橡胶胶浆制备车间VOCs排放全过程管理与智能监控系统研究与应用

李沛贤; 党小庆; 翟晨; 韩巍; 赖志强; 李朝阳; 屈嘉鑫; 王赫; 郑华春

doi:10.13205/j.hjgc.202606002

橡胶胶浆制备车间VOCs排放全过程管理与智能监控系统研究与应用

doi: 10.13205/j.hjgc.202606002

1. 西安建筑科技大学环境与市政工程学院陕西省环境工程重点实验室, 西安 710055;
2. 中国启源工程设计研究院有限公司, 西安 710018;
3. 陕西延长石油集团橡胶有限公司, 陕西咸阳 712000;
4. 中国重型机械研究院股份公司, 西安 710018

基金项目:

陕西省重点研发计划（2024SF-ZDCYL-05-06）；国机重装重大科技研发项目（2023ZLKY-13）；陕西省重点研发计划（2024SF-YBXM-569）

详细信息

作者简介:
李沛贤(2000-),男,硕士研究生,主要研究方向为大气污染控制工程。651433445@qq.com

通讯作者:
党小庆(1964-),男,教授,主要研究方向为大气污染控制工程。dangxq@163.com

计量
- 文章访问数: 37
- HTML全文浏览量: 0
- PDF下载量: 0
- 被引次数: 0
出版历程
- 收稿日期: 2025-08-13
- 录用日期: 2025-09-08
- 修回日期: 2025-09-01
- 网络出版日期: 2026-07-06

Research and application of whole-process management and intelligent monitoring system for VOCs emissions in rubber paste preparation workshops

1. Shaanxi Key Laboratory of Environmental Engineering, Environmental and Municipal Engineering Department, Xi'an University of Architecture and Technology, Xi'an 710055, China;
2. China Qiyuan Engineering Design & Research Institute Company, Xi'an 710018, China;
3. Shaanxi Yanchang Petroleum Group Rubber Company, Xianyang 712000, China;
4. China Heavy Machinery Research Institute Co., Ltd., Xi'an 710018, China

摘要

摘要: 全过程监控体系的建立可有效促进工业源VOCs高效减排。针对橡胶胶浆制备车间，采用混合数据库存储、物联网通信、角色访问控制及微服务架构相结合的方法，开发了集成软件工程和物联网技术的全过程智能监控系统。以某大型橡胶企业胶浆制备车间作为实施案例，制定了VOCs排放全过程管理数据清单，实现了对吸附/脱附离心风机运行状态的实时感知与数据融合分析。监测数据表明：在典型工况下，抽风系统容积流量为40000 m³/h，通风系统容积流量为30000 m³/h，形成了持续微负压环境，有效抑制了无组织排放；车间净化工艺可有效削减非甲烷总烃排放浓度至10 mg/m³以下，符合GB 27632—2011《橡胶制品工业污染物排放标准》。系统通过多级权限管理模块实现了操作责任的精准划分，并基于企业实际净化工艺构建了净化工艺在线监控体系。该研究结果可为计算机技术应用并推广于典型行业VOCs减排和全过程管理提供理论和实践参考。
- 工业源VOCs /
- 胶浆制备车间 /
- 软件工程 /
- 全过程管理 /
- 在线监控
Abstract: Efficient reduction of industrial volatile organic compounds （VOCs） emissions is a key path to improving regional air quality. Taking the rubber paste preparation workshop， a typical process unit in the rubber industry， as the research object， this study designed and developed an intelligent monitoring system integrating software engineering and Internet of Things （IoT） technology. The core technical architecture of the system integrates a hybrid database storage solution， low-power wide-area IoT communication technology， a role-based access control model， and a containerized microservice architecture， effectively solving the problems of heterogeneous data fusion difficulties and insufficient scalability in traditional industrial monitoring systems. The system was deployed in a large rubber enterprise. By establishing a dynamic data list covering the whole process of VOCs exhaust gas collection-purification treatment-end-of-line emission， real-time perception and data fusion analysis of the operation status of the adsorption/desorption centrifugal fan were achieved. Under typical working conditions， the volume flow rate of the extraction system was 40000 m³/h， and that of the ventilation system was 30000 m³/h， forming a continuous micro-negative pressure environment that effectively suppressed unorganized emissions. The workshop purification process effectively reduced the concentration of non-methane hydrocarbon emissions to less than 10 mg/m³， which met the emission control standards. The system innovatively coupled the permission management module with the enterprise organizational structure to achieve precise division of operation permissions for production， environmental protection， and management roles， significantly reducing the response time for abnormal conditions. Based on the enterprise's "zeolite rotor adsorption concentration + regenerative thermal catalytic oxidation" purification process， an online evaluation model for purification efficiency was constructed. The "data-driven decision-making-intelligent dynamic regulation-precise responsibility traceability" technical paradigm established in this study provides a reusable framework for the application of computer technology in the deep treatment of industrial VOCs. It also has important practical value for promoting the intelligent management of pollutants throughout the process of typical industrial parks， and is especially applicable to rubber products sectors with dense solvent usage， such as tire manufacturing and sealing component production.
- industrial VOCs /
- rubber paste preparation workshop /
- software engineering /
- whole-process management /
- online monitoring

