Inhibition of the Hydrolytic Phase in the Production ... - ACS Publications

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Inhibition of the hydrolytic phase in the production of biohydrogen by dark fermentation of organic solid waste Rubén Angeriz Campoy, A Fdez.-Guelfo, Carlos J. Álvarez-Gallego, and L. I. Romero Energy Fuels, Just Accepted Manuscript • Publication Date (Web): 30 May 2017 Downloaded from http://pubs.acs.org on June 4, 2017

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Inhibition of the hydrolytic phase in the production of bio-hydrogen by dark fermentation of organic solid waste Rubén Angeriz-Campoy*a, L. A. Fdez-Güelfob, Carlos J. Álvarez-Gallegoa, Luis I. RomeroGarcíaa

a)

Department of Chemical Engineering and Food Technology, Faculty of Science, University of Cádiz – Institute of Viticulture and Agri-Food Research (IVAGRO) International Campus of Excellence (ceiA3), 11510 Puerto Real, Cádiz, Spain. [email protected]; [email protected]; [email protected]

b)

Department of Environmental Technologies, Faculty of Marine and Environmental Sciences, University of Cádiz – Institute of Viticulture and Agri-Food Research (IVAGRO) - International Campus of Excellence (ceiA3), 11510 Puerto Real, Cádiz, Spain [email protected]

* Corresponding Author: Rubén Angeriz Campoy (Researcher) Tel. +34 956 01 68 34 Fax +34 956 01 64 11 Email: [email protected]

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Abstract In this paper, a destabilization episode in a semi-continuous dry thermophilic acidogenic reactor was induced. The reactor was fed with different mixtures of organic fraction of municipal solid waste (OFMSW) and food waste (FW). The aim was to determinate the effect of the organic loading rate (OLR) and co-substrate ratio on bio-hydrogen production by dark fermentation. OLR was increased from 21.0 to 88.8 g TVS/L/day testing three mixtures ratios (OFMSW:FW) from 80:20 to 50:50 and four HRTs (6.6, 4.4, 2.4 and 1.9 days). Results have shown that an increment in the OLR (from 21.0 to 59.8 g TVS/L/day) and FW proportion (from 80:20 to 50:50) improves the yield process in terms of solubilized organic matter. However, from ratio 50:50 combined with OLRs higher than 70.3 gTVS/L/day and HRTs lower than 4.4 days showed a clear destabilization episode due to an unbalance of the hydrolytic phase. This fact has been corroborated by the values of two indirect parameters, "non-solubilized carbon" (NSC) and "acidogenic substrate as carbon" (ASC).

Keywords: Anaerobic-digestion; Bio-hydrogen production; Process inhibition; Organic loading rate; Hydrolytic phase unbalance.


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1. Introduction Organic waste disposal is an emerging environmental problem and also a challenging public health concern. At present, however, it is considered that organic waste could be transformed into useful products by microbiological processes [1, 2]. Several technologies can be used to carry out an appropriate treatment of organic waste. One of these methods is anaerobic digestion (AD). The AD provides a wide range of advantages: minimizing the generation of solid waste going to landfills and reducing greenhouse gases (GHG) emissions. At the same time, a valuable biogas production takes place. The use of hydrogen (H2) as an alternative to fossil fuels is an environmentally friendly option. The main reason is that hydrogen combustion does not generate atmospheric pollutants or GHG [3]. Anaerobic acidogenic digestion (AAD) or dark fermentation is an interesting variation of the AD process which permits the production of a hydrogen-rich biogas. The key of the process consists of avoiding the final methanogenesis of the AD. An important parameter to be considered is the type of substrate for bio-hydrogen (hydrogen coming from biological processes) production by AAD. The composition of the wastes is one of the main variables to adequately compare the yields of bio-hydrogen from different kinds of residues [3]. It is well known that only a 20% approximately of organic fraction of municipal solid waste (OFMSW) can be properly reused. This fact is due to the low content of biodegradable organic compounds (carbohydrates, lipids and proteins) and its inappropriate separation of different fractions. Therefore, in order to improve the anaerobic biodegradability and the efficiency of the dark fermentation process, it is essential to increase the organic matter


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content using another waste with a high organic fraction [4]. In this context, the food waste (FW) is a similar substrate that could be considered as an appropriate candidate since it is an important waste material coming mainly from houses and food industrial sector. Moreover FW is much richer in carbohydrates, lipids and proteins and easily hydrolysable waste than OFMSW [5]. Each substrate has its advantages and disadvantages. In this sense, according to many authors [7-10], the high fat content of FW can lead to its hydrolysis and the generation of long chain fatty acids (LCFA). LFCAs are incorporated to the cell wall of the microorganisms, causing a decrease in the efficiency of the transport of nutrients and, consequently, exerting a toxic effect on the AD processes [6-9]. Therefore, the maximum OLR that can be used in an anaerobic digester fed with FW as single substrate is limited by the accumulation of LCFA and the subsequent acidification of the system, which inhibits the production of hydrogen [10-12]. In previous studies, developed by the authors of this work, the feasibility of the codigestion of OFMSW with FW has been analyzed [13]. It was observed that the yield and productivity of bio-hydrogen were enhanced with respect to those obtained for the OFMSW [14]. Now, the main goal of this paper is to optimize the co-digestion process and to study the improvement in bio-hydrogen production when the organic matter content of the system is increased in higher proportions of FW in the feed. To achieve this goal, different OLRs (and HRTs) derived from several OFMSW/FW ratios were imposed in order to reach an unbalance between the rates of the different stages (hydrolysis and acidogenesis) involved in the dark fermentation process. It can permit to determinate the operational limits of the


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co-digestion process and the maximum bio-hydrogen production attainable. In addition, several specific parameters proposed in the literature [15, 16] have been used in this paper to analyse the obtained results. Concretely, the parameters non-solubilized carbon (NSC) and acidogenic substrate as carbon (ASC) have been used to determine when the stability limits of the process were exceeded. NSC and ASC are two indirect parameters closely linked to the hydrolytic and acidogenic phases and, hence, with the bio-hydrogen generation. Therefore, both parameters are suitable to detect the unbalance among the rates of the different process stages. This information could be very important for plant operators in order to predict and prevent failures on AD systems. Finally, the optimal values of the process variables (HRT and mixture ratio) were determined using a set of mathematical and statistical techniques that allow an analysis to be performed to consider the simultaneous effects of several independent variables on the response surface. It was developed in order to obtain the maximum bio-hydrogen yield and productivity.

