In the 21st century, there is a need to deal with threats such as energy scarcity and environmental deterioration. The worldwide usage of fossil fuels accounted for 84.7% of global energy consumption in 2018, which is equivalent to 11.7436 billion tonnes of oil (1–2). Global CO₂ emissions, especially from fossil fuels, will continue to grow rapidly. It is crucial to explore green and renewable energy systems, such as wind, tidal and solar energy, and energy storage such as batteries, to replace fossil fuels. LIBs, with excellent energy storage properties, safety and stability, are among the most promising clean and sustainable energy storage equipment. LIBs are widely used in zero-emission vehicles (mainly electric vehicles (EVs), and plug‐in hybrid electric vehicles (PHEVs)), computers and electronic communication devices (3–6). The newly emerging model for super-performance LIBs, assembled with specific complex three-dimensional (3D), porous and polyhedral geometric structures in cathode and anode materials, is perfectly suited to the scale-up requirements for zero-emission vehicles (3–5). Increasing demand for new energy vehicles contributes to the expansion of the LIBs market. It has been estimated that the world’s production of LIBs would increase by 520% from 2016 to 2020 (7), and about 50 million electric buses will run on the road by the end of 2027 (8). Therefore the number of expired LIBs, as major electronic wastes, will inevitably increase. In China, the weight of retired LIBs was predicted to reach 500,000 tonnes by the end of 2020 (9), and that of the European Union to reach 13,828 tonnes in 2020 (10). Since harmful substances may damage the environment and the metals contained in spent LIBs are precious resources, the recovery of retired LIBs is bound to gain considerable social, economic and environmental benefit.

The cathodes of retired LIBs are rich in valuable nonferrous metals such as lithium, nickel, cobalt and manganese, which are secondary resources worth recycling. Considering potential immense profit, researchers have been working hard to develop various technologies to recycle the metals in spent LIBs. Current recycling technologies mainly include pyrometallurgy and hydrometallurgy. Although pyrometallurgical recovery processes have the advantages of a short process, high efficiency and easy industrial application, high energy consumption and generation of toxic gases still limit their development (11–14). In contrast, hydrometallurgical recovery processes have attracted extensive attention due to low energy consumption, environmental friendliness and high recovery efficiency (15–19).

Leaching valuable elements by chemical reagents is the core of the hydrometallurgical recovery strategy. After leaching, valuable metals in the leachate are extracted by chemical precipitation, solvent extraction and ion-exchange (20–25). The conventional hydrometallurgical process for waste electrodes recovery can be illustrated as follows: (a) acid-reductant leaching and selective chemical precipitation; (b) sulfuric roasting, acid leaching and selective chemical precipitation; (c) mechanochemical activation leaching and selective chemical precipitation, as shown in Table I (26–34). However, sulfuric roasting or mechanochemical activation before leaching may complicate the recovery process and decrease overall leaching rate. Based on the principle: waste substance + waste substance → rebirth resources, Li et al. (35) developed an in situ recovery model for graphite, Li₂CO₃ and cobalt by oxygen-free roasting with an anode graphite + wet magnetic separation process, without pretreating or adding any other chemical reagents. However, this technique is not suitable for recovering complex electrode materials. Prabaharan et al. (36) investigated an electrochemical leaching system: lead as anode + electrode scraps as cathode + H₂SO₄ as leachate, in which the leaching rate of manganese, copper and cobalt exceeded 96% through adjusting pH value. Although it can perfectly achieve integrated recovery of valuable metals, the high electricity consumption and recovery cost limit the use of this method. Surprisingly, Gomaa et al. (37, 38) explored a new multifunctional recovery method to recycle ultratrace Co²⁺ (~3.05 × 10^–8 M and 4.7 × 10^–8 M respectively) with visible selective ion extraction-separation-detection. It can extract almost 100% Co²⁺ from leachate in only 10–15 min and the used ion-extractors after activation can be regenerated and reused in repeated adsorption-desorption processes. Following desorption by HCl eluting reagent, the overall recovery rates of cobalt from waste LIBs or printed circuit boards (PCBs) are 98% and 95.7% respectively.

NCM batteries consist of more valuable metals that are worth recycling compared with traditional LiCoO₂ and LiFePO₄ batteries. However, few reports have been made to systematically clarify recovery techniques for waste NCM materials. In order to avoid loss of valuable resources and risk of secondary pollution, it is urgent to construct a sustainable recycling model for valuable metals in cathodes of spent LIBs. This review aims to describe progress in hydrometallurgical recycling of cathode materials from spent NCM batteries. The hydrometallurgical recovery strategy of waste LIBs can be classified into three steps: (a) pretreatment or separation of active substances; (b) leaching or extracting the valuable metals from the active substances with appropriate solvents; (c) separation of valuable metals by selective extraction from leachate by different methods to obtain the metals or metallic compounds. The conventional process flow for recycling NCM materials from waste LIBs by hydrometallurgy is shown in Figure 1. The advantages, disadvantages, existing problems and current status of each treatment method are analysed. Furthermore, advanced recovery technologies, DESs extraction and crystal repair direct-regeneration/recovery technology are illustrated. The challenges and prospects for metals recovery from ternary cathode materials of used LIBs by hydrometallurgy are described. By comparing the advantages and disadvantages of different methods, it is expected that this information will contribute to exploring economic, green, sustainable, high-efficiency leaching, separation and regeneration recovery systems for closed-circuit recycling of LIBs.

Fig. 1

The LIBs are mainly divided into five types depending on the composition of cathode materials: lithium iron phosphate (LiFePO₄), lithium cobalt oxide (LiCoO₂), lithium manganese oxide (LiMn₂O₄), lithium nickel oxide (LiNiO₂) and lithium NCM oxide (LiNi_x Co_y Mn_{1–x –y} O₂,0<x +y <1) (39–43). Of these, layered LiNi_x Co_y Mn_{1–x –y} O₂(NCM) materials are preferred because of their excellent cycle and rate performance. Currently, there are five types of NCM cathode materials that have been commercialised: NCM333 (LiNi_1/3Co_1/3Mn_1/3O₂), NCM424 (LiNi_0.4Co_0.2Mn_0.4O₂), NCM523 (LiNi_0.5Co_0.2Mn_0.3O₂), NCM622 (LiNi_0.6Co_0.2Mn_0.2O₂) and NCM811 (LiNi_0.8Co_0.1Mn_0.1O₂) (44). It is predicted that NCM batteries will account for nearly 41% of the world’s LIBs market in 2025 (42). The chemical characteristics and potential hazards of each component in ternary LIBs are summarised in Table II (45–48). NCM cathode materials are rich in lithium, nickel, cobalt, manganese and other strategic essential metals. The heavy metals in waste LIBs will pose a huge threat to human health and the environment (49). Furthermore, the scarcity and high cost of nonferrous metals such as cobalt make it imperative to recycle ternary cathode materials from retired LIBs. It is also reported that the global lithium resource reserves in 2018 are over 62 million tonnes, but China only accounts for 7%, about 4.5 million tonnes (50). As a core raw material for LIBs, the value of cobalt is as high as AUD$115,000 per tonne (51, 52). Efficient extraction of these valuable nonferrous metals from retired LIBs is of great significance for green and sustainable development of the batteries industry.

3.1 Recovery Process

3.2 Recovery Process of Hydrometallurgical Method

4.1 Regeneration of Nickel Cobalt Manganese from Leachate

Sustainable recovery and regeneration of NCM materials from leaching solution is the current main trend for recovery of exhausted LIBs. Common methods to synthesise cathode materials include chemical coprecipitation, high temperature solid phase, hydrothermal synthesis, sol-gel, microwave synthesis and electrostatic spinning method (95–100). The parameters of resynthesis methods to regenerate NCM cathode materials from leachate are summarised in Table VII (61, 101–107).

NCM cathode materials regenerated by a hydrometallurgical process hold superior crystal morphology and rate/cycle performance. Their electrochemical performance is comparable to that of commercial NCM materials (102, 104). The conventional hydrometallurgical regeneration process to prepare NCM materials is as follows: leaching liquid → coprecipitation → solid phase synthesis at high temperature, as shown in Figure 6. In the regeneration process, appropriate amounts of cobalt salts, manganese salts, nickel salts and lithium salts are added according to the stoichiometric composition of NCM materials.