HTML全文

参考文献(26)

[1]	KAMARULZAMAN N H，LE-MINH N，STUETZ R M. Identification of VOCs from natural rubber by different headspace techniques coupled using GC-MS[J]. Talanta，2019，191：535-544.
[2]	KWON E E，OH J I，KIM K H. Polycyclic aromatic hydrocarbons（PAHs）and volatile organic compounds（VOCs）mitigation in the pyrolysis process of waste tires using co as a reaction medium[J]. Journal of Environmental Management，2015，160：306-311.
[3]	WANG H，YUAN J，XIE L，et al. Characteristics of atmospheric pollutants emitted from reclaimed rubber production[J]. China Rubber Industry，2019，66（10）：790-794. 王浩，袁进，解磊，等. 再生橡胶生产排放的大气污染物特征研究[J]. 橡胶工业，2019，66（10）：790-794.
[4]	WANG J，LÜ X J，GAO L L. Problems and suggestions on automatic monitoring of VOCs in key industries[J]. Environmental Impact Assessment，2021，43（2）：18-21. 王婧，吕晓君，高雷利. 重点行业VOCs自动监控存在的问题及建议[J]. 环境影响评价，2021，43（2）：18-21.
[5]	XU H H，ZHAO X H，DONG Z L，et al. Problems and countermeasures of VOCs pollution control and supervision in China[J]. Environmental Impact Assessment，2021，43（2）：7-11. 徐海红，赵晓宏，董振龙，等. 我国VOCs污染治理监管存在的问题及对策[J]. 环境影响评价，2021，43（2）：7-11.
[6]	Council State. Notice of the State Council on printing and distributing the Action Plan for Sustained Improvement of Air Quality[J]. Gazette of the People's Republic of China State Council，2023（35）：8-12. 国务院. 国务院关于印发《空气质量持续改善行动计划》的通知[J]. 中华人民共和国国务院公报，2023（35）：8-12.
[7]	CHEN Z Y，LUN X X，TANG G G，et al. Study on the emission factor database of anthropogenic VOCs in China[J]. Environmental Engineering，2018，36（9）：68-73. 陈宗耀，伦小秀，唐贵刚，等. 中国人为源VOCs排放因子库研究[J]. 环境工程，2018，36（9）：68-73.
[8]	WANG X，HU K，HAO Y，et al. Volatile organic compounds（VOCs）emission interferes with real-time regulatory monitoring of ozone in urban atmosphere[J]. Urban Climate，2024，55：101938.
[9]	GAI H L，BEATH J，FANG J，et al. Alternative emission monitoring technologies and industrial internet of things-based process monitoring technologies for achieving operational excellence[J]. Current Opinion in Green and Sustainable Chemistry，2020，23：31-37.
[10]	SUN C，WANG W，HE L，et al. Construction and application of comprehensive data collection and sharing platform for atmospheric environmental science[J]. Research of Environmental Sciences，2021，34（1）：202-212.
[11]	YE J. Research on air pollutant monitoring technology based on data fusion[J]. Environmental Science and Management，2021，46（5）：135-139. 叶继. 基于数据融合的空气污染物监测技术研究[J]. 环境科学与管理，2021，46（5）：135-139.
[12]	YUAN J，WANG W，YING X，et al. Semiconductor sensor-based online monitoring system for VOCs unorganized emission discharge[C]// 2024 3rd International Conference on Energy，Power and Electrical Technology（ICEPET），IEEE，2024：1764-1772.
[13]	CHEN Y，CHEN D，SONG T，et al. An intelligent and portable air pollution monitoring system based on chemical sensor array[C]// 2020 IEEE 4th International Conference on Frontiers of Sensors Technologies（ICFST）. IEEE，2020：21-25.
