Abstract Fault diagnosis of sequential batch reactor (SBR), a widely applied wastewater treatment technology in papermaking industry with huge discharge, has been a significant challenge due to the inherent multi-period characteristics of the process. In this paper, based on the conventional multi-way principal component analysis (MPCA) method (Scenario 0), two sub-period division strategies based on the processing phases of SBR process (Scenario 1) and the similarities of the loading matrices between the adjacent time slices (Scenario 2) are proposed for the detection of faults. Combined with Scenario 0, using the field data of blower current, level of SBR reactor, dissolved oxygen of wastewater and the blower valve opening in the SBR process of paper mill, two different fault diagnosis models with Scenarios 1 and 2 are developed and evaluated, respectively. The study results revealed that, both the calculated statistics of T2 and sum of prediction errors (SPE) of the fault diagnosis models with Scenarios 1 and 2 could detect the faults and identify the fault locations and sources. Compared to Scenario 0 which neglects the correlations between the different stages of SBR process, the fault diagnosis model by Scenario 2 demonstrated a superiority ability in fault identification in terms of the fault’s time onset and fault’s sources with adequate accuracy. The results enable the feasible and reliable implementation of the developed sub-MPCA diagnosis model with Scenario 2 in the actual SBR plants.

中文翻译：

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分时段划分策略结合多路主成分分析对造纸厂废水处理过程序批式反应器故障诊断

摘要 序批式反应器（SBR）是造纸行业应用广泛、排放量大的废水处理技术，由于该过程固有的多周期特性，其故障诊断一直是一项重大挑战。本文在传统的多路主成分分析（MPCA）方法（场景0）的基础上，根据SBR工艺（场景1）的处理阶段和加载矩阵之间的相似性，提出了两种子周期划分策略。相邻时间片（场景 2）被提议用于故障检测。结合场景0，利用造纸厂SBR工艺中鼓风机电流、SBR反应器液位、废水溶解氧和鼓风机阀门开度等现场数据，开发并评估了两种不同的场景1和2故障诊断模型，分别。研究结果表明，情景 1 和情景 2 的故障诊断模型的 T2 计算统计量和预测误差总和 (SPE) 都可以检测故障并识别故障位置和来源。与忽略SBR过程不同阶段之间相关性的场景0相比，场景2的故障诊断模型在故障发生时间和故障来源方面表现出优越的故障识别能力，具有足够的准确性。结果使开发的子 MPCA 诊断模型与场景 2 在实际 SBR 工厂中可行和可靠的实施成为可能。情景 1 和情景 2 的故障诊断模型的 T2 计算统计量和预测误差总和 (SPE) 都可以检测故障并识别故障位置和来源。与忽略SBR过程不同阶段之间相关性的场景0相比，场景2的故障诊断模型在故障发生时间和故障来源方面表现出优越的故障识别能力，具有足够的准确性。结果使开发的子 MPCA 诊断模型与场景 2 在实际 SBR 工厂中可行和可靠的实施成为可能。情景 1 和情景 2 的故障诊断模型的 T2 计算统计量和预测误差总和 (SPE) 都可以检测故障并识别故障位置和来源。与忽略SBR过程不同阶段之间相关性的场景0相比，场景2的故障诊断模型在故障发生时间和故障来源方面表现出优越的故障识别能力，具有足够的准确性。结果使开发的子 MPCA 诊断模型与场景 2 在实际 SBR 工厂中可行和可靠的实施成为可能。场景2的故障诊断模型在故障发生时间和故障来源方面表现出优越的故障识别能力，具有足够的准确性。结果使开发的子 MPCA 诊断模型与场景 2 在实际 SBR 工厂中可行和可靠的实施成为可能。场景2的故障诊断模型在故障发生时间和故障来源方面表现出优越的故障识别能力，具有足够的准确性。结果使开发的子 MPCA 诊断模型与场景 2 在实际 SBR 工厂中可行和可靠的实施成为可能。