2. Experimental Section 2.1. Substrates OFMSW and FW have been used as substrates in this work. The OFMSW came from the Municipal Solid Waste Treatment Plant "Las Calandrias" located in Andalusia, south of Spain. The samples were mechanical sieved by a 30-mm cylindrical trommel.


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The source selected FW samples used were collected from different canteens and restaurants at the University of Cádiz, Puerto Real, Spain. Samples were stored in 20-liters drums in a freezer at -20 ° C to ensure maintenance of the characteristics of the substrate used throughout all tests [13, 17, 18]. Particular attention was paid to the preparation of representative samples of the mixture of OFMSW and FW for reactor feeding. The value of the percentage of total solids (TS) was adjusted to 20% (dry conditions) by adding de-ionized water [19].

(table 1 must be placed here)

2.2. Experimental conditions Four lab-scale stirred tank reactors, operating at semi-continuous regime of feeding (SSTR), were used to study bio-hydrogen production at dry-thermophilic AAD process. Different OLRs and HRTs (see Table 2) were tested at three mixture ratios (OFMSW:FW) 80:20, 70:30 and 50:50 in order to assess the effect of these parameters on the organic matter solubilization rate, the hydrogen production (HP) and the hydrogen yield (SHP). These three mixing ratios were selected as initial screening for codigestion of OFMSW with FW. Indeed, the set of mixtures ratios and HRTs selected have permitted to study the OLR range from 21.0 g TVS/L/day to 88.8 g TVS/L/day. This OLR range is higher than that used in previous paper for biohydrogen production from OFMSW exclusively which was ranging between 12.0 and 44.4 g TVS/L/day for the same hydraulic retention times [15].


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Moreover, in the above mentioned paper [15] the optimum HRT for OFMSW was 1.9 days. Therefore, HRTs lower than 1.9 days have not been tested in this work since it was expected that the increase of FW in the mixture ratio could lead to the destabilization of the process. For each HRT tested, operational conditions were maintained for a time period higher than 3-HRT in order to ensure the achievement of steady state conditions. The test duration was 111 days. A series of 5-liters SSTRs was used to carry out the experiments. The top cover incorporates four ports for the following functions: a port for pH control, a feeding port, a port for mechanical stirring (12 rpm) and, finally, a port for biogas output (collected in a 40 L Tedlar bag). Thermophilic conditions (55± 0.5ºC) were maintained in all the reactors by means the circulation of water (coming from a water circulating bath) through the reactor jacket.


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Fig. 1 Schematic diagram of the semi-continuous stirred tank reactors (SSTR).

2.3. Analytical methods The following parameters and analytical determinations have been used for process monitoring and control: pH, soluble chemical oxygen demand (sCOD), total Kjeldahl nitrogen (TKN), total solids (TS), volatile solids (VS), dissolved organic carbon (DOC), alkalinity and volatile fatty acids. All analytical determinations have been conducted according to Standard Methods [20]. Several parameters (TS, VS and pH) were measured directly from the samples of the feed and effluents of the reactors. For the TS determination, the samples were dried at 110 ± 5ºC. Then, for VS determination, the dried


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samples were calcinated at 550 ± 5ºC. pH was measured daily using a Crison Basic20 pHmeter (CRISON Instrument, Spain) equipped with an electrode resistant to high concentration of protein and sulphides [21]. The alkalinity was determined directly by acid titration on the raw samples. A lixiviation process (10 g of sample in 100 ml of deionized water during 20 min) was performed previously to the analysis of volatile fatty acids, DOC and sCOD [22]. The frequency of these determinations was three times a week. For DOC and sCOD analysis, a filtration through a 0.47 µm glass fibre filter, was performed over the samples coming from lixiviation. Analytic-Jena multi N/C 3100 carbon analyser (measurement range: 0–30,000 mg/l C; limit of detection: 4 µg/l C) was the equipment used to measure the DOC according to Standard Method 5310B. Oxygen 5.0 at pressure of 4–6 bar (Air Liquide, S.A., Spain) was used as oxidizer agent. The volatile fatty acids from C2 to C7 (acetic, propionic, iso-butyric, butyric, iso-valeric, valeric, iso-caproic, caproic and heptanoic) were analyzed by gas chromatography. A Shimadzu GC-2010 with a flame ionisation detector (FID) and a capillary column (filled with Nukol) by Supelco were used. The temperature of the injection port was 200 ºC and the temperature of the detector was 250 ºC. The carrier gas was helium at 50 ml/min. Previously, to the injection, the samples from lixiviation were filtered, firstly by 0.47µm and secondly by a Teflon filter of 0.22 µm. The concentration of Total Volatile Fatty Acids (TVFA) was calculated as the weighted (on molecular weight basis) sum of the individual volatile fatty acids concentrations [23]. Biogas composition and volume generated were measured daily. The volume of biogas was determined using a high precision rotary drum gas flow meter (Ritter TG-01). A gas pump (KFN Laboport) was used for gas impulsion. The biogas composition has been analyzed by


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gas chromatography. A Shimadzu GC-2014 with thermal conductivity detector (TCD) and a stainless steel packed column (diameter of 3.2 mm and 3.0 m length) with Carbosieve SII has been used. Samples of 1 ml were injected. The column temperature program was 7 min at 55 ºC initially and, later the temperature was ramped at 27 ºC/min until 150 ºC. The temperature of detector was 255 ºC and the temperature of the injector was 200 ºC. The carrier gas was helium using a flow rate of 30 ml/min. The limits of detection of the method for the different gases were: 3.1% (H2), 3.4% (CO2), 1.5% (N2), 3.2% (O2), and 0.1% (CH4). To calibrate the system, a commercial mixture (Abelló Linde, S.A., Spain) of the different gases (H2, CH4, CO2, O2 and N2) was used.

