DFT calculations have become an important part of materials research to discover and explain the causes of experimentally observed phenomena at the atomic scale. They provide insights into the physics and chemistry of materials which aid in further optimisation of materials for a specific application. DFT primarily provides a means for calculating the total energy and electron charge distribution of any configuration of atoms.

The atomic-scale processes in materials are described by the quantum mechanical time-independent Schrödinger equation, Equation (i):

(i)

in which the wavefunction for the set of electrons and nuclei is denoted by Ψ({R_j},{r_i}) where R_j are the positions of the nuclei, r_i are the positions of the electrons and Ĥ is the Hamiltonian of the system. The energy E obtained from this equation represents a specific energy level for the system. In general, the ground-state energy of the system, E₀, is the quantity of interest. The Hamiltonian for this time-independent equation is Equation (ii):

(ii)

The first two terms in Ĥ are the kinetic energy operators of the nuclei and electrons, and the third is the potential energy. Nuclei and electrons interact via the Coulomb interaction. Unfortunately, the conventional Schrödinger equation is too complicated to solve beyond just a handful of particles. Therefore, approximations are required in order to solve this equation and obtain the ground-state energy of the system of interacting electrons and nuclei. Since electrons move on very fast timescales compared to nuclear motion, the nuclei can be treated as fixed in space while the electronic-ground state is computed. This is the Born-Oppenheimer approximation, which results in a Schrödinger equation for the electrons, in which the nuclear positions and charges enter as parameters only. The underpinning principle of DFT, the Hohenberg-Kohn theorem (1), builds from this approximation, providing a theoretical basis for working not with the wavefunction, but with the much simpler ground-state electron density, n(r).

Figure 1 shows an example of the calculated ground-state electron density of the atoms in a silicon crystal structure, represented by the smooth surface surrounding the atoms. The total energy of a system of electrons and fixed nuclei is a function of all possible electron density functions. Using the Kohn-Sham ansatz, finding the ground-state electronic density is made computationally feasible by expressing it in terms of auxiliary wavefunctions which describe a fictitious non-interacting system of the same density (2). The full expression for the ground-state energy E_KS may then be written as Equation (iii):

(iii)

Fig. 1.

where the first term, T[n], is the kinetic energy associated with the non-interacting Kohn-Sham particles; the second term, E_NN, is the nuclear-nuclear interaction; and the third term, V_ext, is the external potential of ion cores in which the electrons move. The fourth and fifth terms represent electron-electron interaction energies. The fourth term is the exact classical electrostatic energy; the interaction energy of an electron with the mean field of all electrons. The fifth term is the exchange-correlation energy, which attempts to account for all interactions not accounted for within the first four terms. By dividing up the energy in this way, while the exact exchange-correlation functional remains unknown, it may be approximated in various tractable ways.

The simplest approximation to E_XC is the local density approximation (LDA), where the exchange-correlation energy per particle is taken to be equal to that of a uniform electron gas of the same electron density, at each point in space. Generalised gradient approximation (GGA) functionals improve on the LDA by taking into account both the electron density and the gradient of that density, resulting in a more accurate description of exchange and correlation (3). These functionals have limitations; most seriously, both electron localisation and electronic band gaps are underestimated. So‐called ‘hybrid functionals’ have aimed at semi-empirically correcting the electronic band gap (4) and developing functionals beyond the LDA and GGA is the focus of much of the theoretical work in the field of DFT today, where the ultimate goal is to find an exchange-correlation functional which accurately describes all possible systems (5).

Within this framework, total energies, forces, equilibrium geometries, elastic behaviour and many other properties of interest can be readily and accurately predicted. However, to predict a material’s properties using DFT, it is necessary to know how its atoms are arranged. Thus, in the following section, we describe the method of CSP, which uses DFT to generate structures of novel materials.

There are multiple materials databases. Some contain only the experimental crystal structures and other relevant properties of known materials, while others contain the computed properties of both known and hypothetical materials. These can be leveraged to perform CSP. For example, known crystal structure prototypes can be decorated with any set of atomic species, resulting in new hypothetical materials. The stability and synthesisability of these new materials can then be assessed using DFT calculations and by comparing against thermochemical data in the database. Three of the major exhaustive databases of DFT calculations, the Open Quantum Materials Database (OQMD) (6), the Automatic Flow (AFLOW) framework for materials discovery (7) and the Materials Project (8) have been used to predict new materials and screen for desired properties using a combination of high-throughput ab initio calculations and, increasingly, statistical and machine learning approaches. In addition, experimentally identified structures are found in the Inorganic Crystal Structure Database (ICSD) (9) and the Crystallography Open Database (COD) (10). These databases have been used as a starting point for many theoretical studies, leading to several new discoveries in the field of energy storage, including identifying SrFeO_3-δ as a material for carbon capture (11), verifying Li₃OCl as a solid electrolyte with high ion conductivity (12) and predicting LiMnBO₃ as a Li-ion battery cathode (13). While these databases are useful for comparisons of known structures and enable the discovery of materials that are based on known crystal structure prototypes, it is likely that new structures exist which cannot be classified as one of the currently known prototypes. Therefore, it is necessary to perform CSP in order to explore novel phases of materials.

The search for new thermodynamically stable materials (those favoured to form during synthesis, when kinetic factors are excluded) using CSP can take one of many approaches (14), but all involve a search for the lowest energy minimum in a high-dimensional configuration space. The configuration space for a periodic structure with N atoms per unit cell has dimension 3N+3, taking into consideration the rotational symmetries and unit-cell degrees of freedom, whilst the number of local minima in the space scales exponentially with N (15). Ideally, all low-lying minima would be sampled during CSP since metastable phases may be synthesised experimentally, or indeed be thermodynamically stable under different conditions; for example, graphite is the most stable allotrope of carbon under ambient conditions, but diamond can be easily synthesised under high pressure. Particularly popular approaches to CSP include the use of evolutionary algorithms to ‘breed’ new structures (15) and particle swarm optimisation (16–18).

AIRSS (19) is the focus of this review. Despite the potential for having a high computational cost, AIRSS remains an effective method for structure prediction which allows for a breadth of searching and has proven successful in a wide range of materials. Beyond the ease of its implementation, AIRSS has several advantages. Firstly, individual relaxations do not depend on one another, hence all trials can be run concurrently making the algorithm trivially parallelisable to the largest of supercomputers. Secondly, AIRSS allows for the easy application of chemically intuitive constraints which reduce the initial search space to the most experimentally relevant trial structures. This constraint greatly reduces the size of the search space and makes AIRSS applicable to a wide range of systems, including those at high pressure (20, 21). These chemical constraints include, for example: the phases of conversion and alloying anodes (22–25) were constrained by space group symmetries and atomic distances; high pressure phases of ice (26) were constrained to H₂O units; encapsulated nanowires (27) were constrained by rod group symmetries; metal-organic frameworks (28) were constrained to molecular building blocks; grain-boundary interfaces (29) and point-defects (30) had some atoms fixed to describe the lattice and systematically randomised other atoms to describe interface and defect structures.

AIRSS explores configuration space using random sampling as shown in Figure 2(a) and proceeds as follows.

Fig. 2.

To search for a new phase with chemical formula, A_x B_y , any number of atoms of element A and B are placed randomly (denoted ‘Randomise’ in Figure 2(a)) into a 3D simulation cell in the ratio x :y . The cell and atomic positions are allocated such that they obey a set of chosen symmetry operations (a space group in 3D). Further constraints, such as minimum separation between atoms and a feasible range for the atomic density of the unit cell, may be imposed. These constraints narrow the region of the configuration space of possible structures by avoiding regions that describe unrealistic arrangements of atoms.

