A deep learning based data driven soft sensor for bioprocesses

Gopakumar, V.; Tiwari, Sarthak; Rahman, Imran

doi:10.1016/j.bej.2018.04.015

Cited by 91 publications

(46 citation statements)

References 23 publications

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“…For illustration, Figure 5 shows the detailed flowchart of the PFP. During the cultivation process, many factors, such as temperature, PH, sterile substrate, acid/base and cold/hot water flow rates, and dissolved oxygen concentration, can make a difference to penicillin production [13,33,34]. It is significantly important for humans to monitor and predict the penicillin concentration.…”

Section: Process Introductionmentioning

confidence: 99%

Smart Soft Sensor Design with Hierarchical Sampling Strategy of Ensemble Gaussian Process Regression for Fermentation Processes

Sheng

Xiong

2020

Sensors

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Accurate and real-time quality prediction to realize the optimal process control at a competitive price is an important issue in Industrial 4.0. This paper shows a successful engineering application of how smart soft sensors can be combined with machine learning technique to significantly save human resources and improve performance under complex industrial conditions. Ensemble learning based soft sensors succeed in capturing complex nonlinearities, frequent dynamic changes, as well as time-varying characteristics in industrial processes. However, local model regions under traditional ensemble modelling methods are highly dependent on labeled data samples and, hence, their prediction accuracy might get affected when labeled samples are limited. A novel active learning (AL) framework upon the ensemble Gaussian process regression (GPR) model is proposed for smart soft sensor design in order to overcome this drawback. Firstly, to iteratively select the most informative unlabeled samples for labeling with hierarchical sampling based AL strategy, to then apply Gaussian mixture model (GMM) technique to autonomously identify operation phases, to further construct local GPR models without human involvement, and finally to integrate the base predictors by applying the Bayesian fusion strategy. Comparative studies for the penicillin fermentation process demonstrate the reliability and superiority of the recommended smart soft sensing. The cost of human annotation can be dramatically reduced by at least half while the prediction performance simultaneously keeps high.

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Section: Process Introductionmentioning

confidence: 99%

Smart Soft Sensor Design with Hierarchical Sampling Strategy of Ensemble Gaussian Process Regression for Fermentation Processes

Sheng

Xiong

2020

Sensors

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“…Deep learning, a subset of machine learning, is capable of learning deep, hierarchical artificial neural networks efficiently. In recent years, deep‐learning‐based architectures have been applied in data modeling of soft sensor . Based on quality prediction and process monitoring methods in process industries, deep quality‐related feature extraction with hybrid Variable‐Wise weighted stack auto encoder (VW‐SAE), hierarchical quality‐relevant feature representation and extended deep belief network have been developed for soft sensing modeling …”

Section: Introductionmentioning

confidence: 99%

“…In recent years, deep-learning-based architectures have been applied in data modeling of soft sensor. 36,37 Based on quality prediction and process monitoring methods in process industries, deep qualityrelated feature extraction with hybrid Variable-Wise weighted stack auto encoder (VW-SAE), hierarchical quality-relevant feature representation and extended deep belief network have been developed for soft sensing modeling. [38][39][40] Although the model-driven soft sensor is more effective for specific plants, in the previous research of acetylene hydrogenation reactor, a simple soft sensor calibration scheme based on output correction is proposed.…”

Section: Introductionmentioning

confidence: 99%

Online estimation for catalyst activity of acetylene hydrogenation reactor

Xie

Zhang

et al. 2020

Asia-Pacific J Chem Eng

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In the ethylene industry, the high purity of the ethylene product depends on hydrogenation in acetylene hydrogenation reactor. Because the catalyst deactivation leads to the moving of the operating point, the operation scheme must be adjusted continually according to the catalyst activity. It is necessary to estimate the catalyst activity online. Based on the discrete dynamic model of the acetylene hydrogenation reactor, the extended Kalman filter (EKF) is used to build the soft sensor for catalyst activity. Considering that EKF involves the large computation costs, we propose a method that estimates the parameters of the time‐varying deactivation kinetics model for the tradeoff of accuracy and complexity. The method is effective to reduce computation complexity of estimation, and simultaneously, the accuracy satisfies the process requirement.

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“…Therefore, soft sensor modeling (SSMI) is of great significance. Since most chemical processes do not have clear principles but have strong nonlinear and dynamic time-varying characteristics, the use of data-driven methods to establish an industrial soft sensor model (SSM) [2][3][4] has become the focus of research. In particular, how to establish a suitable nonlinear dynamic model has evolved into an important research object for researchers.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Soft Sensor Development for Time-Varying and Multirate Data Processes Based on Discount and Weighted ARMA Models

Dai

2019

Symmetry

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To solve the soft sensor modeling (SSMI) problem in a nonlinear chemical process with dynamic time variation and multi-rate data, this paper proposes a dynamic SSMI method based on an autoregressive moving average (ARMA) model of weighted process data with discount (DSSMI-AMWPDD) and optimization methods. For the sustained influence of auxiliary variable data on the dominant variables, the ARMA model structure is adopted. To reduce the complexity of the model, the dynamic weighting model is combined with the ARMA model. To address the weights of auxiliary variable data with different sampling frequencies, a calculation method for AMWPDD is proposed using assumptions that are suitable for most sequential chemical processes. The proposed method can obtain a discount factor value (DFV) of auxiliary variable data, realizing the dynamic fusion of chemical process data. Particle swarm optimization (PSO) is employed to optimize the soft sensor model parameters. To address the poor convergence problem of PSO, ω-dynamic PSO (ωDPSO) is used to improve the PSO convergence via the dynamic fluctuation of the inertia weight. A continuous stirred tank reactor (CSTR) simulation experiment was performed. The results show that the proposed DSSMI-AMWPDD method can effectively improve the SSM prediction accuracy for a nonlinear time-varying chemical process. The AMWPDD proposed in this paper can reflect the dynamic change of chemical process and improve the accuracy of SSM data prediction. The ω dynamic PSO method proposed in this paper has faster convergence speed and higher convergence accuracy, thus, these models correlate with the concept of symmetry.

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A deep learning based data driven soft sensor for bioprocesses

Cited by 91 publications

References 23 publications

Smart Soft Sensor Design with Hierarchical Sampling Strategy of Ensemble Gaussian Process Regression for Fermentation Processes

Smart Soft Sensor Design with Hierarchical Sampling Strategy of Ensemble Gaussian Process Regression for Fermentation Processes

Online estimation for catalyst activity of acetylene hydrogenation reactor

Dynamic Soft Sensor Development for Time-Varying and Multirate Data Processes Based on Discount and Weighted ARMA Models

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