2.4. pH regulation The initial pH values for the different mixture ratios were in a range between 4.3 - 4.9. According to different authors [13, 17, 18], in order to ensure that methanogenic Archaea were inhibited the pH was maintained in a range between 5 and 6 through the addition of alkaline agent (NaOH solution). It is well-known that this range is the optimum for the selective bio-hydrogen production [24]. Ensuring the stability of pH is a key factor in maintaining adequate performance of the reactors. In this sense, high concentrations of FW in the feeding could affect the stability of the AAD. It must be taking into account that the FW degradation may produce high concentrations of volatile fatty acids. This fact leads to changes in the pH, the alkalinity and the concentration of ammonia nitrogen (NH3-N) [25].


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The pH was daily measured and, when necessary, regulated. Thus, the pH value was maintained in the optimal range of 5-6. This range is considered to be the most adequate to preserve the activity of the hydrogen producing microorganisms and also to inactivate the population of hydrogen-utilizing archaea [26]. It is widely recognized that a low pH leads to the inhibition of the hydrogenase activity, which is considered as the most important factor to explain the effect of the pH on biohydrogen production [27, 28]. The concentrations of the undissociated forms of volatile fatty acids (acetic and butyric acids, fundamentally) are higher when the pH is lower and it is well-known their inhibitory effect. Therefore, the influent substrate concentration should be limited to prevent the inhibition of bio-hydrogen production by the effect of undissociated acids on the microorganisms involved in the process [29].

2.5. Non-solubilized carbon (NSC) This indirect parameter can be estimated from the total organic carbon (TOC) obtained experimentally from the organic matter expressed in terms of VS. According to FdezGüelfo et al. [17], NSC can be calculated as the difference between TOC and DOC. Moreover, TOC can be estimated from the VS data and considering the experimental ratio “Organic matter/organic Carbon”. NSC represents the fraction not solubilized of organic carbon in the hydrolysis step. Thus, if the rate of the hydrolytic stage is unbalanced in the process, as consequence of the variation of some variable, the expected behavior for the NSC/TOC relationship is an increment. NSC has been calculated as indicated in Eqs. (1) and (2) according to Fdez-Güelfo et al. [17].


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NSC = TOC – DOC

(1)

TOC = VS · 0.51

(2)

Where 0.51 is the average value of carbon fraction in the organic matter according to Navarro et. al [15] for different kinds of OFMSW. All parameters (NSC, TOC, DOC and VS) in the above equations must be expressed in units of concentration (mass/volume).

2.6. Acidogenic substrate as carbon (ASC) The acidogenic substrate as carbon (ASC) depicts the fraction of organic matter that has been solubilized but has not yet been transformed into volatile fatty acids. Therefore, ASC can be used to analyze the performance of the acidogenic step. ASC can be determined as the difference between DOC and DAC. It should be noted that DOC represents the total amount of carbon solubilized in the hydrolysis while DAC is representing the amount of carbon contained in volatile fatty acids produced in the acidogenic step [16]. In this other case, if the rate of the acidogenic stage is unbalanced in the process, as consequence of the variation of some variable, the expected behavior for the ASC/TOC relationship is an increment. ASC was calculated as indicated in Eqs. (3) and (4) according to Fdez-Güelfo et al. [16]. ASC = DOC – DAC

(3)

௜ୀ଻

DAC =෌௜ୀଶ[AiH * ni * 12 / MWi]


(4)

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In equation (3), DOC is the dissolved organic carbon and it is measured experimentally in an organic carbon analyzer. Moreover, DAC can be estimated from the concentrations of the different volatile fatty acids determined experimentally by gas chromatography. Thus, AiH, represents the concentrations of each of the individual volatile fatty acid; ni, is the number of atoms of carbon contained in AiH and MWi, is the molecular weight of AiH. All parameters (ASC, DOC, DAC and AiH) in the above equations must be expressed in units of concentration (mass/volume).

2.7. Experimental design Response surface methodology (RSM) is a good option to design an optimization model [30]. These procedures include interactive effects of different variables and to match them with the different parameters of the process [31]. The main objective of this study is to provide an optimal region which satisfies the best operating specifications taking into account several key parameters of the process and developing a continuous response surface model. The RSM used in the present study was a central composite face-centered design (CCFD). Two different factors (HRT and mixture ratio) and two variables (the yield and productivity of bio-hydrogen) are involved in the study. The experimental design was performed by using MODDE software (version 11.0.1, Sweden). The design consisted of 2k factorial points augmented by 2k axial points and a center point where k is the number of variables. Three different levels, low (−1), central (0) and high


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(1), have been considered for the two operating variables [32]. Therefore, 36 experiments were carried out in a factorial design (including 4 factorial points, 4 axial points and 1 center point) and the remaining 4 involving the replication of the central point in order to obtain a good estimation of experimental error [33].

3. Results and discussion 3.1. Evolutions of the indirect parameters NSC and ASC As previously mentioned, NSC and ASC are two indirect parameter whose values are closely related to the performance of the hydrolytic and acidogenic phases and, therefore, with the bio-hydrogen generation. Based on the above explanations, for each HRT tested, if the OLR is increased as consequence of the increment of the FW proportion, the expected evolution of these indirect parameters may unveil three possible scenarios from the microbiological point of view: 1. DAC/TOC increases and [(NSC+ASC)/TOC] decreases with a simultaneous increment of the hydrogen production. This behavior would be representative of a stable reactor. In this case hydrolytic and acidogenic phases are balanced and, therefore, all hydrolyzed and solubilized substrate is converted into volatile fatty acids (except a fraction resistant to acidogenesis) with significant hydrogen generation. 2. NSC/TOC increases and DAC/TOC decreases with a simultaneous decline in hydrogen production. In this case, this behavior would be representative of an


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unbalance of hydrolytic phase in the process and, therefore, the extension of the acidogenesis would be limited. 3. ASC/TOC increases and DAC/TOC decreases with a simultaneous decline in hydrogen production. In the last case, this behavior would be representative of an unbalance of acidogenic phase in the process. As it can be seen in Table 2, for 6.6 and 4.4 days-HRT, the behavior of the system is very clear and it is representative of the first scenario, stable operation. When the OLR is increased as consequence of the increment in the FW proportion, DAC/TOC increases reaching its maximum value of 0.306 at 4.4 days-HRT and 50:50 ratio. Therefore, [(ASC+NSC)/TOC] decreases reaching its minimum value of 0.694 at the same conditions.