The forces on the atoms and stresses on the cell are calculated with DFT and then minimised using the traditional optimisation algorithms (for example, conjugate gradients). This step is denoted ‘Relax’ in Figure 2(a). The energy of the system is used as a metric to gauge how stable the structure is.

Steps 1 and 2 are then repeated several thousand times in order to generate a representative set of structures in the A-B chemical space. The search is stopped once the lowest energy structures have been found multiple times. The set of lowest energy structures are the candidates for phases that are likely to form experimentally.

Using the DFT energies, one can construct a ‘convex hull’ of all the structures found by AIRSS, as shown in Figure 2(b). The structures A_x B_y which are likely to form, must both have a negative formation energy relative to elemental A and B and lie on the convex hull tie-line between A and B to avoid decomposition into other binary phases. This tie-line is shown by the black line connecting the lowest energy structures in Figure 2(b). This figure illustrates the process of constructing a convex hull using the optimised structures from AIRSS. Suppose at a given point during the AIRSS search, the only structures on the convex hull are AB, AB₂ and A₂B, connected by the dashed line in Figure 2(b). Subsequently, a novel phase, A₃B, is identified using AIRSS and is found to lie below the existing tie-line. In this case, CSP has identified a new ground-state structure which suggests an additional phase, A₃B is likely to exist within the A-B phase diagram. Therefore, as shown in Figure 2(b), the hull is reconstructed to include the phase A₃B, rendering A₂B unstable, given that it is now no longer on the convex hull. Although this example is given for two dimensions (i.e. a binary system containing elements A and B) the convex hull construction is generalisable to N dimensions, in which the tie-lines between the lowest energy structures are computed in a similar manner.

In this way, AIRSS enables the prediction of new thermodynamically stable and metastable compounds in a given phase diagram and the convex hull construction provides a guide to their stability compared to previously known phases, without performing exhaustive chemical synthesis. Synthesis experiments can then be targeted at the most promising compositions and characterisation experiments can be guided by the predicted model structures.

The electrochemical voltage profile is the voltage signal of the electrode measured (vs. a reference, usually Li⁺/Li) as a function of the number of ions (i.e. charge) stored in the electrode. The phase transitions, which occur within the electrode during cycling provide the characteristic shape of the voltage profile; two-phase regions show a constant voltage, while solid-solution regions show a sloping voltage. The voltage drop between two phases is proportional to the difference in their free energies and thus these voltage drops can be computed directly from the free energies of the phases which lie on the convex hull tie-line. The voltage-drop between two phases with active ion concentrations x₁ and x₂ is Equation (iv):

(iv)

where q is the charge of the active ion, F is the Faraday constant and ΔG_rxn is the change in Gibbs free energy between phases. In practice, the change in Gibbs free energy in Equation (iv) is approximated by the change in the DFT total energy, under the assumption that entropic contributions will have a minimal effect on the free energy differences between phases during cycling.

When studying a phase diagram computationally there are a finite number of phases on the tie-line, thus the profile will not be a continuous smooth line, but a sequence of two-phase regions with constant average voltages. Although the profile will not have the same characteristic curve as an experimental voltage profile, it is still possible to calculate quantities of interest such as theoretical capacity, which is calculated from the maximal difference in active ion concentration between the predicted stable phases. Similarly, the energy density of an electrode is found by integrating the voltage profile between the two endpoint phases.

Beyond calculating the voltage profile, one may further validate a crystal structure against experiment by using DFT to predict its spectroscopic signatures. Many spectroscopic methods, including XAS, EELS (31) and Raman spectroscopy (32), can be readily calculated using DFT to aid characterisation.

Solid-state nuclear magnetic resonance (ssNMR) spectroscopy is a tool for investigating the element-specific local structure of materials, even for the disordered and dynamic systems present in battery materials (33). Due to the complex structures and processes that arise during battery cycling, the usefulness of NMR spectroscopy can be greatly enhanced by applying complementary techniques to aid the assignment of spectra to the local environment of each nucleus. Theoretical methods in DFT are sufficiently mature that the calculation of chemical shielding tensors across a diverse range of inorganic systems is now routine (34).

NMR spectroscopy involves the precise measurement of the response of nuclei in an applied magnetic field to weak oscillating perturbations; for a given pulse scheme, the frequency of perturbing oscillations is adjusted until resonance is achieved, at which point a signal is observed. The frequency of this resonance is a cumulative measure of several competing interactions between the spin of the nucleus and its local environment and, when referenced against a model nucleus, is referred to as the chemical shift. The observed chemical shift in most materials is determined by the nuclear spin interacting with the orbital angular momentum of paired electrons. In Figure 3, such a shift is given for the phases of Li-P which form during cycling of a Li-ion battery with a phosphorus anode (22). The ³¹P chemical shift of each Li_x P_y phase is distinct, as shown by the coloured peaks in the figure for each compound.

Fig. 3.

Whilst the theory for computing magnetic shielding for isolated systems (such as molecules and clusters) was developed in the 1960s and 1970s in the context of quantum chemistry (35), these methods were not easily extendable to solids (36). For periodic systems, such as battery anodes and cathodes, most modern implementations of theoretical ssNMR use DFT and the gauge including projector augmented wave (GIPAW) approach (37–39). It is not only possible to compute the full chemical shielding tensor, but also several other effects that can modify the lineshape of the NMR signal, namely quadrupolar coupling (for spin I >1/2 nuclei), dipolar coupling (which can be simulated directly from the geometry using for example the SIMPSON software package (40)) and J-coupling (interaction of electron spins which can probe chemical bonds directly) (41).

Finally, beyond just characterising the static crystal structure of a battery material, it is also possible to predict the dynamics of ions moving through the material, which is especially useful when studying ionic transport in electrodes and solid electrolytes. The charge and discharge rates are key performance factors in battery design, defining the time required to fully charge a battery and the amount of power it can deliver, respectively. Rate capability is determined by the speed with which the charge carriers can move through the materials. Since both ions and electrons move in a battery, the rate capability depends on both the electronic and ionic conductivity of the materials. While the electrodes in batteries must be mixed electronic-ionic conductors, the electrolyte must be electronically insulating. First principles methods, such as DFT, can be used to study both electronic conductivity and ionic conductivity of battery materials. Electronic conductivity can be assessed from electronic structure calculations (42–44), while ionic conductivity can be calculated using ab initio molecular dynamics (AIMD) or the nudged elastic band (NEB) method (45), as outlined below.

The bulk ionic conductivity, σ(T), of a solid electrolyte can be related to diffusion coefficients via the Nernst-Einstein relation (46) defined as Equation (v):

(v)

where n is the diffusing particle density, e the elementary electron charge, z the ionic charge, k_B the Boltzmann constant, T the temperature, D(T) the ionic diffusivity and H_R the Haven ratio accounting for the correlated ionic motion.

3.3.1 Ab Initio Molecular Dynamics Simulations

One way to compute ionic diffusivity of a given material, using AIMD simulations, combines the first principles aspects of DFT with the ability of molecular dynamics (MD) to model ionic forces and trajectories. Methods to screen the mobility of ions along an MD trajectory include mean square displacement (MSD), mean jump rate (MJR) (47, 48), velocity autocorrelation function (VACF) (49–52) van Hove correlation function (53, 54) and others (55, 56). MSD is the most straightforward and robust and thus the commonly used definition of diffusivity.

