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A novel model for assessing the degree of intelligent manufacturing readiness in the process industry: process-industry intelligent manufacturing readiness index (PIMRI) 被引量:1
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作者 Lujun ZHAO jiaming shao +2 位作者 Yuqi QI Jian CHU Yiping FENG 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2023年第3期417-432,共16页
Recently,the implementation of Industry 4.0 has become a new tendency,and it brings both opportunities and challenges to worldwide manufacturing companies.Thus,many manufacturing companies are attempting to find advan... Recently,the implementation of Industry 4.0 has become a new tendency,and it brings both opportunities and challenges to worldwide manufacturing companies.Thus,many manufacturing companies are attempting to find advanced technologies to launch intelligent manufacturing transformation.In this study,we propose a new model to measure the intelligent manufacturing readiness for the process industry,which aims to guide companies in recognizing their current stage and short slabs when carrying out intelligent manufacturing transformation.Although some models have already been reported to measure Industry 4.0 readiness and maturity,there are no models that are aimed at the process industry.This newly proposed model has six levels to describe different development stages for intelligent manufacturing.In addition,the model consists of four races,nine species,and 25 domains that are relevant to the essential businesses of companies’daily operation and capability requirements of intelligent manufacturing.Furthermore,these 25 domains are divided into 249 characteristic items to evaluate the manufacturing readiness in detail.A questionnaire is also designed based on the proposed model to help process-industry companies easily carry out self-diagnosis.Using the new method,a case including 196 real-world process-industry companies is evaluated to introduce the method of how to use the proposed model.Overall,the proposed model provides a new way to assess the degree of intelligent manufacturing readiness for process-industry companies. 展开更多
关键词 Process industry Industry 4.0 Readiness model Intelligent manufacturing Readiness index
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Co-pyrolysis of oil sludge with hydrogen-rich plastics in a vertical stirring reactor:Kinetic analysis,emissions,and products
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作者 Lujun Zhao jiaming shao +3 位作者 Li Xiang Yiping Feng Zhihua Wang Fawei Lin 《Frontiers of Environmental Science & Engineering》 SCIE EI CSCD 2022年第10期131-143,共13页
Pyrolysis is an effective method to treat oily sludge(OS)due to its balance between oil recovery and nonhazardous disposal.However,tank bottom OS contains a high content of heavy fractions,which creates obstacles for ... Pyrolysis is an effective method to treat oily sludge(OS)due to its balance between oil recovery and nonhazardous disposal.However,tank bottom OS contains a high content of heavy fractions,which creates obstacles for pyrolysis due to the high activation energy.The incomplete cracking of macromolecules and secondary polymerization decreases the oil quality and causes coking during the operation process.This study introduced polyethylene(PE)into OS to deploy the H/Ceff ratio of feedstocks for pyrolysis A strong interaction between OS and PE during copyrolysis could be observed from the TG/DTG curves.PE tightly participated in OS degradation,while OS also promoted PE degradation at high temperature.Apparent pits were generated in solid residues from copyrolysis,which was attributed to the uniform and violent gas release.In addition to HCN,other nitrogenous and sulphurous pollutants were inhibited.Accordingly,more gas products were attained after PE addition with more value-added compositions of alkanes and alkenes.Although the oil yield decreased after PE addition,the oil products from copyrolysis possessed higher heating values and higher contents of light fractions with short chains as well as paraffins.Consequently,copyrolysis of OS and PE significantly improved the pyrolysis process and resulted in high oil quality. 展开更多
关键词 Oily sludge PYROLYSIS POLYETHYLENE H/Ceff ratio Oil quality
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