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Biofuels and Biomass

Reducing VOC off-gassing during the production of pelletized steamexploded bark: Impact of storage time and controlled ventilation Eleonora Borén, Sylvia Helena Larsson, Andreas Averheim, Mikael Thyrel, and Markus Broström Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.8b00078 • Publication Date (Web): 08 Mar 2018 Downloaded from http://pubs.acs.org on March 9, 2018

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Reducing VOC off-gassing during the production of pelletized steam-exploded bark: Impact of storage time and controlled ventilation Eleonora Borén1,2, Sylvia H. Larsson3, Andreas Averheim4, Mikael Thyrel3, Markus Broström1* 1 Umeå

University, Department of Applied Physics and Electronics, Thermochemical Energy Conversion Laboratory (TEC-Lab), SE-901 87 Umeå, Sweden

2 Umeå

University, Industrial Doctoral School for Research and Innovation, SE-901 87 Umeå, Sweden

3 Swedish

University of Agricultural Sciences, Department of Forest Biomaterials and

Technology, Biomass Technology Centre, SE-901 83 Umeå, Sweden 4 Valmet

AB, Fiber Technology Center, SE-851 94 Sundsvall, Sweden *Corresponding author: [email protected]

Keywords:

Tenax TA, steam exploded biomass, pellet storage, emissions, terpenes, furans Highlights

•

VOCs are reduced over time in enclosed storage of steam-exploded bark

•

Heavier molecular weight VOCs reduce fastest

•

There is no effect on final VOC profiles by intermittent ventilation during storage

ACS Paragon Plus Environment

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Abstract Volatile organic component (VOC) off-gassing behavior of thermally treated biomass intended for bioenergy production has recently been shown to be vastly different from that of untreated biomass. Simple measures to reduce emissions, such as controlled ventilation and prolonged storage time, have been suggested but not yet studied in detail. In the present study, we monitored how VOC off-gassing was reduced over time (24 h–144 h) in enclosed storage with and without ventilation. Steam-exploded bark was collected directly from a pilot-scale steam explosion plant, as well as before and after subsequent pelletizing. Active Tenax-TA absorbent sampling of VOCs was done from the headspaces of a bench-scale sample storage setup. The impact of storage time and ventilation on VOC levels was evaluated through multivariate statistical analysis. The results showed that relative VOC concentrations in the headspace were reduced by increased storage time, with heavier VOCs reduced at a higher rate. VOC composition was neither reduced nor shifted by controlled intermittent ventilation during storage; instead, VOC levels equilibrated at the same levels as those stored without ventilation, and this was independent of process step, storage time, or number of ventilations.


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Introduction Conventional wood pellets are prone to changing the gas composition in storage vessels by forming potentially hazardous concentrations of CO and CO2 and by causing depletion of O2. The phenomenon of wood pellet off-gassing has been addressed in numerous studies during the last decade, motivated by a number of serious incidents and fatalities occurring during the unloading of transatlanticshipped pellet cargo in European ports , and with the growing number of small scale and domestic pellet storages in both Europe 1, 2 and in the US 3, 4. Together with selfheating behavior and excessive dust formation 5-7, off-gassing poses the main safety concern associated with large-scale bulk storage and transportation of biomass. The main focus of most previous off-gassing studies has been on CO formation and depletion of O2, and only little attention has been given to volatile organic compounds (VOCs) – a diverse group of chemical compounds that are readily emitted from biomass. Exposure to biomass-associated VOCs does not generally bear the same immediate health risk as that of CO poisoning or suffocation. At wood pellet production sites in Sweden, α-pinene levels were reported to not pose a problem, and instead excessive dust formation was correlated with respiratory symptoms 8. However, several VOCs have been targeted in exposure studies because they are known skin, eye, and mucous irritants even at moderate exposure levels 9, 10. Especially hexanal, known as a skin irritating aldehyde, has been found to surpass safety limits in different locations inside and in association with pellet warehouses and domestic storage rooms, but levels are highly dependent on production site, pellet delivery, and season 9. Also formaldehyde has recently been shown to surpass exposure limits in pellet warehouses 4. Thermal pre-treatment of biomass by steam explosion is being developed for upgrading low-value feedstock to an energy commodity having several key fuel properties superior to those of conventional wood pellets. Steam explosion of lignocellulosic biomass is a high-pressure steam treatment, effectively defibrating the biomass fibers when forced through a blow valve in a depressurizing step 11-13. The treatment causes physicochemical changes, for example, deacetylations and depolymerizations of hemicellulose and lignin 14-16. Steam explosion and steam treatment has been used to increase digestibility of cellulases 17-20. However, for