One can extract the diffusion coefficient D(T) from the gradient of the MSD, given a well-converged MD trajectory such that the MSD is a linear function of time. Here, the slope of the line of best fit gives the diffusion coefficient D, times twice the dimensionality d of the diffusion (2d * D). For ionic diffusion in three dimensions, d = 3. Depending on the level of mobility of ions in the system, good convergence of the MSD of ions may require long trajectories, for example 50–100 ps, thereby requiring tens of thousands of time steps. As each step involves DFT energy or force evaluations, AIMD can be a computationally demanding process. Two common solutions to this are: (a) to analyse trajectories obtained at elevated temperatures (500–2000 K) to foster higher mobility and faster convergence of the MSD; or (b) to utilise parameterised atomic force-fields to allow faster evaluation of the interatomic forces in the system compared to ab initio methods like DFT. A drawback of parameterised force-fields is non-transferability, so one needs a new set of fitted parameters for the specific set of atoms in a new system.

The activation energy (E_a) for the ionic transport in a given electrolyte or electrode can be obtained from AIMD simulations using the Arrhenius law, Equation (vi):

(vi)

where D₀ is the theoretical maximum diffusivity at infinite temperature, under the assumption that the diffusion mechanism is not temperature dependent and no phase transition occurs. Analysis of the trajectories from the AIMD simulations can also provide useful information on the crystallographic sites with higher occupation probability, while also revealing the preferred ionic conduction pathways between these sites (47, 57, 58).

3.3.2 Nudged Elastic Band Method

Another way to obtain ionic diffusivity from first principles is with optimisation-based methods, through the exploration of minimum energy paths (MEP) describing a set of predefined ionic migration pathways. To this end, the NEB algorithm is often used. Other approaches are also available for transition-state searches, for example the dimer (59), Lanczos (60) and eigenvector-following (EF) (61) methods as well as others (62, 63). Specifically, the NEB method computes the MEP (at 0 K) for a predefined route connecting the initial and final states of the motion of a single ion or a few, concertedly diffusing ions (45, 64). The ion-transport path is divided into intermediate steps (called NEB images), defined by the interpolation of these two end-point states. The NEB images are concurrently optimised by introducing a set of imaginary spring-forces to ensure the harmonic coupling of the consecutive images and a continuous path on the corresponding high-dimensional potential energy surface. Using the climbing-image NEB that maximises the energy of the saddle point(s) on the MEP, one can also locate the transition states, from which activation energies (E_a) are calculated.

In solids, the change of entropy during ionic diffusion is usually negligible and thus activation free energies are typically approximated by their 0 K values. The diffusion rate can then be related to the ionic diffusivity in the dilute carrier limit (65) (i.e. diffusion carriers do not interact) using Equation (vii):

(vii)

where λ is the hop distance between two adjacent sites, g is a geometric factor that depends on the symmetry of the sublattice of interstitial sites, f is the correlation factor, X_D is the concentration of the diffusion-mediating defects, v* is the entropy difference between the initial and final states, the activation energy E_a is the energy difference between the initial and final states, k_B is Boltzmann’s constant and T is the temperature of the simulation.

Static methods, such as NEB, provide computational efficiency over AIMD: NEB requires only a few hundred DFT steps to converge and is accurate within the regime in which the electronic structure of the model system does not change with the ionic migration (66). NEB calculations also allow for quantitative comparison of different migration routes. Nevertheless, NEB is less likely to reveal new conduction mechanisms compared to AIMD, and the complex cooperative conduction mechanisms may not be as straightforward to sample with NEB as with AIMD. Moreover, NEB usually operates in the dilute regime (Equation (vii)), where vacancy defects are manually introduced in the sublattice of the diffusing ions to have a low diffusion carrier concentration and mediate the ionic motion. These artificial defects not only decrease the accuracy of the simulation models, but also impede the integration of the NEB method in high throughput approaches. AIMD, in contrast, would in principle work with any concentration of diffusing ions by readily addressing the self-diffusion limit (67, 68). Given these tradeoffs, a common practice in the literature is therefore to combine AIMD with NEB calculations, specifically by identifying the potential conduction pathways from relatively shorter AIMD trajectories at a selected, elevated temperature and to probe the MEPs to get E_a and compute the other properties relevant to the ionic transport (57, 58, 69–71).

In many cases, as in the high-voltage high-capacity anode material TiNb₂O₇ (TNO), both ionic and electronic conductivities are relevant to the performance of the battery material (72). In this case, density of states (DOS) calculations were used to determine that the electron-doped TNO is metallic, as compared to the pristine TNO. Additional localised electronic states were confirmed in AIMD as a result of bond distortions, thus exemplifying the need in this case for both AIMD and DOS calculations.

Graphite is ubiquitous in contemporary commercial Li-ion batteries. However, alternative anode materials are a highly researched topic, due to graphite’s low capacity (372 mAh g^–1) and tendency for Li plating and subsequent dangerous short-circuiting due to its low operating voltage (76). These factors make graphite anodes unattractive for applications that require high performance and capacity, such as electric vehicles.

Here we highlight developments in predicting high capacity conversion and alloying anodes to replace graphite, based on tin (990 mAh g^–1) and antimony (660 mAh g^–1). Such conversion and alloying anodes undergo a succession of reversible phase transformations during charging and results in their observed higher capacity retention than other conversion and alloying anodes (77).

Both Sn and Sb were previously employed as anodes in Li-ion batteries, showing evidence of conversion reactions, with unknown phases of Li_x Sn and Li_x Sb forming during Li insertion. An AIRSS search for the thermodynamically stable phases of both Li_x Sn and Li_x Sb was conducted (23, 78) in order to understand the voltage profiles and reaction mechanisms of these two alloying anodes. In this case, a new phase Li₂Sn was identified by AIRSS to lie near the convex hull. The resulting voltage profile is compared with experimental measurements in Figure 4.

Fig. 4.

During the cycling process in conversion anodes such as Sn, the material at the anode undergoes several conversion reactions as Li is inserted (77). In the voltage profile shown in Figure 4, the black line is constructed from the ground state phases in the Li-Sn system, which were predicted using AIRSS (23, 78). Each plateau in Figure 4 represents a two-phase region between one ground state Li-Sn alloy and another, until a critical point is reached at which there is a phase transformation (a vertical line) to the next Li-Sn alloy.

The DFT predictions lie within the voltage range of the experiment and are an accurate match to both sets of experimental data by Wang et al. (79). In many cases, the experimental data has less-sharp distinctions between separated phases, due to reactions which appear to occur gradually rather than at a well-defined stoichiometry.

The Li-Sb phase diagram was found to be somewhat simpler, with only two stable phases predicted during cycling: Li₂Sb and Li₃Sb. Two competing polymorphs of Li₃Sb were found and NMR calculations were performed on both to provide a signature of each phase to aid the interpretation of future experiments.

This work on Li-Sn and Li-Sb anodes provided theoretical confirmation of experimental binary phases in this family of conversion anodes and allowed for more concrete evidence of the specific mechanism of Li insertion into these anodes. Furthermore, this study confirmed the new phase of Li₂Sn.

The electrolyte in a battery forms a conductive bridge between the anode and cathode which allows ions to move from one electrode to the other without permitting the flow of electrons. Conventional Li-ion battery architectures use a liquid electrolyte consisting of a Li salt mixture dissolved in an organic solvent. Two prominent safety concerns arise from the use of organic liquid electrolytes (80, 81). The first is that the organic solvent component tends to be flammable and poses a fire hazard when exposed to air if the battery casing is breached (82). The second is that Li dendrites (83, 84) form, which can eventually bridge the gap between the anode and cathode resulting in short-circuiting.

All solid-state batteries attempt to solve these safety issues by replacing the organic electrolyte solutions with solid equivalents, which exhibit high mechanical strength, suppressing dendrite formation, thus enabling the use of the high energy density Li-metal anodes (85, 86). Most proposed solid electrolytes have sufficient mechanical strength, as demonstrated by high throughput screening based on machine learning methods (87).

