Article(id=1282030075948274387, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282030021623657005, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2025.08.1054, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1741795200000, receivedDateStr=2025-03-13, revisedDate=1747497600000, revisedDateStr=2025-05-18, acceptedDate=null, acceptedDateStr=null, onlineDate=1783589389072, onlineDateStr=2026-07-09, pubDate=1756310400000, pubDateStr=2025-08-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783589389072, onlineIssueDateStr=2026-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783589389072, creator=13701087609, updateTime=1783589389072, updator=13701087609, issue=Issue{id=1282030021623657005, tenantId=1146029695717560320, journalId=1146031787341344770, year='2025', volume='35', issue='8', pageStart='1', pageEnd='276', issueExtLink='null', onlineDate='null', pubDate='1756310400000', pubDateStr='2025-08-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783589376121, creator='13701087609', updateTime=1783589731222, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282031511155228698, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282030021623657005, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282031511155228699, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282030021623657005, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=54, endPage=60, ext={EN=ArticleExt(id=1282030076149600980, articleId=1282030075948274387, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Prediction method for process safety management indicator data of refining and chemical enterprises, columnId=1149733271128420907, journalTitle=China Safety Science Journal, columnName=Safety social science and safety management, runingTitle=null, highlight=null, articleAbstract=

To improve the level of process safety management in refining and chemical enterprises and enhance dynamic monitoring and trend warning capabilities for key safety indicators, a multi-model fusion method for process alarm data prediction was proposed. This approach integrated three time series forecasting models: Autoregressive Integrated Moving Average (ARIMA), double exponential smoothing, and particle swarm optimization(PSO)-based support vector regression (SVR). The method effectively modeled and predicted diverse safety indicators by addressing their trend, autocorrelation, and nonlinear characteristics. Initially, outliers in the raw indicator data were processed. Three types of forecasting models were then constructed and their prediction results were computed. The optimal model for trend prediction was automatically selected based on error comparison. Finally, an empirical analysis was conducted using the time-averaged alarm count indicators from a refinery enterprise over one year. The results show that the proposed method dynamically adapts to varying data characteristics, with selected model prediction errors consistently remaining below 0.1, significantly outperforming the existing requirements for alarm magnitude accuracy. This method effectively enhances the accuracy and flexibility of safety indicator prediction in refining and chemical enterprises and enables the timely identification of potential risk indicators.

, authors=Yang LIU^{1, 2, 3}, authorsList=Yang LIU, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1282030076409647829, articleId=1282030075948274387, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=炼化企业工艺安全管理指标数据预测方法, columnId=1149733271296193071, journalTitle=中国安全科学学报, columnName=安全社会科学与安全管理, runingTitle=null, highlight=null, articleAbstract=

为提升炼化企业过程安全管理水平，增强对关键安全指标的动态监控与趋势预警能力，提出一种融合多预测模型的工艺报警数据预测方法。该方法结合差分移动平均自回归（ARIMA）、二次指数平滑以及基于粒子群优化（PSO）的支持向量回归（SVR）3种时间序列预测模型，针对不同指标的趋势性、自相关性与非线性特征，实现对多样化安全指标的有效建模与预测。首先，处理原始指标数据的异常值；分别构建3类预测模型并计算预测结果；然后，通过比较各模型误差自动选取最优模型用于指标趋势预测；最后，实证分析某炼化企业一年内的时平均报警数指标。结果表明：该方法能够动态适配不同数据特征，所选模型预测误差稳定低于0.1，明显优于现有报警数量级精度要求。该方法可有效提升炼化企业过程安全指标预测的准确性与灵活性，及时识别潜在风险指标。

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²SINOPEC Research Institute of Safety Engineering Co., Ltd., Qingdao Shandong 266104, China
³SINOPEC Management System Certification (Qingdao) Co., Ltd., Qingdao Shandong 266104, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1282030077122679525, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282030075948274387, authorId=1282030076921352928, language=CN, stringName=刘洋工程师, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^{1, 2, 3}, address=¹化学品安全全国重点实验室，山东 青岛 266104
²中石化安全工程研究院有限公司，山东 青岛 266104
³中石化管理体系认证（青岛）有限公司，山东 青岛 266104, bio={"content":"

刘洋（1995—），男，山东烟台人，硕士，工程师，主要从事化工安全智能化技术方面的工作。E-mail:liuyang.qday@sinopec.com。

"}, bioImg=null, bioContent=

刘洋（1995—），男，山东烟台人，硕士，工程师，主要从事化工安全智能化技术方面的工作。E-mail:liuyang.qday@sinopec.com。

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