监控。(2)分析研究了SPC控制图的应用方法,阐述了SPC控制图应用的局限性,通过对其相关解决方案进行分析比较,最终确定本文应用贝叶斯估计理论弥补生产初期碳足迹样本不足的情况下传统SPC控制图应用的缺陷。(3)提出了基于时间维的工位碳足迹核算模型,确定了工位碳足迹的系统边界,为工位碳足迹的研究提供了准确的量化方法。(4)综合考虑碳足迹样本不足时传统SPC控制图应用的局限性与企业实际生产过程中碳足迹先验信息的存在与否,分别提出了基于共轭贝叶斯与贝叶斯假设的工位碳足迹统计过程控制模型;通过应用两类模型对工位碳足迹总体的分布参数进行估计,优化了传统SPC控制图的界限,弥补了传统SPC控制图在小样本条件下监控能力不佳的缺陷。(5)以H仪表集团丝网印刷工位为例,验证了工位碳足迹核算模型与两类基于贝叶斯估计的统计过程控制模型的有效性;通过实例分析比较了两类基于贝叶斯估计的统计过程控制模型的应用效果,认为在存在先验信息的前提下,应优先应用基于共轭贝叶斯的碳足迹统计过程控制模型。(6)以论文所述数学模型为基础,设计开发了工位碳足迹监控系统,实现了工位生产全程碳足迹的分析统计与实时监控、报警功能,对H仪表集团的低碳减排工作具有重要的实用价值。
[Abstract]:The idea that human activities produce a large amount of greenhouse gases to accelerate global warming has been widely accepted. In the face of more and more serious environmental and resource pressures, a "low-carbon revolution" with high energy efficiency and low emissions as the core has been launched around the world. Manufacturing industry is the key field of carbon emission. As a big manufacturing country, it is of great significance to study how to realize "low carbon emission reduction" in manufacturing process from many angles. Although more and more research on carbon footprint has been done in recent years, it is based on spatial dimension to study the accounting and optimal control of carbon footprint in the whole life cycle of products, but there is little research on carbon footprint monitoring in the process of station production in time dimension. Based on the above facts, based on the time dimension, this paper studies the real-time and effective monitoring of the carbon footprint of the whole process of station production through statistical process control theory, which provides a reference for the low-carbon emission reduction work of enterprises. The main contents of this paper are as follows: (1) the research results of carbon footprint based on spatial dimension are summarized and reviewed, the spatial dimension and time dimension of carbon footprint are compared and analyzed, and the definition of carbon footprint based on time dimension is given. This paper creatively puts forward the effective monitoring of the carbon footprint of the whole process of station production through statistical process control theory from the point of view of time dimension. (2) the application method of SPC control chart is analyzed and studied, the limitations of the application of SPC control chart are expounded, and the related solutions are analyzed and compared. Finally, it is determined that the Bayesian estimation theory is used to make up for the shortcomings of the traditional SPC control chart when the carbon footprint samples are insufficient in the initial stage of production. (3) A time dimension based carbon footprint accounting model is proposed, and the system boundary of the carbon footprint is determined. It provides an accurate quantitative method for the study of station carbon footprint. (4) considering the limitations of traditional SPC control chart application when carbon footprint samples are insufficient and the existence of prior information of carbon footprint in the actual production process of enterprises, a statistical process control model of station carbon footprint based on conjugated Bays and Bayesian assumptions is proposed respectively. By using two kinds of models to estimate the overall distribution parameters of the carbon footprint of the station, the boundary of the traditional SPC control chart is optimized, which makes up for the defect of the poor monitoring ability of the traditional SPC control chart under the condition of small samples. (5) taking the screen printing station of H instrument Group as an example, the effectiveness of the station carbon footprint accounting model and the two kinds of statistical process control models based on Bayesian estimation is verified. The application effects of two kinds of statistical process control models based on Bayesian estimation are analyzed and compared through examples. It is considered that the carbon footprint statistical process control model based on conjugated Bayesian should be applied first under the premise of prior information. (6) based on the mathematical model described in this paper, a station carbon footprint monitoring system is designed and developed, which realizes the analysis, statistics, real-time monitoring and alarm function of the carbon footprint in the whole process of station production. It is of great practical value to the low carbon emission reduction work of H instrument Group.
【学位授予单位】：浙江理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TB497

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