A key challenge in developing solid electrolytes is finding solids with room temperature (RT) ionic conductivities that approach those of their liquid counterparts. Among several solid electrolyte families identified to date, the thiophosphide ceramics, for example Li₂S-P₂S₅, chemically-doped sulfides, like Li₁₀GeP₂S₁₂ (LGPS) (88) and Li_9.54Si_1.74P_1.44S_11.7Cl_0.3 (89), are known to deliver the highest RT Li-ion conductivities (1.2–2.5 × 10^–2 S cm^–1). Sulfides, however, have high moisture sensitivity and their chemical stability against common electrodes is low, thus limiting their practical use (90). By contrast, oxides like garnets (for example, Li_x La₃M₂O₁₂, where M = zirconium, niobium, tantalum) display notably higher chemical stability than sulfides but exhibit lower ionic conductivities (91). The latter limitation can be partly remedied by a chemical doping with diverse metals, including aluminium, gallium and scandium (92).

High throughput CSP is useful for exploring new superior electrolytes with combined high conductivity and chemical stability. Various studies have performed extensive screening of superionic conductors within databases such as the Materials Project (8), searching for phases with good phase stability, high Li⁺ conductivity, wide band gap and good electrochemical stability (12, 53, 93–95).

Various LGPS-derived compositions were predicted using ab initio calculations through elemental swapping (95), such as Li₁₀(Sn/Si)PS₁₂ and then verified by experimental synthesis and measurements (96, 97). LiAlSO was discovered solely through structure prediction and proposed to be a superionic conductor with AlS₂O₂ layers, which facilitate faster movement of Li-ions, low activation barriers and a wider electrochemical window (94). Similarly, Fujimura et al. (98) presented a high throughput (HT) screening of the chemical phase space for Li_3.5Zn_0.25GeO₄ (LISICON)-type electrolytes. The authors proposed new electrolytes with higher conductivities than the parent LISICON material. Later, Zhu et al. (93) reported a HT screening of the Li-P-S ternary and Li-M-P-S (where M is a non-redox-active element) quaternary chemical spaces and identified two Li superionic conductors, Li₃Y(PS₄)₂ and Li₅PS₄Cl₂. Particularly, Li₃Y(PS₄)₂ is predicted to exhibit a room-temperature Li⁺ conductivity of 2.16 mS cm^–1, which can be further enhanced with aliovalent doping (93). However, these materials are yet to be synthesised.

Following the structure prediction of these new solid electrolyte phases, it is then desirable to use NEB and AIMD simulations to investigate the atomistic origins of their ionic conductivity. For instance, Li-ion transport was elucidated in the sulfide-based electrolytes, Li₇P₃S₁₁ (99), argyrodite Li₆PS₅Cl (48, 53), LGPS (57, 100), Li-Sn-S/Li-Sn-Se (101, 102) and Li-As-S/Li-As-Se alloys (103), Li₃PS₄ (48, 104, 105), Li₄GeS₄ (57, 103) as well as oxides, for example LLZO (71, 106–108), LiTaSiO₅, LiAlSiO₄ (71), Li₄SiO₄−Li₃PO₄ solid mixtures (109) and several others. The problem of identifying solid electrolyte candidates for all solid-state batteries which are air stable and highly conducting can be solved using a combination of structure prediction techniques and atomistic modelling such as AIMD and NEB.

So far, the battery materials we have discussed (Sections 4.1 and 4.2) are based on Li-ion chemistry. However, cost and sustainability are driving research efforts into ‘beyond Li-ion’ batteries. The philosophy presented in Section 3, using CSP and DFT, is straightforward to extend to ‘beyond Li-ion’ chemistries. A prominent example is Na-ion batteries, where Li is replaced with the more earth-abundant Na.

Unlike in Li-ion batteries, graphite shows poor capacity for Na, although other carbonaceous materials offer some promise (110). As such, the success of future Na-ion batteries will rely on the discovery of new anode materials. There are many classes of anode materials which are applicable to Na-ion batteries including two-dimensional transition metal carbides (111) and group V elements (P, As, Sb) (112). Although this review focuses on one Na-ion anode material in particular, structure prediction has been used to predict phases of each of the anode materials in several cases (113–115). In particular, black P shows a high theoretical capacity for Na of 2596 mAh g^–1, corresponding to the formation of Na₃P (22). Here, P acts as an alloying electrode, so its cycling is expected to involve multiple phase transformations. For these reasons, there has been recent focus on understanding sodiation processes in P.

Applying a combination of AIRSS, data mining (22) and a genetic algorithm (25), the convex hull of the Na-P system has been mapped out and is shown in Figure 5. The Na-P system contains a number of stable crystalline phases (coloured black circles in Figure 5) with compositions varying from NaP₇ through Na₃P, and the voltage curve derived from these phases shows good agreement with experimental measurements (25). In addition to these stable phases, there are metastable phases lying close to the convex hull across a range of compositions.

Fig. 5.

By following the structures which fall on or near the convex hull in Figure 5, from least sodiated (pure P) to most sodiated (Na₃P), the calculations predicted many changes in local structure: the layered black P is broken upon successive Na insertion, forming P chains and helices, then dumbbells, which eventually break apart to form isolated P atoms. These structural motifs are distinctive and have characteristic NMR signatures, which can be accurately modelled. In order to confirm this explicitly, ex situ ³¹P solid-state NMR measurements were taken at different points during both the sodiation and desodiation cycle (25). Since contemporary NMR calculations lack a rigorous treatment of paramagnetic contributions to the isotropic shifts, the chemical shift anisotropies were computed for the thermodynamically accessible range of predicted structures to provide a set of chemical environments to screen against experimental measurements. During the reverse cycle when Na is removed from the system, P helices re-formed in a tangled fashion and the original crystalline P was not recovered. Amorphous phases were encountered experimentally on desodiation and, while modelling of amorphous materials is challenging, the local structural features of predicted metastable phases were discovered to be present even in the amorphous structures.

Aside from P, Sn also shows promise as a Na- ion battery anode. Sn presents a lower theoretical capacity for Na (847 mAh g^–1) but offers better capacity retention than P (24). The results of an AIRSS search for Na-Sn phases (24), predicted that insertion of Na into Sn would result in hexagonally layered structures NaSn₃ and NaSn₂, before passing through an amorphous phase of approximate composition Na_1.2Sn, after which a solid-solution consisting of Sn dumbbells surrounded by Na ions would form. The final product, Na₁₅Sn₄, contains isolated Sn atoms surrounded by Na. Importantly, the computational workflow used to study Li and Na-ion batteries is the same and is equally as applicable to conversion anodes for other chemistries.

The present pilot-scale research study was implemented in an autothermal updraft gasifier reactor with a throughput capacity of 500 kg h⁻¹. The primary purpose of this study is to inspect and to analyse the experimental data obtained including gas concentration, temperature profiles, mass and energy balance. Syngas LHV, carbon conversion and energy output via the ORC turbine are further presented and discussed. Another goal of this work is to demonstrate the possibility of using pelletised olive pomace in cogeneration systems based on the gasification process.

The evaluation of state of the art affirms, therefore, that the combination of two unique technologies, i.e., biomass gasification and ORC turbine, which are both technologies in progress, can be considered as an original approach. Currently, the original combined biomass fixed bed autothermal updraft gasification and ORC turbine pilot-scale plant are both in operation.

In summary, this study was carried out using a pilot-scale (500 kg h⁻¹) gasification plant consisting of an autothermal updraft gasifier, hot syngas burner where the raw produced syngas was not cooled or treated prior to the specially designed syngas burner on the thermal oil heater which runs in an ORC turbine at an output capacity of 240 kW electrical power. The excess 1.36 MWh of thermal heat is used for evaporation of the blackwater, a harmful byproduct of the olive oil production facility. The most important properties of pelleted olive pomace that are known to impact the gasification systems are water content, shape and size, bulk and total density, chemical composition (i.e., ultimate and proximate analysis) and the HHV. This paper will focus on the production of power and heat from the gasification of olive pomace in a pilot-scale autothermal fixed bed updraft gasifier.

