Knowledge and data-driven hybrid system for modeling fuzzy wastewater treatment process

Cheng, Xuhong; Guo, Zhiwei; Shen, Yu; Yu, Keping; Gao, Xu

doi:10.1007/s00521-021-06499-1

Cited by 16 publications

(7 citation statements)

References 53 publications

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“…This serial/parallel hybrid structure with competing expert networks was trained with the EM algorithm. The and deep neural networks (data-based model) [64] . For the latter, a convolutional neural network (CNN) was combined with a long short-term memory network (LSTM).…”

Section: Mixed Microbial Culturesmentioning

confidence: 99%

“…Most of the hybrid modeling studies so far applied simple shallow neural networks. With a significant delay, hybrid modeling is now incorporating some of the advances in deep learning [19,64] . Pinto et al (2022) recently compared traditional shallow hybrid modeling (using the Levenberg-Marquardt training coupled with the indirect sensitivities, cross-validation and tanh activation function) with a novel hybrid deep modeling approach (using ADAM training, semidirect sensitivities, stochastic regularization, multiple hidden layers and ReLU activation functions) [19] .…”

Section: Research Gapmentioning

confidence: 99%

See 1 more Smart Citation

Application of Hybrid Neural Models to Bioprocesses: A Systematic Literature Review

Agharafeie

Oliveira

Ramos

et al. 2023

Preprint

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Due to the complexity of biological processes, developing model-based strategies for monitoring, optimization and control is nontrivial. Hybrid neural models, combining mechanistic modeling with artificial neural networks, have been reported as powerful tools for bioprocess applications. In this paper, a systematic literature review is presented focused on the application of hybrid neural models to bioprocesses by Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) over the last 30 years. This analysis showed that hybrid neural modeling has covered a wide range of microbial processes, animal cells, mixed microbial cultures, and enzyme biocatalysis. Hybrid neural models have been mainly applied for predictive modeling/process analysis, process monitoring/software sensors, open- and closed-loop control, batch-to-batch control, model predictive control, intensified design of experiments, process analytical technology, quality-by-design, and more recently, digital twins. Hybrid modeling experienced a decline in the number of publications after a peak in 2004 and is now surging again. A “model scale” research gap was identified, which will likely narrow by a better integration with deep learning and systems biology in the near future. The biopharma sector is currently a major driver but applications to biologics quality attributes (e.g. glycosylation), new modalities and downstream unit operations are significant research gaps.

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Section: Mixed Microbial Culturesmentioning

confidence: 99%

Section: Research Gapmentioning

confidence: 99%

Application of Hybrid Neural Models to Bioprocesses: A Systematic Literature Review

Agharafeie

Oliveira

Ramos

et al. 2023

Preprint

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show abstract

“…These are now hot topics for the application of HNNs to bioprocesses, especially in the biopharma sector. Only recently has the "deep learning" keyword appeared explicitly or implicitly in connection with hybrid modeling [30,32,33,[36][37][38][39]. Further details of the PRISMA analysis are provided as Supplementary Materials (Supplementary File S1, Supplementary Figures S1-S7 and Supplementary Table S1).…”

Section: Systematic Literature Reviewmentioning

confidence: 99%

From Shallow to Deep Bioprocess Hybrid Modeling: Advances and Future Perspectives

Agharafeie,

Ramos,

Mendes

et al. 2023

Fermentation

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Deep learning is emerging in many industrial sectors in hand with big data analytics to streamline production. In the biomanufacturing sector, big data infrastructure is lagging compared to other industries. A promising approach is to combine deep neural networks (DNN) with prior knowledge in hybrid neural network (HNN) workflows that are less dependent on the quality and quantity of data. This paper reviews published articles over the past 30 years on the topic of HNN applications to bioprocesses. It reveals that HNNs have been applied to various bioprocesses, including microbial cultures, animal cells cultures, mixed microbial cultures, and enzyme biocatalysis. HNNs have been applied for process analysis, process monitoring, development of software sensors, open- and closed-loop control, batch-to-batch control, model predictive control, intensified design of experiments, quality-by-design, and recently for the development of digital twins. Most previous HNN studies have combined shallow feedforward neural networks (FFNNs) with physical laws, such as macroscopic material balance equations, following the semiparametric design principle. Only recently, deep HNNs based on deep FFNNs, convolution neural networks (CNN), long short-term memory (LSTM) networks and physics-informed neural networks (PINNs) have been reported. The biopharma sector is currently a major driver but applications to biologics quality attributes, new modalities, and downstream processing are significant research gaps.

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“…Hansen and co‐authors recently applied LSTMs for modeling phosphorous removal in a wastewater treatment plant characterized by complex mixed microbial dynamics (Hansen et al, 2022). Cheng and co‐authors reported a multilayered hybrid modeling workflow for a wastewater treatment process that combined a mechanistic model, a convolutional neural network (CNN), a LSTM and a FFNN (Cheng et al, 2021). In this paper, we extend the deep hybrid modeling framework proposed by (Pinto et al, 2022) to LSTMs combined in series with dynamic material balance equations.…”

Section: Introductionmentioning

confidence: 99%

Deep hybrid modeling of a HEK293 process: Combining long short‐term memory networks with first principles equations

Ramos,

Pinto,

Poiares‐Oliveira

et al. 2024

Biotech & Bioengineering

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The combination of physical equations with deep learning is becoming a promising methodology for bioprocess digitalization. In this paper, we investigate for the first time the combination of long short‐term memory (LSTM) networks with first principles equations in a hybrid workflow to describe human embryonic kidney 293 (HEK293) culture dynamics. Experimental data of 27 extracellular state variables in 20 fed‐batch HEK293 cultures were collected in a parallel high throughput 250 mL cultivation system in an industrial process development setting. The adaptive moment estimation method with stochastic regularization and cross‐validation were employed for deep learning. A total of 784 hybrid models with varying deep neural network architectures, depths, layers sizes and node activation functions were compared. In most scenarios, hybrid LSTM models outperformed classical hybrid Feedforward Neural Network (FFNN) models in terms of training and testing error. Hybrid LSTM models revealed to be less sensitive to data resampling than FFNN hybrid models. As disadvantages, Hybrid LSTM models are in general more complex (higher number of parameters) and have a higher computation cost than FFNN hybrid models. The hybrid model with the highest prediction accuracy consisted in a LSTM network with seven internal states connected in series with dynamic material balance equations. This hybrid model correctly predicted the dynamics of the 27 state variables (R2 = 0.93 in the test data set), including biomass, key substrates, amino acids and metabolic by‐products for around 10 cultivation days.

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Knowledge and data-driven hybrid system for modeling fuzzy wastewater treatment process

Cited by 16 publications

References 53 publications

Application of Hybrid Neural Models to Bioprocesses: A Systematic Literature Review

Application of Hybrid Neural Models to Bioprocesses: A Systematic Literature Review

From Shallow to Deep Bioprocess Hybrid Modeling: Advances and Future Perspectives

Deep hybrid modeling of a HEK293 process: Combining long short‐term memory networks with first principles equations

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