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production of solid fuels it is mainly the harsher steam explosion that has been shown to improve sought combustion properties, moreover also the material has been proven relatively straightforward to pelletize. Pellets of steam-exploded materials have been found promising for replacement of coal in heat and power production as they require little retrofitting of existing plants. There are currently a few steamexplosion production plants operated in demonstration scale, e.g. Arbaflame 21-23. Untreated dry biomass has been suggested form CO by autooxidation of unsaturated fatty acids. However, the amounts of fatty acids has not been able to account for the high levels of reported CO 24-26. Recently, the bulk of CO in untreated biomass was suggested be formed not primarily by direct autoxidation, but by the hydroxyl radicals also formed as secondary products from the autoxidation that in their turn reacted with hemicellulose, thereby forming CO 24. CO formation has been shown to be inhibited by ozone treatment, effectively eliminating the radicals 27. Although off-gassing of thermally treated materials can be expected to be lowered by the treatment it is still shown to proceed to concerning levels

28, 29

. Pelletizing of

steam-exploded material has been shown to lower CO, CO2, CH4, and H2 off-gassing, and to decrease O2 depletion, compared to untreated pellets 22, 30, but the thermal treatment has also shown to shift VOCs chemical composition to new and potentially harmful compounds 28, 31. Terpenes and aldehydes are generally reduced by thermal treatment, but instead the more concerning furans are formed, especially furfural 28, 31, 32.

To avoid harmful human exposure, it is important to monitor and, if necessary, take actions to reduce VOC off-gassing from different production steps where exposure might occur. Several means to lower off-gassing during storage have already been tested, for example, controlling storage temperature, purging of the storage vessel’s headspace with inert gas 33. Also, cargo shipments require ventilation and monitoring of gas levels prior to entry into ports. Lowering of the fuel storage temperature has been shown to reduce terpene off-gassing 34 and to lower VOC off-gassing from both torrefied and steam-exploded softwood. Storage temperature was also shown to have greater impact than treatment severity on VOC off-gassing31. In a previous publication, shifts in VOC concentration and composition were found in response to treatment severity and at different process steps during the production of steam-


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exploded bark; off-gassing was high when sampled directly after the steam explosion process and was sparked anew after pelletization 28. All VOC samples in that study were collected after 72 h of storage without any further details on the time-dependent variations. In the present study, we evaluated changes in VOC off-gassing from steam-exploded bark over time in enclosed storage. A subset of samples was treated by a sequence of low, intermittent controlled ventilations to also assess ventilation as a reductive means for VOC off-gassing. Samples were collected at three different points along the steam-explosion production line – directly after the process, just prior to pelletization, and directly after pelletization. The fuel samples were identical to those of the previous publication mentioned above 28.

Material and Methods Steam explosion A mixture of softwood bark consisting of 40% Norway spruce (Picea abies) and 60% Scots pine (Pinus sylvestris) was steam exploded in a in a continuous pilot-scale plant (BioTrac, Valmet AB, Sundsvall, Sweden, as described previously 28). The steam exploded bark was pelletized at the Biomass Technology Centre, Umeå, Sweden, in an SPC PP300 Compact pelletizer (Sweden Power Chippers, Borås, Sweden). The steam explosion process temperature was maintained at 180°C, the residence time was 5 minutes, and the process pressure was 9.6 bar. The resulting severity factor 35 was determined to be 3.05 log Ro. Detailed information on the processes, the operational parameters used, and the resulting fuel properties can be found in a previous publication 28. The steam-exploded bark was sampled at three locations along the process line: i) thermally treated bark directly from the steam explosion process outlet (direct), ii) one-day-old thermally treated bark collected from the pelletizer inlet (pre-press), and iii) pellets direct from the press channel outlets (pellet). Sample collection Material from the steam-explosion process (direct) was sampled straight from the process line by holding a sampling collector in the flow stream. Material prior to pelletization (pre-pellet) was sampled from the material stream entering the pellet press. Pellets (pellet) were collected in a beaker at the exit from the die channels. All


