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作 者:叶凌箭[1,2] 王朗[1] 马修水[1] 宋执环[2]
机构地区:[1]浙江大学宁波理工学院,浙江宁波315100 [2]浙江大学控制科学与工程学系,浙江杭州310027
出 处:《计算机与应用化学》2014年第1期15-18,共4页Computers and Applied Chemistry
基 金:国家自然科学基金项目(61304081);浙江省自然科学基金资助项目(LQ13F030007);国家重点基础研究发展规划(973)资助项目(2012CB720505);宁波市创新团队(2012B82002)
摘 要:针对不确定化工过程的实时优化问题,提出了一种基于主元分析的最优状态检测技术。通过分析系统在不同扰动工况下的最优状态必须满足最优性必要条件这一特征,提出使用测量变量对最优状态特征进行等价描述。基于主元分析方法建立最优状态的特征模型并使用多变量统计手段建立统计限,判别当前系统状态是否处于最优操作点。主要的思路是将系统非最优状态视为一种特殊的过程故障,然后通过多变量统计方法检测出来。提出的最优状态检测技术可用于评估系统的优化控制性能,并且为控制系统是否有必要进一步执行后续实时优化措施提供指导意见。For the real-time optimization (RTO) problem of uncertain chemical processes, a novel optimality detection technique is proposed. Firstly, the common features of optimal statuses under various disturbances for continuous processes are explored, namely the first order necessary conditions of optimality should be satisfied, it is found that these common features can be compactly described in a reduced space with the introduction of principle component analysis (PCA), through which the feature model of optimal statuses can be built. For monitoring purpose, the control limits both in principle component space and residual space are determined using the theory of multi-variable statistical approaches. The projective scores of the new operating status onto these two spaces can be computed and compared to their control limits, through which means whether current operation is optimal can be determined. Proposed method is applied upon a simulated exothermic reactor for optimality detection, the results show that with linear PCA, the false alarm rate for/~ and SPE statistics is 0.6 % and 1.2 %, while the missing rate is 11% and 2.2 %, respectively. It is concluded that PCA is effective for optimality detection and moreover, the SPE statistic exhibits a better performance. For future developments, proposed technique is expected to be used for evaluating the performance of optimal control operation, and providing guides to inform the control system of whether it is necessary to perform additional RTO actions afterwards.
分 类 号:TP273[自动化与计算机技术—检测技术与自动化装置]
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