The objectives of the study are to:

The autothermal fixed bed gasifier reactor gasifies a maximum of 500 kg h⁻¹ biomass feedstock. This amount of gasified biomass approximately supplies 1250 Nm³ h⁻¹ of produced gas to the thermal oil heater. After the gasification process, almost 10% of the gasified biomass comes out as char, a byproduct from the reactor. Gasification of the pelleted olive pomace is carried out in an air-blown (680 Nm³ h⁻¹ at the maximum load) updraft gasifier system operating slightly under atmospheric pressure.

The gasification reactor was built using a 6 m long reactor made of SUS 306 stainless steel with a 1.5 m diameter. The reactor body is well insulated to prevent any significant heat losses. A basic plan of the autothermal updraft gasification system is shown in Figure 2.

Fig. 2.

The updraft reactor is made of a cylindrical-conical reaction vessel. The fixed bed gasifier structure is cylindrical with a feed rate of about 500 kg h⁻¹. The biomass is conveyed from the main hopper to the upper part of the reactor using a motorised elevator and screw feeder. Fuel is admitted at the top with a screw conveyor and proceeds by gravity down through inside the unit.

For the start-up, primary air is used to light the biomass. Then, primary blower air is adjusted to maintain the desired temperatures. Once the preferred temperature of the reactor is achieved (about 900°C in that case), the moving grate is activated and frequency of the feeder is regulated to stabilise the feeding rate required for olive pomace. The produced syngas exits the reactor at around 350°C through the channel. Typically, it requires around 1 h or 2 h to stabilise the operating conditions with respect to gasifier temperatures. All parameters are kept constant for at least an hour for the analysis of the produced gas.

In the gasification (reduction) zone, with a high amount of thermal energy from the oxidation region below, a number of endothermic reactions take place between the gases and the char including steam, obtaining a large amount of H₂ and CO, along with CH₄ gases. For instance, the incandescent char in the gasification region reacts with CO₂ gas that should be in the temperature range of 700°C to 850°C and the char volume shrinks as it delivers C atoms to the CO₂ to convert CO.

In the partial oxidation region, the gasifying agent is provided at the bottom of the reactor and is dispersed via movable grates to the pyrolysed char. Not only the incandescent char but also pyrolytic products such as partially oxidised heavy hydrocarbons (tars) enter that region. The pyrolytic molecules oxidise in the gas phase to form CO₂ and H₂O. The thermal energy, which is transferred to and used in other regions, is supplied by the exothermic reactions in this oxidation zone. The temperature of the partial oxidation zone is between 900°C and 1100°C. The basic gasification process is described by the simplified chemical formulas in Table I (Equations (i)–(vii)) (24).

The moving grate inside the reactor is shown in Figure 3. Transferring the char is possible by agitating the grill. Char movement causes a loss in pressure over the char bed at this stage. When the pressure drop across the oxidation zone in the reactor exceeds a threshold, the system activates the moving grate. The ash and char are removed at the bottom of the reactor by a screw conveyor.

Fig. 3.

The gas generated in the reactor is then taken out from the top of the gasifier by repulse and pressure force of the induced draft (ID) fan and the forced draft (FD) fan. As the solid feedstock is transformed into gas, it conducts the remaining material to move through the reactor under a gravitational effect. The char residues formed during the process are automatically discharged into the char box by intermittently rotating the screw conveyor. The produced gas leaves the reactor at a temperature range of 250°C to 350°C. The produced gas is then flared at the well-designed burner and fed to a thermal oil boiler to generate 1.77 MWh of thermal energy. This thermal energy is transferred to the ORC turbine to generate 240 kW (15%) of electrical power. The excess 1.35 MWh useable thermal energy of waste heat from the system is used in the blackwater evaporation unit.

The whole system used in this study consists of an updraft gasifier reactor, hot gas cyclone, syngas burner, thermal oil heater and ORC turbine, ID stack fan, FD air fans and a stack which is illustrated in Figure 2.

Data obtained in gasification system experiments include the flow rates of feedstock and produced gas, produced gas compositions, temperatures, pressure throughout the operation line and electrical power generated in the ORC system. Every 15 s, a programmable logic controller (PLC) records all temperatures for air inlet, oxidation zone, reduction zone, pyrolytic zone, drying region, cyclone outlet and thermal oil boiler. The pressure drop is recorded at the top of the reactor and the cyclone outlet. The air flow rate is measured after the primary ID fan. The generated gas flow rate is calculated from the gas exit channel of the gasifier.

The produced gas exits the gasifier at around 250°C to 350°C. From one exit located at the top of the gasifier, product gas passes through the cyclone and then the thermal oil boiler. The produced gas exiting from the reactor includes some fine particulate matter passing through the cyclone which is used as a dedusting unit to separate these particles. The cyclone eliminates most of the fine particles and dust from the hot gas produced. The produced gas channels and cyclone are well insulated to prevent tar condensation. Afterwards, the produced hot gas is transferred to the syngas burner and combusted in a well-insulated thermal oil boiler. The pumps circulate thermal oil in the heated coils through the ORC turbine to generate 240 kW of electricity and excess heat is transferred to the evaporator units which vaporise the blackwater produced from the olive oil facility.

The main feedstock hopper and the screw conveyor are shown in Figure 2. The main biomass hopper, which has a volume of 3 m³ at the top of the reactor, is packed with the pelleted feedstock. A screw conveyor intermittently feeds pelleted olive pomace into the reactor at the upper part of the gasifier. A frequency converter can convert the needed amount of feedstock. The fuel flow out of the hopper interconnects with the entire reactor and the rotation speed of the drive motor.

A container with an elevator in the basement feeds the pelleted olive pomace to the main hopper. After this, the feedstock is fed into the main hopper where a screw conveyor feeds the biomass to the reactor. The main hopper system with a screw conveyor not only prevents air leakage to the reactor but also avoids gas leakage from the gasifier.

The gas sampling port is located at the syngas exit point between the cyclone and the gas burner of the system as shown in Figure 2. A portable Vario Plus (MRU, Germany) syngas analyser is used to measure the volumetric fractions of the main product gas components. After attaining a steady state condition, the product gas analyser is switched on. A heated probe is sucked into a small stream of the produced gas; then the gas is passed through a filter box filled with glass wool. The gas flows from bottles filled with water which act as a cooler; the cooled and clean product gases are analysed by the MRU syngas analyser. The volumetric fractions of the gas components (H₂, CO, CO₂, O₂, CH₄, ethylene (C₂H₄) and ethane (C₂H₆)) are measured on a data acquisition for a definite period during 24 h of process operation. During the plant operation, the composition of the gas is analysed and data are collected for about half an hour.

The gasification system is controlled by a PLC. The entire control strategy used in that research aims to generate a continuous syngas flow for the thermal oil boiler to produce thermal heat and transfer it to the ORC turbine. To achieve these tasks, the ID suction fan is functioned at a constant rate after reaching steady state. The algorithmic system is equipped with automatic security controllers and can be operated remotely.

Temperatures are recorded with an analogue-to-digital (ATD) converter. Four thermocouples are fixed to the internal refractory wall inside the gasifier, to prevent probable issues with the flow of feedstock while it is consumed. The thermocouples are located at corresponding positions along the vertical axis of the gasifier as shown in Figure 2. The temperature of the gas generated in the reactor is calculated at the outlet channel of the gasifier. Three digital manometers are used to calculate the pressure at different locations and to measure the pressure changes over the system. These fixed locations are the top of the gasifier and the channel between gasifier reactor and dust gas cyclone. The heat exchanger is used for the assessment of the waste heat remaining from the gas combusted in the thermal oil heater. The hot air generated from the heat exchanger is used as a gasifying agent and combustion air in the thermal oil heater. The flow rates of the gasifying agent, produced gas and combustion air used in the syngas burner flow rates are calculated by flow meters. The values are recorded every five seconds. These digital indicators are connected to the PLC system and a supervisory control and data acquisition (SCADA) computer for data retrieval.

