Neuronal Modeling of a Two Stages Anaerobic Digestion Process for Biofuels Production

Camberos, S.U. Aguirre; Gurubel, K. J.; Sánchez, Edgar N.; Aguirre, S. Alarcon; Perez, R. Gonzalez

doi:10.1016/j.ifacol.2018.07.313

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…[59] Maximizing biogas production 2018 [60] Self-optimizing operation of anaerobic digesters 2016 [61] A techno-economic optimization 2004 [91] UASB controller 2018 [92] Optimal anaerobic digestion processes monitoring 2017 [93] Cadmium removal in an anaerobic process 2016 [95] Online diagnosis in biological processes 2004 [76] Anaerobic digester with highly nonlinear dynamics 2001 [77] Interval-based regulation for anaerobic digestion processes 2005 [62] Importance of secondary settling tank models 2012 [3] Continuous anaerobic digestion based on ADM1 2017 [47] Degradation kinetics 2012 [63] Optimizing ADM1 2019 [64] Mathematical simulation of maize silage 2013 [78] state estimation scheme for ADM1 2012 [94] A biodegradability and modeling 2013 [7] Control of an anaerobic bioreactor on the ADM1-based virtual plant. 2021 [65] Modeling of anaerobic digesters (ADM1) 2015 [73] Estimation of Parameters in Anaerobic Digesters 2008 [66] Multi-criteria analyses of bio-processes (ADM1) 2012 [67] Wastewater treatment plant design and control 2010 [84] Two Stages Anaerobic Digestion Process (ADM1) 2018 [74] Simulation of a biogas reactor (ADM1) 2015 [83] ADM1-based AD Model 2021 [68] Wastewater treatment plant (WWTP) control strategies 2012 [75] Anaerobic sequencing batch reactor 2004 [87] The control scheme for the stability of continuous anaerobic digestion processes 2010 [105] Controlling anaerobic digestion processes at an industrial scale 2003 [88] A pilot plant up-flow fixed-bed reactor that is treating industrial wine distillery wastewater 2008 [79] Chemical and biochemical processes 2005 [76] Industrial wine distillery wastewater 2005 [85] Monitoring of volatile fatty acids in anaerobic digestion processes 2020…”

Section: Discussionmentioning

confidence: 99%

Anaerobic Digestion Process Modeling Under Uncertainty: A Narrative Review

Barahmand¹,

Samarakoon²

2023

IJEPM

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Growing concern about global climate change has led to considerable interest in investigating renewable energy sources such as the biological conversion of biomass to methane in an anaerobic environment. Through a series of complicated biochemical interactions, it uses various bacterial species to degrade biodegradable material in the feedstock. Due to the complex and interacting biochemical processes, anaerobic digestion has nonlinear dynamics. Anaerobic digestion is highly at risk of instabilities and uncertainties because of its dynamic and nonlinear behavior, uncertain feedstock quality, and sensitivity to the process's environmental conditions. Therefore, effectively operating a biogas production unit necessitates a thorough understanding of the system's uncertainties. The present study aims to identify and assess the sources and methods of coping with the uncertainties in anaerobic digestion processes through a narrative review. Moreover, the knowledge gap is also investigated to reveal the challenges and opportunities to have a robust model. The results indicate that the unpredictability of model parameters and input variables were the most significant source of uncertainty, and the Monte Carlo technique, confident interval, and interval observers, as well as sensitivity analysis were the most frequently used tools to cope with these uncertainties.

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Section: Discussionmentioning

confidence: 99%

Anaerobic Digestion Process Modeling Under Uncertainty: A Narrative Review

Barahmand¹,

Samarakoon²

2023

IJEPM

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“…Furthermore, the prediction efficiency of regression-based SVR, which assumed a linear combination of variables, was low in biological reactions with complex nonlinear relationships of various factors. For efficient operation and management of real BEAD reactors, it would be more effective to use the RNN method based on the accumulated time-series data when predicting the methane yield of the BEAD reactors with nonlinear relationships with time [36].…”

Section: Multi-step Ahead ML Modelsmentioning

confidence: 99%

Application of Various Machine Learning Models for Process Stability of Bio-Electrochemical Anaerobic Digestion

et al. 2022

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The application of a machine learning (ML) model to bio-electrochemical anaerobic digestion (BEAD) is a future-oriented approach for improving process stability by predicting performances that have nonlinear relationships with various operational parameters. Five ML models, which included tree-, regression-, and neural network-based algorithms, were applied to predict the methane yield in BEAD reactor. The results showed that various 1-step ahead ML models, which utilized prior data of BEAD performances, could enhance prediction accuracy. In addition, 1-step ahead with retraining algorithm could improve prediction accuracy by 37.3% compared with the conventional multi-step ahead algorithm. The improvement was particularly noteworthy in tree- and regression-based ML models. Moreover, 1-step ahead with retraining algorithm showed high potential of achieving efficient prediction using pH as a single input data, which is plausibly an easier monitoring parameter compared with the other parameters required in bioprocess models.

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“…The state variables that represent the concentrations are described in Table 1. The stoichiometric and kinetic parameters are defined as in a previous work of Camberos et al 17 The gaseous output is defined as follows:…”

Section: Mathematical Modelmentioning

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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Neuronal Modeling of a Two Stages Anaerobic Digestion Process for Biofuels Production

Cited by 6 publications

References 13 publications

Anaerobic Digestion Process Modeling Under Uncertainty: A Narrative Review

Anaerobic Digestion Process Modeling Under Uncertainty: A Narrative Review

Application of Various Machine Learning Models for Process Stability of Bio-Electrochemical Anaerobic Digestion

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

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