For 2.4 and 1.9 days-HRT, the trend of the data is analogous except for the mixture ratio 50:50. For this specific ratio, the behavior of these parameters is inverted, reaching DAC/TOC minimum values (around 0.126) and [(ASC+NSC)/TOC] maximum values (around 0.874), which indicates an unbalance of the hydrolytic phase. In order to determinate this unbalance, the evolutions of the NSC/TOC and DAC/TOC relationships were analyzed. As it can be seen in Table 2, for 2.4 and 1.9 days-HRT, NSC/TOC increases for 50:50 mixture ratio reaching its maximum value (around 0.714), while DAC/TOC decreases reaching its minimum value (around 0.126). In according with the above mentioned scenarios, the hydrolytic phase was unbalanced at these conditions. It must be highlighted


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that for 80:20 and 70:30 ratios the system is able to operate efficiently with OLR higher than 70.3 gTVS/L/day. This fact reveals that the distortion of the system at 50:50 mixture ratio is not due to an overloading effect. Therefore, the cause should be associated to the generation of some inhibitory compounds linked to the FW degradation.

3.2. Evolution of the volatile fatty acids profile The volatile fatty acids evolutions were monitored in all reactors and acetic, butyric and caproic acids were the main volatile fatty acids detected in the process (Fig. 2). No signs of inhibition or toxicity by caproic acid have been observed, although the lethal effect of high concentrations of caproic acid on acidogenic microorganisms is well-known [34]. It is well known that the major volatile fatty acids generated by AAD of carbohydrates are mainly acetic and butyric acids and it has been reported as one of the most efficient ways of biohydrogen production, especially by Clostridium sp. [35]. The other β-oxidation pathway (odd carbon volatile fatty acids) was minority. In these tests, valeric and heptanoic acids were not detected or their concentrations were negligible.


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*HAC means “Acetic acid”; HBU means “butyric acid “; HCP means “caproic acid” ; HBu/HA means the ratio between butyric acid and acetic acid and HRT means “hydraulic retention time” expressed in days .

Fig. 2 Evolution of the concentration of the main volatile fatty acids with the hydraulic retention time (HRT) for each mixture ratio tested. The ratio of butyrate to acetate concentrations (HBu/HAc ratio) may change with the activities of the microbial populations during fermentation process. Therefore, the metabolic pathway of bio-hydrogen production from organic waste degradation has been assessment by using the butyric/acetic (HBu/HAc) ratio. This ratio has normally been used as an indicator to evaluate the effectiveness of bio-hydrogen production. In these experiments, the


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average HBu/HAc ratios were included in a range between 0.8 and 1.2 for the different HRTs, OLRs and mixture ratios tested. These results are in agreement with data of the HBu/HAc ratio reported in the literature, which varies from 0.4 to 2.1 [36-38]. As it can be seen in Table 2, the TVFA concentration increases as HRT decreases and the proportion of FW increases, except for 50:50 mixture ratio operating at 2.4 and 1.9 daysHRT, where the TVFA drops sharply and, however, the pH diminishes close to 5. This fact is associated with increment in the NSC/TOC and the decline in the ASC/TOC previously mentioned, indicating that hydrolytic phase have been unbalanced for these conditions. The reason of this unbalance of hydrolytic phase may be related to long chain fatty acids (LCFA) which are not accounted in TVFA data, although contribute to the pH decreasing. It must be taken into account that TVFA only includes volatile fatty acids from C2 to C7. Long chain fatty acids (LCFAs) are the main intermediate in the lipid degradation process. Different operational problems of anaerobic digesters, such as biomass washout and inhibition of the microbial activity, have been reported as consequence of LCFAs accumulation [7-10, 33]. The cell-membrane seems to be the prime common target for most of the described LCFA inhibitory effects over anaerobic biomass. These LCFA associated with the degradation of mixtures with high proportion of FW at low HRT may induce inhibition or suppression episodes at hydrolysis level. The LCFA create a barrier which hinders the substrate and products transfer to the hydrolytic phase with an inhibitory effect on bio-hydrogen production [39, 40].


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About the maximum value of TVFA obtained in the experiments, 358 gHAc/kg, it was reached at 1.9 days-HRT and 70:30 mixture ratio, which corresponds to the maximum biohydrogen production as it will be discussed in the next section.

3.3. Evolution of hydrogen production at different HRTs and OLRs Data about biogas composition (hydrogen and carbon dioxide percentages), specific hydrogen production (SHP) and hydrogen production (HP) are presented in Table 3.


As it can be observed in Table 3, the biogas was composed mainly of hydrogen and carbon dioxide. In this study, the values of hydrogen percentage are in agreement with the data reported in the literature, which are ranging between 25% and 50% [41-43]. Highest hydrogen percentages and HP data were obtained operating at an OLR of 75.6 gTVS/L/day and 1.9 days-HRT with 70:30 mixture ratio.


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Fig. 3 a) and 3 b) Hydrogen yield and productivity for the different HRTs and mixture ratios tested. As it can be seen in Figures 3a and 3b, the HP and SHP were gradually increased as the HRT decreasing and the fraction of FW in the mixture is increased. However, as it was mentioned previously, for 2.4 and 1.9 days-HRT and 50:50 mixture ratio, the values decrease, due probably to an inhibition episode of the hydrolytic phase linked to the accumulation of LCFA in the reactor. It is very important to highlight that the best SHP, 53.8 mLH2/gVSfeed, was achieved at 4.4 days-HRT and 50:50 mixture ratio, which was more than double with regard to OFMSW fermentation alone (24.3 mLH2/gVSfeed) according to Romero et al. 2013.