[14]	THELOKE J，FRIEDRICH R. Compilation of a database on the composition of anthropogenic VOC emissions for atmospheric modeling in europe[J]. Atmospheric Environment，2007，41（19）：4148-4160.
[15]	ZHENG C，SHEN J，ZHANG Y，et al. Quantitative assessment of industrial VOC emissions in China：historical trend，spatial distribution，uncertainties，and projection[J]. Atmospheric Environment，2017，150：116-125.
[16]	JENSEN S S，BERKOWICZ R，HANSEN H S，et al. A danish decision-support GIS tool for management of urban air quality and human exposures[J]. Transportation Research Part D：Transport and Environment，2001，6（4）：229-241.
[17]	LIU J Y. Research and application of multi-protocol fusion gateway for field-level industrial networks[D]. Qinhuangdao：Yanshan University，2023. 刘金雨. 面向现场级工业网络的多协议融合网关研究与应用[D]. 秦皇岛：燕山大学，2023.
[18]	ZHOU S Q，KONG Y X. Optimization of cost management for tire enterprises under the trend of low-carbon environmental protection[J]. China Tire Resources Comprehensive Utilization，2025（3）：58-60. 周仕权，孔宇轩. 低碳环保趋势下轮胎企业的成本管理优化[J]. 中国轮胎资源综合利用，2025（3）：58-60.
[19]	BOUTROS A，BETZ V. FPGA architecture：principles and progression[J]. IEEE Circuits and Systems Magazine，2021，21（2）：4-29.
[20]	GUPTA I，SINGH A K，LEE C N，et al. Secure data storage and sharing techniques for data protection in cloud environments：a systematic review，analysis，and future directions[J]. IEEE Access，2022，10：71247-71277.
[21]	WANG Q，LI F J. A unified identity authentication scheme for laboratories based on single sign-on[J]. Experimental Technology and Management，2020，37（5）：219-223. 王群，李馥娟. 一种基于单点登录的实验室统一身份认证方案[J]. 实验技术与管理，2020，37（5）：219-223.
[22]	ZHANG S X，ZHANG S C，XIE X D. General operation and maintenance management platform for monitoring equipment based on InfluxDB[J]. Computer Systems and Applications，2021，30（12）：123-127. 张世贤，张少春，谢晓东. 基于InfluxDB的监控设备通用运维管理平台[J]. 计算机系统应用，2021，30（12）：123-127.
[23]	ROLDÁN-GÓMEZ J，CARRILLO-MONDÉJAR J，GÓMEZ J M C，et al. Security analysis of the MQTT-SN protocol for the Internet of Things[J]. Applied Sciences-Basel，2022，12（21）：10892-10910.
[24]	LAKSHMINARAYANA S，PRASEED A，THILAGAM P S. Securing the IoT application layer from an MQTT protocol perspective：Challenges and research prospects[J]. IEEE Communications Surveys& Tutorials，2024，26（2）：1058-1083.
[25]	XIONG H R，CHEN X Y，DU X H，et al. Review of security analysis research on role-based access control models[J]. Application Research of Computers，2015，32（11）：3201-3208. 熊厚仁，陈性元，杜学绘，等. 基于角色的访问控制模型安全性分析研究综述[J]. 计算机应用研究，2015，32（11）：3201-3208.
[26]	KHAN J A. Role-based access control（RBAC）and attribute-based access control（ABAC）[M]// Improving Security，Privacy，and Trust in Cloud Computing，IGI Global Scientific Publishing：189-224.