Process Monitoring and Fault Diagnosis of Penicillin Fermentation Based on Improved MICA

Abstract

In the process monitoring and fault diagnosis of batch processes, traditional principal component analysis (PCA) and least-squares (PLS), are assuming that the process variables are multivariate Gaussian distribution. But in the practical industrial process, the data observed of process variables do not necessarily be the multivariate Gaussian distribution. Independent component analysis (ICA), as a higher-order statistical method, is more suitable for dynamic systems. Observational data are decomposed into a linear combination of the independent components under statistical significance. The higher order statistics will be extracted and the mixed signals are decomposed into independent non-Gaussian components. Traditional method of ICA has to predefine the number of independent components. This paper proposed an improved MICA method of realizing the automatically choosing the independent components through setting the threshold value of the negentropy. The method can solve the problem of predefining the number of independent components in traditional methods and meanwhile it reduces the complexity of the monitoring model. The proposed method is used to do the process monitoring and fault diagnosis of penicillin fermentation and the results verify the feasibility and effectiveness of the method.

Quality Prediction in Complex Batch Processes with Just-in-Time Learning Model Based on Non-Gaussian Dissimilarity Measure

“…50,51 Implementing an effective online prediction 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 approach is very important to ensure operating conditions stable and safety, improves the product quality and yield, and increases the economic profits for Penicillin Fermentation process. Generally, in the typical Penicillin Fermentation process, microorganisms are cultivated and accumulated to up to adequate cell densities for penicillin production in the initial pre-culture phase.…”

“…In this section, a benchmark fed-batch Penicillin Fermentation process is used to verify the effectiveness of the proposed online quality prediction method. The Penicillin Fermentation process is a typical nonlinear, multiphase, dynamic and non-Gaussian industrial process. , Implementing an effective online prediction approach is very important to ensure stable and safe operating conditions, improve the product quality and yield, and increase the economic profits for the Penicillin Fermentation process. Generally, in the typical Penicillin Fermentation process, microorganisms are cultivated and accumulated to up to adequate cell densities for penicillin production in the initial preculture phase.…”

Variable moving windows based non‐Gaussian dissimilarity analysis technique for batch processes fault detection and diagnosis

“…In this section, a benchmark fed‐batch Penicillin Fermentation process is used to verify the effectiveness of the proposed fault detection and diagnosis method . The Penicillin Fermentation process is a typical nonlinear, multimodal, dynamic and non‐Gaussian industrial process . Implementing an effective fault detection and diagnosis approach is very important to ensure operating conditions stable and safety, improve the product quality and yield, and increase the economic profits for Penicillin Fermentation process.…”

Deep Koopman Neural Network Based Process Monitoring for Stochastic Production System

“…As multiway PCA is essentially a linear method, it is incapable of handling nonlinearity in dynamics, which motivated the use of the kernel method to map data into a highdimensional feature space where data is linearly separable [9] . Other notable efforts include an improved Independent Component Analysis (ICA) method [10] , a two-step modeling strategy named kernel ICA-PCA method [11] and a multiway kernel entropy ICA method [12] developed for capturing nonlinear and non-Gaussian features embedded in SPS data. Additionally, Support Vector Machines (SVM) integrated with PCA or fuzzy reasoning are able to obtain robust decision functions for anomaly detection in SPS [13], [14] .…”