Fig. 6

Regeneration flow of LiNi_1/3Co_1/3Mn_1/3O₂ cathode material from leaching liquid by coprecipitation

Li et al. (108) resynthesised NCM333 (R-NCM) materials from spent cathode materials leaching liquids by a one-step sol-gel method, and the related details and recycling mechanism are shown in Figures 7 and 8 (108). In the recovery system, H₂O₂ is used as reducing agent, and lactic acid plays the role of leaching agent in the leaching stage and chelating agent in the regeneration stage respectively. The mechanism of leaching stage can be shown using Equation (i):

(i)

Fig. 7

Resynthesis flow of LiNi_1/3Co_1/3Mn_1/3O₂ cathode material from leachate of spent LIBs by sol-gel method

Fig. 8

(a) Possible products and mechanism in the lactic acid leaching process; (b) XRD and SEM patterns of R-NCM and F-NCM samples; (c) electrochemical performances of R-NCM and F-NCM samples: charge/discharge profiles at 0.2 C; (d) cycling performances at 1 C; (e) rate performances at different currents and (f) Nyquist plots in the frequency range of 100 kHz to 0.01 Hz. Reprinted with permission from (108). Copyright (2017) American Chemical Society

Under optimal conditions (lactic acid = 1.5 M, s/d = 20 g l^–1, H₂O₂ = 0.5 vol%, temperature = 343 K, where s/d means solid/liquid (S/L) ratio) the leaching efficiency of the metals reached 98%. Electrochemical results proved that the R-NCM cathode material held excellent reversible discharge capacity of 138.2 mAh g^–1 while capacity retention reached 96% at 0.5 C after 100 cycles (105). Due to low charge-transfer resistance (R_ct) of R-NCM (58.78 Ω) compared with fresh NCM333 (F-NCM, R_ct = 70.02 Ω), which can provide higher lithium ion diffusion coefficient (D_Li⁺) and faster lithium-ion intercalation/deintercalation kinetic properties, the electrochemical performance, especially cycle capability, of R-NCM is self-evidently higher than that of F-NCM. It is indicated that the chelating agent lactic acid can efficiently recycle and resynthesise NCM materials by a sol-gel method with closed-loop recovery process.

4.2 Integrated High Value Utilisation of Electrode Scraps

4.3 Potential and Challenges of Mediate/Direct Regeneration Method

5.1 Extractive Methods Based on Deep Eutectic Solvents

Compared with traditional extractants, DESs have the advantages of being non-toxic, cheap, biodegradable and possessing extremely high dissolution and reduction capability for metal oxides (114, 115). However, there are few reports on recycling electronic waste using DESs. Tran et al. (116) first applied DESs to recycle cathode materials from waste LIBs. The adopted DESs were synthesised by choline chloride and ethylene glycol, and the possible synthesis reaction is shown in Equation (ii):

(ii)

The aluminium foil and binder PVDF were recovered respectively while valuable metals were extracted by DESs. The results showed that the leaching rate of cobalt in LiCoO₂ cathode material was as high as 99.4% at 180ºC, which was comparable to that of traditional leaching agents such as sulfuric acid (99.70%) (70) and formic acid (99.96%) (72). Cobalt in the DESs liquids can be recovered as Co₃O₄ by electrodeposition and calcination.

Wang et al. (117) developed a recycling process using DESs to detach active substances and aluminium foil of retired LIBs, as the recovery flow shown in Figure 10. The specific synthesis reaction of DESs is shown in Equation (iii):

(iii)

Fig. 10

The separation of cathode materials and aluminium foil from spent LIBs by using choline chloride-glycerol DESs

The DESs attacked the hydrogen atoms in PVDF molecular chain, forming unsaturated double bonds, which were further oxidised into hydroxyl and carbonyl to construct an unsaturated ketone structure on the molecular chain. Therefore, PVDF was forced to deactivate and dissolve, the active materials and aluminium foil were separated successfully. Hence, it can be indicated that DESs have great potential in regenerating cathode materials. High selectivity of valuable metals is the key to promote leaching performance of DESs in recycling spent LIBs.

5.2 Nickel Cobalt Manganese Particles Recovery

Despite high recovery rates of R-NCM from leaching solution, electrochemical performance is damaged to some extent due to inevitable contact with organic solvent during the treatment process. In the future, recovery technologies with low efficiency must be avoided. Sieber et al. (118) directly recovered NCM particles from used cathode scraps. The active material was detached from the aluminium foil by strong stirring in water to maintain the electrochemical performance and morphological characteristics of NCM particles. However, during the separation process, side reactions occurred when the NCM cathode contacted with water, resulting in a rapid increase in pH value and degradation of NCM particles followed by forming of Al(OH)₃ precipitate and LiAl(OH)₄. The main chemical reactions during separation stage are as follows (Equations (iv–vii)):

(iv)

(v)

(vi)

(vii)

Such side effects can be avoided by adjusting pH value with superior buffer solution, aiming to detach active materials from aluminium foil and hinder degradation of NMC particles. Under buffer solution control, the aluminium foil was successfully dissolved into solution while NCM particles were well-preserved.

The waste cathode materials in spent LIBs contain valuable metals, which can be recycled by a hydrometallurgical process which offers low energy consumption, environmentally friendly and excellent recovery efficiency. Based on the present analysis and summary of hydrometallurgical recycling processes, the following conclusions are drawn. The following flow is considered the most competitive process to recycle valuable metals from waste cathode materials at industrial scale: alkaline solution dissolution/calcination treatment → reductant (H₂O₂) → sulfuric acid leaching → coprecipitation → resynthesis at high temperature. The leaching capability of organic acid with reducibility is greater than that of inorganic acid leaching united with reductants. Bacterial leaching shows high selectivity for lithium, which can be selected to recycle high purity lithium-containing products. A combination of processes for recovering valuable metals may complement each other, which contributes to improve the quality of the recycled products. The DESs separation technology can achieve efficient overall recovery of valuable components from spent LIBs, which is conducive to sustainable development. The highly efficient direct-regeneration or direct-recovery of NCM materials will face huge challenges and potential. In the future, both will become research hotspots in the field of spent LIBs recovery.

In 2011 the European Commission presented “A Roadmap for Moving to a Competitive Low Carbon Economy in 2050” outlining the milestones, among them also the 83–87% CO₂ reduction of the industry sector (1, 2). Primary steel production via the blast furnace/basic oxygen furnace (BF/BOF) route or so-called integrated steel plant, is a predominant and well-established process, contributing 70.8% of the world’s 1807 million tonnes of crude steel production in 2018 (3). The reduction process of the iron ore to crude steel is linked to CO₂ emissions, resulting in 2016 for a total of 7% (160 million tonnes of CO_2eq) of EU-28’s greenhouse gas emissions (4). The energy efficiency potential of a modern integrated steel plant has already been exploited to a great extent through conventional process optimisations. It is, therefore, necessary to transfer steel production to climate-friendly processes through new and innovative approaches. Hydrogen-based direct reduction processes and electrolytic reduction methods are alternatives for the reduction of iron ore, but they require on the one hand huge amounts of renewable energy, for instance for green hydrogen production in water electrolysis, and cause on the other hand significant investment demand as the existing production infrastructure has to be replaced (5). Carbon capture and utilisation (CCU) processes are a second option which are near-term actionable, as they can be added to the existing infrastructure without a significant change in the steel production itself (6). The first step of a CCU process chain is the energy intensive separation of CO₂ from diluted exhaust or process gases. If these gases also contain carbon monoxide, as is the case in the steel industry (Table I), carbon monoxide is avoided of utilisation since the carbon capture processes selectively separate CO₂ (8). The separated CO₂ is then either biologically or catalytically converted to usable products (6).

The process gases in a steel plant, BFG, BOFG and COG, contain high shares of carbon monoxide and CO₂ (Table I). These low-calorific gases are currently utilised in an integrated steel plant as an energy carrier, i.e. in heating processes, and as fuel in the power plant. In Figure 1 the energy flows in an integrated steel plant are depicted. The main part of the BFG (white letter A in Figure 1) is directed to the enrichment process where it is mixed with BOFG (white letter B in Figure 1). The main share of the enriched gas fuels the power plant. COG (white letter C in Figure 1) is mainly used in internal processes and in the power plant as well. The power plant covers almost the total electricity demand of the steel mill. NG (white letter D in Figure 1) is used for heating in downstream processes, like in the hot strip mill, and in the power plant as well. The quantity of the process gases BFG, BOFG and COG covers up to 40% (9) of the steel plant’s energy demand, where the remaining part is provided by electrical energy and fossil fuels, like NG.

