Hydrogen production automatic control in continuous microbial electrolysis cells reactors used in wastewater treatment

Alcaraz-González, V.; Rodríguez-Valenzuela, G.; Gomez-Martinez, Juan Jose; Dotto, Guilherme Luiz; Flores-Estrella, René Alejandro

doi:10.1016/j.jenvman.2020.111869

Cited by 12 publications

(10 citation statements)

References 23 publications

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“…The initial condition for the model in (21) is: [2 g L −1 , 20 µm, 0.3, 0.3] T . The numerical method ode15s in MATLAB was used to solve numerically the model in and the model in ( 23)- (25).…”

Section: Open-loop Dynamical Behaviormentioning

confidence: 99%

“…Other categories can be related to the complexity of the model, for instance, spatial dimension (1D, 2D, or 3D), time dependence, or steady state models. In this regard, most MEC models have been proposed as either ordinary differential equation (ODE) systems [21][22][23][24][25] or partial differential equation (PDE) systems [12,15,[26][27][28]. Generally, both model types also include algebraic equations (AE), resulting in ordinary differential algebraic equations (ODAE) and partial differential algebraic equations (PDAE).…”

Section: Introductionmentioning

confidence: 99%

“…Biofilm growth and retention are modeled with constants or AEs. The approach has the advantage of simplicity and is a reliable alternative for optimization and control applications [25,31]. However, scaling up is limited if hydrodynamics does not hold.…”

Section: Introductionmentioning

confidence: 99%

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A Catalytic Effectiveness Factor for a Microbial Electrolysis Cell Biofilm Model

2022

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The aim of this work is to propose a methodology to obtain an effectiveness factor for biofilm in a microbial electrolysis cell (MEC) system and use it to reduce a partial differential equation (PDE) biofilm MEC model to an ordinary differential equation (ODE) MEC model. The biofilm mass balances of the different species are considered. In addition, it is considered that all the involved microorganisms are attached to the anodic biological film. Three effectiveness factors are obtained from partial differential equations describing the spatial distributions of potential and substrate in the biofilm. Then, a model reduction is carried out using the global mass balances of the different species in the system. The reduced model with three uncertain but bounded effectiveness factors is evaluated numerically and analyzed in the sense of stability and parametric sensibility to demonstrate its applicability. The reduced ODE model is compared with a validated model taken from the literature, and the results are in good agreement. The biofilm effectiveness factor in MEC systems can be extended to the reduction of PDE models to obtain ODE models that are commonly used in optimization and control problems.

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Section: Open-loop Dynamical Behaviormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Catalytic Effectiveness Factor for a Microbial Electrolysis Cell Biofilm Model

2022

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“…In DMEC, a proton exchange membrane (PEM) prevents the wastewater solution from transferring the anode to the cathode chambers and reduces the hydrogen diffusion from the cathode to the anode resulting in increased pure hydrogen. Although some researchers [18][19][20] observed the pilot scale of MECs and calculated main objectives such as organic load, applied voltage, temperature, inoculum, and pH experimentally, the other ones [21][22][23][24] prioritized analytical solutions to optimize operational conditions including physical, chemical, and biological properties for increasing hydrogen production rate and improving wastewater treatment process. In this regard, Pinto et al [21] conducted a mathematical model for a continuous operation MEC reactor fed with synthetic wastewater where acetate was the primary substrate in the dilution.…”

Section: Introductionmentioning

confidence: 99%

Mathematical Modeling of Microbial Electrolysis Cells for Enhanced Urban Wastewater Treatment and Hydrogen Generation

Rahimi

Eicker

2023

Processes

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Conventional wastewater treatment plants (CWTPs) are intensive energy consumers. New technologies are emerging for wastewater treatment such as microbial electrolysis cells (MECs) that can simultaneously treat wastewater and generate hydrogen as a renewable energy source. Mathematical modeling of single and dual-chamber microbial electrolysis cells (SMEC and DMEC) has been developed based on microbial population growth in this study. The model outputs were validated successfully with previous works, and are then used for comparisons between the SMEC and DMEC regarding the hydrogen production rate (HPR). The results reveal that the daily HPR in DMEC is higher than in SMEC, with about 0.86 l H2 and 0.52 l H2, respectively, per 1 L of wastewater. Moreover, the results have been used to compare the HPR in water electrolysis (WE) processes and MECs. WE consume 51 kWh to generate 1 kg of hydrogen, while SMEC and DMEC require only 30 kWh and 24.5 kWh, respectively.

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“…Nevertheless, to the best of our knowledge, advanced control methods of model-based robust optimal design for continuous MEC systems have never been studied until now. A minimal amount of literature is available on conventional control of continuous MEC systems based on algebraic manipulations of nonlinear dynamics in continuous MEC processes [30]. Model-based feedback-robust control is necessary for the optimal output of this recently developed renewable energy MEC system.…”

Section: Introductionmentioning

confidence: 99%

A Robust Controller of a Reactor Electromicrobial System Based on a Structured Fractional Transformation for Renewable Energy

et al. 2022

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The focus on renewable energy is increasing globally to lessen reliance on conventional sources and fossil fuels. For renewable energy systems to work at their best and produce the desired results, precise feedback control is required. Microbial electrochemical cells (MEC) are a relatively new technology for renewable energy. In this study, we design and implement a model-based robust controller for a continuous MEC reactor. We compare its performance with those of traditional methods involving a proportional integral derivative (PID), H-infinity (H∞) controller and PID controller tuned by intelligent genetic algorithms. Recently, a dynamic model of a MEC continuous reactor was proposed, which describes the complex dynamics of MEC through a set of nonlinear differential equations. Until now, no model-based control approaches for MEC have been proposed. For optimal and robust output control of a continuous-reactor MEC system, we linearize the model to state a linear time-invariant (LTI) state-space representation at the nominal operating point. The LTI model is used to design four different types of controllers. The designed controllers and systems are simulated, and their performances are evaluated and compared for various operating conditions. Our findings show that a structured linear fractional transformation (LFT)-based H∞ control approach is much better than the other approaches against various performance parameters. The study provides numerous possibilities for control applications of continuous MEC reactor processes.

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Hydrogen production automatic control in continuous microbial electrolysis cells reactors used in wastewater treatment

Cited by 12 publications

References 23 publications

A Catalytic Effectiveness Factor for a Microbial Electrolysis Cell Biofilm Model

A Catalytic Effectiveness Factor for a Microbial Electrolysis Cell Biofilm Model

Mathematical Modeling of Microbial Electrolysis Cells for Enhanced Urban Wastewater Treatment and Hydrogen Generation

A Robust Controller of a Reactor Electromicrobial System Based on a Structured Fractional Transformation for Renewable Energy

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