Soft sensors development for on-line biomass estimation

2005 ◽  
Author(s):  
LeiZhi Chen ◽  
Sing Kiong Nguang ◽  
XueMei Li
Keyword(s):  
Biomass Estimation ◽  
Soft Sensors ◽  
On Line

On-line biomass estimation in biosurfactant production process by Candida lipolytica UCP 988

2008 ◽  
Vol 35 (11) ◽  
pp. 1425-1433 ◽  
Author(s):  
Clarissa Daisy da Costa Albuquerque ◽  
Galba Maria de Campos-Takaki ◽  
Ana Maria Frattini Fileti
Keyword(s):  
Production Process ◽  
Biosurfactant Production ◽  
Biomass Estimation ◽  
Candida Lipolytica ◽  
On Line

Biomass estimation using on-line glucose monitoring by flow injection analysis

1990 ◽  
Vol 24-25 (1) ◽  
pp. 591-602 ◽  
Author(s):  
F. Valero ◽  
J. Lafunte ◽  
M. Poch ◽  
C. Solà
Keyword(s):  
Flow Injection ◽  
Flow Injection Analysis ◽  
Glucose Monitoring ◽  
Biomass Estimation ◽  
On Line

Development and industrial application of soft sensors with on-line Bayesian model updating strategy

2013 ◽  
Vol 23 (3) ◽  
pp. 317-325 ◽  
Author(s):  
Jing Deng ◽  
Li Xie ◽  
Lei Chen ◽  
Shima Khatibisepehr ◽  
Biao Huang ◽  
...  
Keyword(s):  
Industrial Application ◽  
Bayesian Model ◽  
Model Updating ◽  
Soft Sensors ◽  
On Line

scholarly journals Monitoring of Biopolymer Production Process Using Soft Sensors Based on Off-Gas Composition Analysis and Capacitance Measurement

Fermentation ◽  
2021 ◽  
Vol 7 (4) ◽  
pp. 318
Author(s):  
Pavel Hrnčiřík
Keyword(s):  
Chain Length ◽  
Estimation Error ◽  
Capacitance Measurement ◽  
Composition Analysis ◽  
Gas Composition ◽  
Soft Sensors ◽  
Process Data ◽  
Medium Chain ◽  
Medium Chain Length ◽  
On Line

This paper focuses on the design of soft sensors for on-line monitoring of the biotechnological process of biopolymer production, in which biopolymers are accumulated in bacteria as an intracellular energy storage material. The proposed soft sensors for on-line estimation of the biopolymer concentration represent an interesting alternative to the traditional off-line analytical techniques of limited applicability for real-time process control. Due to the complexity of biochemical reactions, which make it difficult to create reasonably complex first-principle mathematical models, a data-driven approach to the design of soft sensors has been chosen in the presented study. Thus, regression methods were used in this design, including multivariate statistical methods (PLS, PCR). This approach enabled the creation of soft sensors using historical process data from fed-batch cultivations of the Pseudomonas putida KT2442 strain used for the production of medium-chain-length polyhydroxyalkanoates (mcl-PHAs). Specifically, data from on-line measurements of off-gas composition analysis and culture medium capacitance were used as input to the soft sensors. The resulting soft sensors allow not only on-line estimation of the biopolymer concentration, but also the concentration of the cell biomass of the production bacterial culture. For most of these soft sensors, the estimation error did not exceed 5% of the measurement range. In addition, soft sensors based on capacitance measurement were able to accurately detect the end of the production phase. This study thus offers an innovative and practically relevant contribution to the field of monitoring of bioprocesses used for the production of medium-chain-length biopolymers.

Digital Compressor Analytics

2018 ◽  
Author(s):  
Martin Bakken ◽  
Erling Lunde ◽  
Lars E. Bakken
Keyword(s):  
Dynamic Model ◽  
Performance Monitoring ◽  
Rotating Stall ◽  
Soft Sensors ◽  
Laboratory Setup ◽  
Transient Disturbances ◽  
On Line ◽  
Plant Data ◽  
And Performance ◽  
Production Losses

Norwegian gas export is a high value business, where small and transient disturbances may cause substantial production losses. Process experience has shown that the compressor system may suffer considerably owing to surge and rotating stall in situations where the compressor is forced to trip. One of the main challenges concerns analysis of the actual trip trajectory to validate whether the compressor has entered the unstable area of the performance characteristics. This type of analysis is paramount with regard to compressor operation and tuning of the compressor safety system. Recent advances in data analytics and digitalization capabilities give promise of new ways to handle and analyse such challenges. The current work presents data from a real compressor trip. The investigation reveals that plant data alone may not be sufficient for analysis of the trip trajectory. Hence, the trip scenario was analysed in light of experimental data, fan law principles and utilization of a detailed dynamic model. The results reveal that utilization of a dynamic model gives fruitful insight into the compressor system dynamics during a trip. These findings form a basis for future digitalization of the plant. This idea will be developed into the specification of a concept called a Digital Compressor. The digital compressor may run in off-line or on-line mode with the aim of providing: high resolution estimates (soft sensors) for non-measured or inaccurate process variables; or identification of process parameters and characteristics, such as gas density. Use cases include: off-line “what happened” analysis; identifying the minimal viable instrumentation; on-line advanced condition and performance monitoring. A digital compressor laboratory setup will be introduced, containing both a dynamic simulation system as well as a complete gas compressor rig — with all necessary computational and communication infrastructure.

Soft sensors for on-line biomass measurements

2004 ◽  
Vol 26 (3) ◽  
pp. 191-195 ◽  
Author(s):  
Xue Mei Li ◽  
Xiao Dong Chen ◽  
Lei Zhi Chen ◽  
Sing Kiong Nguang
Keyword(s):  
Soft Sensors ◽  
On Line

Nitrate estimation in the denitrifying post-filtration unit of a municipal wastewater treatment plant: the Viikinmäki case

2012 ◽  
Vol 65 (8) ◽  
pp. 1521-1529 ◽  
Author(s):  
M. Mulas ◽  
F. Corona ◽  
H. Haimi ◽  
L. Sundell ◽  
M. Heinonen ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Wastewater Treatment Plant ◽  
Municipal Wastewater ◽  
Linear Models ◽  
Treatment Plant ◽  
Municipal Wastewater Treatment ◽  
Soft Sensors ◽  
Intrinsic Nonlinearity ◽  
On Line ◽  
Filtration Unit

In this work we present and discuss the design of an array of soft-sensors to estimate the nitrate concentration in the denitrifying post-filtration unit at the Viikinmäki wastewater treatment plant in Helsinki (Finland). The developed sensors aim at supporting the existing hardware analyzers by providing a reliable back-up system in case of malfunction of the instruments. In the attempt to design easy to implement and interpretable sensors, computationally light linear models have been considered. However, due to the intrinsic nonlinearity of the process, also nonlinear but still computationally affordable models have been considered for comparison. The experimental results demonstrate the potential of the developed soft-sensors and the possibility for an on-line implementation in the plant's control system as alternative monitoring devices.

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