Fig. 1

The Sankey diagram of Figure 2 indicates the composition of the different byproduct gases and their internal use. It is obvious that a withdrawal of these low-calorific gases has to be compensated by the supply of other energy carriers, either synthetic natural gas (SNG) or external electricity, since the main part of the gases are used in the power plant downstream of the enrichment process. In the enrichment process, the byproduct gases are mixed and buffered in gasometers.

Fig. 2

However, due to their high carbon monoxide and CO₂ concentrations, the process gases may be conceived for further use as a carbon source for catalytic conversions. As summarised in the review from Frey et al. (10) the alternative utilisation of steel plant process gases for the production of ammonia, methanol or recovery of its derivatives has already been under examination since the early 1950s. The latest review from Uribe-Soto et al. (11) outlined three alternatives: (a) thermal use of the process gases (state-of-the-art); (b) recovery of the valuable compounds (hydrogen, methane and carbon monoxide) via different separation technologies; and (c) thermochemical synthesis to high-added value products (methanol, dimethyl ether, urea), with the focus on the latter. The consideration of their utilisation as a potential carbon source and coupling it with renewable energy within the context of power-to-X technology, has gained attention in the last years especially in Europe, resulting in various theoretical studies as well as research projects. The largest German steel producer, thyssenkrupp, is leading the “Carbon2Chem” project (12), where different scenarios for the synthesis of methanol (13, 14), ammonia or urea (15), as well as higher alcohols and polymers (16) from BFG, COG and BOFG are being investigated. In an ongoing research project the possibilities of converting BOFG and BFG into methanol and methane are explored under dynamic conditions (17, 18).

All studies referenced above utilise pure CO₂ which is separated in a first step from the process gases, and is subsequently converted with hydrogen in a catalytic synthesis. In this study, the catalytic conversion of BFG and BOFG to methane is investigated without a prior separation of CO₂. The process gases of the steel plant are only pre-cleaned upstream of the catalytic conversion by dust removal (i.e. by a venturi scrubber and a bag house filter) and separation of the sulfur compounds sulfur dioxide, carbonyl sulfide, mercaptans (i.e. in a series of two adsorbers in order to remove the catalyst poisons), see Figure 3. Consequently, the catalytic conversion is carried out with significant shares of nitrogen in the feed gas. Therefore, the following advantages arise:

Fig. 3

The aim of the present study is the assessment of different process chains for the direct utilisation of BFG and BOFG in catalytic methanation without a prior CO₂ separation. Since the conversion of CO₂ and carbon monoxide to methane requires renewable hydrogen, the hydrogen supply is ensured by a water electrolysis powered by renewable electricity (P2G plant) as well as by an additional biomass gasification plant (Figure 3). Therefore, a variety of possible implementation scenarios arise, and the following fundamental research questions have to be answered:

A withdrawal of process gases, particularly of the comparatively high calorific COG, would result in a shortage of internal energy supply in the integrated steel plant (Figure 1) which has to be substituted by NG or electric energy sourced externally. Therefore, in order to avoid significant changes of the existing steel production infrastructure, in this study COG was not considered, and BFG as well as BOFG are solely used as a carbon source for a potential utilisation process (Question 1(a)). Furthermore, it has been deliberately decided that the product gas from the methanation, nitrogen diluted SNG, substitute fossil NG currently used in the integrated steel plant, mainly for heating processes. Alternatively, it is used as reducing agent in the blast furnace, for example as substitute for pulverised coal injection (PCI). An injection into the NG grid is not possible since the required specifications are not met (Question 3). The technoeconomic and ecological questions (Questions 1(b), 2 and 4) have been treated by Rosenfeld et al. (19). Supporting laboratory experiments for biomass gasification have been published by Müller et al. (20). The focus of this study is on Questions 5 and 1(b).

Figure 3 provides an overview of the possible integration of a P2G plant (Figure 3(b)) as well as a dual fluidised biomass gasification plant (Figure 3(c)) into the integrated steel plant (Figure 3(a) in dashed lines). By integrating a P2G plant, renewable energy is used for the production of hydrogen by water electrolysis and subsequently for the catalytic methanation of the process gases BFG and BOFG. The combination with a dual fluidised biomass gasification (20–22) provides an additional biogenic hydrogen source. The biogenic CO₂ is vented to the atmosphere. Alternatively, it could be stored in a carbon capture and storage (CCS) process resulting in negative CO₂ emissions (bioenergy with carbon capture and storage (BECCS)) which is not further considered here (23). In addition, the oxygen from the water electrolysis has the potential for utilisation in steel production as well as in the biomass gasification process. The produced nitrogen diluted SNG is directly utilised in the steel plant as a substitute for NG in various processes, and PCI in the blast furnace.

To explore the integration potential, a number of different scenarios has been defined and three of them, supported by the experimental results at a laboratory catalytic methanation plant, will be presented in detail in the present work. The three chosen scenarios provide a good overview of the order of magnitude of the required renewable energy, as well as the resulting CO₂ reduction potential.

The scenarios with different integration variations were based on Austria’s biggest steel production sites. The integration of renewable energy by a P2G plant and a biomass gasification plant has been analysed by three extreme value scenarios and three constrained scenarios. The results are reported in (19). The three extreme value scenarios described a maximum utilisation of the process gases, either individually or in combination. The required hydrogen for the methanation was balanced, half from water electrolysis and half from biomass gasification. The constrained scenarios are realistic in the medium term. They are limited by the maximum plant size of the biomass gasification plant (100 MW_th), based on the current biomass fuel availability and already installed gasification capacity in Europe (21). The main cost influencing factor throughout all six scenarios is the energy supply cost, both for electricity and for biomass (19).

The aim of the aforementioned scenarios was the minimisation of, or complete substitution of, the integrated steel plant’s demand for fossil fuels like NG and PCI. The steel plant process gases (BFG and BOFG) were used as carbon source for the methanation. The three scenarios which are the basis for the considerations in this paper are:

For these three scenarios, the required amount of the renewable electricity, biomass as well as the withdrawal amount of the process gasses (BFG or BOFG) has been determined. The main evaluation criteria for all scenarios were set by the CO₂ reduction potential.

Dual fluidised bed gasification systems consist of two reactors, the gasification reactor (650°C) and the combustion reactor (900°C). In contrast to the conventional systems, the presented system uses the sorption enhanced reforming (SER) process. It allows selective transport of CO₂ between the gasification reactor and the combustion reactor, by the use of calcium oxide as bed material, resulting in a product gas with a high hydrogen (up to 75 vol%) and low CO₂ concentration. The hydrogen rich product gas of the biomass gasification substitute green hydrogen from the electrolysis, and thus reduces the demand of renewable electric power (22, 24). Additionally, when pure oxygen is used instead of air for the combustion (oxySER), an almost pure CO₂ stream can be obtained as an exhaust (flue) gas, suitable as biogenic CO₂ source (Table II). The data given in Table II are based on the gasification of wood chips. A thermal gasification power of 100 MW_th consumes 50,400 kg h^–1 wood chips from Austria as fuel, and produces 28,800 Nm³ h^–1 product gas with the composition according to Table II (21).

The experimental tests were performed at a laboratory test plant, which consists of three fixed-bed reactors (R1–R3) connected in series with the purpose of achieving a multi-stage fixed-bed methanation. A detailed description of the test plant can be found in Kirchbacher et al. (25) and Medved (26). The conversion of CO₂ and carbon monoxide was investigated for synthetic gas compositions of BFG and BOFG under different flow rates, variation of hydrogen surplus and presence of nitrogen, with the focus on achieving a complete CO_x conversion.

A commercial bulk catalyst with 20 wt% nickel load was used. The operating pressure was set to 4 bar, which coincided with the steel producer’s gas supply system. The reactor load was limited to gas hourly space velocity (GHSV) of 4000 h^–1 (GHSV = V̇_feedgas/V_catalyst) for the synthetic BFG and BOFG gas composition and added hydrogen. The temperature in the reactor was determined by multi-thermocouples (Figure 4).

