Performance analysis for online chemical process optimization systems

在线优化在化工流程工业中被广泛应用，以确保通过高水平的流程性能来确保竞争力。在线优化在这些行业中的常见应用包括实时优化(RTO)、模型预测控制(MPC)、反应调度、供应链优化等。这些在线优化技术通常很复杂，需要定期维护以确保其持续的高性能(即，这些自动化系统的性能会因多种因素而随着时间的推移而下降)。因此，在线优化系统的一个关键但目前缺少的要素是一个有效的性能分析子系统，它将包括：性能监测、故障检测和故障诊断。

Online optimization is widely used in the chemical process industries to ensure competitiveness though a high level of process performance. Common applications of online optimization in these industries include real-time optimization (RTO), model-predictive control (MPC), reactive scheduling, supply-chain optimization, and so forth. These online optimization technologies are, typically, complex and require regular maintenance to ensure their continued high performance (i.e., the performance of these automation systems degrade over time due to a number of factors). Thus, a key, but currently missing, element of online optimization systems is an effective performance analysis subsystem, which would include: performance monitoring, failure detection, and failure diagnosis.