3.4. Statistical analysis In this study, several responses and degree polynomial models have been analyzed and regression equations were used for data fitting (Table 4). For the purpose of determining


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curvature effects the data from the experimental approaches were fixed to higher degree polynomial equations, i.e. two-factor interaction (2FI), quadratic and so on. The RSM have been calculated by Design Expert Software, MODDE (version 11.0.1, Sweden) which were analyzed by default [30, 44, 45]. For the hydrogen yield parameter, the interaction term, was significant for mixture ratio and the interaction HRT-mixture ratio equations. Relevant statistical data are contained in the Table 4. The models were highly significant with very low probability values (0.0172 for the interaction HRT-mixture and 0.0470 for the mixture ratio). In the case of hydrogen productivity parameter, the interaction term was significant only for the HRT equation, with a very low probability value (0.0020). These statistical conclusions confirm the previous analyses for both the yield and productivity of hydrogen. Fig. 4 shows the three-dimensional plots (response surfaces) for HRT, mixture ratio, yield and productivity, obtained from equations presented in Table 4. It can be observed, from the curvature of the graphs, that the interaction among the variables is relatively strong. This fact is also reflected in the low p-values obtained.


The optimal values found applying the different statistical designs with MODDE Software (version 11.0.1, Sweden) were: •

For the hydrogen yield (or SHP) 58.4 mLH2/gVSfeed at 4.4 days-HRT and 50:50 mixture ratio.


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•

Page 22 of 39

For the productivity (or HP) 3.4 LH2/Lreactor.day at 1.9 days-HRT and 68:32 mixture ratio.

However, for the combination of SHP (mLH2/gVSadded) and the HP (LH2/Lreactor.day), the optimal values using the RSM design, for the mixture ratio would be of 63:37 (OFMSW:FW) and 3.2 days-HRT, where the SHP would be 60 mLH2/gVSfeed and the HP 2.1 LH2/Lreactor.day respectively (Fig. 4)

Fig. 4 Response surface methodology (RSM) plots for hydrogen productivity (a) and hydrogen yield (b). Both plots show the different HRTs (6.6, 4.4, 2.4 and 1.9 days) and the different mixture ratios (80:20; 70:30 and 50:50).


22

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Energy & Fuels

4. Conclusions

Results of HP and SHP improved with the increase in FW in the mixture (OFMSW:FW) from 80:20 to 50:50 in all HRTs tested, except for 50:50 mixture ratio at 2.4 and 1.9 daysHRT. Indirect parameters NSC and ASC indicate that operating at 50:50 mixture ratio and 2.4 and 1.9 days-HRT, an unbalance of the hydrolytic phase occurs. Best results for HP were obtained at 1.9 days-HRT and 70:30 mixture ratio and for SHP at 4.4 days-HRT and 50:50 mixture ratio (53.8 mLH2/gVSadded and 3.33 LH2/Lreactor·day respectively). The predicted optimal values of SHP (mLH2/gVSadded) and HP (LH2/Lreactor·day).obtained by RSM design would be at 3.2 days-HRT and 63:37 mixture ratio.

Author Contributions All the authors designed research, performed research, analyzed data and wrote the paper.

Conflicts of Interest All the authors declare no conflicts of interest.

Acknowledgements This research was supported by the projects CTM2016-79071-R (Spanish Ministry of Economy, Industry and Competitiveness), UNCA08-1E-035 and CTM2010-17654


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Page 24 of 39

(Spanish Ministry of Science and Innovation) and financed by the Spanish State Research Agency (“Agencia Estatal de Investigación” – AEI), and by the European Regional Development Fund (ERDF). The authors would like to thank to Agri-food Campus of International Excellence (ceiA3).

Nomenclature AAD, anaerobic acidogenic digestion AD, anaerobic digestion ASC, acidogenic substrate as carbon CCFD, central composite face-centered design DF, dark fermentation DAC, dissolved acid carbon DOC, dissolved organic carbon FID, flame ionization detector FW, food waste GHG, greenhouse gases HAc, acetic acid HBu, butyric acid HCa, caproic acid HP, hydrogen production HRT, hydraulic retention time LFCA, long fatty chain acid MBTs, mechanical biological-treatment plant MSW, municipal solid waste NSC, non-solubilized carbon OFMSW, organic fraction of municipal solid waste OLR, organic loading rate RSM, response surface methodology sCOD, solubilized chemical oxygen demand SHP, specific hydrogen production SSTR, semi-continuous stirred tank reactor TCD, thermal conductivity detector TKN, total Kjedahl nitrogen TOC, total organic carbon TS, total solids TVFA, total volatile fatty acid VS, volatile solids


24

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Energy & Fuels

References and Notes

[1] M. Lesteur, V. Bellon-Maurel, C. Gonzalez, E. Latrille, J.M. Roger, G. Junqua, J.P. Steyer, Alternative methods for determining anaerobic biodegradability: A review, Process Biochemistry 45 (2010) 431-440. [2] Z.-K. Lee, S.-L. Li, P.-C. Kuo, I.C. Chen, Y.-M. Tien, Y.-J. Huang, C.-P. Chuang, S.-C. Wong, S.-S. Cheng, Thermophilic bio-energy process study on hydrogen fermentation with vegetable kitchen waste, International Journal of Hydrogen Energy 35 (2010) 13458-13466. [3] L. Alibardi, R. Cossu, Effects of carbohydrate, protein and lipid content of organic waste on hydrogen production and fermentation products, Waste Management 47, Part A (2016) 69-77. [4] M.M. Søndergaard, I.A. Fotidis, A. Kovalovszki, I. Angelidaki, Anaerobic co-digestion of agricultural byproducts with manure for enhanced biogas production, Energy & Fuels 29 (2015) 8088-8094. [5] X. Gomez, M.J. Cuetos, J.I. Prieto, A. Moran, Bio-hydrogen production from waste fermentation: Mixing and static conditions, Renewable Energy 34 (2009) 970-975. [6] S.A. Silva, A.J. Cavaleiro, M.A. Pereira, A.J.M. Stams, M.M. Alves, D.Z. Sousa, Longterm acclimation of anaerobic sludges for high-rate methanogenesis from LCFA, Biomass and Bioenergy 67 (2014) 297-303. [7] K. Hanaki, T. Matsuo, M. Nagase, Mechanism of inhibition caused by long‐chain fatty acids in anaerobic digestion process, Biotechnology and Bioengineering 23 (1981) 15911610. [8] I. Angelidaki, B.K. Ahring, Effects of free long-chain fatty acids on thermophilic anaerobic digestion, Applied Microbiology and Biotechnology 37 (1992) 808-812.