Fig. 4

Seven measuring points in each reactor, five in the catalyst bed and one below and above the catalyst zone, gave an understanding of the axial temperature profile in the catalyst bed. The methanation gas composition for BFG and BOFG for the stoichiometric ratio with hydrogen according to Equations (i) and (ii) is listed in Table III.

(i)

(ii)

The experimental results obtained from the methanation of BFG and BOFG were used as support for the further analysis of the three selected scenarios. In the following, the experimental results and the scenarios are presented separately in the subsections.

5.1 Methanation

The methanation of process gases is a combination of CO₂ and carbon monoxide conversion according to Equations (i) and (ii).

A suitable parameter for the description of the stoichiometry is the ratio r_{H 2} of molar hydrogen flow and molar flows of CO and CO₂, respectively, in the feed gas given in the Equation (iii):

(iii)

r_{H 2} equals 1 for stoichiometric mixtures, r_{H 2} <1 for sub- and r_{H 2} >1 for over-stoichiometric mixtures, respectively.

Achieved CO_x conversion rates for each reactor (R1–R3), with variation of hydrogen surplus (r_{H 2} = 1; 1.02; 1.04; 1.05) with and without nitrogen for a synthetic BFG and BOFG feed gas compositions, can be seen in Figures 5 and 6. On the right y-axis, the mean reactor temperature represents the average of the measured catalyst bed temperatures, as well as the calculated heating values of the product gas in each reactor. For experiments without nitrogen in the feed gas, marked with -N₂, the H₂:CO_x ratio and the amount of the reactive gas in the experimental series remained the same, meaning that the GHSV was reduced to 3260–3280 h^–1 for BFG and 3680–3690 h^–1 for BOFG due to the absent inert gas flow.

Fig. 5

Methanation of BFG with and without nitrogen and hydrogen-surplus variation

Fig. 6

Methanation of BOFG with and without nitrogen and hydrogen-surplus variation

5.1.1 Methanation of Blast Furnace Gas

Complete CO_x conversions are achieved downstream of the third reactor with an over-stoichiometric ratio of 1.05, both with and without the nitrogen in the feed gas (Figure 5). The mean reactor temperatures are approximately 50°C lower with nitrogen present, which is a result of the additional heat capacity of the inert gas. Despite these lower temperatures, approximately 4% better conversions are reached in R1 for all ratios in the absence of nitrogen. The withdrawal of nitrogen from the feed gas resulted in lower GHSV, consequently prolonging the residence time in the reactor leading to slightly better CO_x conversions. The temperature decrease in R2 and R3 was expected, since the majority of the reactive gas converted in R1, resulting in lower release of the exothermic reaction heat. Therefore, nitrogen in the feed gas only has a significant influence on the heating value of the product gas. In the case of the product gas (R3) with nitrogen the heating values vary from 19.4–19.8 MJ m^–3 (r_{H 2} = 1.05–1), whereas without nitrogen the values almost double (36.0–37.9 MJ m^–3). Although with the higher hydrogen surplus better conversions are achieved, the unconverted hydrogen decreases the heating value of the product gas, due to its lower volumetric heating value compared to methane.

5.1.2 Methanation of Basic Oxygen Furnace Gas

As for BFG, similar test series were conducted for the methanation of BOFG. As shown in Figure 6, on account of lower nitrogen share in the feed gas (8.2%), no noticeable effect on the temperature and consequently conversion can be recognised. Furthermore, a complete CO_x conversion at 4% hydrogen surplus is achieved with or without nitrogen in the feed gas. When comparing the mean reactor temperatures and CO_x conversion in R1 of BOFG with the BFG test series, temperatures are 50–100°C higher and conversions 5–10% lower, respectively. This can be attributed to the higher carbon monoxide share in BOFG, resulting in higher reaction heat release. Therefore, the conversion in the first reactor (R1) is clearly thermodynamically limited. Since its lower share in BOFG compared to BFG, the influence of nitrogen on the heating value of the product gas is lower, and values vary from 27.2–28.8 MJ m^–3 (r_H2 = 1.05–1) with nitrogen and between 34.9–37.7 MJ m^–3 (r_H2 = 1.05–1) without nitrogen.

The product gas composition downstream of the methanation is given in Table IV. A complete conversion is achieved at 5% hydrogen surplus for BFG and at 4% hydrogen surplus for BOFG, where the unconverted hydrogen is a result of its over-stoichiometric addition.

Table IV

Product Gas Composition for the Methanation of BFG and BOFG

	Product gas molar fraction
	CH4	CO2	CO	N2	H2
BFG (rH2 = 1.05)	0.446	0	0	0.434	0.120
BOFG (rH2 = 1.04)	0.679	0	0	0.215	0.106

5.2 Results for the Selected Scenarios

In this study, three different scenarios for the implementation of a P2G plant and a biomass gasification in an integrated steel plant have been investigated. The aim was the quantification of the CO₂ emission reduction potential of steel production, avoiding significant modifications in the existing steel plant infrastructure. Furthermore, a carbon capture step shall be avoided as well, in order to improve the efficiency of the CCU process chain, resulting in a catalytic conversion of BFG and BOFG to methane in the presence of nitrogen.

Basic evaluation of the three chosen scenarios confirmed the possibility of CO₂ emission reductions between 0.81 and 4.6 million tonnes CO_2eq per year without considerable interference with existing steel production. The required plant sizes and the necessary fuel demand (renewable power and biomass, respectively) substantially exceed the current realistic possibilities of a P2G plant (electrolyser power 784–2877 MW_el) as well of a biomass gasification (100–3162 MW_th). Even for the scenarios realistic in the medium term, the amount of required renewable electricity beyond 700 MW_el cannot be provided in the foreseeable future. This underscores the need for new technologies for the production of CO₂-free hydrogen.

Experimental tests have shown that the methanation of BFG and BOFG is technically possible without separating the inert gas nitrogen, and thus saving the energy intensive carbon capture unit with the additional benefit of carbon monoxide utilisation contained in the process gases from the steel production. A complete conversion of CO_x was achieved with a 4–5% hydrogen surplus for both process gases, BFG and BOFG, with and without nitrogen, with three-stage methanation. The lower heating value enrichment of the unrefined BFG (up to 19.8 MJ m^–3) and BOFG (up to 28.8 MJ m^–3) via methanation without the necessity of nitrogen removal as lean product gas showed a utilisation potential in the integrated steel plant as a substitute for NG and PCI.

The first evaluation presented here provides a good overview on the order of magnitude of required renewable energy and biomass for the transition of the integrated steel plant towards renewable gas supply by adding a CCU process chain. Additionally, particularly for the utilisation of the product gases of catalytic methanation within the steel plant, intricate CO₂ separation is not required as has been shown by the experimental investigations.

TiO₂ nanoparticles were successfully synthesised by a simple sol-gel method (37) using Ti[OCH(CH₃)₂]_4, generally referred to as titanium tetra-isopropoxide (TTIP), as a precursor purchased from Sigma-Aldrich Company (USA) with purity of 97%. Titanium(IV) isopropoxide was dropped slowly into the mixed solution of distilled water and ethanol in the ratios of 1:4:1 (TTIP: water: ethanol). The solution was stirred continuously for 1 h at room temperature to obtain a white slurry. HNO₃ was used to adjust pH value in the range 2–3. The white slurry mixture was dried at 120°C for 3 h on a hot plate; the dried powder was sintered at 450°C for 3 h. Finally, we obtained the required TiO₂ nanoparticles. The flow chart of synthesis of TiO₂ nanoparticles is given in Figure 1.