FD and ID fans are placed in the system. One of these supplies gasifying agent from the bottom of the reactor through the gasification system generating the updraft effect. The other is located near the stack for a suction effect into the system so that the produced gas is pulled over from the gasifier, resulting in a slight pressure drop. Negative pressure is provided at the top of the reactor for safety reasons. The gasifying agent flow rate is controlled to keep the temperature of the oxidation zone between 900°C and 1200°C. As mentioned earlier, four thermocouples are located in the different reaction zones of the reactor to measure the temperature. In addition, there are nine thermocouples located at the cyclone gas inlet and outlet channels, the syngas burner, the thermal oil heater, the ORC turbine inlet and outlet, the combustion air inlet channel and the stack.

In the primary phase of the start-up, the gasifier was ignited with charcoal to reach the desired temperature for gasification. First, charcoal was ignited using a natural gas burner through the ignition point. The optimum amount of air supplied to the oxidation zone was regulated by FD and ID fans located at the inlet of the gasifier reactor and at the stack respectively.

The experimental conditions, energy and mass balance data are presented in Figure 4 for autothermal updraft air gasification. After layer embers in the gasifier were attained, feeding of the olive pomace pellets was started in the gasifier. From the bottom of the gasifier, at around 680 m³ h⁻¹, the gasifying agent air at maximum load was supplied to obtain the updraft effect in the reactor. When the temperature of the gasification region reached between 300°C and 400°C, biomass pellets were fed at 8.35 kg min⁻¹. During this period, 500 kg h⁻¹ feedstock was fed to the gasifier reactor forming around 5 m bed height. This was provided to reach the maximum, to keep the bed height steady during the operation modes of the gasifier. Air permitted to form 0.25 equivalence ratio was preserved throughout the updraft process. The gasifier temperature was stabilised by achieving steady-state conditions; then gas sampling was carried out to analyse the gas composition. The temperature and gas composition were measured during the gasification experiments until all of the material in the bed was gasified.

Fig. 4.

The pelleted olive pomace was gasified with the method described above. The method was repeated several times to attain reliable results. For power generation, the produced gas was passed through the combustion chamber of the syngas burner, which is placed at the top of the thermal oil boiler. Then, the burner increased the temperature of the thermal oil, the heated fluid was transferred to the ORC turbine to generate electricity and excess heat was passed through the blackwater evaporation units to vaporise the blackwater.

The operation of the gasification system could be portioned into three parts as described below.

For the initial application, an amount of charcoal is ignited by the natural gas burner from the oxidation region to warm up the system. Pre-weighed olive pomace biomass in the form of densified logs (pelleted) is charged through the main hopper from the top of the reactor. The maximum bed height level of the gasifier is determined by a mixer; then, the ID stack fan and FD fans are adjusted for the updraft gasification process. The start-up period comprises all operations needed until a steady state whereby the gas quality for the thermal oil boiler is reached.

The gasification system generally attains a steady state about an hour after the initial ignition. Afterward, the temperature of the oxidation region reaches between 900°C and 1200°C and the generated gas is ignited at the syngas burner. When the produced gas steadily burns in the syngas burner and thermal oil reaches 290°C, then the ORC turbine is started up to generate 240 kWh electrical power. The data collected during the steady operation of the gasification system are temperature and pressure, fuel-flow rate, gas composition and char rate. Temperatures were measured with an ATD converter every 15 s for oxidation zones, pyrolysis zone, drying zone, gasifier gas outlet, cyclone outlet, thermal oil boiler, thermal oil inlet and outlet and stack. Pressure data were collected at the gasifier gas outlet pipe, cyclone outlet and thermal oil boiler outlet channel. The flow rate of the produced gas was measured by carefully calibrated gas flow meters placed before the gas burner and cyclone outlet to measure the air flow from the inlet channels.

Lastly, the shutdown procedure refers to all operations needed to seal the gasification system safely. Gas suction ID and FD fans are turned off; gasifier inlet valves, outlet and stack gas valves are switched off in a systematic arrangement. The off-gas burner remains on as a secondary natural gas burner until no more product gas is generated.

The quality of syngas is affected by feedstock characteristics (water content, particle size and composition). Proper homogeneous feedstock size is an essential factor in generating better quality gas. Compared with small size feedstock, larger sizes produce lower quality syngas. However, feedstock which contains fine particles has low porosity in the reactor and as a result, tends to lead to higher pressure loss in the gasifier. Gasification of small size feedstock could lead to high pressure drop as well as excessive fine particle content in the produced gas. Also, inconvenient build-up issues arise in the reduction region of the gasification bed with small size and low-density feedstock.

Conversely, larger particle size feedstock decreases the reactivity of the fuel and triggers bridging and channelling obstacles that reduce the amount of gas produced. Feedstock size homogeneity also influences the operation performance of the reactor. The gasifier efficiency rises with increasing feedstock size homogeneity. For all these reasons, as shown in Figure 6, feedstock fuel is prepared by pelleting to fractions of the preferred particle diameter (d_p) (10 mm < d_p < 12 mm) with a bulk density of 589 kg m⁻³.

Fig. 6.

The water content in the feedstock also affects the quality of produced gas. Feedstock with lower water content produces better-quality product gas than that with a higher moisture content. The heating value of the produced gas can be influenced by the feedstock water content. Feedstock with high water content generates produced gas with low CV. Feedstock with higher than 30% water content reduces the CV of the produced gas due to low heat transfer to the endothermic pyrolysis zone reactions during the gasification process. More of the heat is absorbed by biomass to evaporate water in the drying process. Thus, the heat required in the pyrolysis zone for reactions is insufficient.

For this reason, biomass with high moisture content (>30%) must be dried during feedstock fuel preparation before the gasification process. Prior to the gasification experimental tests, raw olive pomace with an original moisture content of 60% by weight was dried and then pelleted during the feedstock preparation process at 105°C for 6 h. Proximate, final analysis and gross CV (GCV) of olive pomace sample results are compared with oak woodchips and presented in Table II. Proximate analysis supplies the composition of a substance in terms of FC, moisture, ash and VM as well as GCV. The ultimate analysis provides elemental compositions containing C, H, sulfur, N, O and moisture. Absolute and bulk densities of both olive pomace and woodchips are shown in Table II for comparison.

The GCV (also known as HHV) based on the ultimate analysis was derived using the Institute of Gas Technology (IGT) method, as shown in Equation (viii) (25):

(viii)

The HHV of olive pomace is theoretically calculated as 17.65 MJ kg⁻¹ (IGT method formula). In wt%: C = carbon; N = nitrogen; H = hydrogen; O = oxygen; S = sulfur; A = ash and M = moisture content of olive pomace.

To generate thermochemical conversion systems such as gasification reactors, determination of the LHV rather than the HHV of fuel in the calculation is more effective. The water heat of vaporisation and the moisture content of the feedstock can be overlooked as these do not contribute any CV to the biomass.

A method of relating HHV to LHV is shown in Equation (ix) (8):

(ix)

where the LHV of olive pomace is theoretically calculated as 17.48 MJ kg⁻¹.

Standard biomass feedstock for gasification has LHVs of around 15–17 MJ kg⁻¹; woody feedstock that has been the conventional fuel for gasification systems has HHV in the range of 17–21 MJ kg⁻¹. The LHV that was calculated as 17.48 MJ kg⁻¹ for olive pomace demonstrated that this feedstock is suitable for gasification in terms of CV equivalent to woodchips.

