Skip to main content
SpringerLink
Log in

Multiple feature fusion transformer for modeling penicillin fermentation process with unequal sampling intervals

  • Research Paper
  • Published:
Bioprocess and Biosystems Engineering Aims and scope Submit manuscript

Abstract

The quality prediction of batch processes is an important task in the field of biological fermentation. However, dynamic nonlinearity, unequal sampling intervals, uneven duration, and multiple features of a batch process make this task challenging. Thus, the multiple-feature fusion transformer (MFFT) model is proposed for the time series quality prediction of a batch process. First, the application of sequence-to-sequence architecture enables MFFT to perform a wide range of sequence prediction tasks. Second, the transformer parallel operation model imposes no rigid requirement for the order of sequence input, allowing the model to deal with problems of unequal interval sampling and utilize the sequence information. Third, MFFT integrates a pretrained ResNet50 as a mycelium status classifier for fusing image information into the features. Moreover, a multiple-feature encoding structure is proposed to integrate sampling time and mycelium status. Finally, multiple tasks in penicillin fermentation have shown that MFFT significantly outperforms existing methods for time series prediction.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Jovic S, Guresic D, Babincev L, Draskovic N, Dekic V (2019) Comparative efficacy of machine-learning models in prediction of reducing uncertainties in biosurfactant production. Bioprocess Biosyst Eng 42:1695–1699

    Article  CAS  PubMed  Google Scholar 

  2. Carvalho M, Riesberg J, Budman H (2021) Development of new media formulations for cell culture operations based on regression models. Bioprocess Biosyst Eng 44:453–472

    Article  CAS  PubMed  Google Scholar 

  3. Chen T, Guestrin C (2016) Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM sigkdd international conference on knowledge discovery and data mining, p 785–794

  4. Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu T-Y (2017) Lightgbm: a highly efficient gradient boosting decision tree. Adv Neural Inf Process Syst 30:3149–3157

    Google Scholar 

  5. Oliveira ÍM, de Jesus RA, Nascimento VRS, Bilal M, Iqbal HMN, Ferreira LFR, Cestari AR (2022) Bioremediation potential of Dicentrarchus labrax fish scales for dye-based emerging contaminants by ANN–GA hybrid modeling. Bioprocess Biosyst Eng 45:1189–1200

    Article  CAS  PubMed  Google Scholar 

  6. Gregor K, Danihelka I, Mnih A, Blundell C, Wierstra D (2014) Deep autoregressive networks. In: International conference on machine learning, PMLR, p 1242–1250

  7. Zhang GP (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50:159–175

    Article  Google Scholar 

  8. Qing X, Jin J, Niu Y, Zhao S (2020) Time–space coupled learning method for model reduction of distributed parameter systems with encoder-decoder and RNN. AIChE J 66:e16251

    Article  CAS  Google Scholar 

  9. Liu K, Zhang J (2021) A dual-layer attention-based LSTM network for fed-batch fermentation process modelling. Computer aided chemical engineering. Elsevier, Amsterdam

    Google Scholar 

  10. Jin-Dong C, Feng P (2010) Hybrid modeling for penicillin fermentation process. CIESC J 61:2092–2096

    Google Scholar 

  11. Montague G, Morris A, Wright A, Aynsley M, Ward A (1986) Modelling and adaptive control of fed-batch penicillin fermentation. Can J Chem Eng 64:567–580

    Article  CAS  Google Scholar 

  12. Patnaik PR (2001) Penicillin fermentation: mechanisms and models for industrial-scale bioreactors. Crit Rev Microbiol 27:25–39

    Article  CAS  PubMed  Google Scholar 

  13. Douma RD, Verheijen PJ, de Laat WT, Heijnen JJ, van Gulik WM (2010) Dynamic gene expression regulation model for growth and penicillin production in Penicillium chrysogenum. Biotechnol bioeng 106:608–618

    Article  CAS  PubMed  Google Scholar 

  14. Tang W, Deshmukh AT, Haringa C, Wang G, van Gulik W, van Winden W, Reuss M, Heijnen JJ, Xia J, Chu J (2017) A 9-pool metabolic structured kinetic model describing days to seconds dynamics of growth and product formation by Penicillium chrysogenum. Biotechnol Bioeng 114:1733–1743