Fig. 1

The synthesised sample of TiO₂ nanoparticles were analysed with XRD pattern using a SmartLab^® X-ray diffractometer (Rigaku Corporation, Japan) (with λ = 1.5406 Å CuK_α radiation) operating at 40 kV, 30 mA and at room temperature. The XRD patterns were used to determine the crystallite size, lattice parameter and phase identification. The structural and morphological analysis of TiO₂ nanoparticles were done by HR-TEM and the selected area electron diffraction (SAED) pattern using the model Tecnai^TM G² F30 field emission gun transmission electron microscope (FEI Company, USA) operating at 200 kV accelerating voltage with resolution point:0.17 Angstrom line:1.24 Å and magnification 1500 LM to 520 kx. Tescan MAIA3 field emission scanning electron microscope (Tescan, Czech Republic) operating at 12.0 kV and magnification 21.4 Kx was used for SEM-EDX analysis of the morphology and average particle size of the TiO₂ nanoparticles. The UV-vis absorption spectrum was recorded using Shimadzu UV-2330 spectrometer (Shimadzu Corporation, Japan) in the range 200–700 nm. The UV-vis spectrum was used to determine direct energy band gap of the TiO₂ nanoparticles.

TiO₂-ethylene glycol nanofluids were prepared at different concentrations, 0.2 wt%, 0.5 wt% and 1.0 wt% of TiO₂ nanoparticles. When TiO₂ nanoparticles are added to the ethylene glycol base fluid, the nanoparticles produce a sediment within a few minutes because they remain in clusters without being dispersed. For the uniform dispersion of nanoparticles in the base fluid, we used an ultrasonic homogeniser VC 505 (Sonics & Materials Inc, USA) working at 20–40 kHz, 500 W.

The crystal phases of the synthesised TiO₂ nanoparticles were determined by XRD patterns as shown in Figure 2. The obtained peaks in the diffraction pattern are identified with the JCPDS Card No. 88-1175. The interplanar spacing has been calculated using Equation (i):

(i)

where λ represents the wavelength of CuK_α (1.5406 Å) radiation, θ is the angle between incident beam and the reflection lattice planes and n = 1 is the order of the XRD spectra. The highest peak is observed at 2θ = 25.4° which was indicated to plane (101) and d spacing corresponding to this peak is 3.12 Å. The other peaks in XRD pattern are observed at 2θ = 27.6°, 37.9°, 48.2°, 54.1°, 55.1°, 62.8°, 69°, 70.4°, 75.1° and 82.8° correspond to the (110), (004), (200), (105), (211), (002), (116), (112), (215) and (312) planes of TiO₂ nanoparticles and d spacing are calculated as 2.83 Å, 2.43 Å, 1.88 Å, 1.69 Å, 1.66 Å, 1.47 Å, 1.35 Å, 1.33 Å, 1.26 Å and 1.16 Å, respectively. The intensity of the obtained peaks indicates the well-formed crystalline nature of the sample. The average crystallite size has been computed with Scherrer’s equation (Equation (ii)) (38):

(ii)

where β_hkl represents the full width at half maxima (FWHM) and K is the Scherrer constant. From this formula, the calculated average crystallite size of the given sample is approximately ~23 nm.

Fig. 2

The TEM image of a crystalline sample is shown in Figure 3(a). The average particle size of the TiO₂ nanoparticles ranged from 20–26 nm as shown in the histogram (Figure 3(b)). The SAED pattern in Figure 3(c) shows principally 10 rings which are ascribed to (101), (110), (103), (004), (111), (200), (105), (211), (002) and (116) planes, respectively. These planes are consistent with the XRD results. The d spacings are in agreement with the tetragonal structure of TiO₂ nanoparticles (JCPDS Card No. 88-1175). For the structural analysis TiO₂ nanoparticles were also examined by HR-TEM as shown in Figure 3(d). The crystalline nature of the nanoparticles is visible in the HR-TEM micrograph. The lattice spacing 0.31 nm and 0.28 nm corresponds to (101) and (110) planes respectively. The size and morphology of the TiO₂ nanoparticles were also determined using SEM. Figure 4 shows typical SEM images of TiO₂ nanoparticles. The SEM image shows random distribution of TiO₂ nanoparticles having sizes in the range 18–26 nm. In Figure 4, there is a soft agglomeration of the nanoparticles: isolated particles are connected to each other by attractive physical interactions like Van der Waals force. The agglomeration of nanoparticles in the base fluid probably affects the thermal conductivity performance of the nanofluids. Agglomeration of nanoparticles affects the Brownian motion of the nanoparticles resulting in a decrease in thermal performance of the nanofluids. To remove agglomerations of nanoparticles in the base fluid, a sonication process has been used to break the intermolecular interactions. The EDX spectrum (Figure 5) of the TiO₂ nanoparticles provides information about the constituent components of our sample, which contains titanium and oxygen. The high intensity peaks for titanium and oxygen justifies that the sample contains mainly TiO₂.

Fig. 3

Fig. 4

Fig. 5

The UV-vis absorption spectrum at room temperature of TiO₂ nanoparticles has been recorded in the wavelength range 200–700 nm and is shown in Figure 6(a). It is obvious from the UV-vis absorption spectrum that the peak observed at 315 nm represents a blue shift compared with its bulk counterpart. This indicates that the particle size of the TiO₂ nanoparticles has been reduced (38–40). The optical absorption of the TiO₂ nanoparticles is analysed by Equation (iii):

(iii)

where E_g represents the optical band gap of nanoparticles, B is a constant, α is the optical absorption coefficient of the nanoparticles. The exponent m depends on the nature of the transition, m = 1/2, 2, 3/2, 3 for allowed direct, allowed indirect, forbidden direct and forbidden indirect transitions respectively. Figure 6(b) shows the Tauc plot of TiO₂ nanoparticles, a satisfactory fit is obtained for (αhν)² vs. hν indicating the presence of a direct band gap. The optical energy gap of the TiO₂ nanoparticles has been determined as 3.28 eV by extrapolating the linear portion of this plot at (αhν)² = 0.

Fig. 6

The thermal conductivity of the nanofluids was measured by using a Hot Disk TPS-500 S thermal constant analyser. The Hot Disk TPS-500 S is the newest transient plane source (TPS) thermal constants analyser. The TPS technique has been used to determine the thermal conductivity of a nanofluid. The temperature dependent thermal conductivity of the TiO₂-ethylene glycol nanofluids is plotted in Figure 7(a) at 0.2 wt%, 0.5 wt% and 1.0 wt%. The results show that the thermal conductivity of TiO₂-ethylene glycol nanofluids increases with concentration of TiO₂ nanoparticles. The thermal conductivity exhibits a slow increase for 0.2 wt% nanofluids while it shows relatively fast increase for 0.5 wt% and 1.0 wt% nanofluid in the temperature range 20–80°C. At 20°C, the value of thermal conductivity of pure ethylene glycol is 0.285 W mK⁻¹ and it has been increased to 0.314 W mK⁻¹ for 1.0 wt% concentration of TiO₂ nanoparticles in ethylene glycol base fluid. The expression of TCE is given by Equation (iv) (41):

(iv)

where TC_nf and TC_bf are the thermal conductivity of nanofluid and base fluid respectively.

Fig. 7

A number of investigators have developed models for determining the thermal conductivity of nanofluids containing spherical particles. They only consider the effect of volume fraction of the particles. However the thermal conductivity of nanofluids depends on various factors such as size, shape, volume fraction of the suspended particles as well as temperature of suspensions. A few models also propose that the TCE is due to the ordered layering of liquid molecules near the solid particles (42, 43).

In addition to describing the TCE in nanofluids, we consider the effect of three possible mechanisms for heat transfer in nanofluids: (a) translational Brownian motion, (b) the existence of an interparticle potential and (c) convection in the liquid due to the Brownian movement. In the low temperature region, the mean free path due to the collision of nanoparticles increases and leads to TCE due to Brownian particle (K_Brownian) as given as Equation (v):

(v)

where ϕ is the volume fraction, C_N is the heat capacity per unit volume of the nanoparticles, l is the mean free path and V_N is root mean square velocity of the particles.

This model explains the individual effect of temperature on the TCE in nanofluids with the help of Brownian motion but does not consider the effect of surface functionality and particle loading of nanoparticles. To overcome the shortcomings of this model, Prasher et al. (44) presents an order-of-magnitude justification to show a local convection effect caused by the Brownian movement of the nanoparticles. Based on the Brownian motion induced convection effect from multiple nanoparticles, the model of Prasher et al. for the TCE ratio of a nanofluid is given in Equation (vi):

(vi)

where K is the thermal conductivity of nanofluids, K_f is the thermal conductivity of fluids, A and m are the best fit constants, and should be same for different experimental data for a particular fluid. R_e and P_r are Reynolds and Prandtl numbers respectively (Equation (vii)):

(vii)

where d_N is the particle diameter, R_b is the interfacial resistance (Equation (viii)):

(viii)

The Reynolds number (R_e) is based on the root-mean-square velocity (ν_N) of a Brownian particle defined as Equation (ix) (45, 46):

(ix)

where ρ_N is the density of the particles, k_b is the Boltzmann constant and T is the temperature in Kelvin scale.

