预测性流程监控可以在业务流程运行过程中提供及时的信息,以便采取措施来应对潜在风险,如何提高流程预测的准确度一直受到高度关注。现有的研究方法大部分都在静态环境下引入,很少有结合数字孪生技术用于动态环境的流程预测。为此,提出...预测性流程监控可以在业务流程运行过程中提供及时的信息,以便采取措施来应对潜在风险,如何提高流程预测的准确度一直受到高度关注。现有的研究方法大部分都在静态环境下引入,很少有结合数字孪生技术用于动态环境的流程预测。为此,提出了一个基于概念漂移检测的方法,并构建数字孪生流程预测模型(digital twin based on concept drift,DTBCD)预测下一个活动。首先利用事件流行为关系和权重散度将流程中的活动进行特征提取,得到数据流的特征集,其次进行漂移检测,动态选择特征集输入人工智能模型中训练并预测下一个活动,然后运用物联网和云计算等先进技术创建数字孪生虚拟环境,最后得到基于概念漂移的数字孪生模型。通过公开可用的数据集进行评估分析,实验结果表明,提出的方法能够有效提高预测的准确性。展开更多
Predictive Emission Monitoring Systems (PEMS) offer a cost-effective and environmentally friendly alternative to Continuous Emission Monitoring Systems (CEMS) for monitoring pollution from industrial sources. Multiple...Predictive Emission Monitoring Systems (PEMS) offer a cost-effective and environmentally friendly alternative to Continuous Emission Monitoring Systems (CEMS) for monitoring pollution from industrial sources. Multiple regression is one of the fundamental statistical techniques to describe the relationship between dependent and independent variables. This model can be effectively used to develop a PEMS, to estimate the amount of pollution emitted by industrial sources, where the fuel composition and other process-related parameters are available. It often makes them sufficient to predict the emission discharge with acceptable accuracy. In cases where PEMS are accepted as an alternative method to CEMS, which use gas analyzers, they can provide cost savings and substantial benefits for ongoing system support and maintenance. The described mathematical concept is based on the matrix algebra representation in multiple regression involving multiple precision arithmetic techniques. Challenging numerical examples for statistical big data analysis, are investigated. Numerical examples illustrate computational accuracy and efficiency of statistical analysis due to increasing the precision level. The programming language C++ is used for mathematical model implementation. The data for research and development, including the dependent fuel and independent NOx emissions data, were obtained from CEMS software installed on a petrochemical plant.展开更多
文摘预测性流程监控可以在业务流程运行过程中提供及时的信息,以便采取措施来应对潜在风险,如何提高流程预测的准确度一直受到高度关注。现有的研究方法大部分都在静态环境下引入,很少有结合数字孪生技术用于动态环境的流程预测。为此,提出了一个基于概念漂移检测的方法,并构建数字孪生流程预测模型(digital twin based on concept drift,DTBCD)预测下一个活动。首先利用事件流行为关系和权重散度将流程中的活动进行特征提取,得到数据流的特征集,其次进行漂移检测,动态选择特征集输入人工智能模型中训练并预测下一个活动,然后运用物联网和云计算等先进技术创建数字孪生虚拟环境,最后得到基于概念漂移的数字孪生模型。通过公开可用的数据集进行评估分析,实验结果表明,提出的方法能够有效提高预测的准确性。
文摘Predictive Emission Monitoring Systems (PEMS) offer a cost-effective and environmentally friendly alternative to Continuous Emission Monitoring Systems (CEMS) for monitoring pollution from industrial sources. Multiple regression is one of the fundamental statistical techniques to describe the relationship between dependent and independent variables. This model can be effectively used to develop a PEMS, to estimate the amount of pollution emitted by industrial sources, where the fuel composition and other process-related parameters are available. It often makes them sufficient to predict the emission discharge with acceptable accuracy. In cases where PEMS are accepted as an alternative method to CEMS, which use gas analyzers, they can provide cost savings and substantial benefits for ongoing system support and maintenance. The described mathematical concept is based on the matrix algebra representation in multiple regression involving multiple precision arithmetic techniques. Challenging numerical examples for statistical big data analysis, are investigated. Numerical examples illustrate computational accuracy and efficiency of statistical analysis due to increasing the precision level. The programming language C++ is used for mathematical model implementation. The data for research and development, including the dependent fuel and independent NOx emissions data, were obtained from CEMS software installed on a petrochemical plant.