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Page 26 of 39

[9] L. Dong, Y. Zhenhong, S. Yongming, K. Xiaoying, Z. Yu, Hydrogen production characteristics of the organic fraction of municipal solid wastes by anaerobic mixed culture fermentation, International Journal of Hydrogen Energy 34 (2009) 812-820. [10] C. Zhang, G. Xiao, L. Peng, H. Su, T. Tan, The anaerobic co-digestion of food waste and cattle manure, Bioresource Technology 129 (2013) 170-176. [11] S. Dahiya, O. Sarkar, Y.V. Swamy, S. Venkata Mohan, Acidogenic fermentation of food waste for volatile fatty acid production with co-generation of biohydrogen, Bioresource Technology 182 (2015) 103-113. [12] C. Liu, W. Wang, N. Anwar, Z. Ma, G. Liu, R. Zhang, Effect of organic loading rate on anaerobic digestion of food waste under mesophilic and thermophilic conditions, Energy & Fuels (2017). [13] R. Angeriz-Campoy, C.J. Álvarez-Gallego, L.I. Romero-García, Thermophilic anaerobic co-digestion of organic fraction of municipal solid waste (OFMSW) with food waste (FW): Enhancement of bio-hydrogen production, Bioresource Technology 194 (2015) 291-296. [14] M.A. Romero Aguilar, L.A. Fdez-Gueelfo, C.J. Alvarez-Gallego, L.I. Romero Garcia, Effect of HRT on hydrogen production and organic matter solubilization in acidogenic anaerobic digestion of OFMSW, Chemical Engineering Journal 219 (2013) 443-449. [15] A.F. Navarro, J. Cegarra, A. Roig, D. Garcia, Relationships between organic matter and carbon contents of organic wastes, Bioresource Technology 44 (1993) 203-207. [16] L.A. Fdez-Güelfo, C. Álvarez-Gallego, D. Sales, L.I. Romero, New indirect parameters for interpreting a destabilization episode in an anaerobic reactor, Chemical Engineering Journal 180 (2012) 32-38.


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Energy & Fuels

[17] S. Zahedi, D. Sales, L.I. Romero, R. Solera, Dark fermentation from real solid waste. Evolution of microbial community, Bioresource Technology 151 (2014) 221-226. [18] S. Zahedi, R. Solera, J.L. García-Morales, D. Sales, Effect of the addition of glycerol on hydrogen production from industrial municipal solid waste, Fuel 180 (2016) 343-347. [19] J. Fernández-Rodríguez, M. Pérez, L.I. Romero, Dry thermophilic anaerobic digestion of the organic fraction of municipal solid wastes: Solid retention time optimization, Chemical Engineering Journal 251 (2014) 435-440. [20] APHA, Standard Methods for the Examination of Water and Wastewater,20th ed. American Public Health Association, Washington, DC, USA., ( 2005). [21] K. Aboudi, C.J. Álvarez-Gallego, L.I. Romero-García, Improvement of exhausted sugar beet cossettes anaerobic digestion process by Co-digestion with pig manure, Energy and Fuels 29 (2015) 754-762. [22] C.J. Alvarez-Gallego, Testing Different Procedures for the Start Up of a Dry Anaerobic Co-digestion Process of OFMSW and Sewage Sludge at Thermophilic Range.(Doctoral Thesis), Chemical Engineering, Environmental Technologies and Food Technology, University of Cadiz, 2005. [23] J. Fernández, M. Pérez, L.I. Romero, Effect of substrate concentration on dry mesophilic anaerobic digestion of organic fraction of municipal solid waste (OFMSW), Bioresource Technology 99 (2008) 6075-6080. [24] A. Ghimire, L. Frunzo, F. Pirozzi, E. Trably, R. Escudie, P.N.L. Lens, G. Esposito, A review on dark fermentative biohydrogen production from organic biomass: Process parameters and use of by-products, Applied Energy 144 (2015) 73-95.


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[25] M. Murto, L. Björnsson, B. Mattiasson, Impact of food industrial waste on anaerobic co-digestion of sewage sludge and pig manure, Journal of Environmental Management 70 (2004) 101-107. [26] X.-Y. Cheng, C.-Z. Liu, Hydrogen production via thermophilic fermentation of cornstalk by Clostridium thermocellum, Energy & Fuels 25 (2011) 1714-1720. [27] S.K. Khanal, W.-H. Chen, L. Li, S. Sung, Biological hydrogen production: effects of pH and intermediate products, International Journal of Hydrogen Energy 29 (2004) 11231131. [28] N.H. Mohd Yasin, N.A.A. Rahman, H.C. Man, M.Z. Mohd Yusoff, M.A. Hassan, Microbial characterization of hydrogen-producing bacteria in fermented food waste at different pH values, International Journal of Hydrogen Energy 36 (2011) 9571-9580. [29] S.W. Van Ginkel, S.-E. Oh, B.E. Logan, Biohydrogen gas production from food processing and domestic wastewaters, International Journal of Hydrogen Energy 30 (2005) 1535-1542. [30] N.R. Draper, Response surface methodology: Process and product optimization using designed experiments: R.H. Myers and D.C. Montgomery, (Wiley, New York, 1995, $59.95, ISBN: 0471581003, pp. 714), Journal of Statistical Planning and Inference 59 (1997) 185-186. [31] D. Baş, Đ.H. Boyacı, Modeling and optimization I: Usability of response surface methodology, Journal of Food Engineering 78 (2007) 836-845. [32] A.A.L. Zinatizadeh, A.R. Mohamed, A.Z. Abdullah, M.D. Mashitah, M. Hasnain Isa, G.D. Najafpour, Process modeling and analysis of palm oil mill effluent treatment in an upflow anaerobic sludge fixed film bioreactor using response surface methodology (RSM), Water Research 40 (2006) 3193-3208.


