Dynamic model reduction for two-stage anaerobic digestion processes

Duan, Zhaoyang; Bournazou, Mariano Nicolás Cruz; Kravaris, Costas

doi:10.1016/j.cej.2017.06.110

Cited by 20 publications

(22 citation statements)

References 19 publications

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“…Because of the established convergence properties of the observer error linearization method, the estimate x s in (18) will asymptotically approach the slow state x s of the reduced model (16). Our goal is to apply this observer back to the original system (12) with the actual outputs y, which leads to the following reduced-order observer: wherex s ,x f , and y are the corresponding state estimates and measurements for the system described by (12). The question is whether the convergence properties will hold for observer (21) since it is originally designed based on the reduced system (16).…”

Section: Approach 1: a Full-order Observermentioning

confidence: 99%

“…To address this issue, one option is to apply model reduction to the multi-time-scale models, by applying time analysis tools and only keeping the desired time-scale dynamics. 11,12 It is recognized that controllers designed for two-time-scale systems can be at risk of inducing instability. Many researchers have studied control design problems particularly for these systems, mainly from the point of view of singular perturbation methods.…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear observer design for two‐time‐scale systems

Duan

Kravaris

2020

AIChE Journal

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A two-time-scale system involves both fast and slow dynamics. This article studies observer design for general nonlinear two-time-scale systems and presents two alternative nonlinear observer design approaches, one full-order and one reduced-order.The full-order observer is designed by following a scheme to systematically select design parameters, so that the fast and slow observer dynamics are assigned to estimate the corresponding system modes. The reduced-order observer is derived based on a lower dimensional model to reconstruct the slow states, along with the algebraic slow-motion invariant manifold function to reconstruct the fast states. Through an error analysis, it is shown that the reduced-order observer is capable of providing accurate estimation of the states for the detailed system with an exponentially decaying estimation error. In the last part of the article, the two proposed observers are designed for an anaerobic digestion process, as an illustrative example to evaluate their performance and convergence properties.

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Section: Approach 1: a Full-order Observermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonlinear observer design for two‐time‐scale systems

Duan

Kravaris

2020

AIChE Journal

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“…Municipal sludge is carbon‐rich in nature, and treating it with AD could realize carbon resource recovery. The AD of organic material basically follows the steps hydrolysis, acidogenesis, acetogenesis and methanogenesis and AD processes mainly contain a two‐phase anaerobic treatment process and a two‐stage anaerobic treatment process . VFAs, as useful by‐products of the sludge AD process, are generated from hydrolytic acidification of the dominant forms of carbon resource.…”

Section: Strategies Of Sludge Valorizationmentioning

confidence: 99%

Strategies of valorization of sludge from wastewater treatment

Sheng

Zhang

2018

J of Chemical Tech & Biotech

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Sludge is regarded as a potential source to achieve valorization via strategies such as resource recovery, sludge based adsorbents preparation, bioflucculants production, sludge manufacturing construction materials preparation, sludge composting and thermal valorization, which are currently common and effective strategies. Appropriate treatment strategies of sludge are of great importance worldwide for the fast growing population and rapidly increasing waste. This mini review summarized some widely used and effective strategies to achieve sludge valorization based on whether the strategy would utilize or reuse the potential of sludge to obtain valuable product and eliminate secondary pollution. Anaerobic digestion of sludge is perceived as a potentially cost‐effective method to achieve sludge reduction and resource recovery including carbon, nitrogen, phosphorus resource recovery coupled with other technologies. Utilizing sludge as raw material for preparation of valuable products including sludge based adsorbents, bioflocculants and construction materials is another aspect to achieve sludge valorization. Sludge composting and thermal valorization are also introduced in the mini review since the two strategies could also achieve sludge valorization. In addition, the strategies mentioned were discussed and analyzed in the mini review from environmental and economic aspects. © 2017 Society of Chemical Industry

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“…A stable performance of the TSAD is obtainable by a suitable operation conditions and by the enrichment of the specific microorganisms in each stage. Mathematical models that reproduce the most relevant phenomena of the anaerobic digestion processes with biofuels production have been proposed, nevertheless, there are still nonlinear states that are hard to measure . Feedforward artificial neural networks (ANNs) modeling for specific biogas prediction at various temperatures have been proposed, where the ANN model is able to predict the behavior of biogas production curve and the optimum temperature of substrate for maximum biogas production.…”

Section: Introductionmentioning

confidence: 99%

“…Mathematical models that reproduce the most relevant phenomena of the anaerobic digestion processes with biofuels production have been proposed, nevertheless, there are still nonlinear states that are hard to measure. [6][7][8] Feedforward artificial neural networks (ANNs) modeling for specific biogas prediction at various temperatures have been proposed, 9,10 where the ANN model is able to predict the behavior of biogas production curve and the optimum temperature of substrate for maximum biogas production. In the work of Jha et al, 11 ANN and response surface methodology modeling are employed in an upflow anaerobic sludge blanket bioreactor for optimization of hydrogen yield and chemical oxygen demand (COD) removal efficiency.…”

Section: Introductionmentioning

confidence: 99%

Inverse optimal neural control via passivity approach for nonlinear anaerobic bioprocesses with biofuels production

Gurubel

Sánchez²,

Coronado-Mendoza

et al. 2019

Optim Control Appl Methods

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Summary This paper proposes an inverse optimal neural control method of a nonlinear anaerobic bioprocesses model for simultaneous hydrogen and methane production in presence of disturbances. Based on the fundamental properties of the system, a passivity approach is designed such that asymptotic stability is guaranteed. A recurrent high‐order neural network for unknown nonlinear systems in presence of unknown bounded disturbances and parameter uncertainties is proposed to identify nonmeasurable state variables of the system, which are directly related to biofuels production. Optimal control laws based on the neural model are proposed so that the passivation of the entire plant is preserved. The neural control strategy performance for trajectory tracking in presence of disturbances is proven. Results via simulation show the optimal control methodology efficiency to stabilize the H2 and CH4 productions along desired trajectories even in presence of disturbances.

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Dynamic model reduction for two-stage anaerobic digestion processes

Cited by 20 publications

References 19 publications

Nonlinear observer design for two‐time‐scale systems

Nonlinear observer design for two‐time‐scale systems

Strategies of valorization of sludge from wastewater treatment

Inverse optimal neural control via passivity approach for nonlinear anaerobic bioprocesses with biofuels production

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