Robust Luenberger observers for microalgal cultures

Benavides, Micaela; Coutinho, Daniel; Hantson, Anne-Lise; Impe, Jan Van; Wouwer, Alain Vande

doi:10.1016/j.jprocont.2015.09.005

Cited by 11 publications

(7 citation statements)

References 24 publications

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“…In the first category there are observers that correct the values generated by the process model using some readily measurable process value with a constant gain selected during the observer design. The constant gain can also be continuously adapted by some available technique [84], and interval observers can limit the state trajectory based on the known intervals of uncertain model parameters [73]. The second category are variants of the Kalman filter which alters the correction gain in each iteration recursively using the comparison between noisy state estimations and noisy measurements, using covariance matrices of the system and measurement noises whose selection is critical for the proper functioning of the filter [85].…”

Section: Computer-aided Monitoring and Software Sensorsmentioning

confidence: 99%

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On-Line Monitoring of Biological Parameters in Microalgal Bioprocesses Using Optical Methods

et al. 2022

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Microalgae are promising sources of fuels and other chemicals. To operate microalgal cultivations efficiently, process control based on monitoring of process variables is needed. On-line sensing has important advantages over off-line and other analytical and sensing methods in minimizing the measurement delay. Consequently, on-line, in-situ sensors are preferred. In this respect, optical sensors occupy a central position since they are versatile and readily implemented in an on-line format. In biotechnological processes, measurements are performed in three phases (gaseous, liquid and solid (biomass)), and monitored process variables can be classified as physical, chemical and biological. On-line sensing technologies that rely on standard industrial sensors employed in chemical processes are already well-established for monitoring the physical and chemical environment of an algal cultivation. In contrast, on-line sensors for the process variables of the biological phase, whether biomass, intracellular or extracellular products, or the physiological state of living cells, are at an earlier developmental stage and are the focus of this review. On-line monitoring of biological process variables is much more difficult and sometimes impossible and must rely on indirect measurement and extensive data processing. In contrast to other recent reviews, this review concentrates on current methods and technologies for monitoring of biological parameters in microalgal cultivations that are suitable for the on-line and in-situ implementation. These parameters include cell concentration, chlorophyll content, irradiance, and lipid and pigment concentration and are measured using NMR, IR spectrophotometry, dielectric scattering, and multispectral methods. An important part of the review is the computer-aided monitoring of microalgal cultivations in the form of software sensors, the use of multi-parameter measurements in mathematical process models, fuzzy logic and artificial neural networks. In the future, software sensors will play an increasing role in the real-time estimation of biological variables because of their flexibility and extendibility.

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Section: Computer-aided Monitoring and Software Sensorsmentioning

confidence: 99%

“…Two software sensors are proposed in the form of extended Luenberger observers, based on the Droop model [84]. Parameters of the Droop model were identified using on-line measurements of temperature, optical density and extracellular nitrate concentration.…”

Section: Observersmentioning

confidence: 99%

On-Line Monitoring of Biological Parameters in Microalgal Bioprocesses Using Optical Methods

et al. 2022

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“…In this context, continuous performance monitoring of the process in order to prevent interruptions and stoppages of the plant, is of utmost importance. Unfortunately, most of supervision attempts are hampered by the lack of appropriate devices, high instrumentation costs and unfeasibility of installing sensors inside the system (Benavides et al, 2015;Mohd et al, 2015;de Battista et al, 2011;Jenzsch et al, 2006). An alternative to mitigate this difficulty is to design and implement robust and reliable analytical methods (by combining the information available through process models and available measurements) to perform on-line estimation of process variables and parameters (Benavides et al, 2015 andDochain, 2003).…”

Section: Introductionmentioning

confidence: 99%

Observer-based virtual sensors for microalgae cultures monitoring

Astorga‐Zaragoza¹,

Vélez-Landa²,

de-León³

et al. 2022

Renew. energ. biomass sustain

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In this paper, a nonlinear observer design is presented for simultaneous parameter estimation and state variables estimation. The case of study is microalgae cultures for biodiesel generation, where reaction rates, biomass concentration, intracellular quota and nitrogen concentration are critical variables that provide information about the state of the process. However, these variables might be difficult to measure due to the lack of specific instruments, high sensor costs or infeasibility of installation in the process. Therefore, two observer-based virtual sensors are presented in this paper as an analytical alternative to perform estimation of the main important variables or parameters of the process: a nonlinear adaptive observer and a nonlinear high-gain observer. The observers are based on the Droop´s mathematical model that describes the ability of microalgae to store nutrients and the decoupling between substrate uptake and biomass growth. Numerical simulations are made in order to evaluate the performance of the proposed observers.

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“…For improving the quality of the automatic control of production step, it is necessary to have real time measurements of biomass concentration. To that end, complex and expensive instruments are applied [1] (although recently cheaper sensors are being developed [2]). Moreover, most commercial sensors have limitations to provide reliable measurements of this and other variables of bioprocesses [3].…”

Section: Introductionmentioning

confidence: 99%

Biomass estimation of an industrial raceway photobioreactor using an extended Kalman filter and a dynamic model for microalgae production

et al. 2019

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Production of microalgae is one of the emerging biotechnological processes due to its potential applications to produce high value-added compounds. In photobioreactors for microalgae production, the biomass concentration is a desirable variable to be measured on-line to optimize the yield of the systems. However, biomass concentration can hardly be monitored in real time. There are few expensive commercial sensors that in fact provide uncertain measurements. State estimators, also known as software sensors, are algorithms designed to estimate unmeasured (or non-easily measurable) variables of a process. In this work, a state estimator using the extended Kalman filter algorithm is developed to estimate biomass concentration for an outdoor industrial raceway photobioreactor. The state estimator is based on a dynamic model for microalgae production specifically designed for this type of photobioreactor. Results demonstrate that, despite the complex non-linear dynamics that characterise this kind of bioprocess, a state estimator can provide a relatively accurate estimation of the biomass concentration. Furthermore, a state estimator could be used to optimize the operation of industrial photobioreactors by utilizing the estimated biomass concentration for automatic control of the process.

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Robust Luenberger observers for microalgal cultures

Cited by 11 publications

References 24 publications

On-Line Monitoring of Biological Parameters in Microalgal Bioprocesses Using Optical Methods

On-Line Monitoring of Biological Parameters in Microalgal Bioprocesses Using Optical Methods

Observer-based virtual sensors for microalgae cultures monitoring

Biomass estimation of an industrial raceway photobioreactor using an extended Kalman filter and a dynamic model for microalgae production

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