Product-based sliding mode observer for biomass and growth rate estimation in Luedeking–Piret like processes

Nuñez, Sebastián; Garelli, Fabricio; Battista, Hernán De

doi:10.1016/j.cherd.2015.10.030

Cited by 8 publications

(8 citation statements)

References 23 publications

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“…The observer estimates the reaction rate of enzymatic hydrolysis and biomass growth rate, based on the measurements of starch and glucose concentrations. In [13], a second order sliding mode observer is proposed for a continuous process, for the estimation of microbial growth rate and biomass concentration, based on a known product concentration. The convergence region of the biomass observer error is determined in terms of the upper bound of the uncertainty as a function of either the growth rate or biomass concentration.…”

Section: Introductionmentioning

confidence: 99%

“…For larger systems, an observer study for a third order model is addressed in [13], where the disturbance δ 1 is persistent, and the convergence of the biomass observer error is analyzed, as the biomass observer error is a linear filter of the observer error x 2 . However, the stability analysis assumes that the observer error x 1 and its time derivative vanish, and the disturbance of the second observer error dynamics is required to be lower than a constant value that is associated with eigenvalue conditions.…”

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confidence: 99%

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Design and Evaluation of a Robust Observer Using Dead-Zone Lyapunov Functions—Application to Reaction Rate Estimation in Bioprocesses

2022

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This paper addresses the design and evaluation of a robust observer for second order bioprocesses considering unknown bounded disturbance terms and uncertainty in the dynamics of the unknown and known states. The observer design and the stability analysis are based on dead-zone Lyapunov functions, and a detailed procedure is provided. The transient response bounds and the convergence region of the unknown observer error are determined in terms of the disturbance bounds, considering persistent but bounded disturbances in the dynamics of both the known and unknown observer errors. This is a significant contribution to closely related observer design studies, in which the transient response bounds are determined, but persistent and bounded disturbances are not considered in the dynamics of the known observer error. Other important contributions are: (i) the procedure for defining the observer parameters is significantly simpler than common observer designs, since a solution to the Ricatti equation, solution to LMI constraints, or the accomplishment of eigenvalue inequality conditions are not required; (ii) discontinuous signals are not used in the observer; and (iii) the effect of the gain sign associated with the unknown state in the dynamics of the known state is explicitly and clearly considered in the observer design and in the convergence study. In addition, the guidelines for selecting the observer parameters are provided. Numerical simulation confirms the stability analysis results: the observer errors converge within a short time, with a low estimation error, if observer-parameters are properly defined.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Design and Evaluation of a Robust Observer Using Dead-Zone Lyapunov Functions—Application to Reaction Rate Estimation in Bioprocesses

2022

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“…Other approaches for kinetic rates and state estimation are based on adaptive system theory [4,12,[14][15][16][17][18][19], high gain approach [20][21][22], sliding mode theory [23,24], interval SS [25], probabilistic observers [26], etc. All of these methods are very dependent, to different degrees, on the process kinetics knowledge.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous State and Kinetic Observation of Class-Controllable Bioprocesses

et al. 2022

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Monitoring of bioprocesses is a challenge in designing modern systems for control. In the biotechnology industry, the lack of reliable hardware sensors for key variables related to the metabolism of microorganisms is a topical problem. This predetermines the progress of a scientific field that relies on the development of software sensors for immeasurable variables. In this paper, a new approach for the monitoring of class-controllable bioprocesses that evolve through various physiological states (metabolic regimes) is proposed. At the core of the approach is the potential to present total biomass as a sum of the biomass concentrations obtained during each of the metabolic regimes. Algorithms for estimation of immeasurable variables and their kinetics are here derived and applied using real experimental data. As a case-study, a fed-batch process for phytase production by E. coli is considered. Effectiveness of the method is proven by using two sets of real experiments. One is used to tune the software sensors and the other to verify the approach. The stability analyses are provided, as well. The obtained results and successful verification confirm the adaptive properties of the approach. The considered software sensors will be further built into an interactive system for training specialists/students of biotechnology.

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“…Various kinetics models have been derived for biohydrogen production, as previously reviewed [ 21 , 22 ]. In particular, and because of its simple initial form, the Luedeking-Piret model, developed in 1959 to describe lactic acid production and others processes [ 23 , 24 ], has recently been applied to fermentative hydrogen production. In this widely used mathematical model, the rate of product formation (like hydrogen) can be related to both biomass concentration and microbial growth rate.…”

Section: Introductionmentioning

confidence: 99%

Reliable determination of the growth and hydrogen production parameters of the photosynthetic bacterium Rhodobacter capsulatus in fed batch culture using a combination of the Gompertz function and the Luedeking-Piret model

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Willison

et al. 2021

Heliyon

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In this study, experimental results of hydrogen producing process based on anaerobic photosynthesis using the purple non-sulfur bacterium Rhodobacter capsulatus are scrutinized. The bacterial culture was carried out in a photo-bioreactor operated in a quasi-continuous mode, using lactate as a carbon source. The method is based on the continuous stirred tank reactors (CSTR) technique to access kinetic parameters. The dynamic evolution of hydrogen production as a function of time was accurately simulated using Luedeking-Piret model and the growth of R. capsulatus was computed using Gompertz model. The combination of both models was successfully applied to determine the relevant parameters (λ, μ max , α and β) for two R. capsulatus strains studied: the wild-type strain B10 and the H 2 over-producing mutant IR3. The mathematical description indicates that the photofermentation is more promising than dark fermentation for the conversion of organic substrates into biogas.

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Product-based sliding mode observer for biomass and growth rate estimation in Luedeking–Piret like processes

Cited by 8 publications

References 23 publications

Design and Evaluation of a Robust Observer Using Dead-Zone Lyapunov Functions—Application to Reaction Rate Estimation in Bioprocesses

Design and Evaluation of a Robust Observer Using Dead-Zone Lyapunov Functions—Application to Reaction Rate Estimation in Bioprocesses

Simultaneous State and Kinetic Observation of Class-Controllable Bioprocesses

Reliable determination of the growth and hydrogen production parameters of the photosynthetic bacterium Rhodobacter capsulatus in fed batch culture using a combination of the Gompertz function and the Luedeking-Piret model

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