Biological H2(g) Production and Modelling with Computational Fluid

Dynamics (CFD)

RUKİYE ÖZTEKİN*, DELİA TERESA SPONZA

Department of Environmental Engineering,

Dokuz Eylül University,

Tınaztepe Campus, 35160 Buca/Izmir,

TURKEY

*Corresponding Author

and 12 days), different pH values (4.0, 5.0, 6.0, 7.0, and 8.0), and increasing feed rate as organic loading rates

(OLRs) (0.5, 1.0, 2.0, 4.0, 8.0 and 10.0 g COD/l.d) on the bio-H2(g) production rates were operated in

municipal sludge wastes (MSW) with Thermoanaerobacterium thermosaccharolyticum SP-H2 methane

bacteria during dark fermentation for bio-H2(g) production. The effect of HRT, pH, and feed rate on the bio-

H2(g) efficiencies and H2(g) production rates were examined in the simulation stage. Production of volatile

fatty acids (VFAs) namely, acetic acids, butyric acids, and propionic acids were important points influencing

the bio-H2(g) production yields. The artificial neural network (ANN) model substrate inhibition on bio-H2(g)

production to the methane (CH4) bacteria was also investigated. The reaction kinetics model used Thermotoga

neapolitana microorganisms with the Andrews model of substrate inhibition. Furthermore, the ANN model was

well-fitted to the experimental data to simulate the bio-H2(g) production from chemical oxygen demand (COD).

Received: August 18, 2022. Revised: October 15, 2023. Accepted: November 17, 2023. Published: December 12, 2023.

1 Introduction

The technologies of energy production based on

burning fossil fuels constitute the world's main

energy production source and cause pollution and

degradation of the natural environment, [1], [2].

Fossil fuels and non-renewable energy; It causes

environmental damage and climate change by

to be developed, [4], [5]. The development of these

technologies is supported by social pressure, carbon

neutrality requirements, the political environment,

and appropriate legal regulations. Because it reduces

carbon dioxide [CO2(g)] and methane [CH4(g)]

some inconsistency and drawbacks, the H2(g)

production processes by dark fermentation are less

well developed than the CH4(g) production. During

anaerobic digestion of organic wastes, such as solid

waste and wastewater, CH4(g) is produced and its

production processes have been well established

commercially. H2(g) is a more valuable energy

carrier and chemical feedstock compared with

treat agro-industrial effluents and also valorized

communication. Different ways of H2(g) production

methods are summarized at Figure 1 (Appendix).

Bio-H2(g) is a feasible, promising, and clean

alternative fuel with no CO2(g) emissions and high

energy content per unit weight (141.9 J/kg);

Additionally, only H2O is produced as a result of

H2(g) combustion, [15]. Using the dark fermentation

process, organic wastes can be decomposed into

bio-H2(g), CO2(g), and VFAs, and converted into

metabolites, which can be utilized in other

alternative to reduce fossil fuel consumption, [17]. It

compared different methods applied for H2(g)

production, such as photo-fermentation, dark

fermentation, electrolysis, electrodialysis and

photocatalysis, which include environmental,

economic, energy and energetic impacts, [18].

According to Bio-H2(g) production methods, the

most economical and efficient process occurs in

dark fermentation (Figure 2, Appendix).

Biological H2(g) production offers many

produced predominantly using biomass. Various

fuels such as ethanol, methanol, H2(g), and CH4(g)

can be obtained from biomass, [19]. Plant biomass,

theoretical detention time. This type of flow is

approximated in long open tanks with a high

length/width ratio where longitudinal dispersion is

minimal or absent, [28]. Operational factors to be

considered in selecting the type of reactor to be used

in the treatment process include the nature of the

wastewater to be treated, the nature of the reaction

(homogeneous/heterogeneous), the reaction kinetics

that governs the treatment process, process

performance requirements, and local environmental

modeling in packed bed bio-H2(g) production, [30],

[31]. CFD methods can be used to optimize the

reactor configuration and therefore improve the

performance of a bio-H2(g) production reactor. CFD

software is able to predict the hydrodynamics of

fluid flow, heat and mass transfer, chemical

reactions in a reactor, and other related events by

solving a series of partial differential equations.

Processes that describe mass, momentum, energy,

and species balances are advanced methods that are

optimizing performance costs without increasing the

cost of prototyping, [35]. To see the flow pattern of

the liquid, CFD can be used, which focuses on

predicting hydrodynamic patterns through porous

media. Flow dispersity patterns through a packed

bed reactor resulted in arranged spherical particles

using the discrete element method (DEM) coupling

with a biokinetics model, which is an efficient

method to predict bio-H2(g) concentration in a

continuous tube reactor, [36]. Parameters such as

data due to the data classification and learning

ability of the ANN model; It is a good tool that is

widely used and works according to human nervous

nonlinear functions without the need for a

mathematical explanation of events in the system.