The feedstock moisture content greatly affects both the quality of the produced gas and the operational parameters of the gasifier. Excessive water in the feedstock drops the operational temperature of the reactor and that leads to long chain hydrocarbons in the form of heavy tars in the produced gas leaving the reactor. The water content of the feedstock specifies the type of gasifier design that is used. Higher moisture contents of biomass feedstocks are accepted for updraft reactors.

The absolute and bulk density of biomass is essential for process design, handling and storage. Biomass with lower bulk densities frequently causes deficient current under the gravitational force that leads to insufficient gas CV and char burnouts in the gasification region. However, biomass with higher bulk densities requires lesser reactor vessels for a definite refuelling time. The bulk density of olive pomace is higher than that of woodchips (589 kg m⁻³) and the experiments verified that there was minimum char burnout that appeared in the reduction region. Due to minimised reactor dimensions and the feeding charge capability of the gasifier, the feedstock is compressed in the form of pellets. Figure 7 presents the pelleted feedstock (10–12 mm diameter): olive pomace obtained from Marmarabirlik’s olive oil facility was used in this pilot-scale system. Commonly, pellets are produced by pressing the pomace under high pressures using standard compress equipment. Intensification of fuel could decrease the space occupied by the feedstock in the reactor. Fuel intensification has some advantages such as reduction of gasifier dimensions, convenience of feedstock management and inhibiting dust exposure. Pellets that have uniform dimensions enable identical flow by gravitational force and homogeneous pellets create a uniform void field in the gasifier which avoids channelling throughout the gasification section.

Fig. 7.

Gasification characteristics of pelleted olive pomace obtained during three runs are presented in Table III and compared with oak woodchips. Table III also illustrates the different flow rates that cause different characteristics of produced gas. The equivalence ratio and air intake of the gasifier are also shown.

During Run 1, the gasifier reactor operated at the lowest quantity of gasifying agent. Therefore, the reaction slowed and feed consumption decreased. In Run 2, the gasifier operated at 300 kg h⁻¹ (half the capacity) and the quality of the gas slightly improved. However, in Run 3, the gasifier operated at maximum capacity (500 kg h⁻¹) and the syngas CV was highest. Therefore, it is understood that the gasifier reached its maximum efficiency at the highest load.

During the pilot-scale updraft reactor operations, produced gas was taken by a probe and analysed externally. The analysis results during steady state conditions are given in Table III and graphical results are illustrated in Figure 8. Measured compositions show CO in the range of 23 ± 1%, H₂ 7 ± 2%, CH₄ 3.5 ± 0.8%, CO₂ 10 ± 2% and the balance N₂. During steady state operating conditions, power generation at 240 kW was continuously observed via the ORC turbine with the pelleted olive pomace whose moisture content was around 25 wt%. Gas with a typical LHV of 5.0–6.5 MJ Nm⁻³ was generated in the reactor. The characteristics of the syngas composition are presented in Table III.

Fig. 8.

Alternatively, the CV of the gas can be calculated using Equation (x) (8):

(x)

where X_H2, X_CO and X_CH4 are the mole fractions of the main combustible gases, H₂, CO and CH₄ respectively.

Figure 8 illustrates the composition of the syngas generated by the gasification reactor in Run 3. During that run, the composition of produced gas was quite stable, so the ORC turbine operated smoothly and stably. The flow rate of the gas produced by the gasifier in steady state operation is between 270 Nm³ h⁻¹ and 1251 Nm³ h⁻¹. In the operational run, the hot gas generated had an average LHV of 6.30 MJ Nm⁻³ and the gas was subsequently used in a thermal oil boiler to run the ORC turbine. The turbine is designed for the conversion of 1.36 MWh thermal energy input to 240 kW electricity power output, which means 15% efficiency of electricity generation. The gasifier was operated with a turndown ratio of around 5:1 and syngas generation was stable enough to operate the ORC turbine. Water was used for the turbine cooling system; the input temperature of the 50 m³ circulating water was 60°C and the output temperature was 90°C. The hot water obtained from the waste heat of the ORC turbine was used within the facility for the blackwater evaporation unit.

The performed runs indicated that the particle size and shape of the pelleted olive pomace significantly affect gasifier operation. Therefore, pelleted feedstock of size 12 mm × 50 mm was selected and used in the reactor. Referring to the size of the gasifier, it is assumed that there is an upper limit for the particle size of 12 mm. This feedstock size is optimised for smooth movement and to prevent bridge formation inside the gasifier.

The design and actuation system of the grate is important to discharge the char in the gasification operation. The amount of char byproduct from olive pomace feedstock gasification was observed to be quite low. Hence, it can be discharged from the gasifier less often, without interfering with the continuous production of high-quality syngas. A small amount of ash in olive pomace is beneficial to forestall probable clinker agglomeration in the gasifier owing to higher operation temperatures in the reactor. Clinker agglomeration could cause bridging and channelling problems on the grate just below the oxidation region. This can block the grate operation and high pressure drops can occur in the zone. Consequently, the feedstock characteristics, fuel preparation and sizing, gasifier design and operation parameters are all critical and interdependent factors and need to be carefully evaluated to avoid these problems. In this pilot-scale system, all these features were evaluated and the operation was terminated without any problems.

Determination and evaluation of the energy and mass balance of the system are essential to reveal the energy production potential of the autothermal updraft gasifier from pelleted olive pomace feedstock. The calculation of the energy and mass balance for the gasification system constitutes a significant factor in establishing the efficiency of conversion of feedstock to the product gas and the determination of energy production. The determination of the energy and mass balance varies according to the type and characterisation of the feedstock and the differences between the thermodynamic equilibrium and reaction kinetics and the three-reaction equilibrium that is essential in the gasification as specified in the introduction section. It may also be changed according to the type and operation of the gasifier reactor.

The energy and mass balance calculations on the process need an assessment of the inputs to and outputs from the reactor. To verify the mass and energy balance outputs, the results obtained from the olive pomace analyses, the fixed bed updraft gasifier capacity, the thermal oil boiler and the ORC turbine efficiency were determined and calculated. There are difficulties in getting 100% closure and obtaining these data. Nevertheless, the average energy balance closeness for three experimental runs was detected to be 96%, indicating a reasonable figure for the initial demonstration of olive waste gasification. The schematic diagram shown in Figure 4 is the energy and mass balance of the pelleted olive pomace as biomass feedstock in the gasification process. According to the energy and mass balance diagram, 500 kg h⁻¹ of olive pomace is used. It has 17.65 MJ kg⁻¹ chemical energy according to the fuel characteristic analysis. Pelleted olive pomace has 25% moisture. The net energy value of the 500 kg h⁻¹ fuel fed to the reactor is 7881 MJ (2189 kWh). As stated in the literature for the updraft gasifier, the air:fuel ratio is determined to be approximately 1 kg of fuel to 1.6 kg air flow rate (1:1.6) (10). For the autothermal gasifier, 1% heat loss can be estimated (79 MJ). Depending on the feedstock, gasifier output char is about 17% of the fuel input. Thus, in this process, 89 kg h⁻¹ of biochar is produced, the equivalent heat is 1043 MJ (290 kWh).

