Design and Optimization of a Penicillin Fed-Batch Reactor Based on a Deep Learning Fault Detection and Diagnostic Model

Hematillake, Dylan; Freethy, Daniele; McGivern, Jacob; McCready, Chris; Agarwal, Piyush; Budman, Hector

doi:10.1021/acs.iecr.1c04534

Cited by 10 publications

(5 citation statements)

References 24 publications

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“…In this paper, a state-of-the-art penicillin fermentation simulation platform, Pensim V 2.0 [40,41], is employed to emphasize the advantages of CDVAE.…”

Section: Process Description and Modelingmentioning

confidence: 99%

Time-Specific Thresholds for Batch Process Monitoring: A Study Based on Two-Dimensional Conditional Variational Auto-Encoder

Zhu,

Liu,

Lou

et al. 2024

Processes

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This paper studies the use of varying threshold in the statistical process control (SPC) of batch processes. The motivation is driven by how when multiple phases are implicated in each repetition, the distributions of the features behind vary with phases or even the time; thus, it is inconsistent to uniformly bound them by an invariant threshold. In this paper, we paved a new path for learning and monitoring batch processes based on an efficient framework integrating a model termed conditional dynamic variational auto-encoder (CDVAE). Phase indicators are first used to split the data and are then separated, serving as an extra input for the model in order to alleviate the learning complexity. Dissimilar to the routine using features across all timescales, only features relevant to local timestamps are aggregated for threshold calculation, producing a varying threshold that is more specific for the process variations occurring among the timeline. Leveraged upon this idea, a fault detection panel is devised, and a deep reconstruction-based contribution diagram is illustrated for locating the faulty variables. Finally, the comparative results from two case studies highlight the superiority in both detection accuracy and diagnostic performance.

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“…In this paper, a state-of-the-art penicillin fermentation simulation platform, Pensim V 2.0 [40,41], is employed to emphasize the advantages of CDVAE.…”

Section: Process Description and Modelingmentioning

confidence: 99%

Time-Specific Thresholds for Batch Process Monitoring: A Study Based on Two-Dimensional Conditional Variational Auto-Encoder

Zhu,

Liu,

Lou

et al. 2024

Processes

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“…For example, Hematillake, D and others put forward an automatic sketch generation method based on CNN. By learning a large number of design samples, the model can generate a preliminary sketch that conforms to the design semantics [7] . However, there is still room for improvement in details and diversity of these methods.…”

Section: ⅱ Related Workmentioning

confidence: 99%

The Analysis of Optimization Strategy of Industrial Design in Automatic Sketch Generation Based on Deep Learning

2024

IEEE Access

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This study is devoted to exploring the strategy of automatic sketch generation and optimization of industrial design based on deep learning. By combining the Generative Adversarial Network (GAN) with the optimization algorithm, this paper proposes an innovative method to realize the automatic generation of highquality and diverse industrial design sketches. In the experiment, this paper selects SketchyCAD and other public data sets, trains them through deep learning model, and introduces genetic algorithm(GA) and differential evolution algorithm to optimize the parameters. In terms of experimental results, we observed that the quality of generated sketches was significantly improved, and the design sketches generated by the mode (GAN+GA) were more realistic and innovative. The introduction of optimization strategy further improves the generation effect and intelligently adjusts the model parameters to adapt to different design styles. In this paper, the influence of hyperparameter tuning is analyzed in detail, and it is found that the adjustment of learning rate plays a key role in generating quality and diversity. However, the experiment also revealed some challenges and room for improvement. We noticed that the generated results may have the risk of over-fitting in the training process, and with the increase of training times, the diversity gradually decreased. This suggests that more complex model structure and richer data sets are needed to improve the generalization performance. Generally speaking, this study provides new ideas and methods for the integration of deep learning and industrial design. By innovatively combining generation model and optimization algorithm, this research has contributed beneficial research results to the development of industrial design automation. This research is of great significance to promote the intelligence and innovation in the field of industrial design.

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“…The hyper-parameter search is implemented using a Keras-tuner. Firstly, a grid of hyper-parameters is defined, for example the number of encoder layers = [1,2,3], number of LSTM units for each of these layers ranging from 2 to 200 with an interval of 2 = [10:2:200], learning rate = [0.1, 0.2, 0.3, 0.01], value of weights in the objective function, etc. Keras-tuner trains the model using different combinations of these hyper-parameters values, and the averaged validation accuracy is evaluated at every epoch.…”

Section: Counts Of Predicted Label I Counts Of Predicted Label Other ...mentioning

confidence: 99%

“…The operation of industrial plants employs sensors and control loops to mitigate the economic losses resulting from these faults. However, in the presence of process faults and manipulated variable constraints, these control schemes are not sufficiently resilient to avoid abnormal operation [1,2]. Thus, process faults must be diagnosed and addressed by implementing a suitable corrective measure.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Deep LSTM for Fault Detection and Diagnosis for a Chemical Process

et al. 2022

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A hierarchical structure based on a Deep LSTM Supervised Autoencoder Neural Network (Deep LSTM-SAE NN) is presented for the detection and classification of faults in industrial plants. The proposed methodology has the ability to classify incipient faults that are difficult to detect and diagnose with traditional and many recent methods. Faults are grouped into different subsets according to the degree of difficulty to classify them accurately in the proposed hierarchical structure. External pseudo-random binary signals (PRBS) are injected in the system to enhance the identification of incipient faults. The approach is illustrated on the benchmark process (Tennessee Eastman Process) in order to compare across different methodologies. The efficacy of the proposed method is shown by a comprehensive comparison between many recent and traditional fault detection and diagnosis methods in the literature for Tennessee Eastman Process. The proposed work results in significant improvements in the classification of faults over both multivariate linear model-based strategies and non-hierarchical nonlinear model-based strategies.

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Design and Optimization of a Penicillin Fed-Batch Reactor Based on a Deep Learning Fault Detection and Diagnostic Model

Cited by 10 publications

References 24 publications

Time-Specific Thresholds for Batch Process Monitoring: A Study Based on Two-Dimensional Conditional Variational Auto-Encoder

Time-Specific Thresholds for Batch Process Monitoring: A Study Based on Two-Dimensional Conditional Variational Auto-Encoder

The Analysis of Optimization Strategy of Industrial Design in Automatic Sketch Generation Based on Deep Learning

Hierarchical Deep LSTM for Fault Detection and Diagnosis for a Chemical Process

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