Trajectory tracking of batch product quality using intermittent measurements and moving window estimation

Lopez-Montero, Eduardo Benedicto; Wan, Jian; Marjanović, Ognjen

doi:10.1016/j.jprocont.2014.11.009

Cited by 13 publications

(13 citation statements)

References 34 publications

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“…The lack of historical data, for example, is an important problem that is encountered when data-based modeling is implemented. This problem is commonly the result of the missing data problem, the insufficiency of historical batches, and the lack of quality variables [14,15]. There are two major problems related to missing data: one is missing measurements in historical data, and the other is missing observations in online data [16].…”

Section: Background and Literature Reviewmentioning

confidence: 99%

“…x 1 (t) = t + 1 + e 1 x 2 (t) = t 2 + 6t + 2 + e 2 x 3 (t) = t 3 − t 2 + 3 + e 3 y(t) = ax 1 (t) + bx 2 (t) + cx 3 (t) (14) where x 1 , x 2 , x 3 are the process variables, t represents the latent variable, e 1 , e 2 , e 3 are independent Gaussian noises, and a, b, c represent model parameters in the equation between process variables and output variable. The above system is run for a finite time, and a total of 200 samples are generated in each batch.…”

Section: A Numerical Simulationmentioning

confidence: 99%

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Quality-Relevant Monitoring of Batch Processes Based on Stochastic Programming with Multiple Output Modes

Shen

Zheng

et al. 2020

Processes

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To implement the quality-relevant monitoring scheme for batch processes with multiple output modes, this paper presents a novel methodology based on stochastic programming. Bringing together tools from stochastic programming and ensemble learning, the developed methodology focuses on the robust monitoring of process quality-relevant variables by taking the stochastic nature of batch process parameters explicitly into consideration. To handle the problem of missing data and lack of historical batch data, a bagging approach is introduced to generate individual quality-relevant sub-datasets, which are used to construct the corresponding monitoring sub-models. For each model, stochastic programming is used to construct an optimal quality trajectory, which is regarded as the reference for online quality monitoring. Then, for each sub-model, a corresponding control limit is obtained by computing historical residuals between the actual output and the optimal trajectory. For online monitoring, the current sample is examined by all sub-models, and whether the monitoring statistic exceeds the control limits is recorded for further analysis. The final step is ensemble learning via Bayesian fusion strategy, which is under the probabilistic framework. The implementation and effectiveness of the developed methodology are demonstrated through two case studies, including a numerical example, and a simulated fed-batch penicillin fermentation process.

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Section: Background and Literature Reviewmentioning

confidence: 99%

Section: A Numerical Simulationmentioning

confidence: 99%

Quality-Relevant Monitoring of Batch Processes Based on Stochastic Programming with Multiple Output Modes

Shen

Zheng

et al. 2020

Processes

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“…15 Biotechnological applications increasingly consider the propagation of input uncertainty to ensure good modeling practice, 16 to achieve high quality of the developed models even though sensor input is not ideal, 17,18 and to meet end-product specifications by ensuring that the trajectories in which product quality was accepted were never breached. 19 If distributions are well known, Bayesian approaches may be employed 20,21 to impute meaningful values when high quality data is missing or otherwise unavailable, 22,23 a concept that blends prior expert knowledge with mathematical expressions and allows simulations of experiments in silico to get statistical confidence in the models developed. In conclusion, dealing with uncertainty in biological systems using simulations is a natural exercise in risk-minimization.…”

Section: Uncertainty In Literaturementioning

confidence: 99%

“…When the error ranges are well known, ordinary differential equations (ODEs) can be set up to calculate trajectories which are based on the maximum lumped uncertainty like in guidance systems . Biotechnological applications increasingly consider the propagation of input uncertainty to ensure good modeling practice, to achieve high quality of the developed models even though sensor input is not ideal, and to meet end‐product specifications by ensuring that the trajectories in which product quality was accepted were never breached …”

Section: Introductionmentioning

confidence: 99%

A robust feeding strategy to maintain set‐point glucose in mammalian fed‐batch cultures when input parameters have a large error

Konakovsky

Clemens

Müller

et al. 2017

Biotechnology Progress

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Industrial CHO cell cultures run under fed-batch conditions are required to be controlled in particular ranges of glucose, while glucose is constantly consumed and must be replenished by a feed. The most appropriate feeding rate is ideally stoichiometric and adaptive in nature to balance the dynamically changing rate of glucose consumption. However, high errors in biomass and glucose estimation as well as limited knowledge of the true metabolic state challenge the control strategy. In this contribution, we take these errors into account and simulate the output with uncertainty trajectories in silico in order to control glucose concentration. Other than many control strategies, which require parameter estimation, our assumptions are founded on two pillars: (i) first principles and (ii) prior knowledge about the variability of fed-batch CHO cell culture. The algorithm was exposed to an in-silico Design of Experiments (DoE), in which variations of parameters were changed simultaneously, such as clone-specific behavior, precision of equipment and desired control range used. The results demonstrate that our method achieved the target of holding the glucose concentration within an acceptable range. A robust and sufficient level of control could be demonstrated even with high errors for biomass or metabolic state estimation. In a time where blockbuster drugs are queuing up for time slots of their production, this transferable control strategy that is independent of tedious establishment runs may be a decisive advantage for rapid implementation during technology transfer and scale up and decrease in campaign change over time. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:317-336, 2017.

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“…The nonlinear system dynamics imposes great challenges on model-based controller design. However, the acquirement of a comprehensive plant model is often difficult and time-consuming . Moreover, the models which are able to retain high prediction accuracy over wide operational ranges would require large computational efforts in receding horizon optimization, which is detrimental to real-time control .…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control of a Solar Power System with Microturbine and Thermochemical Energy Storage

Xiao

Yang

2022

Ind. Eng. Chem. Res.

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This paper presents the model predictive control (MPC) application on the solar power system with microturbine and thermochemical energy storage (TCES). To investigate the potential of a solar-powered turbine, a solar receiver and a TCES are introduced to the Brayton cycle as the replacement of the combustor. MPC is applied to offer the constrained multi-variable real-time optimization control. To increase the practicability in the solar industry, a custom-made multi-modeling approach is proposed based on the close relationship between direct normal irradiance and system states. Feedback correction mechanisms are designed to improve the prediction and target tracking accuracies. During the regulatory control under both extreme and realistic conditions, the multi-MPC (MMPC) shows stronger adaptability and reliability than proportional–integration–differentiation (PID) and higher tracking accuracies than the single-linear-model-based MPC. Although the control performance could be further improved by employing nonlinear MPC (NMPC), the much longer optimization time of NMPC was unsuitable for real-time control. MMPC is further adapted to track the grid demand, which is technically unachievable by PID in the current system. While the output power precisely follows the demand, the performance parameters can still stay close to their design values, retaining a high system efficiency. Overall, the proposed MMPC enables power demand tracking operation of solar air turbine systems, and can ensure high stability and system efficiency.

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Trajectory tracking of batch product quality using intermittent measurements and moving window estimation

Cited by 13 publications

References 34 publications

Quality-Relevant Monitoring of Batch Processes Based on Stochastic Programming with Multiple Output Modes

Quality-Relevant Monitoring of Batch Processes Based on Stochastic Programming with Multiple Output Modes

A robust feeding strategy to maintain set‐point glucose in mammalian fed‐batch cultures when input parameters have a large error

Model Predictive Control of a Solar Power System with Microturbine and Thermochemical Energy Storage

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