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[33] I. Elez Garofulić, V. Dragović-Uzelac, A. Režek Jambrak, M. Jukić, The effect of microwave assisted extraction on the isolation of anthocyanins and phenolic acids from sour cherry Marasca (Prunus cerasus var. Marasca), Journal of Food Engineering 117 (2013) 437-442. [34] A. Rinzema, M. Boone, K. Vanknippenberg, G. Lettinga, BACTERICIDAL EFFECT OF LONG-CHAIN FATTY-ACIDS IN ANAEROBIC-DIGESTION, Water Environment Research 66 (1994) 40-49. [35] D. Evvyernie, K. Morimoto, S. Karita, T. Kimura, K. Sakka, K. Ohmiya, Conversion of chitinous wastes to hydrogen gas by clostridium paraputrificum M-21, Journal of Bioscience and Bioengineering 91 (2001) 339-343. [36] H.-S. Shin, J.-H. Youn, S.-H. Kim, Hydrogen production from food waste in anaerobic mesophilic and thermophilic acidogenesis, International Journal of Hydrogen Energy 29 (2004) 1355-1363. [37] Y. Ueno, S. Otsuka, M. Morimoto, Hydrogen production from industrial wastewater by anaerobic microflora in chemostat culture, Journal of Fermentation and Bioengineering 82 (1996) 194-197. [38] S. Zahedi, D. Sales, L.I. Romero, R. Solera, Hydrogen production from the organic fraction of municipal solid waste in anaerobic thermophilic acidogenesis: Influence of organic loading rate and microbial content of the solid waste, Bioresource Technology 129 (2013) 85-91. [39] Ž. Zonta, M.M. Alves, X. Flotats, J. Palatsi, Modelling inhibitory effects of long chain fatty acids in the anaerobic digestion process, Water Research 47 (2013) 1369-1380. [40] J. Palatsi, J. Illa, F.X. Prenafeta-Boldú, M. Laureni, B. Fernandez, I. Angelidaki, X. Flotats, Long-chain fatty acids inhibition and adaptation process in anaerobic thermophilic


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digestion: Batch tests, microbial community structure and mathematical modelling, Bioresource Technology 101 (2010) 2243-2251. [41] M.A. De La Rubia, F. Raposo, B. Rincon, R. Borja, Evaluation of the hydrolyticacidogenic step of a two-stage mesophilic anaerobic digestion process of sunflower oil cake, Bioresource Technology 100 (2009) 4133-4138. [42] H. Zhu, A. Stadnyk, M. Beland, P. Seto, Co-production of hydrogen and methane from potato waste using a two-stage anaerobic digestion process, Bioresource Technology 99 (2008) 5078-5084. [43] I. Valdez-Vazquez, R. Sparling, D. Risbey, N. Rinderknecht-Seijas, H.M. PoggiVaraldo, Hydrogen generation via anaerobic fermentation of paper mill wastes, Bioresource Technology 96 (2005) 1907-1913. [44] N.R. McIntyre, T. Wagener, H.S. Wheater, S.C. Chapra, Risk-based modelling of surface water quality: a case study of the Charles River, Massachusetts, Journal of Hydrology 274 (2003) 225-247. [45] M.-S. Fan, A.Z. Abdullah, S. Bhatia, Hydrogen production from carbon dioxide reforming of methane over Ni–Co/MgO–ZrO2 catalyst: Process optimization, International Journal of Hydrogen Energy 36 (2011) 4875-4886.


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Energy & Fuels

Ratio OFMSW/Food Waste Parameter/Unit

OFMSW

Food Waste

80:20

70:30

50:50

pH a

5.7 (0.44)ab

5.3 (0.01)

5.5 (0.08)

5.4 (0.3)

5.4 (0.12)

Density (kg/L) ab

1.12 (0.03)

0.98 (0.04)

1.10 (0.04)

1.07 (0.05)

1.10 (0.09)

Alkalinity (g/kg) ac

17.6 (1.8)

10.9 (1.1)

16.3 (1.2)

14.1 (0.8)

13.7 (2.2)

TS (%)ab

52.8 (4.8)

31.1 (4.2)

20.0 (1.5)

20.2 (1.7)

20.3 (0.9)

TVS (%)ab

33.0 (3.9)

27.2 (3.6)

12.6 (1.5)

13.6 (1.7)

15.3 (0.6)

0.63

0.88

0.63

0.67

0.75

C/N ratio

18.6 (2.9)

37.4 (2.4)

27.7 (2.1)

28.7 (1.8)

30.9 (2.3)

sCOD (g/kg)ac

137 (18.4)

288 (45.2)

167 (39)

182 (18)

244 (55)

DOC (g/kg)ac

32.9 (1.2)

54.4 (3.5)

38.4 (11)

41.9 (3.8)

46.1 (2.1)

TVFA (g/kg)ac

13.1 (0.8)

10.1 (1.3)

12.1 (0.7)

11.9 (0.6)

11.7 (0.7)

TVS/TS

a

Data are the average value from different replicates. The standard deviation is included into the brackets. Data calculated on wet basis. c Data calculated on dry basis. b

Table 1. Characterization of the different raw wastes and feed mixtures.


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HRT (days)

6.6

4.4

2.4

1.9

Page 32 of 39

MR*

pH b

TS ab (g/kg)

VS ab (g/kg)

DOCab (g/kg)

sCOD ab (g/kg)

TVFA ab (gHAc/ kg)

DACabc —— TOC

ASCabc —— TOC

NSCabcd —— TOC

(ASC+NSC) ———— TOC

80:20

5.55 (0.1)

9.5 (12)

63.4 (10)

137 (23)

420 (19)

150.1 (19)

0.162

0.208

0.630

0.838

70:30

5.40 (0.6)

103 (10)

70.0 (7)

162 (17)

612 (24)

197 (34)

0.201

0.211

0.589

0.799

50:50

5.50 (0.5)

109 (13)

77.0 (9)

215 (18)

695 (32)

271 (11)

0.263

0.261

0.476

0.737

80:20

5.53 (0.1)

86.0 (11)

57.0 (12)

161 (28)

497 (21)

181 (23)

0.188

0.232

0.581

0.813

70:30

5.44 (0.9)

97.4 (12)

66.3 (7)

189 (25)

680 (24)

245 (21)

0.230

0.258

0.522

0.780

50:50

5.52 (0.2)

102 (8)

77 (10)

267 (21)

