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Multivariate statistical projection methods such as principal component analysis (PCA) are the most common strategy for process monitoring in wastewater treatment plants (WWTPs). Such monitoring strategies can indeed recognize faults and achieve better control performance for fully observed data sets but can be more difficult in the case of having missing data. This study presents a variational Bayesian PCA (VBPCA) based methodology for fault detection in the WWTPs. This methodology not only is robust against missing data but also reconstructs missing data. Furthermore, a novel historical data preprocess method is proposed to deal with diurnal behaviors in the WWTPs with fast sample rate. These methodologies have been validated by process data collected from two WWTPs with different process characteristics and different sample rates. The results showed that the proposed methodology is capable of detecting sensor faults and process faults with good accuracy under different scenarios (highly and lowly instrumented WWTP).

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Soft-sensor is the most common strategy to estimate the hard-to-measure variables in the chemical processes. Recent research has shown that accurate prediction of hard-to-measure variables can significantly improve system performance. However, deterioration of predictive ability resulting from dramatic changes in the operation conditions always renders a generic soft-sensor inadequate. This study developed an adaptive soft-sensor with Moving Window and Time Differencing technique accounting for both of long-term and short-term information for modeling. At each step of model update, the most insensitive variables were removed by VIP (Variable importance in projection). With further integrating Variational Bayesian PLS (VBPLS) as predictive model, not just prediction values are obtained but also the credibility of information for hard-to-measure quantities can be generated. The proposed methodology was first demonstrated by applying the design algorithm to a WWTP simulated with the well-established model, BSM1, then extended to a real WWTP with data collecting from the field. Results showed that the proposed strategy significantly improved the prediction performance.

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