Figure 7(b) shows the variation of the TCE with temperature ranging from 20–80°C. It is clear from Figure 7(b) that the enhancement in thermal conductivity of TiO₂-ethylene glycol nanofluids is achieved with increasing concentrations of nanoparticles and temperatures. At 20°C, we observed 3.3% to 6.5% and 11.2% TCE on 0.2 wt%, 0.5 wt% and 1.0 wt%, and at 80°C, the TCE becomes 9.9%, 18.3% and 23.8% for 0.2 wt%, 0.5 wt% and 1.0 wt% respectively for TiO₂-ethylene glycol nanofluids. This enhancement is due to better uniformity and stability of suspensions. It has been found that ultrasonication increases the stability and uniformity of the nanofluids. The achieved values of TCE are higher than any of the results reported previously for TiO₂-ethylene glycol or TiO₂-water based nanofluids (13, 14, 30, 31) at such small concentrations. In preparation of the nanofluids, we used very small amounts of nanoparticles, so the fluidic properties of the liquid are almost unaffected, allowing for the easy flow of liquids and better transfer of heat. As the temperature increases, the TCE in the TiO₂-ethylene glycol nanofluids may be attributed to Brownian motion of nanoparticles. It is obvious from Equation (ix) that the root-mean-square velocity (ν_N) of a Brownian particle depends upon particle diameter. If the particle diameter is small, root-mean-square velocity of a Brownian particle is large. Since the synthesised nanoparticles are small in diameter (approximately 22 nm) this results in the increase of Brownian motion, causing convection which in turn increases the thermal conductivity of the nanofluids. The high TCEs are probably due to the small size of nanoparticles because as the particle size decreases, the surface-to-volume ratio of particles increases, which can lead to enhanced thermal conductivity of nanofluids.

The ultrasonic velocity in nanofluids was measured using an ultrasonic interferometer (model nanofluid-10X, Mittal Enterprises, India) at 3 MHz frequency in temperature range 20–80°C. The measured ultrasonic velocity in ethylene glycol matrix and three nanofluids samples containing 0.2 wt%, 0.5 wt% and 1.0 wt% of TiO₂ in temperature range 20–80°C are shown in Figure 8. It is obvious from Figure 8 that the ultrasonic velocity in the nanofluids increases with the temperature. The plot also indicates that the ultrasonic velocity in the nanofluids is larger than that of pure ethylene glycol matrix (1410 m s⁻¹) at 20°C and the velocity increases with the particle concentration (1430 m s⁻¹) for 1.0 wt% loading at the same temperature of 20°C.

Fig. 8

If we consider (ρ_m,ρ_s) and (k_m,k_s) are the density and the compressibility of fluid and suspended particles respectively, B and ϕ are the bulk modulus and the particle volume fraction; then the effective density (ρ_eff) and compressibility (k_eff) of the suspension becomes as Equation (x) (47–49):

(x)

The ultrasonic velocity (V) in a medium is given by Equation (xi):

(xi)

where B, ρ and k represent the bulk modulus, density and compressibility of the medium respectively. λ and μ are the material dependent quantities known as Lamé moduli or Lamé coefficients. The compressibility and density of a fluid medium are changed by the dispersion of nanoparticles and are the function of the particle volume fraction. From Equation (x), it is clear that the evaluation of the effective bulk modulus and compressibility of the suspension is performed with calculation of effective Lamé moduli, which depends on particle volume fraction of suspended particles.

It is obvious from Equations (x) and (xi) that the bulk modulus and change in density of the nanoparticles suspension as a function of volume fraction causes an enhancement in the ultrasonic velocity. An increase in the wave velocity with increase in the particle concentration of given nanofluids indicates that there is positive change in the bulk modulus and density of the nanofluids. It may be predicted that the comparative change in the density with respect to bulk modulus is small. As the particle concentration in nanofluids increases, the compressibility of the given matrix decreases. A strong cohesive interaction occurs among the molecules after dispersion of TiO₂ nanoparticles in the ethylene glycol matrix. Thus for the TiO₂ nanofluids, the ultrasonic velocities are larger in comparison to the ethylene glycol matrix and increase with the nanoparticle concentration.

In the low frequency region, the velocity in nanofluids is independent of particle size (49, 50). Here all the nanofluids have been prepared with nanoparticles fabricated at low evaporation rate and velocity of the ultrasonic wave is measured at different temperatures and low frequency (3 MHz). Thus it was concluded that the temperature dependent velocity at low frequency in the nanofluids depends only on the particle concentration. At low frequency, the ultrasonic velocity in a nanofluid is a quadratic function of temperature (Equation (xii) (51):

(xii)

where V₀ is the ultrasonic velocity at 0°C, V₁ and V₂ are the absolute temperature coefficients of velocity and T is the temperature difference between experimental and initial temperature (0°C). The first and second terms in Equation (xii) are in good agreement for a simple liquid system, but the third nonlinear term is caused by non-linear change in bulk modulus and density of the nanofluid system with temperature.

The acoustic particle sizer APS-100 (Matec Applied Sciences, USA) was used to examine the PSD in the nanofluids. The APS-100 works on Epstein and Carhart theory (52) and is mainly based on the ultrasonic spectroscopic method. The APS-100 computes the sound attenuation (dB) per unit length (cm) over the 1–100 MHz frequency range in particle-liquid suspensions with high precision. This attenuation spectrum can be converted to PSD data. According to Epstein and Carhart theory (52), the attenuation of the ultrasonic wave in a nanofluid can be understood with the understanding of the thermal wave length (; K_S, ρ_S and C_S: thermal conductivity, density and specific heat of the dispersed particle: ω ; frequency of the wave) and the viscous wave length (; η: viscosity of the matrix). When the viscous wave length is comparable to particle radius (r), the viscous loss is a prominent cause behind the ultrasonic attenuation; while the viscous drag, scattering and thermal losses are effective when the thermal wave length λ_T ≈ r. The expressions for the ordinary viscous dissipation (α_V), the viscous drag loss (α_VD) (47, 50) of the sound waves are given as Equation (xiii) and Equation (xiv):

(xiii)

(xiv)

where η_d and η_V represent the dynamic and the volume viscosities of the nanofluid, k̅ is the wave number, . Biwa (53) calculated the change in the ultrasonic attenuation with respect to volume fraction caused by scattering at microscale in low frequency limit. The expression to compute the ultrasonic attenuation is given as Equation (xv) (53):

(xv)

where γ^sca represents the scattering cross-section which depends on the frequency of the ultrasonic wave, particle size, bulk modulus and density of the base/carrier fluid and suspended particles. The thermal attenuation is caused by temperature variation produced by propagation of the sound waves in different components of suspension. The thermal loss mainly depends on the frequency and particle size. The particle size has been obtained by APS-100 in range of 19 nm to 24 nm as visualised in Figure 9. It has been confirmed from Figures 3(a) and 9 that the PSD obtained by APS-100 is in good agreement with that obtained by the TEM micrograph. Ultrasonic spectroscopy is sensitive to particles with radius between about 10 nm to 1000 mm. The maximum particle concentration which can be analysed varies between about 1 wt% to 50 wt% depending on the nature of the system. On the other hand, the technique is unsuitable for analysing dilute suspensions i.e., particle concentrations below about 1 wt%. In the present study (Figure 9) the weight percentage of TiO₂ is 1 wt%. Systems with different weight percentages of TiO₂ show the same PSD because nanoparticles are dispersed in base fluid with the same technique and the same ultrasonication time.

Fig. 9

Taking into account that more than 99.4% of the diesel vehicles currently in China are compliant with Euro III–V, three typical LDDTs compliant with Euro III, Euro IV and Euro V, respectively, were selected from the market and their specifications are provided in Table I. These trucks have similar dimensions and powers, and their biggest difference is their aftertreatment technology. To eliminate the impact of fuel quality, all the diesel fuel used in the study was from a specified filling station, conforming to the China VI standard.