In the updraft gasifier, the ratio by mass of the feedstock and produced gas after gasification is approximately 1:2.5 and the volumetric flow rate of the product gas is 1251 Nm³ h⁻¹. When the density of the syngas is about 1.18 kg Nm⁻³, the production of hot gas is 1475 kg h⁻¹. Assuming that the temperature of produced gas is 350°C, the volumetric flow rate of syngas at this temperature is calculated as 2660 m³ h⁻¹. If the heat losses are calculated, the energy of produced gas at 350°C is 1878 kWh (6760 MJ). The hot product gas is transferred to the syngas burner when gas is combusted in the thermal oil heater; the boiler thermal energy is calculated as 1596 kWh with 10% heat loss. This thermal energy produced is transmitted to the ORC turbine with thermal oil circulation; heat loss is not calculated because it has sufficient insulation. Since the ORC turbine efficiency is 15%, the turbine generates 240 kW gross, 221 kW net electrical power as 8% parasitic load is internally consumed. The ORC turbine also produces 1356 kWh of thermal energy in the form of waste heat. After the gasification of the gas produced in the boiler and the heated thermal oil in the ORC turbine is transformed into electricity and waste heat energy, thermal power can operate the blackwater evaporation system.

Blackwater produced in the production of olive oil is an environmental problem. Work continues on the vaporisation of this blackwater using excess heat with an evaporation system. In this study, the remaining solid substance from blackwater vaporisation will be used in the gasification system as a feedstock by mixing with olive pomace biomass. In future studies, the produced steam will be converted to a superheated gasification agent in the reactor. Thus, the produced thermal power can also be evaluated efficiently within the facility.

Figure 9 shows the temperature profiles of the oxidation, reduction, pyrolysis and drying zones in the updraft gasifier observed during 8 h of continuous operation in the test runs. In general, there is only a small dependence on the feed rate. However, the variation is more pronounced at the gasifier outlet, which could be due to variations in the aeration rates, especially at higher throughputs. However, as the air to fuel ratio increases, the zone temperatures increase. Because of very high temperatures around the moving grate zone (>1000°C), some forms of clinker were observed over the grate during the clean-up. In the literature, studies seem to have reached a consensus about the temperature (>1100°C) in the oxidation zone of an updraft gasifier (8). Reasonable residence time is necessary to destroy the refractory unsubstituted aromatics (tars) in the product gas, without catalytic tar cracking.

Fig. 9.

Therefore, the optimum operating temperature should be adjusted for each different fuel used in the reactor by considering tar cracking versus clinker formation. Obviously, ash fusion temperatures of the fuels are decisive in selecting operating temperatures of an updraft gasifier. Clinker formation has a more significant impact than tar formation, which can be easily treated by improving the clean-up of the system. Although tar formation above 900°C is small, the benefit of reducing clinker is substantial for the operation of the gasifier.

The operation parameters of the process are 1600 kWh energy generated in the thermal boiler as a result of burning syngas is transferred to 60 m³ h⁻¹ Therminol^® 66 (Eastman, USA) fluids. Therminol^® 66 is a high performance, highly stable synthetic heat transfer fluid. The chemical composition of this fluid was carefully selected to minimise the formation of low boilers and eliminate the risk of insoluble high boiler formation and fouling, provided proper attention is given to system design and operation is within the maximum bulk (345°C) and film (375°C) temperatures. To calculate the physical properties of the fluid such as density, heat capacity, thermal conductivity, kinematic viscosity and vapour pressure, formulas are given below (Equations (xi)–(xv)) (26):

(xi)

(xii)

(xiii)

(xiv)

(xv)

According to these formulas, at 280°C physical properties of the fluid are 824.6 kg m⁻³ (density), 0.097 W m⁻¹ K⁻¹ (thermal conductivity), 2.492 kJ kg⁻¹ K⁻¹ (heat capacity), 0.56 mm² s⁻¹ (kinematic viscosity) and 19.46 kPa (absolute vapour pressure).

Niall McEvoy’s research is primarily focused on the synthesis and characterisation of nanomaterials, particularly two-dimensional (2D) materials, and their subsequent assessment for use in a wide array of applications. A key aspect of this work involves developing and refining industry-relevant synthesis protocols for emerging 2D materials. One potentially industry-compatible way to produce these materials is using vapour-phase methodologies, such as chemical vapour deposition (CVD). Identifying 2D materials that can be synthesised at relatively low temperatures is vital if these materials are to be considered for real-world applications. Vapour-phase-grown 2D materials are of interest for diverse fields, in areas such as electronics, optoelectronics, telecommunications, sensing of analytes, detection and measurement of strain and catalysis. The innovative potential of these materials has led to considerable interest and investment from private enterprise, particularly in the information and communication technology sector.

McEvoy leads the Architecture and Synthesis of Integrated Nanostructures (ASIN) group at Trinity College Dublin, Ireland. He is a funded investigator in the Advanced Materials and BioEngineering Research Centre (AMBER), also at Trinity College Dublin. He has co-authored over 100 peer-reviewed articles in the area of nanomaterials. His group benefits from an extensive research network involving active collaborations with research groups in Ireland, the UK, China, Germany, Italy, Austria, Switzerland and Denmark. He was the recipient of a Science Foundation Ireland Technology Innovation Development Award in 2015 and a Starting Investigator Research Grant in 2016.

Since the isolation of graphene in 2004, research has unveiled the ever more impressive and diverse properties of 2D materials, prompting them to be linked with use in an increasing array of applications. While the properties of 2D materials are certainly revolutionary, and the associated physics and chemistry indeed very exciting, the hype surrounding the field should to some extent be tempered by practical considerations of how best they should be fabricated and subsequently processed. Many of the experimental reports on their properties have used materials prepared by mechanical exfoliation, a laborious, serendipitous and inherently unscalable production technique.

Efforts to improve the scalability of 2D materials production broadly fall into two approaches: liquid-phase exfoliation, a top-down method; and vapour-phase growth, a bottom-up method. Enormous progress has been made in these fields in recent years. The scalable production of 2D material dispersions by shear exfoliation was reported by Professor Coleman’s group at Trinity College Dublin (1). On the vapour-phase growth front, recent reports from researchers in Interuniversity Microelectronics Centre (IMEC), Belgium have demonstrated wafer-scale growth of the 2D material tungsten disulfide (WS₂) in a semiconductor fabrication setting (2).

Much of the research undertaken by the ASIN group is centred on developing sensible and scalable vapour-phase growth approaches for the synthesis of 2D materials. Particular focus has been placed on developing growth recipes for less-commonly studied 2D materials, for instance those whose bulk form is not naturally abundant. A recent example of the group’s research efforts is the vapour-phase growth of platinum diselenide (PtSe₂). PtSe₂ can be found in nature in the form of the mineral sudovikovite but this is quite rare. In its 2D form PtSe₂ benefits from a high charge-carrier mobility, good stability in ambient conditions, a thickness-dependent band structure and promising electrocatalytic behaviour. McEvoy and coworkers developed a simple, but robust, vapour-phase process for the growth of thin films of PtSe₂ (Figure 1(a)). The relatively low growth temperatures involved (~400°C) mean that the material could potentially be integrated with back-end-of-line processing in the semiconductor industry (3).

Fig. 1.

The PtSe₂ films grown in this manner have shown very promising results in laboratory-based prototype devices. Like other 2D materials, PtSe₂ possesses a near ideal surface-area to volume ratio which is in part responsible for its impressive performance in gas-sensing devices (4). The relatively low growth temperature means that PtSe₂ can be grown directly on flexible polymer substrates (5). These polymer/PtSe₂ films show a piezoresistive effect (Figure 1(b)), i.e. when they are bent the resistivity changes, suggesting potential use as gauges to monitor strain. PtSe₂ films grown in the ASIN laboratory have also shown promise for use in photodetectors (6), transistors (7) and pressure sensors (8).

Other ongoing projects in the ASIN group are focused on CVD synthesis of 2D material heterostructures, synthesis and electrochemical applications of transition metal ditellurides, tailored functionalisation of 2D materials, resistive switching in 2D materials and scanning-probe studies of defects in 2D materials.