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sampled material was immediately transferred to a sealed container. Material moisture content was determined by a quick moisture content analyzer (Mettler Toledo HB43 Halogen Moisture Analyzer), and the amount required from each sample of material to obtain 30 g on dry basis (d.b) was calculated. The samples were placed in 1 L glass bottles capped with double-piped gas washing tubes (Duran group, VWR) that collect gas close to the sample while letting purge gas in close to the top. The sample bottles with pellets were left open for 10 min prior to closing the lids in order to remove excess moisture. All process set-points were analyzed in three replicates. The material was kept at ambient temperature (approximately 20°C) throughout the storage time. More details can be found in a previous publication 28. Storage and ventilation The stored bottles were sampled after 24 h, 72 h, or 144 h to assess the changes in the sealed bottles over time. The bottles sampled at 24 h were then subjected to complete ventilation through active pumping of air at 200 ml/min for 5 min, the equivalent of a total air exchange in the headspace volume. This was done to mimic controlled forced air ventilation. Other combinations of storage and ventilations are illustrated in Figure 1, giving a set of samples stored for 24 h, 72 h, and 144 h with 1 or 2 ventilations.

Figure 1. Overview of the sampling and the data analysis. Steam-exploded material was collected directly out of the process (direct), prior to pelletization (pre-press), and directly when exiting the pellet press die as pellets (pellet). Sampling was done after 24 h, 72 h, or 144 h and with or without ventilations after the first sampling time point. Ventilated samples are indicated by V for one ventilation and by 2V for two ventilations. All sample set-points were performed in triplicate.

VOC sampling and analysis The active sampling protocol for VOCs involved the use of Tenax-tubes filled with absorbent, as previously described 28, 31. Briefly, the VOCs in the headspace of the


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stored bottles were actively sampled on the Tenax-TA absorbent by an extractive pump unit operating at 100 ml/min for 1 min. For each process set-point, at least one second Tenax sampling tube was connected in series to the sampling tube to detect any VOC breakthrough. VOCs were released from the Tenax absorbent by thermal desorption and transferred onto a GC/MS system. An external standard was used for furfural (FLUKA) with methanol (Sigma-Aldrich) and spiked into the Tenax sampling tubes with an in-house built spiking unit. Peak identification was performed with AMDIS (2.7) using NIST libraries and two pyrolysis-specific libraries 36, 37. Furfural and toluene were identified by standards. The chromatograms were converted to NetCDF format, run through a dataprocessing pipeline utilizing in-house software (Swedish Metabolomics Centre (SMC) RDA (v.3.99)) to correct the baseline and to align peaks, and integrated according to their base peak, thus allowing for a semi-manual integration process. The integrated peaks were subjected to principal component analysis (PCA) using SIMCA v.14 (Umetrics, Sweden) and then mean centered and scaled to unit variance. The TenaxTA absorbent has a volatility range of C6-C26. Therefore, VOCs of lower volatility range cannot be quantified correctly. Some VOCs have a low breakthrough limit or are present at very high levels, meaning they will also be found in the second Tenax sampling control tube. These VOCs were identified if possible, but not integrated. To improve the assessment of the impact of different process variables, all integrated peaks were assigned (when possible) to a chemical functional group and subjected to multivariate data analysis. This allowed patterns to be identified that correlated to VOCs that are co-varying not only by amounts, but also by chemical functionality. Specifically, to first gain an overview of the enclosed stored and ventilated samples, they were compiled into a PCA. Then, to better interpret the impact of non-ventilated storage over time on the VOCs, the integrated VOCs were analyzed as the X-matrix and storage time was used in the Y-matrix of an orthogonal partial least square (OPLS) 38 model. Previous data analysis of these samples 28 showed systematic variation due to GC run order drift. The drift was positively correlated to the increasing storage time, partially seen in the PCA (Figure 2A-B). Therefore, in order to mitigate the influence of the drift, data were compared group-wise to separate the effect of the co-varying systematic drift into the orthogonal components. To assess the differences in composition over time, OPLS-DA models 39 were compiled for


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VOCs measured after 24 h and 144 h and compared to measurements after 72 h to evaluate how the compositions and amounts were affected by storage time. The two models were compared in a SUS plot 40 to identify any differences between the two OPLS-DA models. Finally, to interpret the effect of ventilation, separate OPLS-DA models were built between the closed samples and the different combinations of once or twice ventilated samples.