The test route was designed to simulate the real driving conditions of most diesel trucks in Zhengzhou, Henan province. The total length of the test route was approximately 68 km, including 14 km of urban roads, 18 km of connection roads and 36 km of highway. VOCs were sampled only when the trucks travelled on the urban and highway roads and cold start emissions of VOCs were not included during the whole test. Table II shows the driving condition parameters during each road type. The average speeds on urban and highway roads were 18.1–20.8 km h⁻¹ and 72.8–76.5 km h⁻¹, respectively. Driving conditions on urban roads are more aggressive than those on highway roads. The average accelerations on urban and highway roads were 0.22–0.26 m s⁻² and 0.10–0.13 m s⁻², respectively. During the measurement, the trucks were not in service and the load of each truck was approximately 500 kg during the experiment, containing the PEMS equipment, four batteries, two testers and one driver.

Under real driving conditions, some gaseous emissions may transform to secondary fine particles when the exhaust is cooled or diluted with the ambient atmosphere. Thus, VOC emissions might be overestimated if sampled directly from the vehicle exhaust because the temperature is very high. Therefore, a combined PEMS (Sensors Inc, USA) was employed to sample the exhaust VOC emissions. The schematic diagram of the emission testing and sampling system is shown in Figure 1.

Fig. 1

The microproportional sample system (MPS), a partial flow dilution system, was used to dilute and cool the exhaust from tailpipe. After the MPS, the gas temperature decreased from about 120ºC to about 40ºC. Two 3.2 L SUMMA^® canisters (Entech Instruments Inc, USA) were used to sample the VOCs during each test trip, one for the urban roads and the other for the highway roads. VOC emissions during the connection roads section were not sampled because the actual running speed could not meet the requirement due to unexpected road repairing. The sampling flow rate was controlled by a passive restrict valve at 0.1 L min⁻¹. Teflon^TM tubes were used to connect the canister and PEMS system to minimise the adsorption of VOCs. A laptop was used to control the system and collect data from the test module. It should be noted that these trucks were driven by their owners throughout the test to ensure these trucks were running under ordinary working conditions and each vehicle was tested twice to enhance the reliability of the results.

Analysis of the VOCs was carried out following the United States Environmental Protection Agency (US EPA) TO-15 method by a gas chromatography-mass selective detector (GC-MSD) (22). Samples collected in the SUMMA^® canister were preconcentrated using an 8900DS preconcentrator (Nutech Instruments Inc, USA) with three cold traps and a canister autosampler (Nutech Instruments Inc, USA, mode 3600DS). The moisture, CO₂ and methane would be removed through the traps. Then the concentration of the individual VOCs in the samples was determined by a GC-MSD system (7890A GC with a 5975 MSD, Agilent Technologies Inc, USA). Separation of the VOCs was achieved through a capillary column (60 mm × 0.25 mm internal diameter, 1.4 μm film thickness, DB-624 column, Agilent Technologies Inc). During sampling and analysis, strict quality assurance and quality control procedures were conducted to assure the data quality (22). The detection limits of the target non-methane hydrocarbons ranged from 7 parts per trillion by volume (pptv) to 141 pptv and the accuracy of the measurements was about 1–10%. Detailed description of the analysis procedures can be found in our previous study (23).

A total of 102 VOC species were identified and quantified, including 29 alkanes, 35 halocarbons, 17 aromatics, nine alkenes, five carbonyls and seven other compounds, which are presented in Table III. Due to the detection limitation of GC-MSD used in this study, some species (ethane, ethylene, propylene, acetylene, formaldehyde) were not detected and included.

EF per kilometre of a certain pollutant was calculated with the corresponding concentration, total exhaust volume and running distance during the test process. Prior to calculation, the results of the VOC measurements were time-aggregated. The total exhaust volumes in various driving conditions were the integration of the instantaneous exhaust flow rates, and the same for the total running distance. The EF of compound i was calculated as Equations (i)–(iii):

(i)

(ii)

(iii)

where V (m³) is the total exhaust volume of the sampling process; V_ins (m³ s⁻¹) is the instantaneous exhaust flow rate; DR_ins is the instantaneous dilution ratio of MPS; S (km) is the distance that the test vehicle travelled during the sample period; S_j is the travel distance at j second, which is equal to the value of instantaneous speed at time j recorded by the global positioning system (m s⁻¹); EF_i (mg km⁻¹) is the EF of compound i; C_i (parts per billion by volume) is the concentration of compound i; M_i (g mol⁻¹) is the molar mass of compound i; and V_m (l mol⁻¹) is the molar volume of compound i. The volumes and concentration data were all normalised to the standard ambient temperature and pressure condition (273.15 K, 101.33 kPa). The total EFs of the VOCs in a certain driving mode were summed by the individual VOC EFs in the driving mode.

The OFP refers to the amount of ozone generated by VOCs per unit mass (mg O₃ mg⁻¹ VOCs), which can reflect the ozone formation capacity of VOC species. In most cases, ratios of VOCs to NOx from the diluted exhaust were much higher than 20 in this study, which illustrated VOCs had the greater effect on the ozone formation (24). Therefore, the MIR scenarios developed by Cater (25) was applicable to evaluate the OFP of VOC species here. The OFP of a certain VOC is calculated according to Equation (iv) (26, 27):

(iv)

where OFP_i (mg O₃ km⁻¹) is the ozone formation of compound i; and MIR_i (mg O₃ mg⁻¹ VOCs) is the maximum incremental reactive of compound i obtained from Cater (25, 28). The total OFPs of a certain driving mode were summed by the individual VOC OFPs of the driving mode.

Figure 2 presents the EFs of regulated gaseous pollutants of three LDDTs compliant with different standards. Obviously, NOx, CO and HC emissions from LDDT-3 (Euro V) were the lowest and those from LDDT-1 (Euro III) were the highest, except for CO. In general, updated emission standards had a great effect on the reduction of regulated gaseous emissions. This is mainly because the three trucks adopted different aftertreatment technologies to meet different emission standards (29). For example, both selective catalytic reduction (SCR) and diesel oxidation catalyst (DOC) were utilised by LDDT-3 to be compliant with Euro V standards. SCR was often used to purify the NOx emissions and the DOC device could oxidise the CO and HC emissions efficiently (30–32). It is not difficult to understand why LDDT-1 produced the worst emissions because there is no aftertreatment requirement for Euro III trucks in most of China.

Fig. 2

As shown in Figure 2, NOx, CO and HC emissions under urban conditions were significantly higher than those under highway conditions. To be specific, NOx, CO and HC emissions under urban conditions were 1.3–1.8 times, 1.4–2.2 times and 2.5–4.1 times those under highway conditions. This phenomenon could be explained by the fact that the combustion quality in the engine was associated with the operation speed and frequent acceleration and deceleration (18, 33, 34). During this experiment, no traffic signals were encountered on the highway and the average speed was up to 73.8 km h⁻¹. However, there were 26 traffic signals on the urban roads and the average speed was only 19.4 km h⁻¹. In this operating condition, the combustion was insufficient and the temperature of aftertreatments might not be high enough for proper function, which caused the emissions to deteriorate.

Average weight percentage of individual VOC species of the entire trip was calculated based on the test trucks. On the whole, alkanes were the dominant group, accounting for 65.5 ± 10.3% of the total VOCs, followed by aromatics, carbonyls and alkenes, taking up 19.6 ± 5.0%, 5.4 ± 1.9% and 4.4 ± 1.8%, respectively. Additionally, though 35 halocarbons were quantified, they only took up 3.6 ± 1.5% of the VOCs. Thus, the following discussions on the VOCs are mainly focused on alkanes, aromatics, alkenes and carbonyls.

Weight percentages of the top 15 VOC species from the exhaust are presented in Table IV. These species accounted for approximately 83.4% of the total VOCs. Dodecane, n-undecane, naphthalene, n-decane and acetone were the major species, and their total weight percentages were over 80.1%. These results are partially consistent with the results obtained by Wang et al. (14), who indicated that n-decane, n-undecane and n-dodecane were the most abundant species. However, a study by Yao et al. (20) showed that carbonyls were the top group, which could account for 42.7–69.2% of the total VOCs. The difference was mainly attributed to the different VOC species quantified between the two studies. For example, Yao et al. (20) reported formaldehyde and acetaldehyde took up 47.9% and 21.0% of carbonyls, while these two species were not detected in this study.

