Integrated design of monitoring, analysis and maintenance for filamentous sludge bulking in wastewater treatment

Abstract

Stable operation of activated sludge process is often compromised by the occurrence of filamentous sludge bulking in wastewater treatment. Standard maintenance is implemented as a single task, which is inefficiently manipulated based on purely chemical dosage by experiences. This paper proposes a novel and efficient maintenance framework, including a set of activities: incipient fault detection, causality analysis, remaining useful life (RUL) prediction and maintenance schedule, to develop a collective strategy for sludge bulking management. In this framework, Canonical Correlation Analysis (CCA) work together with Kurtosis (KU) and an autoregressive moving average (ARMA) to identify incipient sludge bulking and to capture the evolution of sludge bulking over a long horizon. The proposed framework was tested by the collected the field data. The results showed that the proposed framework is capable of detecting (Type I error, 2.1%; Type II error, 0%), locating (root cause: temperature), calculating RUL (32-days) and maintaining sludge bulking.

Condition-Based Maintenance Policy for Systems With a Non-Homogeneous Degradation Process

“…Since V n (k, X k ) is non-decreasing function in k and X k , and X k+1 | X k is stochastically non-decreasing in k and X k , we can conclude that E [V n (k + 1, X k+1 )] is non-decreasing in k and X k . Since the right-hand terms of Eq (21) are all non-decreasing in k and X k , we can conclude that V n+1 (k, X k ) is a non-decreasing function in k and X k , which completes the proof.…”

“…We focus on the control limit policy as it is most commonly used in maintenance practice. The control limit policy works in such a way that the system is preventively replaced whenever the inspected degradation level exceeds a preventive replacement threshold [21], [51], [52]. Although both corrective replacement and preventive replacement restore the system to the as-good-as-new state, they differ in nature as corrective replacement is often implemented at an unplanned time and at a more deteriorated state; as a result, additional cost may be incurred for the potential damages caused by failure.…”

“…Due to the varying environment, non-homogeneous degradation processes exist extensively in reality. Actually, for most systems such as civil infrastructures and airplanes, which are designed to operate for decades, the assumption that a system operates under a stationary environment is too restrictive and therefore unrealistic [18]- [21]. More often than not, the degradation process may accelerate as a result of cumulative damage/degradation.…”

Efficient batch process monitoring based on random nonlinear feature analysis

“…[ 3 ] Considering that advanced automatic control systems yield vast amounts of running data, data‐driven methods have been widely applied in the field of batch process monitoring. [ 4 ] These methods utilize statistical analysis and machine learning tools to mine data features for status monitoring purposes. Typical methods include principal component analysis (PCA), [ 5,6 ] independent component analysis, [ 7,8 ] partial least squares, [ 9 ] and support vector data description.…”

A Batch Process Monitoring Method Using Two-Dimensional Localized Dynamic Support Vector Data Description

“…The process complexity leads to high difficulty to ensure the continuity and safety of the process running. Therefore, process monitoring and fault detection technologies are particularly important to batch processes and have attracted widespread attention from scholars in the process control field [7][8]. Due to the abundant data recorded by the advanced computer control systems, data-driven process monitoring methods are becoming the hot topic [9][10][11][12].…”