727 (28)

335 (14)

0.306

0.304

0.390

0.694

80:20

5.48 (0.3)

90.4 (14)

56.4 (10)

171 (35)

552 (23)

200 (22)

0.221

0.251

0.528

0.779

70:30

5.33 (0.4)

89.2 (14)

59.8 (9)

225 (40)

761 (27)

276 (36)

0.283

0.296

0.422

0.717

50:50

5.17 (0.2)

121 (9)

88 (14)

119 (10)

481 (23)

130 (12)

0.123

0.159

0.718

0.877

80:20

5.54 (0.1)

80.9 (11)

49.4 (10)

207 (19)

629 (35)

236 (16)

0.266

0.319

0.415

0.734

70:30

5.33 (0.6)

87.3 (9)

57.4 (7)

257 (34)

835 (12)

358 (33)

0.375

0.299

0.325

0.625

50:50

5.04 (0.2)

122 (8)

91.5 (4)

126 (11)

507 (22)

140 (13)

0.129

0.161

0.710

0.871

*

MR means “mixture ratio”; TS means “Total Solids”; VS means “Volatile Solids”; DOC means “Dissolved Organic Carbon”; sCOD means “Solubilized Carbon Organic Dissolved”; TVFA means “Total Volatile Fatty Acids”; DAC means “Dissolved Acid Carbon”; TOC means “Total Organic Carbon”; ASC means “Acidogenic Substrate as Carbon”; NSC means “Non-Solubilized Carbon”. a Data calculated on dry basis. b Data are the average value from different replicates. The standard deviation is included into the brackets. The operating conditions were maintained under these same conditions for more than 3 times HRT to ensure that steady state conditions were achieved. c DAC and ASC has been calculated from VFA through Eq. 3 and 4, respectively. d NSC has been calculated through Eq. 1 and 2.

Table 2. Characterization of effluents obtained in the different HRTs and mixture ratios tested.


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Energy & Fuels

HRT (days)

MR

80:20

6.6

70:30

50:50

80:20

4.4

70:30

50:50

80:20

2.4

70:30

50:50

80:20

1.9

70:30

50:50 *

H2 (%)

CO2 (%)

HP (LH2/Lr/day)

SHP (mLH2/gVSfeed)

47.7

52.3

0.64

33.7

(4.1)

(0.1)

(0.1)

(2.2)

*

48.9

51.1

0.82

43.7

(2.7)

(1.9)

(0.3)

(0.3)

50.4

49.6

1.17

50.1

(1.3)

(1.1)

(0.2)

(0.4)

49.1

50.9

1.03

35.5

(3.1)

(3.2)

(0.3)

(0.6)

49.8

50.2

1.38

48.1

(2.5)

(2.4)

(0.2)

(0.8)

50.6

49.4

1.88

53.8

(1.4)

(0.9)

(0.2)

(0.9)

50.1

49.9

1.89

36.3

(1.1)

(0.9)

(0.2)

(0.5)

49.2

50.8

2.57

49.6

(1.6)

(1.6)

(0.2)

(0.3)

49.0

51.0

1.49

23.3

(1.1)

(1.2)

(0.3)

(1.2)

49.2

50.8

2.51

37.9

(0.9)

(2.6)

(0.1)

(0.4)

51.0

49.0

3.33

50.9

(1.5)

(1.5)

(0.2)

(0.9)

49.4

50.6

2.02

21.3

(1.4)

(0.8)

(0.2)

(0.5)

OLR (g TVS/L/day)

21.0

21.8

25.6

31.4

32.6

38.4

57.6

59.8

70.3

72.8

75.6

88.8

Data are the average value from different replicates. The standard deviation is included into the brackets

Table 3. Biogas composition, hydrogen productivity (HP) and hydrogen yield (HSP) for the different HRTs and OLRs tested.


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Page 34 of 39

Hydrogen yield. MIXED MODEL (YIELD) Linear mixed-effects model: Fixed effects: yield ~ HRT * mixture + HRT2 + mixture2 Value

Std. Error

DF

t-value

p-value

Intercept

-210

108

36

1.93

0.062

HRT

35.5

11.4

6

3.09

0.023

mixture

6.48

3.36

6

1.92

0.104

HRT2

-1.26

1.06

6

-1.48

0.28

Mixture2

-0.044

0.021

6

-1.69

0.24

HRT:mixture

-.0339

0.10

6

-3.23

0.017

(Intercept)

Residual

8.33

0.019

Std. Deviation ANOVA

DF

F-value

p-value

Intercept

36

366

0.0001

HRT

6

2.78

0.1464

6

0.223

0.0471*

6

1.40

0.280

Mixture

6

2.88

0.1404

HRT:mixture

6

10.64

0.0172*

mixture HRT

2 2

Shapiro-Wilk Normality Test

W

p-value

0.9765

0.4442

Hydrogen productivity. MIXED MODEL (PRODUCTIVITY) Linear mixed-effects model: Fixed effects: Prod ~ HRT * mixture + HRT2 + mixture2 Value

Std. Error

DF

t-value

p-value

Intercept

-8.08

5.39

36

-1.41

0.142

HRT

-0.11

0.57

6

-0.206

0.843

mixture

0.34

0.16

6

2.03

0.088

HRT2

0.05

0.05

6

0.986

0.362

Mixture2

-0.002

0.001

6

-1.82

0.117

HRT:mixture

-0.0099

0.005

6

-1.91

0.103

Std. Deviation

(Intercept)

Residual

0.414

0.017

ANOVA

DF

F-value

p-value

Intercept

36

207

0.0001

HRT

6

26.8

0.0020*

6

0.084

0.929

6

0.973

0.362

Mixture

6

3.33

0.117

HRT:mixture

6

3.68

0.103

mixture HRT

2 2

Shapiro-Wilk Normality Test

W

p-value

0.9863

0.8411


34

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Energy & Fuels

* The null hypothesis was rejected for uniformity, the mixture and the interaction effect HRT: mixture shows statistically significant

(p-value< 0.05)

Table 4. Statistical Experimental Design for Hydrogen yield and Hydrogen Productivity.


35

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Inhibition of the Hydrolytic Phase in the Production ... - ACS Publications

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