On-line estimation of VFA concentration in anaerobic digestion via methane outflow rate measurements

Lara-Cisneros, Gerardo; Aguilar-López, Ricardo; Dochain, Denis; Femat, R.

doi:10.1016/j.compchemeng.2016.07.005

Cited by 28 publications

(22 citation statements)

References 28 publications

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“…With the development of anaerobic digestion soft sensors, various soft sensors based on state estimation have been proposed [56][57][58][59][60][61]. Among them, the nonlinear observer presented by Dochain under the improved anaerobic digestion model can estimate the VFA concentration online under different working conditions [62]. The improved anaerobic digestion model can be expressed as:…”

Section: Soft Sensor Based On State Estimationmentioning

confidence: 99%

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Review of Soft Sensors in Anaerobic Digestion Process

et al. 2021

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Anaerobic digestion is associated with various crucial variables, such as biogas yield, chemical oxygen demand, and volatile fatty acid concentration. Real-time monitoring of these variables can not only reflect the process of anaerobic digestion directly but also accelerate the efficiency of resource conversion and improve the stability of the reaction process. However, the current real-time monitoring equipment on the market cannot be widely used in the industrial production process due to its defects such as expensive equipment, low accuracy, and lagging analysis. Therefore, it is essential to conduct soft sensor modeling for unmeasurable variables and use auxiliary variables to realize real-time monitoring, optimization, and control of the an-aerobic digestion process. In this paper, the basic principle and process flow of anaerobic digestion are first briefly introduced. Subsequently, the development history of the traditional soft sensor is systematically reviewed, the latest development of soft sensors was detailed, and the obstacles of the soft sensor in the industrial production process are discussed. Finally, the future development trend of deep learning in soft sensors is deeply discussed, and future research directions are provided.

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Section: Soft Sensor Based On State Estimationmentioning

confidence: 99%

“…The prediction accuracy of the model will significantly decrease when fermentation conditions and fermentation batches change. The robust nonlinear observer proposed by Dochain [62] is adopted to predict the VFA concentration during . S = u S f − S − k t µ(•)X (15) .…”

Section: Practical Application Of Soft Sensors For Anaerobic Digestionmentioning

confidence: 99%

Review of Soft Sensors in Anaerobic Digestion Process

et al. 2021

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“…Although a specific sensor could be available, this kind of hardware is usually expensive. According to this, there are many studies for state estimation in chemical and biochemical processes (Bogaerts and Coutinho, 2014, Bogaerts and Wouwer, 2003, Dewasme et al, 2015, Hulhoven et al, 2006, Lara-Cisneros et al, 2016, Heidarinejad et al, 2012, Kravaris et al, 2007, Hulhoven et al, 2008, Oliveira-Esquerre et al, 2002, Pantano et al, 2017b.…”

Section: Neural State Estimation Designmentioning

confidence: 99%

State Estimation and Trajectory Tracking Control for a Nonlinear and Multivariable Bioethanol Production System

Fernández¹,

Pantano²,

Rossomando³

et al. 2019

Braz. J. Chem. Eng.

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In this paper a controller is proposed based on linear algebra for a fed-batch bioethanol production process. It involves finding feed rate profiles (control actions obtained as a solution of a linear equations system) in order to make the system follow predefined concentration profiles. A neural network states estimation is designed in order to know those variables that cannot be measured. The controller is tuned using a Monte Carlo experiment for which a cost function that penalizes tracking errors is defined. Moreover, several tests (adding parametric uncertainty and perturbations in the control action) are carried out so as to evaluate the controller performance. A comparison with another controller is made. The demonstration of the error convergence, as well as the stability analysis of the neural network, are included.

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“…then the performance signal z k satisfies the  ∞ criterion (26) with = max (P). Consequently, the observer parameters L l are computed as L l = P −1  T l , l = 0, … , s, and K l i are free solutions of (29).…”

Section: New Synthesis Conditionsmentioning

confidence: 99%

“…Last but not least, the exponential stability of the system equilibrium point is required to design the proposed observer. Within the same context, another type of nonlinear observers that is based on the rewriting of the AD model by including the dynamics of the methane flow rate to estimate the volatile fatty acids in presence of model uncertainties has been synthesized in the work of Lara-Cisneros et al 29 This latter is based on the modified structure of the model. It is composed of a linear and sigmoid injection of the estimation error, which enables the rejection of the model uncertainties.…”

Section: Introductionmentioning

confidence: 99%

A discrete‐time nonlinear state observer for the anaerobic digestion process

Draa

Zemouche

Alma

et al. 2018

Intl J Robust & Nonlinear

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This paper is concerned with the design of an LMI-based  ∞ discrete-time nonlinear state observer for an anaerobic digestion model. In presence of disturbances in both the dynamics of the model and the output measurement signals, the proposed observer robustly estimates all state variables including bacteria concentrations, which are costly and difficult to measure. In the goal to increase applicability of the proposed observer for other systems, we present the theoretical results in a general way. First, due to the use of Young's inequality in a convenient way, we get new sufficient conditions, expressed in terms of bilinear matrix inequalities (BMIs), ensuring the  ∞ criterion. Then, to render the BMIs convex, two alternative solutions are proposed, where both lead to linear matrix inequality (LMI) conditions. It is shown analytically and numerically that these two solutions provide less conservative LMI conditions compared to the existing methods in the literature. To validate the proposed methodology on a real-world model, an application to an anaerobic digestion model is given.

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On-line estimation of VFA concentration in anaerobic digestion via methane outflow rate measurements

Cited by 28 publications

References 28 publications

Review of Soft Sensors in Anaerobic Digestion Process

Review of Soft Sensors in Anaerobic Digestion Process

State Estimation and Trajectory Tracking Control for a Nonlinear and Multivariable Bioethanol Production System

A discrete‐time nonlinear state observer for the anaerobic digestion process

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