Bayesian optimization using multiple directional objective functions allows the rapid inverse fitting of parameters for chromatography simulations

Jäpel, R. C.; Buyel, Johannes F.

doi:10.1016/j.chroma.2022.463408

Cited by 10 publications

(4 citation statements)

References 21 publications

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“…To compare RNN and LSTM models more objectively, this paper adopts the BOA to choose important hyperparameters for each model and aims to achieve the best fit [37]. BOA is stable and efficient, and widely applied in hyperparameter optimization tasks [38,39]. The details of BOA are not repeated here but can be referred to in [37].…”

Section: Model Hyperparameter Optimizationmentioning

confidence: 99%

Is the LSTM Model Better than RNN for Flood Forecasting Tasks? A Case Study of HuaYuankou Station and LouDe Station in the Lower Yellow River Basin

Wang,

Zang

et al. 2023

Water

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The long short-term memory network (LSTM) model alleviates the gradient vanishing or exploding problem of the recurrent neural network (RNN) model with gated unit architecture. It has been applied to flood forecasting work. However, flood data have the characteristic of unidirectional sequence transmission, and the gated unit architecture of the LSTM model establishes connections across different time steps which may not capture the physical mechanisms or be easily interpreted for this kind of data. Therefore, this paper investigates whether the gated unit architecture has a positive impact and whether LSTM is still better than RNN in flood forecasting work. We establish LSTM and RNN models, analyze the structural differences and impacts of the two models in transmitting flood data, and compare their performance in flood forecasting work. We also apply hyperparameter optimization and attention mechanism coupling techniques to improve the models, and establish an RNN model for optimizing hyperparameters using BOA (BOA-RNN), an LSTM model for optimizing hyperparameters using BOA (BOA-LSTM), an RNN model with MHAM in the hidden layer (MHAM-RNN), and an LSTM model with MHAM in the hidden layer (MHAM-LSTM) using the Bayesian optimization algorithm (BOA) and the multi-head attention mechanism (MHAM), respectively, to further examine the effects of RNN and LSTM as the underlying models and of cross-time scale bridging for flood forecasting. We use the measured flood process data of LouDe and HuaYuankou stations in the Yellow River basin to evaluate the models. The results show that compared with the LSTM model, under the 1 h forecast period of the LouDe station, the RNN model with the same structure and hyperparameters improves the four performance indicators of the Nash–Sutcliffe efficiency coefficient (NSE), the Kling-Gupta efficiency coefficient (KGE), the mean absolute error (MAE), and the root mean square error (RMSE) by 1.72%, 4.43%, 35.52% and 25.34%, respectively, and the model performance of the HuaYuankou station also improves significantly. In addition, under different situations, the RNN model outperforms the LSTM model in most cases. The experimental results suggest that the simple internal structure of the RNN model is more suitable for flood forecasting work, while the cross-time bridging methods such as gated unit architecture may not match well with the flood propagation process and may have a negative impact on the flood forecasting accuracy. Overall, the paper analyzes the impact of model architecture on flood forecasting from multiple perspectives and provides a reference for subsequent flood forecasting modeling.

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Section: Model Hyperparameter Optimizationmentioning

confidence: 99%

Is the LSTM Model Better than RNN for Flood Forecasting Tasks? A Case Study of HuaYuankou Station and LouDe Station in the Lower Yellow River Basin

Wang,

Zang

et al. 2023

Water

View full text Add to dashboard Cite

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“…The root-mean-square error (RMSE) between observed and predicted peak maxima was used as a model quality indicator for different types of gradients, and the corresponding determination coefficients for the training (R 2 ) and test (Q 2 ) datasets were >0.99, whereas an empirical logarithmic model achieved values as low as 0.93 (R 2 ) and 0.85 (Q 2 ) depending on the chromatography setting. Similarly, ML has been used to model the purification of inclusion bodies ( Walther et al, 2022a ) and antibodies ( Robinson et al, 2017b ), to predict antibody retention on a hydrophobic interaction chromatography (HIC) resin ( Jain et al, 2017b ), to improve peak detection ( Chetnik et al, 2020a ), and to predict the elution behavior of host cell proteins (HCPs) from an ion-exchange matrix ( Buyel et al, 2013 ) as well as to fit SMA isotherm parameters ( Jäpel and Buyel 2022 ).…”

Section: Modeling Approachesmentioning

confidence: 99%

“…Data analysis is also more complex for dynamic methods. Typical office computation power can be sufficient for the parameter fitting task, but the specific approach can have a substantial impact on the precision and speed of the parameter fitting ( Section 5.2.1 ) ( Saleh et al, 2020 ; Jäpel and Buyel 2022 ).…”

Section: Challenge Ii: Obtaining Experimental Data To Set Up the Modelmentioning

confidence: 99%

“…Parameter estimation algorithms can also get stuck in local minima and take impractically long timescales to converge, e.g., several days ( Heymann et al, 2022 ). This can be overcome by using modern genetic algorithms ( Heymann et al, 2022 ) or Bayesian inference ( Briskot et al, 2019 ; Jäpel and Buyel 2022 ). The sum of squared differences between the simulated chromatogram and the experimental chromatogram is frequently used as an objective function to assess the quality of the model prediction.…”

Section: Challenge V: Mechanistic Model Fitting–parameter Value Optim...mentioning

confidence: 99%

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The use of predictive models to develop chromatography-based purification processes

Bernau

Knödler

Emonts

et al. 2022

Front. Bioeng. Biotechnol.

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Chromatography is the workhorse of biopharmaceutical downstream processing because it can selectively enrich a target product while removing impurities from complex feed streams. This is achieved by exploiting differences in molecular properties, such as size, charge and hydrophobicity (alone or in different combinations). Accordingly, many parameters must be tested during process development in order to maximize product purity and recovery, including resin and ligand types, conductivity, pH, gradient profiles, and the sequence of separation operations. The number of possible experimental conditions quickly becomes unmanageable. Although the range of suitable conditions can be narrowed based on experience, the time and cost of the work remain high even when using high-throughput laboratory automation. In contrast, chromatography modeling using inexpensive, parallelized computer hardware can provide expert knowledge, predicting conditions that achieve high purity and efficient recovery. The prediction of suitable conditions in silico reduces the number of empirical tests required and provides in-depth process understanding, which is recommended by regulatory authorities. In this article, we discuss the benefits and specific challenges of chromatography modeling. We describe the experimental characterization of chromatography devices and settings prior to modeling, such as the determination of column porosity. We also consider the challenges that must be overcome when models are set up and calibrated, including the cross-validation and verification of data-driven and hybrid (combined data-driven and mechanistic) models. This review will therefore support researchers intending to establish a chromatography modeling workflow in their laboratory.

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Towards Digital Twin for Biopharmaceutical Processes: Concept and Progress

Ding,

Yang,

Ierapetritou

2023

Cell Engineering

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Bayesian optimization using multiple directional objective functions allows the rapid inverse fitting of parameters for chromatography simulations

Cited by 10 publications

References 21 publications

Is the LSTM Model Better than RNN for Flood Forecasting Tasks? A Case Study of HuaYuankou Station and LouDe Station in the Lower Yellow River Basin

Is the LSTM Model Better than RNN for Flood Forecasting Tasks? A Case Study of HuaYuankou Station and LouDe Station in the Lower Yellow River Basin

The use of predictive models to develop chromatography-based purification processes

Towards Digital Twin for Biopharmaceutical Processes: Concept and Progress

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