Soft-sensor modeling for l-lysine fermentation process based on hybrid ICS-MLSSVM

Wang, Bo; Shahzad, Muhammad; Zhu, Xianglin; Rehman, Khalil Ur; Ashfaq, Muhammad; Abubakar, Muhammad

doi:10.1038/s41598-020-68081-4

Cited by 12 publications

(5 citation statements)

References 40 publications

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“…The cell concentration and lysine product concentration during fermentation are important biochemical process variables that directly reflect the fermentation quality. Effective control of these process parameters plays an important role in implementing optimal control of the fermentation process, constructing an optimal growth environment for microorganisms, and improving l-lysine yield and quality [24,25]. These biochemical process variables that directly reflect the state information of the fermentation process are determined by various nutrients added (commonly known as feed) during the fermentation process, and are also related to the environmental variables of the fermentation process (fermentation broth pH, dissolved oxygen concentration DO, temperature, pressure, airflow rate, and motor stirring).…”

Section: Case Study Ii: L-lysine Fermentation Processmentioning

confidence: 99%

Data-Driven Soft Sensor Model Based on Deep Learning for Quality Prediction of Industrial Processes

et al. 2021

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Fermentation process is a time-varying, nonlinear and multivariable dynamic coupling system. Therefore, it is difficult to directly measure the key biological variables using traditional physical sensors during the process of fermentation, which makes the monitoring and real-time control impossible. To resolve this problem, a data-driven soft sensor modeling method based on deep neural network (DNN) is proposed in this paper. This method is suitable for large amount of data and it enjoys high efficiency and robustness. At the same time, an adaptive moment estimation (Adam) algorithm is used to optimize the hyper-parameters of the DNN model, which is a technique for efficient stochastic optimization that only requires first-order gradients with little memory requirement. The consistent correlation method is used to determine the auxiliary variables of the soft sensor model. The penicillin and l-lysine fermentation processes are taken as the research object, substrate concentration, cell concentration, and product concentration are selected as a target variable. The performance of established soft sensor model is evaluated through the indexes of mean square error (MSE), root-mean-square error (RMSE), and mean absolute error (MAE). The simulation results show that the prediction performance of the soft sensor model based on DNN-Adam is good and compared with model based on stochastic gradient descent (SGD) with momentum optimization algorithm. It is verified that the proposed method can make a more accurate real-time prediction of quality variables in the fermentation process, and it has higher prediction accuracy than DNN-SGD method.

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Section: Case Study Ii: L-lysine Fermentation Processmentioning

confidence: 99%

Data-Driven Soft Sensor Model Based on Deep Learning for Quality Prediction of Industrial Processes

et al. 2021

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“…The simulation results show that the established soft sensor model has higher measurement accuracy and better effect, which can meet the practical requirements of the project. Wang et al [11] constructed a multi-output least squares support vector machine (MLSSVM) regressor model to solve the problem of multi-input and multi-output for l-lysine. They also introduced the Improved Cuckoo Search (ICS) algorithm to optimize the essential parameters of the model.…”

Section: Introductionmentioning

confidence: 99%

Soft Sensor Modeling Method for the Marine Lysozyme Fermentation Process Based on ISOA-GPR Weighted Ensemble Learning

Lu,

Wang,

Zhu

2023

Sensors

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Due to the highly nonlinear, multi-stage, and time-varying characteristics of the marine lysozyme fermentation process, the global soft sensor models established using traditional single modeling methods cannot describe the dynamic characteristics of the entire fermentation process. Therefore, this study proposes a weighted ensemble learning soft sensor modeling method based on an improved seagull optimization algorithm (ISOA) and Gaussian process regression (GPR). First, an improved density peak clustering algorithm (ADPC) was used to divide the sample dataset into multiple local sample subsets. Second, an improved seagull optimization algorithm was used to optimize and transform the Gaussian process regression model, and a sub-prediction model was established. Finally, the fusion strategy was determined according to the connectivity between the test samples and local sample subsets. The proposed soft sensor model was applied to the prediction of key biochemical parameters of the marine lysozyme fermentation process. The simulation results show that the proposed soft sensor model can effectively predict the key biochemical parameters with relatively small prediction errors in the case of limited training data. According to the results, this model can be expanded to the soft sensor prediction applications in general nonlinear systems.

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“…The process data implies information on the operation status of the fermentation process. Therefore, using data-driven modeling techniques to predict and control key variables in the fermentation process can effectively improve plant productivity. − Deep learning has been an important research area in machine learning and artificial intelligence research in recent years. Deep learning methods are composed of multiple layers to learn nonlinear features of data with multiple levels of abstraction, which have been widely used for supervised or unsupervised feature learning and representation, classification, and pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Bayesian Deep Learning Approach for Online Forecasting of Fed-Batch Fermentation

et al. 2023

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The microbial fermentation process often involves various biological metabolic reactions and chemical processes. The mixed bacterial culture process of 2-keto-l-gulonic acid has strong nonlinear and time-varying characteristics. In this study, a probabilistic Bayesian deep learning approach is proposed to obtain a highly accurate and robust prediction of product formation. The Bayesian optimized deep neural network (BODNN) is utilized as basic model for prediction, the structural parameters of which are optimized. Then, the training datasets are classified into different categories according to the prior evaluation of prediction error. The final forecasting is a weighted combination of BODNN models based on the Bayesian hybrid method. The weights can be interpreted as Bayesian posterior probabilities and are computed recursively. The validation of 95 industrial batches is carried out, and the average root mean square errors are 1.51 and 2.01% for 4 and 8 h ahead prediction, respectively. The results illustrate that the proposed approach can capture the dynamics of fermentation batches and is suitable for online process monitoring.

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Soft-sensor modeling for l-lysine fermentation process based on hybrid ICS-MLSSVM

Cited by 12 publications

References 40 publications

Data-Driven Soft Sensor Model Based on Deep Learning for Quality Prediction of Industrial Processes

Data-Driven Soft Sensor Model Based on Deep Learning for Quality Prediction of Industrial Processes

Soft Sensor Modeling Method for the Marine Lysozyme Fermentation Process Based on ISOA-GPR Weighted Ensemble Learning

Probabilistic Bayesian Deep Learning Approach for Online Forecasting of Fed-Batch Fermentation

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