Integration of distributed multi-analyzer monitoring and control in bioprocessing based on a real-time expert system

Cimander, Christian; Bachinger, Thomas; Mandenius, Carl‐Fredrik

doi:10.1016/s0168-1656(03)00121-4

Cited by 42 publications

(18 citation statements)

References 20 publications

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“…The analyzer/bioreactor system was connected to a real-time knowledge based expert system (G2, Gensym Inc., USA), which allowed full control of the analyzers and bioreactor as well as versatile and flexible data processing [23].…”

Section: Methodsmentioning

confidence: 99%

Bioprocess control from a multivariate process trajectory

Cimander

Mandenius

2003

Bioprocess Biosyst Eng

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A multivariate bioprocess control approach, capable of tracking a pre-set process trajectory correlated to the biomass or product concentration in the bioprocess is described. The trajectory was either a latent variable derived from multivariate statistical process monitoring (MSPC) based on partial least squares (PLS) modeling, or the absolute value of the process variable. In the control algorithm the substrate feed pump rate was calculated from on-line analyzer data. The only parameters needed were the substrate feed concentration and the substrate yield of the growth-limiting substrate. On-line near-infrared spectroscopy data were used to demonstrate the performance of the control algorithm on an Escherichia coli fed-batch cultivation for tryptophan production. The controller showed good ability to track a defined biomass trajectory during varying process dynamics. The robustness of the control was high, despite significant external disturbances on the cultivation and control parameters.

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Section: Methodsmentioning

confidence: 99%

Bioprocess control from a multivariate process trajectory

Cimander

Mandenius

2003

Bioprocess Biosyst Eng

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“…Examples of such platform alternatives are the ASPEN software (Aspen Technology, Inc., Bedford, MA, USA), Wonderware (Schneider Electric Software, Lake Forest, CA, USA), G2 (Gensym, Burlington, MA, USA), SIMATIC PCS 7 (Siemens AG, Munich, Germany), LabVIEW [35][36][37][38] and WinErs (Schoop GmbH, Hamburg, Germany). Applications with bio-ethanol production plants have previously been reported for these software [see e.g.…”

Section: Remarks On the Subsequent Implementation For Realizing The Cmentioning

confidence: 99%

Conceptual Design of an Operator Training Simulator for a Bio-Ethanol Plant

2015

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Conceptual design methodology for the configuration and procedural training with an operating training simulator (OTS) in a large-scale plant for commercial bio-ethanol production is described. The aim of the study is to show how the methodology provides a powerful way for finding the best configuration and training structure of the OTS before constructing and implementing the software of the OTS. The OTS principle, i.e., to use a computer-based virtual representation of the real process plant intended for efficient training of process operators, has long since been applied in aviation and process industries for more efficient and flawless operations. By using the conceptual design methodology (sometimes referred to as bio-mechatronics) a variety of OTS configurations with this capacity was generated. The systematic approach of for targeting the users' (i.e., the plant management and process operators) needs resulted in better understanding and efficiency in training of hands-on skills in operating the plant. The training included general standard operating procedures for running the plant under normal operation conditions with different starch materials, handling of typical frequent disturbances as well as acting in situations not described in the standard operation procedures and applying trouble-shooting.

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“…← for complementary pathway r 1 , r 2 u 1 = u 1 u 1 ← overall regulation for both pathways r 1 u 2 = r 2 /SM r 1 /EM + r 2 /SM ← for complementary pathway r 2 , r 1 u 3 = 1 ← for pathway r 3 u 4 = r 4 r 1 + r 4 ← for substitutable pathway r 4 , r 1…”

Section: Appendix A: Model Summarymentioning

confidence: 99%

Design and Evaluation of an Optimal Controller for Simultaneous Saccharification and Fermentation Process

Murthy

Johnston

Rausch

et al. 2011

Appl Biochem Biotechnol

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Ethanol from corn is produced using dry grind corn process in which simultaneous saccharification and fermentation (SSF) is one of the most critical unit operations. In this work an optimal controller based on a previously validated SSF model was developed by formulating the SSF process as a Bolza problem and using gradient descent methods. Validation experiments were performed to evaluate the performance of optimal controller under different process disturbances that are likely to occur in practice. Use of optimal control algorithm for the SSF process resulted in lower peak glucose concentration, similar ethanol yields (13.38±0.36% v/v and 13.50±0.15% v/v for optimally controlled and baseline experiments, respectively). Optimal controller improved final ethanol concentrations as compared to process without optimal controller under conditions of temperature (13.35±1.28 and 12.52±1.19% v/v for optimal and no optimal control, respectively) and pH disturbances (12.65±0.74 and 11.86±0.49% v/v for optimal and no optimal control, respectively). Cost savings due to lower enzyme usage and reduced cooling requirement were estimated to be up to $1 million for a 151 million L/yr (40 million gal/yr) dry grind plant.

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Integration of distributed multi-analyzer monitoring and control in bioprocessing based on a real-time expert system

Cited by 42 publications

References 20 publications

Bioprocess control from a multivariate process trajectory

Bioprocess control from a multivariate process trajectory

Conceptual Design of an Operator Training Simulator for a Bio-Ethanol Plant

Design and Evaluation of an Optimal Controller for Simultaneous Saccharification and Fermentation Process

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