Results and discussion We found 134 chromatographic peaks in the data processing, out of which 77 could be successfully integrated and used for the data analysis (the remaining were either too low or of insufficient chromatographic quality). Totally 42 were confidently identified either by external standards or by comparison to reference mass spectra. Another 12 VOCs could also be confidently identified, but could not be integrated due to either too high volatility for the Tenax-TA absorbent or too poor spectral quality. The full list of compounds is published in the supplementary material of a previous publication28. VOC overview The resulting changes in VOC amounts and composition over 144 h of enclosed and ventilated storage of steam-exploded softwood samples were compiled in a PCA plot (Figure 2 A, B), where the first two components explained 86% of the data variation (model values in Table 1). Samples from the three different process steps, all sampled at either 24 h, 72 h, or 144 h were included. The spread along the first principal component of the score plot shows that storage time was the main cause of changes in VOC off-gassing (Figure 2 A). The lowest VOC concentrations were measured at 144 h, seen by the left side clustering of the 144 h samples (Figure 2 A, encircled), while all VOCs are right side-clustered in the loading plot (Figure 2 B). A clustering according to the different process steps could also be detected; all three partially overlap, but pre-press and pellet samples do so to a greater extent. Two time replicates of the direct samples, sampled after 24 h and 72 h, were grouped according to the crossing arrows, and two of them were behaving differently compared to the rest of the samples (being outside of the t2 Hoteling’s ellipse). The individual samples in the two groups were more similar to each other by an unknown factor than by storage time. In effect, the difference was caused by the collection time from the


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process line – these outlier samples that clustered further away were collected prior to a small process deviation, whereas the remaining samples were collected afterwards. To minimize the effects of unwanted systematic process disturbances, all collected direct samples, nine in total, were randomized before grouping technical replicates for the three storage times (3 × 24, 3 × 72, 3 × 144), meaning that the samples taken prior to the disturbance were distributed into all different sampling time points (Figure 2 A, arrows). Collection time impacted the VOC off-gassing to a greater extent than storage time, which highlights the VOCs' sensitivity to process variations. The loading plot separates a number of compounds to the upper right corner, especially a group of terpenes, indicating that at the time of the process fluctuation, treatment was probably less severe and thus retaining more terpenes in the feedstock. The samples were separated on the basis of sampling time, independent of whether they were subjected to controlled ventilation. No apparent grouping caused by ventilation was seen in the first and second principal component because the coloring of the samples by both sampling time and number of ventilations (Supporting information, Figure S1) indicates that the effects of total storage time and process step had a stronger impact.

Figure 2 PCA (A) score plot and (B) loading plot of steam-exploded softwood bark sampled after 24 h (circles), 72 h (squares), or 144 h (triangles) from enclosed and ventilated storage. Each sampling time point is comprised of samples from three production steps (encircled): direct, pre-press, and pelletized. VOCs are colored by chemical functionality. PCA R2X[1] = 0.76, R2X[2] = 0.1. Other model values are in Table 1.


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0.18

0.04

0.08

-

0.76

0.1

0.59

0.26

Figure

0.86 0.011 0.51

R2X [2]/R2Xo [1]

0.15 0.62

R2X [1]

Q2 (cum)

1 PCA 24-144 46 2 0.9 2a OPLS-DA 24 vs. 72 15 1+0+0 0.6 3 OPLS-DA 72 vs. 144 15 1+1+0 0.8 4 OPLS-DA 72 vs. 721V 14 No model 5 OPLS-DA 144 vs. 1441V 15a 1+1+0 0.5 6 OPLS-DA 144x1V vs. 1442V 17 No model 7 OPLS-DA 144 vs. 1442V 15 1+0+0 0.7 a Samples affected by process fluctuations have been omitted.

