Semiadaptive Fault Diagnosis via Variational Bayesian Mixture Factor Analysis with Application to Wastewater Treatment

Mainly due to the hostile environment in wastewater plants (WWTPs), the reliability of sensors with respect to important qualities is often poor. In this work, we present the design of a semiadaptive fault diagnosis method based on the variational Bayesian mixture factor analysis (VBMFA) to support process monitoring. The proposed method is capable of capturing strong nonlinearity and the significant dynamic feature of WWTPs that seriously limit the application of conventional multivariate statistical methods for fault diagnosis implementation. The performance of proposed method is validated through a simulation study of a wastewater plant. Results have demonstrated that the proposed strategy can significantly improve the ability of fault diagnosis under fault-free scenario, accurately detect the abrupt change and drift fault, and even localize the root cause of corresponding fault properly

Optimized forecast components-SVM-based fault diagnosis with applications for wastewater treatment

Process monitoring of wastewater treatment plant (WWTP) is a challenging industrial problem, due to its exposure to the hostile working environment and significant disturbances. This paper proposed a novel fault diagnosis method, termed as optimization forecast components-support vector machine (OFC-SVM). The method firstly improved the forecastable component analysis (ForeCA) for feature extraction. Secondly, in order to further enhance the method, the quadratic Grid Search (GS) algorithm is utilized to optimize the parameters of the proposed method. Thirdly, to properly evaluate the method performance, a new evaluation index is proposed, named Pre Alarm Rate (PAR), aiming to achieve the quantitative trade-off between false alarm rate (FAR) and missed alarm rate(MAR). Then, the new ROC curve can be further derived by PAR. Finally, the performance of OFC-SVM is strictly compared with other five methods as well as validated by a Monte Carlo model and a full-scale WWTP

Modeling of adaptive multi-output soft-sensors with applications in wastewater treatments

Given the multivariable coupling, strong nonlinearity and time-varying features in the wastewater treatment processes, adaptive strategies, including just-in-time learning (JITL), time difference (TD), and moving window (MW) methods have been chosen in this paper to enhance multi-output soft-sensor models to ensure online prediction for a variety of hard-to-measure variables simultaneously. In the proposed adaptive multi-output soft-sensors, multi-output partial least squares (MPLS), multi-output relevant vector machine (MRVM) and multi-output Gaussian process regression (MGPR) served as the multi-output models. The integration of adaptive strategies and multi-output models not only provides a solution for multi-output prediction, but also offers a potential to alleviate the degradation of multi-output soft-sensors. To further improve the adaptive ability, four adaptive soft-sensors, termed TD-MW, TD-JIT, JIT-MW, and TD-JIT-MW, have been proposed by mixing the three aforementioned adaptive strategies to upgrade multi-output softsensors. All the adaptive multi-output soft-sensors are analyzed and compared in terms of simulation data and practical industrial data, which exhibit stationary and nonstationary behaviors, respectively

Adaptive ranking based ensemble learning of Gaussian process regression models for quality-related variable prediction in process industries

The proper monitoring of quality-related but hard-to-measure variables is currently one of the bottlenecks limiting the safe and efficient operations of industrial processes. This paper proposes a novel ensemble learning algorithm by coordinating global and local Gaussian process regression (GPR) models, and this algorithm is able to capture global and local process behaviours for accurate prediction and timely process monitoring. To further address the deterioration in predictions when using the off-line training and online testing strategy, this paper proposes an adaptive ranking strategy to perform ensemble learning for the sub-GPR models. In this adaptive strategy, we use the moving-window technique to rank and select several of the best sub-model predictions and then average them together to make the final predictions. Last but not least, the least absolute shrinkage and selection operator (Lasso) works together with factor analysis (FA) in a two-step variable selection method to remove under-correlated model input variables in the first stage and to compress over-correlated model input variables in the second stage. The proposed prediction model is validated in two real wastewater treatment plants (WWTPs) with stationary and nonstationary behaviours. The results show that the proposed methodology achieves better performance than other standard methods in the context of their predictions of quality-related variables

Experiments and modeling for flexible biogas production by co-digestion of food waste and sewage sludge

This paper explores the feasibility of flexible biogas production by co-digestion of food waste and sewage sludge based on experiments and mathematical modeling. First, laboratory-scale experiments were carried out in variable operating conditions in terms of organic loading rate and feeding frequency to the digester. It is demonstrated that biogas production can achieve rapid responses to arbitrary feedings through co-digestion, and the stability of the anaerobic digestion process is not affected by the overloading of substrates. Compared with the conventional continuous mode, the required biogas storage capacity in flexible feeding mode can be significantly reduced. The optimum employed feeding organic loading rate (OLR) is identified, and how to adjust the feeding scheme for flexible biogas production is also discussed. Finally, a simplified prediction model for flexible biogas production is proposed and verified by experimental data, which could be conveniently used for demand-oriented control. It is expected that this research could give some theoretical basis for the enhancement of biogas utilization efficiency, thus expanding the applications of bio-energy.Sichuan Provincial Key Technology Suppor

A SEVA soft sensor method based on self-calibration model and uncertainty description algorithm

Soft sensors are widely used to estimate process variables that are difficult to measure online. However, due to poor quality of input data and deterioration of prediction model as time passes, soft sensors make poor performance. We have been constructing a principal component analysis (PCA) model before performing a prediction. Furthermore, the just-in-time (JIT) learning model has been improved and served as prediction model for self validating (SEVA) soft sensors. The proposed soft sensor not only carries out internal quality assessment but also generates multiple types of output data, including the prediction values (PV), input sensor status (ISS), validated measurement (VM), output sensor status (OSS) and the uncertainty values (UV) which represent the credibility of soft sensors' output. The effectiveness of the proposed SEVA soft sensors is demonstrated through a case study of a wastewater treatment process