Fault detection and isolation in a FED-batch penicillin fermentation process

2003 ◽  
Author(s):  
Hongwei Zhang ◽  
Barry Lennox
Keyword(s):  
Fault Detection ◽  
Fermentation Process ◽  
Fault Detection And Isolation ◽  
Fed Batch ◽  
Penicillin Fermentation

scholarly journals The emergence of feedforward periodicity for the fed-batch penicillin fermentation process

2018 ◽  
Vol 51 (32) ◽  
pp. 130-135
Author(s):  
Chi Zhai ◽  
Tong Qiu ◽  
Ahmet Palazoglu ◽  
Wei Sun
Keyword(s):  
Fermentation Process ◽  
Fed Batch ◽  
Penicillin Fermentation

scholarly journals Design of Robust Fault Detection Scheme for Penicillin Fermentation Process

2015 ◽  
Vol 48 (21) ◽  
pp. 589-594 ◽  
Author(s):  
Abdul Rehman Khan ◽  
Abdul Qayyum Khan ◽  
Muhammad Taskeen Raza ◽  
Muhammad Abid ◽  
Ghulam Mustafa
Keyword(s):  
Fault Detection ◽  
Fermentation Process ◽  
Detection Scheme ◽  
Robust Fault Detection ◽  
Penicillin Fermentation

scholarly journals Fault Detection for Industrial Processes

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Yingwei Zhang ◽  
Lingjun Zhang ◽  
Hailong Zhang
Keyword(s):  
Fault Detection ◽  
Fermentation Process ◽  
Industrial Processes ◽  
Statistical Techniques ◽  
Tennessee Eastman Process ◽  
Detection Approach ◽  
Principal Directions ◽  
Residual Space ◽  
Simulation Results ◽  
Penicillin Fermentation

A new fault-relevant KPCA algorithm is proposed. Then the fault detection approach is proposed based on the fault-relevant KPCA algorithm. The proposed method further decomposes both the KPCA principal space and residual space into two subspaces. Compared with traditional statistical techniques, the fault subspace is separated based on the fault-relevant influence. This method can find fault-relevant principal directions and principal components of systematic subspace and residual subspace for process monitoring. The proposed monitoring approach is applied to Tennessee Eastman process and penicillin fermentation process. The simulation results show the effectiveness of the proposed method.

Multiple faults detection and isolation approach applied to a kraft recovery boiler

TAPPI Journal ◽  
2014 ◽  
Vol 13 (1) ◽  
pp. 33-41
Author(s):  
YVON THARRAULT ◽  
MOULOUD AMAZOUZ
Keyword(s):  
Fault Detection ◽  
Early Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Principal Component ◽  
Fault Isolation ◽  
Fault Detection And Isolation ◽  
Detection Scheme ◽  
Multiple Faults ◽  
Recovery Boiler

Recovery boilers play a key role in chemical pulp mills. Early detection of defects, such as water leaks, in a recovery boiler is critical to the prevention of explosions, which can occur when water reaches the molten smelt bed of the boiler. Early detection is difficult to achieve because of the complexity and the multitude of recovery boiler operating parameters. Multiple faults can occur in multiple components of the boiler simultaneously, and an efficient and robust fault isolation method is needed. In this paper, we present a new fault detection and isolation scheme for multiple faults. The proposed approach is based on principal component analysis (PCA), a popular fault detection technique. For fault detection, the Mahalanobis distance with an exponentially weighted moving average filter to reduce the false alarm rate is used. This filter is used to adapt the sensitivity of the fault detection scheme versus false alarm rate. For fault isolation, the reconstruction-based contribution is used. To avoid a combinatorial excess of faulty scenarios related to multiple faults, an iterative approach is used. This new method was validated using real data from a pulp and paper mill in Canada. The results demonstrate that the proposed method can effectively detect sensor faults and water leakage.

Sign in / Sign up

Export Citation Format

Share Document