R2Y (cum)

R2X (cum)

Components

No. of observations

[h]

Group 1 (SE-180°C)

Model type

Table 1 Multivariate statistical model values for the different comparisons.

Model no.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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Figure 2 No fig. Figure 3 No fig. No fig. No fig. No fig.

Impact of storage time on VOC off-gassing in enclosed storage To assess the differences in composition over time, OPLS-DA models were compiled for VOCs measured after 24 h and 144 h and compared to measurements after 72 h to evaluate how compositions and amounts were affected by storage time. There was very little difference between the 24 h and 72 h head space VOCs of the steamexploded bark samples, irrespective of process step, rendering a very weak OPLS-DA model (Model 2 in Table 1). Instead, larger differences were found between the 72 h and 144 h storage time, which rendered a stronger OPLS-DA model (Model 3 in Table 1, Figure 3 A-C). The strength of OPLS-DA is the inter-class separation, i.e. in this case separating according to differences between VOCs sampled at the two storage times (separated along the x-axis) from the intra-class separation, i.e. differences within the sample group of respective storage time (found along the y-axis). However, the 72 h and 144 h samples cannot be fully separated by the discriminant analysis, i.e. they are not fully separated by the x-axis zero-line of the OPLS-DA score plot. Instead, 72 h pre-press samples cluster to the right together with the 144 h pre-press samples, indicating that very few changes occurred for the pre-press samples with storage time. The intra-class separation is partially attributed to process steps (encircled), but also to process fluctuations (the 72 h direct sample at the very top) causing the outlier observed in the PCA (Figure 2 A), but unwanted systematic variations of the analytical process were also identified.


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Figure 3. OPLS-DA (A) Score plot, and (B & C) loading plots of VOCs from enclosed storage of steam-exploded softwood bark sampled after 72 h (circles) or 144 h (squares). Each sampling time point is comprised of samples from three production steps: directly from the process (direct), before the pelleting (pre-press), and newly pelleted (pellet). VOCs are colored by chemical functionality (B), and by a color gradient of retention time (C). OPLS-DA R2X[1] = 0.59, R2X[2] = 0.26. Other model values are shown in Table 1.

VOC concentrations were higher in the headspace at 72 h and decreased in the 144 h samples, as indicated by the left-side orientation in the OPLS-DA loading plot (Figure 3 B-C). The majority of VOCs are also clustered far to the left and only a few scattered more to the right. These more right-shifted VOCs remained more similar in amount between the 72 h and 144 h samples. Moreover, all VOCs are clustered on the positive side of the y-axis, indicating higher levels in the corresponding overlapping direct samples of the score plot (Figure 3B). Coloring VOCs by their retention time, which is indicative of their molecular weight, revealed that lower-weight compounds, i.e., more volatile compounds (blue), are the rightmost compounds, while heavier compounds, i.e., those of low volatility (yellow), are shifted to the left. The color distribution reveals that the heavier VOCs were present at higher concentrations in the headspace at 72 h than at 144 h, while the composition of higher volatility compounds was more similar between the two storage times (Figure 3 C). To compare the shared and unique variation among the VOCs over time, the two OPLS-DA models of 24 h versus 72 h (model 2 in Table 1) and 72 h versus 144 h (model 3 in Table 1) were compared in a SUS plot (Figure 4). The plot should be interpreted as follows: Any VOCs located along the positive diagonal (quadrants one and three) were affected in the same positively correlated manner in both models, whereas those located along the negative diagonal (quadrants two and four) were affected in the same, but negatively correlated, manner. Compounds uniquely