However, when critical interactions of complex

nonlinearities such as biomass conversion are

included in a data set, ANN models are attractive for

outcome prediction, [67], [68], [69], [70]. Therefore,

very little work has been reported on modeling

biomass gasification using the ANN model, and

nothing in the field of downdraft gasifiers coupled

with water-gas shift reactors for bio-H2(g)

was investigated. Different operating parameters,

increasing HRTs (1, 2, 4, 8 and 12 days), different

pH values (4.0, 5.0, 6.0, 7.0 and 8.0) and feed rate

(0 .5, 1.0, 2.0, 4.0, 8.0) and 10.0 g COD/l.d) on bio-

H2(g) production rates, bio-H2(g) yields and H2(g)

for its production, it was run in MSW with

Thermoanaerobacterium thermosaccharolyticum

fermentation in an ACPFR. Rates in the simulation

phase; The production of VFAs, namely acetic,

on online measurements of ORP, pH, and dissolved

2 Materials and Methods

The hydrogen-producing bacterium SP-H2 was

isolated from a thermophilic acidogenic reactor

inoculated with municipal sewage sludge and

processed a carbohydrate-rich simulated food waste.

The experiments were conducted by an up-flow

anaerobic continuous plug flow reactor (ACPFR)

packed with immobilization materials. ACPFR is a

tube reactor. The tube reactor diameter was 10 cm,

whereas the length of the reactor was 45 cm to

2.4.1 Computational Fluid Dynamic (CFD)

written in C code. The model contains protein,

which makes up ≈ 27-30% of the dry weight of

Thermotoga neapolitana bacteria. A numerical CFD

study of hydrodynamics-biokinetics aspects for

interactions between multiphase is an important part

of how hydrodynamics influence biokinetics for bio-

H2(g) production. This numerical model contains

kinetics information, which is applicable to a set of

process parameters, thereby complicating process

analysis and the design of the fermentation system.

continuity, momentum, and energy equations). In

traditional CFD software, the solutions to these

equations are found by solving a set of partial

differential equations called the Navier-Stokes

equations. The Navier-Stokes equations describe the

motion of a fluid and how the pressure, velocity,

temperature, and density of a moving fluid are

related. For a three-dimensional (3D) system, they

consist of one continuity equation for the

conservation of mass, three equations for the

sludge reactors (aerobic, anaerobic, and nitriding-

anammox) is reviewed from a mechanistic modeling

perspective (i.e., mass balance-based models with

transport and reaction terms), [25]. Some studies

have reported using CFD to study the transport of

solid, liquid and gas phases, [25]. While we discuss

the forces involved in momentum transfer between

phases, no details are given about CFD modeling.

mass flow rate of H2(g) production (smH2) from

different operating conditions (HRTs, pH, and feed

rate). Then, an attempt is made to investigate the

relative impact of biomass properties and operating

parameters on smH2. At the end, to have a

comprehensive analysis, variations of the inputs on

analyzed together.

The ANNs always consist of three layers

including (i) input, (ii) hidden, and (iii) output

(4):

 

mathematical models, [104]. Therefore, artificial

intelligence technology can predict nonlinear

relationships extremely quickly and reliably.

Advances in computing power are minimizing the

time required to develop models as well as the time

required to retrain models to incorporate new data

and reflect process changes. The four main types of

artificial intelligence approaches based on major

branches are summarized and shown in Figure 3

(Appendix), [105]:

intelligence techniques to optimize the dark

fermentation process. While it is understood that the

application of artificial intelligence in biogas

production is still a growing phenomenon, its

application in bio-H2(g) production is still a very

new application.

Nature-inspired computing (NIC) is a recently

developed branch of artificial intelligence

techniques. Natural systems (living and non-living)

have an innate ability to evolve, often in parallel and

this forms the basis of algorithm development for

optimization problems in process engineering.

Algorithms are iterative procedures for providing

calculations or guidelines in a step-wise manner

tailored for specific goals. Computational

optimization aims to create algorithms to design,

implement, and test for solving optimization

problems, [106].

Optimization works on various levels, including

maximization of performance, efficiency, and profit,

ACPFR, computer simulation becomes an

indispensable tool.

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The Lagrangian–Eulerian approach is adopted to