The mean EFs and weight percentages for each VOC group for the entire trip of the three test trucks are plotted in Figure 3. The total VOC EFs of LDDT-1 (Euro III), LDDT-2 (Euro IV) and LDDT-3 (Euro V) were 186.9 ± 34.9 mg km⁻¹, 106.5 ± 26.2 mg km⁻¹ and 61.1 ± 16.9 mg km⁻¹, respectively. In other words, the VOC emissions decreased significantly as the standards tightened gradually from Euro III to Euro V. Most of the other species also showed a decreasing trend. Especially, dodecane and n-undecane presented the most significant decline, from 85.2 ± 3.7 mg km⁻¹ and 38.6 ± 11.7 mg km⁻¹ for Euro III to 16.7 ± 2.8 mg km⁻¹ and 9.7 ± 3.0 mg km⁻¹ for Euro V, respectively. The trend was partially consistent with that found by Zhang et al. (10), though the VOC EFs were a little higher than those in this work. This might be mainly attributed to the fact that Zhang et al. (10) employed tunnel measurement, which included evaporative emissions.

Fig. 3

Most VOC groups presented similar variation trends as the emission standards changed, especially for the dominant groups. For example, both alkane and aromatic emissions decreased noticeably as the standards varied from Euro III to Euro V. The progress in engine technology and application of aftertreatment devices played a major role in the subtraction of VOCs emissions. Additionally, there were no significant differences between emissions of carbonyls, alkenes and halocarbons from LDDT-1 (Euro III) and LDDT-2 (Euro IV), but they were much higher than those of LDDT-3 (Euro V). No coherent order was observed for other emissions among these diesel trucks, possibly because the absolute values of these species were too small to quantify accurately. On the whole, implementing stringent emissions standards could reduce most of the VOC species effectively in the freight transportation sector.

Figure 3 indicates that alkanes were the dominant group in tailpipe VOCs emissions from the test LDDTs, accounting for 57.2–80.0%, followed by aromatics (12.5–22.9%), carbonyls (3.1–7.7%) and alkenes (2.2–6.5%). This result was consistent with that observed by Wang et al. (14) (carbonyls < aromatics < alkanes) but inconsistent with that by Yao et al. (20) (alkenes < aromatics < alkanes < carbonyls). Discrepancy of the quantified VOC species was the main cause of the inconsistency. It can also be found that the proportion of alkanes decreased significantly, from 80.01% for LDDT-1 to 57.15% and 60.41% for LDDT-2 and LDDT-3, respectively. Additionally, LDDT-2 and LDDT-3 had similar VOC group distributions, while the aromatics weight percentage of LDDT-2 was significantly related to that of LDDT-3. This is probably due to the different aftertreatment used in LDDT-2 and LDDT-3 (as shown in Table III). Jung et al. (35) also observed that heavy-duty trucks equipped with DPF emitted higher quantities of aromatics compared with those with SCR.

Figure 4 shows the EFs of the top 15 VOC species from the exhaust of LDDTs. The EF of dodecane for LDDT-3 (Euro V) was 51.0% and for LDDT‐2 (Euro IV) it was only 19.6% relative to LDDT‐1 (Euro III). For several other species, LDDT-3 had the lowest EFs, while the EFs of LDDT-2 and LDDT-1 were comparable or even higher, such as naphthalene, acetone, 2-propenal. A hypothesis is that much higher temperatures and more oxidising conditions during the DPF regeneration process favour carbonyl formation (36). However, there is no direct evidence that DPF regeneration occurred. Additionally, there were several individual species whose emissions were not affected by the emission standards. Overall, most of the top 15 VOC species presented a decreasing trend as the emission standards tightened.

Fig. 4

Figure 5 shows several VOC group emissions from the exhaust of LDDTs under urban and highway driving conditions, respectively. It can be seen that VOC emissions on highway roads were much lower than those on urban roads. EFs of each VOC group decreased significantly, especially for alkanes and aromatics. Specifically, EFs of alkanes under highway conditions were only 20.4–46.2% of those under urban conditions, which was mainly attributed to the sharp decline of the most abundant alkane species, such as dodecane, n-undecane and n- ecane. For aromatics, the significant reduction of the EFs during highway driving could be attributed to the sharp reductions of naphthalene, 1,2,3-trimethylbenzene and 1,2,4-trimethylbenzene. Lower average speed and more acceleration and declaration were found during the urban road episodes, causing more incomplete combustion on non-highway road driving, resulting in higher VOCs emissions than on highways (37). Caplain et al. (38) also reported that tailpipe emissions in urban driving cycles were approximately four times those in motorway driving cycles. In addition, the reduction degrees of VOC EFs (urban vs. highway) for LDDT-3 were highest while those for LDDT-2 were lowest. This discrepancy is mainly due to the different aftertreatment devices used. For instance, optimum conditions of a DOC + SCR system used for LDDT-3 could be maintained under highway conditions because of the high exhaust temperature, resulting in more efficient reduction.

Fig. 5

A breakdown of the C1–C12 VOCs for different driving conditions of the tested trucks is presented in Figure 6. There was no obviously consistent trend in the carbon number distribution of the VOC species between highway and urban road conditions except for C3 and C11, which showed a decreasing trend when driven on the highway compared to urban roads. This phenomenon illustrated that driving conditions had a weak correlation with carbon number distribution. On the whole, the carbon number of the VOCs was concentrated in C3–C4 and C10–C12, showing a distinct ‘double peak’ phenomenon. Lu et al. (39) summarised several previous studies and reached a similar conclusion. VOCs are expected to be a mixture of unburned and partially burned fuel species (40). Propane and acetone are the dominant species in C3–C4 group, and this portion of the VOCs is likely generated as a result of the high efficiency of the diesel engine. For the C10–C12 group, these species are considered to be components of diesel fuel. Durbin et al. (41) reported the C1–C3 species contributed most to non-methane organic gases (NMOG) and ethene, ethyne, acetaldehyde and formaldehyde made the largest contribution. The differences may be attributed to the VOCs species detected between these two studies.

Fig. 6

According to the EFs of each VOC species, OFPs based on the travelled distance were calculated and the results are plotted in Figure 7. As expected, the magnitude of OFP based on emission rate presented a decreasing trend. To be specific, LDDT‐1 and LDDT-2 had the higher OFPs, approximately 239.6 ± 57.3 mg O₃ km⁻¹ and 227.7 ± 69.2 mg O₃ km⁻¹, respectively, and that for LDDT-3 was 124.8 ± 47.6 mg O₃ km⁻¹. The OFP values in this work were lower but comparable to those for diesel trucks in some studies (20, 37, 42). The lower OFP values in this study were mainly because DOC and SCR dramatically reduced VOCs emission. Additionally, engine technologies, driving cycles and fuel quality were also important factors.

Fig. 7

The chemical structure of OFPs was different from the trend of VOCs emissions based on distance travelled, shown in Figure 3. Aromatics were the primary contributor to OFP, accounting for 49.3–57.6% of the OFPs. It was noteworthy that although alkenes accounted for only approximately 5.0% of the VOC emissions, the OFP contribution of alkenes (13.4–22.3%) was comparable with that of alkanes (13.7–27.9%), which was attributed to the higher MIR scales of alkenes related to alkanes. Similar conclusions have been reached in previous studies. Therefore, priority measures should be taken to reduce the VOCs with high MIR values, such as aromatics and alkenes, to control the formation of ozone originated from diesel exhaust.

The top 20 VOC species ranked by their OFP are given in Figure 8. The contribution of these substances accounted for approximately 90.0% of the total measured OFPs. Naphthalene, 1-butene, dodecane, 1,2,3-trimethylbenzene, 2-propenal, 1,2,4-trimethylbenzene and 3-ethyltoluene were the dominant species in the photochemical ozone formation process, and their OFP values were over 10 mg O₃ km⁻¹. Among the top 20 species, 11 belonged to the aromatic group and four were alkenes, which accounted for a lower mass percentage but higher MIR values. This indicates that substances present in small amounts but with high MIR values should not be ignored.

Fig. 8