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(positively or negatively) correlated for each model are located on the axis of the respective model. The SUS plot comparison shows that all VOCs are located in the third quadrant, indicating that there was relatively little difference between the 24 h and 144 h samples. However, there is a small positive spread along the diagonal, and this is correlated to the compounds’ retention time; heavier compounds are leftmost oriented and lighter compounds are spread more to the right. This indicates that, within both groups, there were relatively greater amounts of the heavier compounds in the earlier sampling time and that there was a slightly smaller apparent difference in concentrations of lighter compounds. No VOCs are located along the negative diagonal, meaning no VOCs were higher in 144 h samples than at 24 h. Also, there are no unique compounds that occur in one group that are not present in the next, meaning that no VOCs were completely degraded between the time points and no new VOCs were found. The finding of a reduction of heavier molecular weight compounds over time could possibly be explained by a faster sorption back onto the material surface. Soto-Garia et al, 2015, showed that untreated pellets (hardwood, softwood and blended) all reduced formation rates of methanol, pentane, pentanal, and hexanal, over time. The results of the present study indicate that VOCs of lower molecular weight may instead increase over time. However, those results must be put in relation to our samplings stretching over maximum 144h, whereas those of Soto-Garcia et al. were measured over a month 26.

Figure 4. Shared and unique structures (SUS) plot for the comparison of VOC off-gassing profiles of enclosed stored steam-exploded softwood bark at shorter and longer storage times.


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Results of the initial PCA overview showed no apparent impact of ventilating the stored steam-exploded bark, and closer discriminant analysis between enclosed stored and ventilated samples showed that the samples were almost identical. No significant difference could be found between the headspace gas compositions of the steam-exploded bark stored enclosed for 72 h and the sample subjected to ventilation at 24 h and stored for 72 h, (Model 4 in Table 1), nor between the samples stored enclosed to those ventilated and stored until 144 h (model 5 in Table 1), nor between those that had been ventilated once or twice prior to sampling at 144 h (Model 6 Table 1). Overall, ventilating had very low impact on reducing VOC amounts, irrespective of process step. Two rounds of ventilation did not have any significant impact, but the effect of more extensive ventilation was not evaluated. The negligible effect of ventilation is consistent with previous results for steam-exploded material that, in addition to undergoing ventilation, had the headspace gas replaced with nitrogen but still emitted compounds similarly to samples stored under air 28 In summary, our analysis of off-gassing during enclosed storage of steam-exploded bark showed that there was an overall reduction of VOCs over time and that heavier compounds reduced faster. This was possibly due to condensation onto the solid product because no increase with time was found for either new or for already existing possible secondary reaction products. Also, Meier et al. found a reducing trend in total VOCs in wood pellets during low ventilation storage 41, which is in accordance with the results presented here. The concentrations of lower weight compounds were more stable over time, and off-gassing from pre-press samples changed less with storage time compared to direct and pellet samples. Intermittent ventilation did not reduce VOC off-gassing under the prevailing conditions.

Conclusions VOC off-gassing from pilot-scale produced steam-exploded bark decreased with increasing storage time over the course of 144h, and was more rapidly decreased in the beginning of storage. The largest reduction was seen for heavier molecular weight VOCs, whereas VOCs of higher volatility remained unchanged with increased storage time. The reduction of VOCs was seen irrespective of sampled process step – directly


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from production, prior to pelletization, or as pellets. Intermittent ventilation at one or two occasions during the storage time did not reduce VOC off-gassing under the prevailing conditions. Instead, VOCs were formed anew to levels corresponding to the samples stored without ventilation. This shows the need of further studies on how different groups of VOCs will behave during longer storage times, and most importantly, also at what rate and frequency ventilation will be needed to secure a safe working environment.

Acknowledgments We thank the J. Gust. Richerts Foundation (Sweco) and Bio4Energy, a strategic research environment appointed by the Swedish government, for supporting this work. We gratefully acknowledge Mats Persson and Jan Detlefsen of the Fiber Technology Center, Valmet AB, Sundsvall, Sweden, for operational and technical work with the steam explosion. We also thank Gunnar Kalén and Markus Segerström of the Biomass Technology Centre, Swedish University of Agricultural Sciences, Umeå, Sweden, for their technical support with pelletization and analysis work on pellet quality. We also thank The Swedish Defence Research Agency, FOI, Umeå, Sweden, for kindly providing access to their ATD-GC/MS instrument, and Roger Lindahl, Umeå University, Umeå, Sweden, for help with developing the protocol for Tenax-TA sampling.

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