Big Data Analysis Approach for Real-Time Carbon Efficiency Evaluation of Discrete Manufacturing Workshops

Chaoyang Zhang,Weixi Ji

doi:10.1109/access.2019.2933353

Chaoyang Zhang, Weixi Ji

Open Access

https://doi.org/10.1109/access.2019.2933353

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Abstract

Due to the huge consumption of materials and energy during machining processes, reduction of manufacturing carbon emission is an essential key to decrease the environmental burden of various manufacturing systems. To achieve this target, one critical step is to calculate and evaluate the carbon emissions of machining processes. However, this step is a little difficult for discrete manufacturing processes, because they are always complex and the data sources are diverse. Considering the complexity of discrete manufacturing workshops, a Big Data analysis approach for real-time carbon efficiency evaluation of discrete manufacturing workshops is proposed in an internet of things-enabled ubiquitous environment. Firstly, the deployment of data acquisition devices is introduced to create a ubiquitous manufacturing workshop, and data modeling of production state and carbon emission is described to realize data acquisition and storage. Then, a data-driven multi-level carbon efficiency evaluation of manufacturing workshop is established based on Big Data analysis approaches. Finally, an auto parts manufacturing workshop is studied to verify the feasibility and applicability of the proposed methods. This method realizes the combination of manufacturing Big Data and low-carbon production. Meanwhile, the evaluation method can be used in other production information systems and then assist the production decision-making.

Highlights

The growing energy and resource consumption has led to concerns about economic development in many countries
The energy consumption and carbon emission evaluation methods have been studied in many literatures, the study on manufacturing carbon emission and carbon efficiency analysis based on real-time data and Big Data analysis is not enough, especially for discrete manufacturing workshops
Definition: Carbon emission efficiency is defined as the ratio of carbon emission caused by material removal energy consumption to the total carbon emissions of a machine tool or a workpiece

Summary

INTRODUCTION

The growing energy and resource consumption has led to concerns about economic development in many countries. The energy consumption and carbon emission evaluation methods have been studied in many literatures, the study on manufacturing carbon emission and carbon efficiency analysis based on real-time data and Big Data analysis is not enough, especially for discrete manufacturing workshops. The current Big Data analysis methods focused production data, fault data, logistics data and energy data, but the carbon emission data analysis is lacked, especially for the real time carbon emission data, which includes cutting tools and buffers, logistics, etc These analysis methods about manufacturing does not consider the data complexity of discrete manufacturing workshops. Through the above deployment of RFID, energy consumption and flow monitors, a ubiquitous production environment for carbon efficiency evaluation is established Within this manufacturing system, machining processes and logistics operations are reengineered and rationalized. Data Model 3: The energy consumption data of a storage unit are modeled as SE =< SEID, BID, EData, T >, where

BIG DATA ANALYSIS APPROACH FOR CARBON EFFICIENCY EVALUATION

MANUFACTURING DATA CORRELATION ANALYSIS FOR CARBON EMISSION EVALUATION

Findings

CONCLUSION