    Article  CAS  PubMed  Google Scholar 

  15. Haoguang L, Yan P (2020) Study on soft sensing technology of penicillin fermentation based on PLS and SVR. In: 2020 15th IEEE conference on industrial electronics and applications (ICIEA), IEEE, p 980–984

  16. Li L, Li N, Wang X, Zhao J, Zhang H, Jiao T (2023) Multi-output soft sensor modeling approach for penicillin fermentation process based on features of big data. Expert Syst Appl 213:119208

    Article  Google Scholar 

  17. Shen F, Zheng J, Ye L, Ma X (2020) LSTM soft sensor development of batch processes with multivariate trajectory-based ensemble just-in-time learning. IEEE Access 8:73855–73864

    Article  Google Scholar 

  18. Wang K, Gopaluni RB, Chen J, Song Z (2018) Deep learning of complex batch process data and its application on quality prediction. IEEE Trans Ind Inf 16:7233–7242

    Article  Google Scholar 

  19. Ghorbani M, Prasad S, Brooks BR, Klauda JB (2022) Deep attention based variational autoencoder for antimicrobial peptide discovery. bioRxiv. https://doi.org/10.1101/2022.07.08.499340

    Article  Google Scholar 

  20. Geng Z, Chen Z, Meng Q, Han Y (2021) Novel transformer based on gated convolutional neural network for dynamic soft sensor modeling of industrial processes. IEEE Trans Ind Inf 18:1521–1529

    Article  Google Scholar 

  21. Zhou H, Zhang S, Peng J, Zhang S, Li J, Xiong H, Zhang W (2021) Informer: beyond efficient transformer for long sequence time-series forecasting. Proc AAAI Conf Artif Intell 35:11106–11115

    Google Scholar 

  22. Zhou T, Ma Z, Wen Q, Wang X, Sun L, Jin R (2022) Fedformer: frequency enhanced decomposed transformer for long-term series forecasting. In: International Conference on Machine Learning, PMLR, p 27268–27286

  23. Li S, Jin X, Xuan Y, Zhou X, Chen W, Wang Y, Yan X (2019) Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. Adv Neural Inf Process Syst. https://doi.org/10.48550/arXiv.1907.00235

    Article  PubMed  PubMed Central  Google Scholar 

  24. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30:15

    Google Scholar 

  25. Gillioz A, Casas J, Mugellini E, Abou Khaled O (2020) Overview of the transformer-based models for NLP tasks. In: 2020 15th conference on computer science and information systems (FedCSIS), IEEE, p 179–183

  26. Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. Adv Neural Inf Process Syst 27:3104–3112

    Google Scholar 

  27. Liu X, Yu H-F, Dhillon I, Hsieh C-J (2020) Learning to encode position for transformer with continuous dynamical model. In: International conference on machine learning, PMLR, p 6327–6335

  28. Ege T, Yanai K (2017) Simultaneous estimation of food categories and calories with multi-task CNN. In: 2017 fifteenth IAPR international conference on machine vision applications (MVA), IEEE, p 198–201

  29. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86:2278–2324

    Article  Google Scholar 

  30. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, p 770–778

  31. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, p 2818–2826

  32. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, p 4510–4520

Download references

Funding

This work was supported by National key research and development program of China (2021YFC2101100), and National Natural Science Foundation of China (21878081).

Author information

Authors and Affiliations

  1. Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, MeiLong Road No. 130, P.O. Box 293, Shanghai, 200237, People’s Republic of China

    Yifei Sun, Xuefeng Yan & Qingchao Jiang

  2. State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, 200237, People’s Republic of China

    Guan Wang, Yingping Zhuang & Xueting Wang

Authors
  1. Yifei Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xuefeng Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qingchao Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Guan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yingping Zhuang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xueting Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. GW, YZ, and WX provided the dataset of the penicillin fermentation process and tagged the mycelium status images. YS proposed the methodology and carried out experiments; Analysis and investigation were conducted by XY and YS. The first draft of the manuscript was written by YS. XY and QJ commented on the manuscript and experimental results. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Xuefeng Yan.

Ethics declarations

Conflict of interest

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, Y., Yan, X., Jiang, Q. et al. Multiple feature fusion transformer for modeling penicillin fermentation process with unequal sampling intervals. Bioprocess Biosyst Eng 46, 1677–1693 (2023). https://doi.org/10.1007/s00449-023-02929-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00449-023-02929-7

Keywords

Search

Navigation