ON-LINE ESTIMATION AND DETECTION OF ABNORMAL SUBSTRATE CONCENTRATIONS IN WWTPS USING A SOFTWARE SENSOR: A BENCHMARK STUDY

2007 ◽  
Vol 28 (8) ◽  
pp. 871-882 ◽  
Author(s):  
F. Benazzi ◽  
K.V. Gernaey ◽  
U. Jeppsson ◽  
R. Katebi
2019 ◽  
Vol 33 (1) ◽  
pp. 141-151 ◽  
Author(s):  
Pavel Hrnčiřík ◽  
Tomáš Moucha ◽  
Jan Mareš ◽  
Jan Náhlík ◽  
Dagmar Janáčová

In this study, the potential of two software sensors for on-line estimation of biomass concentration during cultivation of filamentous microorganisms is examined. The first sensor is based on common bioreactor off-gas analyses, and uses the assumption of the biomass concentration linear dependence on the square root of cumulative O2 consumption. Parameters of the semi-empirical data-driven software sensor based on off-gas analysis were calculated from experimental cultivation data using linear regression. The second sensor is based on biocalorimetry, i.e., the on-line calculation of metabolic heat flux from general enthalpy balance of the bioreactor. The software sensor based on biocalorimetry thus essentially represents a model-driven approach, making use of a fundamental process model based on the enthalpy balance around the bioreactor. This approach has been combined with the experimental identification of the specific biomass heat production, which represents the main process-specific parameter of the software sensor based on biocalorimetry. For this sensor, the accuracy requirements on the process variable on-line measurements were also analysed. The experimental data from the pilot-scale antibiotics Nystatin production by a bacterium Streptomyces noursei were used to calculate the specific bioprocess heat production value using linear regression. The achieved results enabled us to propose a new on-line indicator calculated as the ratio of the outputs of both sensors, which can serve as a timely warning of the risk of undesired nutritional conditions of a culture characterized as underfeeding.


1998 ◽  
Vol 37 (6) ◽  
pp. 2436-2445 ◽  
Author(s):  
Raju B. Mankar ◽  
Deoki N. Saraf ◽  
Santosh K. Gupta

2012 ◽  
Vol 34 (6) ◽  
pp. 1009-1017 ◽  
Author(s):  
John Dahlbacka ◽  
Jan Weegar ◽  
Niklas von Weymarn ◽  
Kaj Fagervik

2001 ◽  
Vol 43 (7) ◽  
pp. 115-120 ◽  
Author(s):  
C. Aubrun ◽  
D. Theilliol ◽  
J. Harmand ◽  
J. P. Steyer

In this paper, a method for unknown input estimation in stochastic system is presented. A key problem in bioprocess systems is the absence, in some cases, of reliable on-line measurements for real time monitoring applications. In this paper, a software sensor for an anaerobic digester is presented. Unmeasured components of the influent are estimated from available on-line measurements. Unknown input Kalman filter is discussed to estimate the state and unknown input of the process. First, the theory of unknown inputs optimal filtering in the stochastic case is exposed and a design procedure is proposed. The observer is applied to an anaerobic fluidized bed reactor to estimate the variations in Chemical Oxygen Demand (COD) concentration and experimental results are presented.


2003 ◽  
Vol 36 (5) ◽  
pp. 981-986 ◽  
Author(s):  
Pawel Rzepiejewski ◽  
Michal Syfert ◽  
Sergiej Jegorov

2002 ◽  
Vol 41 (2) ◽  
pp. 127-143 ◽  
Author(s):  
Oscar A.Z. Sotomayor ◽  
Song Won Park ◽  
Claudio Garcia

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